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<title>Cinética de la digestión anaeróbica de excretas porcinas para la obtención de biogás en laboratorio</title>
<meta content="modelo cinético, Gompertz, constante de velocidad, metano, Kinetic Model, Gompertz, Kinetic Constant, Methane" name="keywords">
<meta content="Jairo Enrique Granados Moreno" name="author">
<meta content="Diego Adrés Abril Herrera" name="author">
<meta content="Andrés Mogollón Reina" name="author">
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<header>
  <div class="toctitle"> Ingeniería Agrícola Vol. 12, No. 4, octubre-diciembre, 2022, ISSN:&nbsp;2227-8761</div>
  <div class="toctitle2"><img src="data:image/png;base64,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" id="codigo" alt="Código QR" height="85" width="85"><script>
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  <div class="toctitle2"> CU-ID:&nbsp;<a target="_blank" href="https://cu-id.com/2284/v12n4e04">https://cu-id.com/2284/v12n4e04</a></div>
  <div class="toctitle2">ARTÍCULO ORIGINAL</div>
  <h1>Cinética de la digestión anaeróbica de excretas porcinas para la obtención de biogás en laboratorio</h1>
  <h2>Kinetics of anaerobic digestion of pig excreta to obtain biogas in the laboratory</h2>
  <div>
    <p><sup><a href="https://orcid.org/0000-0003-4060-7547" rel="license"><span class="orcid">iD</span></a></sup>Jairo Enrique Granados Moreno<span class="tooltip"><a href="#c1">*</a><span class="tooltip-content">✉:<a href="mailto:jegranados@ucundinamarca.edu.co">jegranados@ucundinamarca.edu.co</a></span></span><a href="#aff1"></a></p>
    <p><sup><a href="https://orcid.org/0000-0001-5950-4631" rel="license"><span class="orcid">iD</span></a></sup>Diego Adrés Abril Herrera<a href="#aff1"></a></p>
    <p><sup><a href="https://orcid.org/0000-0003-1354-5897" rel="license"><span class="orcid">iD</span></a></sup>Andrés Mogollón Reina<a href="#aff1"></a></p>
    <br>
    <p id="aff1"><span class="aff"><sup></sup>Universidad de Cundinamarca, Facultad de Ciencias Agropecuarias, Prog. Zootecnia, Sede Fusagasugá, Cundinamarca, Colombia. </span></p>
  </div>
  <div>&nbsp;</div>
  <p id="c1"> <sup>*</sup>Autor para correspondencia: Jairo Enrique Granados MorenoI, e-mail: <a href="mailto:jegranados@ucundinamarca.edu.co">jegranados@ucundinamarca.edu.co</a> </p>
  <div class="titleabstract | box">RESUMEN</div>
  <div class="box1">
    <p>La
      biodigestión anaeróbica es un proceso bioquímico natural, mediante el 
      cual materiales orgánicos complejos son desdoblados por algunas 
      comunidades microbianas hasta producir biomoléculas elementales y 
      biogas, en ausencia de oxígeno. El proceso se realiza en cuatro etapas: 
      hidrólisis, acidogénesis, acetogénesis y metanogénesis. La generación de
      CH<sub>4</sub> se puede seguir mediante análisis y modelos cinéticos 
      como los de Gompertz y Monod. El objetivo del trabajo fue evaluar el 
      comportamiento cinético de biogases: CH<sub>4</sub>, CO2, H<sub>2</sub>S,
      producidos por biodigestión anaeróbica de excretas porcinas tratadas 
      con inóculo microbiano, mediante modelos cinéticos de Gompertz y Monod. 
      Se trabajó con datos recolectados en campo provenientes de un 
      biodigestor de geomembrana instalado en un predio cercano a Fusagasugá, 
      en Cundinamarca, Colombia, y con tres prototipos de biodigestores 
      emplazados en laboratorio, cuyo afluente eran excretas porcinas e 
      inóculo microbiano (IM)<b>,</b> el seguimiento a la producción de biogas
      se realizó mediante termohigrómetro digital y medidor multiparamétrico 
      de gases A-ALT5X-ALKB. Mediante DCA se encontraron diferencias 
      significativas (p &lt;0,05) entre los parámetros cinéticos evaluados, 
      expresados a través de los modelos cinéticos trabajados, lo que es 
      atribuible al control de variables como: tipo y composición del 
      afluente, temperatura del biodigestato, pH y evolución del DQO. El mejor
      comportamiento cinético se observó en el biodigestor que operó a 
      temperatura cercana a 38 ºC, dado que el cambio de volumen del biogas y 
      las concentraciones de CH<sub>4</sub>, CO2, H<sub>2</sub>S y NH<sub>3</sub> en el tiempo, fueron explicados adecuadamente mediante los modelos cinéticos de Gompertz y Monod.</p>
    <div class="titlekwd"><b> <i>Palabras clave</i>:</b>&nbsp; </div>
    <div class="kwd">modelo cinético, Gompertz, constante de velocidad, metano</div>
  </div>
  <div class="titleabstract | box">ABSTRACT</div>
  <div class="box1">
    <p>Anaerobic
      biodigestion is a natural biochemical process, through which complex 
      organic materials are broken down by some microbial communities to 
      produce elementary biomolecules and biogas, in the absence of oxygen. 
      The process is carried out in four stages: hydrolysis, acidogenesis, 
      acetogenesis and methanogenesis. The generation of CH4 can be followed 
      by analysis and kinetic models such as those of Gompertz and Monod. The 
      objectives were to evaluate the kinetic behavior of biogases: CH<sub>4</sub>, CO<sub>2</sub>, H<sub>2</sub>S,
      produced by anaerobic biodigestion of pig excretes treated with 
      microbial inoculum, using Gompertz and Monod kinetic models. We worked 
      with data collected in the field from a geomembrane biodigestor 
      installed on a property near Fusagasugá, in Cundinamarca, Colombia, and 
      with three prototypes of biodigesters located in the laboratory, whose 
      tributaries were pig excreta and microbial inoculum (IM), biogas 
      production was monitored using a digital thermohygrometer and a 
      multiparametric gas meter A-ALT5X-ALKB. Significant differences (p 
      &lt;0.05) were found between the kinetic parameters evaluated using DCA,
      expressed through the kinetic models worked on, being attributable to 
      the control of variables such as: type and composition of the tributary,
      biodigest temperature, pH and evolution of COD. The best kinetic 
      behavior was observed in the biodigester that operated at a temperature 
      close to 38 ºC, given that the change in biogas volume and the 
      concentrations of CH<sub>4</sub>, CO<sub>2</sub>, H<sub>2</sub>S and NH<sub>3</sub> in time were adequately explained by the kinetic models of Gompertz and Monod.</p>
    <div class="titlekwd"><b> <i>Keywords</i>:</b>&nbsp; </div>
    <div class="kwd">Kinetic Model, Gompertz, Kinetic Constant, Methane</div>
  </div>
  <div class="box2">
    <p class="history">Received: 02/4/2022; Accepted: 09/9/2022</p>
    <p><i>Jairo Enrique Granados Moreno,</i> Profesor Titular, Universidad de Cundinamarca, Facultad de Ciencias 
      Agropecuarias, Prog. Zootecnia, Sede Fusagasugá, Cundinamarca, Colombia,
      GRIPEZ.</p>
    <p><i>Diego Andrés Abril Herrera</i>, Profesor Titular, 
      Universidad de Cundinamarca, Facultad de Ciencias Agropecuarias, Prog. 
      Zootecnia, Sede Fusagasugá, Cundinamarca, Colombia, SISPROS, e-mail: <a href="mailto:adiego@ucundinamarca.edu.co">adiego@ucundinamarca.edu.co</a>.</p>
    <p><i>Andrés Mogollón Reina</i>,
      Profesor Titular, Universidad de Cundinamarca, Facultad de Ciencias 
      Agropecuarias, Prog. Zootecnia, Sede Fusagasugá, Cundinamarca, Colombia,
      SISPROS, e-mail: <a href="mailto:amogollon@ucundinamarca.edu.co">amogollon@ucundinamarca.edu.co</a>.</p>
    <p>Los autores de este trabajo declaran no presentar conflicto de intereses.</p>
    <p><b>CONTRIBUCIONES DE AUTOR: Conceptualización:</b> J. E. Granados, D. A. Abril, A. Mogollón. <b>Curación de datos:</b> J. E. Granados. <b>Análisis formal:</b> J. E. Granados, D. A. Abril. <b>Captación de fondos:</b> J. E. Granados, D. A. Abril, A. Mogollón. <b>Investigación:</b> J. E. Granados, D. A. Abril, A. Mogollón. <b>Metodología:</b> J. E. Granados. <b>Administración de proyectos:</b> J. E. Granados. <b>Recursos:</b> J. E. Granados, D. A. Abril, A. Mogollón. <b>Software: Supervisión: Validación:</b> J. E. Granados, D. A. Abril. <b>Visualización:</b> J. E. Granados, D. A. Abril <b>Redacción-borrador original:</b> J. E. Granados, D. A. Abril, A. Mogollón. <b>Redacción-revisión y edición</b>: D. A. Abril, A. Mogollón.</p>
    <p>La
      mención de marcas comerciales de equipos, instrumentos o materiales 
      específicos obedece a propósitos de identificación, no existiendo ningún
      compromiso promocional con relación a los mismos, ni por los autores ni
      por el editor.</p>
    <p class="copyright">Este artículo se encuentra bajo licencia <a target="_blank" href="https://creativecommons.org/licenses/by-nc/4.0/deed.es_ES">Creative Commons Reconocimiento-NoComercial 4.0 Internacional (CC BY-NC 4.0)</a></p>
  </div>
  <div class="titleabstract | box"><a id="content"></a>CONTENIDO</div>
  <div class="box1">
    <nav>
      <ul class="nav">
        <li><a href="#id0x5e87880"><span class="menulevel1">INTRODUCCIÓN</span></a></li>
        <li><a href="#id0x5edcb00"><span class="menulevel1">MATERIALES Y MÉTODOS</span></a></li>
        <li><a href="#id0x5edcd80"><span class="menulevel2">Materia prima</span></a></li>
        <li><a href="#id0x5edd400"><span class="menulevel2">Etapa experimental</span></a></li>
        <li><a href="#id0x5eefa80"><span class="menulevel3">Análisis biofisicoquímico</span></a></li>
        <li><a href="#id0x5ef0980"><span class="menulevel3">Análisis cinético</span></a></li>
        <li><a href="#id0x345e700"><span class="menulevel1">RESULTADOS Y DISCUSION</span></a></li>
        <li><a href="#id0x345e980"><span class="menulevel2">Análisis fisicoquímico de materia prima</span></a></li>
        <li><a href="#id0x345f080"><span class="menulevel2">Producción de biogás en los biodigestores</span></a></li>
        <li><a href="#id0x4cc3180"><span class="menulevel2">Producción de gas metano en los biodigestores</span></a></li>
        <li><a href="#id0x6307e00"><span class="menulevel1">CONCLUSIONES</span></a></li>
        <li><a href="#ref"><span class="menulevel1">REFERENCIAS BIBLIOGRÁFICA</span></a></li>
      </ul>
    </nav>
  </div>
</header>
<div id="article-front"></div>
<div class="box2" id="article-body">
  <section>
    <article class="section"><a id="id0x5e87880"><!-- named anchor --></a>
      <h3>INTRODUCCIÓN</h3>
      &nbsp;<a href="#content" class="boton_1">⌅</a>
      <p>Actualmente,
        la generación de residuos alimentarios equivale a un tercio de la 
        producción para el consumo humano en el mundo, siendo aproximadamente de
        1300 𝑀𝑀𝑡𝑜𝑛/𝑎ñ𝑜 según <span class="tooltip"><a href="#B19">Zapata (2019)</a><span class="tooltip-content">Zapata,
        E. I. V. (2019). Valorización de residuo alimentario como fuente 
        potencial de producción de biogás y ácidos grasos volátiles [Tesis para 
        optar al título de Ingeniero Civil Químico]. Universidad Técnica 
        Federico Santa María, Departamento de Ingeniería Química y Ambiental.</span></span>,
        los sistemas productores agropecuarios enfrentan un reto fundamental 
        con el aprovechamiento de residuos de biomasa vegetal y animal generados
        en los diversos procesos productivos, dado que al ser desechados y no 
        tratados adecuadamente, generan impacto ambiental produciendo gases con 
        efecto de invernadero (GEI), contaminación de suelos y fuentes de agua, 
        dicha biomasa es considerada como la principal fuente de energía 
        renovable en el mundo destinada a reemplazar recursos de combustibles 
        fósiles en clara reducción según <span class="tooltip"><a href="#B5">Deepanraj <i>et al.</i> (2015)</a><span class="tooltip-content">Deepanraj,
        B., Sivasubramanian, V., &amp; Jayaraj, S. (2015). Kinetic study on the
        effect of temperature on biogas production using a lab scale batch 
        reactor. Ecotoxicology and Environmental Safety, 121, 100-104. <a href="http://dx.doi.org/10.1016/j.ecoenv.2015.04.051" target="xrefwindow">http://dx.doi.org/10.1016/j.ecoenv.2015.04.051</a> </span></span>, causado por el incremento en la demanda de energética de actividades antropogénicas (<span class="tooltip"><a href="#B3">Benavidez et al., 2017</a><span class="tooltip-content">Benavidez,
        E., Toledo, J., Palacios, Y., Andino, F., &amp; Ortiz, W. (2017). 
        Producción de biogás a partir de la codigestión de estiércol bovino, 
        melaza e inóculo bacteriano. Revista RedBioLAC, 1(1), 52-57. <a href="http://redbiolac.org/revista/" target="xrefwindow">http://redbiolac.org/revista/</a> </span></span>; <span class="tooltip"><a href="#B9">Gutierrez &amp; Ochoa, 2019</a><span class="tooltip-content">Gutiérrez,
        N. L. C., &amp; Ochoa, N. L. D. (2019). Determinación del potencial 
        energético para la obtención de biogás, a partir de la codigestión 
        anaerobia del cosustrato cascarilla de arroz con excretas porcinas 
        [Tesis Ingeniería Ambiental]. Universidad Santo Tomás, Facultad de 
        Ingeniería Ambiental.</span></span>; <span class="tooltip"><a href="#B12">Orjuela, 2015</a><span class="tooltip-content">Orjuela,
        C. G. C. (2015). Producción de biogás mediante la fermentación 
        anaerobia de los residuos orgánicos de la cadena de restaurantes Wok 
        [Tesis Ingeniería Química]. Universidad de los Andes. Facultad de 
        Ingeniería, Departamento de Ingeniería Química.</span></span>; <span class="tooltip"><a href="#B16">Valladares, 2017</a><span class="tooltip-content">Valladares,
        C. F. (2017). Modelamiento del proceso de digestión anaeróbica de 
        estiércol vacuno y cáscara de cacao [Tesis en Ingeniería 
        Mecánico-Eléctrica]. Universidad de Piura. Facultad de Ingeniería. 
        Programa Académico de Ingeniería Mecánico-Eléctrica.</span></span>). </p>
      <p>Aunque
        Colombia contribuye únicamente con el 0.37% de las emisiones globales 
        de GEI, se considera como un país vulnerable frente al cambio climático 
        en razón de su posición geográfica según <span class="tooltip"><a href="#B15">Valdés <i>et al.</i> (2001)</a><span class="tooltip-content">Valdés,
        L. Y., Vavilin, V. A., Kettunen, R. H., Rintala, J. A., Holliger, C., 
        &amp; Nozhevnikova, A. N. (2001). Evaluation of kinetic coefficients 
        using integrated Monod and Haldane models for low-temperature 
        acetoclastic methanogenesis. Water Research, 35(12), 2913-2922.</span></span>, está claro que los recursos renovables jugarán un papel crucial en la limitación de las emisiones de CO<sub>2</sub>,
        ya que biomasa y residuos se consideran fuentes de energía renovables 
        futuras de mayor impacto, puesto que pueden proporcionar generación de 
        energía continua de acuerdo a <span class="tooltip"><a href="#B2">Appels <i>et al.</i> (2011)</a><span class="tooltip-content">Appels,
        L., Lauwers, J., Degrève, J., Helsen, L., Lievens, B., Willems, K., Van
        Impe, J., &amp; Dewil, R. (2011). Anaerobic digestion in global 
        bio-energy production: Potential and research challenges. Renewable and 
        Sustainable Energy Reviews, 15(9), 4295-4301.</span></span>, es así como
        la producción de biocombustibles a partir de biomasa puede mitigar 
        estos efectos al reducir sustancialmente las emisiones de GEI (<span class="tooltip"><a href="#B1">Ampudia, 2011</a><span class="tooltip-content">Ampudia,
        M. M. J. (2011). Investigación de las condiciones óptimas y de la 
        cinética del proceso de biodigestión anaerobia de desechos orgánicos 
        agroindustriales y estiercol vacuno [Tesis de grado en Ingeniería 
        Química]. Universidad San Francisco de Quito.</span></span>). </p>
      <p>Entre
        algunas tecnologías renovables, la digestión anaeróbica es una 
        metodología atractiva, verificada y ampliamente utilizada para la 
        conversión de biomasa proveniente de residuos agrícolas y forestales, 
        desechos sólidos municipales, etc. (<span class="tooltip"><a href="#B1">Ampudia, 2011</a><span class="tooltip-content">Ampudia,
        M. M. J. (2011). Investigación de las condiciones óptimas y de la 
        cinética del proceso de biodigestión anaerobia de desechos orgánicos 
        agroindustriales y estiercol vacuno [Tesis de grado en Ingeniería 
        Química]. Universidad San Francisco de Quito.</span></span>). El proceso
        se desarrolla mediante reacciones bioquímicas microbianas enfocadas a 
        transformar la materia orgánica en biogas y residuos denominados bioles,
        ricos en nutrientes como N, P, K, que los posibilitan para usar en 
        fertilización y enmienda de suelos agrícolas (<span class="tooltip"><a href="#B4">Cerdán, 2020</a><span class="tooltip-content">Cerdán,
        M. J. M. A. (2020). Potencial de producción de ácidos grasos volátiles 
        en lodos de Ptar, residuos urbanos y agroindustriales: Enfoque hacia una
        economía circular [Tesis para optar el grado de Magister Scientiae en 
        ciencias ambientales]. Universidad Agraria la Molina.</span></span>; <span class="tooltip"><a href="#B6">Ferrer &amp; Pérez, 2010</a><span class="tooltip-content">Ferrer,
        Y., &amp; Pérez, H. (2010). Los microorganismos en la digestión 
        anaerobia y la producción de biogás. Consideraciones en la elección del 
        inóculo para el mejoramiento de la calidad y el rendimiento. ICIDCA. 
        Sobre los Derivados de la Caña de Azúcar, 43(1), 9-20.</span></span>; <span class="tooltip"><a href="#B9">Gutierrez &amp; Ochoa, 2019</a><span class="tooltip-content">Gutiérrez,
        N. L. C., &amp; Ochoa, N. L. D. (2019). Determinación del potencial 
        energético para la obtención de biogás, a partir de la codigestión 
        anaerobia del cosustrato cascarilla de arroz con excretas porcinas 
        [Tesis Ingeniería Ambiental]. Universidad Santo Tomás, Facultad de 
        Ingeniería Ambiental.</span></span>; <span class="tooltip"><a href="#B12">Orjuela, 2015</a><span class="tooltip-content">Orjuela,
        C. G. C. (2015). Producción de biogás mediante la fermentación 
        anaerobia de los residuos orgánicos de la cadena de restaurantes Wok 
        [Tesis Ingeniería Química]. Universidad de los Andes. Facultad de 
        Ingeniería, Departamento de Ingeniería Química.</span></span>; <span class="tooltip"><a href="#B17">Vaquerano et al., 2016</a><span class="tooltip-content">Vaquerano,
        P. N., Salazar, R. T., &amp; Porras, A. M. (2016). Medición automática 
        del metano en biogás, por columnas de desplazamiento. Revista Tecnología
        en Marcha, 29, 86-96. <a href="http://dx.doi.org/10.18845/tm.v29i8.2988" target="xrefwindow">http://dx.doi.org/10.18845/tm.v29i8.2988</a> </span></span>). </p>
      <p>La digestión anaeróbica (DA) de biorresiduos
        es la forma más convencional de producir biogás rico en metano, que 
        tiene un gran potencial para reemplazar el combustible fósil utilizado 
        en múltiples aplicaciones, como el transporte vehicular (<span class="tooltip"><a href="#B7">A. K. García, 2009</a><span class="tooltip-content">García,
        A. K. (2009). Codigestión anaeróbica de estiércol y lodos de depuradora
        para producción de biogás [Tesis para optar el grado de Máster, perfil 
        Investigador]. Universidad de Cádiz.</span></span>; <span class="tooltip"><a href="#B14">Sihuang et al., 2016</a><span class="tooltip-content">Sihuang,
        X., Faisal, H. I., Xinmin, Z., Wenshan, G., Hao, N. H., William, P. E.,
        &amp; Long, N. D. (2016). Anaerobic co-digestion: A critical review of 
        mathematical modelling for performance optimization. Bioresource 
        Technology, 222, 498-512. <a href="http://dx.doi.org/10.1016/j.biortech.2016.10.015" target="xrefwindow">http://dx.doi.org/10.1016/j.biortech.2016.10.015</a> </span></span>). Muchos países y empresas están involucrados en el 
        diseño y construcción de sistemas DA eficientes y económicos. El diseño y
        optimización de estos procesos para la producción de biogás se puede 
        mejorar a través de modelos matemáticos validados desarrollados a partir
        de estudios mecanísticos que conducen a una mayor profundidad 
        comprensión de los fenómenos de transporte muy complejos, la cinética 
        bioquímica microbiana y la estequiometría (<span class="tooltip"><a href="#B18">Yu et al., 2013</a><span class="tooltip-content">Yu,
        L., Wensel, P. C., Ma, J., &amp; Chen, S. (2013). Mathematical modeling
        in anaerobic digestion (AD). J Bioremed Biodeg S, 4(2). <a href="http://dx.doi.org/10.4172/2155-6199.S4-003" target="xrefwindow">http://dx.doi.org/10.4172/2155-6199.S4-003</a> </span></span>). </p>
      <p>La digestión anaeróbica consta de cuatro 
        etapas basadas en reacciones bioquímicas enzimáticas (RBE) a saber: 
        hidrólisis: bacterias con enzimas hidrolasas descomponen polímeros 
        presentes en la materia orgánica de la biomasa en monómeros; 
        acidogénesis: bacterias acidogénicas convierten monómeros en 
        biomoléculas elementales como: ácidos carboxílicos, dióxido de carbono, 
        hidrógeno y alcoholes; acetogénesis: en esta fase las moléculas 
        anteriores se convierten en ácido acético; metanogénesis: bacterias 
        metanogénicas reducen CO<sub>2</sub> hasta CH<sub>4</sub> a partir de ácido acético (<span class="tooltip"><a href="#B5">Deepanraj et al., 2015</a><span class="tooltip-content">Deepanraj,
        B., Sivasubramanian, V., &amp; Jayaraj, S. (2015). Kinetic study on the
        effect of temperature on biogas production using a lab scale batch 
        reactor. Ecotoxicology and Environmental Safety, 121, 100-104. <a href="http://dx.doi.org/10.1016/j.ecoenv.2015.04.051" target="xrefwindow">http://dx.doi.org/10.1016/j.ecoenv.2015.04.051</a> </span></span>).</p>
      <p>El biogás producido durante las etapas de DA, es una mezcla de metano (CH<sub>4</sub>: 55-65 % por volumen), dióxido de carbono (CO<sub>2</sub>: 30-40 % por volumen) y trazas de sulfuro de hidrógeno (H<sub>2</sub>S), hidrógeno (H<sub>2</sub>) y amoníaco (NH<sub>3</sub>) (<span class="tooltip"><a href="#B5">Deepanraj et al., 2015</a><span class="tooltip-content">Deepanraj,
        B., Sivasubramanian, V., &amp; Jayaraj, S. (2015). Kinetic study on the
        effect of temperature on biogas production using a lab scale batch 
        reactor. Ecotoxicology and Environmental Safety, 121, 100-104. <a href="http://dx.doi.org/10.1016/j.ecoenv.2015.04.051" target="xrefwindow">http://dx.doi.org/10.1016/j.ecoenv.2015.04.051</a> </span></span>).</p>
      <p>Múltiples trabajos de investigación se han 
        propuesto para efectuar seguimiento del comportamiento cinético de la 
        producción del biogás como son <span class="tooltip"><a href="#B11">Nopharatana <i>et al.</i> (2007)</a><span class="tooltip-content">Nopharatana,
        A., Pullammanappallil, P. C., &amp; Clarke, W. P. (2007). Kinetics and 
        dynamic modelling of batch anaerobic digestion of municipal solid waste 
        in a stirred reactor. Waste management, 27(5), 595-603.</span></span>; <span class="tooltip"><a href="#B1">Ampudia (2011)</a><span class="tooltip-content">Ampudia,
        M. M. J. (2011). Investigación de las condiciones óptimas y de la 
        cinética del proceso de biodigestión anaerobia de desechos orgánicos 
        agroindustriales y estiercol vacuno [Tesis de grado en Ingeniería 
        Química]. Universidad San Francisco de Quito.</span></span>; <span class="tooltip"><a href="#B18">Yu <i>et al.</i> (2013)</a><span class="tooltip-content">Yu,
        L., Wensel, P. C., Ma, J., &amp; Chen, S. (2013). Mathematical modeling
        in anaerobic digestion (AD). J Bioremed Biodeg S, 4(2). <a href="http://dx.doi.org/10.4172/2155-6199.S4-003" target="xrefwindow">http://dx.doi.org/10.4172/2155-6199.S4-003</a> </span></span>; <span class="tooltip"><a href="#B10">López y Ruíz (2014)</a><span class="tooltip-content">López,
        A. A. M., &amp; Ruíz, R. C. (2014). Evaluación de la producción de 
        biogás a partir del buchón de agua mediante codigestión anaerobia con 
        estiércol bovino [Tesis (en opción al título de Ingeniería de 
        Procesos)]. Universidad EAFIT, Escuela de Ingeniería, Departamento de 
        Ingeniería de Procesos.</span></span>; <span class="tooltip"><a href="#B5">Deepanraj <i>et al.</i> (2015)</a><span class="tooltip-content">Deepanraj,
        B., Sivasubramanian, V., &amp; Jayaraj, S. (2015). Kinetic study on the
        effect of temperature on biogas production using a lab scale batch 
        reactor. Ecotoxicology and Environmental Safety, 121, 100-104. <a href="http://dx.doi.org/10.1016/j.ecoenv.2015.04.051" target="xrefwindow">http://dx.doi.org/10.1016/j.ecoenv.2015.04.051</a> </span></span>; <span class="tooltip"><a href="#B16">Valladares (2017)</a><span class="tooltip-content">Valladares,
        C. F. (2017). Modelamiento del proceso de digestión anaeróbica de 
        estiércol vacuno y cáscara de cacao [Tesis en Ingeniería 
        Mecánico-Eléctrica]. Universidad de Piura. Facultad de Ingeniería. 
        Programa Académico de Ingeniería Mecánico-Eléctrica.</span></span>, los 
        cuales, mediante modelos matemáticos que incluyen parámetros cinéticos, 
        buscan comprender la cinética de reacciones bioquímicas enzimáticas 
        desarrolladas por las bacterias anaeróbicas en las etapas de digestión</p>
      <p>La
        presente investigación analizó el efecto de la adición de un inóculo 
        microbiano (IM) en la biodigestión de excretas porcinas y bovinas en 
        biodigestores prototipos escala laboratorio y el comportamiento de 
        parámetros cinéticos en la producción del biogas, los resultados se 
        estudiaron a la luz de modelos cinéticos de Monod y modificado de 
        Gompertz. </p>
    </article>
    <article class="section"><a id="id0x5edcb00"><!-- named anchor --></a>
      <h3>MATERIALES Y MÉTODOS</h3>
      &nbsp;<a href="#content" class="boton_1">⌅</a>
      <article class="section"><a id="id0x5edcd80"><!-- named anchor --></a>
        <h4>Materia prima</h4>
        &nbsp;<a href="#content" class="boton_1">⌅</a>
        <p>Las
          excretas utilizadas en este estudio experimental se recolectaron en 
          tres predios del municipio de Fusagasugá, Cundinamarca, Colombia, los 
          cuales corresponden a sistemas productivos pecuarios con explotaciones 
          porcinas y bovinas. Esta biomasa es un sustrato muy deseable para la 
          digestión anaeróbica debido a su mayor biodegradabilidad y rendimiento 
          de biogás/metano, por la cantidad de materia orgánica que poseen. 
          Además, el análisis del contenido de nutrientes mostró que las muestras 
          contenían nutrientes bien equilibrados para los microorganismos 
          anaeróbicos (<span class="tooltip"><a href="#B11">Nopharatana et al., 2007</a><span class="tooltip-content">Nopharatana,
          A., Pullammanappallil, P. C., &amp; Clarke, W. P. (2007). Kinetics and 
          dynamic modelling of batch anaerobic digestion of municipal solid waste 
          in a stirred reactor. Waste management, 27(5), 595-603.</span></span>). 
          Las excretas se mezclaron y almacenaron en botellas de plásticos de 1 L,
          esterilizadas, luego se refrigeraron en nevera a 54 °C hasta su 
          introducción en el digestor anaeróbico. Se añadió agua para obtener la 
          concentración deseada de sólidos totales. Las características 
          biofisicoquímicas de afluentes y efluentes se determinaron antes y 
          después de la digestión. </p>
      </article>
      <article class="section"><a id="id0x5edd400"><!-- named anchor --></a>
        <h4>Etapa experimental</h4>
        &nbsp;<a href="#content" class="boton_1">⌅</a>
        <p>El
          montaje de los biodigestores prototipo se realizó en los laboratorios 
          de nutrición de la Universidad de Cundinamarca, sede Fusagasugá, en 
          condiciones de: altura: 1600 msnm, temperatura promedio: 19,1ºC, humedad
          relativa promedio de 80%, precipitación anual de 140 mm; el municipio 
          se encuentra ubicado a 59 km al suroccidente de Bogotá.</p>
        <p>En todos 
          los experimentos se utilizaron digestores discontinuos anaeróbicos a 
          escala de laboratorio hechos de vidrio con un volumen total que oscilaba
          entre 1 L y 2 L, con volumen de trabajo de 1,5 L; la producción de 
          biogás de los digestores se midió diariamente por el método de 
          desplazamiento de agua. Las mezclas de reacción se agitaron 
          continuamente en termoagitadores magnéticos diariamente, igualmente se 
          controló temperatura y humedad relativa mediante un termohigrómetro 
          digital.</p>
        <article class="section"><a id="id0x5eefa80"><!-- named anchor --></a>
          <h4>Análisis biofisicoquímico</h4>
          &nbsp;<a href="#content" class="boton_1">⌅</a>
          <p>Sólidos
            totales y sólidos volátiles, demanda química de oxígeno del sustrato y 
            digestato se determinaron según el método estándar (APHA); sólidos 
            disueltos totales-TDS, conductividad eléctrica (CE) y pH en afluente, 
            digestato y bioles, se determinaron usando un medidor multiparamétrico 
            Hanna instruments (2019); Carbono orgánico (CO), se determinó mediante 
            método espectrofotométrico modificado de Walkey Black <span class="tooltip"><a href="#B8">García y Ballesteros (2005)</a><span class="tooltip-content">García,
            G. J., &amp; Ballesteros, G. M. I. (2005). Evaluación de parámetros de 
            calidad para la determinación de carbono orgánico en suelos. Revista 
            colombiana de Química, 34(2), 201-209.</span></span>, mientras que N-NO<sub>3</sub> y P-PO<sub>4</sub>,se hallaron en fotómetro multiparamétrico de Hanna Instruments-HI 83399. La composición de: CH<sub>4</sub>, CO<sub>2</sub>, H<sub>2</sub>S y NH<sub>3</sub> en el biogás se midió utilizando analizador digital multiparamétrico de gases A-ALT5X-ALKB</p>
        </article>
        <article class="section"><a id="id0x5ef0980"><!-- named anchor --></a>
          <h4>Análisis cinético</h4>
          &nbsp;<a href="#content" class="boton_1">⌅</a>
          <p>Los valores de producción de biogas (<i>Vac</i>) y metano (<i>μ</i> <sub> <i>CH4</i> </sub> ), se analizaron mediante modelos matemáticos cinéticos de Gompertz modificado y Moned (<span class="tooltip"><a href="#B5">Deepanraj et al., 2015</a><span class="tooltip-content">Deepanraj,
            B., Sivasubramanian, V., &amp; Jayaraj, S. (2015). Kinetic study on the
            effect of temperature on biogas production using a lab scale batch 
            reactor. Ecotoxicology and Environmental Safety, 121, 100-104. <a href="http://dx.doi.org/10.1016/j.ecoenv.2015.04.051" target="xrefwindow">http://dx.doi.org/10.1016/j.ecoenv.2015.04.051</a> </span></span>; <span class="tooltip"><a href="#B11">Nopharatana et al., 2007</a><span class="tooltip-content">Nopharatana,
            A., Pullammanappallil, P. C., &amp; Clarke, W. P. (2007). Kinetics and 
            dynamic modelling of batch anaerobic digestion of municipal solid waste 
            in a stirred reactor. Waste management, 27(5), 595-603.</span></span>; <span class="tooltip"><a href="#B13">Pommier et al., 2007</a><span class="tooltip-content">Pommier,
            S., Chenu, D., Acosta, M. M., &amp; Lefebvre, X. (2007). A logistic 
            model for the prediction of the influence of water on the solid waste 
            methanization in landfills. Biotechnology and bioengineering, 97(3), 
            473-482.</span></span>). El modelo de Gompertz modificado (1) y de Monet
            (2), describen la producción acumulativa de biogás de los digestores 
            asumiendo que la producción de biogás es una función del crecimiento 
            bacteriano; usando estos modelos, se calcula potencial de producción de 
            biogás del sustrato (B), tasa ó velocidad máxima de producción de biogás
            (R<sub>b</sub>) y fase de latencia de la reacción (λ), tasa o velocidad máxima de producción del gas metano (μ<sub>máx</sub>)
            y constante de Michaelis-Menten (Ks), para luego comparar y ajustar 
            resultados experimentales. Las ecuaciones son las siguientes:</p>
          <div id="e1" class="disp-formula">
            <math>
              <msub>
                <mrow>
                  <mi>V</mi>
                </mrow>
                <mrow>
                  <mi>a</mi>
                  <mi>c</mi>
                </mrow>
              </msub>
              <mo>=</mo>
              <mi>B</mi>
              <mi>*</mi>
              <mi>e</mi>
              <mi>x</mi>
              <mi>p</mi>
              <mfenced separators="|" open="{" close="">
                <mrow>
                  <mo>-</mo>
                  <mi>e</mi>
                  <mi>x</mi>
                  <mi>p</mi>
                  <mfenced separators="|" open="[" close="]">
                    <mrow>
                      <mfrac>
                        <mrow>
                          <msub>
                            <mrow>
                              <mi>R</mi>
                            </mrow>
                            <mrow>
                              <mi>b</mi>
                            </mrow>
                          </msub>
                          <mi>*</mi>
                          <mi>e</mi>
                        </mrow>
                        <mrow>
                          <mi>B</mi>
                        </mrow>
                      </mfrac>
                      <mfenced separators="|" close="">
                        <mrow>
                          <mi>λ</mi>
                          <mo>-</mo>
                          <mi>t</mi>
                          <mo>)</mo>
                          <mo>+</mo>
                          <mn>1</mn>
                        </mrow>
                      </mfenced>
                    </mrow>
                  </mfenced>
                </mrow>
              </mfenced>
            </math>
            <span class="labelfig"> &nbsp;(1)</span></div>
          <div style="clear:both"></div>
          <div id="e2" class="disp-formula">
            <math>
              <msub>
                <mrow>
                  <mi>μ</mi>
                </mrow>
                <mrow>
                  <mi>C</mi>
                  <mi>H</mi>
                  <mn>4</mn>
                </mrow>
              </msub>
              <mo>=</mo>
              <mi>&nbsp;</mi>
              <mfrac>
                <mrow>
                  <msub>
                    <mrow>
                      <mi>μ</mi>
                    </mrow>
                    <mrow>
                      <mi>m</mi>
                      <mi>á</mi>
                      <mi>x</mi>
                    </mrow>
                  </msub>
                  <mi>*</mi>
                  <mi>S</mi>
                </mrow>
                <mrow>
                  <msub>
                    <mrow>
                      <mi>k</mi>
                    </mrow>
                    <mrow>
                      <mi>s</mi>
                    </mrow>
                  </msub>
                  <mo>+</mo>
                  <mi>S</mi>
                </mrow>
              </mfrac>
            </math>
            <span class="labelfig"> &nbsp;(2)</span></div>
          <div style="clear:both"></div>
          <p>La ecuación de Gompertz (<span class="tooltip"><a href="#e1">1</a><span class="tooltip-content">
            <math>
              <msub>
                <mrow>
                  <mi>V</mi>
                </mrow>
                <mrow>
                  <mi>a</mi>
                  <mi>c</mi>
                </mrow>
              </msub>
              <mo>=</mo>
              <mi>B</mi>
              <mi>*</mi>
              <mi>e</mi>
              <mi>x</mi>
              <mi>p</mi>
              <mfenced separators="|" open="{" close="">
                <mrow>
                  <mo>-</mo>
                  <mi>e</mi>
                  <mi>x</mi>
                  <mi>p</mi>
                  <mfenced separators="|" open="[" close="]">
                    <mrow>
                      <mfrac>
                        <mrow>
                          <msub>
                            <mrow>
                              <mi>R</mi>
                            </mrow>
                            <mrow>
                              <mi>b</mi>
                            </mrow>
                          </msub>
                          <mi>*</mi>
                          <mi>e</mi>
                        </mrow>
                        <mrow>
                          <mi>B</mi>
                        </mrow>
                      </mfrac>
                      <mfenced separators="|" close="">
                        <mrow>
                          <mi>λ</mi>
                          <mo>-</mo>
                          <mi>t</mi>
                          <mo>)</mo>
                          <mo>+</mo>
                          <mn>1</mn>
                        </mrow>
                      </mfenced>
                    </mrow>
                  </mfenced>
                </mrow>
              </mfenced>
            </math>
            </span></span>), se puede transformar matemáticamente en la <span class="tooltip"><a href="#e3">expresión 3</a><span class="tooltip-content">
            <math>
              <mi>L</mi>
              <mi>n</mi>
              <mfenced separators="|">
                <mrow>
                  <mi>L</mi>
                  <mi>n</mi>
                  <mi>P</mi>
                </mrow>
              </mfenced>
              <mo>=</mo>
              <mfrac>
                <mrow>
                  <msub>
                    <mrow>
                      <mo>-</mo>
                      <mi>R</mi>
                    </mrow>
                    <mrow>
                      <mi>b</mi>
                    </mrow>
                  </msub>
                  <mi>*</mi>
                  <mi>e</mi>
                </mrow>
                <mrow>
                  <mi>B</mi>
                </mrow>
              </mfrac>
              <mi>*</mi>
              <mi>t</mi>
              <mo>+</mo>
              <mfrac>
                <mrow>
                  <msub>
                    <mrow>
                      <mi>R</mi>
                    </mrow>
                    <mrow>
                      <mi>b</mi>
                    </mrow>
                  </msub>
                  <mi>*</mi>
                  <mi>e</mi>
                </mrow>
                <mrow>
                  <mi>B</mi>
                </mrow>
              </mfrac>
              <mi>*</mi>
              <mi>λ</mi>
              <mo>+</mo>
              <mn>1</mn>
            </math>
            </span></span> </p>
          <div id="e3" class="disp-formula">
            <math>
              <mi>L</mi>
              <mi>n</mi>
              <mfenced separators="|">
                <mrow>
                  <mi>L</mi>
                  <mi>n</mi>
                  <mi>P</mi>
                </mrow>
              </mfenced>
              <mo>=</mo>
              <mfrac>
                <mrow>
                  <msub>
                    <mrow>
                      <mo>-</mo>
                      <mi>R</mi>
                    </mrow>
                    <mrow>
                      <mi>b</mi>
                    </mrow>
                  </msub>
                  <mi>*</mi>
                  <mi>e</mi>
                </mrow>
                <mrow>
                  <mi>B</mi>
                </mrow>
              </mfrac>
              <mi>*</mi>
              <mi>t</mi>
              <mo>+</mo>
              <mfrac>
                <mrow>
                  <msub>
                    <mrow>
                      <mi>R</mi>
                    </mrow>
                    <mrow>
                      <mi>b</mi>
                    </mrow>
                  </msub>
                  <mi>*</mi>
                  <mi>e</mi>
                </mrow>
                <mrow>
                  <mi>B</mi>
                </mrow>
              </mfrac>
              <mi>*</mi>
              <mi>λ</mi>
              <mo>+</mo>
              <mn>1</mn>
            </math>
            <span class="labelfig"> &nbsp;(3)</span></div>
          <div style="clear:both"></div>
          <p>donde: </p>
          <div id="e4" class="disp-formula">
            <math>
              <mi>P</mi>
              <mo>=</mo>
              <mfrac>
                <mrow>
                  <mi>B</mi>
                </mrow>
                <mrow>
                  <msub>
                    <mrow>
                      <mi>V</mi>
                    </mrow>
                    <mrow>
                      <mi>a</mi>
                      <mi>c</mi>
                    </mrow>
                  </msub>
                </mrow>
              </mfrac>
            </math>
            <span class="labelfig"> &nbsp;(4)</span></div>
          <div style="clear:both"></div>
          <p>La <span class="tooltip"><a href="#e3">expresión 3</a><span class="tooltip-content">
            <math>
              <mi>L</mi>
              <mi>n</mi>
              <mfenced separators="|">
                <mrow>
                  <mi>L</mi>
                  <mi>n</mi>
                  <mi>P</mi>
                </mrow>
              </mfenced>
              <mo>=</mo>
              <mfrac>
                <mrow>
                  <msub>
                    <mrow>
                      <mo>-</mo>
                      <mi>R</mi>
                    </mrow>
                    <mrow>
                      <mi>b</mi>
                    </mrow>
                  </msub>
                  <mi>*</mi>
                  <mi>e</mi>
                </mrow>
                <mrow>
                  <mi>B</mi>
                </mrow>
              </mfrac>
              <mi>*</mi>
              <mi>t</mi>
              <mo>+</mo>
              <mfrac>
                <mrow>
                  <msub>
                    <mrow>
                      <mi>R</mi>
                    </mrow>
                    <mrow>
                      <mi>b</mi>
                    </mrow>
                  </msub>
                  <mi>*</mi>
                  <mi>e</mi>
                </mrow>
                <mrow>
                  <mi>B</mi>
                </mrow>
              </mfrac>
              <mi>*</mi>
              <mi>λ</mi>
              <mo>+</mo>
              <mn>1</mn>
            </math>
            </span></span>,
            es una ecuación lineal de la forma y=mx+b, por lo tanto, al asumir que 
            y=Ln(LnP) and X=t, se puede graficar y ajustar por medio de regresión 
            lineal por mínimos cuadrados, luego con los valores obtenidos de 
            pendiente (m) e intercepto (b), se calculan los parámetros cinéticos de 
            Gompertz , de acuerdo a las <span class="tooltip"><a href="#e5">ecuaciones 5</a><span class="tooltip-content">
            <math>
              <mi>B</mi>
              <mo>=</mo>
              <mi>P</mi>
              <mi>*</mi>
              <msub>
                <mrow>
                  <mi>V</mi>
                </mrow>
                <mrow>
                  <mi>a</mi>
                  <mi>c</mi>
                </mrow>
              </msub>
            </math>
            </span></span>, <span class="tooltip"><a href="#e6">6</a><span class="tooltip-content">
            <math>
              <msub>
                <mrow>
                  <mi>R</mi>
                </mrow>
                <mrow>
                  <mi>b</mi>
                  <mi>&nbsp;</mi>
                </mrow>
              </msub>
              <mo>=</mo>
              <mfrac>
                <mrow>
                  <mo>-</mo>
                  <mi>B</mi>
                  <mi>*</mi>
                  <mi>m</mi>
                </mrow>
                <mrow>
                  <mi>e</mi>
                </mrow>
              </mfrac>
            </math>
            </span></span> y <span class="tooltip"><a href="#e7">7</a><span class="tooltip-content">
            <math>
              <mi>λ</mi>
              <mo>=</mo>
              <mfrac>
                <mrow>
                  <mn>1</mn>
                  <mo>-</mo>
                  <mi>b</mi>
                </mrow>
                <mrow>
                  <mi>m</mi>
                </mrow>
              </mfrac>
            </math>
            </span></span> </p>
          <div id="e5" class="disp-formula">
            <math>
              <mi>B</mi>
              <mo>=</mo>
              <mi>P</mi>
              <mi>*</mi>
              <msub>
                <mrow>
                  <mi>V</mi>
                </mrow>
                <mrow>
                  <mi>a</mi>
                  <mi>c</mi>
                </mrow>
              </msub>
            </math>
            <span class="labelfig"> &nbsp;(5)</span></div>
          <div style="clear:both"></div>
          <div id="e6" class="disp-formula">
            <math>
              <msub>
                <mrow>
                  <mi>R</mi>
                </mrow>
                <mrow>
                  <mi>b</mi>
                  <mi>&nbsp;</mi>
                </mrow>
              </msub>
              <mo>=</mo>
              <mfrac>
                <mrow>
                  <mo>-</mo>
                  <mi>B</mi>
                  <mi>*</mi>
                  <mi>m</mi>
                </mrow>
                <mrow>
                  <mi>e</mi>
                </mrow>
              </mfrac>
            </math>
            <span class="labelfig"> &nbsp;(6)</span></div>
          <div style="clear:both"></div>
          <p></p>
          <p>e=
            base de los logaritmos naturales </p>
          <div id="e7" class="disp-formula">
            <math>
              <mi>λ</mi>
              <mo>=</mo>
              <mfrac>
                <mrow>
                  <mn>1</mn>
                  <mo>-</mo>
                  <mi>b</mi>
                </mrow>
                <mrow>
                  <mi>m</mi>
                </mrow>
              </mfrac>
            </math>
            <span class="labelfig"> &nbsp;(7)</span></div>
          <div style="clear:both"></div>
          <p>Teniendo en cuenta la tendencia hiperbólica de la producción de CH<sub>4</sub>, se aplicó el modelo cinético de Monod según <span class="tooltip"><a href="#B16">Valladare (2017)</a><span class="tooltip-content">Valladares,
            C. F. (2017). Modelamiento del proceso de digestión anaeróbica de 
            estiércol vacuno y cáscara de cacao [Tesis en Ingeniería 
            Mecánico-Eléctrica]. Universidad de Piura. Facultad de Ingeniería. 
            Programa Académico de Ingeniería Mecánico-Eléctrica.</span></span>, acorde a las <span class="tooltip"><a href="#e8">ecuaciones 8</a><span class="tooltip-content">
            <math>
              <msub>
                <mrow>
                  <mi>μ</mi>
                </mrow>
                <mrow>
                  <mi>C</mi>
                  <mi>H</mi>
                  <mn>4</mn>
                </mrow>
              </msub>
              <mo>=</mo>
              <mi>&nbsp;</mi>
              <mfrac>
                <mrow>
                  <msub>
                    <mrow>
                      <mi>μ</mi>
                    </mrow>
                    <mrow>
                      <mi>m</mi>
                      <mi>á</mi>
                      <mi>x</mi>
                    </mrow>
                  </msub>
                  <mi>*</mi>
                  <mi>S</mi>
                </mrow>
                <mrow>
                  <msub>
                    <mrow>
                      <mi>k</mi>
                    </mrow>
                    <mrow>
                      <mi>s</mi>
                    </mrow>
                  </msub>
                  <mo>+</mo>
                  <mi>S</mi>
                </mrow>
              </mfrac>
            </math>
            </span></span> y <span class="tooltip"><a href="#e9">9</a><span class="tooltip-content">
            <math>
              <msub>
                <mrow>
                  <mi>μ</mi>
                </mrow>
                <mrow>
                  <mi>C</mi>
                  <mi>H</mi>
                  <mn>4</mn>
                </mrow>
              </msub>
              <mo>=</mo>
              <mi>&nbsp;</mi>
              <mfrac>
                <mrow>
                  <msub>
                    <mrow>
                      <mi>μ</mi>
                    </mrow>
                    <mrow>
                      <mi>m</mi>
                      <mi>á</mi>
                      <mi>x</mi>
                    </mrow>
                  </msub>
                  <mi>*</mi>
                  <mi>t</mi>
                </mrow>
                <mrow>
                  <msub>
                    <mrow>
                      <mi>k</mi>
                    </mrow>
                    <mrow>
                      <mi>s</mi>
                    </mrow>
                  </msub>
                  <mo>+</mo>
                  <mi>t</mi>
                </mrow>
              </mfrac>
            </math>
            </span></span>, para ajustar valores de producción en función del tiempo de retención:</p>
          <div id="e8" class="disp-formula">
            <math>
              <msub>
                <mrow>
                  <mi>μ</mi>
                </mrow>
                <mrow>
                  <mi>C</mi>
                  <mi>H</mi>
                  <mn>4</mn>
                </mrow>
              </msub>
              <mo>=</mo>
              <mi>&nbsp;</mi>
              <mfrac>
                <mrow>
                  <msub>
                    <mrow>
                      <mi>μ</mi>
                    </mrow>
                    <mrow>
                      <mi>m</mi>
                      <mi>á</mi>
                      <mi>x</mi>
                    </mrow>
                  </msub>
                  <mi>*</mi>
                  <mi>S</mi>
                </mrow>
                <mrow>
                  <msub>
                    <mrow>
                      <mi>k</mi>
                    </mrow>
                    <mrow>
                      <mi>s</mi>
                    </mrow>
                  </msub>
                  <mo>+</mo>
                  <mi>S</mi>
                </mrow>
              </mfrac>
            </math>
            <span class="labelfig"> &nbsp;(8)</span></div>
          <div style="clear:both"></div>
          <p>donde:</p>
          <p><i>µ</i> <sub> <i>CH4</i> </sub> -
            velocidad especifica de producción de gas metano; </p>
          <p><i>µ</i> <sub> <i>máx</i> </sub> -
            velocidad de producción máxima de metano; </p>
          <p><i>S-</i> concentración de sustrato; </p>
          <p><i>K</i> <sub> <i>s</i> </sub> <i>-</i> constante de Michalis-Menten. </p>
          <div id="e9" class="disp-formula">
            <math>
              <msub>
                <mrow>
                  <mi>μ</mi>
                </mrow>
                <mrow>
                  <mi>C</mi>
                  <mi>H</mi>
                  <mn>4</mn>
                </mrow>
              </msub>
              <mo>=</mo>
              <mi>&nbsp;</mi>
              <mfrac>
                <mrow>
                  <msub>
                    <mrow>
                      <mi>μ</mi>
                    </mrow>
                    <mrow>
                      <mi>m</mi>
                      <mi>á</mi>
                      <mi>x</mi>
                    </mrow>
                  </msub>
                  <mi>*</mi>
                  <mi>t</mi>
                </mrow>
                <mrow>
                  <msub>
                    <mrow>
                      <mi>k</mi>
                    </mrow>
                    <mrow>
                      <mi>s</mi>
                    </mrow>
                  </msub>
                  <mo>+</mo>
                  <mi>t</mi>
                </mrow>
              </mfrac>
            </math>
            <span class="labelfig"> &nbsp;(9)</span></div>
          <div style="clear:both"></div>
          <p>Para lograr tal ajuste, se grafica el inverso de μ <sub>CH4</sub> versus el inverso del tiempo (1/t), luego se aplica regresión lineal de
            mínimos cuadrados y con base en la pendiente (m) e intercepto (b) 
            hallados, se determina los parámetros cinéticos:
            <math>
              <msub>
                <mrow>
                  <mi>μ</mi>
                </mrow>
                <mrow>
                  <mi>m</mi>
                  <mi>á</mi>
                  <mi>x</mi>
                </mrow>
              </msub>
              <mi>&nbsp;</mi>
              <mi>y</mi>
              <mi>&nbsp;</mi>
              <msub>
                <mrow>
                  <mi>k</mi>
                </mrow>
                <mrow>
                  <mi>s</mi>
                </mrow>
              </msub>
            </math>
            , teniendo en cuenta las <span class="tooltip"><a href="#e10">expresiones 10</a><span class="tooltip-content">
            <math>
              <msub>
                <mrow>
                  <mi>μ</mi>
                </mrow>
                <mrow>
                  <mi>m</mi>
                  <mi>á</mi>
                  <mi>x</mi>
                </mrow>
              </msub>
              <mi>&nbsp;</mi>
              <mo>=</mo>
              <mi>&nbsp;</mi>
              <mfrac>
                <mrow>
                  <mn>1</mn>
                </mrow>
                <mrow>
                  <mi>b</mi>
                </mrow>
              </mfrac>
            </math>
            </span></span> y <span class="tooltip"><a href="#e11">11</a><span class="tooltip-content">
            <math>
              <msub>
                <mrow>
                  <mi>K</mi>
                </mrow>
                <mrow>
                  <mi>S</mi>
                </mrow>
              </msub>
              <mo>=</mo>
              <mi>&nbsp;</mi>
              <mfrac>
                <mrow>
                  <mi>m</mi>
                </mrow>
                <mrow>
                  <mi>b</mi>
                </mrow>
              </mfrac>
            </math>
            </span></span>.</p>
          <div id="e10" class="disp-formula">
            <math>
              <msub>
                <mrow>
                  <mi>μ</mi>
                </mrow>
                <mrow>
                  <mi>m</mi>
                  <mi>á</mi>
                  <mi>x</mi>
                </mrow>
              </msub>
              <mi>&nbsp;</mi>
              <mo>=</mo>
              <mi>&nbsp;</mi>
              <mfrac>
                <mrow>
                  <mn>1</mn>
                </mrow>
                <mrow>
                  <mi>b</mi>
                </mrow>
              </mfrac>
            </math>
            <span class="labelfig"> &nbsp;(10)</span></div>
          <div style="clear:both"></div>
          <div id="e11" class="disp-formula">
            <math>
              <msub>
                <mrow>
                  <mi>K</mi>
                </mrow>
                <mrow>
                  <mi>S</mi>
                </mrow>
              </msub>
              <mo>=</mo>
              <mi>&nbsp;</mi>
              <mfrac>
                <mrow>
                  <mi>m</mi>
                </mrow>
                <mrow>
                  <mi>b</mi>
                </mrow>
              </mfrac>
            </math>
            <span class="labelfig"> &nbsp;(11)</span></div>
          <div style="clear:both"></div>
        </article>
      </article>
    </article>
    <article class="section"><a id="id0x345e700"><!-- named anchor --></a>
      <h3>RESULTADOS Y DISCUSION</h3>
      &nbsp;<a href="#content" class="boton_1">⌅</a>
      <article class="section"><a id="id0x345e980"><!-- named anchor --></a>
        <h4>Análisis fisicoquímico de materia prima</h4>
        &nbsp;<a href="#content" class="boton_1">⌅</a>
        <p>El
          análisis de las excretas porcinas indicó que poseen pH entre 8,36 y 
          8,53, conductividad eléctrica en rango de 1,04 mS/cm a 1,85 mS/cm; 
          sólidos totales disueltos en intervalo de 0,98% a 1,33%; oxígeno 
          disuelto (3,16 g/dL-6,.10 g/dL); humedad, (52.54%-70.45%); carbono 
          orgánico en rango de 1,89% hasta 2,57%; Nitrógeno total en intervalo de:
          1,97% hasta, 2,98%; Fósforo (0,57 ppm -0,.75 ppm) y potasio 
          (0,11%-0,13%) y demanda química de oxígeno (55,65g/L-62,76g/L)</p>
        <p>El 
          análisis de excreta bovinas resultó: pH entre 7,70 y 7,.6, conductividad
          eléctrica en rango de 1,33 mS/cm a 1,76 mS/cm; sólidos totales en 
          intervalo de 15,33% a 17,04%; oxígeno disuelto (3,31 g/dL-4.00 g/dL); 
          humedad (52,54%-70,45%); carbono orgánico en rango de 1,84% hasta 2,01%;
          Nitrógeno total en intervalo de: 1,77% hasta, 1,98%; Fósforo (0,32ppm 
          -0,92ppm) , potasio (0,52%-1,05%) y demanda química de oxígeno 
          (51,76g/L-60,97g/L)</p>
      </article>
      <article class="section"><a id="id0x345f080"><!-- named anchor --></a>
        <h4>Producción de biogás en los biodigestores</h4>
        &nbsp;<a href="#content" class="boton_1">⌅</a>
        <p>La <span class="tooltip"><a href="#f1">Figura 1</a></span>,
          muestra la Producción de biogás acumulado en biodigestores (Vac) 1 y 2,
          detectándose un comportamiento sigmoideo similar en ambos, en función 
          del tiempo de retención más marcado en el biodigestor 2, que fue 
          inoculado con 0,5% de inóculo microbiano y superado en el intervalo de 
          tiempo de 5 a 12 días de reacción, tal situación se puede explicar en la
          segunda fase de la digestión anaeróbica, considerando la acción 
          mesoofílica de las bacterias a las temperaturas de cada reactor: 25.5℃ y
          32,1℃.</p>
        <div id="f1" class="fig">
          <div class="zoom">
            <svg xml:space="preserve" enable-background="new 0 0 500 300.532" viewBox="0 0 500 300.532" height="300.532px" width="500px" y="0px" x="0px"  version="1.1">
              <image transform="matrix(0.6649 0 0 0.6649 0 0)" 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+tGP5PHJ%20ycn8/Hx2FQBAmpnHQjIpij4vpGqgCQQC7733nnL3woULwWBQXaC7u5tAAyDNuFyuqakpZXHRULdv%2035Y7V8c/WWl6eloOnVm7du1yq0+j0chFhSUONH6/f3h4WNzo7e39xje+IX7IDbOLAg8MDPh8PpvN%201tXV1dTUxALBANKGXEtG3CgoKIjUpiLTzPzcv38/RWtm27ZtXB5I1UAjWSyW8vLyzz777PPPPxd3%20KysrN27cmJ2dnZWVRaUDSLM0E3Mtmb6+vlWrVhmYpgQkTbIGBT98+PDu3btKf1N1dbX4Mb506ZLf%2076fSAaRZmom5lszo6Ch1BSRV4ltozGZzYWHh4OBgfn6+crCgoMDpdDY2Norbu3btor8JQCpSxsGo%20hY6JUca+KFJ9bC+tRNC/xLfQmEymw4cPq4/Y7fZt27YVFRUZZgfTvPnmm9Q7gFS0kHEwyxADe7GY%20kjKGxuFw3Lx5U7bHWCyWs2fPym5jcXt4eJjmGQCp/XtzmY2DYWAvUkKyxtDIPbeFiYkJmWBEylFu%20AwAApECgiSQQCPzgBz+QKwgDABbN47mj0kCgMdTU1GSEYzabWc8bgD65XK6Ojg7qQY1xMEgVSRlD%2043Q6e3p6qFwAKSTm4njKWjKpiHEwSHuJb6EJBAJydeCwGBcMQJ9pRr04XtiGZNaSAfQsiVsfuN3u%20wsJCq9X6T//0T8ePHzc83bTy2LFj8i4ALL6wa8kY/no5mdCFZAxpsU8kkMaSNYZGbn0gF9nr6uqS%20o4AdDkd1dfWtW7cYawZgqaT0WjIM7AUWO9BIJpPJbrd7vd729nZx1+fzXbx4cWhoiJ8xAEsrc+6W%209g3n5OTM+7EM7MWy+KFO+DMqWx+IELN//365QPCBWbLA9u3bl/xXAwCkluLiYioBiCKJWx+8/fbb%204+PjVVVVpaWl6gJyo0qqHgAA6DfQGGbHyly7dm3t2rXr168X+aavr89sNstT9fX1+/fvp94BJAlr%20yQDLU7JaSioqKkZGRuTtvLy8qakp6hpAokSaqSR/4Qihc5QApLfEt9D4fL7s7OyMWKqqqhgaDGB+%20UnemEtOUgCRZsrEsLpdr3bp1AwMDVquVbwOA+fz+Wja7XjNNCdBvoBEmJydPnjzJInsAlgn2HwCS%20J/FdTnl5eePj42az2WKxfPXVVzNPXbt2TfyRYbfbp6enxd2GhgZRmEX2AACAHgONcOjQIZFdLl++%20rN6zqaKiwu12/+d//udPf/pTcbeurk6EHhbZA5ByGAcDLItAI5cDzsnJWb9+fdgCyk4IC3mJ7Ozs%20/v5+9cFAIFBWVqYMOtacjVkg5sMBYCEYBwMkVbLG0Hi93j179pw/f14ZtSdSyPbt24PBoLw7Pj4+%20PT09vzRjtVr9fr8mjthstsHBQeWIuOt2uysrK+MpEPPhACAxDgbQp6SModm5c6dhdh5TVlaW0uax%20bt06mUJ27dr12WeficQgwo24re6Wisnj8WzYsEGTZoSWlhYRR5QBOnJZrX379ilNQdELxHw4gMXE%204ngAlj7QCKdOnVKWBtawWCxNTU3K3ZqamjifU3Yzbd26VTzzu+++qz4VCATErz/xzGfPnpUNQg6H%20o6Ghwev1Xr9+PWaBmA8HsJhGRkauXLkyNDQ0MDAQtkBfXx+1BGAxAo1cGljOY1Jzu90TExMrVqwY%20GxsLBoOtra1z7dMRD3nw4MHmzZvVB/1+//DwcElJiXpRCrkpplytOHqBmA8HsJhp5vTp0/L2uXPn%20wmaa0dFRKgrAYgQayel0zvw1Jb44HA5xd06bOsmQFPYhcjjOli1b1IkkPz/faDTeuHEjZoGYDwew%20mGnmyZMnsp86Uqapq6ujrgBopM+u18XFxeq7Vqs1Kysr/gIxHw5gkdOMIP7yEZlG3CgrK6OKACxN%20oNFMR7JYLMPDw3MaApwS2tvbJyYmlLvKL2IA0YVuMFlVVRW25PT0dMI3m2RtGIBAExen09nY2Kg+%204vV6V65c2draOqeepvhpuodCp4VHLxDz4ZHs3btXfbe5uZmrCss5lKQ31pIBlleg6ezs1KQZxYED%20BwoKChK7vovsHpK7KCjjYOS4Y9mRFL1AzIcDiGThaWZJNphkLRkg/SR+UHAgEHjvvffEjdzcXBEL%20NHs5GWYXfUlsY6/ZbC4sLNTsonDz5k3xrwhPMQvEfDiAmKFkrqg0AHoPNHIWtEgzAwMDVqtVOS73%20chKZJuH7N5lMJrvdLtcmls8sm4gsFkt5eXnMAjEfDgAA9P6XVXp8GUeOHHHNUk9NOnPmjDIGOXqB%20mA8HAAB6lvgWGtmDMzk5WVZWNj4+rhz3eDxyuwPNEnYJYTKZ3G53aWmpckSzE1P0AjEfDkCH2PUa%20gCLxLTQiHBw+fLi2tlZkmvz8/NACR44cWWCgccwKfd1IC6XHUyDmwwGkB2YqAQSaOQSOjz76qKen%20J/RUfX09LR8AFo6ZSgCSHmiE7u5uzcJ6ocOEAQAAdB1oDE93X6KKAQBAsj1DFQAAAALNX/h8vuzs%207IyMjObmZnkjkk2bNj169IiqBwAAugs0AAAABBoAKaavr49KAJBugUYOAZ6ZmTl69Ki8EcnExAQr%208AIpweVydXR0RDo7OjpKFQFIt0ADIM2MjIxcuXJlaGgo0pqTdXV11BKA9A80TqdTMxy4v7+fGgdS%20Jc2cPn1a3j537hzraAPQuaSsQ6NZUk9hs9nsdvv58+cTvpcTgISnmSdPnoi/Q8TdmZkZkWnEjbKy%20MioHgD4lpYXm0KFDoWlGcrlcP/3pT6l3ICXSjMKQtHYaNpgEoNNA4/P5Ll68KG5YLJavvvpKGQt8%207949s9ksjnd1dbEODZASaUYejJlpFj+UsMEkAI1kdf2INDM8PKyezZSXl/fJJ5/YbDYqHUihNKNk%20GkO4vqecnJyHDx/OO5SUlpZS7QB0GmhEcNm5c+fg4GCkArt27WLaNpBCaSZKpikuLtYU+3iW4Wm7%20jmygFYV3795NJQNInqSMoTl69OiXX365Z88edcOyx+Ox2Wxms/nNN9+k3oHUSjNKpok5nmbHLBl9%20SDMAFk3CWmhCZza5XK6srCxNsWAw+OKLL2p6owDoP80omcYQa96TDDQff/wxaQbAomFhPWBZ6+vr%20a2trizPNKJlGaaeRsSZsptm4ceOqVatIMwAWB+vBAMva/LYvUNppojycdYQBpGSgkXs5UaGA3rhc%20LvGzWVtbGyl2uN3u0N7hOK1du5YaBqAHdDkB6SzmfkzCvNOMcP/+fSoZgB7Q5QSkc5pR78dkiLp3%20wTw2JGHRXgD6QQsNkM5pRj3alz0mAaQxWmiAZZFmDOwxCSCt0UIDLIs0QzsNAALNPHk8nmeffVb+%20Gu3v7/f5fMXFxWxLCSx+miHTACDQzEcgECgrK9u6dWswGFQOjo+P/+EPf3j++efFDeodWOQ0Q6YB%20QKCZs5aWltDNKcfGxkS+mZycPHnyJPUOLH6aIdMAINDMQSAQcLlc4kZ9ff3MzExHR4c87nA45O2u%20ri46noAlSTNkGgAEmnj5/f7h4WGLxdLU1KQ59dJLL5nNZiodSKwF7sdEBQIg0IQhIkthYaHX6z12%207Jjm1KFDh0TcKSkpmccSXsBy5nK5lMbOUPPejynO9AMAyzHQmEwmu90ubpw4cUL8upQ7yNhsNnG7%20p6dH3K6uribQAPGLuX1BXV3djh07DLMrzQhxPq0szJo0AAg0ER05cqS0tDTsKZF1XnvtNeodiD/N%20qLcviJRpdsyKP9MoaWb37t1UMgACTXgmk0n82g1tIXe73b29vUlqnvH5fNnZ2RlPtbW1qc/KmeTK%202f7+fs3DYxYAlirNxLl9QfyZhjQDgEAzBw6HY+avVVZWJum1PB7Phg0b/H6/cuTAgQNvvfWWElZs%20Npt6Jrm4q44sMQsAS55mEpVpSDMA0lKajGVpbm4OBoOtra379++X+UYkkq6urqamphUrVsh1cex2%20+/nz5zMzMzs7O2tra/ft2zc8PCzOGp4unBOlALC0aUYelDfCbsl0+/btO3fuiBvZ2dnV1dXxvMTV%20q1epZwBpI2EtNJoenyg2bdqUjHVoLBbL3r175e2KioqjR48+fPjw7t27cl0ccfbs2bOyt8vhcDQ0%20NHi93uvXrxueLpwTpQCghzSjZJqw7TQyzQAAgSa1lZSUiPzR3t4u73o8nubm5pycnPXr18t1cTRz%20xYuKiuTHhuHpwjlRCgA6STPRM42QOXdUOID0kCa/zpxOp2F23Iwgj1gsFtlh9Nlnn01PT2/ZskX9%20uzs/P99oNN64ccMwu8lU9AKAftKMkmkMEfqe5u3x48fUP4DUlbAWmry8vKmpKTne8Nq1ayINiEjx%201VdfKSOC7927Zzabc3NzL1++nPCBKcp+CwpNh1FxcbH6rNVqzcrKUh+JWQDQSZpRMo0eti8QP+l8%204wDoQVJaaJqbm5977jnNiFqReD755BObzXby5Mnjx48nNs3IOUrKoGCfzycSSVVVFfvUIFX09fWJ%20rG+YywK+6naaOAcCR7Jt2za+BQAINH9FhImLFy+uWbMmUgFl8lGiXvHzWfX19TLNaMLTq6++Ko5o%20Oo9kN5P6SMwCkbS3t09MTGg+Y4C5mvf2BTLTUIEACDRJ4fV6CwsL1Y00Iuhs3749GAwm6RUj9RnJ%20G7du3Xr8+LEySmZsbEy8E/mQmAWiU6ZWSc3NzVxVmIe6urqPZ8l0EmcyZvsCAJASP8spLy9v586d%20MtOsXLlSma29bt06ufDdrl27krG4S6QmFrlZ5tDQkHrM482bN8W/BQUF8RQAFgfbFwCAjgKNcOrU%20KZESwp6y2+0///nPE/ty35p14sQJZbsDpTWopqZGbpYp0tWePXtkZOns7GxsbLRYLOXl5Yanu2lG%20KQDoMNOQZgAg6YFGznhqaGjQHE/SXk4ikXz44YdGo/HAgQPq1qD6+nq52YLcLNPlcmVlZSkbgJ85%20c0ZpKIpZAFg4cYGFbnA2v0xDmgGAxQg0ktPpXLS9nCoqKv74xz+qm4VaW1uVuVQi8Ygspd4AXNxV%20v5mYBYAFGhkZuXLlytDQUDwz76JnGtIMAIRKn3VCZbNQpLNyA/AoD49ZAFhImjl9+rS8HedSeDLQ%20hI4RJs0AQFjPUAXAIqQZ9XJ5cS6FF9pOQ5oBgEjYyQVY1DRjmMuWBZp2GtIMAERCCw2wqGlmIe00%20pBkAINAAS59m5p1pNm7cuGrVKtIMAESSxC4nuaGSXEzPoNr+mkrHck4zSqYxzKXvqa6ujloFgCiS%201ULjdDqVpYEluXCwsvYdsGzTjJJp9LBdNgCkh6S00MiVdsOeOnDgQEFBAUu8YJmnGSXTGFTtNOJv%20gDt37izk1dXbdwDAspL4FppAIPDee++JG7m5uWNjY8qqeteuXTMajeJ4S0sLv3aRlvr6+tra2uJM%20M0qmUdppFphm5k3+YAJASkt8C43f7x8eHhZpZmBgwGq1KscrKircbrfNZpPbQCZ8AwRgyY2Ojs7j%20UUo7zV9+Juf+oyH/Qti2bRvfAgDLFrOcgISpq6ub03bZkrJcHhUIADoKNGazubCwcHJyUvyCHh8f%20V457PB6bzRYMBktKSmieQbqKf7tsTZphSjYA6CvQmEymw4cPixsi0+Tn52c8tXXrVpFmDLNbWxNo%20QKYhzQCArgON4HA4qqurw56qr69nihPINKQZAEisZLWUdHd3axbWCx0mDKR3pjGE2y6bNAMAqRRo%20hLy8vKmpKaoYZBpNpiHNAEDCMcsJSG6m0fQ9kWYAQNeBxufzZWdnZ8Rh06ZNjx49ouqRilwuV0dH%20x0IyDWkGAJKB2UZAvEZGRq5cuSJuFBQUzGnZGHXfE2kGAJKBLicg3jRz+vRpeXse20mKTLNx48ZV%20q1aRZgAgGRLWQqMeAizX0HvuueeGh4dXrFghD8pJT0aj8fLly8pBIIXSjNykyaDaTnJO7TR1dXXU%20JAAkSVJaaJqbmzVpRiaeTz75xO/3nzx5knpHiqYZhWFe7TQAgJQJND6f7+LFi1EKdHV1MSgYKZpm%205EEyDQCkf6CRvF5vYWGhOriIoLN9+3a5+wGQummGTAMAyyLQ5OXl7dy5U2aalStXKk3069atk6sG%2079q1izE0SOk0Q6YBgPQPNMKpU6fMZnPYU3a7/ec//zn1jlRPM2QaAEj/QCNnPDU0NGiOu93u3t5e%20ttpGeqSZJGWax3PHtwyAhlzt9q233pJ3a2pqIi1sGwgEysrKxNm2trbQJ7FYLOPj45qSUn9/f/T3%20EKW8eD+hL6fHQCM5nc6Zv1ZZWSm+vB/84AcMCoZu9fX1iR+zONNMaKaR07nnJycnZ96PNRqNfO8A%20ROf1eq9fv645+PmssOUPHTq0e/duuau0+Pi22WyDg4PKWXE3SqaJXv7o0aPHjh1TolJCJKuxRISv%20np6esKdE3OOqgm6Njo7O41HK+jTC1atX5/fSa9euLS4u5lsAIB7T09NZWVnxl8/Nzf3GN77R3d1d%20WVmpPn7hwoVvf/vbv/3tbzXlOzs7f/Ob35w4cULebWlpEenEbrefP38+MzNTnK2trd23b59miRZF%209PIVFRXbtm07efLk8ePHE1UhSWmhcTqdkdIMoHN1dXWa7STjoWzStJC8fv/+feofQJx++ctf/uIX%20v4i/fE5OTlVVlWbllEAg4HK5XnnlldBs9NFHH73++utK84woJn6/nT17Vo4bcTgcDQ0NYZt84iz/%206quv/upXv0pgI03iA438MiKdFV9epDQH6EToFtlxphllW4PMuaPaAcRPRJlbt279/ve/n1Om+f73%20vx8MBu/evasc+fzzz7/44ovt27drSno8ngsXLtTU1Mi7fr9ffHaXlJSof1kVFRUZZgcdhr5QPOVf%20eukl8WYSuNZu4gON/DIMs0OA7927Zzab6+vr5W/8jo4Okc6OHTvGtYi0yTRsoA1g8dOMiDLBWXPK%20NMXFxc8///ynn36qHBGp5bvf/e7GjRs1JcVx8fH9wgsvyLvj4+PT09NbtmxRB5T8/Hyj0Xjjxo3Q%20F4qnvFzkJYFr7SZrULDFYikvLxfVUVhYqLxdh8NRXV0tQiXzMpAemYY0A2Cp0oy8O6dMIz6U7XZ7%20T0+P/BSWPSoidoT2Nw0NDeXk5Kxfv16Th9R3rVZr9EE8McuXlJQ8fPhQ3WKkx0AjmUwmUXder7e9%20vd3wdFcEUU0EGqRBpiHNAFjaNDOPTFNUVNTf33/79m3D0/lNSr+SQgQd8cGt6TBKBvFmJicn79y5%20o9NAI1tllBAj+8wOHDigLBacvDpyOp3KfPeqqip1bIo5e36u0+uxzDMNaQbAIvuP//iP3/3ud2F3%20EBIHxSlRIOaTyJErstfpwoULzz33XHl5eZxvQNO7JPuVElhed4HGZDIdPnxY3Hj77bfFuxfBorS0%20VF2guro64YFGxpHGxkbliMvl+sEPfqC0qkWfPT/X6fVY5pmGNANgSX4Xbdy48W/+5m9CT4mD4pT8%20TRWdHLkie52Ghobi3IxI9hZpRoyMjY2JbBR2sYm5ltdpoDHMjpW5du3a2rVr169fL/JNX1+fshNC%20fX39/v37E/6Kcr57a2ur/KTx+/0iRYlM8+tf/9qgmg0vsqEcmywO7tu3TxmIFLMA+D2izjSkGQCL%20T0SEn/70p5s2bdJkGnFXHBSn4lyW5tVXXxV/sQ8MDFy8eDG0vyks2feiGTFy8+ZN8W9BQcHCy+s3%200AgVFRUjIyMy98mdEOTHQAKX0FG3r8j57nv37pVHlBR148aNmLPh5zq9HmQa0gwAnWSauaYZw9Ne%20p7a2tjVr1oTtbxKfoeIzUR1HlBGxe/bskQc7OzsbGxvlBKCwzxBPeRFxcnNzlblUOg00i0kOaxIR%20RN1uJlOUyE8xZ8PPdXo9lnOm2bhx46pVq0gzAPSQaeaRZgxPe51OnToVpb8pdP7RkSNHZNeHeK2M%20jIza2lpx8MyZM/IZnE7n6tWr1avkRS8vicxUWVkZOml8iQON3AcrIw5hN8daCNktV1BQ4PF4nn32%20Wc2g4Jiz4ec6vR7LWV1d3TvvvEM9ANBDpplHmlHyimF2k6JIBV5++WXx1756xRqTyeR2u9WDYsVd%20zS4Kmkaa6OXlxOcEDqtNn/VJxTf1v//7v5W7IhV+85vflEv8GeKYDT/X6fUAACxtppE3IjXDTE1N%20KXe7u7vVZ52zIhU2zI4bEZmmp6fntddeUwKHyCgDAwNhX07zhDHLC5cuXTIajd/73vcSVSfPpM13%20V6QZZVAwqxIjChF25bhvAEjpTJPUP7zl2GG5Yk0yqPeKSoiEtdCE5rtFJtKMev6UnC7e1dX1j//4%20j4Y4ZsPPe7p8e3v7xMSEclfuugzdGhkZuXLlimF2pH1ZWRkVAgBhORwOkTkSuyG2wuPxXL16VdnK%20W1+BZgnJ8S6amWBykPb9+/f/9m//VpkNr7SbqWfDq6fLhy0QnTK1SmpububHQM9p5vTp0/L2uXPn%20xL9kGgCI5NSpU+KX5JtvvpnAdhTls7KpqSmxT5v4QOPz+cRb9Pv9kQokfMNtmUi6u7vVo43kys2G%20v54Nr+QV9Wz4mAWQTmnmyZMnshVtZmaGTAMAUeTl5clP0oTTjOlJiGfSo8Z37tx54sSJtrY25aBc%20K2/Xrl1r1qyJPht+rtPrkeppRmGYbaeJMmYNAJAq0mSW06lTpy5evHhglnJQJJKmpibD7Gx41yz1%20+Cn1bPiYBZBOaUYepJ0GANJJ4lto1OsCq8ntCBLe36R+0erqauVIfX39xMSEfKGYs+HnOr0eqZ5m%20lEyTpHaax3PHdwoAFmLxWmhEaPjwww9tNtuxY8eSMWTaELVPLvps+HgKIM3SjJJpDAltp8nJyXn4%208OH8Hms0GvmWAYDeA43h6ejdrq6upqYmenOw5GkmGZkmeRvJAgD0Emg++OADv9+/Zs0a6h06STNh%20M826devu3Lkzv9ddu3bt5s2bqX8ASPlAE3PatmYbSCDh+vr6Ll++bFCNkolJnWnUg7Hm6v79+wQa%20AEiHQBNTAneiAsIaHR2dx6OUTPOXn425X6WM7QWApbLY69BoNigAkqGurm7Hjh0ynSgBJSZZmPnb%20AJCKEt9SsuSbOgGCDDQff/yxDDQxO56UNLN79+6rV69SgQCQWp6hCpDGmSbOdhp1mqHeAIBA83+c%20TmdGBJs2bXr06BFVD51kGtIMABBoIqaZxsZGKhf6zzSkGQAg0ITn8/nef/99ahb6zzSkGQAg0MTm%20drtnwlG2WAKWMNOQZgCAQBON2WwuLCy0WCzl5eXUL3SbaUgzANKYz+fLzs5+66235N2amppIY1gD%20gYD4ZSjOtrW1hT6J+DQfHx/XlJT6+/ujv4ewo2nlo8T7CX053QUak8l0+PBhr9d77NgxLinoNtOQ%20ZgAsN+Kj+fr165qDn88KW/7QoUPi96TVapVpxmazDQ4OKmfF3eiZZmhoKNKpo0ePipCgRKWESMqK%20vQ6H46OPPjoxK/SsiHvDw8P0OmGpMo24/B4+fEiaAZBaLl68GGVbIcNsD8nOnTsjnc3Nzf3GN77R%203d1dWVmpPn7hwoVvf/vbv/3tbzXlOzs7f/Ob3yif4y0tLSLN2O328+fPZ2ZmirO1tbX79u2L9IEu%20ApDIT0p5zdmKiopt27adPHny+PHjiaqfpIyhqamp6enp4eKDPtXV1b3zzjvUA4DUItJMZlTR405O%20Tk5VVVVXV5e610nEDpfL9corr2RlZWnKf/TRR6+//rrSPCOKWSyWs2fPynTicDgaGhrCNvko71Zk%20nS1btkTaRubVV1/91a9+lcBGmqTMchIpkisPAABd+f73vx8MBu/evasc+fzzz7/44ovt27drSno8%20ngsXLtTU1KjTiWZv6aKiIvHvyMhI2NcSSWV6erq4uDjSm3nppZfEmzl58qR+A42Um5s7NjbGLCcA%20AHRCxIvnn3/+008/VY6I1PLd735348aNmpLiuNlsfuGFF9TpRNPckp+fbzQab9y4Efa1RAYQeWXV%20qlXZ2dlyOHBVVZV6B9+8vLydO3dqWoz0FWjkW8zJyVm/fj1XDwAAOiEyit1u7+npkcFCdiSJmBLa%203zQ0NBT6Oa5pbrFaraEPVNy8eVP8+8orrygdYeK11q1bp+5jKikpefjwobrFSF+BxjA7evnLL79k%20lhMAALpSVFTU399/+/Ztw9P5TUq/kkKO59V0MM2VnOKkXpHu2rVrItyo+5jEm5mcnLxz545OA43P%2059u+fXswGDxx4gR7OQEAoB9y5Irsdbpw4cJzzz0X/6Jxmt4l2Q8VqXB3d7cIMeoZVRUVFS+//HIC%20+5iSHmiA9PB47qg0ADonh4XIXqehoaFdu3bFM6pV9i7dunVL/YtOjpKJMux3kRFokJJcLldHR0eS%20njwnJ2fejzUajXx3AOjZq6++2t/fPzAwcPHixdD+prDkHgAiAKkDjRwlU1BQEFperlOsGQWckJ6s%20KBL/pCL9TU1NccUgeUZGRq5cuSJ/kMrKyhL+/Pr5gwMAEk72OrW1ta1ZsyZsf5PJZLJYLIODgyKO%20yPAhjtjt9sbGxj179igL64m7kbY5UtqBrl69qvQ69fb2iuesq6tTAo2IRLm5ucpcqgWihQapl2ZO%20nz4tb587d078kUGdAFgORA6I3usdZ8uHTBunTp2K0t8UOv/oyJEjpaWlLpcrKysrIyOjtrZWHDxz%205ox8BqfTuXr1avUMJvH8ZrPZZrMpI2jFQ0Qqeu2115QyQ0NDIu6EThqfZ/0kvMZ9Pp/Vao2yXiFb%20H2CBaebJkyfiZ8MwuyWTyDTiRjLaaQBAV/7hH/4hUU8l8kpPT0+U/qaXX365ubn5008/lSsFG2Yb%20adxut3o7J3FXs4uCJjbdu3dPXb6+vl690YFchveXv/xlonqgMrlEkIppRgYaMg0ARMoT6uEf3d3d%206rPOWZEKG57OSBKh57XXXlMCh8g0kRrFNU8Ys7xw6dIlo9H4ve99L1FfMl1OSOE0o9ym7wkAEkuO%20HZYr1iSDeq8onQYaGfRCdzzw+/2lpaX0NyFRaYZMAwDJ43A4/v7v/z6Bey2peTyeq1evvvnmmwl8%20zsVroTGZTB9++CErCCOBaYZMAwDJc+rUKfF7NYEbYiuam5ubmpoS2DxjWOQxNHJlnq6uLvFl0EiD%20hKQZJdMYGE8DAAmVl5fn9XqT8cyaMT0JsahjaD744IMos5+A+aUZJdPQTgMAy9YSTNtO3iqBSCd9%20fX2XL19WJ5WY1O00Dx48mPeCv2vXrt28eTPfAgBIIUswy6m6uppAg5hGR0fn8Sgl/Sxk+4L79+9T%20/wBAoImmtbV1//791Dtiqqur27Fjh2G2xUWI81GysDKGJnPuqHkAIND8WaRp29LipJnOzk7xZ3p/%20f79yJBAIiA85ZQFm9ak4C2Dx7ZgVf6ZR0szu3bupPQAg0KQ2j8fzox/9SBNW1KsvC+KuJu5ELwD9%20ZxrSDAAQaBJPbh2uZALZ/qHZSTwZxAu98cYbwWBQfbClpUWEFbvdPj09LT7zOjo6xMF9+/Y9evQo%20zgLQeaYhzQAAgSbxPB7Phg0b/H7/yMiIPCJuDw8Pu1yub37zm0lNCSKajI+Pv/vuu+qII17XYrGc%20PXtWjpBwOBwNDQ1er/f69evxFIDOMw1pBgCQlEDT3NwcDAbV43/lwJrq6mqREpK3UnBnZ2djY2Nv%20b696zq3MUpq54kVFRYbZZU7iKQA9ZxrSDAAgKYFGbghusVj27t2rOXX06FGj0djV1ZWMRhrxugcP%20Hqyvr9fsZj4+Pj49Pb1lyxZ1XsnPzxfv5MaNG/EUgG4zDWkGAJCsQCM9fPjw7t27i/mVHDp0aM2a%20NU1NTWHPFhcXq+/KTRjmVAA6zDSkGQCAlPhVN8xmc2Fh4eDg4IsvvqjeWNvn823fvj0YDCZjpWCn%2009nT0+N2uxd/i6j29vaJiQnlbpxr2mIhmUb8+/HHH5NmAABJDDQmk8lut4tA4/V6V65cGVog4SsF%20ezye5ubm1tZWTWeTmqbzSHYzzalAJJqeNfFOuKoWIdOIrPzw4UPSDABASkqX05EjR0pLS8OeElnn%20tddeS+zLXbhwIRgMHjhwQFkWr7a21jC7lszq1avF3/FZWVm3bt1SzxgfGxsTD5HdTLJ3KUoB6FBd%20Xd0777xDPQAAkhhoTCbTwMCAXM1Fze129/b2LvLq8rILbGhoSJ1Xbt68Kf4tKCiIpwAAAFiOgUZy%20OByafQ+idAkthNPp1LyQzFIiPz148OBb3/qW3W73er179uyRkUXO7rZYLOXl5YanfWRRCgAAAJ1b%207K34AoHAD3/4w/b29sUcvXvkyBHXLPXEpTNnzijvIWYBAACgZ8lqoampqckIx2w2DwwMLPIXaTKZ%203G63eliPuKtuLopZAAAA6FlSWmjkJOol/KocszSZJnqQilkAAADoVuJbaOTWSJHOWiwW9eI0AAAA%20egw0cmskw2yvzb1798xmc319vTJWN6l7OQEaj+eOSgMAAs3/kVOE5IxoZfMmh8NRXV2tWfEFSIac%20nJx5P9ZoNFKBAJBakjvLSc6IbmxsbG9v379/v9y3cs2aNSLQLPJqNNAhl8s1NTUlV0FMOBZFBIBl%20JfEtNLJVxuv1ihAj7hYVFYl/5TK+69at8/v9ydjLCSlnZGTkypUrQ0NDjMUGAOgx0JhMpsOHD4sb%20b7/99vj4eFVVlWYbhITv5YRUTDOnT5+Wt8+dO0emAQDoLtAYZsfKXLt2be3atevXrxf5pq+vz2w2%20y1P19fX79++n3kkzT548kUsTkWkAAAuXrJaSiooK8bklb+fl5U1NTVHXCJtmhJmZGZFpxI2ysjKq%20CAAwD89QBVjaNEM7DQCAQIPUTjNkGgAAgQbpkGbINACAhWO2EZY+zSiZxvDX42lu3759586d+b3o%202rVrN2/eTOUDwDKRrBYap9OZEcGmTZvkwsEgzYRmGnU7zbzTjHD//n0qHwCWj2Tttt3Y2Ejloq+v%207/Lly+qkEpO6naa6uvrP1+jcVy1ibw0AWG4We7dtLCujo6PzeFT86QcAgGQFGmW37dbW1plwJiYm%20VqxYQdUvB3V1dTt27DDMtrgIcT5KFmZNGgDAUgYauZeTxWLZu3cv9Ysds+LPNEqa2b17N7UHAFiy%20QGMymUSaefjw4d27d6lfzCnTkGYAAHoJNMKpU6eCweBPfvITxmYi/kxDmgEA6CjQ+Hw+q9Xq9/td%20LldWVhbTthFPpiHNAAD0FWiAuWYa0gwAgECD1M40pBkAwMIlfmG9vLy8qakpahZRMo349+OPP1YC%20DWkGALBAtNBgaTKN0k5DmgEA6DrQeDyeZ599Vg4E7u/v9/l8xcXFDAeGkmk2bty4atUq0gwAYOGS%20spdTIBCw2WyDg4Pqg+Pj43/4wx+ef/75gYEBq9VK1aOuro5KAAAkRFJaaFpaWjRpRhgbGwsGg5OT%20kydPnqTeAQCArgONsjllfX39zMxMR0eHPO5wOOTtrq4uOp4AAICuA43cnNJisTQ1NWlOvfTSS2az%20mUoHAAB6DzRyc0qv13vs2DHNqUOHDom4U1JSkpmZSdWnAZfLpbTAAQCwhBIfLEwmk91uHxwcPDFL%20HrTZbEqB6upqAk0aGBkZuXLlirhRUFBQVlaWvBdiRzAAQExJGRR85MiR0tLSsKdE1nnttdeo9zRI%20M6dPn5a3z507NzAwkIxXycnJmfdjjUYj3yYAWD6S0lJiMpnEJ1xnZ2dtba36uNvtrqyspNLTI808%20efIkIyPDMLs4nsg04kbC22mKi4upbQBAPJK4sJ7D4Zj5a0lNMz6fLzs7W9nTu6qqSt1VEQgExMet%20cra/v1/z8JgFEJpmFIZkttMAALCUgWYxeTyeDRs2+P1+5YjL5Vq3bt34+Lgh3EJ/4q46ssQsgLBp%20Rh4k0wAA0jbQOJ3OjAg2bdqU2HVoRBx54403zGbz2NiY0hrU0dGhLOInF/qz2+3T09PK0jj79u1T%203kbMAoiUZsg0AIC0DTQizTQ2Ni7a1yBXvnn99dfVOypUVVWVlpZ2dXX96U9/crlcFovl7NmzcnaV%20w+FoaGjwer3Xr183PF0JMEoBRE8zZBoAQBoGGp/P9/777y/m15CXlzc1NXX8+PHQUyUlJV9//bWI%20O5rFb4qKiuSHtJKHohRAzDRDpgEApFugUbjd7plwJiYmVqxYkewvrLe3d3BwcMuWLX/84x+np6fF%20DXVeyc/PNxqNN27cMMzumhm9AGkmnjRDpgEApFWgkSsFWyyW8vLypfqqfD7fwYMH1dsvaCYAW63W%20rKws9ZGYBZanvr6+tra2ONNMaKaR07kBAEi2pKwUfPjw4dra2mPHjoXtBlqENCPiiNFovHz58iI0%20BaW30dHReTxKWZ9GPPz27dt37tyZ36uvXbt28+bNfBcAAEsQaAyzg2o/+ugj9dYHahaLZXh4OElR%20w+Px2Gw2s9k8MDCgHiOs6TyS3UzqIzELRNLe3j4xMaH5LE8bdXV1H88S6ST+r072LZaVle3evfvq%201avzfvX79+8TaAAASxZoampqenp6Fv+LkbOrNIFJdh7dunXr8ePHyiiZsbGxYDAou5liFohu7969%206rvNzc1pdons2LFD/Bt/plGnmf+7zua+exdbOAEA4peUWU4XL15cqjRjt9tHR0fVzT9yTM/Q0JD6%20A/LmzZuG2V0V4ymQ3uLZMXvHLCWszDXNAACQeoFGys3NVS9zl+xZTjLN1NfX9/b2ahoD5O7fXq93%20z549MrJ0dnbKhhw5bDlmgTQmd8wWYS7mjKR4Mg1pBgCQPoEmLy9v586dOTk569evX5yvQVn55sSJ%20E2FXJZa7f7tcrqysLHFQbpl55swZJVfFLJCuaWZOO2ZHzzSkGQBAWgUa4ejRo19++eWxY8cW52u4%20dOmSehenUCaTye12i8iiHNHs+x2zQLqmGfV87IVkGtIMAGBpJX5QsM/n2759ezAYXLRZTo5Z0cuI%20yBL90zpmgfROMzKUyGVjRC5RF9bMu87Ozq6uro70zJo5TWvXruVnDACwCJ6hCpabue6YPe9VZAyz%20866pcADAIsikCkgzSqYxRG6nYd41AGB5BRq5VSQ1m1ppJp5MAwCAbtHlRJrRZhp2lwQAEGj+PCg4%20Ozs7IzI5lZqq12GaIdMAAAg00KkF7phNBQIA9I9BwelvdHS0uLh4y5YtVAUAIF0lZaXgqamp0B0P%20/H5/aWlpUrfaRlh1dXWkGQAAgSYxTCbThx9+uJgrCEMjc+6oNAAAgUbLarVmZWV1dXUxKBgAAKRq%20oPnggw+ib7oEAAAwD0nZy8lqtUYJLiUlJfRlLDesGgwASKolmLZdXV1NoFk+cnJy5v1Yo9FIBQIA%204rHYwaK1tXX//v3U+zxodr1OFcXFxXzvAACpF2jYyylJUjHNAACQqoEG8+NyuUQQrK2tjfENY9dr%20AABCJGwMTcwtnNjLKYqRkZErV64MDQ2xdxIAAEsZaLCQNHP69Gl5m/0gAQCYB7qcdJFm5M6R4u7M%20zIzcD7KsrExX75N+KwDAsgg0jAVeYJpR9sHWbaaZB+ZdAwBSLNAgHqFTr6uqqsKWnJ6evnr1qk7e%209rZt2/jeAQD0LIljaDTDhBkLbGDqNQAAqRVonE7nunXr1BsgeL3elStXtrW1Uenseg0AQAoEms7O%20zsbGxrCnDhw40N/fn6612d7e/sUXX3BVAQCQ8oEmEAi899574kZubu7Y2NjMU9euXZNDRFtaWtJy%20ysxvf/vb//qv/3r48CHzrgEASPlA4/f7h4eHRZoRn+tWq1U5XlFR4Xa7RaYZGhpKv0Aj0sy//uu/%20ytuR1pLp6+tb2jf5eO74CQEApAQGZ8zZ7373u5GRkdDjdrtduR12jtKqVatS8etl6jUAYDkGGrPZ%20XFhYODg4WFZWpm6k8Xg8NpstGAyWlJSk9CjXsGlG/5h6DQAg0MyByWQ6fPhwbW3t5ORkfn5+aIEj%20R46kwbQdNokEAEA/kjLLyeFwVFdXhz1VX19fWVlJvQMAgARKVktJd3e3z+ezWq3KUjShw4QBAAB0%20HWgM7O4EAAAWS8K6nORGB2m8aJ5OMPUaAIAkBhrJZrNlZGSsXr16fHw8tSoiEAiUlZUpO09FSmZy%20H+yUw9RrAEB6S0qXkzK/yW63nz9/Xv9zmkSaEVFscHBQnczcbnfo+OXf//733/nOd5bqfYadev3x%20LHFDBLLdu3dzTQMAlqGEtdDIETMdHR3qgy6XKysrKyMj46233tJzLbS0tIg0I+LX9PT0zMyM/Cr2%207dsXuj34v/zLv+jtze/YsWPjxo2rVq0izQAACDSJ4XA45M5NDQ0N6uMnTpyI3pWzhAKBgAheFovl%207NmzsjFJfBXi/Xu93uvXr6fEd7Guru6dd97hagYAEGgSzOl0hk02Nptt06ZNoS0fS0huPqVZv7io%20qMiQsosCAwBAoFmkZKMf4+Pj09PTW7ZsUQea/Px8o9F448YNLhEAAPQv6cN1RaBpbGzUf0UUFxer%2071qt1qysrCS9FlOpAQBIrCR2OclBM5o0Y7fbR0dHV6xYQdXPFVOvAQCIJMEtNJHaYywWy/DwsJ5z%20jKZ3SfZDJeOFfv/738/7sUNDQ1yyALBUsrOzf/zjH1MPaR5oNDs3SSmxf5PsXbp169bjx4+VYTRj%20Y2PBYFDTD5UQR48e5bJTNDc3UyHUFXVFdaVQXVEJupWsLie32z0zM/PgwQP970ZpNpsLCwuHhobU%20Q1tu3rwp/i0oKOASAQBg2QWa1tZWOacpdI1d3TKZTHa73ev17tmzR2aazs7OxsZGi8VSXl7OJQIA%20gP4lrMsppffWPnLkiGuWembTmTNnogz6YaYSAAD68QxVYJhtpHG73aWlpcqRsBs5SatXr573C61c%20uZLaBgAg4TKpAiXTDAwMxFPy7/7u76Kc/bd/+7d//ud/pj7j9P/+3/+jEqgr6orqoq6wcBk/+9nP%20DEy9AQAAqUnOPqPLCQAApDwCDQAAINAAAAAQaAAAAAg0AACAQAMAAECgAQAAINCkg0AgUFZWlvFU%20f38/dRKF0+nMCEGlafh8vuzsbE21cKXFX1dcZmFrSamKqqoq9RYuXFrx1xWXFoEmndOMzWYbHBxU%20joi7XNxRDA0NUQkxf59arVa/38+VNr+64jLT8Hg8GzZsUNeSy+Vat27d+Pg4l9ac6opLi0CTzlpa%20WsQvArvdPj09PTMz09HRIQ7u27fv0aNHVE7Y/Of1epXqUqTQDu2L//uUK22udcVlpqmNN954w2w2%20j42NKVUhrp/JycmTJ09yac2prri0CDTpfPWL8G6xWM6ePZuZ+ee9sRwOR0NDg7jir1+/Tv2EEp89%20w8PDW7ZskdUFTWNDdnb21q1bxe/Td999lyttfnXFZRa2Nl5//XWr1aocrKqqKi0t7erq+tOf/sSl%20FWddiYTHpUWgSfOrv6SkRH1xFxUViX9HRkaon1Dj4+PiL5vi4mKqIpLW1tYHDx5s3ryZK21+dcVl%20ppGXlzc1NXX8+PHQU+KK+vrrr7m04qwrUUVcWgSaNP941qT1/Px8o9F448YN6ifU2NhYMBhctWqV%20MuZOM+COD579+/dzpS2krrjM4tHb2zs4OCiuqD/+8Y9cWnHWlagiLi0CTZrTpHWr1ZqVlUW1hHXz%205k3x7yuvvKKMe9AMuANXGpdZsvl8voMHD1oslqamJi6tOdUVlxaBBvgLOUHA7XYrg+muXbsmfjXI%20AXcAl9kifEKLvGI0Gi9fvrxixQoqZE51xaVFoElzmoZZ2TtAtYTV3d2tmRFQUVHx8ssvywF31A9X%20GpdZUsl5YeITemBgQD3ulUsrzrri0iLQpC3ZMHvr1i11H6rsZGXUGLjSoCtOp3Pr1q3PPffcF198%20oXxCc2nFX1cg0KQzs9lcWFg4NDSk/l0gO1kLCgqoHw051TZ0iVKv16uZZAGuNC6zhH9CNzY22u32%200dFRdU8Tl1b8dcWlRaBJZyaTSVz04mres2ePvMQ7OzvFT4LFYikvL6d+NPLy8nbu3Olyua5evaoc%20lDMIqqur+XXAlcZlltRP6Pr6elEPmhrg0oq/rri0dO1ns2awAH6/v7S0VFOx6iFjULt37574i1BT%20XaHLbkKu1qq+kLjS4q8rLrOYP3SSSC1fffUVl1b8dcWlpUMyydBCk5g/ncVPvvrXgbjLMthR/noW%20vxHU1RX2LyFwpXGZJcqlS5dCd4fg0ppfXXFp6VaGCDXiv6NHj1IXAAAg5TQ3NxsYQwMAANIAgQYA%20ABBoAAAACDQAAAAEGgAAQKABAAAg0AAAABBoAAAACDQAAIBAAwAAQKABAAAg0AAAABBoAAAAgQYA%20AIBAAwAAQKABAAAg0ACILRAIlJWVZWRkrF69enx8nAoBQKABsMQ6OztFNNm0adOjR4+ilxHeeust%20cffzWeJGb2+v1WpN1Dvx+XzZ2dmEJAAEGgB/lQ8aGhpiljl48KC4IUquWLEizmdubm4OBoOtra2V%20lZUJfMN5eXlvv/325OTkT37yk8ePH/MdBECgAZY7p9O5bt26qamp6MU++OADv9+fm5v7ve99L/4n%207+7unpmZ2b9/f8Lf9ssvv2w0Gl0u19WrV/kmAiDQAMtaZ2dnY2NjzGKBQEBEB3GjsrJy48aNenjn%2035olbrS0tNBIA4BAAyxfNTU1tbW18vaJEycyMjKqqqrChgNlKEx1dXVmZqb6lNPpzHiqra1N80A5%202EUz8kY5qAgdl+PxeJ599ll1GTkoR2Eymex2u7jR399/+/ZtvpsACDQAYrhw4UIwGBQ3CgoKlINy%20BpO6gefAgQNKQopEJJUNGzb4/X71Qa/X+/zzzysjfEVI2rp1q3xFhYhcmrxVVFQk/p2cnPz000/5%20HgEg0ADLVHd3d0dHh7xdX18/MzPT29uraYCRhoaGxL+5ubkvvPCCclAUHhwcFDcsFstXX30lHn7t%202jWj0Rj9ReUwYeUhghyPrOQSn8/3/vvvq59WpJ/S0lJxxOVy/frXv1aeKj8/X77cjRs3+G4CWGSZ%20VAGQWgKBgNfrFTdycnLWr1+vHHzvvffk7TNnzsh5TxUVFUePHo0+KEekKOW2yC5Wq1XTWqMQL3r9%20+vXKykqTyTQwMBBaQDw2KytLxKNbt249fvw4bBQDgCShhQZIMSJwDA8PRzposVjKy8uV47IbKDpl%202M26detC04zZbC4sLJS3bTabLBl9yZmhoSHGBQMg0ABIGKUbKJKamhqlCcdut09PT2uWwDGZTG63%20W/YxKSYnJ8Uzs5IeAAINgERSt6OojY2NaQbzqnk8ngsXLihRJtKQHdnHFDooh5X0ABBoAIQRs0El%20UnYRmcNisRiejnRRjt+8eTPKUylxZ8uWLUqUkSOONWpqatra2ioqKr7++muRbO7duyfehiFC71JJ%20SQkDaAAQaAAY5LjasKeU7PLw4cO7d+8qx1999VV5Y9++fXIVmZjL9Cn56Ve/+pXsPBIP6enpUZeR%20K9CIg2+//bbSwXTp0iU52kadXcTZ6elpTTwCAAINsOzIiUKG2RnR4kakhfVEjDDM9vjcuXNHOehw%20OKqrqw2zjTQrV67MyMiIuQhNRUXFyy+/bHg6JkbzEDn7Wk6VUpdRiuXm5v7iF79QsovS3lNcXMy3%20EgCBBli+8vLyPvnkE6XXKdJ0IblxkrgxMjKiPt7d3a0e0tva2qosbBOJeIiMQVJ9fb2yzExXV5ds%206XE6nUofk8Jut4uD6i27ZffWXLeXAoCEyPjZz34m/pN/gQFICYFAwGazDQ4OilRx/vx5PfTv6PAt%20AVgmmpubDbTQAKlIhxsnKQvhhG4vBQCLgEADpKS6ujqz2ayfjZM++OADkWksFsvevXv57gAg0ACI%20S15e3ttvvy1u9PT0LPlKMIFAwOVyiRsNDQ1y1wUAWGS0DAOpyjlLD+8k0u5OALBoaKEBAAAEGgAA%20AAINAAAAgQYAABBoAAAACDQAAAAEGgAAAAINAAAg0AAAABBoAAAACDQAAAAEGgAAQKABAAAg0AAA%20ABBoAAAACDQAAIBAAwAAQKABAAAg0AAAABBoAAAAgQYAACBFZcr/mpubqQsAAJCinnnhhReoBQAA%20kLpycnL+vwADAC/oQqlbjDkTAAAAAElFTkSuQmCC" height="452" width="752" overflow="visible"> </image>
            </svg>
          </div>
        </div>
        <div class="fig"><span class="labelfig">FIGURA 1.&nbsp; </span><span class="textfig">Producción de biogás acumulado en biodigestores 1 (sin inóculo microbiano) y 2 (con 0,5% de inóculo microbiano).</span></div>
        <p>Continuando con el análisis del volumen acumulado de biogás, en la <span class="tooltip"><a href="#f2">Figura 2</a></span>,
          se describe el comportamiento del biogás en los biodigestores 3 y 4, 
          inoculados con 1,0% y 1,5% de IM, se advierte que el tratamiento 4 
          superó notablemente al tratamiento 3, en la producción de biogás, a 
          partir de los 10 días de retención; es decir, en la fase inicial la 
          tendencia de producción del biogás es bastante similar, por lo tanto, al
          cabo de 10 días las reacciones bioquímicas enzimáticas (RBE) de la 
          digestión anaeróbica para las fases 3 y 4 disminuyeron notoriamente en 
          el biodigestor 3, lo cual se puede atribuir a cambios bruscos de 
          temperatura, pH o presencia de sustancias inhibidoras en el sustrato, 
          que afectan la digestión anaeróbica(DA), contrario a lo observado en el 
          biodigestor 4, donde se mantiene la tendencia de producción sigmoidea, 
          muy acorde a la cinética de RBE bacterianas de las fases 3 y 4 de la DA,
          es decir: la acetogénesis y metanogénesis del sustrato son 
          significativamente superior a lo que estaba ocurriendo en el reactor 2.</p>
        <p>Cuando se aplicó el modelo de Gompertz modificado según <span class="tooltip"><a href="#B11">Nopharatana <i>et al.</i> (2007)</a><span class="tooltip-content">Nopharatana,
          A., Pullammanappallil, P. C., &amp; Clarke, W. P. (2007). Kinetics and 
          dynamic modelling of batch anaerobic digestion of municipal solid waste 
          in a stirred reactor. Waste management, 27(5), 595-603.</span></span>; <span class="tooltip"><a href="#B5">Deepanraj <i>et al.</i> (2015)</a><span class="tooltip-content">Deepanraj,
          B., Sivasubramanian, V., &amp; Jayaraj, S. (2015). Kinetic study on the
          effect of temperature on biogas production using a lab scale batch 
          reactor. Ecotoxicology and Environmental Safety, 121, 100-104. <a href="http://dx.doi.org/10.1016/j.ecoenv.2015.04.051" target="xrefwindow">http://dx.doi.org/10.1016/j.ecoenv.2015.04.051</a> </span></span> a los valores de producción de biogás en los cuatro biodigestores, se obtuvo la gráfica indicada en la <span class="tooltip"><a href="#f3">Figura 3</a></span>,
          es de notar que el comportamiento cinético del sustrato tratado con 
          1,5% IM superó de forma altamente significativo (P&lt;0,010) a los demás
          tratamientos, los cuales alcanzaron un producción acumulada similar al 
          finalizar el tiempo de retención, destacándose pequeñas diferencias 
          entre los 5 y 15 días de reacción, causadas por posibles efectos y 
          cambios moleculares en las RBE bacterianas de las fase 2 y 3 de la 
          digestión anaeróbica: acetogénesis y metanogénesis.</p>
        <div id="f2" class="fig">
          <div class="zoom">
            <svg xml:space="preserve" enable-background="new 0 0 500 291.527" viewBox="0 0 500 291.527" height="291.527px" width="500px" y="0px" x="0px"  version="1.1">
              <image transform="matrix(0.5574 0 0 0.5574 0 0)" 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mit0RAsW%20LJAMqkbNm/yzxkYuP2PGDP3FdNL6azX0jALIGAYOJwLSBTd/jCH19fVVVVUSQOvq6pqbm0MDaG9v%20r9frDX2iPMXkHyOvfpw/f75sL1++HFry1KlTsp0+fbr+YjqND8EbCiDTPpK5lRNjyYQRU8vp06dH%20rEVFCqSympqalpYW2Wlraws7VZMcbG9vr66uDpzKXlH3iWrdlvPmzZNtZ2dn0Hqbkl9VWs3Pz9df%20DACgZdCYn8uMSMi0DKpN/Im0Jt8lJICazebDhw8PNyVnSUmJZFC73R50jb6rq0vNFXrXXXepI4sW%20LTL5JxPVFlhSDh06JNvKykr1dJ3FAADAWPzSRRNkPAmRas3MCAHU5F++SE0d/9RTT2mzgcpzV6xY%20ITuNjY3aepuSHevq6mTniSee0K7dd3R01NbWys7atWujKgYAAMagEfpBJXlEtfQRV1dT0HPPPad2%20iouLwxY4ceKEZFOJjMePH1e9oZ2dnQsXLpQkqnpAq6urg4YErV+/XsrIoxaLpbS0VCspoTMw5uos%20BgBpypDx7NnZ2bQkyKDBglb6RjrSfzeFvN2SEXfu3CnBUT1LguOqVatC7x+VwOpwOPbv3793716t%205Lp164Imb9JZDADSlCHj2QMXokOEuE8jjK0MigwQ1Yxxubm5oWOSwpJ8ucHPkGIAkL4YS5RQEydO%20lKAf5+ynUgktmd4ZVL6r7dq1K8JI+cbGRrpOAQBkUDKoUWbOnBnVXH5hMaNfemdQCaCzZ89Ws+oM%20J6qbRwEAQGYz5Bo6CXKsZ9Bdu3ZFDqAAAAAK19BhWAZVl+CtVuuPfvSj4XrFGRcPAABMXEOHgRlU%202bp1K4OaAQAACRLxiOI26vvvv1+2V65codUAAAAQjyj6QSsqKjZt2lRTU2Pyry1O2wEA0pch08vH%20ebNjKjcOZwhSKIPm5ORs3Lixtra2yi9sGbXiDs0KAEhxhkwvf91112VYs6h5phhIhNTKoF1dXWql%20bwAAMilyxSY1p/aMswtT4uMtt9wS52vgNlAYnEFff/112gsAQAZNzQxqVBcmCRIpl0G9Xq/JPzfT%20Cy+8MFwZ5mYCAGBU0IWJjM2gU6ZMka0EUO74BAAgBZEgkUaiuAZx3333yfbixYu0GgAAAOIRRT+o%20zWZ7/vnn1dxM+fn5BQUFNB8AAEZhRiSMKVH0g9bX18vW5/NVVVUVFhZmhdPV1UWbAgAQFTWZ0VAc%20TMyIhHQT3XrxLpeLJgMAwFgsrQ4yKAAAGAUkSJBBh+VwOGgvAAAAxG8cTQAAAIDUyqA1NTWtra29%20vb1RVer1euvr691uN+0LAMh4g3Gg9UAGHTZNrlmzZurUqTabTcKoWippOF1dXU1NTUVFRRaLpaGh%20YWBggPYFAGTyh+hXy2Mynh2I1gj3g65bt+78+fMej6fdTx0sLS0NKqbKBB6prKxk3U4AQGZjeUwg%20URm0vLx80aJF+/fv37Fjh8/nUwedTmeEp0hC3bx5M+t5AgDGAhIkEJuRxyTl5ORs2LChp6enra2t%20srJyuGJWq7Wurs7j8TgcDgIoAAAAItA7N5MkUZuf3W7v6uq6cuXKRx99JMdnzJgxffr0/Px8KUBr%20AgAAwMgMGkh1c5aXl9N8AAAAiAHzgwIAAIAMCgAAgEzHevEAgLGLWeIBMigAAMkzceLE/v5+NUt8%20PJXQkgAZFAAAvWbOnDl9+vQ4K2FyUIAMCgAACRJIG1GPSer1035sbW0tKyurr6+PvJQ8AAAAEGMG%20tdvts2fPPnfunPpRoueaNWucTmdDQ4PVaiWGAgAAwOAMKhGzqqpKWzVefpToqT0qx+vq6mhQAAAA%20GJlBDx06pHZuuOEG2e7cuVP9WO0nO+3t7TQoAAAAjMygx44dk21jY2NBQYHJf11etmazudnPYrHI%20j11dXbQpAAAAIot6XPzixYtl29HRoS7KL126VB3Py8vzeDw0KAAg0QYHB/v6+uKsJDs7m3HxQDpl%20UOXgwYNqp6SkRO0EDpYHACBxenp6+vv746xEPrbmzJlDYwJpkEEXLFjgdDp379595coV7dbPu+66%20S7Zut9vlcslOfn4+bQoASIJx48bF/Nxr167RgMAo/y+sv+jdd99t8g88WrZsmTpitVpzc3Nlp7a2%20Vv2Yk5NDmwIAkpNBY0brAemUQZcsWaLGv2v27dsX+OMLL7xAgwIAAGBE0d0P2tzcfMcdd3zyySey%20X1FRoTpBhewcOXJEQioNCgAAAIMzqLDZbGGzKU0JAACARGVQ0zCTgF65cuWdd9556KGH6A0FAACA%20kRm0tbV1zZo1EQpIBqVNAQAAEFkUY5K6uroiB1CkMq/XW1NTk5eXl+VXVFTU1NQUOqurHJHjWrGy%20srKOjo6wFeosqb9CAAAwdkTRD/rLX/4ycoG2tjYuxKcmt9tdUlLi8/nMZnNpaakccTqdLpdr7969%20nZ2d2tgyyYuSEdVUr1JMfnT61dXVbdu2LShZ6impv0IAY4QhSxwNDQ3RkkC6i6If9PTp0yb/evHy%20P79kCNnfs2fPkJ+as+nXv/41DZqCJPmpACpvU09Pj8PP4/FYrVbZ1tTUaCV37doleVEdlzLd3d1H%20jhyR4w0NDUE3Aessqb9CAGOE/BXyxO0vf/kLLQmMoQyqqPXi1Xz1b7zxhjrY3NxssVhaWlpYsTMF%20HT16VAKovEHyNmmLCOTm5qrFrpxOp9vtVlFVoqHJP+2r1jNaXl4u3zpkZ/fu3YGhVk9J/RUCGHOf%20PUwvD/B3INonXLlyxeSfr17FFy10ms1m2Z47d442TUHy7lRWVgYdlFxotVplZ2BgQEVVk3+xq4KC%20gsBiFRUVJv/6WNp7rbOk/goBkEHJoAAZdFgLFiyQ7ZYtW7xer8oWsn3qqae6urqamprUPX9IQTab%20ra+vL+z9l4Hvmlp6oKioKDSqBn3B0FlSf4UAAIAMOix1/V1Sy6VLl2TnwQcfNPl7s4qLi9V68SI/%20P582TRetra0mfxep6tU+duyYbGfNmhVacuHChbK9ePGi+lFnSf0VAgAAMuiwJKm0tbVpPz7++OOq%20N0vT2Nio3W6IFOd2uzdt2iQ7Gzdu1PkU1a9pYEn9FQIAgAwT3Rz1NptN9WAJiZsul2vnzp3q0vyq%20VavCLuOJ1AygaqR8aWnphg0b0uI1//M//zNvHAAAYzSDmvw38wXus1J8+gZQq9X62muvpcvL/vLL%20L3nvAAAYixlUsosaQB3BtGnTAkMqUo3dbq+pqVFzhT777LOB905MmTIl8nNnzJgRVUn9Ferx4x//%20OOgIPaMAAIyJDFpbW+t0OkcsZrFYJC6sXr2axk019fX1asLOsMsUzZ8/v729/fLly6FPPHXqlGyn%20T58eVUn9Feo6UydM4B0EACBjGD/RmsfjWbNmjRpzjdRRU1OjAmhbW1vYeZrmzZsn287OzqDjXq/X%205/OZAiY90FlSf4UAAIAMOqwFCxaoOUFN/gl9Sv20IxaLRR1RP3KdNKXU19e3tLTIu3bixInhho4t%20WrTI5J97Sy2bpDl06JBsKysrtQv3OkvqrxAAYjAYB1oPSKcMqjrPVI7p6+tTy453d3d7PB7JE3Kk%20sbFRjpw5c8bk7w1V4+Ux6rq6ulQP6OHDh9VUoGFJIqyrq5OdJ554QnvvOjo61OSva9eujbak/goB%20ILqPLv9qSUNxkKdPnDiRlgRGURT32LW2trpcrra2tqAck5uba7fb8/LyHnjggfPnzxcUFJSWljqd%20zkuXLjE+KRU899xzaqe4uDhsAflSod7T9evXd3Z2yruserV7e3vVQkoSJYPedJ0l9VcIAPpJfLzl%20llvirGT8+PG0JJAeGfSNN94wDT+ORDKo5E632x20ODhGnZ6RZEpOTo7D4di/f//evXvVsyQ4rlu3%20rry8PLaS+isEABIkQAaNZPfu3aE9WBI9VcIYGBjwer1q1DNShLrqpD+GbvAzqqT+CgEAABk0jAUL%20FkjQbG9vd7lcTz755OLFi+Xg2bNnJYC2tLSoMvn5+StXrmTUMwAAAIzJoOvXr5esKfnS4/GoYSVB%206urqtJHOVquVUc8AAAAIK4px8ZIpjx8/brFYwj4aNO35Cy+8QOMCAAAgrOjuBy0oKDh//rzdbv/9%20739/+vRpk389xvnz51dUVGhD4Ddv3tzc3MyIeAAAABiTQRWb33CPMucOAAAAjM+gJv+052fPnr18%20+fK8efMWLVrErZ8AAP1YqQhA1OvFd3R05OXlFRcXr1mzpra2dtmyZVOnTq2vr+/t7aU1AQCRqdWJ%20WOIImefUqVN/7xd4sKqqSh287bbbIjx3x44dqpiUD320q6srKytLAljY56pHm5qa9L9Uu91eVFSU%205Sc7oc8tKyuT44lusej6QeVFh22dhoaGzs5Oh8NBhygAIIKZM2cOt9aJfkxQj2Rqa2uTrBbhLkQ9%20Lly4cPbs2blz54Z99K233hruib29vStWrCgtLQ27vIvb7ZZHo3olNTU1akpNNYWR0+l0uVw///nP%20u7u7tTKNjY2FhYWSTRM6vXcUGdTr9crrVvuBUy9J8Pf5fPIP2LVrV+DQeABAxhgcHOzr64uzkuzs%207PF+tCfSxeuvv/673/3O5O+8D9sNp9/bb78dNoNKjpKEOtyzJFxJytq8eXPoQx0dHQ8//LCalF0n%20eYoEULPZfPjwYTWAR1LsAw88ICkuMHEWFBRUV1fX1tZK9k3c+pdRZNCjR4/KvzPwdWsJ/Sc/+UmL%20HxkUADJST09Pf39/nJXI58WcOXNoTKRXAM3KylJZzeS/th5DPffee+9bfhs3bgx99Be/+IVWJugh%20r9fb0NAgQTBowLccr6ura29vj/aVbNmyRbbbt2/XKpSIeeDAgeLi4h07dgT2ej7zzDOS6ySGOhyO%20BDVvFPeDqvXim5ubgxoiJydHDlosFkmoXV1dnLIAkKnGxYHWQ5oGUI3E0La2thiq+u53vztr1ix1%20OT700TfffFMelTKhD+3cuVO269atCzouoUsCqNlsPnLkiCRUnS9DkqvL5ZKdioqKwOOS61SKC7zl%20NDc3t7Ky0ul0qvA9yhlUGe4+nry8PM5XACCDkkGRYQFUHYkzht57770m/+X4oOPvvvvu5cuX1aNB%20ent71XXz0DtBJTLu2bOnp6cn7E2iw7l06ZJ6bujoHavVKtuPPvoo8OB9990n29bW1tHPoGra+d27%20d4c+JG+JJGXZmTZtGicuAADIpAAafwy95557TOHGHh09elR7NOxDS5cuDX3o/Pnzq1evjnYg+MmT%20J03DdBrOnz/f5B84FXhw4cKFJv949NHPoHfccYds29vbbTZbR0eHmoxJ3gkJyCUlJSpZszwSAADI%20vAAaZwydO3du6OV4n8+nLsSHHav061//WkuHyeH1egN/lFCX0Dsto8igEj1VV63EUDUtqLwNhYWF%20a9asUWOyXnzxRc5dAACQpjo7O9Uo+LABNCiGvv/++1FVriYXCrwc/957712+fFmbdCiImixp8eLF%20o9ggqtM07G2sSc2gYt++fSqGhqqrq4vqpgQAAICU8p3vfOdrX/uayT8T03Bl1FoJ119//be+9a2o%20KlfXkwMvx6sh5+p4KDV+aHQtWLBAthKURz+DFhQUSHvt2bNHS6Jms7mysvLEiRPMygQAANLarFmz%20HnvsMYmh2qJcwwXQVatWzZgxI6rKZ86cKTnqwoUL7777rsl/IV7yqByR48n51914442RC4TeUTni%20U5KXQU3+mZhWr17d3d2t3oa+vj673R40WxMAAEA6+va3vz1cDA0MoFIshspXrlxp+mqw0Xvvvacd%20SQ5112nYxdU//PBDFcGT2dRMlgEAADBCDI0/gJq+uuz+5ptvmr66EL98+fKk/bvy8/NN/kv8oTFU%20XfefN29e0PEEXYVXRlgnqb6+/vTp0yb/yqE/+9nP1H4EUixxazoBAAAkLYb+9Kc//dvf/qZlUAmg%20cjCezkJ1Od7tdksMfeutt+69916z2TxcYavVauwtoTk5OarOQ4cOrV69Wjve1dXl8XjCTkSqgl+C%20rsiPkEHld6uJPzdv3qztRxB2PVMAAIC0jqHxB1Bl5cqVkkEbGxtlv6ysLELJoqIiyYsnT5408I7H%20rVu3Llu2bNOmTXPnztXWi1+1apXshF1H9NSpU7K99dZbE9HCXIsHAAAYNoZ+7WtfMyqAmr66+K5m%20g//e974XoaS6cK/u1IxBV1dX6Cyn5eXl1dXVPp+vuLhYMq6E4MLCQo/HY7VaAxeLV7xer5Q0m80J%20usQ9Qj+oROM777zT5F8ASduPgHWSAABAJsXQuro6SXISQw2pUCLdvffeO+KFeHHXXXdJgfb2dmNX%20KmpubpZ0+/zzz6sL/ZI+H3zwwdAAavpq7JTNZktQ22YNDQ1t375d9jZt2sSphjSizlv5YkpTAEnw%208ccf9/f3T5gwIeYavvzyyylTpsyZM4fGBEZ08803m/zT2re0tLS1tSUuCEagbgbweDwGroIZmDlj%20vxbf5Rd2hD8AAADi9Mwzz8j2wIEDyf/VXq9XAmhpaWnilmGPOoPa7XbJxVlZWcV+U6dOzc7Obmpq%20IowCAAAYSPJfXV2d0+lM0IrtEezcudPkn+8ocb8iugxaU1NTVVUVNFOAz+erra1dtGgRMRQAAMBA%2069evN5vNzz33XDJ/qdfrbWlpkfib0Ak3o8igksHlBQ33qMfjSeZc/wAAABkvJyfn8OHDTqezo6Mj%20ab+0pqbGarVK/E3ob4kig77++usm/3iuPXv2fP7550NfkfRZWVkpD0kDSXDmdAEAADDKkiVLJG6F%20TiCfOA6Ho7u7W+JvqmRQlS+3b9++evXqwJeVm5trt9stFovsX7p0iXMFAAAAkUUxy4YaGKUWvA+V%20l5fn8XiYHxQAMtjg4CCNACDZGfShhx5qaWnZvXt36JpRbrfb6XRaLJbEDeAHAMQcHPv6+uKs5Otf%20/7rJv2R2PJVMnDiRtwOArgwq4XJgYED7sbq6Wg1L+sEPfjB58mR18Je//KUcNJvN//qv/0qDAkCq%206enp6e/vj7OSKVOmFBUVxVnJ+PHjeTsA6MqgtbW1Tqcz6GC7X2jhwsLCEydOhPaSAgBG3bhxsS9K%20cu3aNRIkgKRmUAAAGVRlUABIXgZdsGBBVNXdcMMNtCkAAADiyqDbtm2jjQAAAGCscTQBAAAAyKAA%20AAAggwIAAABkUAAAAJBBAQAAADIoAAAAyKAAAABA7Bm0t7e3tbW1rKwsy6+rq6upqUm2NCUAAAAS%20kkHdbvfs2bPXrFkTuIj8sWPHiouLa2pqaE0AAAAYnEF7e3tLSkp8Pl/YR1taWurr62lQAAAAGJlB%20Dx06JAHUbDbv2bPH4/GUlpaq4+vWrbNarbLT0NBAg6aFpqYmdR9F6ENFRUVZ4ZSVlYV+J5F68vLy%20tAIdHR1hv7roKQYAQFo7derU3/sFHqyqqlIHb7vttgjP3bFjhyom5UMflc9r+QAd7tNTPSoftTpf%20Z319fdYwtDLyYS15IIUy6BtvvCHb5ubm1atX5+bmasfLy8u7u7stFotqCM7CFGe322tra4d71OVy%206alEkqWcoFKP+jYiX0KcTueyZcuC+sJ1FgMAILNduHDh7Nmzwz361ltvRfjAXbFihXyGStwKfdTt%20dsujUb2S06dPj1imsbFR8oD+XBubCdE+Yfr06WGP5+XlSc7gJEtxcj5FCKDqK4QkRflSEbmeXbt2%20ydkpJdvb29UXEvl+JuGyoaHh7rvvXrJkSVTFAABIBX/84x8/++wznYWnTJnyne98R3/lb7/99ty5%20c0OPnzp1ShJqhA9cn8+3efPm0IfkI/Xhhx8e7ibJ4aghPZ9//nlOTs5wZQoKCqqrqyUwSPaV/QS1%20dhT9oCpD7N69O2wrqH/StGnTOINTk+TLoqKiCAFUnDx5UrZLly6NXJV8J1P3Xezbt0/rEZfvZ/K1%20KfAM0VkMAIAUIcnsmm59fX1Xr17VU+29995rGr6z8xe/+IVWJojX65VPUgmCQb02ctxmsy1btiza%20AKp1NkUIoMozzzwj28ixIXkZ9KGHHpJte3t7Xl5efX39+fPn5cfXX39dtYLsWyyWwGv0SKkAWlxc%20rLokz5w5M1yxDz/8ULa333575NqOHj2qzuCg70YVFRXqDJH0qb8YAAApZYIOUVX43e9+d9asWcNd%20jn/zzTflUSkT+tDOnTtN/oE3QcclccnHqNlsPnLkiDY+Rw/1AkbsbDL5ex4rKyudTqfb7R79DCoZ%20vLq6WnY8Ho+kcnXlvaWlRVpBFThw4AAnbsqSc7Stra27uztCp7q6GfTgwYPayCT5vtHU1BQUFj/5%205BOTf/RS6Pkq/z/Izrlz5/QXAwAg46luzrfffjvo+Lvvvnv58uWwnaDy4SspSz4xQ+8ElQy6Z8+e%20np6esDeJRqAC5enTp7WJ3rOzs2tqarxeb2jh++67T7atra2jn0FN/gFJdXV1ocelLSSJc3tfypK3%20xuFw2Gy2CGXk/FPfK+RLhc/nU0OI5Ehtba2cqYEx9NixY7KVL22hlSxcuFC2Fy9e1F8MQHIMxoHW%20A+J0zz33mMJdjlfXDNWjYR8K22d5/vz51atXj3g9PVRnZ6fJf0voqVOnSv3kE1+Srnzih/Z3qg9r%20u92eoDaJekzStm3bHn/8cXnpqpdLzJs3L9oYjhQ0MDCgQufhw4e1rxNyRj7xxBMul2vlypWSYnVW%20pZ0bhhQDEKeJEyf29/cPDQ3FWQktCcRs7ty52uV4bWSS5D91IV6O/OpXvwp6yq9//WvZzp8/38CX%20oUaQ79mzRyKsOtLb2/vUU0+1t7eXlJT09PQE5trc3FyLxSLlu7q6EtHPOCGG5+T6cT5lmIKCgtDh%208HJQzks5BeU7U0dHxyh+2VD3xACIwcyZM4eb0kS/8ePH05JAPGpqajZs2BA4Ov699967fPnycPMV%20qg/lxYsXG/gaQruTJHTa7XaXyyVZc9euXdu2bQvNrJKbRyGDysuKqrOqoqKCeJph5A0tLS2VDPrR%20Rx+NYgbVOfYQAAkSSE133HGHyX85fuPGjYGJUB0PpXPGbkM8+eSTtbW1oVOHLliwQAKABOVE/NIR%20MuiBAwcCl4YfkaR1MmjmUaegGjVv8s+IFrn8jBkz9BfTSU0SEYieUQBAGpk5c2ZBQYHb7X733Xe/%20//3v+3w+yaNyRI6P+mubN2+eyT9TadDxG2+8MXG/dBznBDS9vb1hR8ap6cfUUlimr+5NCfutSJ2+%206qqfzmI6XReC9wsAkF5Wrlxp+mqw0XvvvacdSaawcy1duXIl8IM+OUbIoKtWrWoMUFlZKQfNZnN1%20dbV2UC0WLwdlnznq05fNZps6dWrYzkV1S4rWbam+LamxdYEkv6q0mp+fr78YAABjhLrs/uabb5q+%20uhC/fPnypP321tbWrKysBx54IPSh3//+92EzaIKuwisTRswlgdFhx44dkjil1QKHTW3YsMFut1dV%20VckL5UJ8+iopKWlvb5e38tlnnw18f7u6utQtKXfddZc6smjRIpP/PhX5LhU42+ihQ4dkK19U1NN1%20FgMAYIzQLsdLDH3rrbfuvfdeNWF2WJK4jL0l9NZbbzX5Z3kPGueuJiI1fTUhaCB1h2iCrshHcS1+%20//79Pp9v69atodFBoqq0VENDAyvfpK+Kigr5P0He4qeeekp7H+U0XbFihew0NjZqXzDkBFDTxD7x%20xBPatfuOjg61otfatWujKgYAwNihLr6rZavLysoilFSLvKhltA0h8VctqvT0009rV+TlA1pehpoX%20PHQecXXvnAqvo5lBQ0dLhWLlm/QlkfH48eMSQ9vb22fPni1npJz9xcXFcl5WV1dv2LAhsPD69evV%209zOLxaJKqvVaJXQGfrXSWQwAgNTxpQ4xV64uvl+4cEG23/ve9yKUVBfutQHB0erq6lIrIQUefO21%2019TncmFhYZmffECrpbzloaAa1L1zEgwirLCYpAyqbNmyJXTYSmtrazJnEECCyEkm76MkzuzsbKfT%20Kftqhc/m5ubQwOpwOORrnJo6VJU8cuRI0LxiOosBAJAKIlwZD3XjjTfGMEBWfoVamTPyhXiT/xY4%201TFk4D9Q+1yW0On0kx35Meg2S0WNnYq8yGI8soaGhrZv3y57mzZtilxUguaaNWvUviSJO++8U8Vz%20Na+pata+vj7OYCSHOm8fe+wxmgIAYJRr167pLDluXAInF7r55ptN/mntW1pa2traEhcEIygqKlIZ%20z8DRPoGZM4p1klavXr1v3z7V36myc1CBV199lXMXAACkr4Qmy2g988wzkkEPHDiQ/Azq9XrV1cvE%20DTePrqEdDoeanimIxWI5cuQIq8YDAAAYRfJfXV2d0+ns6upK8q9WczWqsVMJEt168WpR0YaGhlOn%20Tqk1PG+88cZbb701QTerAgAAjGXr169vaWl57rnnQpd6Txyv1yu/VOJvQgPehBiek+vHaQEAiTM4%20OBj/HfbZ2dmsFA+ktZycnMOHDxcXF3d0dCTtgnNNTY3VapX4m9DfMoF3FwBSUE9PT39/f5yV9Pb2%20zpkzh8YE0tqSJUuGhoaS+RuT0+dKBgWA1BXP8Aj9w3sBIPnIoABABgXS24ULF7KzsydPnkxTkEEB%20AACSoaur65133pk0adJjjz32zW9+kwZJm+/YNAEAAKnvwoULV65coR3CBlDZ+e///u+f/vSnFy9e%20pE3IoAAAwLCktWfPnhdffPHTTz+lNYICaFZW1rhx42QrMfTAgQPE0EzOoF6vt6mpyWazlZWV1dfX%20d3R00I4AACQ0aZlSpqvPwB7ZeKrSAqiQH9UOMTSNRH0/qITOhoYG7Ue1YqfVat23bx8z1QMAkIgA%20qgLW0NCQylirVq2aPn16bJkvzrE7Bt58GU9VQQFUUftxNhGSJrp+0KAAqnG5XCUlJV6vlwYFACAR%20AdQUd1df/Bf0DeyRjaeqsAFUi6H0hmZgBnW73SqAms3murq6xq+UlpbKQZ/PJwdpUAAAAsV8uXm4%20rr7YMlb88dHAmy/jqSpCACWGZmwG/dnPfmbyX3bv6enZtm3bhq84HI62tjZ5qL29vbe3lzYFAEAL%20TLF1PRrb1Rd/fDSwRzaeqkYMoEFN9PLLLxNDMyGDnj59WrZbt27NyckJeshms0k2lZ1z587RpgCA%20zBDn4JuYux6N7eqLPz4a2CMbT1U6A2hgnQMDA8TQTMigAAA9BgcH/xy3JC8PjbCJJ567J2PuejS2%20qy/++Ghgj2w8VUUVQImhmZZBFyxYINstW7aEXnC32+0ul0t28vPzaVMAY1xPT48nbn/5y19oydEN%20oPHcPRlz16OxXX3xx0cDe2TjqSqGAEoMzagMevfdd5v8Q+AXLVqkpgWVc6K1tdVms1VVVclDpaWl%20oZfpAWCM/nmNA60XP0Mmnozt7smYux6N7eqLPz4a2CMbT1Xvv/9+bAE0tIlYaCpdM+iSJUsqKytl%20R76gNzQ0LFu2rLi4eM2aNe3t7apAY2MjDQoAZNBRF89l9Djvnoy569HYrr7446OBPbJxVhX/cGf1%20e6XOvr4+/u9IywwqXnrpperq6tDjZrP5yJEjzFEPAEiFAGrsxJNJuNxsbFdf/PHRwB7Z+KtasWLF%203Llzh/xiOyXUE5cvXz5r1iz+B0nXDJqTk9Pc3HzmzJm6urpSv8rKSvmu2dPTU15eTmsCAOI3WpfR%2047x7Mp6uRwO7+n71q1/FmfkM7JE1qqpHHnkk5hiqniUBdMmSJfzPlVImxPCcAj/aDgBgOAPXb4xq%205UY9XZgRaou261HlRQlYjz76qFS1YsWKy5cvnz17Vns0hqQl23/8x388efJkbJlPvaR9+/Z98cUX%208fTIav+0W2+99ejRo4ZU9cMf/lBi6CuvvBJtExFAUxl3HQEAjBHnbJqm0buMHufdk4ZcuY6/q0+e%20/h//8R9xXtCXABrnaaBlxz/96U9GVaXu44y2iQig6Z1By8rKsqIh/x/SpgAwBhm1FnnyL6PHefek%20gVeuY4uhWtK68cYbDcl8Wp2xVaLdfPnwww8bfh+n/iYigKZ9BgUAjKLBOCQ5gBq1Fnki1m80duLJ%20wOx45MgRY6cNiqerz8CxO0bdfJmI+zj11EkAJYMCAGL96+yfoWkoDvL0iRMnJi2AGrgW+YjBMYYQ%20GfYyevxdmL/97W/jbL3QaYPi6eozKvPF2SMbmPwMrEpnnQTQdJEl79P27dtlb9OmTSOWrqmpaWlp%20aWxsrKioyM3NVQc7Ojq2bNni8XiOHz/OWCUkjTpvH3vsMZoCqebjjz/u7++fMGFCzDV8+eWXU6ZM%20mT17dpyvZPz48UkLoEGXcSdNmjTiMCA9QTBybdGGSFXbDTfc8Oijj/7xj398++23Y+7C1GqTN1re%20r5jrGS4wqfE3EaqNkLRGfK7O2qKqJ3LyM7CqyHVmTAC9+eabM/izW2XOKPpB7Xa7CqAbNmzQAqgo%20Ly93OByy88ADD/DZAwBGGT+S//zP//yf//mfCAX0/67YhhMldC3yEWuL8zK6ZNB4e3H8v1cC6Le/%20/W3Dpw2Kp6vPqLE7Bt58mYj7OEPrpAc0vUSRQd98803ZLl68OPShnJychQsXejwet9tNmwJAEsQ/%20BijOqpKwFnlQbcZeRv/DH/6Qm5tryN2Tq1evNvxyc4TcpidpGZX5DLz5MhH3cQbWSQDN5Aza398v%2025MnT4Y+1Nvbe+rUKZP/jhbaFACSEEDjHAMUZ1VJW4s8KDiqGGrUqkJ//vOf/+Ef/mFUBt/EnNvi%206SaM7WUYePNlIu7jDKyTAJqxGVRdf6+tra2pqdHmYHK73Xa7fdGiRT6fT36cNm0abQoASQig8YwB%20irOqJK9FHhpDjbqMLrWVlJQkf/BNzLktnm7CeF5GPD2yiasqsM7Fixffc889BNCMzaCrV69WOy0t%20LcXFxerPQWFhYVVVlcfjkeOVlZWB94kCABIXQGOYw8iQqkZlLfKg2gy8jD5r1iwDuzATNG1QPF19%20RmU+A2++TMR9nCtWrLj99tv5+5CxGbSgoKCtrW24R61W60svvUSDAkDSAmhgMos2hsZWVfwLAqXg%20ZXRTEgffxJPbYu7qMyrzGXjzJfdxwhTt/KA2m+3MmTPV1dUWi0UdMZvNlZWVkk27u7tzcnJoUABI%20ZgCNLYbGVpUhCwKl5mV0U1IG38QZtuLp6jMq8xl48yX3cSLqOeoLCgqam5vPnz+vzpu+vj673S7Z%20lKYEgFEJoNHG0NiqMqr/8g9/+EPM/ZdakjMl4DK6KcGDb0Y9bBmV+Qy8+ZL7OMmghvH60aYAkOQA%20GpQdQ8cAxV9Vb29vnP+KOPsvh0tyxo4EStDgmxTp7TMq8xl48yX3cZJB9ZK/QU1NTWXhFBUVWSyW%20S5cu0aYAkPwAGpgdg8YAGVKVgWuRx9B/GTkIGjsSKEGDb1LkcjOZD6kjiqXkJIAuWrRIDYEHAEQ2%20ODiY5ACqZUeTv7tRsuOjjz6qrW8Zf1WSqNTqiNpD8cTHaGsb8TJ6hNpiGAOu1aaeHs/gm8OHD+fk%205JD8gCBR9IMeOnQocgC1Wq3MDwoAEydO1HJPbFQlhowBUr2hRlWV/P5L/SHS2JFABnZh0vUIhBVF%20P+jx48dV0HzwwQd//vOfu1yuysrK+fPny8EdO3b4fL6tW7cyPygAzJw5U+t9FB988IHD4Yic/1TW%20mTRp0sqVK9WX+d/85jdxjgEyfdWFeeuttx49etSQqn74wx8ms/8y2hAZWlucY8DpwgRSIoOqtTol%20aJaXl8+YMaOqqio7O3vDhg1ysLS0tLCwcN26dfIQbQogTQ0ODvb19cVZifxhHO+nftTfASlRSf7M%20Hjx4UF1AN2QMkNQp2fFPf/qTUVVJ+0yePFl/DNXZf5mal9FN/i5M/r8ARj+DKvLXR7YLFy6UbWdn%20pzpYUFAgMdTpdLrdbtmnWQGko56eHvVlOx6SHefMmRNtADWF3Hwp6efy5cux3XypxS+TfwyQJLCY%207+MMqmrWrFl6gmO08XG42mK+jK7VxsSTQMqK4n7QKVOmyPbkyZMm/9rxZrPZ4/GohePlb+6pU6fU%20X0/aFEB6/1mMQ2A98S8pFNvNl2HTm4FVBUY9AxcEMnZCTSaeBDIqg5aUlMi2trZW5c6lS5fKtri4%20uKysbPbs2T6fT3684YYbaFMAZNAUHANk7HCiyHWO+lKQJkYCAZmUQSsqKqxWq/bj2rVr1Y7T6VQB%20VB7lQjwAGLWkkMTQK1euGLgUkLGrCg1XZ4osBQkgczJoTk6Ow+Gorq5WP8qfhrq6Ou1Ri8Wyb98+%20GjQtNDU1ySec6s8OopYhyMvLU5+CZWVlHR0dYSvRWVJ/hUDGMHBJITVGysA5jIydDim0zpRaChJA%20hmRQFUObm5u1vwvbtm3zeDwn/M6fP08naFqw2+21tbXDfXBKRpRH5W0tLS21Wq1Op3PZsmX19fWx%20ldRfIZBJjF1SSH921Jn/jF1VKLTO+PsvuYwOkEFHlpubu8SPpkwLTU1NVVVVwz26a9cul8slSVEi%20o8Ph6O7uPnLkiBxvaGgI6jTVWVJ/hUCGSfExQMYOJ9LqpP8SgH7/d8q37du3y96mTZtCH66vrz99%20+rT+6hobG+kNTU2S+Z5++mlJhNqREydOBH5U9Pb2Tp06VXbOnDkT+CZKbK2tra2srLTb7VGV1F9h%20bNR5+9hjj/Hmwigff/xxf3//hAkTYq7hyy+/nDJlijY3k5ohKKobQ0fMf2HrjC01GlgVAGPdfPPN%20GfnvCsycI/SDSgB1RoO5mVI2gBYXF6suSUmEYcscPXrUFG5gWUVFhWzb29u1W9x0ltRfIZCpUnwM%20kLHDiQAgKuNogjGitLS0ra2tu7t7uI7qTz75RLZFRUVBx9VcsLJz7ty5qErqrxAghppGaQyQscOJ%20AEC/ES45rVq16s4779RfnVrmGKlGPlEcDkfkMseOHZOtNgAi0MKFC51O58WLF6Mqqb9CIONjqIFL%20CoXWGWdqZFUhAKmYQW02G20ERfVrGlhSf4VAZsfQmMcAHT58OCcnJ/4h5AZWBQDGZFAgRezcuZNG%20QEbG0HiugK9YscKol2dgVQBgcAbVM0aecfFIkKtXr9IIyLwYyi2YAMigI1Nj5COX2bx5M22apqZM%20mRK5wIwZM6Iqqb9CPZ555pmgI/SMIt1jqMqgBFAAYxPj4vH/zJ8/X7aXL18OfejUqVOynT59elQl%209Veox3UheMuQvjGUJdEBIIp+0OHGyF+4cEFNNv7qq6/m5+fTpmlq3rx5su3s7Ny2bVvgca/X6/P5%20ZEd7c3WW1F8hMAZjKGOAAJBB9YowRn716tWFhYUHDx4sLy+nTdPUokWLZOtyudxud+BNvYcOHZJt%20ZWWlfF5GVVJ/hcAYxBggAGOcMdfiJWGUlpay8k1ak0RYV1cnO0888YTX61UHOzo6amtrZWft2rXR%20ltRfIQAAGGsMm5vp/PnzJv/KN9zblL7Wr1/f2dnpcrksFot8qZBvFGp9eYmSQW+rzpL6KwQAAGTQ%208Nxud9jl4M+ePXv8+HGPx0NrprucnByHw7F///69e/eqORAkOK5bty70FgudJfVXCAAAxpSsoaGh%207du3y96mTZsiFy0rK4s8N5PZbO7r66NNkRzqvH3sscdoChjl448/7u/vH245TT3kL+qUKVPmzJlD%20YwKIx80335zBn90qcxq5TlJzczMnDYD0NXHiRMmgkiPjrISWBIARRZFBFyxYEOGhu+++mzv8AKS1%20mTNnBk5b+8EHHzgcjiy/4Z6ipvmcNGnSypUrp02bJkfGjx9PSwKAkRk0aJZHAEgdg4OD8d8LlJ2d%20rSXIrq6ud955J3IA1WJof3//wYMHH3300ahWXgAAMigApLeenh4JgnFW0tvbq27l1B9ATV+t/D4w%20MPDyyy8TQwEgURm0o6Pjo48+Gu7RioqK3NxcmhXAqBg3LvY5j69du6Z2ogqgxFAASEYGHXFo/OLF%20i8mgANI3g8YQQImhABDLX2z9RVtbWyMHUABIaz6fL7YAqsVQoWLolStXaE8AiCCKftA33njD5J8E%201GazzZo1K2wZNSwUANLR1atX46xBMujQ0JDE0L6+vsmTJ9OkAGBABlWam5slg9JwADLPTTfdNHfu%203LNnz5q+urYeLTW36PLly4f7og4AUKK4Fq/mB+WbPYAM9sgjj0gMVbN+xhBAhQRQJksGACMz6OOP%20Py7bLVu2eL1eGg4AMZQACgAxi+JafG5ubmNjY21trcViKS0tDVtGChQUFNCsANI9hr7yyiv6L8oT%20QAEggRnU6/Xu2LFD7Q83QH7z5s20KYAxFUMJoAAQgyiuxe/fv9/n89FkAMZODB3xojwBFABiE0U/%206OnTp03+uZk2btw4XBnmZgKQYTE0Qm8oARQAkpFBlVdffbW8vJyGAzDGYygBFADiEcW1+Pvvv5/2%20AjA2Y2jQRXkCKAAkL4NWVFSYzWbmZgIwxmMoARQA4hfFtfijR48uXbq0vb3dYrFIGF24cGFoGeZm%20ApDBMVS7KE8ABYDkZdADBw5oUzL5fL6w0zMxNxOAzI6hhw8fzsnJuf3222kNAEhSBgUArFixgkYA%20gKRm0MbGxhG7OfPz82lTAAAAGJZBudETAAAAyc6gdrv9k08+iVymoqIiNzeXZgUAAIAxGTRwTNJw%20Fi9eTAYFEJXBwcG+vr44K1Ezd0pVtCcAZFoGBYBE6Onp6e/vj7OS66+/XkuiMZs4cSJvBwCkXAZd%20sGBB2OPnz5/3eDxqHXnWiwcQm3HjxsX83GvXrkl8vOWWWz744AOHw5Hlp//pas75SZMmPfzww7wR%20AJByGXTbtm3DPWS326uqqi5fvsyFeACjkkFlKwH0nXfeiTaAajG0v7//5ZdffvTRR6dPn87bAQAJ%20/7NvSC02m81qtba0tNCgAEaFz+eLOYAK9cSBgQGJoVeuXKE9ASA9Mqj2GdDV1UWbAki+q1evxlmD%20Cq8SQ+MfIAUAGJEBY5LcbvdvfvMbl8tFawIYLTfddNPcuXPVYu6xdYWq8UzLly+fNWsW7QkAiRZF%20P2hZWVlWOIWFhWvWrFFlWCcJwGh55JFHJIaqAUYxBFAhAXTJkiW0JACkVgYdUV1dXU5ODm0KIL1i%20KAEUANI1g1qt1sbGxggD5wEgNWMoARQARkUU94M6HA7aC0BaxNBXXnlFz72hBFAAGC3jaAIAGRlD%20R+wNJYACQDpl0N7e3tbWVrfbrR2pqalpamqS47QmgHSJoQRQAEinDCrRc/bs2WvWrBkYGNAOer3e%202trasrIyYiiAtIihBFAASKcMKlmzpKTE5/OFfdTlcv3kJz+hQQGkeAwlgAJAmmXQQ4cOqQBaV1c3%20bdo07XhjY2NlZaXssFYngBSPoQRQAEi/DHrs2DHZ7tmzZ9u2bbm5udrxgoICu91usVhkn7U6AaR4%20DCWAAkAqiHqtTvk7HvZ4Xl6ex+OhQQGkbAw9fPhwTk7O7bffTmsAwKiLoh90ypQpst29e3foQ263%202+l0ys4NN9xAmwJITStWrCCAAkCKiKIf9L777mv36+/vv/POOxcvXiwHz549KwHUbrfLvsViKSgo%20oE0BAABgWAa12WzPP/+8y+Vy+oUWePHFF2lQAAAAjCi6+UEdDkdpaWnocbPZ3NbWVl5eToMCAABg%20RNGNScrJyZEY2tHR8f77758+fdrkv0m0pKTkrrvuChwpj/RVX1/f0NAQ9qGgWb7tdrvqF5d9q9X6%204IMPbtiwIewT9ZdE2hkcHOzr64uzEnVqSVW0JwCQQSMp96PtMpL6ajGimpoaNR2sZEr5ZuJ0OiVi%20/vznP+/u7o65JNJRT09Pf39/nJVcf/31oV9yojVx4kTeDgDI8AwaVm9v79GjR/Pz8xmWlNbUnb6f%20f/655MXhynR0dEisNJvNhw8fVvMsut3uBx54QMJlU1NTYB+n/pJIa+PGjYv5udeuXZP4eMstt8j+%20Bx984HA4svz0PFfN9zlp0qSVK1d+61vf4o0AgHT67IiqdFdXV1lZWVY4U6dOraqqClxHHmlHLTGg%20OiwjFNuyZYtst2/frk30LV88Dhw4IDs7duyIrSTSPYPGTNUwfvx4CaDvvPPOoN+X+qjC/f39Bw8e%20vHjxIm8EAGRmBvV6vStWrAg7Ih6Z4ezZs7JdunRp5NNA3dlZUVEReFxSpsVi8fl8HR0d0ZYE5PuP%20BFD9PaAa9RT59vvyyy8TQwEgMzOotl78cOrq6vLz82nT9OV2u03+W0K13u7s7OyamhpJk1qZS5cu%20mfxzwYb2lVqtVtl+9NFH0ZbEGCd/WGILoKEx9MqVK7QnAGRaBlXrxVdWVp44cUK2KnSe8FOR4sYb%20b4x8DRcprrOz0+S/JfTUqVOlfhIOWlpa5P1V8VScPHnS5F+aNfTp8+fPl+2FCxeiLYkx7urVq3HW%20oMKrxND4B+kDAFIugypr165dsmTJD37wA9n3eDxL/BwOh9ls5g6/dKfy4p49e+SD3OH3+eefy/cN%20SaIlJSW9vb16KgnsNDWqJDLbTTfdNHfuXDXAKLYa1BOXL18+a9Ys2hMA0kKM4+IXLVpk+qrbzOSf%20N3ThwoVOp7Orq0sbfYK0I6Ez6Ii8s3a73eVyyfeNXbt2bdu2bbRe2/PPP88blMEeeeSRV155Rd2R%20HO0VeRVeJYDyxwcA0kgU/aBqFvrdu3f39vZKNFHDSpqamkz+8QSMVcpgTz75pEn31KEJ8rcQvC+Z%20F0Nj6A0lgAJA5mfQO+64Q7bt7e3nzp0zfTV6ura2Nisrq7i4WJWZNm0abZp55s2bJ9tTp06Z/Hf9%206vmuElVJPX4UgveFGEoABYAxkUFtNlt1dbX24zPPPBNUoLKykhU705029iiQGmtssVhkKxHB5F+S%20ILTYhx9+KFvthjz9JfX4WgjerDEeQwmgADBWMqhobm5ua2u74YYbTP5OrCNHjqhcIiSevvTSSzRo%20+mptbc3KynrggQdCH/r973+vZVA1/ZbL5QoNl2o2UNVpGlVJINoYSgAFgLGVQU3+3lBtNc7y8vLz%2058+rDwOJp0zMlNZuvfVWk3+uA7VakkYSpFrw/b777jP5RympqbgOHToUWEyeJc81m81yVqgj+ksC%20UcVQAigAjMUMikwlXy1KS0tl5+mnn9auyHu93rKyMp/PJw/J1w91cOvWrbLdtGmTllal/KpVq2Rn%2048aNgXXqLwnojKEEUAAggyLTvPbaa1ar1eVyFRYWlvlZLBb5UQ7KQ1qx8vLy6upqCabFxcVFRUVS%20TMp7PB4ptmHDhsAK9ZcE9MRQAigAkEGRgXJychwOR2Njo2REp5/syI9yMOhGC3VnsAqsWrHu7u7Q%20OvWXBCLHUAIoAGSSCTQBgmLoBr8RS9r89NSpvyQQNoZq09cTQAGADAoAyYuhhw8fli9It99+O60B%20AGRQAEiSFStW0AgAkEm4HxQAAADJFl0/qNvt3r59u8vl8ng8YQucOHGCu7WA1Dc4ONjX1xdnJdnZ%202bQkACDhGdTr9ZaUlPh8PloNSHc9PT39/f1xVqKtgCWJliYFACQqg+7fv58ACmSSceNivxvn2rVr%20sp04caJk2RHXdo9MKuG9AAAy6LBOnz4tW6vVunXr1smTJ4cto5YIBzBGMujMmTOnT58uOx988IHD%204cjy0/N0NdnnpEmTVq5c+a1vfYv3AgDIoCOQAMoy3wA048eP7+rqeuedd/QHUC2G9vf3Hzx48NFH%20H1VBFgAwdkTRC3L//ffTXgCCxBZAhXrKwMDAyy+/fPHiRVoSAMig4VVUVJjN5i1btmgDEQCMce+/%20/35sATQ0hl65coX2BICxI4pr8UePHl26dGl7e/vs2bMXLlwYtkxjY2NBQQHNCowR8X8jlQw6NDQk%20MbSvr2+4G80BAGM6gx44cMDpdMqOz+dTO6E2b95MmwJjx4oVKy5fvqwWc4+tK1SNqV++fPmsWbNo%20TwAYO1gnCUBcHnnkkblz56px7jEEUCEBlLUtAGCsiaIfdNWqVXfeeWfkMtOmTaNNgYQyZImjOGf0%20DI2hr7zySrS9oQRQACCD6mKz2WgvYNQZssTRddddZ+yrijaGEkABYIyL/Vp8lx9j5IHR+V83Dgl6%20SfovyhNAAQBRfxpJ7rTZbFlZWcV+586dkx+bmpoIo8AYz6A6YygBFAAQdQa12+2SO9vb2wMP9vf3%2019bWlpWVEUMBRI6hBFAAQNQZ1Ov1VlVVqX2LxWI2mwMfdblcK1eupEEBDBdDCaAAgFgy6P79+2Vr%20tVrPnDlz/vx5bZr65ubmuro62XE6nXSFAggbQwmgAIAYM+jp06dlu3Xr1qCVkHJzc7dt2ybZVPbP%20nTtHmwJjxBdffKEzhhJAAQCxZ1AACPTpp59evHhRZwwlgAIAYsygCxYskO2WLVu8Xm/QQ/X19S6X%20S3by8/NpUyD1Xb16Nf5Kvvzyy5dffvnKlSuRY+jixYvvueceAigAIFAUc9Q//vjjDQ0NkjUtFovV%20avV4PHLw6aeflh2fzyf7paWlOTk5tCkQjwsXLmRnZ0+ePDmhv+XatWuGTNI0MDDQ19cX+dWuWLGC%20txUAECSKD6Hc3Ny2tja1L0lU5U5tx2w2Nzc306BAPLq6uvbs2fPiiy9++umnCf1F119/vSH1LF++%20fNasWbxxAIBoTYiqtM1mmzx58rp161QnqKa6uvqZZ56RkEqDAhFEXuq9p6fnd7/73YwZM0z+WSaW%20LFkSNAOaYuBS7/J64nn6TTfddNttt/G2AgASnkFFuZ/X67106ZI6kp+fzyV4QI/IS72PGzdu/vz5%202o99fqHFDFnqXV2IjzPOTps2jfcUAJCkDKrk+tF8QMz5LzbXrl0z5DVMnDjxlltuUft2u/3cuXNZ%20fiM+UQ1yLysru+2228aPH8+7CQBIRgbt7e3dtWuXmig0rMbGxqDZQ4F0F/kCuvL555//3d/9nQS7%204QpkZ2driS0VMugXX3yhvZ6VK1e+8sorZ8+eHTGGMssSAGAUMqgE0NmzZ6sRSMPZvHkzbYoME/kC%20uuby5cuR//eZM2dO6vyjPv3006lTp06fPl39+Mgjj6gYKvvDxVACKADAQFH0x+zatStyAAUy+X+V%20OBj7ShI0tedwi7wTQAEAiRBFP6i6BG+1Wn/0ox9p3SdBmKMeGZxBY36uURfQtdoSNLXncL2hBFAA%20wGhmUGXr1q3l5eU0HDBarr/+ekO6QsNO7RkaQwmgAIBRzqD333+/0+mMvC4fkFIMGU5k4HycRv2j%204q8kwtSegTFU/fMJoACA0cygFRUVmzZtqqmpMfknq6ftkPoMGU5kyHycBvrrX/8a/0sKO/t92BhK%20AAUAJEIUd5Xl5ORs3LjR5/NVVVVlDaOrq4s2Rcqd5SkznMiQUX2qX3YwDnp+i8TQxYsX33PPPQRQ%20AEAiRNEPKvmytraWJkM6ZtCYn2vscKKrV69+7Wtfi7OSL7/8UiqJ8w6BCPceaFasWMHJAwAY/Qz6%20+uuv015APG666SY99wZENjAwIBk08G7OERc6ClzcSB1hiSMAQNpkUK/Xa/LPzfTCCy8MV4a5mWCI%20q1ev/ulPf4pc5hvf+IYEqT//+c/DFcjOzk6df9EXX3xhYJANGk4UeaEjBrYDANI7g06ZMkW2EkD5%20JEOiuVyuzz77LHKZmTNnytbj8QxXoLe3N3X+RWpdIkOqCjuciKk9AQDpJYr75O677z7ZXrx4kVZD%20ks7O1BhLZOC6RLJNXHOFLnREAAUAZEIGtdlsVqu1pqbGbre73W7aDmMkgxo1LGlgYMCQOKszhhJA%20AQAZkkHr6+tN/sllqqqqCgsLmZsJY8T1119vSD0SB/WMRjcwhhJAAQApK7r14l0uF02GscbAdYk+%20/vjjJLxgiaGHDx/Oycm5/fbbefsAAGmfQYGxKQnrEhmOqT0BAJmTQR0OB+2F9OLz+YabMlO/VFsv%20HgCADDCOJkAGS/QYIAAAEJso+kHr6+tPnz4duUxjY2NBQQHNCk1vb+/+/fv37t2rJvIsLS1dt25d%20eXl5cn67IesSGc6QG0wBABgrGVQCqNPpjFxm8+bNtCkCA2hZWZkayibpU350+tXV1W3bti3Rv/2L%20L774+te/Hn898a/wrpk4caJk4iQs9Q4AQOZkUCBau3btkgBqtVrb29tzc3PlSEdHx7JlyxoaGu6+%20++5ETxv06aeffvOb34y/nuuuu86oa/ozZ86cPn16nJWw1DsAYGxl0FWrVt15552hxy9cuGC322Xn%201VdfZb14aHp7eyVrys6+fftUABXl5eWNjY21tbW7d++OkEE///zz+F/Al19+KTE0JyfHkH+OURfQ%20SZAAAESXQW0223APrV69urCw8ODBg0m7zw+p7+jRo7K1Wq1BtwhXVFRIBm1vb3/ppZeGC4h/+9vf%20DMlqhqyNqVZd4gI6AACjk0EjkJBRWloaOVVgrPnkk09kW1RUFHQ8NzfXbDb7fL5z584N1xU6bdq0%20zz77LP7XcNNNN8VficTHW265JUIBu90u/xbZKSsru+2228KWofsTAIBAhs3NdP78edmqT2JAHDt2%20TLazZs0KfWjhwoWyvXjxYkJfgARQo2aGHx/RypUr5V+k7nAdrgznAwAAgaLoB3W73QMDA6HHz549%20e/z4cTXzDqCf6ihNnGQuTcS6RAAAJCqD1tbWRp6bST7yEz3SGdBP9bNOmjQpzuFEUs8HH3xAewIA%20kmbTpk0Z/280cp2k5uZmThqklL/+9a8m/3CimGmVAACQNNu3b8/4f2MU/aALFiyI8FASpntEepky%20ZUrkAjNmzEjoC7BYLLfddlvkofGtra3//u//LjsPPvjg97///fD/k0wYo9PoPv/883/7299+9KMf%20GThLPzS7du2Srzfr16+//vrraQ3DvfDCC1988cXTTz9tyEIVCPUv//Iv//u///tP//RPTPpBAE1G%20Bk3CwjbIJPPnz29vb798+XLoQ6dOnZJt/LO16zrFIybIp5566tVXX73pppuWLl3KWwYAQNKMowmQ%20IPPmzZNtZ2dn0HGv1+vz+WQnRVY0ePjhhwmgAAAk2Qj9oHa7ParByxUVFdqKOBjjFi1aJFuXy+V2%20uwOnqT906JBsKysrR5xK1qiliQAAQJpl0AMHDkQeCx9k8eLFZFAoEjHr6uoaGhqeeOKJwPXia2tr%20ZWft2rURnnvDDTd89tlncS5NJJXwLgAAkJYZFIjH+vXrOzs7XS6XxWIpLS3t7e2VfTku2TTyCLZ5%208+bNmTMnQgHGEgEAkNayhoaG1PCrsDNR1dfXnz59Wn91jY2NQYuDY4yT3Ll///69e/eqVQwkia5b%20t668vDz+mhlLBABAegnMnCN0FDEWHnHKycnZ4Gd4zQ8//DDNCwBAmmJcPAAAANIhg9rt9rKysuzs%207KysrKKiovr6eq/XS1MCAABAp+gGbfT29kr6VMNKFJdfS0tLc3OzzWajQQEAADCi6PpBV65cGRhA%20NT6fr6qqqquriwYFAADAiKLoB+3o6FBzhaqhzZMnT1bHT548uWPHDomhTz/9dHd3N20KAAAAwzLo%20O++8Y/Ivb2O32wOPL1myRFJpYWGhy+Xyer3MUQ8AAIDIorgWrwYehV3epqCgQGKo7Fy6dIk2BQAA%20gGEZVLl48WLY4+fPn6c1AQAAYHAGvfPOO2VbU1PjdruDHpKDahWcyAswAgAAAKao7getqKiora31%20+XyFhYVWq3Xp0qU33njjhx9+2NnZKQelQHV1NQ0KAACAEUXRD5qbm9vY2Kj2XS5XQ0ODRNL29nYV%20QM1m87PPPkuDIgm6urpsNluWX15eXk1NTW9vL81iCGnJrGE0NTXRPvE0bHZ2dllZWVqf0vLa1IuU%20V5hqr03OT3lhYacILCoqCntKD/d2QDtppVW11lNnZtglaex2u1ZMdvhbEdVnmVrxR52QQWO+M/9v%208tDQUIPfkD5tbW0SN4MqKS0t9Xg8Q0DiyRmozjqLxSInnjobZcsZaIgTJ04M97dCvoLSPrH5/PPP%20rVar+lOZvqf0kSNH1OuUf0vK/lmQEzj00eFO6bBvB4JOWtVQgWemnAmBJbVLoFJejU5OzZMkxT/L%20Als7s/8mB2bOqDOo9seo0W/Pnj1nzpzhZEJyyKey+jsoJ572h1L91ePjxBDqWkddXR1NYRT5Cznc%20p4shp7R8ROl/v6SknnwQ+LGnpbrKykr1eSmvMEFtpfPlhT1ph8ug6t9CJIqWOgml3bTvQvK+q7gp%20Z6x2DqhvJnJEa3k54eUk4Vurzv/FtP/x1UH11yCw6TLvb3IUGZSIiZSiPqLkszDo+7r6/5Zz1agP%20nqB+DsRGzszAeBQ2VsZzSmuZQH9aVS+jra0t2gyqRp3Kq0ro/2Xqt+iPL/LatHw/XAbla1U8b0Ro%20Z7zKl1pyUu0fGKS080fOFlpyOOr/XNkGHVedo4FNl3l/kwMz5wj3g77xxhuFhYV5eXmtra1h7wIB%20kqm9vV22P/jBDwIP5uT8f+ydzWsTXRTGo7j1o32z8wMpCEqFiBoLmgoKaoluhKKWuFPshysRG41d%20WvzAlUhaRUGEWl7RnQ1RwWyiqK3SheKmiP+AkZI/wPd5c+jhcmcymSROTafPbxHGcZLO3Dn3nud+%20nHv+OXXqFA6ePn3KImqSjx8/4rOrq4tF0TypVCqdTouissToHzHphw8fjo2N+b+fa9euycGhQ4fq%20fZaOjg44jFKpFIvFgisx/BXRNDdu3Ki5IrZYLHZ3d8/MzOArUMbVLvv8+TM+9+3bR4P0T7lchtZE%20wTqTzshw+Pz8fKSya7ik7+7t7TWvSSQS+PqvX79yuRwL0xXpZB47dsw6L3VTwmyWQ5vsKyYJPaGB%20gQGYFJpF54JZQhYN6Z1v3brVOi9+8dOnTyyiZoBHQduHxvH8+fMafRKPx1nrG2PdunWZTAZGOzw8%20/NdNGopNki0PDg5C47ZsoV28eFF8MBR2zYuPHDkyOTk5PT3toYxFJD1+/NiMrbl16xYDGT1Aec7N%20zbkm3zZtUrLSQBs4LUr6El++fGFhujI6OopOXTKZtM5/+/ZtWbXJNTTo8ePHZeBdu+x9fX3t7e2u%20u4QSEiga8ersmnd2dkaYKKFppMONJg81Ha0eHDyqP1w4an0LxkG3PvAT8DQe6YsbNml8Ea5IBlkB%20xKU4J9eocOHJkydysH//fuevaVS+h3uTwHNnODncJMxDHSQcBH7NeSdWCHA1P7Jnzx45uH//vnfx%20JhKJfD4vA8Ye3SpR+bKFi0R+4AyKDk9BGVoveF/Sk5EJ4nfv3kUquyU4r9y5cyc+f/z4wUKri7t3%2070YWBpuXQ5tcQ4P29/ejEZydnUXXWcPhURxjY2Ocoyethq5hIg0jE3AoyenpaTh4VP+pqSnUfVR5%207reydE0aYktn7a2JeCjO7u5uWRIQqYwawr1duHDBvyiBweDH9VbFX+I3TS2rf0UnGdWPWNO1Oh2P%20H2x+JrdcLuPXJGIGxgyThmFLlBieNJVK0cbqsqKzZ8+KAPW5JIPyoN5eq9REMyl6uNtkX3PxsLZs%20NlsqlfDkphjlHD0hYQIVWSbgzGG5ZDKJ6o+DdDrNcaMlyocPH9SfmdOmkAhQnM7rZf7aD5K4JLIQ%20lWJu6DMyMqKXyZiNRk9rNBU4ffq0ZVfxeFwO3r592+SDw3nBnuG8zBx+OCme/uXLl1yw6F+A9vT0%20wDDwEicmJlggQQhQqYw3b95Ucw19m1xfvnh5ctTnyclJHSuOGHP0nKAnJJTaVA6stUrkbwEXZe5J%20pHHx1bIl67I8a9rUXHOJX5MNd1w3gXZFZ2Yhbfv7+yOVaCpZ0BkxBnGLxaLoVPiIbdu2yWVwJfJX%208F+vX782f3bz5s1yENwKb3h0mU3mgsV6BWihUGjl9cRLXYCib+axfDx8bfLKxr4mA5+y84g5R18u%20l2lMJCBWr17tfYG5dpn8WWRw6+vXryyKpWjSb968kYODBw+a51+9eiUH4vlEW6B5l1GWmsRiMdG+%203kux9TGhSqPRqLiP79+/l0ol+bq1pnPv3r1yIAI3IHbt2hVZiJon3j2Nrq4u2X8An+Ys/Nq1a2tq%20fRZgTa5evSoCdHx83GfVC02bvKqxr6H5QM/1wYMH/qdsCGkSbfvQKbc64lIPXZfGk3qrNvyKc5xD%20xrE2btzIIgqTSWsDbgUqaWCQf5ny/v17fELUOtew4jEhpvX8vxVEYZ87d+7MmTOBjquhbOfn551i%20SEyaHVdv0FsYGhqScK6JiQnrTUnknOt0sIh7HdIm1YwzlUqhr9XW1gb16RpgF+42ub5xUJTFvXv3%204vE46u3AwIAlQHGyZreekGaQnp8ubjNdoA5skIZpb29HLbYmRqWhFAGxfv16llIoTXrDhg3mP/0P%20X+VyOQjlHTt2wCOYkUkWz58/tzaTj1SGRdPp9JYtW4JbxAWnHo1Gb9++7fwv2Xho06ZNNEIPAdrX%201wetMzg4mM/nnTJIVlZACThlqMiD7du3sxg9BGhPT48I0EKh4CpAQ98m+9KgaCC8pWcmk5mdnZ2b%20mwt0+2JCDh8+HKls9WfVRgmJO3HiBIuo+eJ99OiRdV5WDaKms4KH1aStGT2f4czFYvHo0aOaQunk%20yZPjFZxXSmyQFdUqQOJcv349oOc6cOCAaClLJOHOxZE1sF3/MgG9i5oTxFCl0rV49uyZVbyShNa5%20/yVRZImtc4XD8mqTa+bqrDZVodKTSbfIYmaQY774RUhhbGZKVEnhzIJI/CPxQ382X7wzJsk7BWvE%20kQNTz1vJQl0zsDv/nMa2m0nkzTyf1e4HjsNyLtUyhfovYVcr1ayneEDzDp1ZuYlrudUsIuaLb6ZB%20QLmpWS6fNrmOfPHaQlF6khZB0umKKcI+paHEpzOvMWm+eFUl1MwwThrToM2YtIpCkYC4vpo/021M%20LFlgas1qcfEeGlQdhJmNHceWgtQzu3fvrnb/rn63roTj1bwyvJUWqexRL1c6U3UTV8NwxTQk7Yqg%20bNUkrHdNXCW+9/hgWNvkRjQopSdpHeBm1KfCMtECenclSQPFq24bxxwBDVSDNmzSTqFQ7U1VGzH1%204wv9jIPiR8Q7WPpVLtOxXlPp4qTKwWrKuK7JDY8SwN/CrarzlgyftEkPnMNPHhpU3ru+TRxwBLRm%20G+tnjjqsbXIdGhT1ltKTEEJaELTMlt+ampry9nnWiOPvylyqU4aa8UMeGtQUl0pbBeu7Ov3txCk0%20VdpSyhASPkzNWSMmKZvNjo6OMhCBEEJaDbTMUJCqFyHm1qxZ43plIpEQCQjVaEXnJJPJQqFgjsJC%20+eXzeT830NHRYX0X8nFmZkYjfDVJPW4A5zOZjKluZTzS+bckYj2ykJScEBJWVkCHSkzilStXWByE%20EBJKhoaGJGU8ZGsrRytDIkejUVG03lvfE0KWIqbmXMniIISQ0CO5NMGLFy9a+T51q9RLly7xrRES%20bqhBCSEk/MRiMZnalp1HWxbZKrWtra23t5dvjRBqUEIIIUuekZGRSGVP+Fwu15p3+PPnT0njefny%205UATeBJCqEEJIYQsEolEQkLO79y505p3qNlfhoeH+b4IoQYlhBASErLZ7O/fv32GvS8+kJ64PYYi%20EUINSgghhBBCSCCs0iOJlieEEEIIISRo/h8H7ezsZEEQQgghhJCgUdm5QtPsEkIIIYQQsjhwPSgh%20hBBCCFls/hNgAApaoX93c7CUAAAAAElFTkSuQmCC" height="523" width="897" overflow="visible"> </image>
            </svg>
          </div>
        </div>
        <div class="fig"><span class="labelfig">FIGURA 2.&nbsp; </span><span class="textfig">Producción de biogás acumulado en biodigestores 3 (inóculo microbiano 1,0%) y 4 (con 1,5% de inóculo microbiano).</span></div>
        <div id="f3" class="fig">
          <div class="zoom">
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OouUbfe6%20Vd0kSS97Zf4jzqLJ06SG7K63Z13jmSNtwyhHr9+zjrHaX375pc6czZBKpfX19f7+/syauLi49PR0%20ZkCISCQi0ZndgJ2kubcCAAA89JeOeeZipGgP8SPjxOT4H2458aeN96tWk5R8r1P1+d8v5JwoKr4g%20NzxnO42T0HHVdkK7F9f+5A8f/+zdPcv/4/0IR1dJW0vH2YxvyWsxSc7mGTbiHGC0sY5ebev6BoOT%20CQAw4untwTX2KkZabu96aV1dTRMdct37YKRHf6M+tJEvACQ6T5omdRondvVwJA/62V/O6XwOxvZA%20f/2vq18kXzTktTBHfuq5x16ImYuPCozmTMiz3ilsAAAAhtHgr2Kko6vJ7jerGsgdErU1GvC17mig%20A6xJVnZ1f4QE68nTpHYCWwOfg7Gjq0lubr6rfHLhNL3fGeiRz2WWhEb543MCgKgNAABgyrTdX8Yl%2062sqFddL60i2rumbeVHvQ7D7s0mMJmGaBGuncWJyy31RpuHjPQxP24Z3UZMjh69ZgE8IAKI2AACA%20KdO2xlyMAoHt5bxrJFvfrFQYXcHahj9j1gSPSc6F/65UtnS4ejjGvB1s4LWGF3PKDBm7wn7+OSeK%20MN4DwORsn332WfK/oKAgnAuTyMvLw8kEALA08tI6B5GQPb6iP2pVd+XV2nHjHzHwyCT7PuYvu3q5%205m5tS0XJ7dLC6mtXbtsKbKUejl//6+pXWVdKL9fcvn5Xe3yItsnTpF6PenSre9rb7pGUvH7L0sDn%20Z02bNeHxgCnM8X3nexryKsihOjtUJDrrHe9Bn79K3fP8ygAbWxt8VABMmAkRtRG1AQBGvoriW3//%2079PlRTf15lQ67iLnRJGTq8TD4JohZC8HB8H1ivrv7yqbFPcvZ+zp6W1r7iDruXf09HF/1F82e+G0%20Z8KeiFj7E7+nvApzr9VUKjR6ozXSvIFpe4bvxLFahbH7S9vefpOQswHMEbUxgAQAAEY4dm0+jhHJ%20RtXma2vpkJfWXS+7Q24balvYmzhKhdCLFyd5uU6aJtWI8qa9ihEALASiNgAAjHCG5FRDriPkiNeU%20dlU+ei3jVO/xJFuTO4akfFNdxQgAiNoAAADDn7Y5km5nexdZzxGvtXM239amt7vnERfx8teDpswc%20b8jTw1WMAIjaAAAAIzBt68zZNZWK0ss110vrDJ2X0YbvN9/T22+Sp487OSCdw+XzQ/kG9j2jajXA%20SIXLIk0Ml0UCAFgs7esLyUomZ78Uu7ChtuXcP4tTk766dKa8uqL++6Z2jqNN8nK1s7PtaO8SOgjX%20v7d0fojP+EnOQnsBrmIEAB4uiwQAgFFIo29bLBlTebXWxs5GILQjmVvv7p4+7lO9x0/1cadznt+s%20asBVjADAAVEbAABGXdp+ae1PDn90pq5vTpleHq9b3dPW3G8H9v3rGn3cpz8+geRsugZXMQIAojYA%20AFgreWkd98TjDJJ6SeMZvhMNOSwJx9dKbn9XWN3c0Mas5PcTx8kxp3qPJ7faTwNXMQIAojYAAFgl%20kmIPf5gj9XDU2xPM9C5Hxi6cHThdZxtaQqT0cg25JffZmzRq89kJbJl47erhyPG4uIoRABC1AQDA%20KplkxpnmhrbvCquvldwmDXTuzuRsGzubHnWPi9vYN7b+1PAxHoZ3UZO0Hb5mAd5WAERtAACA4TeY%20GWcaaluKL8iL86/rrYHt9aj743OnPuovo7X5GutbMaIaAEzOvKWC2tvb58yZw9dy8OBBnHoAAOBO%20246uEpq2NQZ+aOdsEqxz0r7Z+7s08ifnRFF/Odu2r0Ce3Ri713+/bO3GJXODvSWODtyPBQBgiVE7%20Pj6eRGqxWFxYWKi9NTY2lmZuWnEQAADAwLTNztkvxS4sLazWm7BdPRznL3508jRpd3cP2esXv3th%20kper4ckeAMCCovaRI0dIhk5ISDCkcVBQkIuLi1wuxzsBAAB60zaTs20Ftg4iIbnPkbBJtn4hZu6v%203494MyGs8c73NZUKvbX5kLYBwKKjtkKheO2115jFpKSk3n5s3bqVtmlqalq0aFFnZyfeDAAA4EjA%20f932xf73TpCc3dvXt80u2Mfm6eMetmbBxg9W/OK9pU8995irh6NRtfnoY5EEj5MPAINnlssic3Nz%20AwMDudvE9yF38vPzX3nlFbwTAACgk71IuOBZn+x/XFbcbuZpVehjPOovm/74BHIr0ZreHLX5AGCE%20RG2pVNrW1mbULvPmzSsvL8c7AQAAGiqKb5XkXy++IFerupmVfK2E7TN7MrnlrhyC2nwAMBKitiGO%20HDkSExMjk8nKysrs7e3xHgAAAFtDbcvlryov511ra+lgr2f3Z3v7TXosYIrehA0AMOqiNgAAjDAm%20mUe9s72r+IL8m68qayoV2ltpzvbydq+71dTRdq+lqR05GwAQtQEAYIQb/Dzq3xVWl+RfJ7fsgSIP%20seEvCPF+5qdPSBwdmhvauGe3AQCwHDY4BQAAMBjsedQ5auRpz6Pe1tKR+3nxng3/IOs1BmRTdMYZ%20gb3gl1t/umz1fHq9I6ryAQCiNgAAjBaGZF+N+R1JyP7sL+d2/Oex7JRCnTX7pj8+wX2yC51x5rXN%20z2tUDkHaBgBEbQAAQNp+KGcLHYTzQ7xJyP6/PaeKL8h1Hic0yv8/EiP4fH5dTSNmnAEARG0AAAD9%2086jb2Nl0q9Tn/lmsPbmjncB2duD0X7y3NG7ny0HLfMu/vYUZZwBgZDDxZZEKhcLT01OpVOLMAgCM%20zrTNvmaRrExKzLpzs6mXx+tW9/B17RK09HHf+Z7sqxsx4wwAIGoDAABwpe0Pfpt6r72ru7tH5/yO%20JF6TMK2z6h8PM84AAKI2AACAto72rklTxzU3tLW3dvK05lGXODrMDpz+VOij2tOnAwAgausxgInZ%20AQBgZKgovpWXWSIvrWNna+aOp497wKKZvvM9caIAAFEbAADAUJfzruVmlmhc78j0Z4skY16NWzzR%200xUnCgBGG/NWIImPj+f3Y8qUKZ2dnXgDAACsV2d719f/urrjP4+lJn2lnbPHOomCw594ZJykve3e%20iU++Rj0+ABiFzNirTXJ2QkICTjEAgKWRl9Z5yFwMmdJcreomjbUvXrw/0WNmyeW8azoDNN+Gv/il%202U8v8yX3/YNmYB51ABi1zNWrrVAodu3ahfMLAGBpKopvfbrnlCHTvtCS2P+35xSJ1MzK2urG1KSv%20dvznsa//dVXnEQRj7N7Y+lOas3mYawYAELXNKjc3t1eXGzdu2Nvb4w0AABhiUg9HiaOD3uDLnkrd%20va++NQnZZM2ft2awkzdlJ7B1dBGTO/fnUf/dC5hHHQDAvFFbLBZ7e3vLZLKAgACcZQAAy2FI8GXn%20bDoTDQ3Z3xVWa7Qkqf2ZsCdk06UtjUrMow4AMERRWyQSbdiwobq6esuWLTjLAABWlLbZOTv8ZwvO%20nCjSGbLJQSJjF8btfFkkGVP1XR3mUQcAGLqoTURHR0dEROzZswcVSAAArCVtMzlbYC+YMMXlsz+f%200w7ZJE/HvB1MQvbswOl2AtunnnuM/OHO2ewHDQj2xjzqAICoPViRkZFpaWk4xQAA1pK2mZzNt7Xp%206lTJv6vTGbJ/mRD2qL+Mvf6FmLl6czbzoOFrFpCAjvMPAKOBuYr9KRSK7OxsnF8AAMtP20wxPuEY%20u+qK+l4er7e7h68Vshe96KeRsAEAYHiiNuXs7FxQUODpiWl4AQAsOm0f2PYFSds81hSPDE8f9wWh%20jyJkAwBYUNSWSqWhoaGFhYUeHh44ywAAFqutpeN06jdtze08XSF70Yt+5BZnCQDAsqI2sXnz5qCg%20oC1btuzevRsnGgDA0tBp1XMzS9Sqbt7D/dlTZriFvDQbIRsAYJDMOFtkSEhIV1cXKpAAAFggErL3%20/i4t50SRRs627btg8d49tYGXOQIAAAc7nAIAgFHlu8Lq7JTChtoWZg3N2VIPxxdi5pJb5irJdZuW%202IuEOGMAAIjaAACgR211Y1byRXnpQyX8SM4WScYsfmn23GBvuoZdkwRpGwDAEqO2VCpta2vD+QUA%20sATNDW3ZKYXFF+Qa60nOnrto5tJV89iFrjUqACJtAwAMmInHaisUColEkpeXZ/gu+fn5M2fOxLht%20AABz6GzvIiF77+/StHO2jZ3Nzzc8F/6zp7QnlOGeuR0AAIYnalNBQUH02seDBw/21yY+Pp62mT9/%20/r179/BOAAD0R15aZ2DYVau6K4pvMYuX866RkJ37eTG99pFNYC94/ffLpj3Wbz1WdtrOOVGEdwEA%20YABMPIBEKpX+7W9/i4mJoYuxfbh3cXZ2PnPmjL29Pd4MAABtJDof/jBH6uGodyAHM616ZOxCd5nL%20Pz89X1Op0GjjIXNZHOV/reT2kwun6a0xQtP2ucyS0Ch/vBEAAANg+l7t6Ojo3t7erVu3GtI4Nze3%20sbER00kCAPSHhGyJo4PegRxMzhY6CK+X3/nz1gyNnE0OQiL4LxPCZvhOfCFmroG1/EjaDl+zQHuE%20CQAADE/UpuLj40ngViqV/v46+kLi4uJ6+wQGBuI9AADgDrt6h00zOdtujJ2tLf9y7jX2VhKUg1/0%20i9v58uzA6TifAABDybzF/kQiUUFBAc4yAMDg03Z/JUGYnM23tVHdU6vvqdn7zvCdGLZmATkCTiMA%20wNCzwSkAALCWtK3dt01y9qEPviQ5u5fH6+nu4T+8y6txi8kf5GwAAERtAAAwLm2TnP3XbV9UXq1l%20plWn6IiRX78fMcN3Is4bAMAwwmyRAABWlrbpSJIDf8zsaLunbO3UyNkYMQIAYDnM26t95MiRKVOm%200OlpyH1aSNvFxUUul+PUAwAMOG2PEQsbals0cjZGjAAAjKKoTbJ1TExMa2trbW1tfn7+z372M7q+%20qanJy8vLqBklKToVJf+B8PBwtfrHq3/a29vnzJnDbNU+PncDvbsDAFiCmkrF4b2n7yl/GKvN7/uD%20ESMAAKMrapPkunPnTmYxMzOzq+uhAlWpqalGHZCE9UmTJimVSmZNRkaGm5sb7SAnDxcUFFRYWMhs%20JYvsuMzdQO/uAADDTq3qzk4p/OsfM+tqmshib98fQuhwf97H4IgnUf0aAGC0RG2SicvKysid9PT0%208ePHk1jM65sYsqqq6sKFC0KhMCUlhQ4sMTC4//KXvxSLxWT33geSk5Obmpr27dtHGuzYsYME5bCw%20MJVKRTeRlatXr2YegruB3t0BAIZXTaXio60ZuZ8X00UasiVj7UWPOHR1qP7xtzwDZ24HAICRELUp%20mUwWEBBQ0ocsBgYGTp482dPTUyAQDCC4r127lj2vZHh4uL+/P4nsd+/eJVGePNaxY8fs7O5f6Bkd%20Hb1169bq6upLly7RpM7RgHsrPiIAMLyYzuyG2hZ2zvYPnP4f/xX5xu+Xcs9uAwAAIzZq04HazOiR%20iIgIkmW//PJL9jgQQ0il0ra2tt27d2tv8vPzu3fvHgni5A4NypSPjw+5LS8vZ5J6fw24t+IjAgDD%20qKL4Frszm+ZskWTMmrjFkbEL7UVCQ+aSBACAkRa1xWKxt7c3vQIyISGBrpw5c2Z8fHxMTAy5HxUV%20ZW9vP5iHSE9PLywsnDFjxs2bN1UqFbnDzsrkcYVC4ZUrV8h9uVzO0YB7Kz4iADAsSGhOTfrq//ac%20Yjqzac72mT35N9tfYl/+iLQNADDqorZIJNqwYQN7TVhY2IIFC2hvsbOz81tvvTWY4ysUitdee00m%20k23bto2umTVrFruB9jAV7gZ6dwcAGDIVxbf2/i7tct61h9by+T99Zf7qX4ewZ2VH2gYAsGRmnMIm%20Ojq6tLSUdmmzR0KT+2VlZYPp0iY5m0RhoVB45syZQXaND97hw4dv3LjB+quQj08VAAwYSclfJF/U%20DNk8nq3ANva3z0/ycu1vR/bsNjknil6ImYuTCQAwkqM2Ed9HI38Tgzlmfn5+UFCQWCwuKChgXyWp%20MdiDDgthr+FuoHf3/qxatYq9mJiYiE8VAAyMvLTus7+ca2vpYK909XB0l7kELX3cQ+bCvTtN2+cy%20S0Kj/HEyAQBGftTuT3t7+yuvvHL48GFj+6RJcE9ISNDoF6eDPSoqKtRqNTPeuqqqqquriw4L4W6g%20d3cAAHNTq7pzThSxL3+kgpb5Br/oZ3jBbJK2w9cswPkEALAQ5q1AEhkZydeF9kkbezSas8PCwior%20K9kZnV6CWVRUxJ48srS0lNd3IabeBnp3BwAwiry0zsDR0iRhVxTfaqht+Xj7SY2c7erh+Iv3loZG%20+WNiGgAARG3dyTgtLc2ERyM5Oy4uLj09nV0qhNd3CSbJ39XV1StWrKBx+ciRI7TzOyAgQG8DvbsD%20ABiOROdP95wy5NpEkrOTP8z5vz2n9v0+vaZSwd4UtMz3zYSwydOkOJ8AAFaN//7775P/bd682bTH%201Z7qnM3YKyPpdZA6q3HTQ/X09Gg/XG5ubmBgIMfzYRpwbzVKYmKiyU8mAFiR5oa2pO0nWxra3GUu%206zYt0a4WwuTsQx98WXm1ls5Hw1xPLXF0iIxdyK7lBwAA1ohev2f2idlJYK2vrxeLxXFxccyE6tXV%201Vu2bDH8aHpnvRGJROSB/P39+wvK3A307g4AYCBD6u6RnH3wv7K0czZJ2L9+PwI5GwBgxBiKidnp%20YOiUlJTOzk5eXxGSiIgIehmigcchu/T248aNG7R3nMTlgoICZr12UOZuoHd3AACTpG2yuG/LiZtV%20DeycbSewDVuz4NW4xf31ggMAAKK2bsxg6MOHD/P6RoNkZ2drXIYIADAa0vadmsY9G/7RqGhl52wP%20mcubCWFzg71x3gAAELUNQnuymXhNJ4mMjY3l8/lubm5KpdLPz0/j6kYAgJGdtgvOln8U/09yh52z%20g5b5/uK9pa4ejjhjAACI2oZiJmZ/99135XJ5eHg4eyQ0ERERgagNAKMnbe9+9x8nPvm6t6eXydkS%20R4dX4xajnB8AAKL2QERHR1+4cMHV1dXDw4Mk76ysLLFYTDfFxcWtW7cOZx8ARkPaDnt1Pt/W5l7H%20/TEkTM7GFZAAAKOBefuV582bV15eTu9LpdK2tjaccQAYVYovyFOTvurt7uE9CNl2AtsXYuZiZDYA%20wGhgrl5thUIhkUj4+oSHh+PiSAAYkdSq7oxPz3/2l3PkDq+vP5t2aTu5Snzne+L8AAAgaptdRkaG%20m5ubXC7HOwEAI0lbS8fH209ezCmjizRkP/mU1yPjJA21LYbMJQkAAIjaJtDU1LRv3z68EwAwYshL%206/b+Lo2Za53kbDs7m5diF768Pui13+qZ3QYAABC19ZNKpXK5XCwWy2Syjo4OZmqYCxcuCIXCsLAw%20lUpFFrdu3UoaGzWdDQCAJcv9vPhjVowmOXusk2j975fNDpzOM2wuSQAAQNTWb/369SRVnzlzhs7m%20SM2bNy83N/fkyZObNm0ii7/61a9IHMd0NgAwApDcnPxhTnZKIbOG5GyvR91/ve1FD5kLsxJpGwAA%20UXuw6JSQY8eO9fDw0NmAmacdAGAEqK1uPPDHzO8Kq9k5e+6imWs3LtGeax1pGwAAUdsEqqurV6xY%20we6xJhE8JCSkq+uHv1fkcrlKpcLbAABWrfiC/K9/zGyobfkxZ/N5L679SfjPnupvF3bazjlRhHMI%20ADAimauutlQqDQ0NTUtLy8jIEAgE2g2ioqK+/fbboKAgErvJffYgEwAAa6FWdX+RfJGpNEKNdXIg%20OXvmE5O496Vp+1xmSWiUP84kAMCIZMZe7QMHDjDTQ2qQyWTbtm1jFiMjI/FOAIDV6WzvYlf0o+YG%20e/9mx8t6czaTtsPXLMDE7AAAiNpGo9ND0hojbLm5uTdu3LC3t6+qqurq6kpKSgoMDMQ7AQDWpba6%208aOtGUxFP17fNJCRsQvDEJ0BAID5q8HcDxDfR+em6D54DwDA6lQU3/rsL+fYlzM6uUpi3g5mVxoB%20AACwwykAgFFOXlpHIrJ2nRBtalU3adxQ2/JF8kX2+hm+E5e/8bQhRwAAAERtU1IoFJ6enkqlki7K%20ZLKysjJcBAkAFqKi+NbhD3OkHo7rNi3hzsokZyd/mEPaa6yfG+wdtmYBziQAAGgzb7G/+Ph4Nzc3%20Jmfz+sr/OTg4HDx4EKceACwBCdkSRwe99a1Jzj70wZckZ/f2FcxmkJCNnA0AAMMQtY8cOZKQkKBz%20U2xsbF5eHs4+AAw7Q2aTITn7092nKq/W0pDN77u1FwlfjVs8N9gb5xAAAIY6are3t+/cuZPccXZ2%20rqqq6n3gwoULQuH9f6LdsWMHJmMHAMtP2yRnf7z95PWyOnbOJrusf2/pDN+JOHsAADAMUVupVJaV%20lZGcXVBQ4OnpyayfN29ebm4uSdtFRUWI2gBg4Wmb5Oz/+X+f11Qq2Dl78jTpmwlhrh6OOG8AADA8%20URsAwNrTNsnZ+7acuHOziZ2zZwdOX6vvAkoAAADzRm2xWOzt7d3U1DRnzhy5XM6sz8/Pp5Ox+/n5%202dmh1CAAWGjaTvqvrD3v/qNR0crO2aFR/pGxCzFDDQAADHPUFolEGzZsIHdI2vby8uI/MH/+fJKz%20yfqNGzciagOAZabtsS7iOzVNbd93MDmbxOuYt4ODlvniFAEAwPBHbV7fZJARERE6N8XFxWEydgCw%20TPc6ujrb7vEehGzyR+Lo8Iv3lj7qL8PJAQAAo5i3Xzk1NVVjChvtCyUBACzH9bI7/7vzX93dPT/2%20ZwvtYn/7/Dj3R3ByAADAWGa/LFIqlba1tTHF/hobG5GzAcAylVy8/vGOkw/l7DF26i710T+f5Zjd%20BgAAYNiiNgCAVSg4W37so7O9Pb1Mzp4b7P3rP77IPbsNAADA0EVthUIhkUj4fH5iYiK9058pU6Z0%20dnbiDQAAS/Dl8csnPvma92DSdZKzg1/0C1uzwJC5JAEAAIYoagMAWJ3jf807m/EtO2dHxi4MjniS%20bkXaBgAARG0AAKOpVd2f7s7+5t+VTM4WCGxfjVs8O3A6uxnSNgAAWETUZi6C3Lx5M/tqSG03btyw%20t7fHGwAAw4Uk5r/+MfNayW0mZzuIhGs3LZnhO1G7MTtt55wowtkDAABDYBIZABiNmhvaPt1zqqG2%20hVnj7CqJeTvYQ+bS3y40bZ/LLAmN8scJBAAAQ5h9AEl8fLzGBZF5eXk47wAwjGqrGw/8MZOdsyfI%20XNZtWsKRs5m0Hb5mASZmBwAAA5mxV1tj8hpGUFBQWFjY8ePHMTE7AAy9iuJbn/3lHHu89eRp0lfj%20FtuLhDg5AABgWmbs1V6/fr12zqYyMjI2bdqEsw8AQ+xy3rXkD3PYOXuG78S1m5YgZwMAgDVFbYVC%20kZ2dTe7IZLKOjg7masj6+nqxWEzWp6SkoK42AAxxzk5N+kqt6mbWzA6c/mrcYgwIAQAAK4vaFMnZ%20ZWVl7EojUqn09OnTQiE6kABgSF3MKSM5m70m+EW/yNiFODMAAGB9UZtE6tDQUI4GUVFRKPYHAEMj%20J+2bjE/Ps9eErVnATFIDAABgZVGb2Lx5c11d3YoVK9RqNbMyPz8/KChILBa/9dZbOPsAMDQ5W6MS%20dmTswrnB3jgzAABgbiauAaJddSQjI0MgEGg06+rqWrRokcbYEgAAc+dsO4Ht8jeeftRfhjMDAABD%20ABOzA4CVkZfWGTg1+ueH8jVydszbwcjZAACAqA0AoENF8a1P95w6uP2k3rR9/K95F059p5GzdU66%20DgAAA6NQKCQSyTvvvEMXIyMj+Xz+lClTtKvMtbe3z5kzh2w9ePCg9kFkMplcLtdoaeDUh9zt6VMi%20wsPD2UOa6V7MM2e3136GFhS1pVJpW1tbrwFu3LiB0SMAYPQvGQ9HiaNDXXUjd9omOfubf1eSO719%20f+xFwl+8txQ5GwBgCFRXV1+6dEljZUkfne3Xr1+/fPlyT09PmoCDgoIKCwuZrWSRI21zt4+Pjz97%209mxVVVV9ff3p06fZk7qkp6eTcK996eDmzZu3bNnC5H6Li9oAAGbl5CqJ3bTE0VXCkbbZOZtwEAnX%20GjDpOgDAiKfsVP/uk8I7zR3mewhnZ2cfH5/U1FSN9ZmZmb6+vtrlno8cOULSMBN5d+zYQXJzWFiY%20SqXq7e1NTk4mK1evXt3fZCwc7UkKz8jICAwMnDx5slQqfffdd5lJXcimnTt37tq1i+Z7tnnz5i1Y%20sGDfvn2I2gCAtK2ZttWq7sP/fRo5GwBAZ85+528XS240mTVtjx07Njw8XGOmQpp6X375Ze1SGUeP%20Hl27di3TpU2ayWSyY8eO2dndL90RHR29detWnd3ketsrlcqysrIZM2bQTURra2ttbS2vr0u7oaFh%201apVOl/CypUrP/74Y1N1bCNqA8AISdv3c/be06Xf1DA529lV8mZCGHI2AADN2bWN7TZ8fsP3nWZN%2028uWLevq6qKhliopKbl9+3ZISIhGy/z8/MzMzMjIyB+eZF849vPzY8Ix4ePjQ27Ly8t1vCjO9mKx%202Nvbu6Kigg7RLioqIl8DPDw8aJc2SeT9jWR+9tlnyfM3Vcc2ojYAjIS03dbSQXL2tZLb7Jy9btMS%200gznCgCQs2nO5vP4ZJHcmjVtz5o1a8KECTk5Ocwakqfnzp07efJkjZZkPQnEEyf+cCGNXC5XqVTs%20fmjCy8tLKBReuXJF+4G424tEorCwsLy8vJqaGoVCkZ2dTedP3LFjB0eXNu/BPIwaHfMDZofPHwBY%20ddpO2n6SpO0PNqd2daiYnC31cFwTtxg5GwBGnqa2rk9OXTt1+bbhu/D5PFsbPs3ZP6zpS9uxH3zV%2022vcoz/j6x4VOMXLfSxHG5KeScZNS0tbs2YNCcF0mMeiRYu0R48wPc0aSZ296Onpqb2jge3j4+PJ%20Q5DwTe7HxcXt3r2bZO5du3bt3buXuziHn5/f2bNna2trtQdzG8vsvdr5+fljxoxhyq+QV0jOiEm+%20JQAAkDD983cW2wpt2Tl7gsxl/XtLkbMBYETa/89So3I2oZGzmbTdt944Z4vr/pR6VW8zHx8f2p3M%20e1B7hBklwiARvLq6WmP4h8mlpqbS2nckZ98/e/v3jxs3btWqVbRMIQ2o2tX9yPNvamq6devW4J+A%20GaM2LVg4f/78rq4fL1qSy+XXrl2bMGGCCauoAMCopVZ1f34ov7ur+8HfHDyPSc5rNy2xFwlxcgBg%20RLrTZPSoj+5+uq57e3i9xj+BprZ7etvQ4c50DElmZqa7u3tAQICBx9cYK0JHiZikPe3SPnToUE9P%20z/PPPx8SEkJaJicnx8bG6q3ebYlRm5Zf0VhZVVVFTj35omDCKioAMGpzNnt8Nv0LowfnBQBGtPUv%20zBzv5GDULr09vWqtkSI9Pb3dvUYnbbG93apFXnqb0eHOaWlparW6qKiIDpLWuxcd+8FcyMiOjhqj%20RAbWfv/+/SReL1iwgHa0R0RE2NnZhYeH+/v7a1cnNBVz9djTcTm8ByNjjhw5EhMTw+srwkJuyf2U%20lJRt27ZhFhsAMEnOJsZPcOq8p6q/2XRw+8l16NgGgBHKd6rzwd8sNHavz/Nv/v1MVUfnD5FUYMt/%20Ysa438f4me95rly58s033ywoKMjOzs7KyjIox/fVDCHRnERnZlRJaWkpuZ05c+Yg29MubfJMmJbs%20NjSvm2Moi7l6tWn5FZlMRvK0xqZnn32WnBr8qADAYBz76Cw7Z0+Quby25YXXfvs89+w2AACj07J5%20k15Z5OVgb/dDzvZyMWvO5j0YQ3Lw4MFx48bpHD0iEolIUKRBmVkTFhZWXV29YsUKuvLIkSMJCQmk%20WX9HMLz9+vXraZc2s4ZdQFCjjAnJ687OzkxpFEuM2vR7BnnxW7Zs0X6pJIibexQ8AIxgx/+ax66f%20TXI2HZ9tyFySAACjOW3bC22HIGfzHowhOXDgAMfoEZIGmWllqI0bN/r7+2dkZAgEAj6fT8dEHDp0%20iB4hPj7excWFfb0fd3sGLeBNGtPw+XgfOr4lPT29sLBQ46pN8gWATjNpuVGbfs8gd/bs2cO88qCg%20IHKfvDByn46PweceAAaQs9nzQTI5m25F2gYA4EjbW6KfGIKczSRpcqtde4SxdOlSpVLJrsBNAmRu%20bi5Jz8waskhSL0fgNKR9YmLikiVLmC5tsldWVtbp06dJQCcZNSkpib0LLcJtqqTKf//998n/Nm/e%20bPLz297eTrK19pWRBEnhx48fH5FRm7yX5jiZAKAzZzu7Sta/t1TiqHmFUHNDW9L2ky0Nbe4yF4zb%20BgCwWCSId3d3W1QsPHLkCB1lPsii2iQT8sxagYR8YyDPMjk5WWM9+baRnp6OLm0AMFZq0lcaOZvE%20aO2czXu4bzvnRBFOHQCAZVq5ciVTgdtCHD16dO3atYOfvIYy+xQ20dHRvQ/j+FcAAID+ZKcUXs67%20xizqnXedpu2AYO/QKH+cPQAAy0SC4jPPPGM5NaDz8/PPnz//1ltvmeqANtb4rtAJfjSKjcfHx/O1%20sNvQKXV0btK7FQCGV07aN7mfFxues5m0Hb5mgZ3AFicQAMBiHThw4LPPPrOQyQ0TExO3bdtmqi5t%2000dt9iyX3KZMmTKw6dnJQ5DXr1QqNdYXFXH9G7H2wHGyyORp7q0AMOw5mz0IxMmwnA0AAFZBKpVW%20V1ebMN0ORmpq6rp160x4QCvr1c7Pz580aZJ2ziZZmbxJYWFhKpVK52AVOnUl04COIF+9ejWN+9xb%20AcBycra9SBjzdjByNgAAWAWridq0v3z+/PlisfgPf/iDxlY6Y45G+XF2EM/IyJDJZMeOHaMNoqOj%20t27dStL5pUuXuLfiIwJgUTl77aYlHjIXnBkAABiNUVsqlba1tdEe5QsXLgiFQhJhOzo6mG7m+vp6%20kpWdnZ3PnDkzgFnZk5KSGhsbp0+frrFeLperVCqd890zQVxj0hwfHx9e30RB3FvxEQEYLpfzriFn%20AwAAorZuiYmJ7u7uJMWyIzXJ4qdPnybp1thLTWmI72/0TFVVVVdXl5OTEzNSPDw8nJnnkwZxjT5v%20Ly8v8k3gypUr3FvxEQEYFt8VVqcmfYWcDQAAiNo60Il2OBqkpKSYcCR0aWkpuX355ZeZYdwZGRlu%20bm7sq1k1+rw9PT0FAoGBWwFgKFUU3/rsL+eYRTuB7atxi5GzAQDA6ph3Hpnq6mpvb292xzaJ4CEh%20IV1dJp4qmZYfYU/FmZ+fHxQUtG/fvt27d5v1NR4+fPjGjRvMIp/Px6cKYDBqqxuTP8xRq7qZnB3z%20dvDkaVKcGQAAQNT+gVQqDQ0NTUtLI2nbwUHHXG5RUVEDGKvdn9TUVI018+bNW7p0aUpKyrZt2+ga%20jdEgdNwIs8i9lcOqVavYi3QSTgAYcM7+ePtJJmcTkbELZ/hOxJkBAABrZMax2gcOHBCLxTo3hYWF%20bd++fcheJB0NUlFRwYze5j0Y3j1r1izurfiIAAyZ5oa25A9zOtu72Dnbd74nzgwAACBqa6IXMm7d%20ulVjfW5ubnp6us6qfAND6wCyr4PkPai0TeuKkMTv7e1dVFTEbkCHd8+cOZN7Kz4iAIMkL61jp+f+%20tLV0HPyvkyRtM2uCX/SbHTgdJxAAABC1+xUfH9/7MGY4tQkzfWhoaEZGxvnz55mVJM0XFhZGRESQ%20qC0SicLCwkjyXrFiBc3TR44cSUhIkMlkAQEB3FvxEQEYjIriW5/uOXVw+0nutE22fro7u/nujzk7%20aJlvcMSTOIEAAGDV7EbGyzhw4EB2dnZQUBB7JQnQa9asofc3btyY0YddV+TQoUN0vDj3VgAY+Ddh%20D0eJo0NddSNJ2+s2LbEXCbXbqFXdJGfX1TSR+719a/wDp4dG+ePsAQCAtbMZGS9DKpXW19f7+//4%20d3NcXBx7mIpIJMrNzWU3YJcr4d4KAAPm5CqJ3bTE0VVC07Z23zbJ2Yf3nr5Z1cDk7Mf8ZZGxC3Hq%20AABgBLDKXu3oPhorSVwuKCjg2Iu7gd7dAWAwaTtp+0mdfdvH/5p3reQ2k7Nn+k5c/sbTOGkAADAy%202OAUAMDQpG3tvu0Tn3xdcvE6k7MnyFxi3g62E9jijAEAAKI2AMDA0/aXxy8XnC1ncrbUw3HtpiXI%202QAAVoRWgXvnnXfoYmRkJJ/PnzJlivaM4O3t7XPmzCFbDx48qH0QmUzGzPDNtKTy8vK4n0N8fDxf%20C7MXfUqEdqk68ijMM7fiqE3fA+aV6zz7ADDa0vaH7504m/Etk7OdXSVr4hbrvGgSAACsS3V19aVL%20lzRWlvTR2X79+vXLly/39PSkCTgoKKiwsJDZSha50zadMry/FH727Nmqqqr6+vrTp09v2rSJ2ZSe%20nk7C/VtvvWXdUZu8Qjc3N6VSyT77Dg4O2l9oAGD0pG0HyZjWpnYmZzuIhOs2LSGbcH4AAEzu/PrX%20cn76Qn9/yFbTPpyzs7OPj4/2NN6ZmZm+vr5CoWaXypEjR0gaZiLvjh07SM4OCwtTqVS9vb3Jyclk%205erVq/vrqKXzqDDt2aWlyaaMjAxyZ/LkyVKp9N13301JSaHHIZt27ty5a9cumu+tNWrT6tQ6N8XG%20xur95wAAGJHqapqYsdp8krMdhGuRswEAzKbj9i0bgaC/P2SraR9u7Nix4eHhTKhlAjFJvS+//DK7%20qjJ19OjRtWvXMl3apJlMJjt27BgtIhcdHb1161ad3eSUUqksKyubMWOG9tyI2ptaW1tra2t5fV3a%20DQ0Nq1atGoLzb66oTb8u0C83VVVVzJeMCxcu0C805FsLe8QMAIwGN6saju4/09vT2/ugS1s0dowz%20cjYAwAiybNmyrq4uGmqpkpKS27dvh4SEaLTMz8/PzMyMjIxkh2M61TfTxsfHh9yWl5frfCy5XK5S%20qWbNmqW9iU4HXlFRQQNnUVER+Rrg4eFBMypJ8EMzfYq5ojY9WSRnFxQUsDvn582bl5ubS9K2xkTo%20ADDi3a1rOZiY1d3dQ0M2n+TsRxwa61v1ziUJAABWhATfCRMm5OTkMGtInp47d+7kyZM1WpL1JBBP%20nDiRnZs1uqi9vLxIbrxy5YrOx6qqqiKx3snJibkykLn8kU4HnpeXV1NTo1AosrOzo6KiSLzesWPH%20kHVp80bMbJEAYOHaWjr+HP9PtbqbydlhaxbM8J3YX71tAADQqaupqep/P649lW2qA+b89AXDG49/%20+hlZ1AqJlxdHG5KeScZNS0tbs2YNCc10WMiiRYu0R48wPc0aSZ296Onpqb0jo7S0lNy+/PLLzBry%20WG5ubrSrNz4+njyEV9+zjYuL2717N8ncu3bt2rt375DNCG6uXm3aad/U1DRnzhymeguv718KgoKC%20yPcPjX8dAIARrLO9a+/v0rruqZmcHbTMd26wt965JAEAQEP5R/tMmLONdefc2e8+2KO3mY+PD+1O%205j2oPcKMEmHQKxoHGQhp+ZHc3Fz2WGWlUrlv3z7aIDU1la4nOZss7t+/f9y4catWrWKXyDNruQ5z%20RW2RSLRhwwZyh6Rt8mWCKfY3f/58krPJ+o0bNyJqA4wGalX33s1pJEYzOXt24PTQKH+6FWkbAMAo%20HXfuDO8T6Gpq1Nvm2WefJXmPjiHJzMx0d3cPCAgw8PgaY0XoqJL+GtMkHRgYyKyZN2/e0qVLNa7L%20pGiX9qFDh3p6ep5//vmQkBBy5OTkZLOW6zBjBZLo6OiIiAidm+Li4tgnBQBGcs7+XVrb9x1Mzp7h%20OzFszQJ2G6RtAADDzfjFevvx44fr0e3Ekqkxq/U2k0qloaGhaWlparW6qKiIDpLWuxcdK8JcyEjR%200dg6L3w01v79+0m8XrBgAe1oJzHVzs4uPDzc399fuzqhyc6YWd8P8rzJFwhy4pjS2toXSgLACHZ0%20/5nmhjYmZ3vIXJa/8bT2lJA0bdNx2zknil6ImYtTBwCgk5PvE08lfWJ4e71DsYP/+YU5nufKlSvf%20fPNNkvqys7OzsrIM2YUOP6aVM5ixD3Q09syZM7Xb05BJ0vPx48eZ9v2NS6Fd2uSZMOvZx6T53hwD%20Lsw+WyT5WtPW1sYMoGlsbETOBhglii/Iy4pu8h7kbJKnY94O7u/aR5q2A4K9mbElAABgvegYkoMH%20D44bN07n6BGRSCSTydgl6WjNEBKUV6xYQVfSSVpIM51HoH3nGRkZ58+fZ1amp6cXFhbSHmt24/Xr%2019MubWYNu4Cgzsrc1hG1AWB0kpfWpSZ9xeRskrDXxC3mnqqGbA1fs0C7zxsAAKwOzcEHDhzgGD3i%205+fHTCtDbdy40d/fn6RngUDA5/NjYmLIykOHDtEjxMfHu7i4sOttkOOLxeKgoCDmskCyC8nra9as%20YT8QLeDNXCj4eB86voVGc+2rNi00arMv5+Q2ZcqU/ubYBABrV1vdmPxhjlrVTRdJeo55O9jVwxFn%20BgBgiNnaO/SoVP39IVvN99AkSZNbjhS7dOlSpVLJrsAtEolyc3NJ2mbWkEWOC/xIoK+vr2e3j4uL%20I+lZo4s6MTFxyZIlTJc2eZSsrKzTp0+TQE+ieVJSkvmuIeS///775H+bN282VdRmj8zmIJPJysrK%20hqyo4ZAh76WpTiaAlWpuaDu4/SS5ZdaQnP2ovwxnBgAANJAg3t3dzR5sPZIyIQ8DSADAtDrbu5I/%20zGHn7Bdi5iJnAwCATitXrmQqcI9IJo7a7IsgL1y4IBQKZTJZR0cHc1lkfX29WCx2dnY+c+bMyOvS%20Bhjl1KpukrNrq38sufrUc4+RPzgzAACgU3R09DPPPMPMOIOobYTExER3d3eNUSIki58+fZo9iw8A%20jBipSV/JS+uYRd/5nijbBwAA3A4cOPDZZ5+xL3ZE1NZPoVBkZ3PNGqpzFh8AsF7ZKYXFF378Renp%204x4ZuxCnBQAAuEml0urq6pFaDNq8Y7XJifP29mZHahLBQ0JC6NzsADBiXM67lvt5MbPo6uEY83Yw%20yvYBAMAoZ66oTYsp0rTt4ODA1Phzc3Oj9UkMnKITACyfvLQu49Mfpw+QODqsiVvc31Q1ADCMvi77%205Mtv/2TJBwRA1DYULSquc1NYWNj27dtx9gFGgOaGNo0S2q/qm6oGAEF2WA7Y3aP69sbn1+r+fbf1%20umUeEABR2wi0GsnWrVs11ufm5mqXFgcAa9TZ3nVw+0lyy6xZ/sbTHjIXnBkYhcnY8oPs1Zp/0TuX%20Kj+zzAMCIGobLT4+vvdhgYGB7e3tL730Ei6LBLBqtLQfu4R2aJQ/SmjDqE3GFh5kyev95noqvV+t%20KGy/12xpBwRA1DZaZGSkzlnZxWJxQUEBzj6AVcv49Dy7tN/cYO+gZb44LWAmFp6MLT/Iktfbfq+F%20z+Pb8G16etVF19Ms7YAAiNrGiY+PT0vDDx6ANSHRmT0ahMPZ9KLLedeYRU8fd5TQBg2m7YS28GRs%204UGWeb18cjz+/b/6S6pPDuZVm/yAAIjaxmlvb8/IyOhvq0wm05jaBgCGXUXxrU/3nNIYe61TycXr%20X6Z+wyyitB/ojGIm7IS28GRs+UGWeb19RzPBqzb5AQEQtY2jVCpJmOb1XQRJJ2OPi4ujY7WTk5Or%20q6u3bNmCsw9gUaQejhJHh7rqRu60fb3szmd/OUfu9Pb9sRcJUdpvZLDkTmgLT8aWH2TLbuXQ10sX%206Z3iG1lVdy7cbrxi7J/L8tTLVanaB0THNoA285YBkclkAQEBPT093t7eKSkp27Zts7e3j46OPnr0%20aEVFhVqtRh0SAMvh5CqJ3bQkaftJmrbXbVqiHaDv1rV8uiu7t6e3t29RILCNeTsYpf1GANoJTe48%206Rk5buzUwR9NoxNaNMbJRMmYz+vtIZHOb2rEgI/5cJDlMUH2Ke+fmyzI9vaQIDve6VF7wY8/Hc3K%20W+1depJoi/I2aaNovqZ9wG+vf36n5Zqtjd1DBzQ42vL5Px7wQXzvPlW0q3eg74uuAw7qNAIgag+Q%20SCQKCwtLSEg4fPjwunXr6Jzt48aNQ9QGsK603dbS8Zf/97la3U3/bubzeJGxCz193HHeRgB2J/SS%20JzcM/mgPsix/8PHLhMn4buv1eyplSfUXOoLsjUxybIHdDyMbO7paSJDVeZDbjVeGJ8jyeuqbywZy%20QPLV+P53FNuHj3//VZNvzfe3mO6A317PGsy3IABEbUOJxWJvb+/CwkIar318fMjK2D60QUhICHI2%20gBWlbbWqe//v0+91qJicHbTM13e+J87YcPm67JP2e03PPvGbwR/Kwjuh++kz/oKdjInG1hv31MqH%20gvX31zXWcCZjcswMCw+yAzzg/a887Nf7UHwf2PeA/g/YnXHx6MrA1/ETCmDeqC0SiTZs2BATE/Pu%20u+8GBweHh4f7+/uT5M00iIiIQNQGsJa0bSew3fu7NGVrJ5OzScgOjfLHuRouph3vMcSd0Oxe4dYO%20RWtnPbNY23T1oU0d9VaQjIc0yPaY5PUyxyT/2drYujl583kGvfDWDlVTa5WdnZrjgHdavsq+/ELo%20bJTYBzBn1Caio6O9vLxeeeUVDw8Pe3v7rKwsT09PpfJ+B0NcXNy6detw9gGsJW13q7qbG9qYnD15%20mjQydiHO0jAy4XgPk3RC04EZvL7RwzoumOvtKbr+T/rdYKQlY2OC7FgHN4m9K/fxym6ebrt3l+OA%209gLxs0/8xoZvRw841kHKcbTr9fknv9mp6/X+cBr5vT3dPd2e0nm+U5bpfa0FFXcPnE/1n1nOfcAx%20go70/H/weSsXz56AH1UA8/Yrz5s3r7y8nN6n87TjjANYY9rm9RUboX/Vk/Uo7TcAFjveg7sTmula%20bu9qpmOXe3rUd1p++MWuc9Qyr59xxvwHn6JhTMZ2NkJ1TxdHkBXYjfGV/ZQ83Bg7cX//XDDeaaat%20jcDkQZYesIDkbM4DdqqUTW03DTzguSuH+16Ybf9n16a3tzu/4h+PTX6OvijqZgN5lPtliCprW5Wd%20anLn9Le36xo7n/G7aMgBZ0y6vDf9sT+lXf08YTF+9kc2hULh6en5+uuv7969m9c3d2FaWprOms7t%207e1BQUGFhYVJSUka/a3kIHPmzDl79iw5lPbBs7KyAgMDOZ4Dc2S6mJuby25PnxK5ExYWdvz4cWZI%20Bd1r0aJF9Jmz25OWJuwRxhAOAOiXxNHByUXc0jf1Ou08pKX9yHqcHGPDsWWO9yDpWec1gpbSCa2v%20z5hJxk7iiSLhQ983xgg0s7IhyVilvmcvGGtgkL107ZghuZN8L9IIskNzwNNF5zpUt/p7vexXre5p%20TTj8Pyr1U3eaOu80d/TX2M2pxlHSaMgBxwg6prhfrbqN6WNHqerq6kuXLmnk45I+OtuvX79++fLl%20OnM2HQ1heM4myCKTtuPj40mCr6qqkkgk5GibNm1ignV6erpcLk9JSdE44ObNm1988cXg4GCN52Mp%20UdvA88LDLDYAFk+t6j70wZc3yu8w/dnEi2t/4urhiJMzgHD8Q5YajvEetO/5/hgPtZIprMHukB7K%20TmimS5gg4dhR/OMYg/GOrE1jnMjztORkXNPwzd22G4bkTvK9iHwA9D5J0x7wgxNXmjv+6eHC0zuB%20Bn3Vrs75py7N6O7h+teqKR7f9f3foAPOmlr0wS/ews++BSI/+xNcZpnv+M7OzuPHj09NTdWI2pmZ%20mb6+vsXFxRrtjxw5QtLwnj172Cvz8/NJYu7q0j978Y4dO0jOZnqsydFiYmJWr15NQmZPT09GRgZ5%20GpMnTyab3n333Y8//pgWniYBfefOnbt27dLO0/PmzVuwYMG+ffs0erstJWoDwIjJ2Yf3nq68Wsvk%207N6+27MZ3057zAMT1gwsHPPMNt7j4rXkGR5PkweiQztoLQ7yQP2Vq/uRKTqh74+1eGTq/d6W5muq%20nnscndCiMY5hAfGG9O8Sp4r+ZMnJ+OrNbMNzpyFP0tgDTnBedLvxfhD5Vt5EbutbOuubO7pUPaU3%20W8jio1MuzJgk73uHe7p79Y+0GSPoWPbU39K/6rdyCDmgh4sRB+TzlX87ter15z7DbwCLQn4tfF7w%20h1C/uKlu88z0EGPHjg0PDyeRl4baHx63bxLxqKio0tJSjfZHjx5du3YtE3mZTlsS2X/fh+vl9B1W%20JpMdO3aMjgyJjo4mD5GQkHDp0iVvb28SuBctWsQMGmltba2trSXHT09Pb2hoWLVqlc7Drly58s03%2033zrrbdM0rGNqA0AOnJ28oc510pus/uzn3zK63pFPcfsNmBAODbXeI/vbn5Zeuv0QI6orxOa6f1y%20HTtVYCcid5ihGuQLA7nP7HN/eEajnk5oA1Os5SfjCxWHyOs1PHeSJ8mdOwdwwJTzP+dIxq6OtYP5%200D7h6cy7Pz7Hxnvi/X/FEtvbtauyWzvx02z1CiqPkk/512X/a76oTSxbtuzTTz+loZauKSkpuX37%20dkhICMnf7Jb5+fmZmZlffvmlxhHoeG6S17kfiM5NrlE/mhaYLi8v9/f3J2mbmTOxqKiIfA3w8PCg%20Xdpbt27tb2zFs88+29XVZaqObRNHbVz7CDAycnZF8S12zvb0cX9x7U/aWjq455IEbQOr73G/f7q5%20vEutbGi9Tod86Kl8N7DxHvpGQovGOC7132JgJ7Rph2dYeDKubbxq4gB07ZKtSa80zv024ulbufPq%208g3fZcyCZ17ZvIik6n62b6/6349vfHbM8AO6Lw7lPYffARak/V7ztbp/k98fXep28hNhvrQ9a9as%20CRMm5OTkMFGb5Om5c+dOnjxZoyVZLxaLJ06cOLAkKZfLVSrVjBkz2FHby8tLKBReuXKFTqG4d+/e%20mpoaiUSSnZ39+uuvk3gdHx/P0aVNn0NoaCgzzbllRW0AGHk5myk5YsjM7SOGqQqGGDLT4d3W6+Sv%20wDst5W2dDSRPk5BNorbh4Zh7vAcJoOOdZvIedEuPtb9fHo6s7Ohq1jsSerg6oS0/GUfMv98zV1Bx%209w/J36i6e21s+LZaJ7+7p5e8KeTObyIeo2XvutQ9ZTdbmDEeJTfuD/ygwz94vB9OCDkOORr39yN1%20j/5z4jPJcV5BvlEv6t75s2L733I0MCpnE3Wnsh/9zzj8XjVHYs6/dphO7WSU+z+hfR8xVXfnv77Z%20ObCpTKe7L3zSM4L78m6SnknGTUtLW7NmDQnBdJjHokWLBALN78NMT/Mgkz17keR75oFIqiYPQcI3%20r6/S9O7duxUKxa5du0j+5s7Qfn5+Z8+eZXfMW1zU1nt9JC6LBLDcnN3XQcouOcJE6lGStk1YMIRj%20psMmZU1rh0L/cOqH/6rkGO8x1sFt5oRneA+uL+Quupzy73d5ltoJbaZkzGBHZPJR1wjEBuLO2X1f%20cvi8nvsH/1Pa1b/nVCla9Ay/MLYTutLDr3LhcmaMh5e7ROIgcJYIJ7mKaYOcE31vicCgf5ToUakM%20fFyTHxCMlffdX+X1+QPYkXwft7GxefCrhPwwDmSKpWt1X5HfWi8/tVPPlz0fH9qdTNIgrT3y3//9%2035rfGdrbq6urSag165yGqamp7MX9+/ePGzdu1apV7KSqXX+QPP+mpqZbt25ZbtQGAKtzMaeM5Gze%20/XzQywSH5W88rVFyhJ22c04UvRAzd+SdClMVDLlen6/V1zuImQ71jffo7rk32zPSkPEeFt4JrZGM%20TUszIpMz9yAQk60Gpu1PTlV8lnvjfu7sJ2drpG29OZswKmcT02qLXvv5f+EX1yjEjCUzSt+H3Ubj%20B3xgHdvtXc1629DhznQMSWZmpru7e0BAQGtrqzlOyJUrDxX4p6NKdLakXdpZWVk9PT3PP/98SEjI%208ePHU1JSYmJiZs6cyV29G1EbAAbrqece+zr7u6aGNiY4kBg9w3eidkuats9llozIudkHUzDk/ujq%2076/faSlvbL3RrLzV8L18wPU9JrjMIqHZzXEGnUulqa06r/SgqcZ7WHgndH8OZJU3td3bFDXwas06%20u6LZ3c8cabuq7v5kLt/Km9rvqf9VeNvYTuiScbOypj7f39b7lyEW9L0l6DMGvb+rvX9+5spHxgZu%20Vpf2g5/vAXVsk99Ic7yW621GhzvTMSRFRUVRUVH29vYmj9p0rAhz4eMPP6pVVSTla4wqofbv30/i%209YIFCwoLC0tKSn71q1+RvcLDw/39/bWrE1p61O5vVDutNN7Q0IDRIwCW5ovki82snD07cDoJ3/01%20Jmk7fM2CEXkejCoYcj9Pt14nt7VNV8ktyeUaWdWQmQ7pSI/xjjMl9q5O4omuj0wV2ok1HujfpR/z%20TDTew7o6odkp+cTX1eTOnGnjBjbjN8eQD420PUkqVnaoS2+2kFRNErbOiV2M7YR+/O6V09OX+kx2%20pEM+hHY25D5Z7zvVmTbIScEvITAI+R6+Kmi/UbvkXv2f8tvneh++dpr80njE3jXGyEMZjpbMKygo%20yM7OzsrKMsdDiMVib29vEuXZUZvWE5w5c6ZGY6ZLm2nJbqOR160gavdHJBL9+c9/Jml7y5YtpqoN%20DgCDdzGn7Ot//dg3OXmaNGyEJmlueguGdKmVd5or7rSU17dU1DdX3FMrOTqRuMd7jBE4LHnytx7O%20j+l9VqYd7zFkndCmzdkkJdP7Ro30MCRn60zbBjKqEzr19yFDc7q6mprab9YMYMevXl1F9sXvwxGG%20Fh7p1VGjyLylSOgYkoMHD44bNy4gIEBnJpTJZIWFhQPOuLTGSEJCwooVK5gpbMgiOaz2I65fv552%20aTNrysvLmZ5sjTImJK87OzuzS6NYTdTmPejtN1UJFQAYPHlp3RfJF5lFpuTIKDwVOguG/Ls0adwj%20Xo2tN+60VBjxj7a6u7R/jMX3VB0N38sNidqmHe8xNJ3QJs/Z7KsYjU3bn5yquHPk7/9huvEelvKT%20e/jv5La1rKynq0ulVLZVVQ4+o+P34chTUHm0p0etc5Oqu9N8NbbpGJIDBw7ExcX1l/eMLfQRHx+/%20d+/egoICpv3GjRsz+rDLmxw6dEjjEZkC3jRPP96Hjm9JT08ncV/jqs2ioiI6zaRVRu39+/crlUry%20FQeffgBL0NzQlvxhjlrV/cMvBYEtydkSRwfrehWmqs2ns2DItbqvK++cN/AI451mOokn1igut3c1%20W+Z4D+vN2cZexVh8velOU8ed5s7P82+9afx4D42oPd7JYbyzvZf7WNEYuyc8nRsKhv/kXD98CL/B%20QK+au0Xqnn5nOG/rvNvaoeCoVjQYJEmTOBsZGdlfg6VLlyYmJrIrcBtLJBLl5uYGBQWRuEzXkEXt%20UdfkUZYsWcJ0aZO9srKymLKASUlJ7F0UCkV2djbJ9CYZTzJsxf7MXdsFAAxBC/x1tv/4W3j5G097%20yFys61WYpDYf+cvm2+sZuguGcE4QM8Fl1njHmY6iCa6PTKWPXtPwDYnso3m8BzXEVzEywbrkRpPO%200dU8I8d7LA+aKh5j5zPZkV1Bj5Ez4B+6B93Pypqarub7vcgtxcX4XQTmsyroI/MdXOPaPI3KevF9%20+mtMzJs3j6RtpgK3xsGj+3AckMnNBQV6vvtqPDEe51w5X375pVAoDA4ONskpGrawGxERgagNMOw+%20+8u52upGZjE0yv9Rf5nVvYoB1+ZrVt663XiltunqDyNDDJsgxnXs1HGPeLo9Mn2800ydyX6Uj/dg%20UvLQXMWYkne9S92rM1gP0s8XTzftAXNXLlcrh2JCZWffJ8htU/G3xu4Y/M8vOLbm/PQF/M4Ek6NX%20T9IK3BbylI4ePbp27VpTPZ/hCbvapcIBYOhlpxR+V1jNLPrO9wxa5mt1r8LY2nxMvL7ddMWogiHu%20zj5zp0WTeI3xHgamZHp/YFcx/vuDj+6dyvgPg9tbxdBqk+fsqatW24klY/tmwrN3G28/fjySMVid%206OhoEm337dtnIdUy8vPzz58/v2fPHlMdcKiL/QGAhSi+IM/9/Md/tp48TRoZu9AaX4ghtflIHK9p%20uFxzt6im4Zt+r2vUVzDk+/ZavTmbZ83jPUybswd5FeOUUxlGPaj20Gpikqt4vLO990THSa6inoEO%20rW6rqiIRmQ72uFdf33nnzr2B1vcwOc9Vr+BXGYwABw4cmDNnzltvvWUJHduJiYnbtm0z4TPBEA6A%200ai2ujE16StmUeLosPyNp62x5Ah3bb67rddvN16pufsNSdj6j6WvYIiBE8RY6XgPc+Rso65iLL3Z%20UlXbKr/TRm7J/Xf7Vho1tJodrMc7O/hMemiW0wEMrT770os9XV0mP0VCZ2fxpPtlDR7x8eELBEy3%209OXNm/CrCUYnqVRaXV1tIU9Ge1S3RUft+Pj4hIQEnZtkMhlmsQEYFm0tHdolR5xcJdb4WnTW5jt7%205SOHMU41DZc1x4dosbURTHCZ5eH8WNnN0y0ddSYpGIKcbchVjF3qnrKbLZUPsnVVnQnmkPuft58y%207csxec7mHgkNACOSGaM2R84GgOFCS440N/w4visyduHkaVIrfTk6a/PdUBRyzHk+xk5M4zW5ZQqG%205JOcbaKCIdZrkAVDDLyKMfPSLWWn+maDcii+VfaN/Wgq/rZb2T5qf+RNPoU75oQHsIioTWe/xPkF%20sDRfJF+sqVQwi08995jvfE8rfS3X6/MNr83nOnbqZNfZU6QB4500Z+s1bcEQKzXIgiFGXMXYN2ba%20HFcxNhd/S1N1W1VlR/2dzjt3THJYidc0gVgsmjxZ4ORk7zbeYfx4obOzaNJky7/ocKyPd2tpmeHt%20xzx8VeUQHBAAUdsEdBYSB4Bh8fW/rl7M+fFvyhm+E1+ImWuNL6RZeeta3VffVPWN0u6/Nh8JxDLX%202ZPG+ZGQ3d8EDSgYwht0wRCTXMXoLBF6eYz1nujoM8mR3Pkm2uhaBCYf6/zM8RM2QuEQvxcm7DMO%202PWBaZ+byQ8IgKg9cGKx2Nvbu6GhQees9wAw9OSlddkphcyik6tk+RtPD1voH9DkjjRhV9b9m9x5%20EKz7rc3n5f5U6BNxeo+JgiGDKRii7FSX1rTkFN35ed+isVcxTpaKPcdLnvB09nIfK7a3uMv0hzhn%20o88YAFHbCCKRaMOGDTExMVu2bLGQQokAo5nG7Ov2IuGauMXkdliejLGTO2on7Pv01eara7pKHkjv%20SA8UDDG2YEhVXSuJ12W3vie3gxlyrfMqxs47dzrr7wxg7hWd7MePd3AbL/GaZisWmWkOc0vuhAaA%20kRy1eQ9qku/po70VFUgABk9eWuchc9GbmP8/e3cC19SZ7wH/ZAWSsBMWlSgoggsubIoF64JdbLF4%20a1uXuZ2pnc7WmWnrtHp9+95bvX1bbxc7nant3GvH1ukdlyq3LlRrC4qKiiKgCCiLLAaQnbAkAbK+%20T3LwGEMSAob99/10mOTk5CSck6O/PD7n/ycJe/9fz5h1X/cJcB+ut21nc0dld2vx3fSSu+ceSNj3%2047RjavMhZ/dZMIQeui6qbiu4IyM3VBqdo94A3aK8o7y8s6ZaWUXye9HDFP3wDJ9Dp2pygysUiYwV%209BgOj9oYhAaAYY7aq1evPnr06GBsubGxMSgo6NSpU6azwJVKZXx8fG5uz7+P954jbnuFPp8OMNKU%205tfs/yxdHOC+ccvjttP2oS/O1VXJmLsr1kSEhE8crrdtT3PH23UXy+suVRinUFtma0i7J22jNt8A%20cnbvtP2PtNstctVgvIGL/7peJZM5cIPzdnwwlDsQg9AAMJxRm6Th1NTUwcvZCoXCRowmyF3TuGx7%20hT6fDjACkZAtcnepk7Z89cGPNtL26e+uFV03dLajK3LMGe7u6zaaO7YqavKlJ8tqL3ZrrE5LCPKN%20cXXxu3EnBbX5HqY2nwPbngf7u4YFulP978Xo2JxtJxSqA4Ahxh7UrXt6epaXl+stuXPnzgBmj2Rl%20ZU2aNMksZxMffvghCcqJiYlqtZps/MCBA2Thhg0burq67Fmhz6cDjEAePqKXtzzu7iOi07bp/JD7%20Celq5bmUG0zOniDxGt7u6w82dzT8+VMg/bG5Q0oC8bcXXyf/kRsWczZJ2EtmvfrSsr2PzXurvbPe%20nj++6O2TlyMvOvaOPl2b73x+fdq1u/197t600u7+Fwy5/we7iB8Z4r1+SfB/vRR55N+XffbbBa8+%20Heawv5P4fM/wORNXOv7bkWtYaL/Wx3wPAHCIwRrVFovFK1asIPk1ICDAIRtkBrNJfP93I+YhpVKZ%20kpIikUgOHTrE5Rp+o7Vr1xYVFW3fvj07OzsuLs72ChEREbafjk8JjPC0veeDHy2ObddXtRz+7/NM%20znZ1d1n3h6XD233dYnPH/8v8k97K+n4e02dMXBHkF83nCuklqM1HPXRtvitFTZPpXNufgiFJsZLp%20E93CAt39PFyYh1QyWWNRkbyibMC/i1tYmIuvn8ukSSRhO/v6Od8LuDUnTzh2p2G+BwCMqahNbN26%20NT4+3rEVSPbs2bNx48aDBw+aLiT5u7i4eNmyZXRQpoWFGUZZSkpKSFa2vUJoaKjtp+NTAqMxbcvb%20Onf/fz/odXo6xfJGRvd1i80d6RrYpqsJnDxCJyydPuFRD6H5nHLU5htYbb6i6rb8Cln+HRl5+sBe%2095UnDK1/NApFc052e3FRR3FxR3nZw0wCif7r52ZXLgIAIGrbq7GxkYRXlUrlqAokYrFYLpdbfKii%20okKtVoeEhJhm5eDgYD6fX1hY2OcKs2fPtv10gFGXtrk8zq63j6lVGjpnkxj75LroYe++bk9zx2n+%20j0z1XzTFN8baRlCbz/7afOV1HTcqZLllzSRkP3zZkIL33iXZ2lEtGClDF8a+czamVgMAovZIMWvW%20LNO7QUFBvAf/bdT2Cn0+HWAUpe0upUqp6GZyduxjM6OXhg7j29Pq1LdrL2bc/B/KenPH6GlrZ0xK%206F2NBKzmbCOz2nxhge7Zpc0Fd1pvVLQoujSOHEDJvDSUvyxK6QEAojYY7N+//86dOya5gYV9AsOe%20tql7g8Tk4xgU5r9iTcRwvStld2uB9GTx3XRyg7LZ3FGj60bOtqb8H1/fOXyI3OijZkgOVUpR7uQ/%2071kKKwVDKMMMeBcHvje3sDDX4KmYWg0wPtGX0v3617+mZwvThZ4tzlxgqr3Rk4HNNhIZGXnu3Dmy%20qd4bNyvu3Nu2bdu2b99utpCpI8fUnk5MTPzuu++YKQz0+1myZInZPGeyPlnT7B2OxKhtY77HIDGb%207EFPGrF/hT6fbs369etN7+7YsQMnHgwXkbuLm7tLW5OcDtl0/h6uSyHrW0tu1aTRk7MN+mruWHI3%20PWrq86iBbRGds+03u7nQrDYfidfhQZ7hkz3IT3I7/fRDfMyCp7oGBwuDgt1Dw9zCemqPODxqA8Do%20JZVKexeWKDCyuP6vfvWr5557zmLO7l10rre8vDwbKZwk+PLycpFIRLa2ZcsWJlgfP36chL3k5GSz%20p2zduvWZZ55ZunSp2fsZcVF7KNGTPUpLSzUaDfNlhexWlUpFTwuxvUKfTwcYFTRq7Tc706rKGpnx%20bBJtn30l7uG7r2cW71V2y5bPecPO9Ytrzt6qSSVR+8E4Pd6bOw64DPbetNIBFAyhjFX55gR5zZ7s%20ERniTY9kK6urZJfO3CzIH8D7D/75S+5hYW6hYWw+H6cbAFjj6enp5+d35MgRs6h98uTJ8PDw/Hzz%20P38OHjxI0rDZdX1ZWVnx8fEqO9rHKpVKkuzNRqyZh1JSUsjbCAwMJA+9+eabX3/99Xvvvefs7Ewe%20+uijjz7++OPeeTomJmbhwoW7du1yVFWPwaqrTb6LkC8QLOsmT57sqKrVQqEwNDSUfKchWZlZWFRU%20RH5Onz69zxX6fDrAqMjZ+/96prK4jsnZhht6/Yl9WRbrbdtPq1PfuHPidt2l5o5K22uqNIprFUf+%2099wrZws/N8/Zxjootps7UmO3Bjb1cGWw88oGUuXjf/4Q+8+3Fm9eM3uxt0p17seC9969+K/rr/zm%20VyVf7Ko/f24AG5z83PMe4XNs52yS8u35DycswFD64cBVa38RkOXkUce+nKur66pVq5KTk01jHp16%20n3322d4Xwn377bcvvfQSE3npALlgwQISz959990+X46uMmdW3MLaQx0dHbW1tZRxSLupqclsYgLj%20hRdeIKG8oqJiREftoSQQCMi3GfKd5vnnn6fjMvmGtH37dolEEhUV1ecKfT4dYOTn7AOfpd8uuHt/%20PJuiFq2YYbu7jZ1uVv1E38guO2wjZGeXHdp//tWs0v30nGxTImcfG0PaTNQmj9MD22MyZ5uWwbYz%20bVc3KY5mSv/05dWSu+0DeFH9xVQSrzNeeO7qH1+9/eX/NGZeGtTujGgQAzBizXtkKvk7ovdfBGQJ%20WU4edfgrPvXUUyqVig61tIKCgrt37y5btsxszaysrJMnT65evdps+Z49e1paWqZNm9bna9Ezfi1O%20Q6DHUulpC5Rxngn5GhAQEEAPab/zzjvW6uAtX76cvP9du3Y5ZG+MkcsiN2/enGJk+m1p3759zE60%20vUKfTwcY4Tm7NL/GNGfPiJA8uT4m9rGZ1rrb2Ilp7khIG3NJjDa7cpEsyas8Wlxz1mKLx2n+j8yY%20tCJfelLe1WRPc0e9XktebmbgY2Npxna/ymCrNLqc0qbcshbyrPrWzod5XRKvh/LXxFWMACNWgMTr%20iXXR5G+KdX9YyvxFQOdsspw86vBXJMF3woQJ6enpzFg1ydPR0dGBgYFma5LlJBBPnHi/hUJ/L/aj%20Z/x6eHiIRCJ6YjczmYQeS/3rX/9aVVVFHk1NTf31r39Not22bdtsDGlT9/owJicn07NNRmjUtran%206Os9yW/Yr6LafSJ7MyMjg76ylV7CXHlqzwp9Ph1gFOVsnwD3536zmOqrl6Q9TJo7snR6DUnVsaG/%20MA3ZhVU/9Z7y4cQVkrhMQrari3icN3e0sww2SdWZtxpzy5oH3F/GTnTbc9fQ0Mr9+3D6AIxS8rbO%201OTcaxdu9+tZ5K+J9189YLbkb++k9PfVwxcExa2cbTugk/RMMu7Ro0dffPFFEnnp2SNLlizpPXuE%20GWke8N6gZ/w+++yzzBLyWr6+vjk5OSTok1RNXiLYWMV/06ZNO3fubGxs/Pjjj0n+tp1C586de+7c%20udra2oe/OHKoR7VJqP3b3/72kF0k1xr13jLZrbZf2sYKfT4dYOTmbOPUbDpnkzD94qYEpuTIw6Rt%20ZkjbMLuDxES9rkD649wpSWR5Tvnh27UXeodsgZPHvClJpsPS47m5Y59lsCvr5eSvuuzS5uomxeC9%20Da5Q5DlnjvvscNOCIYjaAKNXyjeXb+VK+/ss+hoelknOHlhx4vwrFU21bb/dnmh7tbCwMHo4mURV%20uvbIX/7yF/OxFeMVjSTU9p5mbT+6/IjpCCl9SSVzXeORI0dM1//888+9vb3Xr19vWuGkd/1B8v5l%20MllNTc3oi9rUvXogjhqWBxi3rqYXk5xNGaKbnvkTs3f3ddO0nX4s78l10XZu32RI2zD3w1D9Wq85%20kbO9RV5t4ctqr5BNG5/NHe0vg01Mo6gC71mnepXBFjpzo0J8IqZ6RYb4XF/b74EJv8WPGuJ12Aw0%20PwcYY1qbBlhMmWXldn91tPU9vY2e7kzPITl58qS/v39UVFRHR4fD94ZZkqaMJURWrlxpMWfSQ9qn%20Tp3S6XRPPPHEsmXLvvvuO7LmunXrpk+fPkjTGYYhapPvE+Q7BPlKgbMF4GHEPjbzyumi5oYO5k9M%20EqODwvx7r0mn7fMnC/rVy4Yuic1cy0i3dWzuqDLr0+Tq4hsZ/Ny0gEdQEpvxMGWwJ/kIo0K8I6Z6%20R4Z4d9XXN+dk15zMHcB7mLn53/pcB8VAAEajJ9ZFH9lzccCB+yE5C/hLn5nb52r0dGd6DkleXt6a%20NWtI6h2MqN3fCEri9cKFC3NzcwsKCl599VUul7tq1aqIiIje1QlHetTus/D4Q/57AQCkJue2mOTs%20+XHTSPi2tjJJ26teXGj/xisbsprldx4sG9LT1pF1rxUlHbJDJy4Zk7t3iMtgx87wNQ5gGwpgk3gt%20O3v2ys5sZXXVIP12aHsOMHoFhflv+ujZ/j7L9DrIWmnLqQNXTa+SHAwvvPDC7373u5ycnNTU1FOn%20Tg1e2qQHp03bQFqcl8IMaTPLTWs6m/VXGQVRu09JSUmI2gADditXmnHifiOAwKnixP4k6T5l3zaM%20y5pVwqYHtnV6vbdIMn/qs9P8Hxmru5cug01uRE71tlgqxIb8itbJ/X/FzY8HNmZeavwptygnW6dS%20DfYviIIhAOOKWb0RizVJHI6eQ/LVV195e3tbrJ4sEAgkEklubu6AMy4zdn758mVmTPr48eNkm/SI%20tenKv/rVr+ghbWZJSUkJ8yyzytxFRUWenp6mpVEGbHjqaveefg4A9quVthz+7/PMXZG7i2O7r5fX%20Z/Ya0u4J22zjJZIS38ixnbMHUAbbELIrZZ9/XzSwIn0X/3V9yRe7SNoegpwNAOM5Z9OYtP2Qbc76%20zMG7d++mZ49YXGfu3LlMWxl7bNu2zcvLy7S5DNm+UCiMj49nmiSuW7cuMTHxxRdfNH0iXcB78+bN%20dJ6ebUQyOkn5dDQ3q+2dl5dHt5kcuVGbLvantwI5G+Ah/9DUqLX0XZKwSc4madtR269syDp9w3Cd%20uJXmjoYZKyV308dwW0embAjbOCu9z7SdeauBrPP8jrP/9nXOyavVMrkj/97yjoya9sqv8bEHgAE7%20sueixfrZdNomjw7eS5MkTX727lDDWLlypUKhSE9Pf5jA2dDQEBFx/0qkTZs2kfRsNqS9Y8eOxx9/%20nBnSFggEp06dOnPmDI/HI9F8z549phO1GxsbU1NTHTX/gvX++++T/9u6dSs+iw5BjiV2JgwekrD/%2095O0iqI6Zsnqlx+ZHzfNIRu/21KYdXs/3VOdhEw2y/JUY51Oo6f0i0J/ET75qTGcs+nyfFqdXmfs%20Kv9G0kzTmSR0r5nLxU0kZyu6NGbbeTPHUDCkX3O1TfE9PUnC9oyIFC+MpRuhpz/9ZH83uPT7H3C+%20AMDIR4K4Vqs1nWw97A4ePEjPMn/ISn8kE1KDPVebnq5OvjfQ3xXo/jUTJ04cUTsUYBRJTc41zdmx%20j810SM7u6GzMLN5bYew1Q5FkaSgyYnU6ynho62ixDDa5uzjcn2TrK8aETdK2Y9+AW1iYz4JYErKt%20ledDwRAAGHvoqyfpCtwj5C19++23L730kqPezyDmXbqEuEqlYmadKxSK4uLi3NzcqVOnOrZbJMB4%20cDW9OPOn+x1hgsL87S+SbY1Ko8gpO3zjzgnTKG1plvYDUZul1ym7225W/TRmBrYt5uzeafuz47c0%20Or21jQidubEzfBeG+nT1vx1W/LfJXKHQ2qMoGAIAY9XatWtJtGU6zgw7El8vX778ySefOGqDgxi1%20d+zYQXK26RWQ9ATu1atXHz169GG6RQKMQ1VljT8cuMrc9fARrfvD0ofc5rWKI3kVx7o1vYty9nEV%20xxgb2L706RfdaSmv2b2+WccZTxE/MsSHJGySs+Xl5XWnjw6gRJ+NnE2hYAgAjGm7d++OjIz8/e9/%20PxIGtkl8fe+99xz4TgaxrnZqaqpEIlm/fr3ZQ1u3bj158iS6RQLYr7VJbnYp5IubEqxVaMos3qvs%20li2f84aNDda3lpwt/KJVUWPxUb1ep9X3PTtC2d3297T1v37s8Kjet4Yy2Gkp/XoK3XGGJGySrRfP%209guf4kkn7MyPL3XV1+PjCgDQL2KxWCqVjpA307v95AiN2jS6gMvImXwDMBqRhE1yttykEe5zv1ns%20E+BucWWtTk3PBpkXtNrbdYqlfNyadXs/3QnSlKuLL5vFaVPWjrfdy5TB7tdFh//1UmRPwk49nHkZ%20CRsAAIY2aguFwtDQ0Nzc3CVLlphOy25sbFy2bJlKpUK3SAA7Hf7v87XSFubu0mfmzoiQWFv5ZtVP%209I3sssOPz3ur96NZpfvNZow4cYVzg56ZM/npUT0VZMDNHXe+Ep1+rN8v54KEDQAAwxi1BQJBYmIi%20idpSqdTFxULFX3SLBLBHxon8W7n3/1mNhOylSfOsrazVqa9X9vzLl7QxV9ndKnDyoO82d1Rm3PqS%20LuRnKnTi0php65nVRqmBNXesb+08n1+fdr12bf9fseqY5X9hZPP54oWx9efP4aMLAADUoE4g2bx5%20c0pKCknbvR/q3cUHAHojITs1+f4ZFCDxeu43i22sf7PqJ2V3G8tYQ0Sn1+RVHo0N/YVKo7hecexa%20hXk09BBOXDLrd34e00f7XjJr7kh+2k7bii5N5q2G03m1NypkjnoPdML2XhhLV8JG1AYAgEGP2gKB%20ICcn5+DBg+vWrTNdnpGRYdqSBwAs6m/3dWZIm2XsnU7pdQXSH71Ek7Nu71d2t5quSc8YmR+0egzs%20JdMifZT+fgFsi2mbJOyMwgYHlsQ2S9hmj6IMNgAADPoUjrVG2NEA/dK7+/pzv1ns4SOy8RSTIW1D%20qT42i63Ta84VfmFWBTrINyZuxiujfcZI75xtKIbNokzbzTBpu7yu44fsGpKwHdgy3W/xo9YSNoUy%202AAAMGRR2yKlUvmzn/1s//79KPYH40pFUV2AxMtakT4GSdj7/3qmtUnOLHlyXXRQmL/tZ9FFRZjW%20M4Ybeh3Jnax7DVk8hBMXhf4i0Gfe2NiZfTZ3bFeqOlW609dr61s7rW0k2N/1yaiJVP87zszc/G82%20HkUZbAAAGIqoTXersfiQRCLB3odxpTS/Zv9n6eIA941bHredtk/sy6osvl/aIvaxmdFLQ21vvLIh%20q1l+58EujyzDwDalIyGUzebNC0qaH7R6zPRRt6e5456fblt7up+Hy/J5AQnzA8iNrvr6THw6AQBg%201EXtbdu2WcvZAOMQCdkid5c6actXH/xoI21f/KEg55yhTgg98SPYvu7r2bcPGcI19cBMbnpgW6+n%20VkW/6+s+dczsyQE3d6SbzpCQHTbJXadS1Z8/l3PqZHtRET6cAAAwyqK2UqlMSbHagE0ikZgW2wYY%20Dzx8RC9veXzPBz/aSNu3C+7+ZCw5QudsT/u6r1sa0u4J2/TAdkXD5TETtQfW3LEz8Rfxs3xJziZ3%20W/NvFCWnkZxN0jY+lgAAMKjYg7RdhUJBwjRlrDfS0NAgFAo3bdqkNzpw4IBUKn377bex92F8pm13%20HxGdtruUD0S95rq2fZ+eNg5DG7gI+Da6r5vKuPkl1WtIm0nb5H8ld9O1ujFSDSO/oqeaCpvHs+c/%20euXNa2bP99LfOXwo8+VfXNu6pTYtFTkbAABGcdSmSSSSqKgounNkcnJyV1cXZaxJkpSUVFpaqtFo%20cAAAaZteLm/r/Nu277Vanf5eQLbRfZ3Rqqj59uJrSlWrpSFt43ZYbPKIsruN6SI52u18JXoAz7rx%20zr+TkF3+j6/R3xEAAMZO1KbRnSOlUun+/fspY2/21NTUvLw8RG1A2qbTtkat/eI/UlTdGiZnr1gT%20ERI+0fZ2rlUcSc58q1VeQ1kd0u5J2+Tn9cojI2pge/epkg+S8/v1FJVGl3bt7p++vDqAl2vOye69%200NnPL/jnL8Xu2YvPJAAADJLBmqtNj2Tn5uaSeL1x48awsDCy8GUjeoVly5ahMTuM87TNzNtWdarl%207Z1Mzg5fEBT/VLiNp7cqas4WftHTZd3QGtLykDYTtVl6HT2wHT75qZHw6/e3j3p1k+LY5apz+XWK%20LsP386cfcoCBz/db/Kj/8gSP8Dmmy9FxBgAARk3UFggEb7311rp16958882lS5euWrUqIiLCtEl7%20UlISojYgbdNpm7p3HSTJ2YFTxatffsTGE69VHMkpO/zgEHUf/zxF0rZer71eeWRm4GPDXu/P/j7q%20JFiTeH36em1RdZtDXtotLGzCEyvFsYu4QqHpcnScAQCAURa1KeOc7ODg4J/97GcBAQHOzs6nTp0K%20CgpSKBTkoU2bNm3cuBF7H8Y5kbuLm7tLm7FVDeveEhvd1x8YzDah1+u0+r47jSu72/6etv7Xjx0e%209pzdZx91kq1/yK45n1/nqA7qk5973n95gmBSoMVH0XEGAABGX9QmYmJiSkp6YoFYLJbL5djjADSN%20WvvNzrSqskZmPJtisZ59JY6kbYvrF9eczSze261RjN5fuc8+6vQw9rHLVdVNln9NPw+XgTV3DP75%20S/jIAQDAWIvaAGAtZ+/79HRlcR2Ts8kNll7/46GcjVt8zAr8qTSKS8X/oPuuMwROHktmvTqKuqz3%202Uf9p9y7pXfbrQ1jPxruv3xuQGSIN7md/gE+QQAAgKgNAFZy9oHP0stu1t4fz6aoBctCi2/U9O5u%20U99acrbwi1ZFjekWQicuXRT6cz5XOKpztlnaLpS29n4iPYydMH+Cp8iwQzQKRc3JE/gIAQAAojYA%20WM3Zpfk1pjnbZVJdbNKjcU/ONusl2fsKSCeucMns303xjRlFv7KNnG2Wtk0Xmg5jE8rqKmnyYbR4%20BAAARG0A6EfO5nu2ey7KzS5jPz7vLaYmyd//6+TkJ2/d7cg1fbqfx/Tl4a+7uohH0a+8N620/uA/%20X6vLsv8pBd6zfvb5+/QwNkHide0PJ2X5N/D5AQCAUWdwW9hs27aNZcXkyZPp5pEA4zlnc1xUXvFX%20WRydtDFX2d1KVwAUevIbqtquHxbpVPcL880PWp0U897oytmUsY96TH9yNjG7uZDkbI1CUXXsaObL%20v7j54X8hZwMAwCg1iKPaJGdv374duxiAdjW92JCzjZdA9uRsLsvr0ctcYReLxdbpNXmVR2NCNpTI%20jrvGn+o8vUgtc+somO4eUTjqroA0tfOV6PRjxq/1PLvqedN9ZEq+2FWblmpxroiznx+aqwMAwHiP%202kqlMiUlBfsXgDE/btqZo3ldnSpmtrLv4kK2ZzvLMIGZRel1+dJTtbKixvYyjpDyXX6p/eY0t7m3%20SMImOZuk7VH3+6o0uvP5dYcv3Fnb/+davPbROzJq4qpnyM/0p5+k0NwRAADGc9RWKBTFxYbua3v2%207EG3GgB69ki3Sc4OTxC0iMsNLdWNPdXZhoFtbVN7Gf0oR9jpGZ0fE7J+ftDqYX/zu0+VyOTdW9aE%2027l+fWvnD9k1J69W033UHxKbzw9IWCF59jnnez0a0dwRAADGe9QWCoWhoaFNTU3r16/HXgZI+eZy%20RVEdczd8QRAr6Dglp+icTRl7p1N6nU6vZxlrdLi6+C4Pf83PY/qwv/Oc0uZjmVJyI3Kqt7UO6oz8%20ShkJ2efy6xzy0iRbT1qVRHK2WR91NHcEAIDxHrUFAoFEIqmoqKitrQ0KCsKOhvEs40T+tQu3mbuB%20U8URq5zSCu4YGiaymEuTWYaBbUpHgvYU35gls383Espm03X66NsWO6jTmLki1ro89pd3ZFTAEyvF%20sbH48AAAAKK2Zbt37yYh+7XXXvvuu++4XFQVhHHqVq40Nfl+zT4PH9G6Pyw9VfjvhnBNcUzXpAe2%20KYodN+OVkZOz6XrYlP6BDurMOrbnigiduSvIyv3voz5n+7v45AAAAKK2VY2NjSRnKxSKlJQUnqXK%20AxKJpLi42NnZGccAxrBaacvh/z5//3zjcUjOburOb5abDWn3hG3jjG1dvvTEgpANIydnG/rOsCim%20gzqdtm3PFfHzcFkTN/nRcH+SttN34YMAAACI2gDgUK1N8gOfpWvUWmbJc79ZHCDxOnzxINVrSJtJ%202+R/JXfTo6Y+z2HzhuudW+zvyPR0JGn7H2m3W+SWuzZGhng/s0DCdHkEAABA1AYAB6NLjpC0zSxZ%20sSZiRoQk/873LYoqS0PaxqDNYrP0OmV3282qn8InPzVycrZZ2u6ds4XO3EfD/dfETfbzcGEWqmSy%20mh9O4MMAAACI2g4mFovlcjn2L4xbJGfXSluYu+ELguKfCr9dd/FS0T8olrUh7Z60rddrr1cemRn4%202NAPbNvI2WZpm1lCsvWqhYEr5k8gaZtZ2FVfX338GMnZFtvQAAAAIGoDwAClfHO5NL+GuRs4Vbzq%20pZjM4r037pww5GwrQ9pM1B6uge29aaWHM+6QG9ZytsW0/dUbj5g+qqyukiYfrk1LxccAAABg0KN2%20VlZWfHy8yjiylZGRERoaumTJkpycHFwQCWNVxon8q+n3G6x4+IjWvBr9Q9679a0l95axbW9huAa2%208ytaF9dkxNRl2f+UAu9ZFJVA324vKpL+3+HGzEv4DAAAANj1V/7DUCqVkZGRCxYsUJn8C3JFRcXt%2027cnTJhAbmDvw9iTf6XCtLSfs4C/6rfTv7/x/5jkbEqv12l1ahv/6fSGKymV3W1/TxvSDlA7X4nu%20V84mZjcXkp/NOdnXt27JefON3jnbGZ0aAQBgHBvEUe0PP/wwNzfXbGF5ebnKaNeuXTt37sQBgLGk%20oqjuyJ6L988uHidug+vZ8h0kPY/8N1/f2pl84c5M+is4z66hdJ3a8Htd/ePv5eVlvR8VBU+VrHnO%20b/Gj6U8/yawMAACAqO0ASqUyJSWF3Ni0aROJ1AcPHly3bh25u3btWvKT3E5OTn7vvfcwjQTGjKba%20NrPSfqFPtN2SHzNdJ9BnXsKc1we7Pc3uUyUyefeWNeF2rl9U3UZCduatBnJ7Zv9frnfO9gyfE7jm%20Oe/IKPqua1hoR1Gx/Rt0wkA4AAAgatumUCiKi4slEgnJ02YPLV++XCgUYtfDWCJv6/zmk7Qu5f25%20UpLF9a2iq6brRE59Lmrq84P9TnJKm49lSo0v522xibqpc/l1xy9XkajtqFcXxy6SPPucW1iY6cKo%20jz/FJwQAABC1HYmE6dDQ0Nzc3LfffttsosivfvUrEsSXLVuGbu0wwlUU1QVIvJwFfNuradTab3am%20mpbQ9gyv1k68ztx14gqXzP7dFN+YIcjZ7x7oed3eTdQZKo0u7drd5At36ls7HfXSAQkrJGueE0wK%20xMcGAABg0KO2QCBITEwkUfsTI3phfHw8s0JSUhKiNoxkpfk1+z9LFwe4b9zyuO20vf+vZ+qqZPc/%20/FPqBLPv52wf1ynL57zuIZw4NDmbLolN6R9oos6sI5OrTl6tPnZZqujS9N7CJJ+B/HPTxJVPSZ59%20Dpc/AgAADF3UJjZv3pySktL7ykiCpPAXX3wRex9GMhKyRe4uddKWrz740TRtZxbvVXbLls95g757%20bG/m7YK75AZdZdrZt9kj5v5nPsg3Zsns3w325Gyqd+sZFsU0UafTdn1r5/6zFefz61QaXe+nzwny%20XPPIlMgQ7/Qf+/3S03/3e3xaAAAAhjpqCwSCnJwc5oJIRkZGRlxcHHY9jHAePqKXtzy+54MfTdO2%20Vqc2tKGhqHlBq71dp5w7npdzroTJ2U6e7V7x2SxOT5adM/mp2NBfDMFbtdjikWk0Q9J2ypWq27Ud%20Fp9LUvgzCwOD/V1xxAEAAByOPdgvsHbtWv2DkLNhdKVtdx8Rnba7lKqbVT/RD2WXHb5+qez0ketM%20zuYKO73ir7L5amPM5S2f8/ow5mwmbbONS3rnbKEzNylW8s+3Fr+RNBM5GwAAYJBgtjRA32mbHtve%2088EpUdxJennxDemlcxeZnM3hq0nO5ggNVxkKnDwem/uWn8f04c3ZTNo2baJO+Hm4rFoYuGL+BJK2%20zVZWyWQ44gAAAA40uKPa27ZtY1kxefLkrq4uHAAYLWnb3UdUL5VJf5ilV/G1bR7N5yL19wIsm6Pz%20XJTL92ynjBdBPrvwo6HJ2XvTSv/jn9ds5GwmbbNNHv3qjUeSYiVmOZuE7Ntf7s58+Rc43AAAAA40%20iKPaJGdv374duxjGRtr+xVvLd737rVrm1pi2SCMX6HUsOmeTDOsRdcM5oJHcnub/yJLZr3LYvKF5%20V/kVrfQNw1th2fxKzSKr6E2Gth8I2dLkwzU/nNCpVDjQAAAAoyNqNzY2fvzxx9i/MGbc7bwoXnax%20IW2Ruk1E3Zs3QvKt29xbguBqcjsmZP38oNVD+ZZ2vhKd8p9/FmX9ZP9T/BNWUFQCQjYAAMDojtoM%201BuBsaG4Jp3FU7O0PacMPYgsnHbHdWbZkHWoMVNe19GvnE3UpaXOeH0TQjYAAMDojtp0t8impqao%20qCjsZRjtKhuymtqqGr5P0HbzmPFsFk/jNreIzxUkRm/zdp0ylO8nv1J2+EJlTmnzm8a7bJ5dU1Z0%20akN1lNtf7rYYsp39/Kas21D06SfMmgAAAPCQBuuySIFA8NZbb0ml0rfffntofhOLl2BeuHCBflSp%20VEZGRvZezuhzBRjPrhYfrv9+mbabz+RsiqPTq7lNZ2JVnToXvsfDbHz3qZIPkvPtXDnzVsOfvrz6%20b1/nkJw9sJerOnbELGeTkB32+qbYPXsDEla4hoX2a2tOaBIJAABg3SBOIFm7du23335r2pjdlEQi%20KS4udnZ2dtTL5eXlWXuIxOj4+HjTvpXkrunMlj5XgPGs7O6VmwenapXOTM7me3Z4x96oPxehlrk1%20nI7JlZyIC98wsI2TxHwsU0puRE71Nu2g3lvatbuHL9ypblI48Ffje3pK1jw/8cmVbH5PL8yojz/F%20EQcAAHCUQSz2t3r16qNHjw7Nr0GyslQqTUxMVKvVvdvlfPjhhyRGM48eOHCALNywYQNTbbDPFWDc%200qi1Bz+4oVW6MDmbI+z0TcjiecoDVlxlCztJ2j67t00hH0gCpqti07f/fPQmCdO911FpdEczpRv/%20fJGs4MCcTUL2tFd+Hbtnb+AzSUzOBgAAgNERtRsbG1NTU4fs11AoFMXFxSEhIVwut3cKT0lJkUgk%20hw4doh9du3btO++8Q6J5dna2PSvAeM7Zf3n72+52vmnO9icJm6+hjLfptK1qEf7Pe0e7lP27xNC0%20+wxd99osbSu6NPvSyzf++cKXp0rqWzvNnh47w/ez3y5AyAYAABjJBrcCiaenZ05OTlBQ0GD/GhUV%20FWq1etasWdZS+LJly0xTeFhYGPlZUlISFxfX5wr4lIzbnP3FtiNtjWomZ5OE7bcsh24JSTMk74Qr%20dWkLWutc9nxw6uUtTzgL7Aqv5l0eydaNPR1J2iaPRob4HLssPXm1mqTt3s9NmD9h/ZIgPw8XcvtO%20/38vErKRsAEAAEZ31BaLxStWrMjNzQ0ICBiCX6O8vFylUnl4eIhEIhKdyZLExMTvvvuOpGc6hZsN%20eAcHB/P5/MLCQiam21gBxqdDX/3QdFdhmrN9E7K4buZTOLiibr/lmfWnY+ulVPqxvCfXRfc7Z9Op%20/V4HdZK2uWyWRmfeb0bozF0xf8KauCmeoocKysjZAAAAQ2YQ52pv3bq1rq5uaCqQFBUVkZ/PPvss%20nbOJlJQUX19fEqPpu2YD3kFBQbwH66P1uQKMK6fPpBRfbaTu5WwWR+ez+Brfs8PyF1ZRt+/yS26h%20tcv+JXxgOZtJ2/RMErOcTbL1+iXBX78R98oT0x8yZwMAAMBQGsRukcuWLVOpVENTgYQuP2JaMyQr%20Kys+Pn7Xrl0vvPDCoO7B/fv337lz/5/xWSwWPlWj3Ylzf7/6LaXX8pic7RV/lSdu1OqsPoUj7HSN%20yNl77l9//djhgeVsJm3TY9v0XT8Pl1ULA1dGT+Jz2TguAAAAiNrD48iRI2ZLYmJiVq5cmZyc/Mwz%20z5C7ZlNB6Ekjpkv6XMGa9evXm97dsWMHPlWjl0qj+P7crhuHvHWq+/+m4RF1wzmg8eE3vjet9HCG%204VuZtZzdO23Xt3YmxUqsrVmblopDBgAAgKg9nKZMmcLj8UpLSzUaDTMbm57bTU8aoeeK2FgBxomO%20zsbvM/5cclximrPj/mXSY4k/d8j28yta6Rss/b3G7lawWWQVvV5vdYXGzMzKA/vk5WU4agAAAOMx%20aovFYrlcPjS/Q2NjI4nLy5Yto6+DpBfSlbbnzp3r4eERGhqal5dnmqTpud3Tp0+n7vWQt7ECjAfN%20HZUpFz+68324VuHCLFz45OTHEpc46iV2vhL9zemyb89XaEmI1hnGti2uRh7U6g2D2gnzJ7yRNNP8%20feZkl/9jL0I2AADAuI7aQ4mudnL06NHLly8zc7WPHz+em5v76quvurm5JSYmbt++/fnnn6ez+MGD%20B8ldiUQSFRVFGXvI214BxrzKhqyfsnc1pEU/kLOfCFn5/CJHvYSiS3M0Uyr7v31vSjPtfU4Odaty%20xYzXN9H3WvNvVO7fJ8u/geMFAAAw3qM2PdLM1APpzbGXRe7evTs1NTU+Pt50IQnQL774IrmxefPm%20FCPToiL79u1jXr3PFWAMy79z4kL+vuYzsSqZG7Nw3iPBK1+wnLN3nyqRybu3rAm3c/syuSr5QmXq%20tbskbfcjZxvVpaWSqC0vL7/95f/YCNk6+64rAAAAgCE2RsoaiMXihoaGiIgIZsmmTZuOHz9OTwgR%20CAQZGRmmj5rWKrFnBRiTtDr12cIveufsGRGSf/llvMWn5JQ2H8uUns+vt9hE3Ux9a+efj9782Ufn%20j2ZKTZvRsHk8e/6jVy54792rf3y1d84Wxy6K3bPXNSy0X7+yk58fjjsAAMCQGTuXRZK4nJOTM7BH%207VkBRqnM4r3KbtnyOW+YLVdpFD9e/6i6rsQsZ4eET3zuN4ut5ex3D1ynb9NtHRPmT7C4Znldx/6z%20FZm3Gh7+/TdmXuodsqes2yAKDia3oz7+FIcYAABg3EVta5dFKpXK+Pj4pqYmB84eAbBGq1PfuHOC%203JgXtNrbdQqzvKOz8WTuey2yht45e90flnJ5HGs5my6JTenvN1E3S9v5lbL9Z8tvVMh6b+Hhu894%20hs8J/vlLbmFhOLIAAADjOmpbIxAI/va3v5G0/fbbb+/cuRMHAAbVzaqf6BvZZYcfn/cWfbu+teSH%203Pc7laqB5WxDSWwWxTRRZ9L2ufy65At3yussdJT083BZvySIrJZ+boBl10m8nvrzlzzC5+CYAgAA%20IGrbQtexTk5Ofu+99zCwDQ5UUVQXIPFyFvQMHmt16uuVPb2NpI25yu5WgZPH7bqLZws+VynYTWnx%20Grng/scyzN/enG3ENJohafvmndbrFbL61s7ezw32d10TN/nRcP8B/1Ki4KlT1m0Qx8bi+AIAACBq%209+3zzz9XKBTe3t7Y++BApfk1+z9LFwe4b9zyOJ22b1b9pOxuY1EsQqfX5FUe5XEFOWWHtZ1ODSeW%206tT3P/wkoPcrZ5ul7R8tXSI5J8hzzSNTIkMe9nMe/dddOLgAAACI2g/os9jf3LlzmX4xAA+PhGyR%20u0udtOWrD34kaZvnzKKHtFksNonalF5HT9pmcjbdipFlzNkv3Uvn9udss7RtujB2hu+auMlhk9xx%20UAAAABC1h0dSUhKiNjiQh4/o5S2P7/ngRzptL1zPujekbahoyWaxdXqdtkNY/8Ojei3bnpy9N630%20cMYdw3Ot5GyLafuz3y4I9nfF4QAAAABquOpq79mzZ+PGjdj7MBhp291HRNL2qS9rdCoenbMp49i2%20xpCzF9uZs4n8itae5+r7OosMU1R6biNnAwAAwKBHbbrYn94K5GwY1LQt9OR1NTs3nYnVq3titKZd%201GAYz+YwOTsozN9GziZ2vhK9fomhdrVWr9fprMZtvZ6sYBjUTpg/4cT2BBvvrTEzEwcIAABgXMEU%20DhiDaVvyeEFxikQtc6tPi/FLyFJ3CBtSY0zHs23U9WOoNDo/D2ehM0/RpSZpm9IZZpLYyNlvJM20%20EbIrD+yTl5fh6AAAACBqD1yfV0MyJBIJutjAYKhsyGpnlfolVNenLVLLXOt+XKiVC/Q6FpOzxVNZ%20tnO2oktz8mr1sctSmVzFLOydtu3J2a35N8r+8XV7URGOCwAAAKI2wKh3tfSg4ZMtVAWsuHr31EJN%20u9AQi+/lbEFwtfuiOyzOOoqyELXrWzuPX64iOVul0ZG7i2syYuqy+n7JHCr974b/909YMeP1TUzI%20rty/T5Z/A0cEAAAAURtgLMi/832LosrQz5HF7qz1orp7pmLTY9HC4LueC/I6NfqbVT+FT37K9Inl%20dR3HLlelPVgh266cbaIuLZVE7T5Dtk6txpECAABA1O43+mpI7FYYFpUNWZnF3xiDNac9P7j1Rghl%20Mp7NdlJ7Rt4iEVyv116vPDIz8DEOm0cZi2cnX6y8USGztlk2j2fPq9MB+vrWLRZDtjh20ZR1G4q+%20+GtHUbH9v5GTnx8OKwAAAKI2wDDLv3PiUvFeQ6pmUbKscPntSaY5m8VX67p59FWSLG6XsrvtZtVP%209S3zj12uKq/r6L01TxH/yahJVE6/30bvnO0dGRX885dEwYZiJlEff4ojBQAAgKjtMGYXSuJqSHA4%20rU59tuDz23UXDdlay265GNVZ48vkbDZH5xVT6Ownq02NVstcSdr2XX6F4nWdK/z2p6tcrc58xraf%20h8uauMkJ8yfwuez0nQ/1xjzD50xZv8EjfA6OEQAAwPg0uC1stm3b5uvra1qQRCqVuri4fPXVV9j1%204BDK7taU7G10ztapeE2nF5nmbA5f7RV/1XnKHcql3Xf5Jbawk6TthtMxZE0et/Op2L+bbipskvvm%20NbO/euORldGTSM5+yJA9f8cH83Z8gJwNAAAwng3iqPbBgwe3b99u8aGXX355+vTpcXFxOADwMJo7%20Kn+8/lFHZwO5rVW4NJ1doGkXMTmbK+wkOZvv2U6vzBF2krTdcHqRWubWdCbWZ1kmm99zeWLsDN9n%20FgaGT/F8+LeEkWwAAAAY9KitVCo/+ugjQ/Lw9MzJyQkKCqKXZ2VlxcfHq1SqDz/8cOHChVwuJovD%20AFU2ZKXd+FSrM8Rllcyt+ewCXZcTk7O7+fxirwBV8XSzZ3E81FO7GymZW1nK05UTfBPmT1i/JMjP%20w8VR72rejg9waAAAAIA2WBNIFApFcXGxWc4mYmJiMjIy+Hx+Xl6eRqPBAYCByS479OP1j+ic3VUr%20bj4Ta5qzlc5OZQFilaUvclo+r8xP3MXlCFRqn+a2N5JmOjBnAwAAAJjCoDKMMiqN4mzBFxUNPRWv%20leWTWrPn6LX3vzS2CQXVYi8di2VtCxqeIW2L2zpqPNywPwEAAGDwDNaotlAoDA0NlclkkZGRFRUV%20zHJmAsncuXMxewR6yyzee/rGn6092qqoOXLlbSZnd9ycKrsyzzRnN7qKyrw9NXqr26e7qXdxOKHL%20Z6b85wrscAAAABg8gxV2BQLBW2+9tW7dOpK2g40Vhc1s3rwZURvMaHXqG3dOkBvzglZ7u04xe7Sy%20IetswRfdGkNBGxKv23JnKW5PNl2hytO9wdVwWaSWBGodxWazLOZs8jNh/oQ3kmbaeCeNmZk1x4/i%20iAAAAMDDGMRif2vXrk1KSrL40KZNm1B+BHr7MfcIfeOH3P81e4ienE3nbF2XU9OZWNOcrWOxyn08%206Zzdk9r1ep1OP4CcTUL21T/+vuC9/7TRWR0AAADAHoM7rnzkyBGzFja9L5QE6AnTJfW36793MjZB%20b1MWnMopeiIyjOo1OVvV5NmcEUVfBNmTqtnsUrG3wom/uCYjpi6r71fKodKNBbX9E1bMeH2Taciu%20PLBPXl6GYwEAAACjIGoTYrFYLpdjR0NvFUV1ARIvZwHfkH5Lm/95dv+MKZ0sikVwObqz+Ye47F/O%20n6b68fpH7fKm7gaxc0Cj4vbkttxZppOzu7mc22KfLp7hk2xXzjZRl5ZKR+3atFQSsrvq63FQAAAA%20YORGbXoM+9SpU5gfAraV5tfs/yxdHOC+ccvjhTUd73+b8+j8a2Q5i8UmUZvS6wL9ClOu7i9vzNFp%20dS0Z0V21YidfErh9TDcid3a67eOlZbP9PFyejJpI5RgWsnk8e96ATm0oFFh17Kg0+ZBKJrPwLTF2%20UWPmJWZNAAAAgGGO2rT4+HgKc0XAJhKyRe4uddKWXf95ItNZNCGg0IlHD2kbRqzZLDaXo5s15YpO%20y6Zztp6l72rwMb3OscFVVOXpHhnivXxuwKPh/mRJev+7x9z+8n8shuwp6zaIgoOz33y9o6jY/q05%20+fnhyAIAAMDgRm0aU3skMTHxu+++Q70RMOXhI3p5y+N/+/9+aK9vD+J3imfcpoxD2vSjhht6nVbD%20kl2gczZF6e8XytaxWNVir+j4af/vwsBJPkIHvquAhBWSNc8JJgXSd6M+/hRHCgAAAAbMwRVI6JnZ%20Bw4cMF2YkpLC4/FYLNaf/vQn7HFglMm6r7m50Y0bVVfC9GoeE7UNYVvHlV2IMeRsytAEksnZGj5v%207nPRn29/4tWnwxyVs9l8fuAzq2P37A17fROTswEAAABGVtSmrV27Vm/0zjvvmC7/5JNPWEYXLlzA%20rh/n9qaV/sc/rylYnDJ/MSXoVsvcGk/H6lQ9//Sh17Kbzt7L2dT9nO0z2fs//rzmuSdnCJ0d848k%20XKFoyvoNJGRPe+VXzpj+AQAAACM/ajO2bdtmMXPHx8dPnjy5q6sLB2Dcyq9opW94i6v8Ey6whZ0k%20bdenxZC0TXJ2Q2pMV72XWc6udXf947an6YoljhL71d6g9T/je3riiAAAAMAoi9p9Zm4Y7XafKvkg%20OX8AT9z5SvS/LDL0oJk26SpH2OmfcMWYtl1J2q478Uh3s7tpzlZz2KJHwr789F8c/v65QiEOIgAA%20AAySIbpUkUTt7du3Y3ePMTmlzccypeRG5FTvhPkT+vXE03m15/LrggIK3EUtLBbFEXUHrLha+9MC%20krYpw9zs+znbOaDRJ8Lzj2sWYIcDAAAAonbfCRs1ScZGzn73wHX69p+P3iQ/+0zb9a2d5/Prf8iu%20ITcow3j2tRmBVwxXQFIccldRHqDv7OkByUwa8ZhbLJxZKnBy1+pe5LBtFcxuLyrCQQEAAICxH7Wt%20JWyJRFJcXOzs7Iz9PjZytlqrZ7NZlJ7S6fU20rZKozufX3e+sJ48i17ixFPOn37W16PKkLNZlLbD%20tflSeHezO2U6ns3W+S7NcfZv0elYyu624//xbJjPEtM+6vcT/Plz1cePImoDAADAGI/adLdIhUJh%20uhC9bMZwzuaQpExysc5y2iZrkoRNcjZJ28zCAK+K+SHnuNxu+m5HYUh7QQjda/1+zuZqKQ1Hlhvm%20l5DF4pFta1uieLV700yjtkahqE1LJSEbDdUBAABgXERtMxkZGejQPsZzthGHzTJN28EBrqevG6Zi%20y+Qq0+dy2No5UzMCfXv6L2raRbIrc1VNPdU/6JzN4as9FuTxPdsaTi8yXiUZ7bMsk83XagTs0t8J%20lxnXaS8qunvqZP35czqVCkcEAAAAxlfU3rNnz8aNG7Fzx0nOtpi2e/MU1c+fflbkYqjxp9ey5cVB%20HQWh9GA2k7NdAho9F15nOxsGvH2XXzKmbbemM7HGtK0mC2tOnqg+fkxZXYVjAQAAAOMuatPdIrFb%20x0/OnjnlIp+nuF76mGnaNnsih62dO/X6JN9s+q6qyVN2Za6mXcSsQJ7A1WtCO68H3LpD3WIWd09l%20n80WPdotc+s4tGC+7DRXry6hdln4EAtFAQkrJq16JvPlX5C7OrUaBwsAAADGYNSGsWpvWunhjDvk%20xoPzRrST/W+QG+V3m9oVPr3TNp/LXjpP7io8qdIYLojUqXht12YpyyeZbpms6qFtniO/zNebtzRy%201imj5OdI2pZzPK55LJ/fakjbpisIJgWShE1yNptv6GvjGhbaUVRs/y/lhPaQAAAAgKgNjrL7VIlM%203r1lTXh/n5hf0Srq6u7k8ygWhynFF+hbSN+YNjE7t+QJ+jabBHGdzrCyK+sPSZWltWdVGsNykrDb%208mboupzMcvbUrptBXbesve79tM31qBCGh8hzDS/B5/stfnTCEyvdwsJMV476+FMcYgAAAEDUhmEw%204I4zxC+jA/ZfLurkckp8fXQUxzC2zdYGT8ylH/V2v+PEU3arBXo9pddppza1uHV2iUNuldaWUcYZ%20I23XZjKXPzKE2tbpHTleVKvtl6bTdjk/ZKoiz9nPjyTsgIQVaKUOAAAAiNowgnJ2fzvOUMaS2Jm3%20Gq4UN+Xk353MZglU6ukNTXTanuJf6MTrZBkKY7O4HN3UidcKKx4hOTukuV7YqWXx1ZRbo1bh0pY7%20q7Pa32yzARKvp19cePu1Fw13eLw+3wZJ22EdhqnesXv24lACAAAAojaMuJxtZ8cZQtGlyS5tIgmb%205Ox7JbHZt33F0xoa6bRd5u9JD2mzWGxDExq9bqK4oPzujJDGJpbCheRs7/irXdKJ8uIgpsZIT2gW%208ONXzo5/yjCD5TYODAAAACBqw5jJ2bY7ztS3dmbeaswta2baOppaUH9pTmNhrvsySiWMqS3h6TXG%20IW1Dkmaz2ByKmtNW2dUipnhq4VRpS0a0TmU+XB29NHTpM3NF7i7K6qq7P/yAQwMAAACI2jCGcraR%20WQ3sSWJhRkF9dmlzdZPCxqZi6rLIz4i2MyRtd1IemjOxvsuzKCet4TEdtyVjfledmOJoWSy9/NZU%20s+cGhfk/sS7a25XVePHMrVM/yMvLcGgAAAAAURvGWs62mLZt8PNwiZ0hXj4v4M4vDXcFbNUMrws3%20NIvVMreG0wsMjdM5usZzEV213nryClqO4T8THj6iFYkzfFQ1zf/7eVHmJRwUAAAAQNSGEWHAtfls%2052yztG3x6ZN8hCRhL57tF+zvSi+5c++h9uguX8Gle43TYygdW90mNGxFT5m+DFevjpzjHtBwtenD%20vzfhWAIAAACiNowcD1Obz2LHGXvSduwM34ipXpEh3n4eLhaf0hzJ6RZzuKxOv0dz69JiSNqm7rVV%20Z16GTWkndJZNVt7kn+lqs/LS3pFRzTnZOMoAAACAqA3DkLMHUJuPcaO8p1g1S09RLFtrslksHYnK%20xrB85N+X8blsa2sqJ3JaFvKVE9i6Lif5rRBF+SS9qufzw7wCi6NznVI5J6+I19lucSOCSYETnlzp%20t/hRvqdn+tNPUuijDgAAAIjaMPQ52/7afIz8StmNClnu7ebimrZHqq9GN2SadTJvXOykFVD+p7pN%20F+pYHBnPd9bi2XxugsXNVjZk5ZQdbnrWRdMu6siaZtpZ3XQ8WzClzmN+Acu5S6nWuOc8sAVR8FTf%20+MXi2FgStZmF6KMOAAAAiNpgr92nSorUwQ7M2bZr81HGFjPF1W0kXpNsnV8hu1cDm3Lr7OrW+l3z%20WD6/9TSTtvVclmyeoQCfZ47aqVHH5OwCt7hmfoDqwpUZr1sO2Y2t0s47ExRlj5h1fKRzNpuj07P0%20lIajbhOyjJdENkew3fJYLI3eM3yOOH6xd2SUs6WUjD7qAAAAgKgN9kZk49Rq77Rrd/s7tdpqzjYy%20qxYSGeJTVNVacKe1qKqtqNryXOhuHo+v65JzPa55Lo+Un6fTtmxOT22Q5oXOk04ZlugoToEotpnr%20x9GrRRoZ83StTn279mKB9MTdijZF2eSu6tDeRbJJzuY4qVynS0Uh1ZSOXZsarZa5NpxeIF6WqXFR%20tc3melxXz9vxAT4YAAAAgKgND5uzH2ZqtY2cbTFt96mby6ELYMs5HjmixSRtc9ia5oieedjyQErj%20wmJ3sm/cy9nzW0+7agzTu+tbS27VpJWWFLRXixS3p2jaRRa3r2fpPcJvu82sZHF6RscDVlyl03bj%20mYU+yzJbonTuBRp8MAAAAABRGxyQswcwtdrOnG2Wtm1sROjMjQrxmT3ZIzLE++bPdhrStsdyOm1P%20nXxR46I39ndk6bhU83xe7Y0YOmeTIO7Eb2uZx/vq6FtNRcKuWrGmfYHNT43WN+Ei37PdkLJ19xa6%20qH2X0xUA3ZrOxJK0Xfo79jJ8OAAAAABRGxySs/ucWm3N3rTSuweSX+11FaNF9FWMta5ep6Y8QRlb%20zIQFuk+f6DYnyJOpgU2Ql3fWKqLk57JFj5K0fbMjzkd1meukI1Fbr6Fut0R1cX1Jzg71zKie6dqm%20ntZZ7a875mT1g+KspHh6TYeQxVeTGE1ytoUvA8JOs7SNjwcAAAAgaoODcjadOB+c7GExbcvkquom%20xY0KWU2zsqpR0VTROL3XVYyUpYIhzFWMYR1XNq+ZTUK2tQLYNGedkqTtLK9H1W3uJP76JVylOLqW%20C5F0E3WeT2uRLF53i2djC37dUr+uik65a6kowjAE3nze9WCr9dW7p7LPknDfLXPj/DOIehqfEQAA%20AEDUhn669JevFWlHSCx+rY8wTp02DkJPiJjesHx9Y1tXeV1HUVUbUyqE5mTpKkY9p6dgiHce5dRE%20T9Z44CrGR8P97XmrJG0HLLhYfT1OLXOr+2khi9Kr20SGaShaTle92NqzxKoar+7aie7d/jFhnhE/%20946M+url9/27yl1Zij5fjoT7cn7IVEUePicAAACAqA39U5pf8+M1vbA/g9Cq61f2qUusbbD3VYxk%20s62zeq5ibIziTDqlIzm791WM1sjLy++/qynu7So/J492pdJZ0yakejV3ZLBYWnF3zSQ39bSZft4z%20ot3DwkzLYIfIcw3/x+PZE+7DOtAJEgAAABC1of/OlcsGMAjdRzzVKkyvYozozGiO6LkCUh5IqVy4%20NzkLmasYhQ/mbGV1lUomk+Xf6G5oUFRXtRcVyXi+zf6zOpw9O9RibasTdfWBbG0Wsrncbm9Vrbij%206rHtf3QL/Rmbz7fxPtHcEQAAAABRu3+USmV8fHxubi59NyMjIy4uztrKtxo6Ex56EHpOkOckH+FE%20b8HUANfwKZ7pT+80vYrxqtdiD85lDqVhsVhaFvu6d1SH3JfO2a7aVnr2yfWtW7plMpKzdSxOG9db%204ezWJvLq4AR1iucZHtZSVK+5Hqbj2RxBl0tgrbizJvhGPR2gPcLn2NhFaO4IAAAAgKj9sDmbIHdt%20pO2dr0SnH+v3IHTC/Am+7s4kYft6OFu7lpGe6EzSdqfKXXMm1nd5Foura8mY3yUXc6ienK1ku3by%20uG0835v1TkrOdKVfjFZ3b1KHxvifFfT7c/ZpVbUL9Soe20nlOrtEp+3WF7IoO4aq0dwRAAAAAFG7%203z788EOSsxMTE7/77jsul3vw4MF169Zt2LChuLjY2dnZ2rP6Owj9RtJM06e3FxXp1Cq1XCGvKDNL%202yGTLtxsi1fL3OpTYzlcTXezO8XR8V06CjlRCq37A9nZ/i4xHJ1X1C3XadXkplbhYmw301OSr3WW%20zj0HnwIAAAAARG1HUyqVKSkpEonk0KFDJGeTJWvXri0qKtq+fXt2draNaSSUfYPQzMrXt24hP9uK%20i3QqFb2kiyPsZBuuU1RwPTSC2VoWp4Pvo2ezFO3OOoWLIUi3CTX0aLSW3Sn36sdxdZM7ubbz3NuU%209QGqZg8WX+OXkMX37OhJ3cLOe80dDWmbu+iiW143S6PHhwEAAAAAUduRFApFcXHxsmXL6JxNCwsL%20Iz9LSkpsR23qwUHohtMLOHx1V70XxdX4+RbKb7s38QLIOnqerl3krm4wDpB7ihU6Dy1ls46H3PDD%202lWMFjn5NrNduj382BOCvcNmhU3wDk1/85UCXVzvnN07bdddesRz5lnvG0p8GAAAAAAQtR2poqJC%20rVaHhISYRu3g4GA+n19YWGjPFpQzO30FdJdEVzU9CK3h3r07767AZCX9vbnS/WGxKh/XTc5x7ibZ%20Wuju4unnHCgJ9POb5Ooc6+cxnVlHo9be4j/SVStm8dV+CVfNcnbvtH3L/dEFvNP4MAAAAAAgajve%20rFmzTO8GBQXx7CggTciD2N1iDkfX7eSu6FS4UPYNQtuZs52821gsfXebiFJzOSKl54LrHromz3wt%20p1ntUqhb+v0P1p6bfGi/otWXbqLOcW/X6qys56LuaaXe5n59wfzH8DkAAAAAQNQeORoj2Xotu+VC%20TNddHxZfo1Nx7Y/afB8Zi61jcXR8b0PVbbZLN0eo6MgPo2d9+N+b9UFfxaiVC9pyZ7ssuuhaodF3%20GbLzjh07rG3ZLbRYFDrFJaiK79lu+z1whJ0kbbffnOY295aNDQIAAABNJBL94Q9/wH4ARO1+MJsr%20Qs8q6fNZ8iB2lxev5UI0nbP9ErLaboR01npTWjaLpxFNqyQxmqzm1NnNF7aLgoMN8VqgDQoOubcB%20Fz/36Ry2Yfi84PebO324haJ421cx1l16xGtGhtc1w4WVW7duddjvv9r488mxeXDJVwhH7ivAccFB%20ARyUcX9QsBMAUdte9FyR0tJSjUbDTNcuLy9XqVRms0p6a5jPbcmINk6J7gnH4kdz6UFoncKlq87X%20f9HFkIOd+i5DMl76xm4bm7rVwbo1Ic7a7GrTtH1TF7eAd5qnVuHYAQAAAIxkbOwCoVAYGhqal5dH%20ojazsKioiPycPn26jSe2B/Nrby00zdmmsZgt7KQHoZtnOPX5HjRqbZ5fvLLZt8+rGA2bbXPPES/T%20sHg4dgAAAACI2iOaQCBITEyUSqXPP/88nbYPHjy4fft2iUQSFRVl7Vk6FucWnxmEtlxKj07bNzvi%201Dy+7fdwNb24VetPNiVefsVizmY2659whWy2U+0h9V2AYwcAAAAwkmECicHmzZtTjEyrjuzbt89a%20q0j7B6ENUz6Mg9BRtbZK6bGDrolCK1yCqnge1quFGLEEavoqRnVEIQ4cAAAAwEiGUW0DgUCQkZER%20ERHBLCF3bTSvcfggdG3LTfeIwj6rhTCb9YzOpy+4BHu4u7tjJ+C4AA4KDgrgoMDQY73//vuUY2tZ%20jANXSved+79Se0rpUcZSfe03p3lEFP7myW+x6wAAAADGA7pSDSaQDIRxELrEzpXpQWjsNAAAAIDx%20BlF7IJIWvIedAAAAAAC2Ya42AAAAAACiNgAAAAAAojYAAAAAAKI2AAAAAAAgagMAAAAAIGqPN0ql%20MjIyknXPhQsXsE+G3bZt21i94NAMl8bGRpFIZLb/ceKMwIOCE2cYjwWzw1etWqXRaHCmjOSDgjMF%20ELWHNGfHx8fn5uYyS8hdnG/DLi8vDzth5PyNFRQUpFAocOKM8IOCE2dYZGVlTZo0yfRYpKSk+Pr6%20VlRU4EwZmQcFZwogag+pDz/8kPwhmJiYqFar9Xr9gQMHyMINGzZ0dXVh5wzj9x+pVMocFEZcXBx2%20zrD/jYUTZ8QeFJw4w/KH1W9/+1uhUFheXs7scHI6yGSyXbt24UwZmQcFZwogag/pCUm+6UokkkOH%20DnG5hq5Aa9eufeedd8hJmJ2djf0zXEiGKC4uDgkJoQ8KDAv6n18XLFhA/sZ69913ceKM8IOCE2cY%20/7B66aWXgoKCmIWrVq2KiIhITk5ubm7GmTLSDgr5koMzBRC1h/qEnDt3run5FhYWRn6WlJRg/wyX%20iooKtVo9a9Ys7Ipht2fPnpaWlmnTpuHEGeEHBSfOsBCLxXK5fOfOnb0fIidId3c3zpSRdlDIscCZ%20AojaQx3pzL7aBgcH8/n8wsJC7J/hUl5erlKpPDw8mItazK5ogSH762rjxo04cUbFQcGJM3IcP348%20NzeXnCDV1dU4U0baQSHHAmcKIGoPNbOvtkFBQTweD7tlGBUVFZGfzz77LDMb1eyKFsCJAzhxRqbG%20xsZf/vKXEonkvffew5kyMg8KzhRA1Ibxjr42PCMjg7la5cqVK+TPRPqKFgDAiTNiIx1J0nw+/+zZ%20s87OztghI/Og4EwBRO2hZvYPefQ/jmO3DKMjR46YXQweExOzcuVK+ooW7B+cOIATZwSiK8OQSJeT%20k2N6QR7OlJF2UHCmAKL20KH/Ia+0tNR0khY9iwsXTADgxAGw07Zt2xYsWODv73/37l0m0uFMGYEH%20BQBRe0gJhcLQ0NC8vDzTPwfpWVzTp0/H/hkWdDmz3u3WpFKp2YX8gBMHcOKMkEi3ffv2xMTEsrIy%2003kjOFNG4EHBmQKI2kNKIBCQ85CcYM8//zx91h08eJCcnBKJJCoqCvtnWIjF4hUrVqSkpFy+fJlZ%20SF88npSUhD8HceIATpwRGOk2bdpE9rbZfsaZMgIPCs4U6If3jfTwcBQKRUREhNm+Nb1aAoZeQ0OD%20UCg0Oyi9O3vBkKFb3JmeFzhxRuBBwYkzQv6wopE83dnZiTNlBB4UnCnQJzpjY1TbYeNz5E890z8K%20yV10Zx328TnyR6HpQbE4OAE4cQAnzvA6ffo0UzAOZ8poOSg4U8BOLBK3yf9t3boV+wIAAAAAwCF2%207NhBYa42AAAAAMAgQdQGAAAAAEDUBgAAAABA1AYAAAAAQNQGAAAAAABEbQAAAAAARG0AAAAAAERt%20AAAAAABA1AYAAAAAQNQGAAAAAEDUBgAAAAAARG0AAAAAAERtAAAAAABEbQAA6B+lUhkZGclisby8%20vCoqKrBDAAAAURsAwIKDBw+S0Dx58uSuri7b6xB/+tOfyN0CI3Lj+PHjQUFBjnonjY2NIpEI8R0A%20AFEbAGCkI8n1nXfe6XOdX/7yl+QGWdPZ2dnOLe/YsUOlUu3ZsycuLs6Bb1gsFr/55psymey1117T%20aDQ4ggAAiNoAACPRtm3bfH195XK57dU+//xzhULh6em5dOlS+zd+5MgRvV6/ceNGh7/tlStX8vn8%20lJSUy5cv4yACACBqAwCMOAcPHty+fXufqymVShJqyY24uLjAwMCR8M5nG5EbH374IQa2AQAQtQEA%20RpbVq1evW7eOvv3JJ5+wWKxVq1ZZjK3MlOukpCQul2v60LZt21j3fPXVV2ZPpCdVm83wZhYyes//%20zsrKcnJyMl2HnvzNEAgEiYmJ5MaFCxeqqqpwNAEAELUBAEalkydPqlQqcmP69OnMQrq6iOmg+Msv%20v8xkd2tIhp40aZJCoTBdKJVKJ0yYwFzjSOL7ggUL6FdkkC8DZt8EwsLCyE+ZTJaeno5jBACAqA0A%20MIIcOXLkwIED9O1Nmzbp9frjx4+bDVrT8vLyyE9PT8+JEycyC8nKubm55IZEIuns7CRPv3LlCp/P%20t/2i9IWSzFMI+opMJjE3NjZ+/PHHppsluTwiIoIsSUlJ+eabb5hNBQcH0y9XWFiIowkAMBpxsQsA%20YJxTKpVSqZTccHV1DQgIYBZ+9NFH9O19+/bRNUliYmK2bt1qe/I3yffMbZKqg4KCzEa4GeRFs7Oz%204+LiBAJBTk5O7xXIc3k8HgnupaWlGo3G4pcEAAAYyTCqDQDjHYnCxcXF1hZKJJKoqChmOT2pwzZm%20erevr2/vnC0UCkNDQ+nb8fHx9Jq2S2jn5eXhykgAAERtAIAxjpnUYc3q1auZYe/ExES1Wm1W0lsg%20EGRkZNAzRhgymYxsGT1rAAAQtQEAxj7TsWdT5eXlZpczmsrKyjp58iQTsq1NDadnjPSe/I2eNQAA%20iNoAAKNGn4PQ1lI1ScMSiYS6N6OaWV5UVGRjU0wQDwkJYUI2fc2lmdWrV3/11VcxMTHd3d0kczc0%20NJC3QVmZKzJ37lxM1AYAQNQGABih6CsLLT7EpOqOjo7a2lpm+QsvvEDf2LBhA10Vu8+GOEyy//rr%20r+mpIOQpR48eNV2HrqhNFr755pvMdJHTp0/Ts7pNUzV5VK1WmwV3AABA1AYAGBHoIh6UsY4euWGt%20hQ0JuJRx/kZNTQ2zcO3atUlJSZRxYNvFxYXFYvVZVDsmJmblypXUvbnXZk+ha/bRZUxM12FW8/T0%20/Mtf/sKkamaMfNasWTiUAACI2gAAI4tYLD5z5gwzh8RaKQ+Sj+l1SkpKTJcfOXLE9KLGPXv2MIW6%20rSFPoQM6bdOmTUzZ7OTkZHp0fNu2bcyMEUZiYiJZSL4bMEvoySokfy9duhSHEgBgNGK9//775P/o%20IRYAgPFJqVTGx8fn/v/s3TEKgzAAQNFSuomjk+CZnL2Cq7vdcgKvIYJXcPYS3sG5qZbOHQpV+t5i%20xqDLJ4ZknmPv9n1/hN0aB5wSAJ8LIVysagNctu3asWjjYJqmZVmOMKX3wd5lWepsgJOS2gBPdV0n%20SfK+Pv3nuq6LtV0URVVVvg6A1AY4sSzLmqaJg2EYfn6y9bqu4zjGQdu2+53wAJyRn5IAL/fNEWay%2033HjiwCcnVVtAACQ2gAAILUBAEBqAwAAUhsAAKQ2AABIbQAAQGoDAIDUBgAAqQ0AAEhtAACQ2gAA%20ILUBAACpDQAAUhsAAKQ2AAAgtQEAQGoDAIDUBgAApDYAAEhtAACQ2gAAgNQGAACpDQAA/+O2P0II%203gUAAHzRNc9zbwEAAL4rTdOHAAMARXOZY7j3n1gAAAAASUVORK5CYII=" height="563" width="974" overflow="visible"> </image>
            </svg>
          </div>
        </div>
        <div class="fig"><span class="labelfig">FIGURA 3.&nbsp; </span><span class="textfig">Comportamiento de la producción 
          acumulada de biogás para los cuatro biodigestores evaluados, acorde al 
          ajuste del modelo cinético de Gompertz.</span></div>
        <p>La <span class="tooltip"><a href="#t1">Tabla 1</a></span>,
          presenta los resultados obtenidos de los parámetros cinéticos en el 
          ajuste de Gompertz modificado para la producción acumulada de biogás, se
          advierte que las temperaturas se comportaron muy parecido en los 
          prototipos 1 al 3 y superiores a la obtenida en el biodigestor 1, lo 
          cual puede afectar los procesos de DA; el volumen acumulado de PL4 fue 
          de 2518 mL y notoriamente más alto (1243 mL) que la cantidad de biogás 
          generado en PL3 (1275 mL), esto representa un incremento del 49,36%, así
          mismo, la diferencia entre PL4 y PL2 fue del 53,53% y con relación a 
          PL1 indicó: 51,50%.</p>
        <div class="table" id="t1"><span class="labelfig">TABLA 1.&nbsp; </span><span class="textfig">Valores de parámetros cinéticos de los cuatro biodigestores, obtenidos a partir del modelo modificado de Gompertz</span></div>
        <div class="contenedor">
          <div class="outer-centrado">
            <div style="max-width: 1160px;" class="inner-centrado">
              <table>
                <colgroup>
                <col>
                <col>
                <col>
                <col>
                <col>
                <col>
                <col>
                </colgroup>
                <thead>
                  <tr>
                    <th align="center">Biodigestor</th>
                    <th align="center">T (℃)</th>
                    <th align="center">Vac(exp) (mL)</th>
                    <th align="center">Vac(estim) (mL)</th>
                    <th align="center">B (mL)</th>
                    <th align="center">Rb (mL/día)</th>
                    <th align="center">λ (días)</th>
                  </tr>
                </thead>
                <tbody>
                  <tr>
                    <td align="center">PL1</td>
                    <td align="center">25,5</td>
                    <td align="center">1221</td>
                    <td align="center">1215,16</td>
                    <td align="center">1253</td>
                    <td align="center">85,18</td>
                    <td align="center">1,25</td>
                  </tr>
                  <tr>
                    <td align="center">PL2</td>
                    <td align="center">32,1</td>
                    <td align="center">1170</td>
                    <td align="center">1137,34</td>
                    <td align="center">1200</td>
                    <td align="center">95,27</td>
                    <td align="center">2,55</td>
                  </tr>
                  <tr>
                    <td align="center">PL3</td>
                    <td align="center">32,9</td>
                    <td align="center">1275</td>
                    <td align="center">1248,14</td>
                    <td align="center">1210</td>
                    <td align="center">151,35</td>
                    <td align="center">2,43</td>
                  </tr>
                  <tr>
                    <td align="center">PL4</td>
                    <td align="center">33,4</td>
                    <td align="center">2518</td>
                    <td align="center">2505,22</td>
                    <td align="center">2600</td>
                    <td align="center">222,38</td>
                    <td align="center">3,53</td>
                  </tr>
                </tbody>
              </table>
            </div>
          </div>
        </div>
        <div class="clear"></div>
        <div class="table">
          <p class="textfig">PL:
            prototipo laboratorio; Vac: volumen acumulado de biogás; exp: 
            experimental; estim: valor estimado por la ecuación de Gompertz; B: 
            potencial de rendimiento máximo del biogás; Rb: velocidad máxima de 
            producción de biogás; λ: período de latencia.<br>
          </p>
        </div>
        <p>A pesar de
          lo anterior, la tasa (Rb) de producción de biogás (mL/día) exhibió una 
          tendencia de aumento progresivo (85,18, 95.27, 151,35, 222,38) señalando
          que la velocidad de producción de biogás de PL4 superó en 71,03mL/día, 
          127,11 mL/día, 137,2 mL/día, a los biodigestores PL3, PL2 y PL1, 
          respectivamente, es decir, el inóculo microbiano adicionado en el 
          reactor 4, afectó positivamente la rapidez de producción de biogás 
          generando 222 mL de biogás por cada día de retención del sustrato.</p>
        <p>En
          lo que respecta al período de latencia (λ) se destaca que PL1 señaló el
          menor valor: 1,25 días, lo cual indica que al segundo día se inició la 
          primera fase de digestión, es decir, la hidrólisis de los polímeros 
          presentes al sustrato, mientras que los demás biodigestores necesitaron 
          más tiempo: PL2: 2,55 días, PL32,43 días y PL4:3,53 días, lo cual se 
          puede explicar por el probable período de acostumbramiento de los 
          microorganismos incluidos en el inóculo, a los componentes del sustrato 
          alojado en los reactores, lo cual retardó el inicio de las RBE 
          bacterianas anaeróbicas. La <span class="tooltip"><a href="#f5">Figura 5</a></span>, compara los valores de lambda.</p>
        <p>De la <span class="tooltip"><a href="#f4">Figura 4</a></span>,
          se deduce que la tasa de producción del biogás en los prototipos mostró
          una tendencia cuadrática, con un coeficiente de correlación (r) de 
          Pearson de 0,998, lo cual corrobora dicho comportamiento.</p>
        <div id="f4" class="fig">
          <div class="zoom">
            <svg xml:space="preserve" enable-background="new 0 0 500 301.197" viewBox="0 0 500 301.197" height="301.197px" width="500px" y="0px" x="0px"  version="1.1">
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bW3t7++P%20a+sBAAB8szTj+/vr9/+mGbaS+IL9yl9uvcimTLNbPE5TlTJN6QMN1UxeXt7z58/nzJmzdu3ap0+f%20pqSkFFU4OzubAhBWLwAAgG+fZlgstugLITXeyhw2teFVL9OU/qBgAwODwMDAlStXpqWl5ebmyj9K%20RktLC/dyAgAA+NZphsNhszlCQf7nF7fCbx8W5PNYbI4yh6WqzGEyzd0XcQXvKl0ZlX6EhsPhWFlZ%20eXt7jxo1ysTE5MaNG/n5+UUVVlFRwf4mAACAciUQCM8/jDx7PyImOUeVS8GFw2KxstMTXgTv4iir%20mjd1ZXNVReI7EVGmoZY8LpW35dyrk3c/rRzTXFOtcp+MXNalp6RSu3ZtelC3bl2sSQAAAN9RvkD0%20KDwpOjFbWZnNYXNEgvzPr+59fnHbtH5bQ8vGarrGLCXKMf+Ox3DYLJESKz4tJzefglClH6QpU6CJ%20iooaOXJkQkJCsSXr1avn7+9vYGCAtQ0AAKCcqHDZk3vVNzfWDP47Nj0pLvyan7KKqkXz7sb1mnO4%20apRvJHuX6P/5QgHlG7fmtYY719VSq/TXiivTB+DxeI8fP87MzCy2JJWRs0MKAAAAyo7NYtUy0hjl%20Yp2ZkuB7LsCgbusadZto6tdgsVhCfp6kmCTNtG9gMqGHnZFOVbg3UZkCDZvN1tTULEmgwUHBAAAA%2034BQKLx66eydk0fdXF1iVBqIWCpfDpphCWXSjJKI1c7B5MeqkmbKGmjMzc1v3brFnI/96NGjKVOm%20GBgYBAQESI7/jYyMHDZsGIfD2bBhA/Y3AQAAlKvY2FgfHx9VVdXpUyY0btnh1B/RFx58yhOIuBw2%20W3xhPUmaae/wZWzGWKfq3De6TIFGRUXFzs6OecxcXu/QoUOtWrWSFHBwcNi2bZu3t/eTJ09cXFyw%20qgEAAJQHoVB4SqxPnz4dO3a0sLCgicM61REJBBcfReULhNwv+6NY+UJhlUwzSoq6OeWnT59OnjxJ%20NUUJRuYpW1tbHo93+PDhtLQ0rHAAAAAKFxcXN2bMmODg4NGjRw8dOpRJM0RXkzu8c72eLc05HBZf%20IOR/GZtR+nLcTHfbKpZmlBR1c8r8/HxKLQkJCVSPAQEBurq6zPSYmJhp06alp6fn5OQIBAKscwAA%20AAokFApPnz598uTJXr16derUqeBdFCnTjOhcjx5c+CtKIBR1/DI2Y2usq1b1qkIxgcbExMTR0fHS%20pUsXLlxo06YNl8tlplPKefPmDT3o0qULbrgNAACgQHFxcd7e3qqqqqNHj3Zzc1NWLrxNp0zj3rHO%20lX8+C4VKP7haV8k0o7BAo6Ojs3jx4j///DM5Ofn169cyz7q4uMybN09dXR0rHwAAQNkVOzAjO+6g%20q7Z1oiM9qGWoUVXrRGEX0nF0dLx586avr+/u3bsl1+3hcrlbtmzp06ePubk51j8AAICyi4uL8/Hx%20oRZ21KhR3bp1K2pgRoaFiWbVrhZFXhmwUaNGa9asmTp1qkAgYDINh8OpX79+CesaAAAA5KC2lRmY%206dGjR6dOnerUqYM6UUygiYqKGj58eGZm5s6dOx0dv4xl6YqhWgEAABRLMjAzcuTIkg/MINCUCI/H%20++uvv+jfwYMH6+vrMxOZ2zbp6OigcgEAABTi9OnTJ06c6N69e6dOnaysrFAhCg40FhYWjx49osw4%20ceLEp0+fMhPpwatXr1RUVJTEu5xGjx49ffp03PcAAACgFOLj4318fJSVlUeMGEGBBgMz5RJoKLU4%20iAUHB6enpzMTHzx4MGnSJEmZ8PDwvXv3Wltb07+4+wEAAEDJYWDmGwUaibp160oeN27cuH379swN%20nvLz83fs2OHv75+cnIy7bQMAAJQQBma+T6CRxmazGzZsKPlz48aNs2bNUlFRkdyxEgAAAOSnmWXL%20ljk7Ozs5OUkPGcA3DTTSRCLRo0eP1q5dW6NGjW3btkkOHAYAAIBCnTlz5sSJE3369HF3d5dceR++%20c6AhERERV69eNTMzy8vLQ3UDAAAUJSEhwcfHh8PhDBs2rFu3bkgzFSvQ0PehoqKiqamJE50AAAAK%20Sk5O/vnnn588eaKtrT1+/PhOnTrVq1cP1VLhAg0AAAAU5ezZsydOnOjSpcvYsWPV1NRatmyJgRkE%20GgAAgEojMTHRx8eHzWYPGTKkZ8+eyDEVOtCwxBQ1t/j4+FGjRiUkJDBzdnV1XbZsGXMFP5KZmTlj%20xoy///5bJBJpaGhs2bKlRYsW0i8vtsBXFf6quQEAAEg7e/bs8ePHu3bt6uTkhB1MlSDQfHkDBZ06%20//TpUy8vr/v370umhIrt3LmzTp06FC+mTp3q7+8veXbMmDEHDhyQhIyMjIxp06bJKSCt2MJfNTcA%20AAAJZmCGuuVDhgzp1asXBmYqRKCJjo7+8ccf6buRUyYpKSk/P7+MsSY1NXXJkiUUXw4dOmRnZ6f0%203x1HN2zYcOTIkRkzZhw+fPjo0aOOjo5r1qzR0ND4/fffly9fTmsMldHT0xMIBAEBAXIKSL9XsYW/%20am4AAAAS586dO378eJcuXTp16mRjY4MKqSiBJicn5/bt21lZWd8gz967d69v374eHh6SbGRmZhYU%20FHTmzJlBgwbRvyYmJr6+vswYia2tbVRU1MmTJ58+fUorTXp6+qlTp+QUkH6vtLQ0+YW/am4AAABK%20UgMzgwcPxsBMeWCX5cX0xaiqqpakZF5enkgkKvUb1apVi7LLunXrpEd6tLS09PX1DQ0NMzIywsPD%207e3tHRwcmKd0dXXbt2/P4XDCwsKUxHuI5BQQCoUygUaBcwMAADh37tzPP/9MLcXcuXP79++PNFMe%20yjRCY25uHhISIrlJk5xDgNXV1ctyZ0p6efPmzWUm3r1799WrVwMGDEhJScnJyZG5ODRlIFpj3r59%20S4v3+fNnHo9XaAGKJnw+X3JksZJ4P1pRhUsxNwAAqM6Sk5O9vb3pwaBBg3r37o0oU0EDjaqqasGc%208W3Ex8cvWLCAzWZ7eXklJSXRA1tbW+kVxdLSkhaPgo5IJKL8oaysXGgBekpm6EhO4VLMTT5cbBAA%20oAo7d+7cvn37evXq5ezsTK0GKqTiBhoZ/v7+e/fuLfSu2lZWVjt27FDUvZwSExMp6r558+bw4cON%20Gze+ceOGkngURyZsscWU/hs6klNAJmTIL/xVc5Pva8sDAEClQD1tHx8fejBixIj+/ftj5L7SBBqB%20QHDw4MGFCxd+/vy50AI0XVH3cnrx4sX48eMfP35M7zhgwACaIhQKaQE+ffpE/3I4HKZYbGys9IE7%20RRUo9JAXxc5Ngs/nX79+PSoqiubDjM1Q+KNXSWYCAABVI80sW7asTZs2Tk5OzJm5UGkCTUxMzLp1%2064pKM0RR4dTf33/VqlVxcXEXL150dXVlJpqamtK/7969o3wgeaOPHz9SVqhduzaXy61Ro0ZRBSws%20LGTyhGLnJo2eaty4sbW1tWQKj8e7ceMGjiMGAKgyzp8/f/z4cTc3Nw8PDwzMVL5AQw1zVFQUNdjr%201693dHQsWEBDQ6MsBwUz9u3bN3/+fAocQUFBbdu2lUw3NDSkieHh4dJ7uyhhCAQCSg8sFoveuqgC%20NjY2MtfIUezcpNFrmbQkQXO4detWWc7/AgCAiuDSpUu7du2iprBZs2aDBg3q2bMn0kylDDSUVywt%20LbOyskaPHl324FKQUCj09/dfunRpp06d1q5dW6dOHeln9fT0OnbsuHfv3pUrV65bt46mXLt2zc/P%20T0tLq0mTJvSnjo6O/ALlNzf5FLUbDgAAvpeUlBQfHx/qmrq7u5uYmFALVb9+fVRLZQ00xsbGI0eO%20pNY9ICBg2rRpCl/K2NhYmnlkZCSLxfL09MzNzZWMatCqQ6H4p59+CgkJ2bp1661bt5SVlaOjoyMi%20Inbs2EExi8qoqalNmjSpqAICgYDSyZUrV1avXm1jYyO/cLFzwyoFAFB9/P7778ePH+/QoYOTkxNy%20TFUINFwud8KECTdv3ly2bNnRo0cLnrxjZWW1bdu20p3lRNnl3r17r1+/pscRYtLPvnv3Licnx87O%20jkLJmDFj/vrrL2b6pk2bxo4dK1kSBweHogrk5eW9evWKAs2cOXOKLVzCAgAAULUxAzNCobBfv359%20+/bFDqYqEmgoE/j6+oaGhiaJFSwQGRlZ6t0rLBaLwu/9+/cLPdZEcsm+9u3bU1KmNUxJfPhts2bN%20ZFavogooKyvPmDFj5MiR0uG61HMDAIAqDwMzVTbQxMXF/fbbbzExMUUVKOO1EWuKFVvM3t6+FAXY%20bLaFmELmBgAAVRj1Y2fPni0QCPr27duvXz90ZataoMnNzU1LS9PX19+0aVO9evUKFlDIWU4AAADf%200YULF44fP96uXTsnJyf0aatmoDEyMmrevHlsbKy7u7umpiaqFQAAqhLJwEyfPn0wMFMxKeYgVj09%20valTp2ZmZu7duxd1CgAAVcnFixenT59O/XZvb+/BgwcjzVRMCrtSsJ+fH/27fPnyEydOFDzZp06d%20Ops3b1bUvZwAAADKW1BQEDVtUVFRjRo1wsBMdQk0WVlZf/zxB5/PT0xMvHv3bsEC79+/x0XkAACg%20UkhJSZkzZw5z5K+xsXHdunUbNGiAaqkWgYa5AbWcAmU8ywkAAOCbpZlly5Y1bdrU2dkZOabaBRpz%20c/OgoCDpC/jKxB1NTU1DQ0NUNwAAVGSXLl06duyYk5PTiBEjVFVVUSHVJdBERETMnz/f29ubkmyb%20Nm1K8pJXr17t3Llz0aJFxsbGqH0AAKgg0tLSZs+enZ+f36NHj/79+yPNVK9Ak5OTc+rUqUePHpma%20mtJ3P3PmzG7duhVa8siRI/v376cVJT4+Pj09fe7cuah6AACoIJiBmdatWzs7Ozs4OKBCql2gsbCw%20oBCzatWqsLAw+jM8PLxOnToFD6YRCoVUIDY2lvkzICAAwzMAAFARZGRk+Pj45Obmdu/efeDAgRiY%20qaaBRl1dffbs2S4uLnv27Dl69OhHMTnlKfp07tzZ0dFR/hHEAAAA5SozM5M65E+ePNHU1HR3d+/U%20qVOjRo1QLdU30BBdXd0uXbrY29tPnDgxKSlpzpw5b9++lSnTr18/Ly8vSj8UZRB+AQDg+7p8+fLx%2048ebN2/eo0cPDQ0NZ2dntE0INP+S3DzS2tpa5m7bLBarXr16tWvXRl0DAMD3lZaWRh3v3NxcV1fX%20QYMGIccg0BSpcePGqFMAAKiAgoKCjh071rx5cycnJ7RWCDQAAACVTHp6+pw5c3g8XteuXd3d3TEw%20g0ADAABQyQQFBQUGBrZo0QIDMwg0AAAAlUxISAhza8kGDRp069YNp2Qj0HyF27dvHz9+fPny5fr6%20+nfu3Fm7dm1ubq6JicmGDRtq1KiBugYAgG+AuXwrc+SvoaGhjY1NkyZNUC0INCV169YtLy+vuLi4%20qVOn0r8zZ8589OgR81RkZOTWrVuxPgEAwDdIM8uWLbOzs3N2dka7g0Dz1dLS0rZv3/7q1SvKwpmZ%20mX/++efjx48lz96+ffvixYsODg7KytjDBQAA5SU4ODgwMLBdu3ajRo1SU1NDhVQrbIXMJSUl5e7d%20u2w2e+HChaampiEhITRRX19/586d69ev19TUPHfuHKVmVDcAAJSHrKysKVOmUJrp0qXLyJEjkWaq%20IcUMmfD5/Pz8fHNz8xEjRiQkJDx8+FAkEjVr1mzcuHGUddauXRsfH08FUN0AAKBA2dnZs2bNCg0N%20VVdX79u3r5OTU5MmTXB3HQSa0mPWHg6Hk5ube+/ePYovbDa7W7duXC735cuXFJw1NTVR1wAAoEBX%20rlw5duxYw4YNnZ2dNTQ03NzccB4TAk1ZUTQ2NTV98+aNu7t7XFycQCCgtcrGxubgwYPr1q2jQOPo%206IhMAwAACpGRkTF37tzs7OzOnTsPHToUOQaUFHUMjZGRkYeHR15e3oMHD5gbbjcXo3Xu+fPnXC7X%2009MTgQYAAMruypUrP//8s7W19bRp00aNGoU0AwzFjNCoqKiMHz/+xYsXgYGB9KehoeG6dessLS01%20NDRq1aq1efNmJycn7NQEAICyyM7Onj17dlZWlrOz89ChQ3HkLyg+0BBTU1MKMWPHjhUKhbq6um3b%20tqWJbm5uDg4OrVu3RkUDAECpc4y3tzdz5G/v3r2ph9y0aVN0kqG8Ag0xF5OeUkuMIs7u3bs9PDz0%209PRQ4wAAUHIhISHHjx+3s7Nr3769pqZm9+7dMTAD5R5oNm7ceP36dZFIJDOdx+OlpKT0798f1Q0A%20ACWUmZk5b948+tfJyYm6xMgx8C0CjUAgoAS9atWqpKSkQgvUqlULw4MAAFBCISEhx44da9CgAaWZ%205s2bowWBbxRoMjIyAgICikozlpaWfn5++vr6qG4AAJAjJydn1qxZT58+tbGxwZG/8B0CTVpaWmho%20KIfDWbJkiYmJycKFCzt27PjDDz+w2eygoCBK2fHx8fQsqhsAAIpy9erV48ePW1tbt23b1t7evmXL%20lqgT+NaBJj8/n8fjmZmZTZ48WVNTc+fOnVlZWX379qWnaI0MCwu7ePFi//79tbW1UeMAACAjMzNz%202rRpQqHQyclp2LBhGJWB7xZolMR3P9DS0lJXV+dyuW3atDl27Njly5d79Oihr6+fmJgYHx9PiQeB%20BgAAZFy9ejUwMNDa2trNza1FixY4XAZKRzFXCqYcY2ho+Pnz53v37nE4nObNm2dlZc2cObNfv369%20evV6+vSprq4udjkBAIDEzZs3R4wY4ezsfOTIkfbt21OT0bJlS6QZKDXFjNAYGxsPHTp02bJlixYt%20srOzoxXUwsLitRhTwMXFRUtLC9UNAADZ2dnz58/PyMho3bq1vr6+vb19q1atUC1QIQKNiorKTz/9%20xOFw9uzZw+fzbWxs1q1bN3jwYIFAQM/27NlzzJgxVAbVDQBQzV27di0wMNDW1pa6vsgxUOECjZJ4%20kGbOnDlubm7m5uYsFmvAgAFBQUE5OTn0mNJ3nTp1UNcAANXWzZs3f/vtt+jo6Lp163bs2NHDw0Nd%20XR3VAhUx0BBVVdU2bdpI/uzatSvqFwCgmsvOzl6wYEF6enrLli2pXcAN/qBCB5q4uLh58+alpKTI%20KcNisSiVz5gxA5UOAFBNXL9+nTmDadiwYcgxUAkCTUZGBq2yOTk58otdu3YtNDR09erVNWvWRNUD%20AFRhPB6POrqpqant27cfMWIEdjBB5Qg0X2akXPys0tPTDxw40Lx588mTJ5ekPAAAVC7Us6Uc8/Tp%20U1VVVWdnZw8Pj9atW+NkbKg0gcbMzGz37t0TJ06kx5s2bdLT02OmP3nyZMWKFW3atJk9e7ZQKDx4%208OCFCxfu37/v6emJs7gBAKqY7Oxs2uabmJgMHz6cNvL9+vXDwAxUskBDSTw+Pl5HR2fx4sUUViTT%20e/XqpampeeTIEXo8cOBAc3Pz27dvh4WF8Xg8BBoAgKrkxo0bgYGBDRs2HDdunIaGBioEvjHFXCk4%20JiZm06ZNlMRHjRolE3T69Onz8ePHlStXZmRkWFlZ0ZT09HTm+jQAAFDZUTd19OjRXbp0OXToUKtW%20rahPizQD34ViRmiys7OTk5NFItH27du9vb0l03k83sKFC+nZhIQEPp+fmpqal5eHu44BAFQBOTk5%20tIWnDXvjxo07duzYqFEjR0dHHC4DlTvQGBkZOTg4/PHHHytXrrxz547kosDp6elXrlyhB61btw4P%20D1+xYkVaWpqLi4umpiaqHgCg8rp582ZgYKClpaW7u3vbtm1RIVBFAo2+vv78+fP79etHUf38+fMF%20n501axaPx7t06ZJQKOzbty8CDQBAZURd1j179kRHR1tYWLRp02bkyJHYwQRVKtCwWKyePXuePn2a%20VvQLFy5IPzV79mxXV1dHR8cjR45Qmhk9enS3bt0wJgkAULkwhxCkpKQ4ODi0a9eucePGFGiwMYeq%20FmgIm83u168freI//PADBZd/566sTPGFye+Uac6ePduqVStjY2PUOwCAAt15EZedy3dtVotdPgHj%201q1bgYGBtWvXHjhwIKUZVDhU5UDDsBIr9Kl6YqhxAADF+hiX6R/ylpcvqGWo0dBSX4Fzvnv37t69%20e6OioijKUKd01KhR2MEE1SXQ8Hi8GTNmJCUlMX9aWlquW7cOtQwAUE4iE7J2Xw6LiM9SYintCXoz%20saetfW29ss82Ly9v/vz5KSkp9vb2bdq0wQ4mqF6B5uLFi7t375Y5KPjjx48UcRQ7RJmZmblkyZIR%20I0Y0b96cmSIQCOh9T506xefzJcVUVVVnzZpFv0PmT6FQuGrVqufPn9NjNTW1xYsX161bt6i3KLbw%20V80NAKA8RCVk7boY9ld4IpfLoazx7GOK36XwST3t6tfWLd0Mc3NzFyxY8OzZMxUVFUowAwYMaN++%20PeoZqleg+fPPP318fF69eiUz/eTJk5RpDh06VL9+fYW8EY/H8/T0PHHihLOzsyTQUI45e/bs4cOH%20ZQrTr5EJNNTb+OWXX1avXi0Sif7t1kRG+vn5WVtbF9o1kV/4q+YGAFBOaWbnxbAH4QkqXA6X8+Uq%20qarcL5lm16Ww0mWanJwc6qfp6Oj069dPW1t70KBB2MEE1S7QZGRkbN++ndIMl8tdtmyZ5DCap0+f%200s/j0aNHe/fupellv6lHVFTUjBkzKCRR70H6An1ZWVkxMTEtWrSYPHmy5JxwDodDPQxmNOXUqVOb%20Nm2iwDF//nxajKtXr+7Zs2fevHn+/v4y55AXW/ir5gYA8A3SDLMriKv8JdaULtPcunXr2LFjtra2%20EyZMQI6B6htokpOTb968Sbme4suUKVMk05k7k61cuTI4OHjOnDllCTTx8fFLly7966+/KB516dIl%20NDRU+v4JlKg+ffrUuXNn6TtJST97+PBhLS2tbdu2devWjaY4OTklJCTQUr148aJ169ZfVTgzM7Pk%20cwMAUHyaScyiyCKTZghLKtP4XQ6bSJnGvJhMc+/evX379kVHR9eqVYv6hKNHj0aagUpKMfdyys/P%20p2ae2ngPDw/p6WpqaoMHD6Z/09LSynj/psTExJ07d9aoUePUqVM0T2VlZcnJ4SQ2NpaCSFFHsdC7%20U9Ro2LAhJR5miqmpaY8ePdhs9suXL6XnU2xhJfHlj0s+NwAAxYpOzN51MezPMNk0I51pVLmcpx9S%20/C6GhUWlydluUz9z//791tbWffr0GTt2rJeXF24bDNV9hEZdXZ0adeb43+3bt+vo6Pz7w4uOnj17%20NmUdav65XG5Z3sLc3Pz8+fPUgTAzMzt06JBMbqC3ZrFYFHd++umn5ORkmtKhQ4fJkydLFiMrK4vi%20jnSoou4ILVJ4eDifz5fcq4F8/vy5qMJv3ryhwjExMSWfGwCAogiFoj9eJ5z9I+LJu6RC04zMOM3z%20iNTN517WN9ed2MNOTYUjyTHz589nDvtt3bp17969O3bsiLoFBJp/GRkZubu7r1y5kqJGXFycoaGh%20JEncvn2bHnh4eEhSTunQy6kPQQ9EIhH9IKWfomARGRmZnp6+atUqihTMxOPHj4eGhq5YscLY2Jjy%20B4fDsbW1lQ5VlpaWzK2/JQf2MjIyMooqTE9RkKJ8VvK5AQAoSj5feP5B5B+v4rXUKbHIO4X6S6bh%20fCnxz7vkl59Sf3C1YQINbZBp22hiYkI5Rltbe/DgwdjBBAg0/4Pa8vHjx1+8ePHJkyfM3Silubq6%20MjuJyukz5OXlPXjwgHKGk5PTwoUL2ewvXZO//vrL19e3SZMmEydOpLemCKKvr888xdDS0uKIycyN%20yhRVmJkip0DBucmHG48DwFdsr5XZg9pZqimz/wxL4AtEXI5SkReGESkJREKBQNTWvsZI1wa6Glw+%20n0+bx/j4+GbNmnl6euL0BUCgKVKdOnX8/Pzu3Lnj7e0tHQ6WL1/ev39/6hCU32fgcrmTJ0+mzNSz%20Z0/JDuBevXr9/fffgYGBzGE99GOOiooSCASSFJKYmJifn1/wyB6hUFhUYWY/l0gkKvncpNGr/vjj%20j5iYGJoDsxmiKEav+toYBADVE4fNam1nZGagwWaz7r2KVxKxxAM1hWQa2lrxhaKW9mbD25ue9l+1%20+OHfylxuy5Ytf/jhhw4dOkh3xgAQaArRWqxevXpZWVmScYt+/fqVf69FWXJ8rgT1P8zNzR8+fEiJ%20wdjYmDLEmzdvKEBI9hN9/PgxNze3Zs2aMr9tOYVNTU3pvYyMjEo+N2n0FM1BXV1dEmh4PF5qair2%20UgFAyZkba3i62dKDL5mGLyyYaYQsjoqGZmsr/dpK7/y379HW0evWvbu2tvaQIUMwMAMINF+hf//+%203/gzxMTELF++vEGDBhMnTpQkDMoKKSkptWrVUlNTMzExMTQ0fPfunXTaYIZYbGxsZPaFySlsbW1N%20EynQlHxuMoGGCvzPdkcofP78OU6MAoCvUvtLprGhntD9l/FKAqH4cJkvmYbF4ihxuPFv//r0d3AY%20J6tTS9tGjZv88MNY5BhAoClGfHz8smXLsrOz3dzc7t69m56eXlRJChbz5s0r43HBReFwOMHBwadO%20nXJ1dbWzs2MmhoaGPn36dPTo0VpaWnw+v3Xr1idPntyxY8esWbPo2X/++cff35/CR+PGjWU6N7q6%20unIKK4kPTy753OTLycnB8AwAlCrTaI5z+9JB+pJplESqKirKquo5GSlPL+3QUMqZNqS9ob5ufYfG%20HTp0ZLNxAyZAoClOWlqan58fxQV1dfXdu3dL30dJhpmZ2fTp08sp0BgbG69du9bd3d3T05O5oTel%20hCdPnujr648YMYI5VnfixIlXrlz55ZdfHj16xOVyX758SXFnyZIl1tbWQqHwzJkz9+/fnzZtmqWl%20pYaGhpzCSuJz1OUXAAD4VpnGls3hPHqbFvfh7+gnVzLTk5o0bTZz/KDe3VxQP4BA83X+G+dkfcu7%20sObm5qakpEhO3qa3HjBgwN69e6dOnUq5hJnYokWLDRs2NGzYkPmzbdu227dv//HHHwMDA5kpPj4+%20M2fOVFZWzsvLu3r1KiWzIUOGUKChuckpzLyd/AIAAN8q02j80MXq1J7JcfFxOoY1Wzk6Lpr5YxNr%20U9QMINB8HVNT00OHDlGwsLe379ixI4/HK6qknphClpjyRNeuXQMCAiiySCay2WxPT08LC4vo6Gjm%20z+bNmzs4OEi/cODAgTo6OkwBLpfbp08fbW1tJfExxRMmTHB1dZU+wKWowiUvAABQfvh8/qJFi54/%20f66rpd6vfYPz4Y2MrJqsmO5SvwbOmoRqirV8+XJNTU0vLy9cE+Xby8rKOnjw4KhRo3C5cQAouXv3%207p04cYL6VNRR1NXRcR8y9HmMiJ/Pc2pogsqB6obH4+3cuZPaUwXvIrly5crx48clB9NQSJo/f76F%20hQVqHACgjP78809/f/+UlBRtbe1GjRqNHz9ecvpS+y8HKOqgiqA6U1igoXC0cOHCy5cvh4WFSU9/%208+aNh4fHjz/+iLoGACgdgUCwZMmS2NjY2rVrOzg4UJrp1KkTro8HoPhAw+fzd+3atXnz5oJPXb9+%20/dmzZ8xNQ3BJXACAr9q0Uo55/vy5srJyw4YNhw8f3qVLF1QLQDkGmoSEhN27dyuJLw28fPlyXV1d%20Zjr1JxYtWkTP+vr6duvWTV9fHzUOAFAS9+/fP3HihKamZocOHXR0dEaMGIGD7QDKPdBkZGRQdjEx%20Mdm4cSP96mR6GKtWrWLuDIDqBgCQ78GDB/7+/p8/fzYyMmrcuPG4ceOQYwC+XaAxMDCwsbHJy8uT%20STNkyJAhW7dupZ8lrr0NACCHQCBYunQpdQ7NzMysrKxatmzp7OyMA2UAvmmgoZ6Ej4/P7NmzN2/e%20/PPPP0um0y9zwYIFKioq3t7euEwLAEChmFGZhIQEe3t7Dw+PLl26fMtLlQIg0Hw5dObXX39NS0uj%20315ycjJzk8jHjx+rqqoyBSIjI69cuaKurn7x4kXqbSDTAABI55iDBw9GR0cbGxubmZk1btwYV6UC%20+D6BJjU11dfXV/rgGIo1AQEBMsVycnKOHz+OQRoAAIb03iVLS0vq7+H0JYDvGWhKTs59KwEAqlWO%20efHiBYfDqV+//tChQ11cXLB3CeD7BxpTU9O9e/dmZ2cXW1JfX19yLjcAQDX0xx9/nDx5UlVVtVWr%20Vjo6OmPGjMHeJYCKEmi0tbULntYEAAASDx8+PHjw4OfPnw0MDBo0aDBhwgTkGIAKF2gAAKBQf/31%2016FDh5gcU6NGDTMzs1atWrm4uOA0bAAEGgCASkAoFC5btiw6OppyjKmpKeUYV1dXVAsAAg0AQOWw%20ZMkS5oBfGxubIUOGdO3aFQf8AlTKQHP9+vXTp08XekJTzZo1Z86cidO2AaDq+euvvw4cOJCYmGhl%20ZdWsWTMdHZ0ffvgBmzuAShlohELhlStX5syZ8/Tp00ILUKCZOHEifuEAUJWIRKJly5ZFRUUZGRnZ%202Nh4enpiKwdQuQNNZmbm7t27i0ozAABVzNKlS1++fMlms+vWrevu7u7m5oa9SwBVIdCkpKQ8evSI%20HgwaNKhz584FD+M3MDDAdWgAoFJjjvalHMPhcCwtLRs1akSbtbFjx2JUBqDqBBpKMGpqamZmZvRr%20b9CgAaoVAKqYhw8fnjx5kh44ODjo6OiMHz8eOQagCgYa+nnXqlUrOjraxMQEdQoAVcajR48OHz78%20+fNnii+2traTJk2izR2qBaAqBxpvb2/6qe/bt2/27NmoVgCoGjlGV1fXyMjIwcHB0dHR1dUVl8UD%20qOKBJjk5+datWykpKRs2bHjz5o2qqqpMAVNT0+nTp2OEFgAqssePHwcEBEjnmNatW3fv3h01A1CN%20As327dtzcnKysrL27NlTsEDNmjWxyxkAKiyRSLRy5cqIiAh9fX17e3tHR8cePXqgWgCqXaAhAoEA%20tQkAlQ4zKhMfH29ubj5gwIDu3bvjBGyA6htoTExMdu7cmZWVxeFw9PT0JLuZKeWkpqYKhUKctg0A%20FS3HHD58OCYmRkdHh7ZaLVq0GD9+PA74BajugYbCiqenJ/PY19c3JSWFeUw9nilTpqCWAaCC+Pvv%20v48cOUI5RktLi3KMjY1NmzZtevbsiZoBQKD5f7dv3/b399+/f79kCovFevDgwaRJk5o0aYK6BoAK%20kmPq1atHOaZHjx7YuwSAQCMrNDR05syZjx8/lp4oEol27doVFha2d+9eKysrVDcAIMcAQMUKNJmZ%20mf/880/Hjh3pcVZW1vbt25k0M2vWrNq1azNlXr9+TYHmxo0b/v7+8+fPL3g6NwBAeaDe1MKFC+Pj%204/X19ZFjABBo5KEQs2nTplOnTs2ePZu2HSEhIdQHmjNnzrx58zgcjiT0aGpqUtb5/fffp06dikAD%20AOVt1apVr169ogd6enp9+/bt3bs3cgwAAk0xnj59eubMmZycHC8vr4yMDF1dXUotkjRDKOLQU3v3%207k1MTMR53QBQ3jmGzWYbGRlZWFjQ5og2Prj2FQACTfF0dHRWrFhx7Nixc+fOjRkzxsTE5NOnT4sX%20L16zZo2amhpThnLM8uXLs7KybG1tlZWVUd0AoFj//PPP4cOHExISJDkGt1sCQKD5Ourq6h4eHu3a%20tbt3756NjU2vXr02bNiwdevWpKQkU1NTpsz79+/PnDlDD9zd3dFVAgBFefLkydGjR2NiYjQ0NDQ1%20Ne3t7b28vHCxKwAEmtKzEKMH48ePv3DhQlhYGPWWZMpQ6Bk2bJiKigqqGwAUlWO0tLTMzc3btm2L%20o2QAQEmBp23Xr19/27Zt165dW7NmjfT0mTNnDh06lLY7qGsAQI4BgIoeaIirWL169dLT05kpurq6%20Y8eOlT5MGAAAOQYAKnSgYfz444+oVgAoS445duwY5Rg1NTWKMmZmZu3atevTpw9yDACUV6BJTk7e%20tm0bj8fr0KHDo0ePsrKyiippYmIyadIk6mOhxgGgJDnG1NQUOQYAvlGgSUpKWr58uUAgmDx5sp+f%20n5wrzdC2aeTIkQg0AFDQr7/++vr1a+QYAPhugQYAoNTWrl1LOYbNZmtqahobG7dv375fv37IMQDw%20HQINbYM2b96cm5vbokULe3t7ObucjIyMcKkrgGouNDT0xIkTnz9/pseUY7S0tAwMDPT09KZOnYpL%20yADA9ww0tCX66aefmMfOzs6oTQAoNMccO3YsNjZWVVVVXV2dQgxN1NfXp60HcgwAVIhAI4PP52/d%20urV37962trZK4rtXHjlyhDZYQ4YMQUUDVOcco6amZmRk1L59+z59+rDZbFQOAFTcQEObrRUrVvj6%20+mpoaNjY2LBYrLi4uGnTplGH7OPHj97e3tiKAVQHT58+PX78OG0QuFyuioqKgYFBhw4d+vbtiy0A%20AFSCQCMQCPbv309pplu3bi1atGAm6uvrL1u2bNu2bQsXLqSI069fP2zRAKpJjtHT00OOAYDKF2gS%20EhICAgJq1qy5Zs2aJk2aSAKNj4+Purq6t7f3li1bOnfuTNs41DhAVQoxJ06ciImJoccUYijKIMcA%20QOUONOnp6ZGRkcbGxnXr1pV5ysXFhTLN27dveTweqhugCnj27BnlmNjYWA6HQzlGS0uL4kvHjh0x%20CgsAlT7QUGQxMTH5/Pnz2rVrly9fLv3U9u3bs7Oz7ezsqPeG6gaoGjmGfs6UYzp06NC/f3+EGACo%20OoFGX1+/devWR48epUCTnp6ura3NTE9MTPTz86MHtOHT1NREdQNULhs3bnz9+vW/Gwsx5BgAqMqB%20hsKKp6dncHBwcnLy1q1bZZ61srLy8PBQVVVFdQNUChs2bAgLC2OxWPSz1dDQUBJfBw87lQCg6gca%202vC5uLhQlDl69OjFixelnxo2bBilmZYtW6KuASqsZ8+enT59mjm8l37OHA5HTU1NX1//559/pn9R%20PwBQXQINsxEcMWJEnz599u7dm5mZyXTpjI2NPT09lZUVfMcoPp+/c+dONzc3Ozs76ekHDhx4+/Yt%20PaBu5cSJE+ndZV5YbIHymxtAxbRp06aPHz/Sb4p+sCoqKkriPcjIMQBQfQONkvhqNMeOHRs7dqzk%209OwLFy4EBQX17t1bge8iEonmzJmzZcuWU6dOSQKNUCikiDNz5sy8vDxmyuvXr9evX29qalrCAtIU%20OzeACuX58+f022EGY5TER8Zoamo6Ojr269ePw+GgfgCgugeauLi41atXU86gWDNx4kQWi/Xhw4fB%20gwfTtnLBggXU4VPITXRTU1NXrFixceNGLpfL7NpnIk5ISAi9CwWpcePGqaqqPnjw4PDhw7q6ulSS%20/iy2gExgUuDcACpUjqHfqZJ49JQZNzUwMJgxYwYGYwAAgeb/hzQOHDhAacbFxaVZs2bMRGrgFy1a%20tGPHjrlz59arV693795lOZwwJSVlz549d+/ePX/+fPPmzT9+/EhvyjyVmZm5d+9eerB+/fpRo0bR%20g7dv31LUoJAxYcKEJk2aZGVlyS8g/UaKnRvAd0Q/ybCwMOYx06OgHNOpU6cBAwZgMAYAqhjFnLCQ%20kJBw8OBBU1PTdevWOTo6MptO6vzNnz9/9uzZ9Hjz5s0ZGRlleYuYmBiaVVRUFMWIMWPGcLlcgUDA%20PJWWlvbPP/80bNhw6NChzBRra2vmjpjUK6V/6a3lFBCJRNJvpNi5AXyXHDNZ7M2bN2wxyjFdunTZ%20tm3b1q1b3d3dkWYAACM0haMQEBERYWxsTE27zFOurq4aGhrh4eE5OTm6urqlfosaNWrQtpjmVr9+%20/YCAAMnwjJL4vpi0AO3atePz+cxRjcTMzIw24rRBp9wjpwAtGE2UvuifYucGUEYvPqXy8vktrI3k%20lKEkffr0aVozJYMxFGL09fVnzpyJ3UkAgEDzFdTV1SnN0PZ048aNS5YskX5q165d2dnZtra2ZWzm%20DQ0Np06dqiQ+xkVyKC4jJSWFepz0FpJ4QerUqaOqqpqUlEQJIzU1VU4B6Wyk8LkBlEV8Gm93UHh2%20Ln/BULU6NbT+J+i8eHHmzBkmxEiPCxoYGFCOoX9RewCAQPPVmCsFBwYGrl69OjMzU0vr3y0vNfDb%20tm1TKucrBVNnlBKGiYmJ9DE6tEjKyso0nemtyilQrnMrNggq5FhpqJKSM3IP34p4myBQYnGOPUh2%20b80KPrX/5at/r9vLhBj6l9ZDZ2fngQMHYkcSACDQlBWFlbFjxwYHB6ekpKxfv17m2Tp16gwdOrT8%20Tv8RCoUCgSA+Pp4eSEIGLQmfz6fptMWXX6Bc5yaNng0NDU1MTKSZMDkmNzc3Pz8fl16FgrJESgF3%20445fuJkY/kduZvLTM6zbdfT0tbgG2qp8gRAhBgCgXAINNc9du3bdvHlzYGDg5cuXpZ+iKOPh4dG6%20devy+wz6+vqUFcLDw/Py8iQX8fv48SPFBUNDQ5qip6cnp4BMnlDs3GSI/oM1D4qy03d75Kd3n+Kz%20bz2LoV8Wl6OkzGEJhKKn0bljxo1bNL4jLgwAAFBegUZJvKdm9OjRHTp0OHz4sOQYl29zCV1TU1Nd%20Xd33799LX5L48+fPfD7f2tqaurA1atQoqoCNjY3MhYwVOzdp9NoWLVpITxEKhbt378ZhN9Xcy5cv%20z549yxwNw1VmxSdnX/3nc55AqKXONbFqaubQQU3LUChSysvnP/yU8evhJ+O729QyxK1eAQDKJ9Aw%206tatu2jRom/8GSheNG3a9OrVq8ePHx85ciRNeffu3YkTJ0QiUaNGjehPHR0dOQVkjmJR7Nzky8nJ%20wWgNQgyzE5PWBK4yO5uvbu30Q35j9Vy+UJnNEgnyBXm83KzUL68RKXFYojsvvlwZD5kGAKBcAk1K%20SsqBAwfkXGnG2Nh41KhR5XRcsJaW1rhx40JCQry9vV+/fs1cuvfWrVteXl7169dXEh/iI6cANSe3%20b99+/vz54MGDa9SoUca5YZWComzfvj08PJx5LAkxzNEwgwYNYo6GOXAr9tj1lyJRtooyJ/9/X05R%20WYXzZYcmk2l+7G5rZqiBWgUAUGSgSUxMnDNnjszZ1NJMTU379++vqEDD4/GSkpIkb8disdzc3JYv%20X04JY+XKlczEYcOGLV68mDkSWX4Bms/Ro0d3797dsmVLCjRlnBtWKZAehjl//rzkzGpa05jDxinE%20dO7cmUKM9A7KlxGpFx5EXnsSw+awKc0UOtAnyTR3X8bnC4TN6xn2dqytooyDygEAFL3LqSiKPZHH%200dGR8kSDBg2k5z916lRtbW3m9tcqKiqTJk0yMTEpSQHqGQ8ePNjW1tbS0rLscwPEF8kUvhhzjJSB%20gQFFXkNDw0Jfm5svOHLz/fXQWB11Lpcjb7clk2nYLKUr/3y+GRrr0rSmirIKKh8AQDGBhjbTv/zy%20S2Zmpsz0jIyMgwcPcrlcHx8fav4V8l4sFquFWMGnfvjhB/mvLbQABZquYgqZG1QrO3bskOxFko4v%20SkUMwxSd+Flt65tkZOe/jEwTCEQsjrxjsYQiIV8gbGip79KkppoKTtsGAFBcoKHe59y5cwt9inJM%20YGBgy5Yty+/CegDfxsuXL0+fPp2YmMj8WexepJLjcti9WpvXNdX+LSjs6ceUL79MDrvQTEOJKTdf%20aFVDa3x329a2RvhSAAAUGWjkGDdu3G+//bZ48eIzZ86U5V5OAN/Fzp0737x5wzzOz8/PysqS7D+V%20vxepFOwtdMd3s/0tOPxZEZmGSTN1amh5drNBmgEA+KaB5sOHDzk5OdQklPHmlFWSGpetzMJFaCpo%20fFESj8HQesvn85XEYzBdunQZPHhwuV6ct4Gl3o9FZBpJmhnXzaaNnTG+LAAAxQea1NTUQ4cOFTyG%20RiAQBAUFZWRk2Nvb4x7UBT2LSI/P1y7dTgoo1/jCMDAwWLhwoQLHYEqdafhCEdIMAEC5B5qEhARv%20b285p223bdtWQwPXzPgfiem5/tc+xmfUiEvLt1RTQ4WUKz8/P+nswsQXiuD5+f9/tRcKLitWrDAy%20+v67cijTjO9mu+e/TMNmK31JMyZa49yQZgAAyjPQyOfi4jJq1Cg1tNlSUjPzAq6/exmRxuYoB9x4%20P9bNxswAga+84kvB7MLEl5UrV1aE+FIoh/8yTVhUOl8o+jI2Q2mmPtIMAEB5BhobG5tt27YVeqVg%20Lpfr4eGBa7TIpJkDV99e+iuKw2GzlEQ3nsaKREqe3ZBpvtrr169///33uLg46YmFDr1U5OwiJ9OM%206Wo987e/BALRGJd6SDMAAOUVaFJTUw8fPkyNB4fD0dPTMzAwKFiGWpfAwEBNTc1hw4Zhr5MkzVz8%20kmZYX67xKqL/lG4++3I1NmSar80ueXl5OTk5zInTlT2+FKppXQNPV2t60MGhBtYBAIDyCjQJCQkz%20Z86Uc+iMhKmpaa9evRBoKM0c/P80I768/X8Xs/+SaVhK49xsalb7TLN7927mEszSeDxedna2dHZh%20TjsaOnRo1T6qemSXethOAQCUb6D5irfBiTz/pZkL0mlGTHKDnptPv4zTVJNMU2hqURLftTEtLU36%20VKPqk10AAOD7BBpDQ8OFCxcyh84kJCQEBARQB3rSpElaWlpMgaSkpH379jG3p1bUrQ8qqbQs6T1N%20srce/J9MI1Ia393WVF+9qkaWolILw8DAYPXq1VVjhxGAfBs2bIiLi2OxWFZWVrTlRIUAfLdAQ23P%20okWLmMeUZq5du+bp6bl06VJJAZFIRP9evny5Xbt21fbWB1QJ4dHpFx9GBf8dXWiakck0t5/H8QWi%20FjaG3VrUqsg3Ug4LC7t06ZLMQS3SpO+IXnDNWbVqlbExjnKFsqIOFW18srOzlcSnIPTu3btu3boy%20ZSIjI8+cOcMcJ16jRo2RI0eWZM6hoaE3b95kYndJXhUcHBwREdG/f/+SrNiZmZlbtmw5f/58/fr1%20Kd8zb0QdP3V1hfVkpJe/qJqR459//nn+/PnAgQMLbrpLMueS1F4Zl1DhtVSS5Sz5+lOSNfNra+Cr%201jEEmlKKj49ft26dvr6+dJoRN9KsOXPmnDt3bv369W3atNHR0amGVZzHFwbceH/9SYz2lxsps4u9%20kTKfpXTpcdTVJ587NarxXW6kXGxSYeTk5NAvVuaAXAn6vWGgBcrbkydPduzY8dtvv0lv8X/66adu%203bpJ7lBx5cqVnTt3nj17Vnp7NWnSJDmH9FEn5MSJExQ47t+/L5n46dMnLy+vQk99IA8fPqR5xsbG%20tmjRotjGhuZ/69atlStXUmfPycmJptDi+fj4uLm52draKqRmgoKCVqxYce/ePckUqocpU6b06NGj%20JC//8OHD9OnT37596+LiIhNoip1zCWuvjEtYTrVEyzl+/HhKLcyftH07derU5s2b//jjD+ky9F3L%20v95mSdbMr62Br1rHEGhKLyUlhX4Aqqqqu3btkhk43bdvH/VFXr58SUG1egYaNovV0tooJSM3LDpd%20IBCxOSyl/w81LK6aBovz3zWURUoCkZC+kk7W2p0bmepqKCDN7Nmzp6j9PqVLKv9+KDabtnRDhw7F%20BaDhe6HM7e3tfe3atR9//JHZtiQlJfn7+79+/TokJITp6cbExPz888+vXr0aPnx4zZo18/PzAwMD%20Z82apaWl5enpWdQhWXfu3Jk4cWJqairzKmqhHz16tHDhQmpLKKZLdqlLvH//npbk48ePZmZmJfxF%20UPePZuXo6Mj82aRJE8oNtCFVVJ+E2s53794xy09T6DFlphcvXpw+fbpZs2bFDlosWbKEKoHSlUwV%20lWTOJam9Mi6hQjx//nzq1Km0eWSWgbZ4V69epeWkj0xrCPPBqQzlsOTk5AkTJmhra0vK0BTKo0Vd%20XK0ka+bX1kAp1jEEmlLS09OrXbs2pRbadtAWRBJcEhIS1qxZQw/oh1FtL6zHVWb3a1u7Xk3t34LD%20XnxKVRLfWpkyDYvNFuTnvfvjVGZilJI45IhESjn5/Nw8YUodPd13pveOCYTifXalJhQKaRNZktPQ%20kFSgcqF1+8iRI9RmUH969+7dkrGBxMTECxcuHD9+fM6cOTSFylCLNWrUKCrDbIKaNm3q4+NDvec+%20ffowDYkMaolXrVqVnp5O7c26deuYV4WHh9Offn5+bdu2pV+H9P28qG1bvHgx9Z4po5TwoHUWi9VO%20TDKFWjvq8Zuamhb1EuoQnjt3jsfj0bvLP12UOiT06aKioqivv379emb5qUGlJHH06NEDBw7Y29sX%20tTWmYocPH6Ze6LNnz+jjqKiolHzOBw8ebNSoEZWRX3vDhg2jLVKpl1C+ktcSRZPLly9TPhg7diwt%20DPN2V65cGTduHK0zHh4elpaWWVlZ9BHo+505c+aGDRuYF968eZMiTkBAwOTJk+vVq1eKNXP27Nl8%20Pv+raqAU6xgCTekZGRnRt0urbG5u7rJly2Sepa3GtGnTqvlBwQ3r6I13+3Lh1xcR/2UayjAiUV52%20Oi8jgXILZRe+QGhupNmqkZFQyA9/96nsb0qbSByqAt8StRO3b9+m5rBVq1aOjo6S0XVCwSIoKMja%202rpr167SG2VqAn///ffo6OhCG34q7+bmJtOyKolHhQ8dOkQ9pfnz50uX37JlCzWrTCeYGqSLFy9S%20d4u61JLmYcyYMdRu0ZKEhoZSgGCxWAW7wnfv3m3SpMny5cslr6I3+uWXX3r37k2tYP/+/aUby+3b%20t588eZJCUnBwcGxsrOi/TsiHDx+oq02/vhEjRkgCEJWkT0rNrXR28fX1vXPnzowZMywsLIqqWIoI%20tDzU5nXv3l1+Ux0XF7d//35acmoCJctPm4KlS5eGhIRQt5O20oXGhfz8fKo9epfmzZtTR5SaXsoH%20Iqk+VbFzpq+SIsK9e/fk1N7gwYOpaZczn1evXtES0ntRXZWk9kpXS7Si0pdFfe/p06dLlqFLly60%20vtEC0AdhoknDhg1pySnQSF7o7OzcoEEDWsmLGk4ryZr5td9RUesYlEugoRWOgm18fPzZs2efPn0q%20/RR1gyjt0iYAdd3ISp+5mD2TaZSVRGyuir3LWBabKxAKc/mC2kZant2sW1mqoq6g8qLgQo2Eq6sr%20bQ0kjQptgv39/VeuXEldXhcXF+nyaWlps2bNot5qoXOjVNShQ4eCgSYiIiIyMpI2LFZWVtQppxSi%20JD6sctCgQRTiJXOmloOaFskhEZJ5UoT6888/aUkKjkRSc6uqqkqvon+lp1OXXV1d/d27dzweT/K5%20Tpw4QXFk6tSp9JFv3bpFTaCkPMUp6oXTQjo4OLRo0YKmUAHqtdevX1+yPaTGb+PGjZT/qMmk9l7+%200CklM6pG6YxYKE1NTVoeLTHp6UwrSB3Lom4XTwWo80mtJmU+anfp65O5+Lv8OVM4oGWj2qMvS07t%20UbXQA/lLSHOgFr3Y2itLLVEl0Gek+j9w4ACtk0yd0CpBK5KdnR0zdEdLMnfuXJkXUkilJaHXyqxU%20X7VmUlgp+XckZx2Dcgk0zDdESZb6XufPn5ccfkHrNH0HcsZRq3em4XCVRPm8LPGNlAUWxpojOxgj%20zUClRhtid3f3TZs2/f3339SZtrGxYabTJv7ixYvUOA0fPlwmQ9A2ffbs2R8+fCh0ho0bNy50OIHK%20U6NlZmb266+/Lly4ULLNoXehbQ510Jl+MPWyqH2Sbt7ocZ06dajJpKa30LZBX1+fz+d/+vQpPz9f%20+pwjmltmZib1jyV31aD2jxo8SkW06aMGWOZmYdS537p1K4WDDRs2UHc8Ozt7xYoV9GGpcqh1Z4aC%201q5dm5ycvGDBglatWhU72kr1RtVb7L3fjY2NKTgWnH7s2DFaQmpWizqRiurEy8uLeUz1VrBy5M95%204MCB9E1RrJFfe1RRFAXkzGfAgAFMoJRfe2WsJSrTt2/f0NBQPz8/Fotlbm5On5caL3oL5hArmfIU%20uCkEU8Lbt29f+/btKfbVrl270DmXZM0s+Xckfx2D8go0jI5iqFb5mWacONO8FI/TsNksJs2MdbNp%203wB3vIJKj5q0YcOGrV69mrr4M2bMUFZWZk7qCQ8Ppz8L7gClxoN6n1/1FjTDtLS0vLy8Gzdu3Llz%20h5pSpnWhJnPv3r30RtSNbtKkSWpqqkBMZoieOvHUeBc1UEFxh1rcR48e7dq1i5KWJJBRC0RvSt0z%20Jh5Ru0WtGjXhNF1DQyMrK6vgrKj1Gj169I4dO1q3bk3B5fbt28uXL2/bti099fnz5/Xr19Nnp3cp%206mPm5ORcuHCBSjIfjbr+NIWaSWb3Pb22X79+JTyR8NSpU2vWrBk8eLDMAUBlJ5nzkCFDaM70Rciv%20vYL7+GTm4+HhwdSwnNpTSC3RolJQoLS0ceNGyUSa0rt374IDPM+ePZs8eTLzmNJnUZ+i2DWTPmbT%20pk1L+B2VZB2Dcgw0SuLTmiQDlSYmJrRpQy3LdjrF4zR7g8PffM7gC4VMmumANANVZZCG2jbq+B45%20cmTUqFHUhtH2/cqVK5qamtRWKepgc2pyaPtObYaXlxc1SJJRHOq+U1c+ICDA3t6elqSohkcOAwMD%20akWoMz1v3jzqE1M+o1aKlv/vv/+mpppp6hISEpYsWUL/7tmzx8rKSjI2QM/q6+tLV8XMmTPphdQS%2006x69uz5448/Kv13MvDOnTspyVHPW3IZkuHDh1tbW0teTk0jvVxmZ5zk0l9K4nOjShJo6IugKOns%207Lx06VLFppmCc6Z6kFN79HUUevxHoUtYVO3JKF0tCYXCoKAg+hLp6x4wYACtP7RgoaGh169fp7cr%20OEhjY2NDnyg7O5tW5sOHD9+/f5++eicnp4LRpyRrpsz+uEJrIDY2lj5FsesYlFegoQx74sQJWhuk%20xzA/fvxIWzHm+wDpTDOmq7XPvsdcrio9QJqBqsTCwqJbt25nz5599eoVBRr6l7bvDRs2LHRngXQP%20Wwa1f/Xq1XN1dS14DA21lLRxp3nKnD37008/HT169N69e9SoMEd1FIw1zM3Yi7owAZWnhpOWipqf%20xYsXMxNpGVatWrV582am3x8cHEytGrWUT548oRaXucF7ZGRkenr69u3bGzVqRF1z5jgbc3Pzn3/+%20mbrdtJDU0kuaojp16lCrn5KSkpSUxEyhRo45FlV67IpewhyEQU0p1RKPx3N3d2fmrKura2ZmJv+L%20oL7l1q1b6YP069ePGtdiy5dcUXOWX3sUamUSlfwlLKr2yl5LVPNz586l5onefcKECczE169f0/pD%20TRitddQVl15UCpqSI2BorZs2bdrq1atbtGhR8Fokxa6ZtKpIB5pCa4Dy1uXLlynlNG3atNh1DP4f%20fXlUibT+icqGuQRToW8xaNCgmJgYEfwvoUjkd/Hlz+tPUeRHbUAVc+PGDdrajh8/noLFL7/8QjmA%20Opp8Pr9gSdo4ULtV1AbK0dGR2p6Cr7p27Zqenh51lqj1kp6enJxM/ea6detS15b6uLa2ttTrpU68%20pADlmG3btlHLumDBAur3y/kI1D2bLjZ79uz4+PhPnz7VrFlzzJgxtDze3t5yNqrUXEVHR0vms2nT%20JmYAhjJQqeuTqq5Tp04ODg4UxUr4koiICKp/+hZmzpzJnK9UcnFxcW3btq1fvz5zTk0p5lxo7Y0Y%20MYJa5a+az1fVXslr6eHDh5R1OnbsKLMOhISEUGyiiCOnTaR1zN7evnbt2oW2a8WumZTdi60BWqoZ%20M2aUfB2r5ujLohhDYUYxIzQ0u507d9IXSY+HDBkiOfyb8u/vv/9+6tQp+m1QpMWlTf6n96mkNKF7%203b2R13JzeagZqGpjkI0bN2/enLrL48aNCwoKsrS07NGjR6H7O5jDMJkedkFNmjQp9KBgAwMDdXX1%20sLAwaiekLydD7SU1n9RLVlZWpgLU3w0PD6cykp40m82m/i4927Jly0J/d9QW0gJTahk9ejR19CXT%20fX19aeYWFha0PF27dqW2U3qMhxlnot523759qXcuuUrFnTt31q1bR+9Fr6VeOFULPVuK+kxKSmJG%20lehBUefXSAsNDV26dOmjR4+2bNlCTaYCv1n5c5Zfe8zh2CVfwq+tvZLXEpOtaWFk1gFaMdj/oRx8%209OhRWnOGDx8uM4ZHeYJSSKGXhCl2zZS8o5waoLdwdXVl8nex6xgoeIQmMjKyXr169C1OmTJFOmbS%209zdy5Ej6/qiblZiYiCApg1bxHTt20AqKqoCqZ8+ePbTN7dOnD/27cOFCxY5E0nZm7NixtAWjjixz%202C9j5cqV1BjMmzeP3o6mT58+XabM2bNnqZmhHjZ1jouac79+/ZTEV9mWTKRG2sbGhpqxK1euCIXC%20gq+i+VN3n3rt0rNNSEigZokm3r9/n7p21PWnplF6iKLkYmJiaJmpkZbu3xeF0iFFrmbNmtH7lq56%20ixqhKXbO8msvJCSEqb2SLGEpaq/ktUQf0M7OTk9Pz8/PTzIxKipqxIgRLBZrzZo1tJw0N0on9Cf1%201aVfSy+hJDRp0qRCR5WKXTOZkZtSfEeFrmOg+BEa+oYor9B3T9+Z9Kl6xsbGS5YsoVBJa0aht1YG%20gKrK2dnZ3NycmiJqNoYNG6bYYUjmWibBwcGbNm2idoK5JF16ejr146kZ9vT0ZN6OOlQnTpyQlKGU%204+vry9wPQXI5CeqIUxlqt/r37/9/7L13XBXHG/YtcujSQQRRBFQs2I2ixt4SNaJRsYtBoyK2aKJG%20jbFEY4mJGnvDksT8Yk1i70qsWLCBiooIiKAUDU2KPNfLvNnnPOfA4dDkHLy+f/A5zM7Ozj07e9/X%207M7OWltbI+56e3sjhmH0jIE+DoS9Vq5ciSgCedS2bdtcJxqLewNibW7phV5EvrNnz86bN0+8m4Ni%20161b1759+0LcMjExMcFecNzKbxQrgICKI544ceKjjz66ffv2tWvXpHm4Yk5Sx44dEY8VrFanDuqU%20rLr12rRpg5yqy3FxcenSpYtMJitE66nfSjAZw+8JEyaMHj0aykncwAsICECbQBKhTVAT8XL1lClT%200FuGDx8uehT6GKrk7Ow8bNgwce9QoSVV98wRI0agidQ8R+r0MVL8d2jCw8PRvhiHLVq0SF6TQk1P%20mzYN569hw4boNBSSvEND3h/geRHVEJmgbEqok+/YsaNVq1byDg2x8NChQ6rzTJ48WX5ezr1790R6%20YGCgSEHYWLZsmfwUVAS8qVOnIizlVZPnz58jXCGYieAEN7h//37EpKZNm0quLywszMnJCTEPcVre%20TxZvmx87dkyFw2/evHliYmKuVqu+1aFOyaJVVbdevuVAvqCcgwcPlnTrQVUgPCm8ANWtWzeF/gNd%20ovAFbIU+lmtLquiZ6rek6j5GSuoOjZWVFc7W5s2bv/76a3nxGBISgvEQfvTo0SNfyUwIKUtgrIkR%20M8Y5gwcPzmsxtyIyZMiQJk2abNy4UXywDGMnjKcV3mpRyFO5cmW4KYXBOgIbaivtCBEG0YNwLn1j%20GTFm6NChqu+gYPAN7yemboi4PmrUqK5du0r3PxCP586de+XKlQJ9Xq2gGBsbT5w4Ma874hhbilds%20lK1WNgchX95v51uyuGORb+upLqdx48YoIS0traRbD30SI/B69epJ9cy1/0yaNAmmQZ6KCivnybUl%20VfdMNVtSdR8jufgciBo0k6+vbxE/Hnn9+vVevXpFREQob3J3d9+3b5/8+gpEkJycvH37dlzqVHuk%207IEhtaenJ+L6iRMnICPYIISQkgDyd+3atYinxbYODWT1woULxavz8un9+/f39vammiHkPQHD+kOH%20DsXExIivVPLzqISQd0NxLqw3ZMiQgQMHOjk5JSYmihQrK6sZM2Zw8R9C3qvR0rfffhscHIzfAwYM%208PX1VZ7eSAghGi1oyuW8PS8tp0gIeQ8xMzP78ssvAwMDoWOmTJliYWHBNiGEaI2gwZjs2LFjUVFR%20Dg4OH330kfKHKggh74tPkck+y4FNQQh5l5QvllJiY2N9fHzGjh175cqV7Ny+PUYIIYQQoumCxsjI%20qFGjRnp6eubm5kV8W4oQQgghpKAUzyMnAwMDBweHzMzMefPmlcv5wKlCBktLy08++aSE1qIghBBC%20CAVNMRAbG/v3339nZ2cnJydPmzZNOYO9vX2bNm0oaAgh5MGDBwEBAbmuq6avr9+nTx/pU5ogODj4%202LFjcK3wn+3bt2/UqFGx1yc6Ovrw4cMZGRn4bWNjgwqos1dISMj58+fF1xPz2kudPDBtz549qamp%205XImYHXu3Fl8LoCQ0hE0YhlptiYhhOTrLc+dO/f555/nlSEoKGjy5MlOTk74fePGjbFjx7548cLd%203f3Ro0cHDhxYuXJl3bp1c/2eVOGAtFq7du3OnTvFv+XLl1+zZo23t7fqyQOoyU8//XTq1CkpZfny%205cOHD5e/Pa9OHiie9evXr1ixQkqB6Bk3blxe38wipMQFjYWFxfjx41+9epVXBnt7exMTEzY3IYTo%206elVqFChfv36tWvXhoCQ1zp37tyBZElMTESMh19NSEhANqifpk2bQgZ17979l19+mT9/fnF96TM2%20NtbPz+/27duenp4VK1bMyMj4+++/x4wZAzUzZMgQXV3dXPe6ePHiZ5999vLlS7EXqn3z5s1JkyZF%20RUXNmTNHLDymTh5snTJlyuHDhwcOHChWS4e9u3fvRiMcPXpUSDpCCkCxfJyS8OOUhBA1vx+5ZcsW%20Kysrf39/5a23bt1q3rw5ontAQIDCpo0bN0LHzJs3Lz09vVhqkpmZCfGEMvv16wdfJBJXr14NIdWk%20SZPnz5/nuhcGrp988omOjo6Pj4+0FyRR69at9fX1//jjD/z7+vXrfPOAVatWlctZkVUqHIqqc+fO%20SPzhhx9QPfYWUgofp5S4f/8+hLl4YioGIr179zY1NaVwJIQQ+ZsxycnJcJUKd0Hq1avXsWPHu3fv%20xsXFyafv3bsX/hqbvLy8lG/PpKWlnThxIiYmRvlAUBVOTk5t2rRR3gsV2L9/v7m5+Zw5c6Q76KNG%20jTp9+vTJkychrTp16qT83Ofx48fIgHouWbJE2svd3X3BggWenp4HDhzo2bNneHi46jyICykpKVu3%20bnVxcZk9e/b/fWQgk0FjQVTZ2tpC+eV1i4iQEnzkBHBl/v7779u2bTt+/Lh8emBgIK5ACHO2NSGE%20SDoDkV45YENGHDx4EFvt7e2lRLjWsWPHwov+/PPPuU6YTUxM9PPze/r0aa7H8vDwOHz4sPKSzUlJ%20SbGxsTVq1JD/THT58uVF/kuXLrVr105ZBmEXJNaqVUvhmzaurq5ICQsLg1J58eKFgYFBXnkgiaCl%20oqKinjx5AvWDCiBqiMpD0HTr1g1msoeQUhM0kNL79u0bMmSI8qZVq1Zh6LB582ZcJPJPiwkh5P0E%20njAzMxOOMSMjQ94rimHhzZs3e/ToUbduXUnNTJ48uU+fPhs3bsyrQGgjX1/fhw8f5rq1SZMmua7e%20DmkSExMDPSGvq1AfZ2dnSBZshWNX3svc3Bz1jIyMVHhL6+XLl5Apz549Qzry4G9eeaKjo9PS0iB9%20cKzKlStDvkydOhUpIk/v3r0nTpzYtm1b9hNSOoImISFhyZIl5XK+5eTl5SWpcowb9uzZc+/evUWL%20Fv3666989kQIIfCT6enpv+agvHXYsGF+fn7QKFADO3fu9Pb2zs7OhsJYtmwZFEbTpk3btGmjcGsH%20rnX69OkFrUZcXBwOAXWisLy7lZUVBFBe489q1arZ2toGBgb6+/tPmDBBJEKjIATA4dvZ2clkMkgi%20GxubvPKIG0KIGtBz58+fP3Xq1McffywSIXcg4IKDg3fv3u3u7s6uQkpB0ODCuH//vqGh4eeff75y%205UopHVcLrsY//vgjKCgIPZWChhBCoCEQ9T08PGrVqqWjowM9gRB+5coVCwuLr776asaMGSIb5Ets%20bKx42wg/oqKiIDIgFD788MNSnFxibW09ceJE1PPLL79EDc3NzfH30KFD586dQ91gDqzLNw/+hS1J%20SUlQM4gRq1atEm85AYihH3744Zdffpk7dy4/C0hKQdCgL1asWDEtLU1+ele5nAeiSDl8+DDUN7sm%20IYQIpaKvrz969Ojhw4eLlJs3b/r5+YWGhkpPmsrlLLI3ZcoUdQqE74UyyHVSsLinAg2kPBsGfhuq%20AtpIYeYvBp/izk3uMUMmQ1WRZ8mSJV988YVIRPnz5s1bt26dkZER9BkKVJHH2NgYhWB8iyrVrl17%202bJlkpoB0EB79+4NCAjA7owapBQETaVKlcaOHbt06dKdO3eOHz9eSk9JSYHQNjMz++abbywtLdnc%20hBBSTuktpwYNGqxYsaJ79+4DBgzYvXs3fhSotMTERF9f37wmBbdo0eLQoUPKk4Lt7e3hll+9eiX/%20yAm/w8PDoZDgt/Na2g4yaPr06RimnjlzBv8aGhrOmjULtsyfP799+/ZiyoGKPB06dDAxMTE3N4de%20gaCRVzMCOzu7yMjI9PR09hNSCoIGwiUjIyMhIQE9GNeDtBDk7du3ob4hd6Kjo7dv3y4uG1xXXbt2%205TcsCSFEokmTJl9++aV4TFO9enU3Nzf194U+GDVqVGhoaK5bmzZtmuutDlNTUwiO+/fvQw9JH1uA%20iLlx4wZkFuojk+USIDIzM8+ePQsZNCwHKX3jxo2QaE5OTjgWhAt0TF55qlWrhjwIBPj7+PFjMfNG%20PprExcVB5fCdbVJgimVhvQcPHqg/PwaXEPQN1wLiwnqEvLcL61laWq5atUph7Ti4ArGsnK+vL7KV%20dE3S09P9/PygYGbMmCEd7siRI1WqVKlZs+aTJ09y3QuCo2fPnqjkjh07pETIlzp16kCgHDp0CP+m%20paWpyHP48GFRzuDBg5FH/ujghx9+gJCaMmUKl3slpbawHm8PEkJIUahQocKcOXOuXLny66+/Dho0%206MMPPyzRw+np6Q0ZMmTXrl0LFy7U19evVq0a3PiyZcsiIiLmzZsnLYQDmQWZAt0DsQUdhpyo24kT%20J2bPno1NxsbG2GvJkiUPHz4cNWpU+/bty+XMs1GRp127dshjZGQ0YcKE48ePS0cvl/PsDC3g4uIy%20cuRI3sUnBaXYvuU0ZswY9EU179AorLZECCHvD2/evElISJBWXpGncePGgwcPXrNmjZ+f3549e6pX%20r16iNfHw8FiwYMH69eshI6REX1/fsWPHQmSIf589ezZgwIByOaukNm3aVFdX99NPP3306NHixYuR%20TeQxMTEZN24chshChaiTBzRr1kz56G3atPniiy9q1arFfkJKR9DY2touX76crUkIIflSp04djAAb%20NGigPOsWwX7atGmZmZn/5vAOKjNy5MgPPvjg559/FnfZq1at+t1338lnsLS0hChBVaWZLnp6ejNm%20zKhYsaL44BQ2tWzZcvTo0fJ7qZNH+egwf+bMmfwsJSkcOtDLYpVJ3t979yQnJ2/fvn3o0KHK8/wJ%20IYQQki9paWlr165FPOW3CAghhBCi9cjKhhlv374NCQkJCgqS//iITCZr27at9N21kydPPnv2rFzO%20vdAePXrke1NEdf6ClkYIIYQQDRU0SUlJx44dS01NzTenubl5p06dSu6pVmZm5pocFNL37dvXq1cv%208ePzzz+Pi4sT6VOnTp05c6a09IIyqvMXtDRCCCGEaK6gef78+eDBg3Odq69A5cqVr169WqlSpRIy%20482bNxEREThKy5Ytpcn5BgYGrq6u2dnZV65cGTduHPJ4eXnp6emFhIQsWbLExMRkxowZyitHIf/l%20y5dzzQ/Voqure+nSJfVLI4QQQoimCxqFb7SqIK8PtxYXr1+/hqDp2LHjtm3bFDalpKSsW7fu1atX%20c+bM+fLLL5Fy/fr14cOHr1y5sn///srLcaampq5fvz7X/IMGDapatWpeW3MtjRBCCCHvgCLpDHNz%20888++2zgwIGI9N27dxcrVffq1Wvgf4gvkhgaGnp6epbo2jNxcXGxsbFiaSYFEhMTL1++XKdOHT8/%20P5HSuHFjSJDMzMybN28qazIV+W/duvXy5cvAwED1SyOEEELIO6BId2gqVqwoTVvZvXv3jRs3evfu%20vWrVKilDenr6gAEDLl261Ldv3xKdYhIeHq6jo+Pq6rp//34xpwe/mzVrhh8QOpA7DRs2lJ8v7Ojo%20CPkVGhoKIaLwEdoXL17kml8mkyG/k5NTfHx8/fr11SyNEEIIIZouaCSgAJYuXWpjYyOvZoC+vv6C%20BQvatGmzbNmyxo0bq/+9pwIBbREREQEdAzWzb98+SWSgSr169ULdoHVq1Kghza0B4uto0dHR8rpE%20AAGUV/6oqCjsUqDSVMO1fwghhBDNEjT37t0zNjbetWtXv3795Df9+eefSUlJN27cSE5OLiFBk5GR%20cfXq1fj4+LCwsP79+4v5Ovj92WefVahQAbWSyWSVK1eW/3arra2tnp5erl9zxe7IL266yOeHgkFK%20XlvzKk1BeD1+/PjVq1di3cxyOcsBZWVllfQEI0IIIYSCRi2gG+zs7EJDQ0eOHAlhYWFhIdKjo6Nn%20z54NwVG1atVcv19fLEAc1K5de/jw4bNmzXJ1dRWJkA6enp7+/v6DBw9GBsgI6AlJOkBjZeWQq+xA%20ekJCQq75VW/NV9CIezySoEHLIFF5+XNCCCGElIKgqVixoo+Pz9dff/369esxY8YobLW2tkZiCd2e%20KZfzYOurr75SSKxSpUqDBg2uXLkC9QDR8ODBA6gH6bXq8PDwN2/emJmZKYsJiDNIE+X8aWlpZjnk%20ujWv0v6fts5Z6E8+BUVt2LAhXyVECCGEkHchaBCq/fz8njx5cvz48cePH8tvatWq1YABA4YOHVpy%20NkBFnThxwsHBwcPDQ0rMzMxMSkqClqpataqJicmjR4/kHwlFRUVBkdSsWVN55ZjKlSsjP6xQzl+r%20Vi0nJydjY+Nct+ZammrUWcKHEEIIIe9I0ABTU9N169YdOHBg586diO4i0dDQcM6cOS4uLiVqQ2Ji%204qhRoypVqnT69GlbW1uRGBkZef/+/Xbt2kGg1K5d+9KlS2fPnu3cuTM2xcTEHD169O3bt0hXnr9i%20ZmZWp06dq1evKuTPyspCfisrK/wNDAxUszRCCCGEaJOgEfTIQTn95s2bbm5uJfRSj4ODw8SJE2fP%20nv3999+3aNGiXM6Kf3///TeETu/eve3s7AYOHHj8+PFx48bNnDnTyMgIv/ft29enTx93d/dyOVNb%20goODnz592rJlSwsLCwiawYMHQxsp54fQwb+DBg06depUXqURQgghRLsFzfXr1x89eqScHh8fv3//%20fn9//xL69IFMJps0adLDhw9/ykEkWllZzZo1q2PHjrq6upA1UFSLFi3y9vYWW9u1a4d/TUxMyuXM%20zF27du369evPnz/fvHnz8uXLQ53cvn1bOT/kC37ntVWURgghhBBtFTTZ2dl3794dOXLkjRs3cs1Q%20cl9xEpiamm7bts3V1fXOnTv4V4gYLy8vsdXAwGDBggWQI2KroaHht99+K70Phcxt2rSBjrGzs1Mn%20v+qthBBCCNFWQZOamrpy5cq81Iy5ufnAgQNL9NMHgtmzZ+e1CXolr60ymax/Dmrmz3crIYQQQrRS%200CQmJp4/fx4/WrZsWaFChXPnzjk7O9epU0dHR+fBgwfY6u3tXaKfPiCEEELI+0zxvJWTmpr6/Plz%20S0tLf3////3vf1WqVGnevPnu3bt37dq1fv16Y2PjlStXpqSksLkJIYQQormCJjs7u3z58pUqVYKU%20MTAwqFWr1vHjx+/fv49NHh4ezs7Ojx8/Ft+MJIQQQgjRUEEjk8n09PRev34dGRlpZGTUqlWrFy9e%20LF68eO/evWvXrj137tzLly8L+uFGQgghhJB3KmisrKxat24dFRX1448/JiQkNGvWzNDQ0N/fv0+f%20PmPHjk1OTm7RogXfaiaEEEKIRgsaMzOzL774olWrVr/99ltMTAzky4gRI6Stzs7OXl5eYhEXQggh%20hJBip9gW1vPw8Ni+fbu/v7+dnZ2hoeHcuXNfv34dFxenq6vbp0+fTp06sa0JIYQQoumCBri4uMyf%20P1/8NjU13bRpE9uXEEIIIZouaJKTkwMCAuQ/Ga2jo5OdnS39Fj+QAn3z4YcfGhgYsMUJIYQQolmC%20Jjo6um/fvpA1+easUqVKYGCg9G0BQgghhJBipKiTgtV8GVv+zg0hhBBCSPFSpDs0FSpU8PLySkhI%20gF75999/z5w5A33TpUsXQ0NDkQGJp0+fNjAwQCLfciKEEEKIJgqaSpUqbd26Vfzet29fcHBwt27d%20Nm3aJM2eSUlJ6du3b1BQkLe3t7m5OZubEEIIISVB8axDEx8fv2zZMltb282bN0tqBhgbGy9ZsiQz%20M/Onn35KSkpicxNCCCFEcwXNy5cv79y5ExcXd+DAAYVNJ0+ehJQJDAykoCGEEEJICVE869CYmJhY%20W1s/fvzY29t7+fLllpaWIv3Zs2fTp09PS0tzcHDQ19dncxNCCCFEcwVNxYoVhw4dOnfu3Pj4+GHD%20hilstbCwGDlypJmZGZubEEIIIZoraPT09CZNmvTo0aOzZ89GRETIb2rcuPGAAQN8fHzKly/P5iaE%20EEKI5gqacjm3YXbs2LFnzx78zcjIEIlGRkYLFixwc3NjQxNCCCFECwSNoE8ObFZCCCGEaI2gSUlJ%20uXTpUmZmppOTU1RUVHp6el45TUxMmjdvznnBhBBCCNE4QfPs2bMePXqkpqaOHz9+06ZN+JFXzqpV%20q165coXfciKEEEJISVDUibpZWVnlcr6nLX6oyMZvORFCCCGkhCjSHRoTE5PevXsnJyc3aNAAPxIT%20E/PKWbVqVekDT4QQQgghGiRo7O3tf//9d/F75MiRbE1CCCGElArFvDbMxYsX4+Pjxe+MjIxr164F%20BwezlQkhhBCiHYImJSXF39+/Xbt2Bw8eFNNlnj9/3rNnz/79+584cYINTQghhBBNFzRQMHv37vXx%208alSpYq5ubkQNDKZzMPDIywsbNCgQdeuXeOkYEIIIYRotKBJTEzcsGGDhYXFypUre/bsKb5yYG9v%20v2fPnrFjx2LrkiVLUlJS2NyEEEII0VxB8/Lly9u3b9vY2LRu3Vph06BBg4yNjS9dupSUlMTmJoQQ%20QojmCho9PT2oFsiagwcPKmy6cOFCRkaGmZkZP05JCCGEkBKieL7lZGFhUb9+/SNHjkycOFEmk1la%20Wor0qKioL7/8MjU1tVmzZlA8bG5CCCGEaK6gMTU19fLyOnPmTGxsbL9+/RS2Qt/06tXLyMiIzU0I%20IYQQzRU0urq6/fv3v3Pnzt9//x0aGiq/qV69etjUtWtXPnIihBBCiEYLGmBsbLxs2TJPT8/vv//+%20zZs3ItHIyGjx4sXu7u5saEIIIYRogaARtMmBzUoIIYQQLRY04NKlS2lpaeK3qalpkyZN2MqEEEII%200RpBEx0dffr06REjRkiCxtbWdsOGDW3atLGysmJbE0IIIaSEKLaJui9evJg2bdrgwYMlNSMSe/fu%20/d13371+/ZptTQghhJASonju0GRkZGzevHnHjh343ahRI3Nzc5H+8uXLO3fuLF++vH79+kOHDtXV%201WWLE0IIIURDBQ2Ey2+//aanp9e5c+dNmzbZ29uL9Pv3748cOfLChQu//PKLp6entOAeIYQQQkgx%20UjyPnFJSUiIjI21sbDZu3CipGeDm5rZ69WpTU9NHjx6lp6ezuQkhhBCiuYJGV1fXwMAgLS0tICBA%20Pj0zM/P8+fMZGRnGxsZcWI8QQgghJUSxfcupRYsW+/bt+/zzz5OTk6tXry7Sr1+/PnXqVAiadu3a%20mZiYsLkJIYQQotGCxtfX9+zZs/Hx8SNGjFDY6ujo6OPjw49TEkIIIaSEKLbHQB06dJg0aZLyMnqN%20GzeeNWtWo0aN2NaEEEIIKSGKbWE9XV3db775Zty4cT4+Pi9fvhSJdnZ2K1eudHBwYEMTQgghRAsE%20jcDS0nLfvn1sVkIIIYRojaBJS0u7fft2RkaGfKKOjo78v9nZ2eVyPrvt7u6up6fHFieEEEKIZgma%20qKiorl27JiQk5JvTxcXlwoULdnZ2bHFCCCGEFDtFmhScnZ2t5nJ5CndxCCGEEEKKkSLdoTE2Nu7Y%20saM0BVgFzs7O+vr6bG5CCCGEaJygcXBw+PPPP7XI2nv37okHZLq6ug0bNsxXY6nOX9DSCCGEEKKJ%20gkaZt2/fBgUFZWRkIMa7ubkZGhqGh4dLCweXLhcvXhw2bNjDhw/Fv8uXLx8zZoyBgUHh8he0NEII%20IYRoh6C5d+/eiRMnpkyZkp6eLpPJDh065OjoiKg/f/78jh07lu4rTsHBwT4+Pk+fPm3atCnE1rNn%20zyZNmgT9MWrUqFw/MpVX/tGjR+vo6BS0NEIIIYSUKMUWfUNCQvr27Tt+/HgxTVjcqwgLC7t+/Xq/%20fv0uXLgg3t8uFdLS0tatW/fkyRM/P7/Lly9funRp8+bNEFsLFiyAKClQ/sjIyMzMzAKVRgghhBDt%20EDRQAOvXr797966lpeUHH3zg6uoqk8nevn2LTdWrV09KSvL3909NTS0tIxMTEwMCAtzc3GbPni3u%20oHTu3HnMmDH//vtvUFBQgfLfvHkzLi7un3/+Ub80QgghhGiHoElISDh16pSJicn3339/5cqVefPm%206evrQ9B069ZtzZo1SD979izifWkZCQkSHR1do0YN+cdezs7Ourq6oaGhmZmZauaHSkP+mJiY2NhY%209UsjhBBCiHYImrS0tBcvXlhbW3fq1Klczls/Ojo64vPapqamBgYG+F2Kk0ueP3+Ov66urvISxMnJ%20CaorIiIiKytL/fzh4eGRkZHZ2dnql6YC7IKG4utRhBBCSBEpnknB5ubmdevWPXny5DfffDN27NiQ%20kJCMjIxbt25B2cyePTs+Pr5bt25GRkalaCdqUq1aNfyVUuzt7YWeUD8/lJnIX9DSBJBBsbGx8o/e%20IATRUI8ePSrdxikJ0BQQfLCuFOdO0TRaxxNH02haKWJiYmJjY6M6MmqioOnbt+8///yzMweROGHC%20BGlrr169xA2bUiE7B4VJPG/evHmbQ0HzgwKVJpGVlRUUFCRu8IgTjPzQNGi3svduFEx7+fKltbW1%20vOyjabSOptE6mvae9MnMzExEPR8fH0NDQ20SNDgZgwcPvnHjxqFDhxCw5Te5urp6eXl17969FGM2%20WhMt++DBA7FAjkh8+vQpVAg2KYvHvPJDfBjlUKDS/m9by2Rdu3aVT4Eq2rp1K5quFNVeCSFMGzJk%20CE2jdTSN1tG097NP+vv7v8t5pcW2Do2pqen69euPHTs2b948hHaRiNj/008/NWnSpHSbtXLlypAa%20YWFh8okQXlAkNWrUgM5QP3/NmjWrVatmYGCgfmkqELcZy+TNRppG62garaNp73mffGcPm4pB0KSl%20pT169AhRXJrW2iUHTWtWc3NzVDI4h8aNGyPl1atXFy5cyMrKcnNzU751lFd+KE3kt7a2rl69uvql%208UKlabSOptE6mvZ+9sl3SZEeA4WHh3/88cerV6++evXqnTt3NLZZIVD69OkTGxvr5+cXEBCA2v74%20449btmxp0qRJgwYN0OhRUVG3b99OSUnJN3+9evUsLCw+/fTTvErT/FNehnszTaN1PHE0jaa9t9YV%206Q6Nrq5ufHz85MmT8dvS0hLKpmbNmra2tlWrVtWoNkU9hw4deuXKlW3btrVp00Yk1qlTZ+nSpVAn%20GRkZUCT+/v7Hjh1r2rSp6vxmZmb4PWTIkLxKY2+mabSOptE6mkbTtEzQGBkZtWrV6vbt29HR0QkJ%20CYMGDUJi27Zt58+fX6FChcqVK1esWFFDmtXU1PTnn3+WyWTXrl3Dv8bGxitWrBDyBbi4uLRs2RJ5%201MwPWaNia4GUllivr+z1Y5pG62garaNp7JPv0jodiA8TExNfX99Cv1h19OjRuXPnpqamhoeHQ9ZI%206QMHDpwwYQJET926dcvqCSOEEEJIKZKWlrZ27drk5ORieJW6a9euFy5cuHHjxtKlS5s3b96wYUNL%20S0uk79y5s0WLFk2aNFm3bl1wcDC/CUAIIYSQEqI414YZMWLEpUuXoGyWL1/eoUOHVq1aOTo6ZmVl%20jR8/vnfv3vHx8WxuQgghhJQEJfIkqFevXk2bNk1PT9+4ceOGDRsyMzPT0tLK9tQnQgghhJQRQRMf%20Hx8REZGRkQEds2nTJuk7AAYGBo0aNZL/lCMhhBBCiGYJGknHbNiwAVJGflOdOnWgZjp16jRv3rx3%209jUHQgghhFDQFID09PR79+6tWbMGUkb+iZKrq2uFChXw19/fXyzcQgghhBCioYImMjKyU6dOL168%20EP9WqVLFysrKzs5u3bp1zs7ObFxBWFjY69evy+W8lF+rVq1832AvaH7tsi4kJAQ6GD90dHSsra0r%20V65cZkyTSE5ODg8Pd3R01Fg1X1DTEhIScLG/ffsWZw3XOEwrk32yfPnycFwYjGm+V3nw4EGlSpXy%207WDa5UwKZJoWeZJCWKdFzqSgppWsM5k/f/6PP/6YmpqaXXBgAGpvY2NTv359Dw+Py5cvZ5P/l6tX%20r9apU0dqbUi9N2/eFGN+LbIO3ufw4cMGBgZS/hYtWgQGBmZmZpaBEyeBbPPmzcN1cfDgwbLRJ+/f%20v+/t7S3lb9u27b1798pGn8SmI0eOyH/oeObMmdHR0fC2muxVDhw4gDCAvyXUhzXcNO3yJIU4cVrk%20TApqWgk5EwgYyBiImSIJmidPnnz00Ue///47hUtegg9SD6MijI3gWSpWrIghoL+/f1ZWVrHk1yLr%204GvQT0xNTatVq1bnP7AL1HBAQICmxY+inIj//e9/ujkgUpaBPgkH1LBhQ4yAxSmrWbMm3FCHDh0i%20IiK03Tok7t69u0KFCuiEwjoxWBw5cmRKSopmuhTUGf3K0NAQNqruYNrlTNQ3Tbs8SSFOnBY5k4Ka%20VnLOpHgEDVFBWlra5MmTcZrHjh0r3lr/+++/7ezsnJycwsPDi55fu6yLiopC93V2doaflRL37Nlj%20ZWUFwZ6cnKy9psnz8OFDRBFzc3OEyaNHj2r7WcMAcdKkScbGxjt27BApr1696tOnD9zQokWLMjIy%20tNo69DpPT08Mf//44w+Rcv369Ro1ari4uGigXMO5CA4OXrNmjb6+PjoYIrqKDqZdzqRApmmRJymE%20dVrkTApqWok6E0nQlC9HSobExMQzZ87AP86bN0/cHe3WrRv8C9KDgoKKnl+7rEN/rVKlypgxYxAt%20pERoc+j0a9euJSUlaa9p8jsuWbIEu4waNcrExAQDF20/azExMefOnWvQoEHPnj1FCsL/unXrOnfu%20/DYHrbZOR0cHY0qMFz08PESKu7t7p06dUlJS5D/hoiFAiDRv3nzWrFktW7b09fVFYFDRwbTLmRTI%20NC3yJIWwToucSUFNezfOhJ9YKini4uKePXvWqlUr6X318uXLu7q64m9oaGhmZqbCBL2C5tcu6zA0%20PHHihEIh//77L4aP1atX16hX+gt3IjBKWL16NQYo27Zte/LkiRg3aHufvHPnTnR09OLFi+FSQ0JC%20xNdLbG1tjx07VgauOD09vfbt29+6dWvDhg39+vWDvgkMDNy1a1ezZs00cIYpatuiRQsfHx8vLy+M%20ccWzleLtw1phmhZ5kkJYp0XOpKCmvRtnQkFTUoiphRhGyK8oiKtRX18fwhZKVsGnFDS/dlmXK7/+%20+uvdu3c9PT01yg0VzrRLly79/PPP48aNw2BF3NXX9j4JE5Bf+J0tW7ZgTCxGUR9++OHatWvd3d21%20/YrDv35+fpGRkd/lIBIR9bdu3WplZaVp1lWrVg0RTvzOd7yuXc6kQKZpkScptHVa4UwKZNo7cyYU%20NCUIvIazs7Ourq6UYm9vD5+CsWCx5Ncu6xQ4fvw4okjz5s0RVLCXVpuG0fCMGTPc3NzggxBCNPP+%20cCFMQ2YMfL/55ps3b97UrFkTecTMvp49ex46dKhWrVpabR1sCQoKOnz4sE0OyAMznz59evDgwaFD%20h8oXolGoOV7XLmdSINO0yJMUwjotciYFMu3dOBPOoSnZc4yTJ58opj7l+rywoPm1yzp5kAG6vlev%20XujEmzZtEt9m117T/v333wULFrx8+XLFihUWFhZIEWFDAweLheiTb3JYvnx5SEhIcHAw/mJEJR7w%20p6SkaLV1kZGRPj4+iB+rV68W1gUEBNStW/err746f/58tjZ/e067nEmh0XBPUgi0yJkUok++A2dC%20QVNSoCNmZmaGhobCj0iJGP/hjMqvoFDo/NplnQS679atWz09PevVq7dz505XV1etNg1X6ZkzZ3bt%202gWvih1xld69ezcsLAwlPHjwAIUoBBXtOmvwpKampvCw/fv3lxI7dOiAc3fhwgU4X63ukzhBUVFR%20n3322aeffipSHBwcVq1aVb58+d27d4tF294T56ONaL4nKUTI1yJnUlDejTPhI6eSwtHREb4D3VF+%20qIdBIVxM9erVlZ9hFzS/dlknwMhj5syZ/v7+bdu2/eOPPzRzRFUg0+BrLl68+OLFC/l5GAJfX1+M%20seChGjRooI2mwQFVrVoVQ0OFCSUmJiY2NjbPnz/XtLF+Qfsk6g/tAhvlN1WuXBmFiIf974/z0Tq0%20wpMUFO1yJgVVM+/GmfAOTUlhbm4O3wGV/fDhQ5GSlJR05coVnDk3NzflJ/QFza9d1gFcqN9+++2B%20AwcmTpx4/PhxjfVBBTINFypCILxMrVq13P6jUqVKyIaggnQjIyPtPWvVqlXT09Pbu3cvRsPycfHx%2048diHS2t7pM4Ndh0+fJleesCAgJev35tZmamyXNNSuLy1CK0xZMUIuprkTMpKO/ImXBhvRICg6Hl%20y5fr6+u3adMmMDDw7t27CxcuRIO3bNkyLi4OniUmJiY0NFRqedX5td26tLQ0Mcdt6NChN2/evHfv%203t3/gM/VqCXaCmqaMlu2bLG1tT106FAZOGtjxoxBhq+//vr27dvIj3M3YsQInEf4DU1bR7+g1sXH%20x/fo0QMZZs2aJaw7cuQIYgYGkZA1GutYYMjmzZvRwaTl8LXdmRTINC3yJIWwToucSSFOXMk5E64U%20/C6Ax/Ty8pKXjy4uLmK+YXp6+tSpU6G+r127pk5+bbfuxo0b8l/MUVDuz58/1+oTp8DGjRtNTEzU%20/2KLJpsWERGBKCj/9i/OY7du3VBOGbAOwV7hHj6c8uLFi5FZk4PH+vXr0cH++usvafKvtjsT9U3T%20Lk9SiBOnRc6koKaVnDORBA3n0JQglpaW69atk8lk8JvieeHq1atxRsVWe3v7OnXqyN9FVJ1fe61D%20hwsPD69Ro4aQ4QqPSzXwoX5BT5w8MBC7I0yampqWgT7p6Oh46tSp6dOnizEidvT29p48ebK8V9Je%2065o2bYpQMXDgQIwm8a+BgcGsWbOgAzT8eZO1tTU6mLm5ufSoQtudiZqmaZ0nKcSJ0yJnUlDT3oEz%200YGoQV/39fUtAy+GEY0F3VerJyUQQuhJiGaSlpa2du3a5ORkTgom7wL6IEIIPQkpUShoCCGEEEJB%20QwghhBBCQUMIIYQQQkFDCCGEEAoaQgghhBAKGkIIIYQQChpCCCGEEAoaQgghhFDQEEIIIYRQ0BBC%20CCGEUNAQQgghhFDQEEIIIYSChhBCCCGEgoYQQgghhIKGEEIIIYSChhBCCCEUNIQQQgghWouMTUAI%20KSJJSUlxcXHp6ekODg4mJibvYQUIIaUO79CQ95GMjIynT5+G/0d0dHTZUBVZWVklVDhKfv78Odrq%20xYsXb9++ld8EGTFv3jwXF5cuXbrcuHGjeAtXh6JXoOTqpsmHy87OxlFwrGfPnuXVc96x+SqqGhkZ%20iWq8efNGpERERNy/f//Ro0dpaWnyOVNTU3Fpo7bYRT5RmEnXR0FDSJkCnnHNmjXu7u41/qNjx44I%20hJKv1Ebg3BcuXBgfH19C5aPkXr16VatW7fPPP4dyUjg02tPU1HT9+vUtWrQo3sLVtL2IFVBdt/79%20+6OTjBs3rhB1e8dNUSAgBSZOnAjTunbtGhcXV+r1UUFISMgHH3zQrl27sLAw/Hvt2rVWrVp17ty5%20devWP//8M/SKlPOXX35p2LDhypUrMWiREnF1N2vWrH379pBBdIAUNISUEeAKPTw8vvjii3///Tfj%20P+Au4RlXrFihpZomKCgIvn7v3r0GBgYldAhdXV0nJycEP0dHx/Ll/x+/gQFx3bp1ISa6dOmCbMVb%20uDoUvQKqQa9AJ3k3faOITVFQ0tPTYRqUjY6OjibUJ1cyMzMhU2JjYxs3bly1alX8GDt2LCq2a9eu%20ESNGLFq06OjRo+J+DC7qP//808zMrG/fvvr6+lIJqL+rq+ujR4/2799fiveZSEnDOTTkPQKRb/z4%208VFRUfhtZ2cnwn9WVhZSkpOTZ8+ejZEoXKHkuOElpbvx8PgVK1aUFAM2vX79+tWrVzKZzMHBITEx%20Ef/+f1dUzr8Kx0XMeP78ufC5KBwZFGJDQkICfLE4iqWlZYUKFdQ8CjKg8nPmzEH5xsbGcNmVKlVC%20PTGYFntZWVlh8A0TcER7e3v8lX++oH5tzc3Nly1bhshnYmKCA0mZkc3d3f3XX39F/IiMjLS1tS1E%20EykX/ioHddpWRQXybX+koHFSUlL09PTQaEIMIXwiM8Iemk6cCJGuIJVU9A35SCyKwjnFVkRi/Mah%20K1eujF1wXBwd5cjblVc7qzBBamTpXMvXR3U9hVFoOpQWHR0t7mqYmpqiwirqI1+mwnlR/6Sr04AC%20GA7JgnPUvXt3IyMjjEkePHjQtm3bOnXqoMNDaJ45c6ZHjx44xLlz5y5fvuzj41O7dm35EmAO9sWO%20Bw8eHDRokLW1NZ1h2WT+/Pk//vhjampqNiFlGji+mTNnwoPDdzdp0uTWrVtvcoAf79mzJ1wq3Pro%200aMhLER+aILdu3dLvg+edPHixeLZPBDzNgwNDTF+xaAQ7lJEGowgT58+jUgmHRcxZsWKFdhdihC/%20/fab/BUHp+zp6fn/3zItX37MmDHh4eHqHAXBAEGxX79+8ld03bp1Ede///57hJ8qVarMnTsXR0R6%209erVb968iRGqvDdXv7ZI79ixI6LawIEDEaLkm8jGxkYE10I3EXbp0KGDKBxlIuqruaPqCuTb/mg9%20hDec93r16kFtiPyPHz+G4ED+jRs3it1btmyJHT/99FPEaXX6hgSKcnR0RJxGv1q4cCEsEhX4/fff%20cdInTJhQPgfYdfbsWeWmkA6nwgTRyArn2sXFJSQkJN96wnwvLy+ku7m57dmzBxJfZPvkk09CQ0OV%206yPOO8QNyoR4yrUXqXnS1WxAgM6wfft2oTiDg4ORcvLkSQsLiz59+qB90A6QX35+fuJaQM1h++3b%20t5XLgdaBFTjX4nYOKUvgWoCMgZihoCHvC4hYzZs3h/ds0KABhobym6BpIAvmzJkDlw0HKq6QadOm%20IRRB6FSqVMne3l4MHyF9hNuF4549e7a4US9Gn0BELISW69evi5ITEhIQCOHQ4UxRiK2trXDfEBwY%20DSNDUFAQwicS4a+lDBh6QpTkexRoMmSAABKhDhkQkxB+YB2CitgLmVF/hNWhQ4fiYocViA04EAIM%20/qpTW5Twww8/oD4irkN7iUBbXE0kf2pQuBA06uyYbwVUtz+CK2Iz4qJQezExMWKXhw8fivsTa9as%20yVXQqHNcqShxRHGTADkRfYVROAvYhBRhV+3atZ88eaLQFOJwqk0AUluJc4228vHxQTOig6muJxRA%20//79xb4o0D4HcVMKPRBqTKE+0PrQDZBWGBXk1Ytw0HzPHZpdzQYU45Dp06cjJy7bly9fCmmCxuzW%20rRuu1l9++QU1nzp1KtrhyJEjaBz8RsdTvvzRvOKpGeovLj1S9gQN59CQ94XExET4aPzo2rUr1IP8%20JnhVDPW++eYbxBu4TlwhZ86cWbduHZzp8OHDMS7EjpAIcLsHDx7csWMHMoixNVw2dm/YsOHVq1eR%20Z8aMGfD1iP0XL14UJe/du/evv/5CmStXrgwLC0M6Rq64/OB8k5OTxXA2KioKMePevXsQIteuXXN1%20dcUR//jjj3yPEhAQgLiydOlSMQ7GjoGBgYcPH8bAF5pAPE3o1asXBqyIrBAlCCcQTzji06dPIaSW%20LVuG6BUREaG6tgg/qC2CmagGihXhquhNJP9mjcJjHdU7iicv+VZAdfsj5OMo4ojiWAJkxr/iTp5C%20F1LHcERWhaLwA+2M9g8NDV2/fr2xsTHyIMajyyFl1qxZONb9+/ePHz8uWkBqClEBFSbgpIhGlj/X%20qA+OAvVz4sSJfE+QePSDH717975z5w7K37hxI2qL/MuXL0c9RVNI9UGjwXxUIK9ehHqqOHeXL19G%20z4QiUbMBxcPH06dPo9h27doJsQXxh+sFVwpa7Pz58xAxEDfopSgTrTpkyBBcFMqXPy75Zs2a4cfJ%20kyeh5OgPyyQUNOR9AXIBXg+OFcPxXGaTyWRSAEO08Pf3hyfFOHXhwoUYDsJRjhs3DgEDMRgeH6N2%20ab4F4tN3330nRpkdOnSAzxXPGuC44bIhL5CnVq1an3zyCTI4Ozv7+vp27tx5+/btZmZmCANw7jgu%20tsLJikkSUCdw34hJGI+qcxSMUEX8EIN4fX19EXdRVeyFwbqNjQ32gkOHSMLhBg4cGB0dvWDBAoQT%20xCfsq7q2Xbp02bZtG3aXIg12gRQrehMphC7lCSh57SgG7qorkJCQgIG46vbPtQJ5TY9V33D5XcQU%201I8++sjLywtnx8PDQ9ykQUrLli2R0rp1a7FwjnJl0IA4nAoTzM3NxV4K5xr1zLcPo30k+WhnZzd9%20+nQrKyvsiww9evRAIgQTxgDyrYFjodFU9CJkEBdRXucuPDwcVdq6dav6DSjGIaKfi8rAxtWrV0Oa%20f/XVVxBtGJejDf/55x/onhYtWri4uOR67rAvqoRyMHLAWIL+kIKGEG3u6//plXwXa8HAV9zLcXd3%20hwcXiXC79erVg1uEzoArl96VQFBxdHQUv6Wb59IcBTEBuW7duuKpEOoAL4yRaNWqVVHUkydP4PpR%20FIbpKAS7Y+yLgbiYuyDmkKo+ijBHZMO/UlAUm7CXeCIgUhBOBg8eXKNGDRwdGsXCwkIao6uo7YED%20BxwcHOTfDRFzIIreRCpOgVT/vNpWnQrgPIqlR/Jq/7yEVF51U/O4yobgtMrPlRbzTuRfw9HJQflw%20+XahXM81eP36tfonyNbWVpqxC2ni5uaGPCgB2ZBHoWIqelG+5w6lqXNxyR8O/0K8ipetJAUG1XLh%20woVbt27dvHlz2LBhUK5r1qxB+qhRo6AOcdIfPnyIi0v+PXPsi71wCFQAR6E/LJPwLSfyvoAhHZwd%203FlwcLDy0wSMC+FzxT1tRBERCLGLlBOuEP/CLYpFaeUdvRQYpBQp0oj7+YgW0kMN8YxAbI2JiYGz%20RmZra2uFDIgBCnfO8zqKaqQwg2NhMI2BLBqhSpUqGArDnLZt2yJdyqm6tvLFFlcT5UteO+ZbAQQt%20Mcs1L4vk20deTzx//lx+CRMF1DlurrcHxIGkZlROyev0qTgpeZ1rNevp6uoqSoNWkNSV9ChKuW6o%20AE6r6l6U77kraANKhSiYbJCD+A35cunSpa5du3p4eEDlfPLJJ2I2T9++fb///nvpncHSeu2c8A4N%20IcUMhow1a9bEj6NHj4aEhMhvun//PnzftGnTIiIi4ApNTU0x+kS6mKcpZcO/WVlZ2IpICeeoHIoU%20UkTggUfGoFa6LQQFExsbK5y4mKWIOIHAgMLDwsLwNzIyEtWAg8YgGHvle5Rco7IykHE3btzA4X76%206ac7d+6IV1fEjiKYqa6twkvLCBLF0kTqo7BjvhWoVKmSmO6Tl0WixYRqfJuDFEFVLFWijuH5Vl79%20dlB9UlSgTvuIAI/CHzx4IK1Nh0TpPp/8SRctqboX5dX95CtQuAZUAYTL1q1bse+YMWPS0tL8/PxQ%20jZ07d0J4/fbbb6dPn1ZoaqER6Q8paAjRYszMzLp3744ABveNH6dOnYrK4fbt2yNGjDhz5syaNWu+%20/fZbRAtIHzc3N+yCTdJy6YmJiXDlcIUYSopgoNotimf21apVE+VI485t27a1a9fuyJEjCCEYGcOD%20wyPD7aJihoaGGHTGx8djvIvDFeBGa87YHfFG/Mj14UV0dDTcPTLY29uLhxcYy6IOCFpirIzEvGp7%204sQJDKzlx9kIY0VvokKjTgUQF9G2aGEV7Y/WEI8hsKMkEdA9xGr6ys2oznHln/sU3cx8u1BeGgK2%2059s+0h0LqKV//vlH/EaZ6BjIY21tbWtrK51EHAiiQTwAUtGL8l22Lt+KKTSgdF8qr9tmoaGhEC6Q%20Vh4eHigH/zZo0KB+/fqNGjVKT0/H6EVaEVGUAENKYvVFQkFDyLsDiuGzzz7r3LmzGA5+/PHH1XP4%204IMPzp8/L27hDBgwQLzYPHLkSHj8hw8fTp48GS4yMjJy0aJFf/31F+LZhAkTLC0t852IgwwYs3p6%20esLvo5wlS5Y8ffoUA9wtW7aEhIR4e3sHBQUh6osJmBs2bNi1axfU1eXLl+vVq+fo6Dh9+nShP1Qf%20BfEDtRV3nuDNr1+/LmbeKMQ5/IthMSoPF79x40bUBzWZPXu2WM0Px8WgHIarqO2VK1ekGI/MxdJE%20hUa0reoKWFhYoPVUWCS++gTFgDgKrQB9gHZAeF67di0iX64xT03Di9FMHE51FyqXx5MUiON820c6%20QehpU6dOhcJAC2zduvXgwYPlcl5TxxhAfmYM2gS9CC2WVy8Sywaqtgi7F6gBHRwcYD52hOpSnjeN%20M4XWSEpKQpnIBr2FuuG8ixfCxVqLQseghEePHsEKGC69S08oaAjRVuDIVqxYgQEcholwfGk5iAEc%20/Cacadu2bUXMbt68ObxtxYoV4WcxoIQfRzixsrJCIkSPUBJi9ihKkEal4l/hZ0Ui9MqQIUNwuDVr%201mCoXbduXUgWKKcRI0Y0bNgQGuLrr792d3fHXii2SpUqGGXCKeOIvr6+iEnwwqqPgt+oMIpFMIiJ%20ienUqVPHjh3F21IKe9WuXbt169b4AYsggHB0qDoRKl68eCFCRV61RbRo0qSJtHKaKLPoTSR/AwnF%20lstZiV+SDvm2bb4VUN3+jRs3RuRr06aNs7MzGgERHe3fqlUriAZRExHv5eumznHllZCyscopKBMp%20WTnkejjVJkBAiGVX5NtKoM4Jkho8Pj4epaEFxo8fj5p8+OGHfn5+sAX1FHnEZC/VvUiYoOLcoSgU%20on4DimEG8sDMXO/QQKPs2LHDyckJlS+X87oWRAyqKlbQxg9XV1fxRFXMqkE5uNykJQpJGYOTgsn7%20RfXq1S9evLhv375Zs2ZJ96KR+Ouvv9rb20vOFCPjKVOmNGvWDMJCvEdaoUKFdevWQS5IeTCUrFy5%20svxa+wiQCDkY7ltbWwthhPHoqlWr2rVrN2PGDLH6BVIwqEWIEn7WxcXl6NGjY8aMQZQS6qR79+7w%207zY2NsILqz6KGEB369Zt0KBBGFiLGy2vXr3CMNTR0VG8xCv2wm+MqkePHh0YGIjAg60w5+zZsytX%20rsRYGXWDl8+1tt9+++2wYcMwCEa0gOyTHlUUVxOVy5mrIVZgQ+EiUZ0d1alAvu3fsmVLf39/lICI%20jgCMxh87duyoUaNQoPTdA6lu6hueV53lU6RbKUhB3cTr3MpNodoEnHHltlKzD4sPDuBYkCbz588f%20N27cs2fPRA9cunSpqKF8fdDZIC9U9KLk5GTLHFScO/FgVM0GLJfzpBjDjAsXLoSGhqampsrbiH8h%208tDbUXmxcjH+QmNdu3bt1KlT//zzD4r96KOPxC7IBqmKQ3fu3Fn+mxKkLKGDroCxnRgOsjnI+4NY%20HVsKqHl9R0bKBkevMLATH7ZEOvaVXgXCSBR/9XLIqxxcawqPhDBylW6nK+yr5lFENlE+ciLYKOyl%20cCAkohri9kC5nIVi5aukXFux9ItYr09h+mfRm0jclkCdpRNR6LbNa/Ctuv2lraJwcXcBZiL+yddN%20fcPl70zI11k5RawAhJQCHU7eBOW2Ur8Pi/4gml26p5LXqZHqo7oXqX/u1DlxyLBr1y5vb29o9OPH%20j7u7u0ubIFB69uyJ+LV//37xFEl8SKRv377iA1JDhw6FChQ96ty5cyLzsWPH6tatSwdYlkDvWrt2%20LfQ079CQ9xQ1bzuryKYcWVX4ZdWH08+hKEdRyIZAkut6qQoHQrZcR6vK5YsgWkJNJIJfCbWtOnkU%20tspbqlw39Y+rXGflFDEduyiHU24r9esp3x9yPb+51kd1L1L/3Klz4sQawY0aNQoKCgoPD5cXNI6O%20jufPn4fSkmqOzOIWrHi6hPKlWz4PHjyAcOzQoYN4x4qUSTiHhhBCiOZiZWXVrVu39PT0/fv3S6+X%20l/tvaWxlHQYdY2ZmVqFCBUnNJCYm7t69OysrC+XweRMFDSGEEFIKyGSyQYMG1a5dOyQkpHCfYbp3%20715YWFjTpk179uzJd7bLcldhExBCCNFkXFxcxKz5wt1fgZS5du0apAzfb6KgIYQQQkqTojwqkslk%200gcQSBmGj5wIIYQQQkFDCCGEEEJBQwghhBBCQUMIIYQQChpCCCGEEAoaQgghhBAKGkIIIYQQChpC%20CCGEUNAQQgghhFDQEEIIIYRQ0BBCCCGEUNAQQgghhIKGEEIIIYSChhBCCCGk1AWNrq4u24IQQggh%202oUkYGQ6OjrZ2dnJyckGBgZsF0IIIYRoEW/evIGMgZiRAfyzZcsW/MN2IYQQQogWATUDGaOrqyuz%20s7OLiYlJTU1FEtuFEEIIIdqFTCYzMTH5PwIMAPN9Upeq6nxtAAAAAElFTkSuQmCC" height="453" width="752" overflow="visible"> </image>
            </svg>
          </div>
        </div>
        <div class="fig"><span class="labelfig">FIGURA 4.&nbsp; </span><span class="textfig">Comportamiento de velocidad de producción de biogás (<b>R</b> <sub>b</sub>)<b>,</b> en función de la concentración de inóculo microbiano.</span></div>
        <div id="f5" class="fig">
          <div class="zoom">
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QQMAAKMH%20Hb/f//rrrytv29ra7kfQ1NQkp5mamnr++edv377NsgYAAIYOOpLH45FpZs+ePZGmcblccpqLFy8u%20XbqUBQ0AABZfdlxTFxQU3Lx5M66PVFVVjYyMsKABAMDim9ddV52dnevWrZOXpWTbHYFGOQAAwPRB%20RySbnTt3/vTTT999911/f/9rr70mh09NTRUXF3O/FQAAMGvQCQQC7733nvL23LlzwWBQPcHZs2dZ%20uAAAwJRBZ3p6enh4WLzo6en5y7/8S7fbLV6vWLFibGzs4sWLubm5XV1dSb/TStM/YdRuCTXTayqZ%209McCAIDMDTqS3W7fuHHjH+eIt5s3b37iiSeKiopycnKSXlC/319YWKjun7C+vn7//v06KcfhcKin%20F2+VNKM/FgAAEHQssoGOct2qpqYmOzv7008/nZ6eTnpBP/jgAzHbhoYGedf65OSkzWb76KOPIjV8%20bm1tFTnG6XTOzMyI6Ts6OsTAuro6Wc+kPxYAAGR00BEho7S0VLY7VnpJLikpcblcO3fuFK9feeWV%20vLy8ZJUyEAi43W6RS44cOSKHFBQUHDhwQBTgm2++iTS93W4/c+aMyF5iSG1tbVNTk8/nu3Tpkv5Y%20tgkAADI96Fit1rfeeks9RKSQZ599tqyszDLXWOfNN99MYinFnxsYGOjp6ZG5xDJ3Jevo0aPy2lno%209LIJUUVFhTK9IMs2MjKiP5ZtAgCATA86lge1IPK1unZEvP7222+LiooWqMTyeaKFhYVbtmy5cuVK%202Hqj8fHxmZmZ9evXq6NMcXFxbm7u5cuX9ceyTQAAQND5c+aQLWYmJiZk4BDpR3m9QAYHB+ULt9v9%200ksvzc7ORpqyvLxc/VbTSlp/LAAAyPSgE0kgEBARZIEa9p49e1Z5bqjIOm+//TZrEQAAhJU9nw9v%203769u7s77Ci73b7QRW9sbBRBp6ur6/Dhw2HrkDTXoeQVqxjHRtLe3j4xMaEekpWVxWYEAEC6BR2X%20yxUp5SwOq9Uq4tT169dDR8nrUKOjo7Ozs0pDnLGxsWAwWF5erj9W/4/u2rVLM6SlpYXNCAAAY0r8%20ERCyN+SwRP4YHh5OYksdv9+fn59fXV2tbpEjyuDz+cJOL+9+HxwcVE8/NDRkmbsHXn8s2wQAAJke%20dJRHQHg8Htl3n9KVX0dHh8gfhw4dSmIpCwoKtm7dKqLVhQsXlIGy07+mpqbQRGW1Wp1OpyjGjh07%20ZJrp7Oxsbm6Wt6Prj2WbAAAg04OOJJOBrCBRHm5VW1tbU1MjLwwlsaAnT54Uf8jhcChPpxLRROSV%203bt3W+auo61cuVLdS3JjY2NlZaXIRjk5OWJi2Y3h6dOnZSrSHwsAAAg6f6ZUkLS3t1vmLjP19vZq%20LgzNX0FBweTkpEgnypC2tjZ1F4KhpfJ4POrpxdvNmzfHMhYAAGR00JG1OEq4kd0K19fXy678pqen%20Nf0OJ4XsH/n+A3v27FFGuVyuGzduaHop1EyvyTH6YwEAQOYGHeUREAcOHBgfH6+urlbXjlgePOCT%205QsAAMwXdCxzbXEuXrz46KOPrl69WuSe8+fP22w2OaqhoUFd3QIAAJAS86p0qaqqUp6CWVBQcPPm%20TRYoAAAwjjhqdAKBwD/+4z8mt4kxAACAIYKO0Nvbm5OT09fXJ3vwy4ps3bp1C/SsKwAAgAUJOlJd%20Xd2tW7dYdgAAIH2Cjrwfu6Ojg6UGAABMIe7GyLVzxAuaHgMAAINbwiIAAAAEnZ9FbYNMY2QAAGDW%20oAMAAEDQAZACmjrXqBWrLpcrtDp2//79sU8AAGkVdGT3x/IpmBcvXszNzbXb7bdu3VIejTk5OWmz%202VasWPH555/n5eWxfIFF09nZKR+pqwzx+Xxr1qwZHx+P9JHBwUH9eUadAADSKuiotbS0PPbYY8PD%20w+pAI5LQZ599Jg61J06cYOECiyYQCLz33nvihXLuIU9Fpqam9u3bF7ZDc7/f39vbK160tbXdVzl2%207FiMEwBA2gYd5QgYSVdXF42RgUUjzi7EWYd48corr8hzj6qqqm3btlnmamXCBp3x8fGZmRnxoqSk%20JOw8o04AQOO+Cd27d0++SNeVMq+Hevp8vtLSUnWljghAW7ZsCQaDbO7AYkrgqbpjY2NyV3U4HHKI%203W5X785RJwAA40uwRkccVbdu3SqzzrJly5RWikoTAeW0EkBKKNWukXbGjz/+OPTURd2mJ+oEAJC2%20QUc4efKkzWYLO8rpdB45coSFC6TQ3r17xVnHihUr3nzzzdCxgUBApBZL5DY9USdgCQNI86Ajq8qb%20mpo0wz0eT09PT3Z2NgsXSJXt27d3d3eLF0ePHi0qKgqdQD66TsSXiYkJpU3PwYMHxYu+vr6vv/46%206gQsZABpHnQkl8ulada0efNmFitghJTT1ta2Z8+e2D9YVlY2zwmwEOLtHkkRCAQ2bNgQ2vWRMlw6%20deoUCxkEnV/sb+J8LvaP9Pf3l5SUcAcWYPCUA2NKoHskRWtrq9frDT2Mixmqh9fX19MJJAg6v+Bw%20OKKeBygdqm7atOnOnTssaGAxU47H49FPOeIMZOnSpZq9WLY+fvjhh1evXh11Apb2IkigeyT1Km5p%20aQkd/sEHH8jYJDYSMUPZ/OD48eNxncECaRt0CgoK/v3f/119HhDpoZ7Nzc1yGnpJBhaHOLuQKaeh%20oSH0CrJytaK6ulr8QD49Rww/cOCArBvo7OyUH5d3aUWdgAW+CBLoHklZ3W+88UZoTx9iuNvttszd%20MvLss8+KF2JuIjmxqEHQ+X+1tbXKSUBU4ozhxo0bYdtCAkgiv99/9OhR+VqcnavPOsI26bBarR9+%20+KGsGyguLhaT7dy506K6ZTLqBFgEylN34u2NWl60ev7553/1q19p1rtsY67cMtLS0iLykDgjXbt2%20LQscBJ1fnDuKXUWcbVRWVoaOFSeUNEwGFtOnn36qbsYRi6qqKhFi1Luw2HPVt0xGnQApSbT63SNZ%205urempubRXb5t3/7t5ycnEizEpOJ/Nrd3S2mFOmHM1Kkq3kdsOTJAQsRSLnaOfrThO6tUXdh9nGj%200e8eSSah119/XbwQkbS4uFhnVkNDQ/KFiLMnTpzgEWZIV0sWYqaBQOCll17iTisASKKo3SMpSShs%20Oy0NWTEvWzcfP36cm8xB0Am/14VtiWyz2TgLBICFSDk6HQfIBuN2u/3w4cMxzlZp3dzc3MzZKQg6%202rMBudctGk2vWfLmEf0ShoYw5RZKTZdZ3FoJwNQpx/Lg/n/lEYTinFP2lyPbp1Nnszh0nhNuMeTj%20zdUlJ+j8P+UexbAW4hHH/f39jz/+uLq5pShAYWGhTq9Zg4ODOuV3OBzqLrPEW7IOACOnnKjdI8Vy%20IA3tHkmqqKigmTkIOv9P6d1B7HiTk5PivEG506qjo0OcTxw6dCiJpZR9Qoi/MjY2poRQ8YdkG7pI%20HxHFcDqdMzMzoU+okPdeKmPFrMTAuro6am4BGEpc3SOdPXtWfbhTboyVx2cRknS6R6qpqSHoIC3N%20a7O22+0bN268d+9eaWlpV1fX4cOH8/LyamtrP/7449HRUbHXJWu3kbnq7//+79Xt78SOLfZh5e+G%20/cjzzz8fWgZZHSUKf+bMGTlWlHloaKi5ufnSpUvcEg9DEb9P165d++KLL0x8OrVkifgRZVUmQNM9%20kqA+/MqzzbjI7pEcDofsHkkZLs76du/ezQIHQSfiniN2EpES2tvbxRmD7OZh1apVSQw6stessKMi%20VbeKk5WZmZny8vJIsWnLli3qD8qnFY6MjBB0YJyII/89f/682KeeeOIJM34Lcc7z3HPPiW+RlZXF%20Oo1XAt0jRVVVVXX16lVx0qjMmceigaAThs1mKy0t9Xq9MtzIlFA/R06giRELoaenR3b9GfYPjY2N%20BYPB5cuX5+fny/1ZpLFPPvlETCwz0Pr169UfFCc3ubm5ly9fZpuAcVLOvXv3xAvx748//hgIBDS9%203BrfH/7wh6mpKSWxkXXilVj3SOqz0LBjdU4dAYLO/+8/b7311s6dOw8cOPC3f/u38iqSum3vQl/u%20lZ1i6dxFKfvCevnll5UhsvGysttrKnvE+Y1OF6LA4rs3Z3Z29u7du+LttWvXRHB/7rnnzFJ+j8fz%207bffyogjvsiSJUtYpwBME3TkqUZxcfHf/d3frV69Oi8v7/z580pdaENDw4JWhIqUI/5Wbm6uzuNC%205S1X4lCrXIrq7+93OBwnTpx49dVXE/7T7e3tExMT6iGcpCLplMakIuUEg8GZmRk5/KuvvnrooYee%20eeYZ438Fcdpz5coVJbEpd7GyvwAwTdCxzF3rHRkZka8XrS5U5hXZJ6HOw1nOnj0bWtpt27Z1dXW9%20+OKL4q3mKpW8nhX1r+/atUszpKWlhc0IC5F17t69K4OOur8okXXEhlpSUmLkwg8NDV29elV5K4KO%20+C6ywyrWLAAzBZ3F53K5mpub59lPz5NPPpmTk6O5L0y26QnbeBlY/JQTtkZHunbtmhi+Zs0aYxZe%20RJzJyUn1EKVGh2Y6yLRdOLRHPiPsAkqRNH0YEnR+Jq8ZTU9Pv/vuu4cPH9a5HWAh+gyUKUdpUxy1%20nFu2bFFPKXvWqaioWL58eWlp6eDgoDroyDY9Bj9RRkaRbXREypFtdNRu3Lghtt5HH33UaGUWEeeH%20H34IPaqKL8IKReaknKhDUl48dU/Nab9GTFOjI1NOQ0NDLI/YLSgo2Lp1a3d394ULF5Q2OvIurd/+%209rePPPKIvB9+x44dMgl1dnbKiqKNGzeyl8I4B6N7D4ROcPPmTRGAxMZsnDKL+BW2y83MOZ4CIOgk%20SOk1S9NlluVB1dG//Mu/vP/+++pWOydPnuzt7XU4HOqJlU6xGhsb3XPUd1qdPn06uVVQwDyzjn6V%208q1bt0TWMchGOz09rdPKjZQTdZkYfxFprrlwFRLpGXTULY4PHjy4aKVMoNcsUdTJyUn1A63UtUFW%20q9Xj8ajHqu/PAgz7c6gRDAbv3buX8p+cu3NYWXGt0++++07d4bW5go5sWl5TU0PcQboFnVSJ2muW%20a45mYKTOsmIZC5jF7Oxsyn9sqLCJd1mJeHr+/Pnf//735u3w+te//rWFfiCR9kFHtvkVu6usC5GP%20BF+7dm3UxsIAkLGUpldm7/CafiCR5kFH9mcTDAaV50PJZ0h5vd6/+qu/SvotVwBgduqOA9QdXsva%20EVPo6+uTHV7TDyTSP+i0tLSIlKN+GpxswbN9+/bu7u5Dhw7FcnsUAGRa1pFdQSqtmszb4TX9QCKd%20g458RLndbg/tKfjgwYPnzp3r6uo6fPgwlToAoE45mhodJevIJw0bufDDw8OaDq/pBxLpHHSkn376%206bvvvtN5DgMAQEM20NHcp2a6Dq/pBzJqnDVm58j3oyHo/JnNZistLfV6vc8//7y6OY7f79+yZUsw%20GKyoqKA9MgCE/sxYHlSHaEbJDq9XrVpltDKLEBb6KEP6gUSaBx2r1ep0OkXQ8fl8y5YtC52gpqaG%20oAMAkU6pw44yYIfX33///Z07d3RCG5CeQcfyoHNhpcM9NaUDYgBAXCnBRB1eA2kedGSHe52dnTt3%207lQPp4thAJiPYDAosk7Ku6ihw2tketCRovZZDABIIGSkvKkvV6aQHujUEgAAEHTCcblcWRGsW7fu%209u3bLF8AAGDKoCNSTnNzM0sQAAAYVoJtdAKBgNvtZvEBAKCIpRe+lDd+0hRM/diytJRgjY58fqd4%200dbWFnalTkxM8PwHAACQWvPqGfn69euhz7oCAAAwiARrdKxWq91ul8+6YiECAIC0CjrCyZMng8Hg%20vn37ZmdnWY4AACB9go7f7y8qKpqenna73Tk5OdxeDgAA0ifoAAAAEHQAAABSJsG7rgoKCm7evMni%20AwAARkaNDgAASWau/vfS+wGu8w06/f39S5culQ2Q+/r6/H5/eXk5zZABABmecpQedJWuh41JKXO6%20xp3Eg04gENiwYcOmTZuCwaAycHx8/Msvv1yzZo14wbYOAADMGnRaW1u9Xq9m4NjYmMg9U1NTJ06c%20YOECAABTBh3loZ4NDQ3379/v6OiQw2tra+Xrrq4uLmABAABTBh35UE+73X748GHNqBdeeMFmsy1E%20Wf1+f35+vtInYXV1tX6nzPLimjJ9X19f7GMBAEDmBh35UE+fz3fo0CHNqL1794oYVFFRkZ2dncSC%209vf3P/7442LOyhC3211YWBipMZDIMQ6HQ31xTbxV0oz+WAAAkNFBx2q1Op1O8eL48eNZWVk7d+6U%20WUG87u7uFq9ramqSGHRELnnjjTdEuhobG1Maind0dOg0BpJNiEQhZ2ZmlItrdXV18oKa/lgAAJDR%20QUdobGysrKwMO0oEiN27dyexlPJK2W9+85uioiJlYHV1tShA2MZAsgmR3W4/c+aMzFu1tbVNTU0+%20n+/SpUv6Y9kmAAAg6PxcqTMwMKA0Q1Z4PJ6enp7kXreSHTEfO3YsdFTYa2QyGGlGlZWViX9HRkb0%20x7JNAABA0Pmz2tpaTddDmzdvXpyiizjl9XrXr18fGnTGx8dnZmY0o4qLi3Nzcy9fvqw/lm0CAJAY%20pds9w3YPGKn3Qkv69o+cYNCRN0CtW7cu9LKRy+WK5ZaoeRIFeP3118Pe9qUoLy9Xvy0qKsrJyYlx%20LAAAyNygo2Pbtm25ubmDg4MLF3REyhG5RPyVzz//PC8vj7UIAADCiq8lTX9/v8PhUJ75MD09vWzZ%20skUusSyDzWYbGBhQt00OpbkOJa9YxTg2kvb29omJCfWQrKwsNiMAANIh6FRVVW3btk3eQK4v6f3o%20SC6Xq7m52W63Dw8P69TlyOtQo6Ojs7OzSjHk4ynKy8v1x+oXYNeuXZohLS0tbEZYHCa6gi7bAbDK%20AKRc3JeuDh48mJubqz+N+s7tpKccp9N55coV/StWsj9DzeWzoaEh8W9JSYn+WLYJGDDfGL+FY6Tn%20IbP6AJgs6FRVVd25c2dyclLEBRFobt26FXqMm5iYSHrTGZlyGhoaYrl3XfZn6PP5duzYIdNMZ2en%20rArauHGj/li2CQAAMjfoSLJjm0iBJhAIvPTSS0nsZdjv9x89etTyoCNmNXnnl4hBK1euVD8OQvZn%206Ha7c3JylL6bT58+LQusPxYAAGR00JG2b9+eFY5sKZzEUn766afqp1zFwmq1ejwedd/N4q3Sx4/+%20WAAAkB4Sb0bjcrliaZWcFLVz9AsjhGYdnbylPxYAgMSoW6qZoqgmKvDiBR35uKhIY6PeFQUAQLqm%20HE2GMGDxwuYbekb+Bfm4KMvcFR/ZMLmhoUF5qLjP5zt06BCbOwAAMGXQkeRtSvJubeUp4rW1tTU1%20NbKXGpYvAEQ6sTYd5TFJrD5kStCRlLu129vbLXN3SPX29i7oIyAAAAAWMOjIWhwl3JSVlYl/6+vr%20s7KyCgsLp6enF6hnZCDD6wAoMwAsRtCxWq1vvfWWeHHgwIHx8fHq6mr1rdpCTU0NQQdIYlyI1DOy%20xZAXQVhlAMwddCxzbXEuXrz46KOPrl69WuSe8+fP22w2OaqhoWHPnj0sXAAAkFrzqnSpqqoaGRmR%20r2VfySxQAABgHEtYBAAAgKDzM7/fn5+fnxUD+Qgqli8AIHMYttmcplWfuqcAzahMDzoAAAAEHQAL%20dbIYy0DjnNGy1gCkVnyNkWlxDKQqPeg8ocYgxUvv2u+FXm6a1ZqVlZXyxajTawCrGCZCjQ4AACDo%20AAAAEHQAAAAIOgAAAMYOOv39/UuXLpV95/T19fn9/vLycnrQAQAA5g46gUBgw4YNmzZtCgaDysDx%208fEvv/xyzZo14gULF0gW/R7AlL6/jNxHGXRWbowDU15IVmhce6vpji0EHa3W1lav16sZODY2JnLP%201NTUiRMn2NwBIOpvjMXYHenSa8ACnZ+kkPrUyMjxOsVBJxAIuN1uy9yDysWi6ejokMNra2vl666u%20Li5gAQAAUwad6enp4eFhu91++PBhzagXXnjBZrOxZAEAgFmDjogypaWlPp/v0KFDmlF79+4VMaii%20oiI7O5vlCwAAUijBLGK1Wp1Op9frPT5HDnQ4HMoENTU1BB0AAJBaiTdGbmxsrKysDDtKZKDdu3ez%20cAEAQGolXulitVoHBgY6Ozt37typHu7xeDZv3sySBZIllnu2DfUASPFWua2D1RfvmlWPzcrKMuCG%20x03mMJf5dhhYW1ur2fRJOQAAIE2CDgAAQJoEHb/fn5+fnxWDdevWLVw/OrIYfX19+pO5XK7Qgimf%20kj07hw4HACBhBu+1XBbJEu7SJEHHKETKKSoqmp6ejjrl4OBgpFEi5TgcDnXPzuItWQcAAIJOKvX3%209z/++OOxpBwRZXw+n9PpnJmZCW1CJJ9foYyVvTnX1dXRmzMAAJkbdAoKCm7evCkTw8WLF3Nzc+12%20+61bt5QYMTk5abPZVqxY8fnnn+fl5SWxoPJy1aZNm8T833nnnajTy76b169fH9qdj3x+hSj5mTNn%205Nja2tqmpiYRjC5dusQ2AWARKFcKdC4ZGORRrMpr5aoHkLZBR62lpeWxxx4TYUIdaEQS+uyzz0TI%20WKCHera1td24ceOpp56KOuX4+PjMzEx5eXmkDKTpu7msrEz8OzIywjYBw/4umuiJ5RaedB3zOrUY%20/qGemjTGOkVGBB2/39/b26szQdIf6ikrk/bs2RPj9PI56suXL1daT1dXV8/OzioZSFPZU1xcnJub%20e/nyZbYJAACo0fmZz+crLS1VBxoRgLZs2SISRsq/2NDQkPj35ZdfVhr0uN3uwsJCkXLkW01lT1FR%20UU5ODhsEAAAEnZ/rV7Zu3SqzzrJly5SbtEWSkMHilVdeSW4bnXjJW648Ho9S+3rx4sWFu6YGAAAM%20KPFHQJw8ebK3tzfsDVBOp/PIkSOp/WJnz57VDKmqqtq2bVtXV9eLL74o3mquUsnrWVFn297ePjEx%20oR6S2j7aAQCAjsQvXclGM01NTZrhHo+np6fHyI8uf/LJJ3NyckZHR2WTHUm26QnbeFlt165dB38p%20Pdrlbd++Xd2MKRJNj5Gh3UKG7aRx//797GkAMkfYVvnGaVpuUTWEv3fvniXdbx2Ybz864ofNgM+6%20kr/Hmp9t2bNORUXF8uXLS0tLBwcH1WNlm56SkpIM3CdFyunu7o46WWdnp3JpUhLLc82aNUqzJ4tu%20J41IytGT0qbrLyI306X3PmucJRaaddJ+babns65kEyK3233hwgVlYE9Pj9frrampeeSRR5xOp/iR%203rFjh8w64ie8ubnZbrdv3Lgxo3bC/v7+pUuXxpJyREx87733xAul5yTZkdLU1NS+ffvkYlTuxWtr%20a1MfHI8dO8bxbv6/hWF/Fw3VqXykE0fWIACCzny5XK6VK1eqqxZOnjxps9kcDodyAWXnzp0i3+ze%20vVuMbWxsrKysFEkoJydHjhIDT58+ndoG1Iu/0DZt2hTjLXKy8yGLqpm5bPMka3HU9+1bMrViDABA%200Fk8BQUFk5OTIs0oQxoaGpTGQ1ar1ePxqMeKt0a46Lb47Hb79evX1Ysi0vKUnWLrVM/Idk6WuQeH%20LcKzXQEASM+gU1tbq2kM5HK5bty4UVRUpJ5MpJmBgYFIF1A0YzMz5YjwNzExsWzZsgQ+q1yoUup4%20Pv74Y800oY14AAAwU9CJehsODEukw/m0ntm7d+/09PSKFSvefPNNy4O23hbdRjwAAJgp6IhfytDb%20cJYtW3bq1CmWbHpTbtQ6evSorEhTasgmJiaURjwHDx4UL/r6+r7++msWGqBmivbaYVvBs+5gLon3%20diPvVAo7qr6+vqSkJDMvBmVUymlra9N/+ph8VCqS9ZNjlt8Y9U1YrLi4YoQBixe2YKxZmEiCNTrK%20zcYrVqwYGxtTP2YhNzdXDG9tbeVqRYanHCz0ibWRe1vhFxHss6YrLR0G/oK82ViknIGBAXUT4Kqq%20Ko/HI7KOpjs+pFnKEWtZk3JklzxZWVnqC5eyefLDDz+8evVqlh6ADIk4xuwTWV0wpdrVQoeByFiB%20QGDDhg3q50K4XC6ZchoaGkKvSz49R7w4cOCAvM2qs7NTTp/yJ7wCADJWgkHHZrOVlpZOTU2J30L1%20zcPitN7hcASDwYqKCiM/7grx8vv9R48ela+PHz+ufpSVvNXOarV++OGH8jar4uJipRtGIzzhFQBA%200ImP+FV76623xAvlV01SetptbGwk6KSTTz/9NOyT6tWqqqrE9hCpk0YAABZf4r9AtbW1H3/8cdjH%20JIW9tAHDkneG6w+snZPYrACEpd+O2wjFUxdS80QzVh/SP+gIZ8+e9fv9RUVFyrl+aPNkAMn9vZGy%20srIM9WMT+vvHzyEA0wcdy4NHILEcgcUPFiLrGDOQWX55HwdxB0AKcdcVAAAg6MzRPNlKBw+9AgAA%20KcftMAAAJEfUvsKNQLYrDy0tQQcm2MGuXbv2xRdfmPcrLFmypKamhlWJDNlhLSZpsk0bc2RQ0FE3%20PZZ9Az722GPDw8NKv7fyJqzc3NzPP/+cznAX/4h5/vz53t7eJ554wozfYnR09LnnnjNgG1tg0aoB%20DFgwcfZvoY05MifoqLW0tGhSjkxCn332mQhAJ06cOHbsGMt30VKOrIoU//7444+BQEA+jcFE/ud/%20/mdqako5mJJ1Iv0Qmq4ynJ9DAKYMOn6/v7e3d9WqVZEm6OrqOnz4MJU6i/YbI8zOzt69e1e8vXbt%202vLly5977jmzlN/j8Xz77bfK6eOSJdwMCABIadCRfD5faWmp5tLVli1b5FMgsJgn+iLliMU+MzMj%20h3/11VcPPfTQM888Y/yv4PV6r1y5oq4VkHUAVOoAAFIWdAoKCrZu3drd3S2yzrJly0In4IHVi5l1%207t69K4OOfMy4knVE7ikpKTFy4YeGhq5evaq8FUFHfBfZQwFrFgCQsqAjnDx5sre3N+yDHnlg9WKm%20nLA1OtK1a9fE8LVr1xqz8F9//fXk5KR6iPphOmQdAEAqg468A8vlcjU3N6uHezwenui5yGQbHZFy%20ZBsdtRs3bois8+ijjxqtzCLi/PDDD6G5Td7iAWTUiQptzAEjBh3JNYflmNpjpZJ1wqYEkUdFAHrk%20kUeMU2YRccLWBYbewgoA5j0ymyJqa4IsQQdGPy8MO8GtW7dE1jFIqykRcTSX2Ex3dEjtKhZx1oCP%208+S8H9AcxIy5F6j31gw56s436MgeApWzc7vdrulZBwY5gQgGg0b4gbw7h5UFAFgc8+qwxOVyFRYW%20qq9ByJuwTp06xZI1oFkDoAkOAMAcQaezs1PTDFlRX1/f19fHwgUAAKYMOoFA4L333hMvVqxYMTY2%20plyJvHjxYm5urhje2tqq7tMFABDKLC2ZNM2wWHFI/6AzPT09PDwsUs7AwEBRUZEyvKqqyuPxiKwz%20ODi4cEHH7/fn5+dHrTQSaWzDhg1ZD2im1x8LAIuQHpQX6k6kDEJ9b7mmwED6B50U0jR/1kk5DofD%206/UqQ8RbJc3ojwUM/rtootLy0wjAlEHHZrOVlpZOTU1t2LBhfHxcGd7f3y8SQzAYrKioyM5O/r3r%20Yv6PP/541JRjmbt2JnKM0+mcmZkRh9qOjg4xsK6u7vbt21HHAkbODZoTboNQV0uE1gQAgMmCjtVq%20feutt8QLkXWKi4uVC0CbNm2ST/RsbGxMbtCRl6vE/EXGeuedd/QnDgQCbrfbbrefOXNGFqO2trap%20qcnn8126dEl/LNsEAACZHnRkOKipqQk7qqGhYYGeAtHW1nbjxo2nnnpKfzLZhEhTq1RWVib+HRkZ%200R/LNgEAQNqYV6XL2bNnNS1mQpsnJ4t8tFaME4+Pj8/MzKxfv14dZYqLi3Nzcy9fvvz000/rjGWb%20AAB9XJREpgSdePPHIisvL1e/FfErJycnxrEAsDiJIbSpk0Ee8RG26RW3lyOdg04gEPinf/qnf/7n%20f16IVsZm0d7ePjExoR5itKcOAQCARIKO0Nvbe+TIEY/HU1paqn+PtxEeeqW5DiWvZ8U4NpJdu3Zp%20hrS0tLAZYTHPsM1VWs7+kZm7qun21jSuq0ukbqaurk5kHSN/K3kdanR0dHZ2Vql/GhsbCwaD5eXl%20+mPZRWHk46Zhj0fqSxvkG3BCYjHw08st4TqDSGNx3HVltVoHBgZklzMGJ7v50fTOPDQ0JP4tKSnR%20H5sGP4fmov51BAAgZUFHqq2tnZiYsNvtN2/e1Pn1EtOk8LqVyGROp9Pn8+3YsUOmGfkIUlHsjRs3%206o9lmwAAIHODjjG5XK6VK1eq+2hubGysrKx0u905OTlZWVk7d+4UA0+fPi3jl/5YAFgEZrzMx6VJ%20ZErQUZ6IWV1dbcynlFutVo/HI9KMMkS8Vbox1B8LAIucGwzIQtMrpIUEbxSXnQtbFuBRD7GonaMe%204poTmnUGBgZ0kpDOWAAAkAYSrNEpKCg4cOCAhQdhGvIEUf/8zIBnjepTW0RdpwZcVqGrkp7lABhE%20gpUxfr//6NGj4oXP51u2bFnoBEboRwdI19xj5KxDuAFgKEtYBAAAgKAD8530xzIw5YXkGgcAYOEk%20eOnKyM/yJOKENscxWvG4zAHoN1kzCKXbXIOXE9BBjQ4AACDoRNDf37906dKsOX19fX6/v7y8nPuw%20AACAESTeBU4gEHA4HF6vVz1wfHz8yy+/XLNmzcDAQFFREct38UW9hVtEUqMVjyrxWJabxcC3bUft%20v4A1CJjlOJN+Eq/RaW1t1aQcy4NngE9NTZ04cYKNBgDA2abxO7xO7xOSxB8B4Xa7xYuGhgaxdJRH%20mtfW1srXXV1dXMACAACpNa9HQNjt9sOHD2tGvfDCCzabjSVrqDMJzTTGKaTlwW0dXN1AZu6wFgNf%20Lwjt7dpCt9cwoQRrdESUKS0t9fl8hw4d0ozau3eviEEVFRWL/wwsAEinUxTjXOkwyGkSkIAEs4jV%20anU6nV6v9/gcOdDhcCgT1NTUEHQAAEBqJd4YubGxsbKyMuwokYF2797Nwk3tCaIpiqc+X+RkEQBg%20oKBjtVoHBgaUZsgKj8fT09NDdU7KY4RS86w0gjFaZTiPLgcAGDfoSLW1tZrfsM2bN7NYAQBAOgQd%20AACAtAo6gUBgw4YNWQ+cOnWK5WgQUTuoTblI19EsXL2KYc0adv1qNj+xli08lx6ZuqtqNn7D9mMe%209tiSluJuSdPf3+9wOILBoDKkvr7+8uXLx44dYxMHFu1IaswiEVuBsLnHYoAn8IQ9JaZGJ4yWlhZ1%20ypGOHz/e19fHNg0AAEwcdAKBgM/nEy/sdvutW7fUD38YGRlhaRonqpuF5koWqw/srYZ9LhJ7KzIi%206MgnP4gXTU1NeXl54kV1dbXsTefy5cssTQAAYOKgoygpKZEvrFar3W5nOQIAgPQJOjAyU1Qsh31e%20IAAAyUX/xekWcTTpwZh36Ci3H4eWmZUIAEiiBGt0HA6H0o9Od3e3Ze7GqyyVdevW3b59m+ULAADM%20F3QAAEA6Sdc6dTMFHU2PzFF77nG5XFkhlE/FOzcTbanmuqWcG1bT+BjEakVm7qrGPwKHljON10h8%20QaegoODmzZuxLMqJiQl5/3kSU47D4fB6vcoQ8VY/nQwODiZxbkDKU44pelsJ+3R6AKBGJ4rW1laR%20S5xO58zMjNJRYV1dXaSWQLJvQ2V6zcPV450bAECTvAGCTtKI1OJ2u+12+5kzZ7Kzf75TrLa2tqmp%20SUSZS5cuhf2I7Ntw/fr1cvp5zs1cB6BYLhIZrdaU4yYyMy6YpVvk0GdVAgSdZJKppaKiQp1aysrK%20LJEfPTE+Pj4zM1NeXp6UuQEAAILOQpGpRVM9U1xcnJubG+nRE2NjY8FgcPny5fn5+bK5cXV19ezs%20bGJzAwAABJ2FpameKSoqysnJiTTx0NCQ+Pfll1+enp6WQ9xud2FhoUg5CczNdMxSt6zuM5D6cABA%20RgeduMhbrjwej3JR+eLFiyL0nDhxIu1XamgjGEM11rH88sI/KSeuOGjYEobtlZvVCiDlzPQICM11%20JXkFKtLEZ8+e1Qypqqratm1bV1fXiy++GO/cFO3t7RMTE+ohWVlZbEZYzDyhDhDihUG2QDIrAIJO%204uR1pdHR0dnZWaVhjWyFE7a5sb4nn3wy4bnt2rVLM6SlpYXNKIn1FkBGbfBU1KXx2YgxF5emWl1T%2035+WK8Ucl65sNltpaeng4KBsTSzJVjglJSWh0/v9/vz8fKX1sSR71qmoqFi+fHlcc0uD3Yye5QAT%207bMGudZsoaIOBJ1FY7VanU6niCk7duyQ6aSzs7O5udlut2/cuDF0+oKCgq1bt7rd7gsXLigDe3p6%20vF5vTU3NI488EtfcAACASZmmjU5jY6N7jvreqNOnT8sHTbhcrvfff39gYKCoqEiOOnnyZG9vr8Ph%20UM9E5Jvdu3dHnRsAAEgPprnrymq1ejyeyspKZYh4K5/nEFZBQcHk5KR6+oaGhp6eHtkoJ965mcV9%20M8jAR8oBAFLFTHddiXQyMDAQdpRrTuzTRx0LGDzCGrB4YcMrERZAai1hEQAA4o22LASYRTaLgMOQ%20oaoBWHfIqP3UpBV1AEEHqY84xj90cmoIAFhoXLoCAICT5LStrqNGBwCAJCcG9X2mS5YsMVoJM6rX%20Vmp0AABA2qJGJ23PJ8xSVItRmxMZ9gSRp+cAAEEno38UNb+ORv5F5IcQ5FezHFLEPmu6UymAoAMA%20Rkk8xjwtkRGHR/DCvGijAwAACDoAAAAEHaScGeuWqRIHACwE2uikZ8ox8j0vmdaFAxskCwEAQQdA%20lLhgxi7ISDlgnzXgjqBpXa55LA9BB5w9A8j0/MqhBgQdGO5MQrzIysoy5skEx00AwAKhMTIAACDo%20AAAAmA2XrtKN5mKQcknIINetwj6hIu2bwgEACDpYwFRhnBgR2hyHfJMhWyAAEHQARI+JJrphFWmw%20vYW+ZeWCoANOoOd7SAXIr4baMdk9QdCBQQ+axnwestI5h7qLDgBIm4Owuc6N07silqADAMDCnnMa%20MIdlzkN4uL0cAACkLWp00vw0gk7lAQAEHQAmyK86Yw1YVPIrAIIODPRjuWgM0nWheddg6FPMjBMm%201KVifcW7Qo28Wg1+lwMHYaRJ0AkEAg6Hw+v1yrcej2fz5s0JTx/v3Ey9Oyl9nBi2nOz88/n5MUh5%20iLCsVrBaCTpJSzmCeKuTTvSnj3dupt61ZmdnRdAxWtmWLFlCuJmPu3fvGnO1smrm494co+0arFZW%20q6mZ5nu2traKXOJ0OmdmZsTm0tHRIQbW1dXdvn07genjnZups474RbxvPKJUHP5YrdCsVuUXkdXK%20amW1ZlaNTiAQcLvddrv9zJkz2dk/l7m2tnZoaKi5ufnSpUuh1TD601dWVsY1NzPuV+q3Dz30kGGL%20KqtPueqfACOvVnGmyE9jwnuEYdcsq5XVStBZQNPT08PDw1u2bJG5RCorKxP/joyMhEYT/elLS0vj%20mpsZU474V3w741+FlXWns7OzHApjlJOTY5aL6/woslozs/5GYYqDsCQOwvSMnGLj4+MzMzPr169X%20R5Pi4uLc3NzLly/HO/3TTz8d19xMuV6zs418xq8mFvu9e/fu3LnDITLqoVOsU7NcVhe/3OIX8fbt%2021TURV2zYm9ltabhj6t5DsKyUieND8JmuuuqvLxc/baoqEjsdQlPH+/czCU4x0QFprVjLGbmmKjA%20ZjnQpyrlmHRvZbWytxJ00ll7e/vExIR6SFZWVktLi0GKl5eX53Q6Tb2E33//fTYzjWXLllVXV7Na%2004w4dLBaWa0G1NbWZoRi5Ofn/+53v8u4oKO5riSvTyU8fbxzk3bt2qUZIlLOwYMHU7VM7t27d/fu%20XVHy6enpn3766dy5c9evXxdvlVtyDH5EkG30cnJyli5dKlLaCy+88NRTT4m3Kb9Gw2pltS70mnW7%203X/605/u3LljijXLak3L1Rq6Zv/mb/5m/fr1Npst5Ws2iTUI5gg68rrS6Ojo7Oys0rBmbGwsGAxq%20rkDFMn28czOyJXPE17FarQUFBW+++SZnVGmA1ZoJa3bPnj0sEFYrFmMFmaKUIl2WlpYODg6qb88Z%20GhoS/5aUlMQ7fbxzAwAABJ0FJGKy0+n0+Xw7duyQ6aSzs7O5udlut2/cuDHe6eOdGwAAMCnTtNFp%20bGx0z1HfG3X69Om8vDzxwuVyvf/++wMDA0VFRbFMrz8WAACkB9Pc02u1Wj0eT2VlpTJE/9FU+tPH%20Ozcdf/EXf8FmlH5YraxWsFqRHqs169133xX/pbbJOgAAQHLJW7fopQ0AAKQtgg4AACDoAAAAEHQA%20AAAIOgAAAAQdAAAAgg4AAABBB3/W2dmZFU51dbX6QWBysr6+vqgz9Pv9+fn5sUwJ469WuTYjfRwm%20Xa2BQGDDhg3Kx0+dOsWyTaeDcLwTE3SQodxud2Fh4fj4eFyfEr+LRUVF09PTLMA0WK39/f2PP/64%20em0mtlXAUKtV7KRiYq/Xqwypr6/fv38/izE9DsJyz33ttddYgAQd/ILH47n/SzU1NVNTUydOnIhr%2079L8LsK8q1Wc9L/xxhs2m21sbEz5eEdHR7xbBYy2t37wwQdiJ21oaJCfnZycFGv5o48+Ir+mwUFY%202XODwSCLlKCDKE6fPl1ZWTk6OhrLpQp5gWPTpk3iiPnOO++w9NJgtYrfwuHh4d/85jfKU3KF6upq%208fGurq7bt2+zMM24WsWvoNvtdjqdR44ckUMKCgoOHDggflC/+eYblqR5D8KK1tZWkVk5DmtkswgQ%20ymq12u12r9cr9rHs7Jg2kra2tj179nR2drL00mC1it+/mzdvhh1VUVER4yYBo61WMeXAwIDmLOXo%200aPi4xs3bmRJmv0gLA6/zc3NHo/n6tWrLEA1anQQ/szP5/PF+JMmfxRFymG5pdNqDdXT0yMOu+vX%20ryfopMFqdblcWVlZhYWFW7ZsuXLlSl5eHkvS1KtVZNbXX3+9oaFh8+bNLD2CDqKrq6sTP2k1NTX8%20pLFa1YdRcYp5+PBhlmQarNbBwUH5wu12v/TSS9xPZ/bVunfv3lWrVrF7hsXPGCwOhyN0oPhJ27Vr%20FwuH1Wp5cD9dbm7u559/zql/eqzWs2fPyhcul6u5ufntt98+duwYi9ekq1WsxO7ubo/Hw+4ZFjU6%20CKOhoWFiYoJ9htVqeXA/nUg5AwMD6rbJSI+9tbGxkTbmpl6tYg9taWlpa2vjolUk1Ojg5zsb2UNY%20rZHOFMXpvjizHB4eJvim5d4qG71ev36dZWvS1Xru3LlgMFg/Rz3c4XCsWLGC8xMLNToA9FOO0+mk%20sWp6kD1BaLrclY1eWTgg6ADIxJTT0NDQ09NDm/T0UFBQsHXrVrfbfeHCBWVga2ur1+ttamoiy5p3%20V9V0NtjR0WGZqyW6ceMG1TkEHcTB4XCEPpCFx6mk5Wr95JNPjh49KsYeP35cM2rdunU05jDv3nry%205EmbzaYeKyvtdu/ezULjIEzQAZAp/uM//oOneaSlgoKCycnJyspKZUhbWxuVdkhvWe+++6747+DB%20gywLAACQNlpaWizU6AAAgDRG0AEAAAQdAAAAgg4AAABBBwAAgKADAABA0AEAACDoAAAAgg4AAABB%20BwAAgKADAABA0AEAACDoAAAAEHQAAABBBwAAgKADAABA0AEAACDoAAAAEHQAAAAIOgAAgKADLCKX%20y5UVzrp1627fvp3EP/QP//APCcxQ+VQgENiwYYMoWHV19ezs7EIsiu3bt+t/8SSWQf23FuGrJX29%20pKWkr4ioW1SyipHYHzLmtpTa3QEEHWQQn8+3Zs2a8fHx+c/K7/fn5+efPXt2ET4FljDYlmBk2SwC%20GMfU1NS+ffs++eST7Ox5bZl79+6dnp5etWrVfD5ltVoHBgZSu0AWqAyp+mqJrRdgQbclI+zpWFDU%206CA17Hb7rVu37j8wOTlps9nE8L6+vq+//try4ArXypUrOzo6li5dKl8r9T39/f1yoHTq1Cllztu3%20b+/u7rbMVREtW7ZMXbse16e+//57TYW2Ul0vphFnkzpX3HT+UFji6yszVNefR6pUj2X+srSa5aY/%20W81Hwl6eUKYR9u/fr/mjmuuS6vkntl70NwOdwuiUJKyEv5eis7NTPY3YkiOtr9A1orPx6E+ss5Zj%20/GrzLEakTTfGbVVn/eoUILFtKdKf++Mf/5jwXgZzeHfOfWCxNDU1hQYdZfiKFSvGxsaUt2pOp3Nm%20ZibsKPXYmpqasIkq3k9dv369srJSZxqFUmb1F4n0h9QizVApszhn1ZQhlvkrn4o059DZhn5EfKmy%20sjJ1YZQwGimwhi2YMkFi6yXSZqBfGP2SaMzze+msyra2NjFK/KZG2mZCV4TOxJpiR13LsXw1zaxi%20L0bUTTfGbTXS+tUvQGLbUqQ/98MPPySwl8EUZMIh6MAQQUc5IoceszQHl4sXL+bm5ob9LZS/K8px%20UP0nEvhU6NFfObwq0yiH44aGhtj/UOivY+gPjJxhaBlimX9oqTQl1/lhi/SR0OWjrDL5d5W3yhyU%20ec5nvUTaDHQKE0tJQtfCfL6XZoh6Y1bicugWIibWrAjlbdiJNcWe/yqLFHRiKUbUTTfGbTXs+o29%20APHu4/p/Lq69DAQdIErQiST0yOLxeHR+lsIODJ0mgU/pBB11kaL+6UgDleGa83X1xJHKEO/X1+TI%20WGar+UjY30h1ZlV+GyJVnCS2XsJuBvqF+a//+q+oJYlxVgl/LzGHsNOrN37xKx62xi7SxFH/aLyr%20TKdGJ2oxom66MW6rkXbzGAsQ7z4e9s8ltpfBREGHNjowEHHc/Nd//Vd1S2QxZO3aterGJT6fT7yo%20qKhQTybein9/+umn7777LnS2iX1Kp3XRxo0bw84ksT/08MMPr169OpaJY5m/Ms0rr7ySl5cnJygo%20KNi6dWukbxR2tpqPjI+Pi98A8aK+vl5ptdDc3Kz83afnKM0mYmnWEPvi0mwG+oVZuXJl7CWZ//cK%20+y1cLtfExIRc/kqLKGXO+sskloljWctRv9r8i6Gz6ca1L2jWb7wLLd59PPTPLdzhAjRGBn5RlyPO%20QYuKinSmEedew8PD4sX69evVxyDZmmRqauqbb75J1qdipJ5JYn9Iczyd59dXpoldpNmqiRP3YDCo%20MxOr1Xr+/HlNixD5E6tulpuU9aJfGFGG2EuSxO8VdumJX02Hw+H1epWKhLDtZuKdOJa1HPWr6fzS%20x1gMnU13PjtdXAst6fv4gh4uQNBBBgmtAe7p6Yn6ey8OdqWlpeLF6Oio+v6IoaEhnRO1xD4VI/VM%20EvtDg4ODMXZTFsv8I02TwGzViouL5RWc0EsMN27ckPG0oKDg5s2boVcnW1tbw8424fUStTCxlySJ%203yvs0vvjHMuD60ebN2/WWRGxT5ysVTbPYuhsuvPZ6eJaaEnfxxf0cAGCDhCFOL0WCSn0CCveWkIq%200uf5qUh8Pt+lS5fCziSxP6QzwwS+SNhp/H5/b29vXEtV8xHxu5iTkyNe6HTRJk7Et27dOj4+7nK5%201M0aIv0cJrxeohYm9pLM/3uF/RadnZ3ydmilWqWkpESOUq4olZeXR6qDiTpxslaZflVQ1GIkfV+I%20twALsY8n93ABgg4Qt4MHD4rzVHGE3bFjhzwMiZ8f2amGOOGW7RVCr6Yn9qlI6urqZL8d4vdMzkRp%20KhHLH4prhgl8/VdffVX+CB06dEh+SvaupvONon5Eaf9x/PhxeclGdk2r9D7S398vTnZ///vf79u3%20TxYstK1DYusllH5h/vu//ztqSZL7vTRLT0zw3nvvTU1NFRcXX716VVaryOofMeqNN96Qv+KXL1+O%20VAcTy8TzX2WRNoa4ipH0fSHGAiRrW0r4IAMz4a4rGKQfnbCThe1BJOx9W+rbUtSdcOj3saHzqdj7%200Yml75DQu2aS24+OZv76c05iPzrqFRS2YOoJElgvkTYD/cJELUnss4pxbmEXuPq+Kv2xmvu6k7X9%20RP1q+reX6xQjWftC2PUbSwES28f1/1y8exm4vRxYqKCj7uhCCm2FoByI1XOI61P/+7//G+mmUyUD%20WSLckRv1D2luWJ2YmFB+kPR7+Ih9/uqfIjGB/l3rYTtiCb2fVvMLFPrdNQWLdEd97OtFZzPQL0zU%20ksQ+qxjnpunjTrmpWz1z+UWUXSA0TOtMHLb8Oms5xq8WdjOLWoyom26M22qk9RvLckhgH4896MS+%20F8P4QSfr57AzV1NH5RagT3Y8Lw61w8PDmVB9Lb+vOPrP/+ljALD4WlpaLLTRAWBRPQ5J6SGmv7//%203LlzFt17zgHA+Ag6AP7cstWi6lxu06ZNwWBwxYoVb775JssHAEEHgInV1taGNlyNpQtHADA4aqSB%20WMXbH4m5yG7xWMsA0gw1OgAAgKADAABA0AEAACDoAAAAEHQAAAAIOgAAAAQdAABA0AEAACDoAAAA%20EHQAAAAIOgAAAAQdAAAAgg4AACDoAAAAEHQAAAAIOgAAAAQdAACApMqW/7W0tLAsAABAmlmydu1a%20lgIAAEg/Dz/88P8JMAD3Ak2rlYNs2gAAAABJRU5ErkJggg==" height="452" width="760" overflow="visible"> </image>
            </svg>
          </div>
        </div>
        <div class="fig"><span class="labelfig">FIGURA 5.&nbsp; </span><span class="textfig">Valores de período de latencia (λ) de los cuatro biodigestores, obtenidos a partir de la ecuación de Gompertz.</span></div>
      </article>
      <article class="section"><a id="id0x4cc3180"><!-- named anchor --></a>
        <h4>Producción de gas metano en los biodigestores</h4>
        &nbsp;<a href="#content" class="boton_1">⌅</a>
        <p>A
          partir del volumen medido de biogás y la determinación de la 
          concentración (%) del metano en los biodigestores, se calculó la 
          producción de CH<sub>4</sub>, obteniéndose los resultados mostrados en la <span class="tooltip"><a href="#f6">Figura 6</a></span>. </p>
        <div id="f6" class="fig">
          <div class="zoom">
            <svg xml:space="preserve" enable-background="new 0 0 500 300.532" viewBox="0 0 500 300.532" height="300.532px" width="500px" y="0px" x="0px"  version="1.1">
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kaGOOwsW%20LBCXLS0tEykGgBCxzJmZODPhfEeX70wjA8q50xf1ev2li85AikVUmmEy3mJNppFBoJlKcvBvSkrK%208uXLP/744/j4eLGyra2tr69v1qxZiYmJchhvbm5uf3+/vIuMO179N2lpaUaj8eTJk16Pr6kYAEJk%201uzEjVsfnjk70UemUQeUf/7h8sCLN2x9eA6BBgSaqaUM/rXb7d/61rdkamlqahKXq1evdjqdyq0i%209MgRNpJX/01qaqrBYBjrWTQVA8DkZxqvgDL/7+cGXkyaAYFm6lVWVsoBv0VFRSK1bN26VUk5NTU1%20ynDgd999V4Sb3bt3s6UBRF+mGTWgaCoGokZcpP8CW7ZsEYHm4MGD27dvFynH69YlS5asXLlS3irX%20eO0wkruWxnpwTcVq+/fvP3PmjHqNXq/n3QZggpmmbMcbMqaIRBJniB0roGgqBgg0YcFkMlmt1gsX%20LvitlDuMWltb+/v7lZExctjNyHHEmopHWrt2rdeakpIS3m0AgphpbpiZ4COgaCoGokAk7XJyOByJ%20iYnqcb6663PPiIXOzs6xbpUHK5nN5vT09MbGRnWBHHYzf/58r+fSVAwAk5lp5O4kvwFFUzFAoJk8%20FotlxYoVdrv92LFjyko5/V1RUZHVah15a1VVlbg1Ly9PBBqTyZSTkyPyzZo1a2RMqaioKC4uVo76%20VtNUDACTJnFmQsqcmXI5aXai+AlWMRDR9D/5yU/E/7Zt2xYRL9fhcKSmpioHMUkiebzyyisisvi+%20VTfa2Qx0w4OI5VTCNpvt+eefr6+vFw/it1irkpKSSGlkAGFLPbA33mS8dKHnFmvyhq0Pi+UJFgOR%20Sw7qiLCjnCwWS2dnZ2ZmprKmrKxMmWpv5K2FhYXqifhMJpNIJOoCHwFFUzEATGaaEbnE9/w0moqB%20KBBhPTQRnR9pZADBSjNyKMznF3rKdrwxsutFUzEQBd+wusidhwYAQkrTKSFDfZbHsaaQGXXKGU3F%20QNQg0ACAN02nhAz1WR59T4jnFVN6Ll0JvJhMAwINAEQzTaeEDOlZHgOZ3lcdU57bVhl4MZkGBBoA%20iGZaTwkZuvld3qtuDuQp5AtOMBuvXnHHxMUGUix/u+pXG9nciA6xX/va18T/srOzaYuQqq2tpZGB%20CBJvMt6daT31/icXP7vU+uG5e7JS4wyxk5xmhL+709J7xf31gvv8PoV4wfcsSW1rOr/2B8tuu+Om%20QH47d//gPz26OCaWf9ki4r9hdfTQAMBYwuQsj4GkGeUFP/F/cuemzQ6wOHf9/eqUBkQ0Ag0AaMg0%20nLMaINAAQMRnGtIMQKABgCkzkXllOMsjQKABgKk38XllOMsjQKABgCk2wXll1OuZwQUg0ADA1JjI%20vDKc5REg0ABAZGSaANOMWM9MuwCBBgDCMdMEnmYCyUYACDQAMNmZRmuaIdMABBoACLtMM440Q6YB%20CDQAEF6ZZtTUoumUkJzlEQgHcTQBgGlIzitz6UKPbrR5ZR546O7PLzq/8tU7/c6eJzPN7w9/uOKR%20TFoVmEL00ACYdgKZV0bTKSE5yyNAoAGAKUszzCsDEGgAIOLTDPPKAAQaAIj4NCPXk2kAAg0ARHaa%20IdMABJqp4XK5Fi1apL9u7969Pm6tra31ffeRBeMuBhChaYZMAxBoJpvD4UhJSWloaFDWbNy48amn%20nlLyR3Z2tvpWcVWdQvwWeKWZwIsBhDPmlQEINOHlhRdecDqdhYWFnmGdnZ1ms/nFF19sb28Xt5aW%20lor8kZOT43a7xa3l5eVi5bp163p7e+Xd/RaoaSoGEM4eeOhu8eM7zagzzeJl6cwrAxBoQsXlctnt%20dpEwduzYIddYLJbNmzd3d3efPXtW3mq1Wg8cOBAXNzRhYH5+flFRUUdHx/Hjx5W7+ygY+VwBFgMI%20f8wrAxBowoXJZKqvr6+qqpIJQze8B2rnzp0idixevNjpdDY3N2dkZCi3CgsWLBCXLS0t4tJvgZqm%20YgAAQKAZD5vNptfrU1JSli9f/vHHH8fHx7e3t7vd7nnz5qkjSFpamtFoPHnypFj2W6CmqRgAABBo%20xqOx8dp4Pbvd/q1vfau/v19eXbhwobosNTXVYDCo1/gtGHcxAACYKpF6csrKykq5YLPZiouLt27d%20+uijj4bPy9u/f/+ZM2fUa/R6Pe82AAAINKPbsmWL3W4/ePDgN7/5TXHVa3+Q3HOkXuO3YNzFamvX%20rvVaU1JSwrsNAIAQifiZgk0mk9VqFQt33HGHwWBobW1Vdj8JbW1tfX19cs+R3GHko0BNUzEAACDQ%20BMrhcCQmJubm5qpDhsvl6ujoEAtmszk9Pb2xsVF9a1NTk7icP39+IAVqmooRbt5p/tXRP/00dPUA%20AALN+FkslhUrVtjt9mPHjikr5fR3RUVFN910U05Ojgg3a9askSmkoqKiuLhYHtStG+7L8V2gpqkY%20YWVg0P2nM699dP6PFy+fDkU9AIBAM1F79uwxm83Z2dnK+ZVEyBDJY/369brh8TSZmZki8RgMBnFT%20QUGBWLlv3774+Hh5d98FNpstOTlZTjocyKMhPJ365Hdy4fjHL4eiHgBAoJkoi8XS2dkpcoaypqys%20TJlqz2Qy1dTUqG8VV5cuXapc9VugpqkYYWJg0P3B6WtHwHU4GlxXPw9uPQAgPEXeUU5yvuDx3eq7%20wDZM06Mh3Jz65Heuq5f0Q4fJ6wc9/Y2nDz2Q/r+CWA8ACE8xNAGihtLdotfHiB+x8GHHGz46XbTW%20AwAINEDIqbpbxBtbhJQY2ekSrHoAAIEGCLnms9W64e4WedVvp4vWegAAgQYIrdOddRd7zuj1fwso%20vjtdtNYDAAg0QMgd/+jAUCTRxapX+uh00VoPAJh2gUZO6atMFeM1ty8QdKN1t1yLKLLT5UTHaxOp%20BwBMu0BTUVGRkpLidDqVNXJuutraWpobITJqd4uSUcR/LeeqBwbd464HAEyvQFNXV/fYY4+NelNu%20bq4yCS8QRGN3twzHE32MuMV19ZIyI7DWegDAtAs0JSUlfX19uuEJfD3XFRUViTXd3d27d++mxRF0%20PrtbrmUUcfnB6UrZ6aK1HgAwvQKNw+F48803xUJhYeGGDRuU9TabLS8vTywcPHiwt7eXRkcQfXLh%20Ax/dLUpAUTpdtNbTwgAw7QKNYtWqVV5rHn30UdoaoXDq0zcDeScrnS4nP3lDUz2dNAAwfQNNZWWl%2015rf/OY3tDWC7t3Wfac768SCxzMokoePn0HPgChzXb10xnFcU/0vj6ylnQFgegUai8WyYsUKsbBr%201669e/cq620226FDQzOVPfLII/Hx8TQ6guWzrlM0AgBAF/SzbW/btu3w4cN9fX0bh6lvSkpKevLJ%20J2lxBFFe1nYaAQCgC/oupyVLlohMM+pNO3fuTE1NpcUBAEC4Bxrd8A6mzs5Os9msrLFarVeuXFEf%209wQAABBEcaF4UIvF0tPTQ+MCAIDJwckpAQAAgWaEiooK/Rhuv/12JtbD1GpvOt/r6gukst890Hri%20LC0GANMx0Ig0U1BQQLMiPImA8tKuI3t3vOE304g0U/7z6l/vOvJ+7Ue0G4Dp4LW6Tzf+9A/O3n7f%20ZXV1dTNmzPDRWyEL1LO3jPUgwe3mCGagcblczzzzDO8JhC3LnJmJMxPOd3T5zjQyzYj0M8NkvMWa%20TLsBmA5p5j/fbrtwufepX77nN9PoVGdsdDqds2fPTkhIqK2tDfC5bDZbVlaWPPNjEAUz0Ijfqrm5%20WSzk5OS43W7PCGfOnGFiPUyhWbMTN259eObsRB+ZRp1mNmx9eA6BBsD0SDNXevv1Ov1nXa4AM41k%20Mplef/11s9lcWlra3+//XqtWrSouLn7++eczMzPDN9AotmzZEhcXF6J2dzgciYmJSk9Xbm6uugVF%207hs5dkcdG10u16JFi0a9aSRNxYiCTEOaATBt04y8Oo5MI9JMenp6Y2Oj30AjT2JdU1PjNfVu2AUa%20+SslJSXddtttIWr3urq6uXPnOp1OZY3dbk9JSWlvb5dXRYP6DijZ2dkNDQ3KGnF1rJiiqRhRkGlI%20MwCmeZoZd6YJkJzVZenSpaH4XYIZaEwmU05OTnd3d3V1dSheq0gYmzZtErGpra1N2Y1VXl4unnH3%207t2yoKOjY+QOL6XtSktLRUBRCsR9xcp169aNOihJUzEiPdOQZgCQZsaXaT4cNuWnawzyLqctW7Zk%20ZmZu3rxZ6TIJIjlG59vf/rb6FAq5ubniGQ8ePChyhiyYN2/eqDu8RNyx2+1Wq/XAgQOyID8/v6io%20SGSg48ePT6QYUZBpSDMAppUTp7t//dbHV8bOK8OZ5sq2X9X7fhyHw7F8+XKz2Tzlp2sMZqARv1VK%20SkpDQ0N3d3daWlrQ56GRXVXPPvvsyJsyMjJE7BApyu12L1y40EcekpXKygULFojLlpaWiRQjCjIN%20aQbAtHLPHUkLb59liNX7qLnRZPhh3t2j3rRx40b5zS6+90Wg6ezsnPLTNUb8TMFVVVUiQslemba2%20tr6+vlmzZimjhtVDhmXc8eq/EcHLaDSePHnS62E1FSNyJc5MSJkzUy4nzU4UP7QJgGni/y3I+Pu0%205LEyzQ0mQ/E/fyXtlhtGvVU5bFsQX8ShOxJougQah8Px+OOPW63W7du3i6tNTU3icvXq1cqoYa8h%20w4JX/41IlAaDYazH11SMiKMeN+P7WG4AmFaZxneaCU/BDDRyl5BnbMGdh0akGZEwjEbj22+/LR9W%20HuJUU1OjPOO7774rwo0cMgyMlWY2bH3Y7/w0QOTa83rLjoMnaAcEmGkiMc3oQnS27VG5XK5//ud/%203r9/f1AyTV1dXXZ2ttlsrq+vV/bbVVZWepUtWbJk5cqVBw8elF04gtcOI7lraaxn0VSsJn5Nkd7U%20a/R6PR+bsE0zctyMyDRlO96QmUasjDcZaajp8E3f3XN16yP3hKg+HF55fevFV9/pEAuL7rzpwXtv%20ZaNj1EzzdHnjn9q63AOeCE0zulDschp1ajtBho9gPUVWVtYtt9xy7ty5wEchyR1Gra2t6pl/5LCb%20keOINRWPtHbt2m1f5PF4+MyEc5rRBTaPMKKJ/Kb//Ym/HHn/XCjqw+GVi8qnyz+Qyz89dGrKXznC%20vJ/GFB8XrDSjjBqWnnrqKZ1qalwRCRoaGjo6OhISEpRbw6uHpqKiori4OKSNLtKMeIqcnJxXXnlF%20PQpJ7oFavny5er2cmUYerKSeylApkMNu5s+f7/UsmooRBWlGnWnop5kmaUb9TS8uffdeaK0Ph1cu%20K8W/uWNi9DqPbtDjmdpXjvDPNH/5/MrNsxJ8ly1ZsuTq1avjK5BDUyKgh8b3ySlFBPn4448nuL9J%20ppnCwsKRY6pFM61YscJutx87dkxZKY+BysvLE8Vy3j+Rb9asWSP7XWT8slqtixcv9noiTcWIjjRD%20P810SzPymz5meHew794LrfXh8MrVlbF6fexUv3JEBL9pJpyF5OSUZWVlYjkzM1N8/V+5csXj8YhI%204RU1xsHhcOzcuVMs7Nq1a9QZbvbs2WM2m7Ozs5X1BQUFIpesX79ePoKc90+8EoPBIG8VK/ft2ydj%20lkhLycnJyiFRvosRid6rbg5kvhl1pql+tZF2i+I0E8g3ffgkg8BfiVelXEmmQXQL/hiapKSkZcuW%20mUwmkWYuX7782WefiZUyagR4Ks6xHD16VH0Wp5EsFktnZ6f6BJ5efTniVdXU1KgLxNWxTiqhqRgR%204YGH7hY/gcyeJzPN4mXpKx7JpN2iOM34/aYPn2QQ+CsZtZJMg6gXwqOcHn300UOHDlVXVyvjdr2G%20pGiVP8x3jUghvoce+yiwDdP0aIg4Xy+4L8BKkWly199Pi0V9mlG+6XWD3qNMtNaHwytPSpwxVuWU%20vHIgInto5EDa7u7us2fP6q7PqysyjQgxfjtXAGCqMsGovRda68PjlZ/88X++76Ny5Cv/RtER3huI%20DsHsoZG7mRoaGkpLS++//3555LMcgyILvE6NBACT41dHWl+uGZoayu83/fXei5ND5+bTUH9K/LxW%20/OBUv/JrV/Ue+fLH/resXpQwmwSiSpDH0Gzbts1oNIoQ89JLL1ksls2bN6tvlUcb0egIovam8wEe%20iNTvHmg9cZYWm55OtH/+t296nZ9v+uHYoNdYHz6vfMiAxzM4OOYdRI4Z8IgYNrS/KRQhDIiGQLNk%20yZJPP/3UbDbLyVpsNpsIMfKmsrKyDRs20OIIIhFQXtp1JJCDq+UB27/edeT92o9ot2no2e/ct/Yf%200zR902utD1EyCPorV1f+6xgnUgYiUfD7S7ymzRl5OgIgaG+2OTMTZyb4nQRPPf3MLf6Ob0K0Wrds%206Gt+/9tt4pteNzi0ByeQb3qt9WH+ykkziGIxNAEiVyCT4AUymR6mT6YZq/di1G96rfVh/spJMyDQ%20BEo5R0Ntba3XTatWrRLrc3NzJzIPDaA105BmEEgy8PFNHz7JYIKvnDQDAk1wPProo7rr89DQ6Jic%20TEOaQSDJwO83ffgkg4m8ctIMCDT+KafXTklJkZPNqE8+oJyCgLbGZGYa0gwCTAaBfNOHTzIY9ysn%20zSDqBWFQ8BNPPLFz585A5s1jHhqEOtMoJ8q+YWYCaQa+k4FueOTsWN/0bf/x4pmXDyhXb9XpvjAL%20Rb2u+pdfqL/lwRVf+mHhlL9yr5f9hVc+4jVP5ssGIiDQWCyWX/7yl377YJKSkp577jkCDSYn05zX%206Ugz8JsMRCwQC6P2W3jFAr/OH3kzRMlAa0YJk5cNRF6g0V0/y5LD4UhNTXU6nZzEEVMlcWZCypyZ%20ly4MTRyQNDtR/NAm8MHv5DEx1yc6923Q7Q7di9QarcLkZQMRGWgkrxlogEmmHjcTbzL6nZ8GiCBk%20FMDPZyQUD1pXVzdjxgy9Xp+cnNze3l5RUcEB25jMNCNCjN/5aQAABBpfVq1alZWV1df3t6+QpqYm%20u91+55139vb20uKYhDQzx5ocyJx7AAACzegqKioOHTrktbKxsVFcdnR07N+/nxbHJKQZuZ5MAwAE%20mvFwuVzPPPOMWMjJyXG73UVFRXJ9ZWWlPEWlyDrseMLkpBlNmWbP6y07Dp6gMQGAQDPE6XQ2NzeP%20enj2tm3bjEYjMwVjMtNMgJmmvvXiq+90/P7EX468f44mBQACzTXd3d1nz571WtnW1qYeVQMExXvV%20zYHMnqfONNWvNnqlmafLP5DLPz10ikwDABEqmIdtm83m9PT0hoaGdevWNTc3K+sdDsfjjz+uY6Zg%20BNsDD939+UXnV756p9/Z82Sm+f3hD1c8kumVZtwDnpgYvc6jG/R4RKYR6x+891baFkB0O/bdx6+c%20O+u3LOHW2+7f84XZG+vq6rKzs9WdFFarVXzpx8fHqwt+8YtfbNiwYeQD2my24uJi5WoQJ64LZrww%20mUw/+tGPCgoKOjo6EhIS5Mq0tDSlYMuWLQQaBNfXC+4LsFJkmtz194+aZmL1ep1epxsk00SzkfPt%20AtOZSDOBTG40VugpKyuTecXlcon4Ir70A4kmIs00NDS43W4ZBlatWiXuqzzUBAV5l1N+fr4c/ztS%20YWEh0wcjTHinmWGxMfqY4WX2PUUl0gwQCiaT6fXXXzebzaWlpX6HyYpAU1VVpXRt7NmzR9wxWAcM%20Bb+/pLKy0qs/Kikpqb6+PjU1lQ2PsE0zSqahnya6Md8uEHRywIk87kfTfphx33H0T3cofrclS5Zc%20vXrVc11XV1cQ04zD4UhMTNRf5zUHscvlWrRokXJrbW2t1939Foy7GJGeZpRMQz8NAEwCeXB0sMbX%20xkTWL19XVzd37lzRBMoau92ekpLS3t6uu74nr6GhQblVXFWnEL8FXmkm8GJEhF8daf3xf77vI82M%20zDTfKDpCu01ng253ID+8bExnHw575JFHlHHBATp69Kj4Qs/LywvTQGOz2fRjuP322ydy9gORMDZt%202mQ2m9va2pTun/Ly8u7u7t27d4uC0tJSkT/ktH7yJrFy3bp1ypP6LVDTVIyIcKL9c7mg9/j7YAy9%20YWmwac2j82iqn3HzzeGQUcLtZSPqORyO5cuXi6/mJ598Uut3+jPPPGO1WteuXRuUVxLkMTReh2MF%20l+yb+t73vqfegZWbm5uZmXnw4MF/+7d/s9vtomkOHDggs15+fn5TU5N4PcePH1+6dKloO98FXg0d%20ePE01N50fo41OZCzWPe7B0TxvHtuC4eX/ex37ttX3bb/7bYBj0c3qBs6WnvUbzKPbsAzKC4fvPfW%20f827mz9Y05Nep1/2//02HF7JDQvSLzc1B/6yRUb5b2W/YgsipDYOk8viX/6vvPKK1l6WdevWNTQ0%201NTUaO3XmYxAI0NA6JrPYrH09PSMelNGRsbVq1dF3BE5Ud2mCxYsEJctLS0igsg85KNgZHgKsHi6%20aT1xdv/Pqy1zZm7Y+rDvTKPM5Ltq41fvXXpXOLz4dcuG5hHwkWlIMwg3i3f+jEZAuJngsdY2m+3Q%20oUPiQYL4fRr8Ux/oho/Q9ozmzJkzwQpiiqqqKhHx5s2b9+mnn7rdbrGgjiBpaWlGo/HkyZNDnQrt%207b4LvtADoaV4uhFRJnFmgt8zPqrPS3CLv4nvJjnTrP3HoVgjMs3goIc0AwCTSe7MEVEhKNPPhCTQ%20SElJSVp3pI2bnIPYarVu375drlm4cKG6IDU11fDFozT9Foy7ePoI5IyPgZxlKdwyDWkGAEKtoqJC%20pJmcnJwdO3YE95GDGWgsFsuKFStGPZdTiNKMSBhGo/Htt98OescPJpJpwjzNjJppSDMAEGp1dXWP%20PfbY+MbcTGqg0V2f9S83N1ceRx3SRpk7d65IM15T9nntD5J7jtRr/BaMu1ht//79JV8UfcfMjJVp%20IiLNjMw0pBkACKKNGzeqD3N+6qmnxErxbdjX12e32w0Gg/rWvXv3TvwZg5yPLBbL5s2bi4uL1adw%20Unidv2rc5O43r0eT+4NaW1vVEw7Ks3zLPUd+C9Q0FY808iA0sRWjtZ+mbMcbMtOI+BJniI2UNKNk%20Gt3wGGHSDIBpJTY+YaD3SiBlXmvk3Lk+7uKjoLKyMnS/USQdtq1+ipEdVqPOoNzU1CQu58+fH0iB%20mqbi6cwr09wwMyGC0oySaUSgEQukGQDTxz8cfCXKfqNg7nIK9WHbOtXQaPXZrSSTySRSTkdHx5o1%20a+TJEOTII6vVunjx4kAKND0avDKN3PcUcWlGeq34QfHDpgQAAs0Q5bDtsrKyUBy27XA4du7cKRZ2%207do16hzEW7ZsyczMVHbOFRQUiOJ9+/YpT+q7QKSl5ORkZfSP30eDInFmQsqcmXI5aXai+KFNEJ44%20JwBAoPFP7qZJSkpatmxZKF6rPOmDjwKTyVRTUyNSiLJGXFVP2uO3QNOjQVKPAvZ9LDcwhW5YkK6p%20nnMCAJFF/5Of/ET8b9u2bUF5OLlLiC/+kUpKSoLVyGGbZjZsfTjBZCzb8calCz23WJP9ziMMAEBQ%20vmF1QT9s+4knnjCbzZzBcZoYeYR2IHPuAQAQdMEMNHKmO6fT2dHRkZCQEPSzbSPM04xcT6YBAER2%20oAFphkwDACDQIBrSDJkGABDZgcZisfT09HjGFoqzbWPyvVfdHMh8M+pMU/1qI+0GAAidOJoAWj3w%200N2fX3R+5at3+p09T2aa3x/+cMUjmbQbAIBAg/Dy9YL7AqwUmSZ3/f20GAAgpBhDAwAACDQAAAAE%20GgAAAAINAAAg0ASbw+GwWq3ynNV1dXUzZszQ6/Xqs1gDAACEdaARCWbu3LmffPLJ2bNnRbJZvnx5%20X9/QjGrd3d2LFi0i0wAAgAgINCUlJTLBtLS0HD161Ol0KjeJTLN7925aHAAAhHWgcblcHR0dYqGw%20sHDDhg2/+c1vxLLVar1y5UpRUZFYbm1t7e/vp9EBAEBwBXNiPafT2dzcnJSU9OSTTyrhJiMjIy4u%20buXKlSUlJY2NjSLQiKu0O6ZK23+8eOblA4HX3/Lgii/9sJB2A4AwF6qjnD4cJhbmzZtHgsEk2/N6%20y46DJ0a9SVOaEc4feZP2BIDwF/yo0d3dffbs2U8//VQOplm1apXL5dq0aZO4KntraHSEVH3rxVff%20GeodXHTnTQ/ee+voQd5gCOShBt1u2hMApl2gsVgsK1asOHToUHZ2tlyTlJQ0a9YscbWhoUFczcvL%20I9Ag1Gnm6fIP5PJPD50Sl2NlGgBANAnyLqdHH31UffXb3/72l7/8ZavVqhseHbx27VpaHKFOM+4B%20T0yMPkavl5nmyPvnaBkA8OvyFceHHb/1W6bMMKe4/fbbe3t7vQr27t076t1XrVqlvu9YZVMfaPLz%2088vLy+VyTk7Ojh07dMPjgsXyxx9/HB8fzzsGk5BmYvX6WDINAGjxXyf//VjLr11XPw+kuKyszDPM%206XTOnj07ISGhtrbW771sNtvAwIDb7Zb3LSoq2rhxY7AyTfAHBYtMI19oVVWV3MEkfgFlGVHsneZf%20Hf3TT8MhzciVZBoACNDFy6cv/LVdr9PXf/wbTXc0mUyvv/662WwuLS31OzOLVx7YsmVLZmZmcXGx%20uoMnjALNWFwu17e+9a2gvGiEoYFB95/OvPbR+T+KT0U4pBkyDQBo+Rfpf7gHej06j/gzHmAnjUKk%20mfT0dDkzyxT+CsEPNCJ/6UcjfuH6+vogPpHD4UhMTPTq4xr12dU1IlctWrRo1JtGDWGBF09zpz75%20nVw4/vHLYZJmyDQAEIhr3TP6oUgwONivtZNm3OT0dcE6AjrIgaaioqK4uHgSWkGkmdTUVPWpFSSR%20EH0HFOWQK0lcHSumaCqe5gYG3R+crpTLHY4Grel+3H51pPXH//m+jzQzMtOwsQDAi+yekcvj6KSR%20M8898sgjmkbKyu9xo9H43HPPhV2gEQngmWeeGevWII4LlqfAHJlm5PTE4omUAUfS0qVLZUFpaakI%20KEqBHL+8bt26UXeEaSqe5k598jvX1Ut6ncgNMYOe/sbThybneU+0X/vI6T3+3uhDXWxsKADwpu6e%20kTR10sgTUZvN5ieffDKQemVHSkpKikgz9fX1ItYE5RcJZqCRfUe64cHPYjkzM1OeyEmkgby8PLvd%20fuzYsQk+hdzNlJWVJdru6aefHvUFjDU3sYg74jWIl3TgwAFZkJ+fX1RUJDLQ8ePHJ1JM94zsntEP%209YMMvaM+7Hhjcjppnv3OfWv/MW3oNXg8g4NjhhqPRxQMikvmpAEAH90zgXfSbNy4UcklItB0dnYG%20mEtEoFG6G/793/89LS0tNzc3KINvgj+GJikpadmyZSaTSaSBy5cvf/bZZ2Llnj17AhwCHQgRmLq6%20uu666y6v9e3t7W63e+HChT7ylte+ugULFuiGzw0+kWK6Z2T3zHCamexOmnXL0nxnGnWa+de8u9le%20AOCjeybAThrlsG31Qc1ayale7Hb7Sy+9FI6BRvHoo492d3dXV1crayY+BNpisfT09GzYsGHUW9va%202vr6+mbNmpWYmCiTozr3ybjj1X8jsqHRaDx58uSo2SjA4ijQ3nS+19U3IraPchh2v3ug9cRZ9Zrm%20s9Wye0ZeneROGt+ZhjQDAJq6ZwLvpAmKIH6xBjPQyAO35LmclFd56NAhESmOHj06cshL0DU1NYnL%201atXK88lcl9KSopIJ0qNV/9NamqqYezT+mgqjlwioLy068jeHW+oM82oh2GLNFP+8+pf7zryfu1H%20cs3pzrqLPWf0ep0q4E92J81YmYY0AwDj6J4JsJMmKGRPxFi7VqYs0MjdTLrh4bQixMivfxEpxGVB%20QYFueMrgkE6vJw9xqqmpUfrB3n33XRFudu/ePcnvkv3795d8UTgPSbXMmZk4M+F8R5c604w8DFum%20GZF+ZpiMt1iTr9360dDJq/W6WPUDTn4nzchM4zvNDLrdgfzw9w7ANOyeCVEnjRwFq54XuK6u7rHH%20HsvJyVm/fn14BRph27ZtRqNR7g+zWCybN29W3xrqk1NWVlaqj2kSlixZsnLlyoMHDyqHJnn1a8ld%20S2M9oKZitbVr1277oqEv2HA1a3bixq0Pz5ydqGSakYdhq9PMhq0PzxkONKN1z3yhk+ZEx2uT+Yuo%20M81YaeaGBemaHnPGzTfzVw9AVHbP/OVS89BBomPrH+wbdyeNMmpYeuqpp0QqeOuttzZt2qSszMrK%20+sUvfhGscwkEOV6IAPHpp5+mpqbOnz9fNzyYubGx8dChoV0PZWVlY419mRyyx6i1tbW/v19pu7E6%20uzQVRwGZacp2vCEzzf1r9dfH+epFLnn/o0Ntb8z1SjNjdc8omUb813KuevGda2JjJm8/ncg04nL/%20221j9c0s3vkz/pABwFW3c5b5Nr9l/R73yC/6q1ev+k4CYxX4vW8YBRrd9XG76l6Tydk2coqe5cuX%20v/LKK0oEkTPTyF1d6rmZlQI57EbGLzVNxdGXaQ7vcd60zBA3Y1AEGk+/7r/2X+w9p/dKM2N3zwzH%20GX2M3jMoUtGpT353z+3fmOR+GhFoxALjZgBgLLcmL1x9/zPR9BvFRM1vIoLUihUrvGa7qaqqamho%20kLu6TCZTTk6OyDdr1qyRhz7JeY2tVuvixYu9Hk1TcZRlmoRZsX1d5gtvPeBxGz0DsV219/Wemx0b%20r1OnGZ2f7plrmUZcfnC6cmBwsgejvFb8oPjhDxYATB/BDDRyvI/en2BNoTOSnO0mOztbea6CggL1%20aCN5Yk85TlneKlbu27dPzl9ss9mSk5OVQ6J8F0dxprnjn07FmK+4u2/8y5ElF35/b+85i97onr3s%20DzNv/tu75b2KUh/dM0qgEbe7rl6q+vHqP/9sFx82AEBkBJoAiYhw5513huIEAhaLpbOzU6QQZU1h%20YaF6tJHJZKqpqVEXiKvqQcRqmoqjxunOur/qW29+8I/DmeaGK+dm6439KV97N25Wt/ow7JbPjwXy%20/pFxp2ux4bO3j/BhAwBEVaAROjo69u/fP8EHyc/P9zqmSaaQ+vp6ZYT2s88+OzKmqAvUd7fZbF1d%20XerJm30URyu5Iyk2vn/GzGtz+cSZrxgSh9Knchj2u637etKG9jR5PIMDg24fP4OeAVHWb4pp/b6Z%20DxsAIDICjbqDRD0ZTFFRkewsEcvyHE/iqpxwjw0QVuQ4X91gzMWaRbJvRvbT/OVIls49QzkM+7Mu%20zlkNAAgvQT7KSZ6h2mvXjM1mE5fFxcULFy7csGHDj370o4KCAq8DiBAOjn90wDMQ01W7pHc4zdz8%20YF2ssf+zN+8TmabzaNbs5e/Iw7DzsrZX/4+vD8XhwOZNvjZD3UM0MAAgVILZQyPPUJ2UlHTbbd6H%20tsvTOspeGXlKBJo+3JzurLtw6RN5TJNMM8aky7HmK3NWvCfHCF9464Geyy5lBmEAAKIz0MgzVCvn%20clKTM7hM/OSUCJ33ml/uqrlv+Jima2lGrvfKNA0t9sk/DBsAgMkLNHIyOrGQm5urPh+knMFFd/1c%20TocPH+7r6wv1eZ2gyenz77f+9ubez7zTzMhM0/HbhY2tb9BiAICoDTRyMjqx0N3dnZaWpp4MRhbk%205eUdPHhQhpt58+YRaMJEv3ug8v8eH04z7pFpZmSmObynw21gpyEAIEoDjW54/K9ILaPeJCe4k/ue%20kpKSnnzySVo/TBw8sL/7jHFo9rzl78TO7BrrGGxdwl9TvjY0P01fl/mDrHtpNwBA1AYa3fDJm8rL%20y9VrRHxpa2uTE9w1Njbqhs9IoJ7uBVMr/q6mxPR2kWaMSX/1XRlrviIyjemuMzdm/Jl2AwCEj5Ds%209MkfNlbcodHDTV7Wdl2Wljus0snDtgEACBMxNAEAACDQAAAAEGgAAAAmhgOnMX7XzmkAAMBUo4cG%2043HDgnRN9TNuvplGAwCEDj00GI/FO39GIwAAwsck9dDYbDa9Xn/77bf39vbS6NFqz+stOw6eoB0A%20AJOPHhoER33rxVff6RALi+686cF7b6VBAACTiTE0CE6aebr8A7n800Onjrx/jjYBAEwmemgQnDTj%20HvDExOh1Ht2gxyMyjVg/Of00bf/x4pmXDwRef8uDK770w0K2GgBEGXpoELQ0E6vXx8boY/R63ST2%2002hKM8L5I2+y1QDA/tKxzy/0+K7pdfX9tvw9r5V1dXUzZszQq3gNkJUFe/fu9f3gFRUV8u61tbUE%20GoRXmpErJz/TDL2PDYZAfthkACA9sOJLlWV/uPDZpbEKRNwp/3n1V75656i3lpWVeYY5nc7Zs2cn%20JCRoyiUOh+Pxxx8P8hcBGxVBTDNTmGkAAIGbPWfmqo1fFZnms46uUdOMuOmfCu6bY032/Tgmk+n1%201183m82lpaX9/f0BPvsLL7xw0003+e3FmdRAI0JWYmKi3p/i4uKgbwz51F6R0OVyLVq0SHnekYHR%20b8G4i0kzZBoAiCCzZif+z8IHXy9/r/XEWfV6EXHKf14t4o7fNCOJNJOent7Y2BhgoKmrqyspKSkq%20KkpISAijQDNVRJpJTU11Op1e+SM7O7uhoUFZI66qU4jfAk2PNm396kjrj//zfR9pZmSm+UbREdoN%20AMJNvMlY8INlx99u+XNDh1zT3nReRJxvb31YxJ0QPalIMw8//PD69euD+7ARGWhEuJs7d65XmhFK%20S0tF/sjJyXG73R6Pp7y8XKxct26dMljJb4GmR5u2TrR/Lhf0Hn9vr6GOLf5iAEBYZ5r/53//wwd/%20+Pj92o9aT5ytPfyhiDhiZeCP8OGwRx55JD4+3m9xRUXFoUOHtmzZEhcX5OOsJxpoLBZLT0+PJzBn%20zpwJ5Lf1Qe5mysrKMpvNTz/9tPoml8tlt9utVuuBAwdkM+Xn5xcVFXV0dBw/fjyQAk2PNp09+537%201v5jmlgY8HgGB8cMNR6PKBgUlw/ee+trxQ/yVwMAwlOcIVaEGJFpflv+ntY0I76Xly9fLr6Un3zy%20Sb/F4rv1mWeeycnJuf/++4P+W0RkD01ZWVlXV9ddd92lXul0OpubmzMyMtShb8GCBeKypaUlkAJN%20jzbNrVuW5jvTqNPMv+bdTYsBQDh753enZs1O/FKmtfrVxkDqN27cKEeXpqSkiEDT2dmZmprq916l%20paXt7e3PPfdc0LtndBE3sZ7sEBr1JtFGbrd73rx56mZKS0szGo0nT54MpEDTo0FkGnG5/+02kWl0%20g7qhWfVIMwAQgaoPfdB7xb1q41fFcs1rJyrL/iCXffcsbNiwQdOzOByOnTt3fu973wsk+oxD8Hto%205HkoRzUJJ6dcuHCh+qpoNcMXZx/xWzDu4umZaUb205BmACCCiPgiLr9ecJ+8mv2Ne+5Iv7n859X9%207oHgPtHRo0edTueuXbuUVFBQUKAbPuAmKMcRBznQiDQTiiO0ESmZhjQDAJFCRJaX/+/vRXxZlvcV%209fp7l971la/eKW7qdfUF8eny8/O9RtbKo21qamrE8tKlSyf4+MHc5SQH0k7t5vHaHyT3HGkqGHex%202v79+8+cOaNeE8VH+6j3Pel1HtIMAERKmvnykjvuyRplB9CXMq1xhtjyn1drHSM8hYIZaORAWrFQ%20WFj47LPPTvJvIvcHtba29vf3KwNf2tra+vr65J4jvwWaHs23tWvXeq0pKSmJ4g+GkmlIMwAQEURY%20uX/Fl+bdc9tYBeKmxJkJlWV/EJlmHI+/cZhydRKCQfAHBSclJQVy7FbQqWcqVCJIU1OTuJw/f34g%20BZoeDSMzjQg0YoE0AwDh738W+p9NY441eWSaWbJkydWrV33cy2+BIn9YsH6jYI6hsVgsK1as6O7u%20Pnv27ORvG5PJlJOT09HRsWbNGjn7ckVFRXFxsdVqXbx4cSAFmh4NI71W/OBUzTcz6HYH8sM2AoBo%20FeRBwXv27DGbzbm5ue3t7ZP/y2zZsiUzM9NutxsMBmX49L59+5TZ/HwX2Gy25ORk5ZX7fTSEgxsW%20pGuqn3HzzTQaAESfIO9yslgsmzdvLi4uTktLG3mr1Wptbm4OXSAwmUw1NTXqEzCJq+qB034LND1a%201Nvzekt3z9Wtj9wTzi9y8c6f8TEGAAQ50EzmYduj7nsTKaS+vt536BmrwDZM06NFsfrWi6++M3Su%20skV33vTgvbfyUQEAhLNg7nIKh8O2Eaw083T5B3L5p4dOHXn/HG0CAJgugUY5bLusrCxEJ6fEpKUZ%2094AnJkYfMzx9DpkGABDmgrnLSR7q3N7evmzZMlo2srT9x4tnXj6gXP2XUWKOrlp17ZYHV3zph4W0%20GwAgTASzh0Ye6jxVh21jItRpJhDnj7xJowEAojPQCE888YTZbF63bl2oT0KJkLwbDIZAfmgoAEA0%20BxqHw5Gamup0Ojs6OhISEqbkbNsAAIBAg+noV0daaQQAAIEGke1E++c0AgCAQHONxWLp6enxjI3D%20tsPTs9+5j0YAABBoAAAACDQAAAAEGgAAQKABAAAg0AAAABBoAAAACDQAAIBAAwAAELHiaAIoBt1u%20GgEAEInoocGQGxaka6qfcfPNNBoAIHzQQ4Mhi3f+jEYAAEQuemgAAACBBhPQ3nS+19UXSGW/e6D1%20xFlaDAAAAk14EQHlpV1H9u54w2+mEWmm/OfVv9515P3aj2g3AACmRaCx2Wz6EWpra+WtLpdr0aJF%20I9ePRWt94CxzZibOTDjf0eU708g0I9LPDJPxFmsyb1kAAKZFoGlsbPSRTrKzsxsaGpQ14qqPjKK1%20XpNZsxM3bn145uxEH5lGnWY2bH14DoEGAIDpEGhEBOno6MjJyXG73R6VpUuXiltLS0tFOlFuLS8v%20FyvXrVvX29s76qNprQ9upiHNAAAwTQON0+lsbm6eN29eXFzcyKxjt9utVuuBAwfkrfn5+UVFRSIA%20HT9+fNRspKk+uJmGNAMAwPQNNO3t7W63e+HChWNlnYyMDHXWWbBggbhsaWmZeH0QMw1pBgCAaR1o%202tra+vr6Zs2alZiYKIfx5ubm9vf3K1nHq/MmLS3NaDSePHlyrGwUeH0QMw1pBgCAaR1ompqaxOXq%201audTqdcY7fbU1JSRDqRV706b1JTUw0Gg48H1FoflExDmgEAQJNoO/WBPMSppqZGjgIW6urqsrOz%20d+/e/eijj07ay9i/f/+ZM2fUa/R6vd97Jc5MsNxy46ULPWL5hmRj0uxE3qAAAEzHQFNZWem1ZsmS%20JStXrjx48OA3v/lNcdVrb5Hcr+TjAbXWS2vXrvVaU1JS4vsuctzMRx+e0xvdekP/hU91e3e8sWHr%20w/EmI29TAAB8m0YzBd9xxx0Gg6G1tVUOqZHkmJtRBxHLvUuB10+EMgpYb+yfvfydlK/9McZ8xe+c%20ewAAIAoDjcPhSExMVEYBS3JmmoyMjFmzZqWnpzc2NqpvlWNu5s+fP/LRzGazpvqJp5m4eP3s5X+c%20kXTZkHhVZJr4mToyDQAA0y7QWCyWFStW2O32Y8eOKSurqqoaGhry8vJuvPHGnJwcEW7WrFkjM0pF%20RUVxcbHVal28ePHIRzOZTJrqJ5hmZpiMtz5Ub0z6q14fI35izVeS//G/brzJRKYBAGB6BRphz549%20ZrM5OztbOftSQUGByCXr168Xt27ZsiUzM1MkHoPBIG8SK/ft2xcfHy/vbrPZkpOTlUOi/NYHMc1k%20rzcNmM/ph0YPi40iQk2M3nQ5c43T97kRAABAFAYai8XS2dkpUoiyprCwsKqqSs4lYzKZampq1Leq%20j4caSWv9uNPMhq0Pdw7U6IYOhrq2ReTCx5feWPuvD5BpAADwLS76fiWRQurr68d3q21Y4PUT8V51%20s5JmrsZ/dLHnjF7/t0AjO2kGPf2nL/9u49ZHyna8ITJN9auNXy+4j3ctAADRH2gixQMP3f35RedX%20vnrnHGvywT8eGIowulh1wVC48Qx+2PFGxh15G7c+/PvDH654JJN2AwBgpBiaYAp9veA+kWZOd9aN%206J65FmlkJ82JjtdmzU7MXX9/nCGWRgMAgEATjo5/NEr3jJJpxH8t56oHBt00FAAABJowNXb3zHCc%200ceIW1xXL5365He0FQAABJow5bN75lqmEZcfnK6kkwYAAAJNOHqvotRH94wSaGQnTdWPV//5Z7to%20NAAACDThpeXzY4FsBRl3uhYbPnv7CI0GAMBIHLY9Zd5t3deTNrSnyeMZHPAM+q3vN8W0ft+8nIYD%20AGAEemimzJ/aG2gEAACCgh6aKdN0Jv9/vPrjoVBpMARSP+geHhT8EC0HAIA3emimzLPf4SQGAAAQ%20aAAAAAg0AACAQAMAAECgAQAAINAAAAAQaAAAAIEGAACAQAMAAECgAQAA4NQHYeDaOQ0AAMB40UMT%20ZO1N53tdfV4r97zx08E7Or1W9rsHrtyp7ewHM26+mRYGAGCkaddD43K5srOzGxqunem6pqZm6dKl%20wapvPXF238+OmPo+v/fzo3Gea/0unji95/vm2TfpXv/2N2Y4BuXKQX3shzcuvWi8Myvz5m/8nyd4%20IwIAMBHTq4fGK50I4mptbW2w6i1zZhrczp64We/P+lq//to5tC99+VpqvJg144tpZk6sxz3wp1re%20hQAAEGg0KC0tFekkJyfH7XZ7PJ7y8nKxct26db29vUGpnzU7MfPSWzMGhjNN0tcGjSZ9vLHrvmvJ%20pseqH7zRqDPEfzjrH2Sauffzozf0f867EAAAAk2gXC6X3W63Wq0HDhyIixvqNcnPzy8qKuro6Dh+%20/PjE66X4AedQphl09cTOqk/8h4tfmtGfEKPX6WP0Mbq4mIv3Gv6U+MDFuJtFmlnU83vSDAAABBpt%20nE5nc3NzRkaGTCfSggULxGVLS8vE69WZZnHPf8lMc+ry0sE+g17kGX2MZyDmo66sv6WZAdIMAAAE%20Go3a29vdbve8efPUASUtLc1oNJ48eXLi9V/INIMukWkMcS73pZkX3nrA4zZ6BmK7au/rPZ8SE0ua%20AQCAQDMxCxcuVF9NTU01GAxBrFdnmjlZf4gxX3F33/iXI0su/P7e3nMWvdF90/I/Jhgv8c4DAIBA%20E6Z+daRVWe5JjRmwXr35wT8OZ5obrpybrTf2p3ztXePsy12ZsbQVAABBNO3mofHaWyT3KwWr/kT7%2057dfX3YsGgqLsfH9M2Y6rzgThtrafMWQ2OvR6bru1ic36OOueGRlSUkJb0QAiG6JiYk/+MEPaAcC%20TRDIvUWtra39/f3KsJi2tra+vj6v/Urjqxee/c591a8OLfSkxly1xOoGYy7WLJJ9M3qD2919w1+O%20ZKV87ZjOcLUrIybl2IC817Zt23gjhppIjbQzm4D2px2mdhPQCCE1jXY5mc3m9PT0xsZGEVCUlU1N%20TeJy/vz5E69XcywaOqbpYs0SmWZufrBuzor35L6nzqNZg32Gvy6I8cTqef8BAECg0cZkMuXk5HR0%20dKxZs0ZmlIqKiuLiYqvVunjx4onXK3rSYnuTDUPHNF1PM8aky7HmK9czzY0X3nqgL3bG5wsZwAQA%20AIFGuy1btmRmZtrtdoPBoNfrCwoKxMp9+/bFx8eLBZvNlpyc3N7eHmD9WC4sju+quW/4mKZraUau%2098o0jntmeOLopAEAgECjkclkqqmpERlFWeP7ZJNa64XLt8/4rOn+3s+808zITHP+j1/tujuBtyAA%20AASa8WSa+vp6z3XqdGKz2bq6ulJTUwOsH6nfPfBn41eH04x7ZJoZmWn+7PrvboORd+EkmDlzJo3A%20JqD9wSYg0CAgBw/sd36eItLM7OXvxM7sGhh0j/qjS/hryteG56e5NPODrHtpt0nw/e9/n0ZgE9D+%20YBNEsTiaIIji72pK/MSQkPqJMemvvitjzVdEpvnrqbtuzPgz7QYAAIEmjORlbddlabnDKtoMAIAg%20YJcTAAAg0AAAABBoAAAACDQAAIBAAwAAQKCBDy6Xa9GiRfrramtraZNQs9ls+hGUlmeLhI7D4UhM%20TPRqUr8NzhYJ9Sbw/YlgEwSr2ZUGzM3NVZ/SmI8AgSZK0kx2dnZDQ4OyRlzlzRpqjY2NbJEp+Zue%20mprqdDo1NThbJNSbwPcngk0wcXV1dXPnzlU3u91uT0lJkacF5CNAoIkSpaWl4m2ak5Pjdrs9Hk95%20eblYuW7dut7eXhondCGyo6NDaXOvc1awRSbtb3qAHwG2SKg3ge9PBJtg4n9wNm3aZDab29ralLYV%20bdjd3b17924+ApPtJ8M8CDbxlyUzM9NqtV65ckVZWVRUpBs+wyXtEyKdnZ3ij0thYSFbZDIbXLRh%20UlLS008/rW5Mvw3OFgn1JvD9iWAThOgPjtKqFy5c4CMwOWSSoYcmVMQ7tbm5OSMjIy7ub9MxL1iw%20QFy2tLTQPiHS3t4u/qGzcOFCtshkKisr6+rquuuuuzQ1OFsk1JvA9yeCTTBxFoulp6fn2WefHXmT%20aNWrV6/yEWCXU/R8s86bN0/9Tk1LSzMajSdPnqR9QqStra2vr2/WrFnKGD1lgB5bJHR/0Dds2DCO%20jwBbJNSbwPcngk0QIlVVVQ0NDaJVP/30Uz4CBJro4fUPo9TUVIPBQLOETlNTk7hcvXq1MphAPUCP%20LRKGHwG2yNR+ItgEweVwOB5//HGr1bp9+3Y+AgQaYPzkAR3q3c/vvvuu+FMuB+gBfCL4RIQ0zYg4%20YjQa33777fj4eBqEQBNVvLoNZQcjzRI6lZWV6iM4hCVLlqxcufLgwYPyqAG2SLh9BNgiU/uJYBME%20izzQTKSZ+vp6EWv4CBBooofsNmxtbVXPsCT3Z481QA8hdccdd7BFwuojwGeEP1NRw2azZWVl3XLL%20LefOnVPSDB8BAk2UMJvN6enpjY2N6neq3J89f/582icU5HydI6fp7OjoyMjImDVrFlskrD4CfEam%209hMRFxfHJghWmikuLs7Jyfn444/Ve5r4CEw25qEJHTmdgNeMSV5TDiC48vLydF8cMSCbvaysjC0S%20arI91Y3vt8HZIqHeBL4/EWyCYP2dH2umHz4CkzkPDYEm5HPreSVIpkuanEnG1JQ/FmyRSf429dvg%20bJFQbwLfnwg2QSj+4EgylPARYGK9KGEymcT7Uv1mFVfVo/MQdBaLRfyJUbe5+JdTVVWVnOaBLRJu%20HwG2yNR+ItgEE3T06NGRp5vgIzBV9EO9NDrdtm3baAsAABBxSkpKdAwKBgAAUYBAAwAACDQAAAAE%20GgAAAAINAAAg0AAAABBoAAAACDQAAAAEGgAAQKABAAAg0AAAABBoAAAACDQAAIBAAwAAQKABEHUq%20Kir0ev3tt9/e29srrtpsNv2wvXv3hvkrdzgciYmJycnJ7e3tbEeAQAMgOonv+6KiIr81jz/+uFgQ%20lfHx8ZH1C1osls2bN3d3d//Lv/xLf38/Wxwg0ACINjabLSUlpaenx3fZCy+84HQ6k5KSli1bFom/%205sqVK41Go91uP3bsGBsdINAAiCoVFRXFxcV+y1wul4gCYmHp0qV/93d/F4m/6ZeHiYXS0lI6aQAC%20DYDosWrVqoKCArm8a9cuvV6fm5s76pf9h8PEQl5eXlxc3KjBSH/dqA8iItGiRYuUmpHDWcSLkTe9%208soriYmJ6ho5AkavoozjUdTV1c2YMUNd89RTT6kLTCZTTk6OWKitrf3kk0/Y+gCBBsC0c/jw4b6+%20PrEwf/78kbdu3LhRCUaC3W6/88471YFDxB2z2dzQ0KCs6e7uTktL88oc0urVq51Op+56b5BIKnPn%20zpVrFB0dHbfeeqsSiWw2W1ZWlnyFChHRvKLVggUL5FNXV1ezTQECDYAoUVlZWV5eLpcLCws9Hk9V%20VdWoHTCNjY3iMikp6bbbbhv1oWpqasTdRezIzMyUgeP48ePyJpFIHnvsMXn3trY2dZnIHLW1tV4P%20ZbVar1y5oryYkpISkVSUlYIcv6zkEofDsXPnTvUdlccX0eqll15SHllEKKPRKBZOnjzJ1gcINACm%20F5fLJQKKWLjhhhvmzJkzskCEoaVLl+pUu3WElpYWuSATiVgQASU1NVWW/eIXv5DZYuSIFq+jqETq%20EhnlzJkzYqXc9zTWoB8lRYnHr6+vl+lnw4YNSoF4doPBIBZaW1sZRgMQaABML06ns7m52UfBwoUL%20/YYhq9W6ePFiZb0yRHfkiJaRe7WUCW9SUlK89j0JZrM5PT1dLmdnZ481RketsbGRQAMQaABgomHI%20ZDKJiBPII6xatUrpksnJyXG73V5T5oiHqqmpkfuYFHKMDjPpAQQaAAgCdfeJmtJz41tdXd3hw4eV%20KDPWEB9lH9O7774r92QpsYaZ9AACDYDop4yTHUcoCYTSE6MeJqxTHQfue2KbtrY2Of5m3rx5SpSR%20I5S9rFq1au/evUuWLLl69apINp2dneJl68bYu5SRkTFqMAJAoAEQ2XyMk1VCyeXLlz/77DOtj7xt%202zaZmXJzc+UOIJfLtWnTJplUtmzZ4iNbKHnrxRdflPetqKg4dOiQukbOQCNWbt68WdnBdPToUTna%20Rp1dxK1ut9srHgEg0ACIeMqBP3a7XSyMNbGeiAW64T04Z8+e1foUS5YsEZlGd31ci16vV+akKSsr%20k4dH+bjvypUr1fdVT3gjj74e+fhKWVJS0nPPPadkF6W/x8coZgAEGgCRx2KxvPXWW8pep7EO/5En%20QtKpDsbWxGazKXPDSHLOGPUx1WOprKzMy8tTrhYWFioPdfDgQeW838o+JkVOTo5YKQ8Ul5qammTK%20idDTUQHQRP+Tn/xEN9xLTFsAkFwuV3Z2dkNDg0gJr7zySiTur4mCXwFAgEpKSnT00AAYKQpOhKQc%20QD7W6agARBkCDYBRPPHEE2azOXJPhPTCCy+ITGO1WteuXcvWBAg0AKYpi8WyefNmsXDo0KGIm9nF%205XLZ7XbdiPMqAIhi9MQCGJ1tWCS+cjnzHlsQmFbooQEAAAQaAAAAAg0AAACBBgAAEGgAAAAINAAA%20AAQaAAAAAg0AACDQAAAAEGgAAAAINAAAAAQaAABAoAEAACDQAAAAEGgAAAAINAAAgEADAABAoAEA%20ACDQAAAAEGgAAACBBgAAgEADAABAoAEAACDQAACA6SlO/q+kpIS2AAAAESrmtttuoxUAAEDkuuGG%20G/5/AQYAamsIyaO3kRYAAAAASUVORK5CYII=" height="452" width="752" overflow="visible"> </image>
            </svg>
          </div>
        </div>
        <div class="fig"><span class="labelfig">FIGURA 6.&nbsp; </span><span class="textfig">Comportamiento cinético de la producción de gas metano en cada prototipo de biodigestor a escala laboratorio.</span></div>
        <p>Se
          advierte que los sustrato de los biodigestores presentes en los 
          biodigestores PL3 y PL4 generaron mayor volumen de metano, que lo 
          obtenido en PL1 y PL2, los cuales marcan un comportamiento similar, al 
          final del período de retención, con marcada tendencia hiperbólica, en 
          contraste, los tratamientos 3 y 4 exhibieron una producción alta en 
          todas las etapas del experimento, dada la acción efectiva de las RBE 
          bacterianas estimuladas por la actividad de los componentes del inóculo 
          microbiano, en las cuatro etapas de la biodigestión anaeróbica</p>
        <p>La gráfica expuesta en la <span class="tooltip"><a href="#f7">Figura 7</a></span>,
          resume el comportamiento hiperbólico de la producción de gas metano en 
          los cuatro biodigestores, es claro que la diferencia entre la producción
          de PL4 y PL3 fue significativamente superior a lo indicado en los 
          biodigestores PL1 y PL2, observándose además que estos prototipos 
          alcanzaron una producción de CH<sub>4</sub> similar hacia el final del tiempo de retención.</p>
        <div id="f7" class="fig">
          <div class="zoom">
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0LGeIVRi%20eC6CO69o8SiSVtvNd/NcE/chpvoCMNBCQ9i0adNbb721du1am6xmAgCAPkCnxZCNmUEJY0YHmglj%20VR7gGmbdJe5CDKZBdrm26aIlr3urG0YYQWzGqIWcrfiiujGPg1EkAIa40Eil0qCgILkOd3d3wwBb%20LdsGAAAzWDgtxlTlgTGjg+iIUpdabrnEkI1egxuaL+PdAcABhAYAAAYdy6fFmKo8wOX+YP4lxnqF%20EoOhw0lWjQetmLsdbxAAEBoAAOgdS6bFtMjrSyRHjdev5mi4HI7W4LJeAn+Rz0zDOTEAgGEoNL6+%20vu3t7WhKAMBgYWZaDLPEurbpAtk2U7+aqTww2t2P6MuEMTPuFUagnDUAI0hoAABgcDE6LebkpfeU%203R36q5PMVh4gEnP/1OdGu/uiSQFwOPqYKVgqlXp4eJw6dcqqZ2VkZEycOLGzsxPtDgCwIUamxXA4%20kqaL+jbTW/3q5vZqdMkAMLKEhhIXF8fV8dJLL5mKUSgU0dHRNCwxMREtDgCwLdWNeUanxXANa9xb%20VnkATQrACBIaX1/f77//nsfj0d23336bawKBQMBk2hUKhSdPnsT6bQCADTl/dT/HoN+FWotG1yUz%201is0JmSNp/s4jsnumTtPQWkkAEaW0BBiYmK6urpoecheISpTVVXV3Nw8wKmEAQDDm4KqgyYKU9/q%20pJkasHRFzHaf0UGtHdd7rzyAThoARqDQUFavXk0LX2/bts1oQG5uLjkLlQEA2JbqxrysvNfPVWRw%20zE6LqW48261RoX41AMMem61yMiyEBAAAdlKZ/MoDtypEmp8Wo9Uoulq/LUitby7moPIAABAaAAAY%20Cly9frpQnHWn2LWxpDJ6TqPVdl+XXUHTAQChAQCAwaes/mR+1YG2jkY9Yel9WkxPr4z6vrCnIic+%20gmYEAEIDAABDSWXu0Pu0GK22+2J15tSApVYVYAIAQGgAAKC/dGtUZfU5F6u/Nqoy3qNFzW0SDqbF%20AAAgNACAIasyl2uPXazO6im9ZECY/6Lo4Me/K3oHDQUAsJfQSKXSoKAguVxOd+Pj47/66isXFzgT%20AKC/KuPs5EpUZmbgClpoacXc7WguAIBdhCYjI0OvskF2drarq2tubm5sbCwaGgBgBqVaXlJ77JLk%20sFGViQhYSktno6EAAPYVmry8vCeffNLoqeXLl+fn5yOrHgDAlMoU1XxzqeZwl1qud8rNRRDmvxAq%20AwCwBCebXCU1NVWpVJKNtLQ07W1o7mCZTPbee++hoQEAhipzvnJ/+o+b8isP6NkMUZnoSY+vuf/9%20+WFPwWYAAJZggx4aqVR6/HhPWvGkpKT169czx5OTkwsLC7Oysg4ePLh9+3bUpAQAUBRdLYXVWSW1%20xwwrDBCVmTZx2fSJj/BcBGgoAMCACg1DQkKC3pEnnniCCA1aGQDQq8rw3bxmBq5AthgAwOALTWZm%20pt783y+//BJNDAAgtHVIL1ZnldXnQGUAAENUaHx9fZcsWZKVlfX2229HREQwo07Jycm0e2blypUY%20bwJgJKtMftUBojKGp6AyAIAhJDSEV1999fDhw0qlcoMO9imhULh582Y0NABQGTaj3f2igx+fPH4B%20VAYAYBNss8opJiaGOI3RU2+99ZZt12wrFIro6GjubT755BP22eTkZK4Bp06dMvV09qleX8t8MACA%20rTInSz5Iz/29oc0QlVkYsWlN3Pth/gthMwAAW2GzOTTEJDZt2sTOFCwSicrKymw72KSXjJiwYcOG%20kpKS3bt3093CwkLzghIXF1dQUMAcIbumUv9ZFQwAoNxsq86vPCBuzDM8RXtliMeglQAANsfJhtfy%209fVtb29n8tDU1NTYfOrM+++/T2wmKSmJvkRjY6NAIPj000/FYjFVEIlEEh8fr1KptCwYBdm5cycR%20FCZg37595ODatWs7OzsNX8uqYAAAUZljF3cdPLPF0Ga8BP5MrwwaCgAw1IXG3hBfyc7OJoaxY8cO%20RqFefvllmUxWX19PdonrlJWVhYSEGC0gRZ8uEon2799PA1avXr1t2zbiQOfPn+9PMABQGVMq4zM6%208KGZW55Y8A5UBgBgV+xYy4nBVmNPfD4/Pz+ffUQqlb711lvk+rNnzya7YrFYpVJFREQYfTrVncWL%20F7N1Jzw8nDyWl5frDSRZFQzASFYZUwNMRGWiJz0e6BeDVgIADAC26aExYzN2gk7+9fPzI85RWVlJ%20bamqqkqpVHp5eXl4eNBpvMuXL1er1fQpVHf0+m+Cg4N5PF5JSYne9a0KBmAEcqOl3HyvzGPzd8Fm%20AAADhg16aBQKxa5duwb4vpnJv9nZ2Y8++uhXX31FzKO0tJQceeyxx5gwcpZID7s6pl7/DTnu6mpy%20nYVVwQCMEK41l+RXHSCPhqfu9Y6IFC2DxwAABh4b9NDQ0RmyYTgb136zgzMzM5kSmMRaXnnlFcZy%20cnNzmZc+e/YsuT1UxwTAViqTfT6Z/DO0GaIy8bOTyT/YDABgULBl6YOtW7canY1rV8iLEqGh9S+J%205eidjYmJWbZsGT1Lj+gNGNGhJVMXtyqYTXp6OtE49hEul4tPG3BolTHTKxMd/Dh5RCsBABxbaAQC%20QVhYGPll7+/vP/BfAJ/PF4lETU1NvUbSAaOKigq1Ws2IF512YziP2KpgQ9asWaN3JDU1FZ82AJUB%20AAA7YYMhJ6IU8fHxMpksJyfHrvcqlUo9PDzY83w5t3PPkI3GxkZTZ2fMmEGkhIpXYWEhO4BOuwkN%20DTVqaRYGAzBcVabXASbYDABg+AgNRzfuExUV9fLLL9MEd3aCVsHMzs7+6aefmIM0/d22bdtEIpHh%202UOHDpGzK1asIEJDxYv4zapVq6imZGRkpKSkMKu+DS3NwmAAoDIAADC4cN98802Orrpkny9hWI5A%20DxvWQDD6WsQ86Con82c5xqoZcHSTiGlemeTk5D179jBLoswHW0tqamp/GhmAAVMZUwNMQX4xMwJ/%20PdYLPZQAgKEFndTh5Fg37evr29jYGBUVxRxJS0s7dOgQ9RXDs0lJScxZjq7fhRgJO8CMoFgVDMAw%20UBlTvTJEZVbO37V05hbYDABgyOLicHdsmC/Y8rPmA5J1WHU1AIYB1Y15xZLDpnploic9PmZ0IFoJ%20ADD8hYbWpERTAuCIKpNfeaCprRoqAwCA0PSCQqH4zW9+k56ebvPcegAAqAwAANhYaJKTk1NSUoye%20EolEaGgAoDLAQsSl18eLvEfxeb1GqlXdJDgk0h+NBoBthIYuaUZrAjCUuXr9dKE4CyozxKkork9/%20N8d3vOf6Vx4y7zTEZva9m0PiEzYsmBU7GU0HIDT9xXxxSvaqaQDAoFBWfzK/6kBbRyNUZrCwvNPF%2029fDzZ13XdL8yY6jZpyGsRk3Pm+cyBstDIAti1OmpaWR7aioKJFI1NHRodVqV6xYoZfpDgAwwCqT%20nrvpZMn7hjYT5r/oiQXvLJ25BTZjb4h2fP72CSIonQql+UiiKYfTzyl+7nD3cKNOY/QpbJsh0jMe%20QgOADfPQCIXCRYsW0cpKbW1tDQ0N5ODevXsFAsHOnTvZBQQAAIOuMmviPlgY8XsvAeZeDAS+4z09%20PN3NCIqhpjz2bKynj4fRp8BmALCv0DA88cQTenWd9CoiAQAGXWVGu/uioQYMLx+PDa88ZEpQjGpK%206PQJRp8CmwHAjkJD6zgSiamvrye7wcHBPB4vKyuLSMx3331npiQCAAAqA6cxpSmGT4HNAGAGG8zV%20pcNMBQUFO3funDdvXlBQkKura3Z2NnmkAbTYNdoaALuqjNFpv85OrhEBS6eJHoHHDBGnSdtxVG/C%20rxlNIU/5zX8s/mz3cfKUPa9lcbmctpYO2AwA9hIajq625eHDh4nEfP755+vXr3/55ZfZq7hpsWu0%20NQADrzIzAlfw3bzQSkPWaVxcndk2M9rTXVx6va5S2nJT3tTQ2iBppn05Wg6nvbWDbty3dCpsBgB7%20CU1MTExdXV1QUFBoaE/tuuTk5MLCwqysLI5u6RNRHDQ0ALalW6O62nAaKuPoTuPq6lxbKXV2dR7j%20N/pT01OGuawN+c8daEYA7CU0HIOKTpmZmWhcAOykMpdrj12szlJ0tUBlLGSI5N5tb+2QNrTWVUrH%20TxC2tSiI03B0nS7kRa9V3zT/XO1toam8fJ14jyVfCwAQGgAAVGb4MIi5d2srpU0NrU3XfyYSw4wf%206cG14DpOzk5RcZPvnTjm5L+Kbl5vNZ9zDwAITR+RSqVBQUFyuTw3Nzc2NpZ9KiEhISsrC8mCAYDK%20DCLsNDB2zb1LrkAMhrgLMRjiMWTbfLzWmNAEhY/z8vHwGiMImOz347+Kq8uus2cBT552r+G0YgAA%20x949NE888QQRGpqHBkIDAFRmUDC1vMiUzVi+hogxmOu1sgZJM5EYy++K2oynkK+QK9VKtdBv9Krf%203e8f5MO+Hz2bsfBrAQBCYx2G5bXj4uLQoABAZQYGq6bFSBtazXiA5TbT0tROxIVcpK6qiVyT7Fp7%2023yBm0Le5cxzfmTt3Jnzg11cnclFyI3JGtu+/uwMvTHz9wOnAcDGQrNp06a33nrLkrx5yEMDAFTG%20tvRtWoy1aWAIdO309VpZXaW0tlJKgq26Tw9Pd3LBCcE+40Te3r4exw4UGL6QnqCsS3owM+20ebuC%200wBgS6Hx9fX9+OOPExMTzYcJhcK//vWvEBoAoDI2pG/TYnpNA0PsgXbAEIMhj30wGJ/xnuQi4wKE%20Eyb5sjuQLO90+VvKv36WKXrtK2I/JefrwocT5+BTAUY43DfffJOjy4zX50uYmRQMGFJTU/vTyAAq%20A5XRg47UtDa1E1OxaloM+4ke94y6eumaM885bEZAW7O812m8hvToi85gyGPAJF9iSOa9yoymMDfG%20Hz0qcfPCiaFjLWmEHw9fWpY4x+jrAjByfsNybDIpWC8DDQDAWoprvoHKWIv5YRejDkEXTpP4qbMC%208k6W30kDo+y+fK7akhcl3kCshbiLz7h7qMFY8qxzOWWWTNBhviLiNJfzJZYIDXnK8nXz8GEAgGPb%20VU55eXlxcXFKpVIoFObn5589ezY9PR0LtgEwg5nCBdMn/mqaaBlUpg9Ow9gMz523aPmM0gLJkX3n%20TI0fmU8DQy5IFCQwbCwdSyKPfbjP+UunttyUz1wwqdfFU/Qr+vHwpSUro/D+AjA4QkNTzrCPlJaW%20ZmdnT5o0qaysbNSoUWhrACxUGfTK9MdpXFydP999orrsOtfZqatDeSTjnKnnGk0Dw57GO1437cYm%2092n5HBd0ugAwmEKTkZGhZzOEwsJC8iiRSNLT01HOCQCozAA4zZ7XspSdKmWXmsiKtltjpveFsRkn%20FyeNWkM85tdP3zcxxA/LhQBwUJz6fwmFQrFr1y6yER8fr1Kptm3bRo9nZmauWLGCbBDXUavVaGsA%20iMqk5246WfK+ns3oBpgeWRP3wfywp2Az1tKpUPbMUOG50JLU1GY4xsaSXFyd+QK3ngZ3dX549ZyN%20/7Xsfz598sXUBE8fD/LE7766gMYEwHGxQQ+NXC4vKyszujz71VdfPXz4MDIFA4BeGXtAPObi6cor%20BRI6OYZdkprBZ7znlCjRuACh372elqSBQU4XABwUJ1tdSCaT1dfX6x2sqqpSKpW2vWOFQhEdHc29%20zSeffGLm7KlTp8w/3TCgz8EAmFIZ9MrYlpam9uMHC97e8n//fPtE8Vkxe6qv9vZYEm+U65LHopJ2%20PfYfb65YsjKKOI1Rm2E7jaePB3UaoyUkAQDDX2gEAkFYWBjZWLt2bWdnJ3NcKpU+88wzHJtmCibX%209PPzKygoYI5s2LDhpZdeYvwjLi6OfZbssi2k1wA9m7E8GACojL0hnnHh1NWP/nSYqEzuN8WGZQeI%20yvgHjlny6Kx7xgiUnaric9WW5AKG0wAAobkFn8/fsmULRzf/193dPSUlRSaTBQcHE/OghRG2bt1q%20K6F5//33yTWTkpK0OhobG4lOffrpp2KxmJzduXMn8Q86lYec3bdvn55m9RrAxqpgAKAy9oO4SGba%206Z0v7iePpnLfObs6P7Vl6e+2/er++OnP/PGXbDWxKg0MfWLO14VodgAcC+cHHniA0++6ktOmTSss%20LCwtLTU8ReTj2Weftcm9KhSKP/7xj7Nnz/7444+dnHpUjNiMUqk8cuTIypUrfXx8yFlXV9cff/yR%20x+PRuyKPhw4d+uUvfykSiejTzQQYvpaFwZZw6tQpFO8cISpzrHBX+bWTSrVcT2UiRQ8vmfFS8Nh5%20ri7IYmARLU3tZ45dJhKT910pkQyNRqsXMIrPE4we1dWhIrLyzGsPiyb7McenRokuX6i92dBacena%20sjUxarXm4cQ5vaaBoU9UqTW/fGK2k7MT3gIAHAI6eGKzibqZmZlMYj16hKbXCwoKstVL8Pl8ckH2%20EalU+tZbbxG9IJbT1tZWVla2ePFidm9QeHg4eSwvL4+NjaWTl80EsK9sVTAAHEz7tR1qVXfxWfGl%20vOqK4npTMSGR/pHzgorOVF29dM22JamRBgYAB8WWK49iYmK6uroG5r6Tk5NTUlI4urXiNBlxUVGR%20SqUKCQlhK0hwcDCPxyspKSHbYrHYfAAbq4IBVAYqYxNqK6XnT5ZfKZCYmsLiM95z1oJJs2InEzvZ%20926OKZvpp9MAAEa60AwkNGsfITs7+9FHHyVOQ3cjIiLYYUFBQa6uruwjvQb0ORhAZdhMn/gIVMZC%202ls7Lpy6euF0ZVNDq9EAIiKRc4NmLpjE1E46c+yyVdWRUJIaAAiN1V0mhohEIptXP8jMzGS/7iuv%20vPLEE0/g7QRDQWXC/BdFBz8+2t0XrWQetar7SoHk4ulK80NL02ICic3oVZNGdSQAgF2ExozN2Jut%20W7dmZ2cfPHjw17/+NdnVGw+iI0fsI70G9DmYTXp6ek1NDfsIl8vFp214UN2Yd7YivUVeD5XpMw2S%205vMny4vPik0NLRELmbMwlHiMmVJKqI4EALCx0CgUCqIUg/UF8Pl8kUjU1NQUGBjo6upaUVHBzkpM%20M/vRkSM6YGQmgI1VwYasWbNG70hqaio+bcNAZfIrDzS1VUNl+kZ7aweRmHMny00NLbm4OtOhpaDw%20cWguAMBACw1dEMTRrdDevXu3/e5VKpUSz1i8eDGdBczolEQi4dzO76dXZoGuJA8NDbUkgI1VwQAq%20A5UxD121dKVAYiogYJLv7IWhU6JEmLcLABg0oaEIhcLNmzfb9V59fX2XLFmSlZX1008/MQunafq7%20tLS0MWPGxMfHp6SkrFq1ihpPRkYG2aWLujm6vhzzAWysCgbDmGvNJWfKPoPK9I2mhtYzx68Qj2lv%207TAa4OXjETk3aM7CUDNDSwAAMEBCw3hGfX29DbPOGGXv3r3Hjx/Xy1BHzGPdunWc2/NpCOy1SF98%208QUzH9l8QHJy8p49e5jcOb1eDTgcRE0UXbIHpv/BQpXJrzpAHqEy1tKpUBafFROVMT+0NC0mMCTS%20H80FABgqQsN4xvLly22bSc+oPDU2NrJLLKWlpa1fv55u8/n83Nxc9lmyy06C12sAG6uCwdCnW6Mq%20qvmGbMwMShgzOrBvKhPkFzM/7CmojCmuFEgu5VUTmzEVgKElAMCQFhriGS+//HJKSkpwcLDhWdsu%202zbMF2z5WfMByTqsuhpwIC7XHqMb5ysPPDRzi9GYm23VZyu+qG26aFRloic9bt6ERixNDa0XTlde%20OHXV1NCSh6f7rNjJsxZM8hnvieYCAAxdoRnEZdsAWEK3RnWx+lbuIom0QNHVopfyjqhMfuUBcWMe%20VMZy6NDSxdOVpgpGEuiqJQwtAQAcQGgGd9k2AJZwufaYoquV25MPiKvRqgurs+aHPdWrytzrHXFf%202FNQGUMqiuuJx1wpkKhV3UYDxou8Z+sSyWBoCQDgMELDLNtmT2cBYOjAdM9wuU49GQ61mkuSozMC%20V5Dj+VUHyupzjKpMdPDj5BGtx8aSoaUpUaL5S6ZgaAkA4HhCQ1O2iMXiRYsWoUHBEITVPeNEdp24%20Thqt+pv8lOb2OqiMJXQqlFcKJOdPlpsfWgqfFUAe0VwAAEcVGiZlywAs2wagD9A+GGoztza0mptt%20tXr1KHxGB8aErA3wmYkWY6Dlr4vPik0NLfmM95y/ZMqUKJGHpzuaCwDg2EJD2LRp01tvvbV27Vqb%20F6EEoJ9UN+bdbK/hcu8IDVGank4ajobojPa2ykRPejzQLwbNRaHlr8+dLG9pajcaQMtfz14Y2mtt%20SAAAcBihoRUJ5Drc3Y38lWaPatsAWMj5q/t7FIZzV61m2kmj0Wo93H1jw5+GyjDQVUtmyl9PiRLR%208tdoKwDAcBMaAIYsxrpnbikN7aQJGR8Lm+FYUKPAZ7znrAWTZsVOxtASAABCA8BAc6b0HxyD7hnG%20ach/5ddyZk9a5ezkOjLbhyaSOX+yvEHSbDRgFJ83JUo0e2FowCQkRwYADHeh8fX1bW9vR1OCocaJ%20or/83NlorHtGpzNcJ65Wo+hqvVx7LHLiIyOtcXpNJENrFETODXJxdcZnCQAwIoQGgMHFsOSkUi0/%20eekD8Y2zPSu1OSZ/HxOn0Wq7L1ZnTg1YOkI6aVqa2s/pVi2Zmu1LaxSg/DUAAEIDwIBiWHKS2Ez2%20ueSmtmpdWmDj3TOM0IyQThq1qpvO9hWXXjcVQ8tfT4kS4UMFAIDQADDQ6JWcJH5zy2Zu4WT+6cO+%20k4YmkrlSIOlUKI0GIJEMAABCA8Ago1dysq1DmntlL8tmOFqtplur6fU6iq7Wj0+seW7pgWHTMjSR%20zIXTlU0NrUYDkEgGAAChAWCooFdy8tuCP8nk10Z4mxSfFV/Kq75SIDEVEBLpP3PBpClRIsz2BQBA%20aAAYfAxLTja31zPVDMZ6hS6Leo3nIhghrdFrIhkvH485ulVLmO0LAIDQmEMqlUZHR//www9BQUF5%20eXlxcXFKpVIoFObn56PAE7AHRkpO3q5m4CXwHyE202siGRdXZyIxMxdMCgofh88MAABC0wuMwdTX%2013t4eCxevJhsk+MymYxYDpwG2AOjJSdpNYNlUa8Pe5uxMJHMlCjRKD4PnxYAAITGIlJTU6nBlJeX%2019XVyeVy5hRxmvfee2/37t1oa2BDzJSc9BdOGe0+bNPaWphIZtaCST7jPfE5AQBAaKxAoVBIJD0z%20EJOSktavX5+QkMC5XZDyz3/+c0pKSkVFhVqtdnHBfB1gM8yUnLx6/czc0HV8N6/h9PXSRDKX8qrN%20lI1EIhkAAISmX8jlcuIuQqFw8+bNjNzMmDGDGMyyZctSU1MLCwshNMCG9FJyUqsulnwzN2Tt8Phi%20ayulF09XEptBIhkAALCv0LC5pINshISEwGCAnTDaPcM4DWdYlJxsb+24UiA5c/yK+UQyRGUwtAQA%20ALYUGplMVl9fX1dXRyfTJCQkKBSK559/nuzS3hq0NbAJprtndDrj+NUM6Gzf4rNiUwFIJAMAAHYR%20Gl9f3yVLlmRlZcXFxdEjQqHQy8uL7BYUFJDdFStWQGiArTDbPXPLaRyxmkGvs329fDxmLZg0K3Yy%20EskAAIBdhIbwxBNPEKFhdp9++ulp06aJRCIiNORxzZo1aGhgE2qbLprpnmGExoE6adSq7isFkoun%20K03N9qWJZKbFBIZE+uMDAADoA8XVsshAIYTGIlavXk0eExMTyWN8fPyOHTs4unnB3d3dX331Fbpn%20gK34d9lnuv8Ph5KTNLevmdm+40Xes3W5fZFIBgDQZ77Jq/vn95URE73+X+IMm1yQyTzHHKHrmkeN%20GsUO+PDDD9evX2/+IuPGjWM/cUgIDXUaqjUMycnJ+CQBW35b5v+pRd7TjeHQJSd7ze2L2b4AABva%20zP+erOpUdhdVNb+xr9BWTkNIS0ujvqJQKIiauLu75+bmxsbGWvJcogcpKSk2/2Kd7N2a5Et99NFH%20Ozs7bXVBqVTq4eHBvc3y5cvVajW7mbgGnDp1in0/0dHRRk8ZvXnLg4FdqW26WHez0KG/hIri+sy0%200ztf3J/9+U9GbSYk0v/x392/9Z1V8evmwWYAADaxmY7Onl+Rqm4tdRqbvwqfzz9y5IhAINi5cyf7%2017EpEhISiM3s2bMnKirKtndisx4aM8IlEtks05dhT1d2drafnx9TXaGwsNC8oDBTlSlk15RUWhUM%207MqNlvKjF3cyu85Orglz3xwzOtAhbr69tePCqavnTpabme0bOTdozsJQzPYFANjDZiiM09iwn4ZC%20bCYsLMySnHNSqfT48ePkNymxmc8++2woCk1GRoY9uo8MDeP5558nDVdaWsoUhyIvnZiYSKsr0LR+%208fHxpibuEH8kgsIE0OeuXbvW6BieVcHAfrTI678teLNbo2Js5qGZWx3CZorPis3M9uXocvvOXDAJ%20s30BAOaRtSs/O3H1xIVrlj+F68R14XL1DhKnOVfW9Mi2E1a9+i8ix62MnRg8bnT/vxBfX9/29nb6%20C93mrWSb0ge7du0yddaMXlgLTUn83HPPsUtdLl++nIjewYMHt2/fTgMWLlxo9OXIfWZnZ4tEov37%2099OA1atXEzciKnb+/Hm9fhergoH9aOuQHi54s0t9pzpY3JSNAT4zh/I9NzW00gXY7a0dRgOQ2xcA%20YBXv/6v0zJXGftoMxcmJy9FwurVay6/2Q/H1Wqn83efnmgqgOXU3b948uH/t28wzOLopQuS3flxc%20XFNTE+3GSEhIyMrK+umnn2xiAIzZGUJz94nFYpVKFRERYeY+Fy9ezNad8PBwjq6mpt4dWhUM7IRS%20LT92cWdbx51v45iQNWH+C4fm3dJyS+dPltdWSo0GjOLziMTMXhgaMMkXby4AwHJuyDqsinfmmjtL%20nKa7W2vVBWXtXaZOSaVS8rtSIBAQoRncVrLZpGChULho0SI+ny8Sidra2hoaGsjBvXv3Wj5RqG8c%20OnSooKCAVlqoqqpSKpVeXl7MrGH2lGGqO3o1GYKDg3k8XklJid5lrQoG9qBbozp6cVdTWzVzZGrA%200llBCUPwVonBZKadfnPTPvJo1GaIwSRsWLD1nVXkETYDALCWjQ+HjvWyokO3W6PVckwqi1qjserV%20BaNc1iwM1ju4YcMG+nvWz8+PCE1jYyN78MRRe2j0oEn2cnJymK/NfsUpiRg+88wzRKG2b99OdktL%20S8njY489xgToTRkm6PXfkOOuribzlFgVDGzLd0XvXGu+445BfjFxU54dUndIZ/teOF1pqtySh6f7%20rNjJmO0LAOgnkYHCT/6wwPJ4eaf6pY/PNTQruBz9vprRfNeU38zs/4QYZtn20MEGkkGnNxcUFNTX%2015Nf+bQbgzjNunXrvvvuO7lcPmbMGHvcOrEZ8nLktU6ePEnH7egSJ/ZCJLoqik4ZHshmTU9Pr6mp%20YR/hcrn4nrSc3CsfiRvzmN2xXqEPTH9x6NzelQLJpbxqM+WWIucGhc8KII94KwEAA49glMvuZ+YY%20Oo2tbGZoYgOhocNMRGh27tw5b9482o2RnZ3NdGbYozglNRXiUuzel8zMTL2wmJiYZcuW0SnD9Ije%20gBEdWjL1KlYFszGs9pCamorvMQs5X7n/cu0xZtdL4L8s6rWhkPCXllu6cOqqqdm+Xj4ecxaGzoqd%20jNm+AICh5jTD22Y4tppD8+qrr/J4PCIxn3/+ua+v78svv8w+a/PilMnJyXPnzh03bty1a9csH7Sj%20plVRUcGe0EOn3RjOI7YqGNiQq9dP51feye3Ld/NaFvU6z0Vg7XXEpddNlRTQQ63qNrOymlJ8VvzP%20t0+8veX/cr8pNrQZF1dnIjHP/teypF2PxT0SCZsBAAwdpxnvzdeOAJuxmdDExMTU1dUJBILQ0FAq%20HERi6CmbD7PRDH7x8fGVlZXsFWI0g7Be4mCamYZ2EbEz/zABdNoNve27PgfWBANbca255OSl95ld%20NxcBsZnR7lbPoiWC8vnbJz7ZcbRXpyE2s+/dHCIrF05dNTzb1ND67b5zO1/cf+BvPxqVHsz2BQAM%20faeZ6CcYeJthZg1TXnrpJQ4r1z/5JVtQUEB+Qbu7uzNn+4nNOk701lQbjv7Y0GaSkpIM58SQG1iy%20ZIneKnG6BmrTpk0uOogGkaevWrWKyZVHdkUi0ezZs/WuxufzLQ8GNqFFXn/s4i52Ar0Hpr/YtwR6%20vuM9PTzdr0uaidOsf+UhU8Udqc0QU3Hj88aJvNnHe12AjXJLAABHcZo9v5trwwvGxMR0dXX1LcBM%20+pX+w33zzTc5ujGjof+u0FnAcrnc8BQt9dnW1mYYwM7sZ1jNgMOaRExsac+ePcykHPPB1pKamuoQ%20jTxYKLpasvJeZ6ecWRixqT8pZ1qa2tN2HG1taiemYtRp2DZDAsbrhIYYzMXTlWYqYIdE+s9cMGlK%20lMjF1RnvGgAADAXoLFUbDDnpVYs0hd5gUB+ga6bM/V3u69vY2Miud5WUlHTo0CFmBg+fz6clJCwR%20FKuCQX/o1qi+LdjOtpnoSY/3M4Gel4/Hhlce8vTxoP00eoKiZzNCH49zOWV7Xsv66E+HyYahzXh4%20usc9Epm067HfJj0YOTcINgMAAEMNG/TQmOk4MdqPMjILIaGHxgyHC7bXNl1kdsP8Fy2M+L1Nrmy0%20n4ZtM8vWxlwtqr9SICEHjV6BdslgATYAAAzl37AcG2YKtgSJRJKeno6mB2xyr3zEtpkAn5k2TKBn%202E/D2IwLz8Wdz8v86FTxWbGhzZAnLlkZtfWdVbRLBm8TAAAMcWwgNOyBntzcXO1ttm3bxtEN+pBt%20uVxOA7KysuxXBgE4HMU13+ilnHlw+ou2TTmj5zSf7jhKbEbL4aiU6pYmI3PTiL4QicECbAAAcCxs%20s8pp586dBQUFelNMkpOTyWNKSkpERMT69eu3bNmSmJhovzIIwOGobsz7d9lnzG6fU85Y6DQfpvyL%20OA3ZpQVOuAYxyIkHAAAjWmgUCkV2drZQKPT399c7RctT0zIItCQCWhxQbrSUnyh6h9l1dnLtW8oZ%20S7hw6mpOVmHH7WqxeioTOTdo5oJJIZH+eFMAAGBEC41cLi8rKyOPtJYT+xTNRKeXng6Atg7ptwVv%20MilnCA/N3GpJyhlx6fXxIm9TeWXYqFXdJNjF1fnIvnMNuo4Zzt19M55jBDGLwtAlAwAAEJpbMMUp%20ly9fzq6sRDPRcW7Xcjp8+LBSqbRHXSfgWCjV8uzzyV3qO8vi4qY8G+Azs9cnVhTXp7+b4zve00yu%20PMZm0vd8f/XSNfZBxmacXZ27Vd3uArc5i8IscSMAAABDHxtMCqZJdcmGTCYLDg5mEs8kJibSgBUr%20Vhw8eJDKTUhICIRmJNOtUR0ueFMv5czUgKWWPJed/9dMTQNiM5/uOEpsRntbYhibiYqd/HxK/H++%20ucJUfhoAhix7j5TvOFiMdgDAjkLDubt4kx7EddatW0fHnoRC4ebNm9HoI5nvit650VLO7Ib5L5o9%20aZWFzzWfK4+xmQ/++1BtpZQ9ukS2g6eMIx6TsGHBeJG3JdcBYEiRX3Hz6zOSH4tvnLhwDa0BgB2F%20hqMr3rRv3z72EaIvVVVVNFFvYWEhR1dZyfLi2GD4cabsM3FjHrPbh5Qz5l2E7P5l6/81Xf/5rnVM%20XO6vn77v6a0PsesuwWnAUMDCThdiM2/su5Wr6S9Zl+E0ABjFlqM/q3WY0h209Qjncu2xoppvmF2f%200YF9SzlDXSRtx1G92pPtrR17XssiasK2GScXp6e3LJ0YOtby6wAwMNBOF07PqOuYB2fdyz5V9Y9P%20aw7sZx/5z7ueycm5+1LjHlwy5cUkNCkY4TihCcAAUNt0kZ1yZrS739KZW/uccsawf+VGbfNftn6l%20ZzOubi7P/b9HjNqM4XVyvi7E2wQG0mbMdLro2UyvXD9xHE0KAObnArvTIq//rugdZpG2s5PrQzO3%209DPlDLt/5e//801LU3t3t4axmfEi7/ETx8x9MJzW0O71Oj8evrRkZRTeKWA/rO106flz09Wi/kuN%20SoXmBQBCA+yOUi0/XPAme5G2hSlnLHSav73xzc0bP3NYq7KnRIkSNiywfPyIXGf5unl4p4BdsbbT%20BQAAoQFDCMNF2veFPWVJyhkLKbtYq/i5k3NbZQizYicTm0HLA0vYe6Rc1t71ysrIAXtFdLoAAKEB%20DsnJS++zF2lPDVgaOfERW1386P78099e4rD6Zjw83R9OnINmh6NY4ihm5uQCABwRTAoG9uKCOPPq%209dPMboDPzPvCnrLJldWq7v0f/KBnM6M83NpbO7AGeyRjebIWLIQGAEJjjry8PDc3Ny6X6+3tLRaL%20MzIyli9fjipOI5Pqxry8inRm10vg37dF2oYQX/l89/FL56oZm3F1dU58YdHvt/0KeWVGuM1Y6Cg0%20UtWtdXLiOnG5dnKaGy0dxdWyH4qvf5FT9dLHeXiDALA3NhtySkhIyMrKYh8pLS3Nzs6eNGlSWVnZ%20qFGj0NYjB71K2m4ugmVRr/d5kTab9tYOYjPXa2WMzbjzeb9NejBgUs+aKeSVGeE2Qx2FfDI0Wi1x%20FHKcGUvSW2T0n0Yucdc6IwszuxBraWzpbO9QVV1vJ7uXano+mZUNbfJO/T/kfoU3CQCHEJqMjAw9%20m+HoimyTR4lEkp6evn79erT1CKGtQ3qscBd7kfbDUa/1c5E2pamh9fPdJ1putjM2I/TxIOLi5eNB%20A5ArDzbjzOX2DEBq9J2mD5ldGKGRtSvrmnqW6RWJe3xFfKNd3qmqlcrJcUsu5d3ZLFDJ8TYB4ABC%20o1Aodu3axdGVbfrqq6/+9Kc/7dmzh6PLDky7bQjr1q1DTcphyZmyzxRdsgem/4HuEo85dnGnoquF%20CVg4bdNYr9D+v1BtpfSfb5+gY0nUZu4VeT9toCxwmpEDu9PlP03Kzl39LlYtMnr1s3yjfS0mf5hq%201OPlDW7qLr/ORoFKMaazebSy1bPrZ7xTADiM0Mjl8rKyMqFQ+Ne//lXPWl599dXDhw8XFhaq1WoI%20zfCD6AutZjAzKIGmlvmu6J2mtmomYFZQwuRxNlhEfaVAcuBvP6pV3YzNhEb6J76wyMXV2TCY7TQ5%20Xxdi3dNwxd6ZXWh/jFH8FFK37k6iL6O6u+6VNzhp1fe2N+AdAUOEnzY+03Gt3nyM+73+8/Z+3Lfr%205+XlxcXFKZV3eihFIhF7bgkN+PDDD40OziQnJ6ekpDC7ubm5sbGxQ0VoKDKZrL6+Xq/2ZFVVFftr%20BsOMy7XH6Mb5ygMPzdySV5HOrj0Z5BcTE7Km/69y4dTVzLTT7CMxi8LizabCQ/7fkYO9M7sEtNWi%200wU4FsRmev2+6NV4eiUtLY36ikKhIPri7u5uiZoQmykoKFCpVLSPIyEhgTyXudTgC41AIAgLCyO3%20uHbtWuJozHGpVPrMM8+QjRkzZqB7ZvjRrVFdrL5Vc1QiLSipPX5BfKcEqc/owAemv9j/V8nJuqhX%20ZWnpyqi4R3rPMoL8v8Obz05UTLTzS7x44a8uGhss0uRPCHATCmXFRXjXwLCEz+cfOXIkKCho586d%208+bNM//rnggNe3fv3r3Hjx+31bwUF5t8MVu2bElMTJRIJMTR6MHg4GAmYOvWrRCa4cfl2mOKrlYu%20h0vQaNX/Lr3Tezna3e/hqNf7v0g7+/OfzuXcUWQXV+f4dfNmxU5G44NicYu9hcZam3Hi8TzDwsnj%206LAwF4HH6OBgnlBIbOaWmv/qYbxrYLhC+zX6ML2kz0+0l9AQVq9e/eWXXxoudCIkJSXZangMDB2Y%207hluTyIPLker6dZ0c3UpPWjtSb6bV3+ur1Z1Z6adLj4rZttM4guLQiL90fgjmarrbaW1rWX1P7d3%20DmaCK9rp4hE8yVnAF0ZOJ0e8dI+2BQUQwLCHzsFdvHixTXo9bNZxkpmZqTdRSCgU5ufn682qAcMD%20VvdMT25GJ66ThqPh6mbsPjj9xX7WnuxUKA/87ceK4jtDvKP4vKdfeajX0tlg+KFUa8rqWovEsks1%20MqIyZHeAb4D4iqlOF/uh5Wi5t6qTWYTb2LH4qAzzbwSZrOofnzacOG7Da1reazj2/l+IVq7yYA28%206HFJx+bNm63NOffdd98Rp1mxYsXQEhpCTExMV1eXvd9XqVRKJIk0Ad2la8WZtqCzkwoKCuiu4Ryl%20XgP6HDyiKKvvWQxLbebWhlaj0Wqjgh8L9Ivpp818uuNog6SZOeLl47Eu6UGf8Z5o9hHCjZYO4i6X%20alrIY9X1tsG6jV989TVRmUHpdKE2s+hf3+LDACjlH7wnPfPvQfuW/PEHeV3dnD3vmfqlvHjxYoFA%20QITGqsvSnC8ikWjNmjU2uU8Hm9piuFosOzvbz8+PdgXp+QeB7LItpNcAMzZjPnhEUd2Yd7O9pieB%20GZcpncGlnTTdmn4ZbUtT+yc7jpJH5sh4kfdvkx708HTHT7ThTWld622JabEkYd0ALDWyrc2MDg9r%20Ky1DpwvoGx03bgzuDShlzXpHNugw2rNgIWvXriW/YclvVVvVEuij0Oh1k5hHb4V6nyGG8fzzzxMN%20LC0tZUayMjIyEhMT33vvvd27d+/cuZO0DtOy9BRde0VfvdcANlYFjyjOX92v+yPyrhwwtJPmkuTo%20jMAVfZtA0yBp/ufbJ9pbO5gjQeHjEl9YhMx4wxKlWlMslhGPsXwsSaCSByrqI7U3xjdVOrfedKyv%20d/Zb7+BNB30m5NmNV955u3OQtMZF4BGYuFbvYD/XWicnJ2dlZZGL2LCPwJF6aOjsoeeee449L2f5%208uVRUVEHDx587bXXsrOziTzt37+feuLq1auJ+qSkpJw/f540GfEh8wF68mR58IjvnmF10mjVxZJv%205oastfay4tLr+97NYReVnBIlevx39xtNnQccFFm7skjcXF7/c5FYZuFYklt3V2R345Tu6z43xc43%20atGGYGTiFTl9ftpnFgZbODlmEMc0aW69pKQk25ZFciSh8fX1bW9vN3pqxowZXV1dhpOlw8PDyWN5%20eTlREKOzqdkBhvJkYTC6Zxin6WmfazmzJ62yas02OxEwZVbs5IQNCzjA8aFjSURiyOONlg6Lfipp%201PNHNYcqG4RSMUdSiTYEYDiRkZFBbCY+Pn7Hjh22vXIfhUbPLegIFI/HYy9romM0thpvMsWhQ4cK%20CgoWLlxYV1enUqlCQkLYChIcHEzuqqSkpKcPQCw2H3BXh4E1weieuaUzXCeuVqPoar1ceyxy4iMW%20XtMwEfCiX89YtGImvu0dFHmnmriLVWNJBMEol/tcboR2NYy+UakWX+013onH0yALOQCORl5e3pNP%20Ptm3OTcD1EOzceNGuVxOcwUyB5kxmtdff3337t32aBqajJg40/bt24uKehJxRkREsAPI/bjenQG6%2014A+B4/47plbTqPVdl+szpwasNSSThrDRMAPJ86Zv3Qqvu0di7omOXGXYt26JFqY2hLGernPdmud%203FU/uq6sM//W3wnm08sII6d7RkaOiZp9T3h4H1LVIbMLAAMGe9YwR5eUjphAamqqUqnMzs7W+2Vq%20k+oHNhAaYhXHjx8XCoX+/vpJz+gYTUVFhT2KUzLdQidPnhxqs3TT09Nramru/k3PdfRPZ23TRTPd%20M4zQWN5Jg0TADk1xtYzmuCsSN1tekjp43OiZ7vJJnfUekivy3BLay9Jp9inEXXo8ZlrkmOjZfb5b%20ZHYBIwfnUe7dnR29xvT5+r2maDETkJmZab8v3L7FKUtLS8mjPapt0/XbAoFAL3ef3ngQHTliH+k1%20oM/BbAxX1RMtdfRvkku1R3T/dzIfZkknDRIBOyLEWoi7EIOhme4sfBbPxSk8wHOGlya4rZp/7ars%20ZIFa3q7lcMxPCeZPCCD6QiRGOH26i0DQ/04XZHYBI4f7D341Mr9wWxanXL58ud4cGloi3ObFKekE%20ab3ZOXQ8SK83iJb7piNHvQawsSp4JHC24guJNL/nL12tplvb+6wIRVfrxyfWPLf0gOEpJAJ2IOhY%20kq4bRmb5WNIEHwGRmPAxrhNklW51V2/mnu+8cYM82fzzR40dK4ycLoyKJo88obDXV0FmFwCAjYWG%20z+fHx8cToZHJZMHGUiPbKqsx22YMpxQZrXFFu4hCQ0MtCTC0NAuDRwJV18/Y5DpIBDz0sTbHHWV6%20kDBsgmeYn7tIXtdxqVD2fVF7VWVTr/03QiEznDTKSuFAZhcAgI2FhqOrp52dnc1Oqstg24XmzOJ1%20w1nG1KvI2VWrVjGp8GhHzuzZsy0JsOpqI4q2Dqm8646C3OsdET87uQ/XaWlq//ztE00NrcyR8SLv%20xBcWEafBt+Ig0occdxzduqTpQd5h/veEB3hO7LwhKy6SfZdPHst7e6ITj3drOClyupnqMAAAYBXc%20N998k/zv1Vdf7f+16DptZtfmxSnNpCemw08ajUavWAHn7gJMhtUM2AHElvbs2cPcs/lga0lNTbVJ%20Iw8K2eeTrzXfmk7k5iJYteCdPuQCbpA073s3h13WIGCS72+THkQi4EGhDznuOLfHkiInepFH787m%20m/n5rZeKZUVFanl7r89lhpPu0a0VAAAAW0FnqdpyastqHfa7Y1qW00wAn88nwmGmnGSvAVZdbYRQ%20Vn+SsRnC/LCn+mYzn+44yk4EHBLpn/jCIiQCHkiIuDBFHy3McUcIn+AZGSTU9cR4uctlsuKi1h+O%201OSfr5D1Pi/YJguUAADAEhwpU7AlwkQsJD8/v28ByTqsutqwR6mW5139gtkN8JkZ5r/Q2otUFNcf%20+NuPbJtBIuABgy6uLtaNJVm4uJrn4qQzGM/pQcLIQKFaLpcVFclyjpQXFSnqei8+4BE86bbERNu8%20WjUAAAwHoQEDw5myzxRdsgem/4Fs51ceUHS10OPOTq5xUzZaezXDsgZzFoXFr5uHdrYTfVtcPdbL%20PTzAc5puLCl43Ghy5Gb++dYffsh/r+hn3Vx481i7QAkAACA0wL50a1RFNd+QjZlBCeSRblOmT/zV%20aHdfq66GsgYDw42WjmKxzNrF1URcGIkhQkOOtFdV3cz9tuhSMbGZ3n92CDzGREf3bYESAABAaIB9%20uVx7jG6crzzAzqvKd/OKnvS4VZfK/ab4+MEC2Iyd6PPi6mkThXRaDM+lJ0eioq5W9u/vL10osGRu%20rxOPx/TEYIESAABCA4Yo3RrVxepbealrGvO1nDvjRHFTnpWU3xwv8rZkUZJa1Z316b+LzlSxDyZs%20WICyBv1BqdbQUSSrFlcLPXjhAV5h/vcQgyEec+tSMpns37mygp5V1p03bvR6kXvCw711EuMVOR1v%20BABgmAuN3pptNvYuuA1sxeXaY4qu1p5iTVyuRtvN7amA04PP6EDVDf/0d0/4jvdc/8pD5p2G2Mz7%20//X1zcY22Ez/6f/iarJND2qUSumZM6264SRL5vYyxQcwtxcAMIKExozNAEeB6Z7pKTDJ5XK0Go1W%20S2tqxk551k3t6eHpfl3S/MmOo2achtjMe69/3SxtoybE1RVpevx390+JEqGFLaSuSa7rhun74mqh%20x513p6W4qFnXE4O5vQAACE0vKBSKXbt2oSkdHVb3TM/UCieuk4ajIUYS6Bcz1qun2sOGVx5K23HU%20jNMQm3nnla9+linYNoOSk5ZQWtd6e1Zvc98WV7NPtVdV9eTt1XkMLWdt7keAwEM4fbpwVhR55E8I%20wHsBABi5QiOXy8vKekrEGdZXAg5EWX0OR9c9Q3d7NnSdNBEBv6RHvHw8zDiNoc2Qs79NejBgki/a%201hB2tYH+LK5m6Lxxo2ehdc+IUr4leXtvDyfNxtxeAACERp+tW7fCZhyU6sa8m+01XO4doSFOQjtp%20JE35/mMizTuNUZtBAW09ZO3K0tqWSzUtVk2IMVxcfceKZLq8vbppMRbO7UXeXgAAhMYktDC1WCz2%2098fIgqNy/up+nYjcVYiAdtJckhydEbiCKXdg6DTk4LuvZ7W1dDA2gwLaDH3LEGO4uJpBo1TqhpMK%20yGN7VWWvl+JPCNCNKEWTRxeBAO8IAABCY/on5u3C1Dk5OTYsRQkGDGPdM7eUpqeTRqsulnwzN2Qt%20c5TtNGl/PtLe0iFv62TbDLGckVxAmymZVCRutjBDjGCUi25dkrDnMdDIbNyfS0tvFpxvLS4mHtPr%201XhCIVPOGinvAAAQGivYunVrdnb2yy+/vGjRIjiNw2G0e4ZxGvJf+bWc2ZNWOTu56jnNx38+eqO2%20Z/6Ho9jM3iPlsvauV1ZG2vzKfZjVa2ZCDEVRV0vLWd/MP9/r3F4nHo9ZZY25vQAACE1fkEqlRGJo%20HexgYxMMkYdmKGO6e0bnKFwnrlaj6Gq9XHsscuIj7FOj+LxupfqO9XA440XeiS8sGrI2k19x8+sz%20ErIRPWnMg7Pu7efV+pbmboKPYLpucXVkkFBvQsyty8pkt+f2nldaUM6aWWV9T3g4PswAAAgNGLmY%207Z655TRabffF6sypAUuZTppOhfKvf8xkjzS58FzW/McizzFD12be2HeRbv8l6zJ57IPTMHUf6QIl%20C58VPsGT9sRMD/IWjDLyHadRKpmeGMvLWQujosgjUt4BAACEBnBqmy6a6Z5hhEavk8bQZpxcnNRK%209Rd7cnrNIzyINqPq1jo5cTlajkartdxp+parl87q7emMmeCpN6uX4efSUllxUdPZMxamvGMWWmNu%20LwAA2EVofH1929vb0ZSOyOW647r/O5kPY3fSqDq1ejYzXuT96DOx/7vn+17zCA+6zTj3uBuHo+nF%20aejSpGJdrl4LlyYxs3rZJZMM6akEWVQks6wSJE8oZFZZY24vAADYXWiAg3K24ovqxjyyodVqurW9%20TwFRdLV+dPQ3rd8+qmczT+sMptc8wkPCZnQ4O3ENnaYPS5OEHrzpQd6h/vdMDxIandVLodliLK8E%20SfQF5awBAGAwhYY9O5iDxMFDnobmy1bFa7udbvxrcbfCiM1wessjbFdMrV0yajOGTpN9trZB1mH5%200qTIIGHkRC9Ts3op1maL8QieNCY6GinvAAA2p1uj+vL0iw/N3DJmdKBNLpiXlxcXF6dkLb3UW/pD%20Az788MP169cbPj0hISErK4vZTUtLMxo2mEJjWJ8yOzvb1dU1Nzc3NjYWH6khyIq52+nGyZIPaN0D%20jq6w9mPzjVTmUqu697yW1a1oN2ozlEFxGlNrl8zYjJ7TXG3oZWYMk6t3epA3u+6jIXRaDO2M6fXO%20mUqQmBYDALAflyTfKjqbz5T941ezt9nwsoyIKBQKoi/u7u6W/LpPTk7u7u5WqVS0s4PsbtiwgWzY%20xGlsIzREx5588kmjp5YvX56fn4/kNEMWRVfL1YZTzO400SNGw758/2RLUzs738xvkx409BW20+R8%20Xfhw4hx724zRtUufnag4kFtDNkzZjJ7TGJ7qdWnSnQa8nS3GkmkxqAQJABjg7pkL4iwth9v0s/hm%20W7WtOmnY8Pn8I0eOkN/yO3funDdvnvlhGWIw7F2axC4lJWXNmjX9z+3SF6GRSqXvvfceuQPmSGpq%20Ku19YvcdkfsmMTKZjATv3r0bH6yhSWF1FvnE3/pcunlNHr/AMKaiuL6ssI7Dspn1rzzk4Wl8wIU6%20zY+HLy1ZGTUANmN07VKxuOX++tyY63lW/BEzJuLaokRTBQf06LxxgymihGwxAICh3D2j1arJn3Wq%207k6bd9Iw0ApIhYWFarV6EOeZ9PGFiaC8++67tOuF+M3x4z2LZZKSkti9RkRoyJeXlZV18ODB7du3%20I7HeEESplpfUHmN2ZwauYKcDprQ0tR/424+MzYzi83rNnkfOLl83b8BsxnDtUurT0ae//m+rLjjt%20ZskLT0WbCWCmxSBbDABggFF0teRdTWfmBlgD14nrQnNzXGu+9Pdjj/fh1SePWzAzaIU9enfkcnlZ%20WdnixYttokF9v4RMJlu4cCG5FeZIQkKCXswTTzzBnvsDhhq5p4+rOjlOut+2RGWmBiw1jCE206m4%20NfnLxdWZ2Myg19Dude3S3m/LntcddHJ1teSCGpXK1Kn2qio6LYZ4TK/XYabFkEeeUIgPGADAJpy6%208pG4Ma8PT3TicplRdy7XmaPVaDlaay9y9frpFnm90emVlEs6Nm/ebFXPBV1IxOPx/vrXvw6a0NDE%20M8nJyZ9++in7eGZmpt6EoC+//BIfxCFLWVHND/+Uud4z32fxGSeeKoKVCJghJ+tibaWU2Z0SJQoK%20HzcEbUbPaeRd3f15CWahtSUjSi4CD2aBErLFAADsQVtHY5+eR35C3skCTzOK9e0GFMoWM16yePFi%20gUBAhMaSS9HpKHRbKBTacJZt350oWUfPX6WjRi1ZsiQrK+vtt9+OiIhgz6Gh3TMrV67EeNMQpEVb%206DSqSyW7p+n7+WMfPDcjcIVeAFGZnK8L73wzODvFLps2ZG1Gz2n6cPHbRZTyLVlojWwxAIABY37Y%20UydLPrBWa9jdM7edpi+dNG4uguhg/bGqDTrotlVZWhh54OjWRwcHB9sqyYttJu+8+uqrhw8fViqV%207K+Q8S8LrQ0MMHWKXL8HbjR+dx9xmtYfH3BayGefVau6M9NOs78P1m9dOriDTVatXeoDRdv+n/kA%20/oQAncdEIVsMAGAgudc7Yk3c+5bHd2tU//zhOZW6U+84l+vk6jwqfk5y/yfE2CR/zOrVq8ljYmLi%20559/3v+rOdmkrWNiYojTGD311ltv2WnNtlQq9fDwOHXqlJ76cQ1gxygUiujoaKOnDLEq2LGobsxr%20aqt2FnT4PfBvJ0FH2w3OJzuOMnNlCDlfFzY13Kq/SGR++VPzJ4YO8nhKsfhWnye3t78unEzrjtXK%20L/AYe/8vwl9MWvDP9Ll/2zv52Y2wGQDAEIdZ3GQIXe40dG41ODiYx+OVlJTY4Me1re6JmMSmTZvY%20mYL1Ugfa3GbYr8VQWFhoXlDi4uIKCgqYI2TXVC4gq4IdjvzKA7f6MwQd0asU5dm+7Gx4DZLmM8cu%20MzYTMSdw9v0hg37Pu5+dk/0/f/HIOzYAr8WkvMOIEgDAsaC5Z9TdxoWGy3WyX06aPlBVVaVUKiMi%20Ivp/KScb3hadLKy9TU1NjZ1sJi8vb8KECYY2QxREIpHEx8erVCotC0ZBdu7cSQSFCdi3bx85uHbt%202s7OTsNXsSrYsbjRUt7UVs3s3jfr0Q2vPOTp40GdplOh/DY9T63qpjbD93Bb8dT8IXLndrUZ/oSA%20gF8nTHv9v3/x1dczU3dMfHwVbAYAMJy6ZyiD1UlDx1U++eQT9m/zJ598kvyeXbdu3dASmgFrjrlz%205woEgjfeeEPvLF3RHhISYnRuEdGd7OxskUi0f/9+GrB69ept27YRBzp//nx/gh2Owuqvme0gvxji%206TQbHnWaD7ZlV5fdoDZDWPH0fUOk0uSNlo5bn1pX117/9eH6dETJd/58pI0BADhy90wm+fHt4uxm%206p+ri3t986WbrD9r7cGGDRvYcz9eeuklX1/f77///vnnn2cOkt/mH3744aFDhwYzDw0z4pOUlPTH%20P/7R6OgPg83HnuhcpIyMDL3jYrFYpVKZ6rkymsAnXJe5tby8XG8gyapgx4J8iNn5DKIn3Zq7Tp3m%204z8fbWlqZ2wmNNJ/SpRo0O9Z3qk+cKr66zOS/8CPKwAAMI2zk+vDs15l8r+bwUvg37eXiImJ6erq%206ltAr88dBKEZLOiolqmzdCjOy8vLw8ODChZ7MRjVHb3+G1PTkawKdiyY2TMcXSlK9jCqh6e7i9Ot%20bkquLo2evYsxWcKJC9c+O3FV1q7EjyoAAOiVsV6hI/MLdxlOX0xpaSl5fOyxx5gj2dnZfn5+7Lw9%20ev035Lir6eEJq4IdAkVXi6TpArPLdM9wdOu0//cvJ5qlbUwFSjd3nqmCTQP0hta1fvRtOXnETygA%20AAB2ERq9nhIzvSYDCV3ixF6IlJeXFxcXN/DVMdPT02tqathHuLZbSNyvJmKVovQS+Af6xTA2s+/d%20nKor1xmb4Thx5T93MOueBvg+Ze3Kz05cPXHhGr5FAQAA2FFohiaZmZl6R2JiYpYtW0arY9IjegNG%20dGjJ1AWtCmazZs0avSOpqamD3j6KrhZ2KcqpE5awbaaiuP6OzXA4y38774dvitlruQfsPg/kVh84%20VS3vVLMP8lycfj1fxMnH9ywAAAAj2HKVU0JCwksvvUS38/Ly3Nzc2KuzBh06YFRRUaFW3/lNaWoF%20vFXBjsLlumNM9wzfzYuWomRshqObPUNtJih83OyFoXpruQfgDourZc+9e+azE1f1bGb+FL+/vTD/%20qQcnW3tBjUplyT/8IAAAAAhND3Q1NbuwNv3dv2HDhuXLl7OdwH7Qe9B7OZqZZsaMGS4uLgKBICws%20rLCwkB1Ap92EhupPobIq2CEgKnOl7jizO2XCEmcnV8ZmnHnOWo2WGRX7pW4uMHstt72dRtau/FNG%200R8/za9rumu53AQfwf/8ZtZ/rZ4+1su62TxaKz/abqgrCQAAjoxthpw2btxIVxXRLg1iD/R3P0c3%20LdcmNRp6xdfXl9bI/Omnn5g5NIcOHSooKNi0aZOLjvj4+JSUlFWrVtGlTxkZGWRXJBLNnq2fzJ7P%2051se7BjdM7XHFF23SgcQlZk6YSljMzx3V2dnp27lrSqss2InMzWbqNOk7Thqv7EnpVrz9RlJ+skq%20snGXU45yWbMweMX8Pi4ad3Z21ei6oxb961t8nwMAAHpoeof2gnDuXiOdnJzc2NgoEAjINpGMgemk%202bt3L3nFuLg4JmlPYmIiOwXh1q1bo6KiiGO5urrSs+TgF198QXPkkHv29vYWi8WWBDscxZLDzHZE%20wFK+m9e5nDJiM258XvjMgI72W4kBXFydl6yMYj+R3U/DLr5tE/Irbv5ON8akZzMPzrr37y/cp2cz%20SpkM37EAAADsJTQ0Bx01AHbWFl9f348//pijW3w0MEJDXpFYFLEQ5khSUhI7BSGfz8/NzWUHmKnN%20ZFXwEKe6MY9dd36a6BHyOH/pVPJv9aaFl8/fWZBFjhgu1aZOM3tRmJ7r9Ie6Jvl//+8F8o/J/0sJ%20n+C5+9k5f1gxVehxV1dQ7ddZZ3+3Ed+xAAAAjGLfVU7MwJM9WK3D0ELy882thDETkKzDqqs5Cuxk%20ekF+MaPdfen2w4lzsj//iZZt4ugS6y369QyjVyBOs3zdPJvcDJP213CM6akHJy+bM0Ev/ufS0rIP%203muvqsS3KwAAADsKDZ0/W1BQsHbtWnaJg7y8PLpWmc7JRVsPFteaS9ilKNnJ9BokzedyypjduGXT%20XFyd7XozPxRf/+hIuWHa3xXzRWsWBhOnYR9UymRV//i04cRxvIkAAADsLjR8Pn/Lli2JiYkSicTd%203chSFL2hKDDAXGLNnrnXO4Jd6+Aka07MeJH3/KVT7XcbdU3y9/9VWiTWnwczPUj47C9Dg8eN1jte%20f/ibqn98ppYbydmIhdYAAABsLzQc3ejPl19+yV62zZCUlOTQpRwdHb1SlJGiZcy2uPT6lQIJs7vQ%20xGBT/1GqNeknqw7kVusdF3rwiMr8InKc3vGfS0srPv77zwbjlWPv/4X8Wl37VSvGnrAYGwAAIDTW%20kZmZSesMKJW3RhOEQiG7iBIYFNiLm3xGBzK1DghH9p1jtqdEifpWVXtvz/hR1ysrI00F5Ffc/EtW%20id4YE037u2ZhMNlgH1fL5dXpX9R+rZ/xmT8hIGzTZq/I6XhDAQAA2FdoOHYuCw76gKKr5WrDKWaX%20Lm6inMspa5A0M7t9654hsvL1mZ4+nuhJYx6cda/e2RstHe//q5TE6B2PDhmz6Vfhhonybvz4w9WP%20/q63NtuJxwtMXDvx8VV4NwEAAAyQ0IChBrsUJd/Na/L4BXRbrepmZ5RhZ9Kzymbe2HeRbv8l6zJH%20lzyG7prKlSf04BGVmT/FT1+86mrL339PVlykd9x3/n2hv9/MEwrxVgIAABg4oUlISAgODqZ1renw%2004cffjgAOYKBUYjKlNWfZHZnBq5wdnKl28cPFrS33sr+YphJz3KbUXVrnZy4HC1Ho9UyTlNcLXsv%20u1SvggHh8bjAx2MD9dYxaZTK6n1f1H6dpVHeNSbFnxAw+dmNY6Jn430EAAAwcEIjlUqDgoLkcnlS%20UhI9wtRyysrKYtIHg4Hkcu2xLvUtq+ipdaArRUkgKsNeqj1nUZhhJj3LbcaZy+0paKm55TTHCq6V%20SFr04sMneP4hYeoEH4He8Zv5569+tFdRV8s+6MTjiVY+PnHlKrKBNxEAAMCACs1QqOUE2HRrVBer%207yw6iwhYynTPfLvvnCWZ9Cy1mVvCxKVOo2czglEuG38Zaji9RimTXf3o7zd+/EHv+Jjo2ZOf3cif%20EIB3EAAAgFUMq1pOgEHcmMcuRTkjcAXdbpA0F58VM2Fxy6ZZVWzSqM0wTuN09xHiMZ/+IdbQZuoP%20f3P2dxv1bIYnFE7d+sfpKW/AZgAAAPQBG/TQmK/llJiYSGs5YdRpICkU3+memTw+lu/mRbfZmfQ8%20PN3nLAqzic0wTkP7acj247ETn1oSou++dbVX3nnbMMFMwK8TAtesdREI8MYBAAAYNKExg11rOQFT%20VDfmsWsdMMn0rhRI2Jn04tfNs7zQwWcnKg7k9tSwNGUzek5z4FQN+fdNyoP0OJ38W3Ngv178PeHh%20Ic88Rx7xrgEAABhkoUEtp6HG5bo7xY/+P3v3AhbVee97fGYcBphBBeKARh2FeCGiIfUCtZU2sdp0%2026J4cqmXnngScznZTU+6aWLq6UXc2Ym7NDHtafKkT3axbXoMNPFUI61JE41JIDESRQmiXBQQVJTh%20ZmAGmAHmvLDwZWW4Dgwg+P08echaa94ZhpcF/HzXu95/WEi0rHXgVujAo5X0coo7LmBpXRqNtreW%20Oq1o4mofpunQ7eRfvSlg5oaN09fE8/0CAAyeF+bQKLWcxIZSy0l7TUxMjLJqMLWchlNVXUlZ5Um5%20GzVzjbJxIuOseiW976xf4tHLvvDwkg13hIuNFpertdXVUzORY1pcbRedVnzl5n9sX9Fss+X9Zufn%20237hlmZCv/HNmN+/SpoBAK97O+WzRrujlwbNzpa01z4de184tZzGmuPn3pTbk8bPDA2co5y+7+3J%20ksdvXWgJi5js6SsvLf3w5uNv9Pt9aLI+ibSdP+9WXdIQFBTx4wQWmAGAIXL7129J+d3h9T+6s9t7%20PsSfgzd//9HiO+YM+PXdyhwJFovF7fpMfxaiS01NXb9+vdhIT0/3Sk7QeasH9+7de/ToUYNq7ZCg%20oKCioiJlnT0MD3tTrboU5aJb7lU2PjucL1fS0/RV6ODVdwp+tSfH7eCV2oauM2B6d/V0rluamXHv%20fUuT/0SaAYChM8USLH7Ji9QiV+j40h/r5I/nR8+cvWDqID9LcnKyq53NZps0aZK/v39GRkb/n261%20Wh966CHvfuE6L76WUsvJdU11dTWVKYdZtmrtmUDTVKUUpYgy6uGZpd+e10uhA6U800c5Vw6euCQP%20Fl2u2/rHjlfQ+fj05z+3lzVOm77o+RfDNz3AcnkAMNTCIiZ/deWtKb873DXNzJp/84IYb/5pNhqN%2077zzjslkSkpK6v8SLS+//PJNN920a9cuL74TpraMHW61DuZNW6lspB84JXO63mdc7Kr5vaSZruWZ%208i5c/eVfTtgaB7iSkFJdcvqaeKIMAAyb2Qumin/NigSzdnNHFb/D+04G3mT6yrJZXv9cyr1B/V+i%20Rbln6JVXXvH39/fi2/BCoJF1D3pq4HZ1DUNEXevA6Buo1DqoraxXFzpY+u15PRU66LY8U8mV+n98%20dsGtxmT/TYiIuPXHCayVBwCDoQy0n8g4O4Dnuj1LXZm4nxbEhC1bNX8ANYx7ItLMXXfddf/99+/Z%20s+f6CjS4TuSUHpDbsyZ/Xal10M9CBz2VZ9p7pHQwb2nR8y/yfQGAQUp77VP1KmLD/cflaHFl+dXH%20tsf11OBUu8cff7w/Ixepqan79u1LT0/3+u3POk6UsaGkIrOuoULZlrUOykur1T8Dsavmd7uSXk/l%20mXRaLR0LACOutrJ+ZN9Aneq2EjdWq3X58uUmk0kEmj5fx263//rXv46Li/vqV7/q9TfphUBjNpvr%206+tdXSg3PYn3fe7cOa43DbWTJW/J7bCQaKXWwUHVXOApluBuCx30szzTXV1KMgEAhsd31i8JnBQw%20Up/dz2joOrq/efNmZc25kJAQEWgqKir6cxtQUlJScXHxb3/726FYnW4ILzlFR0dv3bp1+/btVNse%20alV1JVdqC+Tu7WFtwzNnskoLcy7Kg3esieo6PNP/8kz/PHFpAR0NACMhLGJywq/v9vRZ5aXV76R8%20pixII5efGfwN24rk5GRP/7Jbrdbnn3/+0UcfHaI7oIf2ktOqVasMBgPVtoeaejG9m4MjlVoH6kIH%20028xdy108KeDhb/8vyd6STMy03DtCQBGl9rK+n3JH8vl9cQ/aO/9n98QfxfUS8YPs0OHDtlstp07%20d8qKAsrCerGxsWLbo2VsRiDQFBUVORwO5VYuTq8hYm+qLa08IXeVUpQ5R4vVZ+337u/mauWXyjP1%20fpa0nXj0NACMmjSzV5VmFCLT/PeEFe+kfDZSmWbdunVuU1NSUlI07SsFi+3BLxY8hIFGmfvDiTXU%20skv2tbQ6lW1lMb1mZ8vbKZ/JBrcutHR7u51H5ZkMzU10NQBc/xrtDpFmup12I/LN2s1fF5lmxGcZ%20D4XhWIdmiKptK5/3nXfeUcc6kaJiY2Ozsjomw3atENFngwE3HhEiypy9/LHcVRbTUxc6EJF85T0L%20e3q6Z+WZAADXPSXN9LRsjEg5ItOINg88fdeQvo3N7eRuQkLCUJdCGo51aIai2na3Kcotf2jar8yp%20U0ifDTx6tevB6bJ37U0dV46UxfRENk8/cEo2WHLn3ElTJvb0dE/LMwEArnPrf3Rn7w1EphlMmlHK%20HA2mgbSunbe+8CFfh2YoEkBmZua0adO6jgklJSWJ/BEXF+d0OuXFuY0bNzY2NvazgUevdl0Emgvv%20yW1lMb3Db2Wrh2d6KXTQeRIMqDwTAADXjyFch0bh3TRjtVoDAgJiYmJMJtMzzzyjfshut6elpVks%20ljfeeEMZEBK5b9u2baWlpceOHetPA49e7XpQUpFZa+u4MVtZTE9EGXWhg5X3LOyp0MGAtTqd/fmP%20Hy0AwCgLNMMvOTm5urp61qwvVdiy2Wz5+flu83UiIiLEx4KCgv408OjVrgfqWgfKYnrqOpQiynS7%20kt6AuTQuj9r7hobyAwYAGB4DnNrS50RgNS8Wp1RGg7p9qLi42Ol0zp49Wx1BwsPDDQZDbm5ufxp4%209Gojrqqu5FJ15zu5PSy+vLRaPTzTU6GDAWuv8KS58+9v82MDALjejLVaTpGRkepdkbp8vjz5o88G%20A248zNTDM8pieh+8lS2HZ6ZYgpd+e17vr3CltoEfAADA2EC17SHx+uuvnz9/Xn3EuyvT2Ztqz5Z3%20Lqq4wLLKrQ7lHT1U1ZYcza3/kfL5f+NbBQC4kQON26Uf5QqUwWA4fvy4rNGQmpq6fv16L15v6g+3%2060HKlSOPGgy4sdqGDRvcjuzYscOLX+bpC+/KxfTG+4fMDIlO+d1h+egUS3DXQgduXv57XtHlOn4A%20AABjg3cuOT3yyCM2m23//v3qilPytqCf/exnw/CVKNeDCgsL1WUWlNoLypWjPht49GojSESZM6q7%20tRdYVpWds3o0PLPvSOnBE5c4+wEABJpOVqv1vffeCwoKmjrVvYancluQWywYIiaTae7cuW51o/Ly%208sTHOXPm9KeBR682gvIvHpaL6fnqTfOmf/vvr30qH529YGrvwzN5F67++eBZTn0AAIGmGzU1NRcv%20XnT/29meAIanOKXRaIyLiystLb3vvvuUT5eamrp9+3aLxbJ48eL+NPDo1UaQenhm7tQ7Ck6WqyuN%20rei50EHbt6nekfTmKUdzK6c+AIBA081ghthYvXp1cXGxPK4kAM2Q1XLqasuWLQsXLkxLS/Px8ZF1%20yXfv3i1n8PTeIDExMTg4WH4Jfb7aiCirPFlZVyJ3o2bGf/BWttztqQ6llLQnh5ubAAAEmh6HRjTt%20gzTh4eHaa5QEIMTHxw9PoBHvJD09XaQQecSt8EKfDTx6tRGhHp4JC4k+n/uFenim99kzfzp49vPi%20Grkbv9TCDwAAgEDTSRnM6PahhISEBx98cCje+rp167qWVhAp5Pjx470UXuilQWJiYnV1tXpec5+v%20Nsyq6kqKKzLl7u2Wu9NUs2cWxIT1Mjxz5EzFm+klcve2sKBNK2bxAwAAGBu8M3Ci/OFX7tOWB4OC%20gtR3cWPwCi59ILcnjZ957lijug7lv6xf0tMTL1TaXtx3uvNbE2DYcs8CfWvHxCZKLwEARjtvrhSs%20DJlIbqMdGCR7U21u2bty9/YZ96QfOCV3l9w5t6c6lI7m1mdSPrc1dsQXg1739L0LRKY59ewzlGcC%20AIwNrBQ8apy7/LFcTM/oG3g5x6genoldNb+nJ/5m3+kLlZ1VtzatmLVgZtDZ/3q16vgxpTyTYF76%20tfk/+wWdDAAYpbw5QmO1WgMCAuSk4NWrVw/D3do3jpMl++T2gmlrPn77jNztZXhm35HSD3Muy91v%20Lpgcv9Ry8cA/yt7aKw9OiIiY99TT9DAAgEDTdpN2SEiIuv62csNzRkYGvTx4JRWZcjG9cTqfS8eC%205fCMn9HQ0/CM2xp60yaZfhw/rzbn87N/eFUe9AsNXfCzX+oMBjoZAHCjB5rMzMxNmzZ1+5Db4jQY%20mJMlb8ntsMDlxw4Vyd0710R1OzzjtoaeyU//i/W3tVZZc/7jmVaHQzmoNwWINGMICqKHAQAEmrbK%20i472v5HJyclyUvC2bds07YvTvPTSS3T0YFTVlVypLZC7X+SGNTtblG0RZZbcObfbZ7mtofdv8fMm%20+2uyt/2i2dZZVXTelqcDwsPpYQAAgaajlpOmy5IziYmJ8fHxYmPPnj2NjY309YDllB6Q25ONi3I+%20Lpe7savm633GdX3K7sNFbmvoLb015HTSf9ovlMmDsx5+9KZFi+leAACB5kvWrl3rduT73/8+XTxI%209qbas+Wd85Aa8yP7HJ45cqbi9Q86r0kpa+gptzXJg1NXfXf6mni6FwBAoHG3d+9etyN//etf6eJB%20yr90WN6t7a+Zmn/0qnyo2+GZK7UNXdfQs777tvq2pqAFt8166BH6FgBAoOlkNptXrlwpNnbu3Llr%201y55PDExcd++tjuN77nnnpEt6DiqnVJdb2o4ESOHZyZNmdh1eMbR3Jr05im5hp7w9L0LtMV5brc1%20zf85tzUBAAg0XWzdutXQ/gdy8+bNch0apdR2UFDQ448/TkcPjPpu7ZarwRfzHPKhlfcs7Do88+eD%20Z/MudA7hPPydObP9HW63NUVtf0ZvMtG3AAACjbvo6GiRabp96Pnnn6cAwoCdVtXWbimMkdtTLMG3%20LnSvlX3kTMW+I6Vyd+mtId+77aautzUZp02nYwEABJruJSYmVlRUmFT/9LdYLA0NDUNUavtGUGu7%20WFZ5Utl21EywFnaOx9yxJsqtsdvUmdBA/3+Ln8dtTQCAG4Q3azmZzeb6+nr61FvOqIZnHHm3y+2u%20wzNuU2cMet2We+df+etf1Lc1hX7jm9zWBAAYq7wzQrN27VqtVhscHMyiwN7S0urMv/hBR16pmXC1%20ZIJ8qOvwjNvUmU0rZgUVnTz/5hvyyISIiIgfJ9CrAAACTY/kwnoPPPAA02UG6Uj+nw59/qLYyL94%20uKm5ozBWw+lI2aDr8Mzxwiq3qTPLg+x5v9kpj1CtCQBAoOmbyWSaO3cuXTl4La3Oz8//4+zlT6rq%20SuT1JkfNhPrSm2Qbt+GZmnrHi/ty5W5ooP/jd0zJefbf5W1NIsdEPvVTqjUBAAg0fTAaja+88orB%20YHBbhwaeOl32rrLxcd4fK+tKlO36U3Nkg67DM0l7ckSmUbYNet1T8XPOPf+co6az6EHEjxMmRETQ%20twAAAk0frFbr8uXLleKU6nVopBkzZlDLqU8trc6TJR2L+ZZXd9yv5KiZ0HBhsmzjNjzjVrBp04pZ%20rjeTv8jLk0dm3Htf6De+Sd8CAAg0GCany961N13VarQ6rU4j/td+sJfhmZySGnXBpqW3hnzlwqdX%20PvpQHjEv/Vr4pgfoWAAAgQbDRA7PaEWe0bZ9U1pdrl6GZ2rqHb96M0fuhgb6b7r5i6I//1EeMU6b%20zm1NAIAbhxfWoWH5mYEpzrs8xRLsZ2y7+UgOzyhpRqfVtWpa1cMzwebx6uEZ9dQZ4X99LbBkZ2Ln%20N5X6BgCAGwwjNCOjMOfiazsP7vrVPxvtbbkk/+JhTfvwjPKo2HAbnqm21p3IOKtsu02d+cHSKQ2v%20/lrWN9AZDAt+/gu/0FA6GQBAoOkXu92+aNEiJv8OgHnKxICJ/pdLq0WmKTh/pKr+vFbbGWhEpKk/%209aU74X2NhsmWYLGRd+HqnowSefx2y/hbP3yt8coVeWTWQ48ELriNHgYAEGj6xWq1hoSEZGVlySOl%20paU333wziwX3R+CkgM1P3zVxUoDINHt+93mrw0er6SzV5KgZ33Chc4hF76t/8Om7pliCbY3NSW+e%20cjS3KsdNfvp19Udrcj6XLaeu+q74j+4FABBo+uuRRx6x2WxuB2tqap544onm5uaR+noSExO73jee%20kZGhPOo2pCSP98TT9gPINKYgn8Yqv8r3l7qcnSv5fnE6vLOdVvPw//6XKe3DMy/uO32ltkE+8qPQ%20y5XvvS13gxbcNuuhRzinAQAEmv4Sf+lLSzuW209PT3e5XLLUtvirX1ZWNlJfT3Z2di/vOTY2Vj2k%20JHZ7ySieth9YprHcdUpnanDWTLhyMLrV0TZHu6XBt6EsRGng0mgmLSkKmTZebB/47MKRMxXyuevC%20Wlv/niJ3/UJD5/+c+gYAAAKNJ2w2W35+vthISEhYtmyZpv1epz/84Q+a9kGaixcvjsgXo8SsuLg4%20p9PpUlHeYVJSkkgn8tGUlLY0sHHjxp7m/XjafgBKKjK/0BaGrvikPdOMVzLNF7nhrhadkmZ8Jth8%20bzl9uuzdC5W2/3qnQD5xXpD2lo9eV9c34LYmAACBZuAiIyOvny9GiVmzZ8/W6/Vds05aWprFYnnj%20jTeUR9etW7dt2zYRgI4dO9ZtNvKo/cAcO9tWEFtvckxZ+VlHpnkvpr7AoqQZIfD2ttR4omTvb/bl%20yKkz/rrWey683VTRORF43lM/NU6bztkMACDQeEF4eLhhRC95FBcXO53ObjOWknWioqLUWSeivchR%20QUHB4NsPQFnlSXlz0zhTQ0emqQ1wuTrSjG9QnXG6VTze0HS1qeWIfOKj407Z8zoLUk5fs9a8dCmn%20MgCAQDNGFBUVORyOwMDAgIAAZRrv6tWrlRnKStZxG7xRElhubm5P2aj/7QfgdEc97Y5vgc6vyRDQ%20MeFX2/7f+IgSzbXFaWZPOzFO1yI2vqsv0X/6nnyRtonADzMRGABAoBkcdTXKmJgYpURlbGzsiBSn%20zGuvy3j33XfL26/S0tJCQkLkneRugzdhYWE+Pj69vKCn7fvvaOHukopMseFytba0OpudLdYPb2+8%20Euy6drFJo3MZJl8WD7W62nKMr0/Dd5f+YZamdt6JNPkiykRgTmIAAPRj6YtRbnFKT09XZgELmZmZ%20Il299NJL3//+94fznbz++uvnz59XHxHBTr0r62m3ZZoWXXX6ksZys0vbFmfaP2i0rdrKD2ImLT+i%20Mzhly7XFb7lUE4Ejn/opE4EBABhrgWbv3r1uR6Kjo1etWrVnz541a9aIXberRcp1pV5e0NP20oYN%20G9yO7NixQ70bH/OsstHsbPn9r1Iay1s0+hZN8zjttfhjCPB11GjOpX2vIGRSi65tIO2Rqndd1Z0F%20Kef86+MT2uf0AACAAQaaUVeQcubMmT4+PoWFhc3NzXJajDLnpttJxMrVpf63HxiRZlJ+d7jiXIvW%204Bzn52j+omO4xRpgqhgfMKex0uhwzqmoFJlm+dXjE0o608z0NWunrFjJ6QsAgGLsTAq2Wq0BAQFy%20FrBCWZkmKioqMDBw7ty52dnZ6keVOTdz5szp+momk8mj9gNOM4U5F0WaCVyYK9OMUDFxfJN+XIF5%20UqN+nMg0keXlkcWddzlNiIgI3/Q/OHcBABiDgcZsNq9cuTItLe3TTz+VB/fv35+VlRUfHz9hwoS4%20uDgRbu677z4lo6Smpm7fvt1isSxevLjrqxmNRo/aDzjN6Hw1k5YfaVQV1v7C6N/qo9dptSLTnA0x%20O8bpfFo0JwK/1axtm4+sNwVEPvVTVgQGAGBsBhrh1VdfNZlM6nus1q9fL3LJ/fffLx7dsmXLwoUL%20ReLx8fFRHhIHd+/e7efnpzw9MTExODhY3hLVZ/vB+Oxwvkgzej8f49dO6P2bGlSB5nLQBPFxnE6r%20ZJq59kzfFlu9PrDYtEAcn7flab/QUE5cAADUxtSkYLPZXFFRoS7AlJCQ8MILLyjbRqMxPT1d/aj6%20fqiuPG3vkaXfnnfm4CcTL79bOdlRl9N5DWt8c+2jJ1PdGk8eV15ivPUWW/bMDRtvWrSYsxYAgLEc%20aJQUcvz48YE9mtiu/+0H408HC8NO/7+qGIOrxa/+7Ax5fGpDftfGfi22iLq2egthG37AKQsAwNgP%20NKNFTnGtSDH2GfrG8pDWRt+Ob4bLOaWlXNfd2n2t/btdHACAG5OOLhgRLzy8pNmobZiss6uGZ6Y4%20zus0LXQOAAAEmlHDNkPfYvNvLDerAw3dAgAAgWY0sd+it52dKXfHt9SK/+gWAAAINKNGS6uz3qKz%20n79ZHpnaVEy3AABAoBlNyipPNNUGt9j8r30bWkKdZXQLAAAEmlEVaKqyGy92LqZ3k/OK3sV9TAAA%20EGhGV6CpPKm+3hTqYHgGAAACzahS12CtKnOqrzeZneV0CwAABJrR5FJ1bkNp5/CMSDMsPwMAAIFm%20lDlv/UxdjdLsuEifAABAoBltgaa4mOtNAAAQaEaxqrqSq+cnyN2bmq9wvQkAgEGiOOVwu1Sd21Qe%200hloGsooPAkAwCAxQjPcyi6faaq4Se6amy7084m+oaH0HgAA3WKEZrgV5VaIGKNsj9dVyvX0Zm7Y%20GLbhB/QPAAADwAjNsKqqK6m/OFHu3mS7rGwYgoJm3HMf/QMAAIFmFGibQHOl83pTcFPH/U2zHn5U%20ZzDQPwAAEGhGgcKCAnnD9jidc6KzSmwEhN8S+o1v0jkAABBoRoeygiq5HajpuN40c/1GegYAAALN%206FBVV2K7GCh3zXVt15sCwm8xL11K5wAAQKAZHcoqTqlv2A5quqJheAYAAALN6HIuv8TV0tHhvj51%20fi02hmcAACDQjDKlp+1yO6i1bQLN5G+toFsAACDQXHeK8y432h1uB1/954va8HLbJX95JOTqJe3E%204AbLV+gxAAAGj5WCvakw5+Lu3xw0Omq/UntILgHs0mtd/2qaYPC/XNNRk1Kra53YVJVtWPr+//kg%20Znr9d//9h3QdAACDccON0Njt9kWLFmmvycjI8GJ785SJPk5bvT7wROC3mrU+ysGr89tSo3o9PZNv%20zenxS6sMU8a5nC2fZ3AWAgBAoPEszcTGxmZlZckjYreXjOJp+8BJAQuvvu/b0p5pgr7VajBq/QzV%20S9qSTePlzgrbLU2+Spr5Su2h8c21nIUAABBoPJCUlCTSSVxcnNPpdLlcKSkp4uDGjRsbGxu90l7w%20a7G1ZZpWe/24wOMB36i61bfZX6fVaJvKzbJNQ2uASDOL6j8izQAAQKDxjN1uT0tLs1gsb7zxhl7f%20dhlo3bp127ZtKy0tPXbs2ODbqzPN4voPlUxzum5Zq8NHq9WJj7JBR5ppIc0AAECg8ZDNZsvPz4+K%20ilLSiSIiIkJ8LCgoGHz7L2WaVrvIND56u/PqxMr3l7qcqsKTulbSDAAABJoBKi4udjqds2fPVgeU%208PBwg8GQm5s7+PZdM82UmI91pgZnzYQrB6OVgy6NZmLUKX/DVc48AAAINAMXGRmp3g0LC/Px8fFW%20+z8dLJTb9WG6FktT6IpP2jPNeCXN6HyaTbMvVC8cx5kHAIAXsQ6NN+UU1864tm1d1BYWx/k1+060%20NdjaltTTajT+U63aca3V87TBWVp9g0tpuWPHDroOAK4rAQEBP/rRj+gHAs31y+1qkXJdyVvtX3h4%20yeG32jbqw3RN5nGaVl1V+qKGS5O0huZWh14EGkfVRI3TV+PTVB2lC/m0RXnW1q1bORG9S2REepWu%20prcxyN6mE0aXG+iSk3K1qLCwsLm5WR4sKipyOBxu15UG1l7NukjnahFpJlpJM6ErMqfFf6QzNTTX%20GSsOxbQ6fL6I0LnGaTn/AAAg0HjGZDLNnTs3OztbHVDy8vLExzlz5gy+vVQfPq4x2Kc6Y0njtTRj%20CKobZ2qYsvIzZY5w5ftLHeN8ayMppAUAAIHGQ0ajMS4urrS09L777lMySmpq6vbt2y0Wy+LFiwff%20Xqpc7FedLtKMWaYZ5bhbprEu8HXpGaQBAIBA46EtW7YsXLgwLS3Nx8dHq9WuX79eHNy9e7efn5/Y%20SExMDA4OLi4u7mf7btXN8C3P+2pjuXua6ZppLn/y9ep5/pyCAAAQaDxjNBrT09NFRpFHxO6yZcu8%201b7Z2XLG8PX2NOPsmma6Zpoz9m86fQychV43ceJEOoGuprdBb99QtM8995yGG228JHX3X04fbBVp%20ZtLyI4agL3pp2WLzrzj0tVabf8Dc4i0/TaTrAAAYGOWWNNah8Sa/WXkBZT7+YWW9pxlN+zhNyLc+%20+eL0rAlRZ+g3AAAGiUDjTfExz2piPHnCWvoMAAAv4M5hAABAoAEAACDQAAAAEGgAAACBBgAAgECD%20Xtjt9kWLFmmvycjIoE+8JTExUduF7GF63lusVmtAQIBbB/bZvfS/F3u791Od3h5AD8u+Wr16tbpa%20Hyc2gQY9ppnY2NisrCx5ROzyA+At2dnZ9Pww/PYPCwuz2WwedS/978Xe7v1Up7c9kpmZOW3aNHUP%20p6WlhYSEKBVvOLEJNOhRUlKSOPXj4uKcTqfL5UpJSREHN27c2NjYSOcMPiyWlpbKvpWUwhT0/BD9%209u/niU3/e7G3ez/V6W2Pfmk89thjJpOpqKhIdqPorpqampdeeokTeyx4rp0L3iZ+MS1cuNBisTQ0%20NMiD27Zt07QXhKJ/BqmiokL8YkpISKDnh657RY8FBQU988wz6q7rs3vpfy/2du+nOr09+F8asgMr%20Kys5sUcvJckwQjNUxNmfn58fFRWl13cuxxwRESE+FhQU0D+DVFxcLP6RFBkZSc8PneTk5Orq6lmz%20ZnnUvfS/F3u791Od3vaI2Wyur69/4YUXuj4kOrCpqYkTm0tO6O0v7uzZs9Vnf3h4uMFgyM3NpX8G%20qaioyOFwBAYGyvl9cnIfPe+tX/0PPvjgAE5s+t+Lvd37qU5vD97+/fuzsrJEB164cIETm0CD3rj9%20uyosLMzHx4duGby8vDzx8e6775ZzDtST++j5ET+x6f9hO9Xp7QGzWq0PPfSQxWJ59tlnObEJNMDI%20UO77UF+6Pnr0qPiNr0zuAzjV0WeaEXHEYDB88MEHfn5+dAiBBn1wG4pUBi3plsHbu3ev+kYPITo6%20etWqVXv27FHuOKDnR/bEpv+H7VSntwdAuadMpJnjx4+LWMOJTaBBb5ShyMLCQvWqTcrl8J7m98Er%20Zs6cSc+P4InNmc/vmetcYmJiTEzM5MmTL126JNMMJzaBBj0ymUxz587Nzs5Wn/3K5fA5c+bQP4Oh%20rPXZdYnP0tLSqKiowMBAen4ET2zO/GE71fV6Pb09gDSzffv2uLi4c+fOqa80cWKPBaxDM3SUJQrc%20VmFyW8YAAxMfH6/58sQCpXuTk5Ppee9Sek/d1X12L/3vxd7u/VSntwfwO7mnRX04sUf7OjQEmiFf%20W88tQbIEk3fXIlOTv2jo+SH9E9tn99L/Xuzt3k91enuQvzQUSijhxGZhPfTIaDSKc139AyB21ZP7%20MGBms1n8elL3rfhX1/79+5UlIuj5kT2x6f9hO9Xp7f47dOhQ18oSnNhjibZtlEaj2bp1K30BAABG%20nR07dmiYFAwAAMYAAg0AACDQAAAAEGgAAAAINAAAgEADAABAoAEAACDQAAAAEGgAAACBBgAAgEAD%20AABAoAEAACDQAAAAAg0AAACBBgAAgEADYHikpqZqtdoZM2Y0NjaK3cTERG27Xbt2Xefv3Gq1BgQE%20BAcHFxcX830EQKABxibx937btm19tnnooYfEhmjp5+c3ur5As9n85JNP1tTUPPHEE83NzXzHARBo%20gLEmMTExJCSkvr6+92Yvv/yyzWYLCgq68847R+OXuWrVKoPBkJaW9umnn/JNB0CgAcaU1NTU7du3%2099nMbreLKCA2li1bNn369NH4lc5vJzaSkpIYpAFAoAHGjrVr165fv17Z3rlzp1arXb16dbd/7E+1%20Exvx8fF6vb7bYKS9ptsXEZFo0aJFsk3X6SzizSgP/e1vfwsICFC3UWbAaFXkPB4pMzPT19dX3eYn%20P/mJuoHRaIyLixMbGRkZZWVlfPcBEGiAG86BAwccDofYmDNnTtdHN2/eLIORkJaWdsstt6gDh4g7%20JpMpKytLHqmpqQkPD3fLHIq7777bZrNpro0GiaQybdo05YhUWlp68803y0iUmJgYExOjvENJRDS3%20aBUREaF86sOHD/M9BUCgAcaIvXv3pqSkKNsJCQkul2v//v3dDsBkZ2eLj0FBQVOnTu32pdLT08XT%20RexYuHChEjiOHTumPCQSyaZNm5SnFxUVqZuJzJGRkeH2UhaLpaGhQb6ZHTt2iKQiDwrK/GWZS6xW%206/PPP69+onx9Ea1ee+01+coiQhkMBrGRm5vLdx8AgQa4sdjtdhFQxMb48eOnTJnStYEIQ8uWLdOo%20LusIBQUFyoaSSMSGCChhYWFKs1deeUXJFl1ntLjdRSVSl8go58+fFweVa089TfqRKUq8/vHjx5X0%208+CDD8oG4rP7+PiIjcLCQqbRACDQADcWm82Wn5/fS4PIyMg+w5DFYlm8eLE8Lqfodp3R0vWqllzw%20JiQkxO3ak2AymebOnatsx8bG9jRHRy07O5tAA4BAA2CwYchoNIqI059XWLt2rRySiYuLczqdbkvm%20iJdKT09XrjFJyhwdVtIDQKAB4AXq4RM1OXLTu8zMzAMHDsgo09MUH3mN6ejRo8qVLBlrWEkPAIEG%20GPvkPNkBhJL+kCMx6mnCGtV94L0vbFNUVKTMv5k9e7aMMsoMZTdr167dtWtXdHR0U1OTSDYVFRXi%20bWt6uLoUFRXVbTACQKABMLr1Mk9WhpK6urry8nJPX3nr1q1KZlq9erVyAchutz/22GNKUtmyZUsv%202ULmrT/+8Y/Kc1NTU/ft26duo6xAIw4++eST8gLToUOHlNk26uwiHnU6nW7xCAAINMCoJ2/8SUtL%20Exs9LawnYoGm/QrOxYsXPf0U0dHRItNors1r0Wq1ck2a5ORk5faoXp67atUq9XPVC94od193fX3Z%20LCgo6Le//a3MLnK8p5dZzAAINABGH7PZ/P7778urTj3d/qMUQtKobsb2SGJiolwbRqGsGaO+p7on%20e/fujY+Pl7sJCQnypfbs2SPrfstrTFJcXJw4qNworsjLy1NSzigtRwVgiGife+45Tft4Mn0BjG12%20uz02NjYrK0ukhL/97W+j8XrNGPgSAHjdjh07NIzQADeOMVAISd5A3lM5KgA3LAINcAP54Q9/aDKZ%20Rm8hpJdffllkGovFsmHDBr6bAAg0wA3KbDY/+eSTYmPfvn2jbmUXu92elpam6VJXAQAExmyBG0ti%20u9H4zpWV9/gOAugWIzQAAIBAAwAAQKABAAAg0AAAAAINAAAAgQYAAIBAAwAAQKABAAAEGgAAAAIN%20AAAAgQYAAIBAAwAACDQAAAAEGgAAAAINAAAAgQYAABBoAAAACDQAAAAEGgAAAAINAAAg0AAAABBo%20AAAACDQAAAADolf+t2PHDvoCAACMUrqpU6fSCwAAYPQaP378/xdgAN3UCEgH5KkPAAAAAElFTkSu%20QmCC" height="483" width="752" overflow="visible"> </image>
            </svg>
          </div>
        </div>
        <div class="fig"><span class="labelfig">FIGURA 7.&nbsp; </span><span class="textfig">Curvas cinéticas ajustadas de producción de CH4, acorde al modelo matemático de Monod (<span class="tooltip"><a href="#B16">Vallares.,2017</a><span class="tooltip-content">Valladares,
          C. F. (2017). Modelamiento del proceso de digestión anaeróbica de 
          estiércol vacuno y cáscara de cacao [Tesis en Ingeniería 
          Mecánico-Eléctrica]. Universidad de Piura. Facultad de Ingeniería. 
          Programa Académico de Ingeniería Mecánico-Eléctrica.</span></span>).</span></div>
        <p>Con
          base en el ajuste realizado a partir de la ecuación cinética de Moned, 
          se obtuvieron los valores cinéticos presentados en la <span class="tooltip"><a href="#t2">Tabla 2</a></span>,
          se destaca que PL3 mostró la tasa de producción más alta EN PL3 (66,37 
          mL CH4 /día), seguida por PL4 (52,67 mL CH4 /día) y con un valor mínimo 
          de 37,62 mL CH<sub>4</sub> /día, en el biodigestor PL1, así mismo, la 
          constante de Michaelis, señala que PL3 tardó 29.8horas en alcanzar la 
          tasa de producción máxima de gas metano, seguido por PL4, que tardó 32,8
          h en llegar a la mitad de la producción máxima de metano, es decir, 
          26,34 mL de metano/día, es importante notar que PL2 t gastó 45,2 h en 
          alcanzar la mitad de su producción y PL1, tardó 30,5 h en alcanzar a esa
          mitad de producción máxima de gas metano, lo cual llama la atención 
          puesto que este tratamiento no incluía IM.</p>
        <p>Finalmente, la <span class="tooltip"><a href="#t3">Tabla 3</a></span>,
          muestra la concentración promedio de gases presentes en el flujo de 
          biogás a la salida de los biodigestores, notándose que en las cuatro 
          biodigestiones anaeróbicas se generaron los tres gases CO<sub>2,</sub> H<sub>2</sub>S, NH<sub>3,</sub> con valores más bajos en PL4 y más altos en PL1, se destaca que la 
          concentración de gas carbónico en PL1 y PL3 fueron similares; el sulfuro
          de hidrógeno producido en los biodigestores 2, 3 y 4 también mostró 
          comportamiento parecido, lo mismo que ocurre con el amoníaco en los 
          reactores 2 y 3; la presencia de estos gases en la biodigestión es signo
          de que en las etapas se presentaron inhibiciones competitivas o no 
          competitivas de las RBE desarrolladas por las bacterias sobre la biomasa
          de las excretas que actuaron como sustrato, tales RBE inhibitorias 
          fueron las que afectaron significativamente la producción de biogás, 
          metano y los parámetros cinéticos en los biodigestores 1 y 2, siendo más
          leve el efecto en la reacciones anaeróbicas de los prototipos 3 y 4, 
          posiblemente por la acción del inóculo microbiano sobre la fermentación 
          anaeróbica, en razón del incremento en la concentración de sus 
          componentes.</p>
        <div class="table" id="t2"><span class="labelfig">TABLA 2.&nbsp; </span><span class="textfig">Parámetros cinéticos de la producción del metano acorde al modelo de Moned</span></div>
        <div class="contenedor">
          <div class="outer-centrado">
            <div style="max-width: 1160px;" class="inner-centrado">
              <table>
                <colgroup>
                <col>
                <col>
                <col>
                </colgroup>
                <thead>
                  <tr>
                    <th align="center">Biodigestores</th>
                    <th align="center">μ<sub>máx</sub> (mL CH<sub>4</sub> /día)</th>
                    <th align="center">Ks(h)</th>
                  </tr>
                </thead>
                <tbody>
                  <tr>
                    <td align="center">PL1</td>
                    <td align="center">37,62</td>
                    <td align="center">30,5</td>
                  </tr>
                  <tr>
                    <td align="center">PL2</td>
                    <td align="center">39,07</td>
                    <td align="center">45,2</td>
                  </tr>
                  <tr>
                    <td align="center">PL3</td>
                    <td align="center">66,37</td>
                    <td align="center">29,8</td>
                  </tr>
                  <tr>
                    <td align="center">PL4</td>
                    <td align="center">52,67</td>
                    <td align="center">32,8</td>
                  </tr>
                </tbody>
              </table>
            </div>
          </div>
        </div>
        <div class="clear"></div>
        <div class="table">
          <p class="textfig"><b>PL</b>: Prototipo escala laboratorio; <b>μ</b> <sub>máx</sub>: Producción máxima de gas metano en los biodigestores; <b>Ks</b>: Constante de Michaelis<br>
          </p>
        </div>
        <div class="table" id="t3"><span class="labelfig">TABLA 3.&nbsp; </span><span class="textfig">Concentración promedio de gases presentes en el flujo de biogas a la salida de los biodigestores</span></div>
        <div class="contenedor">
          <div class="outer-centrado">
            <div style="max-width: 1160px;" class="inner-centrado">
              <table>
                <colgroup>
                <col>
                <col>
                <col>
                <col>
                </colgroup>
                <thead>
                  <tr>
                    <th align="center">Biodig</th>
                    <th align="center">CO<sub>2</sub> (ppm)</th>
                    <th align="center">H<sub>2</sub>S (ppm)</th>
                    <th align="center">NH<sub>3</sub> (ppm)</th>
                  </tr>
                </thead>
                <tbody>
                  <tr>
                    <td align="center">PL1</td>
                    <td align="center">1552,00</td>
                    <td align="center">12,05</td>
                    <td align="center">20,61</td>
                  </tr>
                  <tr>
                    <td align="center">PL2</td>
                    <td align="center">894,95</td>
                    <td align="center">4,95</td>
                    <td align="center">0,10</td>
                  </tr>
                  <tr>
                    <td align="center">PL3</td>
                    <td align="center">1577,25</td>
                    <td align="center">4,91</td>
                    <td align="center">0,40</td>
                  </tr>
                  <tr>
                    <td align="center">PL4</td>
                    <td align="center">840,06</td>
                    <td align="center">4,19</td>
                    <td align="center">2,52</td>
                  </tr>
                </tbody>
              </table>
            </div>
          </div>
        </div>
        <div class="clear"></div>
      </article>
    </article>
    <article class="section"><a id="id0x6307e00"><!-- named anchor --></a>
      <h3>CONCLUSIONES</h3>
      &nbsp;<a href="#content" class="boton_1">⌅</a>
      <div class="list"><a id="id0x6308080"><!-- named anchor --></a>
        <ul>
          <li>
            <p>Los
              sustratos evaluados como materia prima para abastecer los biodigestores
              mostraron diferencias apreciables en su composición, de tal forma que 
              las excretas porcinas mostraron mayor alcalinidad que el estiércol 
              bovino, así mismo, nutrientes como CO, NT, P y K. presentaron valores 
              más altos en la excreta porcina, lo cual las hace más atractivas para la
              biodigestión anaeróbica de los prototipos PL2, PL3 Y PL4, en contraste 
              la biodigestión del sustrato proveniente del estiércol bovino en PL1 
              exhibió un bajo rendimiento.</p>
          </li>
          <li>
            <p>Variables como Temperatura, 
              demanda química de Oxígeno, pH y actividad microbiana afectaron 
              significativamente las Reacciones bioquímicas enzimáticas en las etapas 
              de la biodigestión anaeróbica de los prototipos 1 y 2, lo cual se 
              evidenció en valores de producción de biogás y metano bajos.</p>
          </li>
          <li>
            <p>La
              producción acumulada de biogás y metano en los cuatro biodigestores 
              estudiados, mostraron comportamiento cinético ajustable a los modelos de
              Gompertz modificado y Moned.</p>
          </li>
          <li>
            <p>Parámetros cinéticos de 
              Gompertz para producción acumulada de Biogás y de Moned para producción 
              diaria de gas metano, mostraron indicadores superiores en los 
              biodigestores prototipo PL4 y PL5, atribuible al rendimiento efectivo en
              la digestión anaeróbica de la materia orgánica proveniente de las 
              excretas porcinas.</p>
          </li>
          <li>
            <p>La presencia de gases como dióxido de 
              carbono, sulfuro de hidrógeno y amoníaco afectaron la producción de 
              biogás y metano en los biodigestores evaluados, siendo más notorio los 
              efectos en el comportamiento de los biodigestores 1 y 2.</p>
          </li>
          <li>
            <p>Los
              componentes del inóculo microbiano causaron efectos sobre los procesos 
              de biodigestión anaeróbica en los reactores 2 al 4, siendo más 
              significativa en 3 y 4.</p>
          </li>
        </ul>
      </div>
    </article>
  </section>
</div>
<div class="box2" id="article-back">
  <section>
    <article><a id="ref"></a>
      <h3>REFERENCIAS BIBLIOGRÁFICA</h3>
      &nbsp;<a href="#content" class="boton_1">⌅</a>
      <p id="B1">Ampudia,
        M. M. J. (2011). Investigación de las condiciones óptimas y de la 
        cinética del proceso de biodigestión anaerobia de desechos orgánicos 
        agroindustriales y estiercol vacuno [Tesis de grado en Ingeniería 
        Química]. Universidad San Francisco de Quito.</p>
      <p id="B2">Appels, L., 
        Lauwers, J., Degrève, J., Helsen, L., Lievens, B., Willems, K., Van 
        Impe, J., &amp; Dewil, R. (2011). Anaerobic digestion in global 
        bio-energy production: Potential and research challenges. Renewable and 
        Sustainable Energy Reviews, 15(9), 4295-4301.</p>
      <p id="B3">Benavidez, 
        E., Toledo, J., Palacios, Y., Andino, F., &amp; Ortiz, W. (2017). 
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    </article>
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