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A universal theory for drying pharmaceutical hydrate/solvate organic crystals

PGR-P-508

Key facts

Type of research degree
PhD
Application deadline
Ongoing deadline
Project start date
Thursday 1 October 2020
Country eligibility
UK and EU
Funding
Competition funded
Source of funding
Research council
Supervisors
Professor Andrew Bayly and Professor Frans Muller
Schools
School of Chemical and Process Engineering
Research groups/institutes
Complex Systems and Processes
<h2 class="heading hide-accessible">Summary</h2>

Around one third of organic molecules are able to form hydrates and solvates and this includes a number of AstraZeneca compounds in commercial manufacture and in development. In this project we aim to develop an universal theory of drying in particle beds that encompasses both drying under vacuum and with convective N2 drying and takes into account the location of the solvent in both liquid, and solid phase (hydrate or solvate crystals) of the drying mass. <br /> <br /> The impact of solvates/hydrates in the model is via a mass transfer resistance internal to the crystals, and depending on solvate content. The new drying model will be based on mass transfer resistances in the horizontal plane (in analogy with Catalytic reactor bed models where this approach has been very successful) coupled to diffusion and flow in the axial direction towards the bed outlet. <br /> <br /> Prediction of drying time of hydrates and solvates is not possible using our current models . Drying time is often the bottleneck in the process and is where we frequently encounter issues on scale-up and technical transfer. A better model will allow us the make changes to increase drying efficiency and robustness, as well as increasing the reliability of a digital twin.<br /> <br /> The postgraduate researcher will work with in a vibrant chemical engineering department and gain a range of numerical, experimental and analytical skills. Working in close collaboration with industrial supervisors will ensure the project is relevant, and helps the student comprehend the nature of their innovations. The opportunity to demonstrate the techniques developed on AZ compounds during a stay at AZ will be an excellent demonstration of the research project&amp;rsquo;s impact.<br /> <br />

<h2 class="heading hide-accessible">Full description</h2>

<p><strong>Preamble</strong><br /> Around one third of organic molecules are able to form hydrates and solvates<a href="#_ftn1" name="_ftnref1" title="">[1]</a> and this includes a number of AstraZeneca compounds in commercial manufacture and in development. Prediction of drying time of hydrates and solvates is not possible using our current models . Drying time is often the bottleneck in the process and is where we frequently encounter issues on scale-up and technical transfer. A better model will allow os the make changes to increase process efficiency and robustness, as well as increasing the reliability of a digital twin.</p> <p>Within the ADDOPT project it became clear that the solvate drying performance of nitrogen drying is better compared to vacuum drying. As illustrated in the figure below, a resistance model of a filterbed with two sources of vapours, drops and solvate crystals, and three mass transfer resistances, the crystal, radially through the bed, and axially towards the top/bottom of the bed. The axial resistance can be significantly reduced by a flow of gas to support transport of material through the bed.</p> <p>In our EU project PROPAT we monitored the fluid bed drying of wet pharma granules using NIR. The detailed water content data could converted into the beds mass transfer resistance, taking into account the absorption isotherms of the micro crystalline cellulose excipients. We observed that the mass transfer resistance is initially constant, but once the internal liquid phase has evaporated we observed an exponential increase the mass transfer resistance, which we attributed to the transport of water through the cellulose crystalline structure. The lower the water level in the microcrystalline phase, the higher the mass transfer resistance.</p> <div> <p><img alt="" 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rt37AjLjN2frV+R8tJZUIAWpxNdeXlocTp1HRPufRkZn98cR+b4QxPwlgnD+d82NCfHt23TfT+0+zJiUTByXxO2K8h9vWUCXjJhzK6fYOgVdUvCz8jvKCuXkO3cXEYSDIKRBEYSGKYfdJJQmCuKR8gi22eJJAyuWYPRJUswumQJ/H+Mk3y5pywXkpWh2QQfl4Ixx2K0uu04uW6dhNScWK9ucRArqeGVK4PXSkjQpay68vKEY8YXLcKZ668PeUy72QxfWpowvqn583VZFMK5r86CAowsW4bRJUtw5vrrQ16ruckFP5cA3Cb1mx/ftk3XfZ1esQKjS5ZgLD0d/oQEXXEN/dnZkmvpsZQYvS+Px4MzJdcDmSYgPSAXzcDApk1niSR8i5Dr3EJCEHhrHCMJusFIQoziEhhJYDAA5RvrhrVSghCDwMXO+gIMl92JSevlaGmUrkAHNm0SXqb+p5K0fyhcHCatl6HVboc/IUE4Tm8A3uCaNcIxepS9x+NRXEuPMvV4iD+dP+bkunVhXUvPfTW7XJiaP18XsWhucmHUcTPAzQT+T0QSUmfodoucuf564Ti97oNWu11CmvRYLYzcl8fjQZcrj7hRnhHdV7wJ7SWHzg5JSE0NZAYFRGyZY9AFRhKME4WQmQ2MJDAYhPKNxWc30CRKJGG85AuA2QQ/F6/wf/PBejCZMPrSzfBb1ImCn0vAycosCbnggx71Kite8RgxY/MrYV9amm5lKlaMRmIgwrmvnu3bdRGLU+V3w8eJ5veewHO+P05B3tSks6BAuJYRt0Hf1q3kGS9ZEvX76qwvgI9LCd5XeoB0fnN21IMXtUlCIPg3N4uRhDDASEKMhJEEBgOYFpLQX50tKKcJ2xWKv3fv2EFW6eaXMGW5SPXLPmG7Es1NLgm5MBL9zisrf0KC4YDCM9dfbzigsD8725Cyj+S+Tq9YoXktr/NhwXUjyPsm4CITfHmphpSpNzNTtyVGPofiNNRjdfnoq9mKk851OGO/Ce1uJZkKdV/tbjOmLBdK7+v/iBXB/z/J6D1iPMVWSzRjEgQXQxZzN4QBRhL0WRAM/52RBAYDiBpJ4H3Gx7dtC6lwSUR9IoktsKRQFdLxbdvQ6rbDxyWrWhE666WBfEbTEsXXMnrMsfz8sAIewxljOMe0m83oysuj/q2/arOSIJhnCArVzyWir0Z/WmOL0xlWzQlx7Yh2txl+8yzhefu4FOoYtO6rpdGJSetl8JtnKb8zT5J/vc6HIyIFchFIQlsbqT6ZmQksXQokrhdZEDIYSQgDjCQwksAw/VC+sW5YS4KtPKL8br5eggZJaHE6g7EECQkYWr1aU7kNl90lvMxPl92muh/MM5UEwZIEb+VDUX3Zny3prC9A99EdOOFcj+Gyu9DRYJxoRKTUjuQoiBdJNZ0h+cxbaTydNBIRAijFYzCg0JubXBgvuRYwx8m+L9L7GnUsjdqYW5xOnH7kEeC660iJ77Y2Urp8924SuKiI7ckiqZAMusBIAl35swZPDGcTyjfWmuekOd28ZGQBD10j2XV0dBTH9u3DyLJl8KWlSYLshlav1kyLO1aXLygrPxePVrfSx9xTu51KEiatlwhuhk+TtDQ64TfPwpTlgsC9zMRAVXSj7bWk++gOimUmDmP2r2LKMi9mylSv+CxpYY9h1HGztK6G2QQfl4wJ29Wy786lEY3x+LZtguWp3WzGyXfeUfbyEAcu5gZiEsQBjAy6wEhC+JYETWEkgcEA1N0NGVnEgpBhCr7cZJaEyccfR+ehQ4LFYHzRIowvWqRqDpbLhO0qwXXQX7VZoVAVvuWAdLn0nf9cFN7NwstI6fKzcl0SzCd1MfjNszBhuxItjU6S5SAjYmdzXgacG+Dn4sIaw3DZXQorBMxx6K/ajMHKhxT3HA7BbHE6hQyVkWXLMLR6NYZXrtQOXCz0BGolFEprjzDoAiMJMRJGEhgMQIUkZJDaCBm5QJYpWAhGTBK2bIHv5pslgYu9OTmGAgD7q7OFgknyFd5w2V3wW2jxCDOpVodPi0xZLpbcDy1wU49S9FnSFAWl1IQE8ykDQKcsFwrBgd7KTJkyjTtr1prjR7ZRY0/8XHzIMZyszKLHrXBx8Hg86KvZKiEQPkuaIpYllHTl5UnSL31paULsjWadBJpFjpEE3WAkwZg1gaVAMsQC6pYEgSRkBCsufu5zpGVvcTGQk4Ox3t6IiikRPzQfwJgq9GbocuVRAutMwn6xsiQ0N7nQ5cpDX83W2NT5P5JDahLIVraGFJYrD/5AZojPkopRx83ULABeiEVmniKYz8clS+Iheo/kSNJNfZY5hpVpOEJcIPQ0V5/lAs0x9FU/oxnY2tLoJG4tkRvDZ0kznOHQWVCAE+vXCzUhfGlpwXnTTIHMYimQEYCRBGMEQWubkQSGcKFOEjJyiT+1MDe4IrrmGsDhEHaNRsXFoYr7wAebjToy0NzkUqy2JS9/S5KhyHu9cqr8HkGJ+LhETNiujroylLsaiDJL1B28SJsbkhGQggHnBsWqu7nJhQnbIoUp3s8lKkiQUpmmRj1dUKF86wsobgKxzMDJSnrhqZ7a7QJZohOMNByryw9k0sRL5iuSOJB2sxl9W7cKFrOQKZBZuQGywLIbjIKRBEYSGKYfGiQhEHTlKYxpq2iiKBLBm5eHyldrFlCCOTaNek6V36tYiUZTGaqteH2WNNXulnJpaXTCZ0mjzo+PS8Gk9XLhXHw1Rfm+fi6Jer2WRqci8G/AuSFmBKHdbdZU8sHxzsZI6TKJi4lYU6SEy2+OhzjI1c/FC2QylnEguns38KXNGUnQDUYSGElgmH6okwQJUYht74YJ2yJBMfvNsylfbKmJPhaNegaqNsLPBa/ts6RGJfah3W1WNKqSKkFl1UktaW5ywVuZqWGin4PTZbdjuOwOxT4+LhnHa15QPbc8sDFWQZWtbjvVBeKnZLIQiROsJTTCRTJGlOcbqngAHk+wwicv4cSBGCYJQlnmLKk1jpEE3WAkgZEEhumHOknIpfhSY0QSjh/ZpqpIfVyS4iXvs6QqlM7xI9twwrkeZ0puhLfyx4au39zkwqTlUoWy1bvCVxM+HiDUD/5U+b2Gz93RUITxkmsVaYO8UpXXB/BxyUIJazU5Y/9izJSpeK4JKYxTPOcuV56Q8UInVIkKwsg/q3a3GZNW6TM8Y/8iPB6KlchgHEjYJMEk6t2Qy9wNRsFIQmiiQAtUZIGLDNGE8o11w9rAqkf0guNXQTEiCc1NLmqVPL95FkYdS3HSuU7yd79Z2lvgVPm9klr9Pi5V97U7GopEdQtEwsUZWuHTleFCgFMqwymrlDiM2xaFfR1CsFI1ffs+Lh6nyu8Jea6higcMKdOe2u04VX4v1eLS7jZjoGqTJE5CcIFQ6hnwmRqDFQ/qekGKnzUf3DjqyFD8zeMhWTTia/osaREVsRJ/97RjEvhuqrkBawKrk2AEjCTESBhJYDAAdZKQJWoXza+CYkQSSFyC0s3g45LR6raTKoGibAefJVUS9T5QtUmijP1cYsgGRe1ucyC/Xl6hLyhDFfeFTRCIMpytuJ/eIzkKZebnEiOav5ZGp0qtAF5mYrDiQcWc8IF9/DZJF4yXzLOaMiUls1Pg5xLg41IF/3+r247Bigfh45Lh5+Jx0vmYcMxw2V3wyeIDfJZU9FU/I+zTX50ti6OYqSAVYvFW/kg41ut8WDav8WhpdJIMCpHFZcpyge70Udp3BmYTBgMVP7VTIDOkrjv+/wy6wEhCaCsCIwkMsYbyjZWeHkzd4kkCnwKpQRKMdDaUy4RtIYUgJAn+8876Aok7Qh55P1D1c1kuvHqDIl6J+bl4DR84kTP2L6LLlYdT5ffq7orIK0N5sJyPSxYKRp10rlMos0jjH0gqobo1wW9Jgs8yB6P2rwmWDB+XLCg74RySDAd1l8vpstsk1h2fZQ6mLBeReQ2Mw88l4Iz9JqLAKzMVsQQ0F0j30R2YssyVnVfdZePjUjBeci06GoooZJLUROhoKJI8Dz+XYMhKJCY+vJvEz83SJgmpqeo9UBh0gZGE0KK7NgIjCQxhgk4STBmkgJK4lGwIkjCwaROm5s+HNzPTUOOjAecGak2EKes8YR/ijpCu+Pm6/u1uM3yc1C8vjmwXr7Z5RaW24pZbFXxcCiEnHAmcO34kdCOok5VZVIIg7oVAU2aR1GVQtEYWZAblM3kQ6FclylAydhWXS6vbrv0S4uLg5xKEtExaQykfl4jhsruo55bcCzcTfo7SsEnx7OJxqvzfJGTSb0kSyKTcnaUnDqSl0YmTzsdIQG3AmuHnEiUkVUISdu4EtmwBXn45SBL4fg2MJBgGIwnGhBVTYogFVEiCygpIhSR0FhRgcM0ayb5j6ekhqy92NBSppwZyKRJ/tjywcdSxVLPrH6+Um5tcOOl8DD4uVTBj08zXfi6BBL6JiUJA2YmV/XjJNaom+L7qZygEIRHDZXcqlbpMmYVb+0Ete2LKcgHG7F9WLUoVJChzJOeTE6jhcqUi93j4gldpVCLi52bDx6Wip3a7akOpUcfNqvdEqycRPDZJlejRPuO/BxO2KySfa8WBNDe5AmWiEyXpuT4uFf1Vm4XvZYvTieGNG4GFC0n9kNdfJyRh/34RSchlJCFMMJIQvmgSBkYSGAxAnSSI87ppZZkBjLW1wfvooxhdsgRDq1cL+48sW4ZWe2jz+Rn7TfS2vgGSIPYbj9m/Kvn7pPUSla5/PIlYLJR9lr7okxTHkB4GV+EfR17VTFcMKqNEeCsfkpAYUtxHXrQoHmOOxdQiR3LLSDi1H7SqKfIxG8SFcIFG7YkZ6Kn9g3BOeUMkmjI9fmQbpiwXq1gvxONIorh04jBecq1mueXxQEos7XzDZXcR4leZpZoGKv0eEDIyUrpc8Qxp1+a/M/y5yXeGuEVaGp2khbndjmaXCx1FRTj11FOA1yv9DTF3Q1TASEJ0iIGCKDCSwGAAOkhCBj0mweGAb9kywd3QUVQk1LTXo+D6araquBmCCk1sCpc36oF5piJzQP53n0AOEuDjkjFcvpJ+TctFaHebiWXDElR8fm42piwXqPYV8FnIarnLlUdRWHGYsC1UVYaT1ksk+xut/aCWSujnEhVlq5ubXCSoT2W+/FwSTpfdjla3HafL7lBVpj212zFhu0oyH34uEadLb8cZ+5dUrUJiMjZpvSxkfMeY/V+o8z3quFkyn+1uM8bs6ZpkxceloKOhSBHc6uOSJHEgSuITBx+XhKGKB9DS6ESL0ykQ4ePbtsGbmYn+7GwWkxBDMJJgjBxo/Z2RBIZwoU4STIFCSnwnSJ4ktLURue46nDGbBZLQ7HLpsh54PLzvm2YuTkS36y3RKi5YsrivZqt2JUaK39pvnhXwVd+LY/XvUwPgfFwwgr+5yaX4+2DFgwHTc4JCIfMKVm4VIMRjnqYylFtGpiwXGSIItFRCP5eoGrVPAu+0zPgJ8HEpOF16qyJTpLf2NYyXfEFGDpIwUnqLxPXCBz5qZVkcP/KS5r0dq8unxiBM2K5UJVw9tduJO0kjC2K4bKXESjRluQjdR3eoEh9xauex/HxMLFggKPmJBQtwct260A2ehNoIgd9SFkuBNAJGEkITg7AyHBhJYDAAdZLAE4TCwqBVYfZs0uBpbAzwesNOgRxzLFZW2+MShEj3McfigOKPE6Lv5b0FlDIDcv/4qGMp2t1mtDQ6MWFbSDXLywMG5St83lzd6rZj1HFzyNUyUUAXajZdollGjHRdpKUS8kIjCQPODbpM87yClJQ3DqyoxXN2xn6TauOlVrdDYo1RkDJLimC1kB+rHqMyA721v9ecExKY+hA1lZY/h5h8+Ll4TFnmy8hBAk6X3aZ4dt7MTIwvWiSQhME1a4S/hSzLbDKRltEZIoscgy4wkhAjYSSBwQDUSYJYVGISwiEJfPEfrZViT+12YR+fJRXNTS5ifVCJP6Aqo0BNguYmFyEllFU3Lb1v1LFUqvBFWRb6VsszcLzmVyHnQW4Z0dt1kY9ARbIAACAASURBVJZKKBZxFgU/38pAQFrWQ+j5HC+5RrMDZ3OTSzNORKyMfVyKJHOCtLO+UGX/2bpSFkkVyi8Yvjc/l4RRx9KQ899RVIT+7Gx4M0WZKprFlAqBrMJgc6dcRhKMgJEEdQm7RgIjCQwGoUESRBUXddRJ0COkZr/yB+DnEiQv6OYml2A14NPNemq3G/4xnHQ+htNltylW0X4uUbWHgaIgD2WF39LoxJSVrtB4hXq67A7N2gfhdF2kpRLKRZw1QGonKDMLlPMxS/O8fi5BV/EhqgvEHC+pnSCZJ0sqxku+gG7XLkzYrtS8L63CVsHCWOruBup9mWfiTMn1EbUeD9m7ISuDxSSECUYSGElgmH6ok4SMrGAxpSiVZT5ddrsym4GbgeHylYp9TzofE1baE7Z/0lg9K90MQeU2Gz6LXEkmSqoAKl76R3Kk5nVZdUePx4OR0ls00/SIApoFH5csqSYoJxpypXbSSW+L7PHwfncdro6A5YPWhpkEDi7AUPl9is/PlHxJ/bxcHMbsX9IsZaxVTbGl0YlT5fdqzBntGUqzIs6U3EglnfIiR3Q3jLrlxFuZqdvNY5gksN4NEYGRhBgRBUYSGAxA+cZ6Nk8UiyCLSYiAJIhdCEplTlIKxYF++twLM/CPmt9g1JGhS4GSgLS7NcdJahior/CHKu6jtikWF9yRKEouGeO2RVRTtnw+Rh0Zin2am1zoq3lWRcHOUPjg/Vx8oHYCLYtjHtrdZmoQqDB2jl6Fkic9XufDCqVK0hFDV1M8VpevCBKkCZkz9aZeWkWO+quzMS5L49T+TiRE1MyL9W6IHRhJ0CYIoYSRBIZoQPnGEgcumkzB6osRkASSz69unueVFJ9SyB83Zk8PqUx4k3730R2aTY5gnoHxkmt1jVWu7PnqjkQxKVfL3srMkKtlP5cgpNOp3R+t6+JQ+fdU78db+WP0Vz+lKElNc+mILSLdR3dSXQ58lcTTpSs0idaUZZ6gVPuqn9FdTZGXgapNgVRD+grfx6UpOzdy8Wh1O6hFjnjrUEujEy2NzkBchPz+52Dcdp3GNZMx5lgcMuBUP0n4FglYzM0gv6Es1rvBKBhJiJEwksBgACokIUOU+hh5TMJw2V3wW+hR68qXdQrO2L+MofJ/V32hixWfeAXYV/2M6kqYnDsJI6XLQvZJkCu9kdJv4njNC5RqikmKaorH6vIxab0MtJbGfDMk3jIxVHGf4u/ic3krMlUU9Wx01O8TrifN+pipsMD4uGTB795fnQ0fp7QyjNpvFgiMvGy0mlI9U3Ij/AqyoaxnQJPBioeocyT9boizERLh45IltS/8XILQuIqvkkhSIeXWlQR01u8LzJVG7IU5SJT0uiBCZjdkBSwIPNFmJEE3GEmgi6GaCIwkMEQIFZJgUq+TIIKYJHQUFaHFqawLQMr3Kl/MPi4Fo47FutwE0pd4cJXv5+IlUe/Ha36l6xw+LgUDVZtUX/zyFf6k5WKli4FSTbGjoQijjqUKxUm79zF7Ok46H5VlOKQKjYqmLBepHi9eydMsH9JxkiyOUFUST5fdJtyHvGw06Z+gHYNBJA4TtkUhFSwhXPpSMmnX4Gtf8GQvWCUxOXDPsyAmmLw7oaXRqXRhUUilj0vGpPUyXQGN2paEQvIbys0NZjgwkqAbjCSERwJY7waGaEKdJGTkGopJ6M/OBkwmDK9cid4cslJubnJhynIxRXElCUqOxCqkqZZnlihHq7IEMd+oR80PT/z29FiBSesCamOloYoHQhINcTVFEl1/p0SR+iypmLBdgTMlN6oSIWIqF7Vm5pIwZZ2vizj5uUTBKqIV6zFY8UNqsSA5WZiwXSncP61s9IjjFlLimmoRIvMcqoAUcXXsoFgfZmvUNxAr9Fk4XXa74BKgVUkktQ5WSJ+5qFGVfK4mbZdquohCNYKSkITiYtK74YkngJSUYNBihizDgUEXGEkwThB07ctIAoMBqJCEDGnvBl5USMKx/HwMr1wp2Xd80SIMWVcrqyRycRITPXnRX6RLSfi5eExaPyf57Iz9i6IeBspzjDq+phkr4OOScar8bolyG6j6b82Sz7wybHXbMVTxgCwbYg6mLBdLgh1J++N5IWIm5BIHbXO8SehOSYvo93MJmLJcpKiSOOrIQEdDkdLnb54lUX7yGJJRB3FHDJd/B/SmTgm6/Pneyh9L5osQLmJ9GKjaFPKevRWZiiqJ4sqa7Q2HAu4e6XGnyu+Bx+PBmZIbpSTBegk6GooCZE5p3ZiwXaV6L80uF4a2bSMNnnJyCEnYuRPYvZtlN0QBjCQwksAw/VAhCSZiJhVWQnSScKa0FINr12J45UpJF8ih1avRVb6LuiL2cSloddup5XCJYgwVhzBHcb4J2yJVS8SE7WrBdy8vLSxWcD7LHPTVPAtvZaYmYfFZ5qCjfl+gJXQ8gql3SYFz0Ls58v0TQvY34OLgN8fBL1Hus3C69A6yYtbsV2GSHCMmB/Iqif3V2bKAxzSJkle4XKyXBypX0uoZzNRVCCpoqSEZIn5zHHyWC9Bf/RS8lQ+Thl+63BozRPeWKFRJ1CrmxDeqkseBwGwSLEKk0NcFwhyLYzl4OZafj57tJLi21W4nJEHucmDZDVEBIwmMJDBMP9RJgrhOAh+ZLSYJe/ZIGjy12u2Ymj8f3TuIG4HWpc/HJaH3SI4iZdHPJWG85BrN3gLqMlNzhS4PBiSBeynq7g2NwEc/l4Ch8n8PtCoOpN5ZkuDnEtFfna0r2I2sWq/XUPYzMCUrPz1pvUKIPyBWEX1Fg3yBeaX51ntqt2PKcoGw75TlAkkQ6GDFg7J7jw+swuXjnoGTzsdxuuw2Q0WJhstWBshJsmLOiSvoEmoPB7FMWebimOsd4Zy0Yk5iMiGQI4t6HYzgHM9WZGgMbNokuNT6s7NxesUK7ZgEsZshg2U3GAUjCYwkMEw/NEhCLuDxEBGThIYGocHTWHW1JLtBHrh4/Mg2kUKOw6hjadDnz8XBZ0nFGftNgah0ZWXAKetFOlaWUsuD3xwPSe8BLlGRzUC6Hd5uKGiSxDYkSVLvfFyKkHqnpRBbGp0YKb0FvUd+SxRQyPoPMqVpmSNdzdblY9I6X5sgWOZoVkkkHS/TRPMkDQKV11Lwc3GUrIFEjNm/IihmWgqnqi+/druyOiOXIHHVELdPqBLScRio2qjZz0L8PSBltVMl80SrdNnRUCQ811a7HWOicuW+tDScWL8eLU6nNknIEhckyyDEm5EE3WAkgZEEhumHCkmQxSTw6VspKcCqVYDXC4yN6UqBbHXbA70Tgq15W912jJTegu6jOwJtlmUEwTwLE7ar0NLoDCjEBdR4A4Vi5JJwunSFTAmkUQvlENNymoJkhCYjcfBxyRiseDBkKiXvZhgv+byGsgt9fT8XL7FSdNYX6HBbzA5ZJVEtCJQnItLUShkZ42YFCFmw0uGk9bKQ1pRglUSl//906QqF1UfvS1FJFqXBkD5LKrpceYEOpNKgRq1Klx6PBz3bt+P0ihXwJyQAJhOm5s8Pkp1QZZnFgYvM3WAIjCSERxRYdgNDNKFCEkxRrZOgvZqlVQa8WKGATzp/EuLHMRMjjm8qzilfIffUbsek9TKZu2MW/FzoQEFSF+EuXeSAtyDQguh4IjRcdgf6al6g1i2QK8ATzifg8fCNkC4Isb94zPQqiR6PBxO2KyT7jpdcIxm7plsj4C4h8RypOH5kW8i5kFdJlMtI6XIFoVCQKB0xGX4ukXTsFHWj9HPxQryI3Dolv64q4XO50LN9O4ZWr0azy6WDJAQyhMRBi4wk6AYjCcZJAKuTwBBtqJMEg3USjApRdsr0SJ8lVTVK/nTZt5RKQ6IckkhAIWWF3OXKUwRK+rhkjJTegs76AgxW/KdmPALMJgxVrNEVd9Dc5EJ/1WaodUOcsF0h+MHlZn/1e4vHmZLrqASBVFmcp3FsgqS2Ai8jpcsVylViadAy31uS4ONS0V+1WXNO+CJH4iqJUIk1oLkr5GMYdSzFcPldmt8Dn2UO2hsOKT4frHgwQI6uDHldvaJdlpknB1msTkIYYCRBmwwYKsXMSAJDmFC+sa59UfpCE7seokQSSJT8VQplLu8GKRdiftY2zxOlMlP2WZIkN99nmSOJ9iftlEMHA/q5RExaL9cM0CNxGMnUcfrNMzFc9h2KIjSSGilTiFwyjtXlKxS+2r7iipMnnOslRMYncgnx90KyHMTzOUPoyxAqFiNY5EhcJXG2qjVAnobp8Xgwblsk2WfSeikZW82L8Ks19gq4hOS1IMbs6fB4SFDtpPVSjNm/hBPO9bHpApn8M5EFIYORhDDASEKMhJEEBgNQvrHW/CVQb96kjEmIAklobnJhzP4lhWL0c7M1m+yQKoA0M7vemAISnyCP9u+sL6BWN9SKrPdxyQq3A8kWuFBBUMT3d7rsdup8GG1xLB4HP2cDVZtkynemStxCnFBxkpRfFjezShPm5viRbVCkpHIzMCjrP6FGlEiRo2ThmqRcc4bmvcrTMD0eD7WeA4nJCOWiMUFuyRE3ieKFr3XRX/WL6JKE1FRlnRFGEgyBkQRGEhimH8o31g1rAy6GjGBEdhQtCSOlt1AyGRI1fdrtbjOmKNH8PsscdLvewpj9S9RAOLFM2K6QRPu3u804XXabzJxNFOKkdb6OcsfEFz9Y8RAmrAtCpuvBPBNdrr8o7o2MQbmyptcjkBIE8ZyRypXBGhI+SxpOCL0M1CpOXiZZbfstSRgsX6PaqZGvOaEmtC6PfGXIbtdb9FLMooBOeRomb42QVKW0JNNjMjjtVFgylniB4LQ0OkmariUV4AiJ0RtrwkjC2QEjCYwkMEw/lG8sPruBj0cQB11FSBKGKu6jdAxMwknnY6rH8MF/8uwGH5cscU30VWdrFkGatH4Ox+ry0eq2C7UGeHcHXyVxxPENTFnnC8piwrpAdh79VgsaqZAXWiJR/rTiTrMxVK4s+hO89xTFnNECNvtqtqKl0Ymhigd0uzSkZEfutknUfL591c8Iz4BUd7wZnfUFgfgTZcyEj0uRjTlBEmTq8XgCKYuimA2O3sCqs75As2AW//xOVq7DQNVGaa0LLpEUydLIBGEk4eyDkQRGEhimHyokQeXlpkESjm/bhr6tW6lNnjwe0mZZSRCSMVRxn+oLuLnJhQnbPyuUAt+0iCcRpEqietR8UOICwYn6qiSOlH5ToUB9qr0LQstg5UPCuUltCLr1Y8pyIbyVj1BJj9acyeeJD9TzeIhbZdy2SGdfiCRJ/YPgtZM0XQ3NTS74LBfgjP0mwW1B4k8WQmH6D8RSyAMT5b0SOhqKtAMoRd8FsfXBx6VokEZCfvjgy5POx3R3fTREEmgFyRhJ0A1GEhhJYJh+KN9Yv7QGysmKcryzAqlcP1gk2VVMEvq2bhVeguImTx4PX5NAGkTGZxZovoCP5CiC3PxcEk5UPYFWtyOQUheMmicFjlIV1QKpysU8O2SVxAHnBtm14zFm/xcDP0ip5WHM/lVhPpSKT9y1MA0jpbcrjieme/U5k1s++EA9+WqfEAX1VMK+mq2BXhhyxZ6Kgaqf61agwfgTOdkIxlLI3Sp8+WQp+aG7cvxcIrUQkscTKJgVIpjzjP0GtLodYZEDKkkoLiby0nvAwd/JfkNZ5DeUlRHbX/RnCIwkMJLAMP1QkgTekiCvFmcyUS0JXe+9h6HVqzG+aJHE6nBy3To0u1zEXCyLMvdzCThjvynk6q2ndrvC10+yImaClGOOF87ns6RJIu61UgJ5RTVe8gVNEzPx84utH7SOkioR9txsnLF/UfLZpPWSQBdEedvpBChM+xQlPmm9VHPO5JYPWqBeu9uM06W3U+MgJPekloHAJWDMsVhXMycSfyIvzZ2M4zUvCPucLrtNofjl55myzlPMj49LQn/V5pDfH+2U2UShjXS4JGEwNxdYvhw4cIB0gNyyBXj5ZeZuiAIYSWAkgWH6oU4SaCImCWNjmMzORs8776Bn+3Z0FBUJ3R+P5ZP2y8fq8lWqKV4ZMkLe4/EomjmpKa2higckQWd9NVtDKEHx8fHwVmZSlS8xdStdAnzHwc669zVIQjwmbFfL7l0ZXMdXl6QFZspl1KFtefE6H1aMQSgtLFQ6DFoRyFiMx1n4zbPg45Ix4NygSlr0xp+Qzo/qaZgnnY8pSKaPS4K3MlN1HrpceRquFVoHyySMORbrClw8lp8vuNSO5edj9IEHiHtBDCEmQSW2h0EXGElgJIFh+hE+SdiwAZOPPy64G5pdLsF6wCtYmpKfslykaxXq8fBNojR86JwJp0tvE/ZXpt5JFfSk9VKVtspkRSkeF+lSqcwwmLQtEPbTaihEzis3kysVFF9d8lT5PfBZkjX7OkzYrtScL5LSKC1J3VW3W1TpMCGgZMn9Hj+yDV2ud8Ku08BnSMjrDBDFrsxgocVSEGtNmmg+LpKkYcrPQ6pe3km9/876ApwpuV5RTZP2vGnEx8/FY8C5Qf37uG4dYDLhWH4+Tqxfj/7sbO3eDWpZQgy6wEgCIwkM0w9jJOHSS4ENG8hL8MknMXrypGp2A82K4ONSDUWQd7nyMGm9HFrlkn1cMvqrs9HS6JS99GdLKij6LHNwrO69gMKMpxZyanXb0eXKUygasfB+/lPld6sE1GlXbZSMXVRdsqXRSa3XIFdkWvNFakmIsgXMs0nMRqBRk59LJFUSRbEYIcsv67qPFJwuux2tbnsg3kJqfdGKpVA0mrIkoa9ma8BNpWz6NWb/qsJ60dFQhFHHUmrBLG9FZrD1syUNfvMsHK/eCp9lDp0sBHpQiL+nHUVFkgZP/oQEDK1eHaLB088o9UYC1UsZdIGRBEYSGKYf6iSB1uDp0kuBPXuAsTEAoVMgidKaIyjzY3X5usjBsbp8nLHfRM+rVyEK4yVfwJg9HX5uNsYci9FbmysrFJQqBLl1NBQJTafI8SkYrPgBxuxflSoaStW+KctFQsCkfAxD5asxUrpcVwaBOIWTZHFcSSEu8ZA2KVIWGxJLc5OLaokI1bHSqCWBnraZQAgJhXyFij+RHzNceodq0y/xedrdZgyX3SkZv8+SignbVYI1gt/nhHM9emq3C3Pe0ujEYMWDVIIkDohsdrkwuGaN0NwJJhO8mUFXh7YlIVdKEpglwRAYSWAkgWH6oYMkRNbgqaOhCGP2r+oKDuusL8BI6S0ypTUTSjO9WhxAAobLv4OWRie1kqG821/vkZxAqlyCVNFwyZiwXYXuozvgrcykKhGp0kzC6bKg24NYQC7RWKHPwIDzv4T9ibKaJTtnMk44H5cUDlLraMlLH7Vs9UwMVjyoGgPSeyQHSuvHDExaP6eRejgTPktq6OJFOuNPJq2XyI5VEp0py8WS+ApS+yE4Pt71odUeW42QktoKQXI4XnItdd+e7dsxuGYNhlavDk0SWIOniMFIAiMJDNMPdZIQ4wZPcuELHAUVYkpgJS1TnpY56K/ODlT2S1H8APxcvGA1kJfuFXf7a3XbMVx2l0TR+bkkTFkukigaEiFP/j5pvYySkhmPMcdixUq5ucmFcds/q/5QfVyyECzHN7sKFnciXRUVpnhZR0vxGCdsV1GDNSet81TnnGbS56Wl0YmOhiJM2P5JQTxGHN9AS6MTw2V3aQaXkl4cH4Z89qOOpZovtSnLXLS7zUJNDKLQgy2qpywX4/iRbYLVQB78qEdIZccEhOofoiBZeho8ZbAGT+GAkQRGEhimH8o31g1rgy2ihaCr2JMEXhmSl/5FgWI+0pWqvCUxKUiUQs2j93EpCgU4YbtCFOUvVTQ+y5zA6j9BUSK63W3WyNRYSF0pkwJP6kWA+OP9XAIGnBvQ6rYHFH2iJAOA1tGS/1uXK4+sgkN0kaSZ+rX6H/gsKRJXiHwMg861IqLxJ6j3qyDzqlbLQJgrWVaGnEwdq3tPCGKVV0nsr85GW4NZalngZsJb+RBcTQ3IqczHik+exoL9a2HauworPnkaHo8HBUc5rDa/gB3VQRLT6rYbIgghSYIQk5AhdT0w6AIjCYwkMEw/1EkCrVpcDEkCKeBzEY4f2UZVsD5LKjXyvNVtx0jpMl1xAHzTI4miCbQ77q9+SjgHiXG4Rghe62gooirUKcs86oq1v2pzSIIgV4ST1svQc/QNRdzGhO0Kyb7jJdeQKH77TbJVvHoq46mKexW+fC0LgFyxy/cddSyFxxOspqhW7Ej87MYci1WDVv9R8zLVCuLnEjFUfj/pP8EHX4qqJLa6HQLhE6wwActCjv1ZJBTcD9PeVRLJtL4Kj8eDnMp84bP5+3+InEp98TK6SUJqKpBVCHg8gEdkjWMkQTemhSSM9wLdrwNtW4C6+4hC5cX9Q/J5705g0hub6zOSwHCOQfnG4t0NGaLIbI1iStEiCVoKNlT5ZrKi3UGqBGoq55lC+WYflywE8rW67VT/up9LxKmK1YHujkpFSlN6/zjysmY1RXkwovR6CYrukvIW0CQzI04yN2fsN2Gg6r8xZZ2vEog3W4hnIFkU87QVOxeHgapNwhhIu+jg3yetl5Bqio7Fiuv5uTjVFEN+zuWWjTH7lxUkxx/oVsk/F94V4K3MRFuDWWRZCP6dtyw0N7mw3bkHpr2rsOhAFjY53kBezUE43bXCNe0NVci0vor5+38okIX0Q0/AXF8eJUvCt+gFyRhJ0I2zRhImvYQYVKYD1mQiWkrWmgpYEoCaWwlh8I1FbyyMJDCcY6CThAy+hGxWsJzsWSAJNAWrp3wzL81NLnidD6taFfzmePgsKRgqXy1xEZAmUpcbKL6TQM3UoFdTjIfEHM/FYbDihxgvuYbau4HPEuD7SZxwrpcodB+XFOjsmCQEV0ruvyJT9eXg45JIBoJCsSdCHiwojt+Ql7n2m2fhdOlyajXFvupnAoTtYmH1L99n0nqZMG5SdEkZW8Knr/L1CwYrHkRbgxn9VZsxZblAYlngXTRvVu3Bko82wtXUAI/Hg+3OPSG/M66mBmxyvIG0fWtg2rsK60p+Fz1LguCuy5WmQzLoQsxJgm+MWAZsc0MTAy3CYLsQ6PpddMbESALDOQY6STBlkLRH/iXHv9w0SEJnQQHGFy3CwKZNaDfrK5YUWsHqK9/MS7vbjOHylThe86tA+2hKYGNgRUsr6Tvg3BBYmaoXNPJzs6kZBsTHT0/bE1spHIcvwn7rPcirOQhbxdNkjBRTu49LwXjJFzBQtUmWypmGvupnNaP49ble+PtJQu+RHEXhqAnbFcL5+mq2ShQ+b4mRExBxLIVA2FTcGj5LCiZsV1OfkZi8DJfdhXa3OdD/I1jbgFhkiGWhpdGJvJqDgkVgfclrhr9/5vpywwQhJEngfzcsJiEsxJQk9O8hyt2q/v0zJLY5gP1youQjASMJDOcYVEiCKehm0Bm42JuTI0nzGl2yRCjPHEq0FKx4xd/qtqOndjtOONdjuHylkAvf6rZjqHy1cOyU5WI0N7lEikXpSiBpjlcqLAKtbjtGHTerEgU/F48TzvUKH784VVE8jla3Hf/78dew9MN/VfjH11i2oaXRCWfJd7Hh4FfQ8IlSofq52ZKVP19sSGs+5T0j1OMVZsBbQTpTni67PZDdQVwrp8u+JZzvWF2+rNCTsvHUqfK7qWMhKbBfUpRoDk1e4tFZX4Ce2u2BKpo8gYwTLAu8W8beUCW4DZYdflKwJIQr2aVv6j6PhCQ0NAA7dwJPPAEk3UYscIWyDCFGEnQjZiSh9SnApvy9RkVK5gHdr4U/NkYSGM4xqJOEwkKSApkrKidLIQm9O3diav58jC5ZIhSc8SckYGDTJl0vZKJglT+KKcvFisJBI6XLJPuMOpbiVPk9VB8/H3gXLJpDj1Xwcwk4VX6vhIwMVdynWYXQzyVg0nopulx5qpUST3BzUVFXCI/Hg59+fI+EHCz/cBmWfLQR2yp2w+PxYH3Ja8LfVhV+nUoWxDJe8nlN68pQxX06SYJotd5wCF2uPGqxpla3XbWttZ+bjVHHzSGf81D5/ZrjUMpMTNj+SVZiOWhZEJ97xSdPw7R3FRbsXyuJOwhH7A1VguvhPywvhdzfu3MncOedwKOPkg6QW7YQkpCSwuokRIiokwTfGHD07tgRBIEoXAQ0/SS8MTKSwHCOQYUk8O6GXCDLFKy4KCMJvptvRlt1tdDwZmLBAowuWYKOIn2ll4mCvVjxJfZZ6OWbibVArGhoSmcmBisfUihRoWgOZUXr5xLg40h6Ja2aopqQiozKfgvOTz6HK/b/h5BuZy5dj52HFqK3mDeVJ0qCE/NrD2O1+QWBKCR+cA92f3SFxrVnKBoliaW/Olu1i6Ma6fFziYrUT7HQSVMcxkuu1SQspIbDFSK3gl6iMEMyvpHSW6jpiQVHOZj2rkJCwf2Ggw7F0uXKE4Iq13DbYNq7CjP23ot9LouU1JrN6MojFqwWpxOnH3mEkAMxqHUSmLvBKKJOEpxfAyxhxh4YFWsqUP9942NkJIHhHIMKSTCRgEWTKVBQKUtJEjIzMfXQQ5LARb3uBY+HL0V8lSLK3sclo8v1jqJpkMfjwYmqDbp+BFp5+f3V2ZKoeamylAUZmvmiR/8S6OcQ2n/5/seLkFDwPZj2rsL1Bx+Dq6kh0MQoaB3wWdKo92euLxdWxaa9q/DmoWtUrzNuU7cmdB/doRoLQOY7TrW2xHjJtQqC1llfQFHuMzBpvUy1miKp4XCNzBJgrPyz3xyPM/abNMt5FxzlsOSjjUJqo1EJVvlMIu2nq7PhampA6r7vw7R3FS7Y96BAPo5v2wZfWhq8mZno3rEDI8uWaWc3CBaELNYFMgxElSS41wKUQNqYii0N6HjR2DjH2mI/Tvfa6Mwpw3kBOklQy25ITQWuu46snA4cCDu7obnJFeiTIF2d+rhkdB/dgdNlKwCzCafLVggllvurs3W1joZZWX5ZLq1uu+CD11RSssBJvnmRmivCcXgeEj64m5h9EQAAIABJREFUF6a9q7DS/Jzg0yaVE0WNl7h4zbiC7NI3cV/xUzhpX0JRzvw5kqgdGPn7o7a4DhR/amvgKxOqZIGI2mdr1VSguSZIJ8YbZeQgCWP2LwfcSkqy4VcpxjRluchQQzAj0u42B74D8myaVDQ3ufBy5bsCWbuh8FF0fn+1xGUwvHIlOgsKQneBlEsWIwl6ETWS0Pc34gI4mwSBl5J5wJAj8nvQQGZmZuxO3v4CULrw/JPKr8RuTj9lUL6xtLIbbrxRsmu4JIEvnSsnCH01W4mpXKIo5mLKMl9QKPIfIVFgYiUzA6fLVugaR/fRHZi0Xqaxwp2J3trfS45paXRivOTzin17ixMx94O7YNq7Ct879D3FClvukhiqeECVwJACQcmAOQ6vH7oaxR/PVxkfvbYCWfnTlO48ybhIj4nL6HUNAhUN1ao5+rgkiQLvrC/AqGOpJJ2Rr+HQ5cpDT+12BXHxc7M0Yz/4c3idD1OtJnk1B7HJ8QYKjnK6v3ukLTdfApxSwMmShP6qzXC6a5FQcD9m7iWk745ttwu/g6n584XzaadAsjoJkSAqJGG8l6zop4Mg8GK7MLq1FGSIKUmovXN65266xH5p7Ob0UwblG0vSKjoj8IIzRa1OAqm9L0+fSxF87KOOJRoPT7niHKz4oULRTFgX6B5PZ/0+QKOwkI9LxqgjQ1DCatUUiz+eh0sLvoP0fbdilEuGj0uVuD0mrAsk+/Mtp8Xkg8RDBJtNvfPR1TDtXYWb9t+nmR3AH9NXs1V15e/n4qkr/+YmV6C8tTFfLV+cKdiJUdpsacJ2lSJNk8ydvHlXoCw2lwb1jJJETFnmKVJPNznekFRR1JJgAGuCBimMI/NY/Sw8HuLKMNeXY8H+tcirOYh2sxn92dkYXbJEiMPR7t1AIdom5U+OgY6okITabwMqxcvOmlgSAc/6qMwJDYwkxEAYSRCgfGNd+2Kw8EuhJ+BuUM9uMEIS+qs2Uy0I4vS5nqNvaOTWz5FlQszAUPlqSRMmXvTU4CeZFaHNkH7zLPgsczBc+m1qsSQ/FwdwcfAWz5ZkJvi4FIzZ09HRUISR0m/K7iVVoqT9XGKwQBAXDz+XiLrKTUJp4fc/+ifpuKi1FRLhp8QbiNtSa83FmGOxrrgLMsbZGLN/RfI8fVwypiwXU2NCWhqdOFmZBb95tugc0iqJp8rv0bymz5KKkdJlAmHjgz03Od7QJAd8MKqa1YIv2CS3yMjF6a7F1vK3JZ/pi0kwMZIQBiImCcPVsc9k0Cu2NGLViAEYSYiBMJIgQPnGuvZFEqiYkRsMWFTJbjBCEnqP5CjMzWSVLk2fm7ReDq0IeLl7Ycz+ZXg8HpwqWyXZb8o6D721rwl1FcRlhoXVve1KyFevfnNCILAxdIYDH7Owq/LPOGi5TTXGwccl4Yz9y7Jj4zFQtZGY9AOreJJlkYyTlVmCW4BfLd+473bZ/dFLMCvHmKirTbdA0mq3Q955U11mBO6PuCZocRZUEiSqkih2IwxUbQp5T4SwXYCBqk1YdvhJmPauElJJ5dclcSypms/SzyVipPQWqpUlu/RNoTiTq6kB1x98DKa9q5Bd+qY+kiCORWAVFw0jYpJQc+v0KxteuFlA4yNRmxsxGEmIgTCSIED5xkpPB7JyRaugjOD/wyQJatUUaelz3spMYR/S4XE2tNIe/eYEnHCuV9RQoIk8Sv5U+d0SMzsfOMm3JNaslSAq9sQrj+2O5zFlmScq/CO+X+V9BFfhcfBxSRiqeECxki2qswsBdNkHrxeOn7RegmN1+ZiwXalRAyIpZAdGuQxWPEgvk0yVGfBziYIlQGE5CgSbBvsrSKsk0giKuCiVn0smpaQpaWs+LgXfLvwWlSTwRbRoz0E8N2OOxZoWlnUlv5NUcBQ3heKvqR2TwOokRIKISMJ4b/illmMl1jkxiU1gJCEGwkiCAOUbSxKTIJMwSIJaNcVJ6+VURUG6Ov4rRh3fEJXgNZY6pya0OgCkdHAqfFwyjte8oBj7eMkXqCb4SeulwuqTVxz2hiqhHDFpxBR6Rc4XCNIyc6fte1C4RvHH84RjecU8XvIFynkTqKWnQ4lizNxMoY8CTfqrfqE4x/Ej20JWSaQJLQtkoGpjII5FaaW5c/8tEndDsDqjunIQB1OGmgvenSG2HPCWnYSC+7Gj+sMQMQmiFMhClgJpFBGRhN6dpFbBdCsbsdjmRl62mYLzgSSYTPT/GznOkDCSIED5xgqTJPCBXGJRC6KbslykGkRHK64ULaG5HDwe0lhKThDE0lezNeDTnoUp63xJZL+9oUpQ4HKFN2b/kmaa5ZTlAuo8yCXT+ir+5QCpAriw4A54i2fDZ0lFZ32B0A1RfF4/FwdvZaZhguDxkKJTQltsbhb8XBz8ZnWLis+SivGSLwgllCetl+mqkqiXpJwqv1dK2ETZFvy8/9TyJCZsV4UgBynUYEotWfLRRpj2rlK0kc60vioQhY9bKpS/Kp4kiK1xjCQYRkQk4VxyNYgl3EqMGjjfSMLZOI6RhCCUbyxJ74ZAxUV+W4MkDGzahIkFC3Bi/Xq0m81CS2LlyzpV1cTb3OSK2ZfsVNkq1cI/eqTVbceAc4NC2fHugAX71yqO6awvwKT1CqjHWMyk9o+gXr/2T1i6/zZSunn/EvgsczBUtlqR9eDnEiV9F8KRvupnQOI+eAsCsQSQvg7qfSDE5bG1qiRqyYRNWmly3LZIQdhIF8x4gSR8ab96wKNWMKWWuJoahKBRWiXHlebnsOSjjWjsalX+qhQNnmTCoAsRkQTdcTVnWeyXR3WOAEYSYnEcIwlBKN9YEpKQpYsktNrtOLluneRF6F+SBHwoDQrkff5aL2c92QZ6RJ7f7+fiIyIJarLPZVVYEkg2wy2yFbVWZ8kkRf8IsWwtfxvvVP0VNZ/Mw9wP7sLSD/8V3uIERc8KPxePMcdi3V0z5dJ9dIdiRS4O7KO5jpQyAxO2q3QRH5qMlC6X3VMilbD1HsmR9MPoMUuj2LWCKfUI31Xy+oP08teupga4mhrQ29uL4v6j8E6cJs2dtmwBXn45QBIygimQrOKiYYRNEsKMRwhboRgRblZM5ilmCJMk8HNpMinnlf+M9jfaPvJnQztG7ZyMJEQO5RvLgLthrLcXJ37+c6FnA7+f74sp8L8tVWA+LlnXam6k9BbATOoIjDqWYNR+s0QBGXnQ4jLOegssGZWV5ucERVVRV4jhsrsUaYETtivQfXQHhkvv0CAKCfBZLqDGTfAdDus/mYvqw/SULr6aYjhEqMuVR6+SKAvsa25yKYpC0cRnScOYY7FuF4NYTjrXSdI7/Vy8ahyDmCT89KN7AC4u4BZSD6Y0Iub6cuTXHlb9e6vdjvdf+AkS3/s3pH+YBe8ff09Iwv79wToJWVkkEJivmWBiJEEvwiYJYaY+nhWSYEkAJr1Rn6doYcOGDdizZ0/wgwhIgpoyD/WZGqnQu7/4M0YSIofyjcWTBHEBGP7/YpJQXY2pFSvQWl8Pj4c0vvEnJGD8sS8oosp9XHJYQXS8YlK2PtYnnfUFOOlcpxqLEA3hMxsu33cvnJ98DnxKpY9LxJR1vsQH3lezVUgDVFWwXBLOlNwoxDzw7oy0fWsCKZtkv90fXYHr9t0ObzEhY1OWeZpBgTTprC8IlMcOjsnPJWkG9k1aL6GMW0ne/OZZYT333iM5kkBRnyVV1fokJgkJBfehz7oIJ52PRWwx4oso0f52fNs2tNrtaHa50FFUBNdvnsbCgw/DtHcV7nRsxZhvQhSTkBusuJiVRSxzJkYS9OKhhx7C5s2bUVxcbEw+2IaxT/STBNqqlbattr8R8R6cg+IDfzZ+Txpy5513Ru1c6enpMJlMuOaaa/D888/jhPlmw/eolwzQPje6T6j9wyUJ4+aLQs5VdXX1dP9EzgroJEGtdwNPEtragPR0jH/wgSS74dR791KqKSbB63w4LHLQX51tIJBRqai8lT+KGTnweEihHr5XQ33xxQElm4gpFYvAsbp8iRvEz8VR21yTeUuGtzITa60vwbR3FVabX8BgxYMAF4cx8yxct+92SXyCkVV7u9scaGqUKHlOegL7Rh1LFePsProDU5aLqQSI5i4IRVzEwa5+Lh791dnUfZd8tBFLPtqIByy/MVSaWUuc7los2L9WEbDY4nRiaDXp39C3dSuGVq9G39at6O3tRfVgK+bu/wF5HmW/FqVAZkgrLjJLgiHccMMNuOyyy7B8+XJjknEddj+rr5MrTeGEUkiRmLNzfpaE5bekG78nDbn88ssxd+7cqMisWbMkJMj8+/CaTemZFyPuAb1zruWmMCInPowPOe9z5849L4gCnSSo9W5ITAQWLgS8XqCtTRK42F+1mRpEJ66mqFdIT4HLVZU/Tc7Yb6J+HgsrAl8gqLX4QqG1s5+Lx/ZD1+Fd++OqZu6WRqei9sJQ2apAbQWlD7XHnIrED8hKeUf1h8K1R0pvQX3xPCR+cA9Me1dhS8mvdY271W3HcNldkjEYDewbrHxI+ozNcUITLm9lprTbJZdkOCagucklzMV4yTUYKV2uu8mTub5cURHRqPAZDQv2r4W9oQoeD+luOrFggfA7mFiwAP3ZhLjwKZCOk41I3PfvpEy05UVR4GIG6aTKYhIM45prrsGPf/xj4weeOKC73fLZJgmwpQEjDdGfrChh+fLlMJlMyMzMJAowwpgEo8QgHJKgdc5YuhuWL1+OYnmb+M8gVEiCKWhNyDDR3Q2QZjdM2BbKfMmJGHXcHJZfeLh8JfXB+SzJgZoJSuLAV1aUfz5pvTyqBIEvEOTjknHg48/BtHcVrtt3B54p/g+Y9q7CogNZQvdHmsgLH42UfhPNTa5g3wZRCuDmonTV4Lmhigfw5qGFQiqepu9c1jSKKPDwAvtIdoGoAJVljiRIMZj6mYgJWWZCtCW/9jDyag7C1dQAc3050vaRNNE1FqUVJ5Q43bVCBUf5fHozMzG+aJGg5AfXrBH+Jq6TcOA4SYfd4nidVFz0eAg54Huf8MKgC2GTBAMxCWedJMQgJiGayMnJQVtbW/CDKJKET5O7gZGEIFRIQkYg0CqQAqmjLHOr244J20Khgc6EbVHYgWPyLoY+LhUnnOvR0uhE99Ed1IfKm6RH7YtFn8/QVTRHjwQLBPH+8jj8tug6mPauworDmyVm6mWHn1Q9z7htkWTcE7arpArWsZgoYS4OA2XfwopPntY0pfMFfxbsX6sgJxLyEbAeRBrYJ3eZ+CypVNdK75EcwzESRoWf77yag/B4SBYIH6OQfugJTeKkdq60fWuE88mlo6gI/dnZ8GYGa1DIiym1jRwX3A3Vm16gEwUGXQibJBjMbghloo5mTEIsshtiCkYSVHGekwSTiCRkKGMSApBXXGxucmHMsRhT1vkRB48Nl6+En4uHtzJToWzk7aRhNuFU+T3weALR8YHPpixzI1ZEPbXbFWmBPi4ZQxX3odVth9Ndi6I6Mr6Co5ywml3xydNwumsV5ztVfq9k3H4uQbHPI8WP463iZbqUrKupAemHnlAU/OEVOm854EsiRxrYR3OZhBNzEguS4PF4sKP6Q+EZmPauwrqS31GPtTdUYWv528Kx60p+hwX71wrPUq+oVVzM3LgEpr2rcGD/G0HXHSMJhhA2SQBiWichoiyIGNRJiCliTBLUUiS19tGbDslIQnSgThJ4X2phblRbRUdLpqzSQk2T1kvh8ZDV+Annepxwro/YikD6N0gj/0+X3aEZJChWUjSzd391tjQmwJIqkIGCo5zE5M37xPWKq6lBsQqesC2C3xwXsiSyEZHHnow6lk7Ld4BGEjweQgD41FS+7wLfrEkuK83PCXOn5SZSE1WS8P+I+ynx/e/Bcd+PWMXFMBARSahaGp5yiDVJiEHFxZgihjEJWopcax+tmAT5/owkRA4NkiASHTEJZ1u8zodFyjseJysfifo1emq3w2dJgZ9LwqhjqSKAbv7+H2LJRxsVq8+iOjsWHcgS3AQ5lfnYWv42zPXl6D66Q2aun4Mc+7NY8cnTguJK27cG2517DI3V6a7F9QcfU/jTWxqdUTf7nym5UUbQLpmW7wAfZKjmHiiqswvWnPUlrykIQvqhJyIOdNTq3XDnX78L095VmPvuv6Fh/4uMJBhERCSh+/Vzr8FTjHo3xBTnSMXFsy6MJAhQJwm5hcTVkFsYXAWdQyTB4/Ggq243+mq2xqSSIi8Dzg3U0sJOd62gbGhuBX5V6mpqEIoh8XJpwXexsOAOUhiJi8PiA8EGTqvNLxi2IPAijk+gjSlaMljxIGA2YcpyIc6UXB92VcNIJRRJkIu9oUoIdIzWGLS6QI4t+waWvk5KOy/88x1ouySZkQQDiIgkjLWdew2eYtQFMqZgJEEV5y9JuGGtqJRsIZCbcU66G6ZbCo5yMO1dhfn7f6i5n9Ndi20Vu7HS/JzQC4CX9z++GjCb8LJ1PbJL31Qt4KNXXE0NWHQgS4iJmO45irUYJQmxECpJ6BkCbk0ETCZ4U2bjutdux9y/3IXqVcvI74pBFyIiCcC51eSJmwU0PhK9yTlbYCRBFecvSXjweWmVuKwsYlFgJEEiOZX5ITMZaOJ01+K98pfwXvlLqHTtjfq4iursAhkRtzhmEhtRtST89PdAoQfweNBWX462+neB3Cx4U2aTqowMIRExSQizPHNMxJZGsi4+bWAkQRXnL0kQZzeYTAHXAz0F8mTfEF75j714YVW+prz584/xyg+C++X8Z4Hk79HejlT+soWTbL+VXYzf3P++5HrLXsiGae8qZLz45Dk3/jW/JK2ML3tzLZ793rtRP7/4Wb6wKl8yN+eb2PYeUf6qVLpAtl2SjOteux3fr3w1Rj/nzxYiJgnAuWFN+LRaEQBGEjTASIJAEtRTIBscx/Dzr7+NDem7BHnuO/nw9p/C735UKHzW1dyH0gK3sO0u74SzqFnYdtnaJdvu8k6U7WtU3e5q7sPHu46o/r2upAOH/1wr2S75e4Nk+8P/rRS2O93Hhe1n7/wbRkZG8M6zVmxI34Unb/kLhrynJPsfP3YCy98lgYa3Pfwsjh87gaI/VWND+i5suvltnBocxnu/cVC3N6TvQl/XSWF//nzi8YrHQ9vubulX/H33czbJfPyy4C94fPGb1Pm1vVePqkMtqttHilsl8yXf7us6iT2vlkmuL5/v/a85Vee71twmmQ/x9qab30Z/txd/euJjbEjfhVd/sA9tR/+B576Tjw3pu/DauoOoNbfh6dv+imt/9ThMe1fh+UPv4unb/ooN6buw7/cVqLd3Cud+51kr2o7+A5tuJt/TNzd9gvIPm/CLb75D3X7jZx/B8tc6YX/59sE3qoRn919f2YU3njik/FWpkIQDX/0cEvNJlcxHj/whRj/pzw6iQhKGq4ESvaXdY6hwPo1WBICRBA0wkiCkQBaqxiR4KnqFlysv+dtKMDQ0JLy0n7/7PXi9Xux68hNBCQ8ODuKD/ykTlPCJ/pPCNv/3vGc46vbzd7+HwcFB/PkXxdS/Z9+6G4ODg/j7yw7JNn/+7Ft3Y2hoSPj7s3f+DUNDQ/jLFgs2pO/CW08Xw+v14tk7/4YN6buwc/NheL1evHDP37EhfRdyMz/E4OAgXlz/Pu5b9Qp+/cj7GBwcxP9mHcCG9F34/SOFGBwcxP+s3U/d5o/n9+e3xfczNDSkuv3ymg8k4+W3d2w8TJ2Pf3/oVayxvyLc/8avvYV/dPXjk7wjqtsn+k+i8A+V1O2X13yAwcFB/HH9IdXnMTQ0hILflUueL7/Nz/+7v7JRt/836wBOnTolzNf+1ytwcsArKOnDb9egr+cENt38Nm549mcw7V2F39cWCH+vc7Sh1tIqfB9rzM2oL20Xtrl3j+KY57jqdtl+N9rqelS3Oxp7Yc2vE7Z3bixW/qpUSAJMJuz+5hVCTMqWht2x+VV/RhAVkgAAPW9Mn9vh05jRIEb7C0DpwvNPKr8ScmoYSTCZRA1q9JOEyo/dcB4Orurfe8WGnq5ePHnLX7AhfRf+8pwZfX19eP7u97AhfRd2/Pwjyfaupw6jr69PUNLy7fdesaG3+x+K82Xfuls4X39/v7A/v/3ymg8k2/z1/vqiBf39/cLx5YfqUWNpEsZf/G41PEc7hO0P/1CGztZubPzaW9Ttj/Oq0N7UJewv3/7wD2X4R89xyfHi+5GPR77991dLFNv/6DmumI9n7/wbHrnlj5i95z6Y9q7Cd558HhvSd+F3D+9Hf38/cv5zHzak78Jvf1gg2f7jE0WS+ZJvf/B7h2T8/PjE1x8YGJA8X/H2n7M/wcDAgPB85Nsf51Whw9MtzJe7qg32gqBSbm/qwifv1GBD+i6BJPz338jftnz7XfT39+Otp4sFgtLf34+//cYmEJ6u9l589OcqYftYW49k+3hvH/a9Vkbdfun7ezEwMIA//ddH+kiCSmzP620HBaKws+NwbH7ZnwFEjSQAgHstEKIDa/QJQhrQ8WJ0xs9wzoGRhMLCYGOaQnpMgpwkPLX8HXR1dWH3rzjhs/KPXXAcOCps2wtrcbRMpITzqyTb9sJaVH5Sr7rt5OpRsq9W9e/WD2ok57N+UIP6KpGpveAIXOUeYdtx4CicxcSUvvkbeWhracffXrIISqKtpR3v5wZN+XVOD3a+ewhX5D6Mrzy5EXVODw7vDprWm91tKPy/MtVt+f51To/kfhwHjqKsqE51u7a0URgvv601Hz/9++sw7V2FWX//Hh5YmYODu8rR2tQhKPn9fyqVbBf9uQKeujbhePl2bWkjzO8FXSXlH7tQetAlGa94fs3vVavON227takDB3YQq8Nzd/0N3d3d2PFzopRffegDdHd347WfEFfW1/7n5zDtXYV/e/nX2JC+C289cxjd3d0CgfrTxiJ0d3dj68p84fienh789ocF1O3X1x9AT08PXvr+Xup2/stWHOvoEuZKkyQk/4z8bsQkgf8dAdjSsFtoL81AR1RJgm8MqLgJUOm6GnWxJAFH747O2BnOSZznJIHvAqndu0FOEv74XwfQ1taGZ+4groZfrtiN5uZm/N9/FwkkoqWlBfmvcoISbnJ7hO3N38hDS0sL8rZ+Qt3+5YrdaGlpwY7NhyTn4/++8WtvwdPYjL/+xizZ3vdHO/XvTy1/B62trcLxf9hQiJaWFjx3F1nV/jaTtMJ+5T+Ikvj1/X9HS0sLfvTC72HauwpX7ngELS0t2P44UVov/2APWltbkbO2gLrNH8/vz2+L70c8Hvn2M3f8lbr9xv87qDofTW4Pbv7LJlKkadeDcFRWo/DNUuGZ1ZTWC9sbv/YWGo42SeZLvP3sd99VXK+1tRV//uXHwvNqbW3F33Oskvnmtzd/Iw/NTS14dxtH3X75B3vQ1tYmzNfuXxejxdOKzd/II0r6VQ4tnlZBSS9/mwSPfv3//YIQwn3VKD0UJEwf/7UC1SVBwlTwhxI01bcIxxf8oQSNdc1BwrTDgYYjQUIj366y1cGyN0iQQpOELKm7ITdIEgBgZ8dhIdOBZTwoEVWSABCiULMs9hYFWxpQ//3ojZvhnMR5ThJMwSqLfK0EHSTho3dLUVZcI2zv+MVBNDQ04Bf/SkzR23/2IdxuN359P/Hv//4n+9DQ0CBs839/9rvvUrd3/rIIbrdbcb5frtgtnE9+frfbjZd/sEfYbmxsFP7++hP70djYKBxf+FYJHIerhPHv/YMFVaVBpfP28x+h7mg9vvlEIGjxzadRd7ReUDLvvvoJaqvqVLf543ml9/bzH0nuRz4e+fauZw5Rt59a/g51vvj73/KD3UjbRQo23X44G//72IfYkL4LL973Htxut7D90oPvo7GxUZgv+bZ8/H/cWCiZf368/7+9s49uo7zzvfYAB4eE4Jb21IfL3k3vbtvcvd1T77lwN22Xrbn3QlNauqLLmrS5pQktWYoDtRvamGJwG1pcFoKDeXF4cQ0xReHF8YviWAkxsiXZsmzHjqNEShzHSsBYuG4ip7aRLSO+94/xM5bkGc1ImhmN5N/nnDnJ6GXm0cx4ns88z+/5Pez47vpJk+DxZuWLXX/zKSvcA4vH07a/D+2NiwGPvfZBHPzjouDcauFSL68rLsUvv1aHkydP4tXyd3hBOeH24I0nrItC1HMc5pc6Bde3Xb8Hx/pPoPF5u+D6r295A0NDQ9hdvBiQG1cSVq3iWuO2LHTZbdm1KA0x1J5rQ55lMzc5FMGjuCQwTm5WL0bBkUtdDMuE5SsJX3qMm7kucpHR3bD966/BPXgcdY8u9te2N/ahvbln8cnuTRdcHYtPYs0vOqLWLa85oyrp2PV39/VEbe+dN7qj3t//h+jt7f+DA0f73Iv9/zHvR35/2/V74DnuhenxNv79Y/0nUP9MO7/e/e4ADu118lH191qexaG9i1H6vfZBmGvsouvs+5HrbfUu0d8Tu97e3LNkPd73I4/vHd+pxMp9d+BhRy1/PEyPt8E9eJxf3/dsR9Txil3vMPdG7a+t3hV1Pg7s6cQR5+Kok8bdtqh1c4096vjHrrs6Bvjj9fBNJng8Hryw7QBfSXs8Hjx//35+ff/RDhQ/8hLuKnge1T9rgcfjQfm39vKC4vF48Njtb/NC5PF4sOsnTYLrT97ZAI/HgyfvbBBcr334IDweD375tejRPPEDF5kcHFjMNSIgCfnWEi4r46Et8M/qdxphrVFNEoCFYMZcwJajjBzYVwKOTwHnW9UpL6E7lq8kCM3dIKO7YdddZvh8PvzW+BZ/A7W+1R/V9H2kw4Pm3YtPcj2Hj0etDzpP8l0Psevbrt8Dd+8p1P2mLWp7rOugOP8VeI4Oof5pW9T6/pe7otabqheD2M6cHoGpgnvSfOTmvTjS4cHvC/ehOJ9rSu85fJxv+v5Vwetw957CCyWt+OzuH8HQaMTTtn14oaSV//6Q5wyeK+Ki8su/+UbU+q8KXod38HRU10vs74l7mN+XAAAgAElEQVQsj9D62bNnl6zv+fVhfj32+MUe3y7XUbxj4ir1uwqeh+sdNw7v7V2Uop6TUccrcn3711/DmdMjfPm3Xb8H3sHTUeUZHjoTdXxPHj8dtT48dAZvPtkuuP77wn3w+Xx8q0bN9kMYGRnhWyn2/r4dQydP860Yseu2xgH02RZjX1prXTjevzjs9u2nbDjlHeKFJ3a9eXcnTgyeFF13HhxER9NiLImkJFzxf6LjEeLMAhkITWPNIS5TZr61BIHQtEJ/3pmNqpIAAPMB4PR9XPdAsrNG2i7nZMNXnnkpl4mUWL6ScPPNgkO3YDBw70VwutePX/zT4pMV68tPZWE3fbH1yKAxoe+ypnf22cj12Pe3f/21JduXs1y+l8toeOf/fXbJe7Hli12Pt7/Y8kit/6rgdcnjFVuWX966B2t2/gdWv/IDbL2hho8fETpeQuuR249d/1XB61Gfj13/zbffjNqf0Hrk8WKCxvbFYkPY+h0PPYlrd/0EX9/6ELZdvyfq+tt2/R787ra3o8qy6y6z6PojN+/Fs/e0iK7/5ttv4vE7GuRLwqpVi6ODtuxa7LITkAQA8M2MI8+ymQ9mpBgFDSSBMecHRh7ipnF2rAY6LpEWA/sqoPvzwPuVnGwQy47lKwkJ0v7aCbRWDyyr5fGaBmyueybt5UhmeWu3E5+r38QF/O3dnvbypLJsrKvk5qkw/TptZTj/wdTSP4pAAFi5MiLfyGng9EJCsjzxJC0DkyPIbdkIQ6MRreP9qf5pZjyaSUIkM17gXAVw5gGgL3/p4ivnxCDo07ZchO4gSYhlYkLFYhBa4rxwCjnmQhgajagcbk53cZKGDSPc1F+V7qJE8cqSV1yyv9s63o+GsW4li5OxpEUSCEImJAmxnDmjYjEyh4axbmzqr8r4G3nlcDMMjUbkmAvhvHAq3cVJCvYb9CYJwpxM6NPBcAgb+nZmtMSlCkkCoWeWtSQsaTSgVgSeYncNDI1GlHrq0l2UlDG6KrghhLbt6S5KUtSea8sISUjmz4f9NkOjEaZRu/KFygBIEgg9s6wlAQDe0bIUGcR6546suXEHQtPY1F+VscPu9CwJQmLgEntDBCakOeZCWCfcipUtUyBJIPTMspYE/j4WeUOj7gYA4IeqDUyOpLsoijIwOZJxXSgDkyMo95p0W+6oDoaJCW49wWYF1tqT27Ix6645KUgSCD2zrCVhEdfiPY0kAcFwiG8CzqYhagOTI8gxFyLHXAjv1Gi6i5MVKNVDFwyHUOAo43MoLCdIEgg9s+wlIfomR50PAFeZGhqNWNu2Nd1FURz2xLq2bWvGCJBvZhzlXpNuZ1JMLFRRnEBoGkZXxbJL20ySQOiZZSsJok9AFLyIQGgaFUP1uq2UUiEy658e+/iFsE64YWg0osBRlu6ixEWw+y5JvFOjMLoqMkbkUoEkgdAzJAmy3yCyhcj8CZkgQnqWhKV/Lsq0xrF5HpbDFNMkCYSeWbaSwKAQhKWYRu0o95oydjSAHFjugXKvKd1FkYRJgt766oVTJ8lPqBSPyKyMmdLikywkCYSeWbNmDfLz81FQUJC2pbVV/QnFqCUhAdhTnFbD0YLhEKp9FpR7TVjv3IF1tu184CTrn2ZBbWzJbdmIAkcZ3xLgmxlPOCo+U5IrMUlYc2hLuosCgBuSWe414X/1PI1vWx/mz4nzPe54bu19Mupc5ZgLUeAoQ8VQPQDAPxuQdeytE26+xScTZC5ZSBIIPZOXl5fWlgSfz4fy8nLV90OSkADsxqxWS4JvZhzVPguKB58HPqhG0PWlqEolchGTBLZU+ywAgFJPHQyNRuRZNmNTfxVMo3ZZ5WcZ//T8tJpOSfDPBlB7ri3q+LAJmmIXx58GAQCb+qsE32eJuap9Fl70NvTtRO25NtFgRdOonf9+ts7zQJJA6JllKwmEMN6pUf4GriSt4/0odtdgbdvWaAno+AzQboCx9QaUe02oGKqHdcIN64RbdCrhgcmRJZ+pGKrnm6cjF/YEKrYtVgHrOT5hYHIEBY4ybOjbqcn+nBdOodRTx7cosYW1LN0zuBulnjqUe01oPu/FwIQF/tmAoF97p0b5c8WkrfZcm6BoMBGJPVeVw83Y1F+VtUGMJAmEniFJIKJoGOtWPIWxfzawpEJY3/I1VBz8IgLWy7hpaTs/DZxPvd8ptoJjGSPLvSasObQFtefallQ2bAIlyp/AtazEytY623ZVYlQGJkdQMVTPdy+x7ggmEZXDzUuEodpnybqsjCQJhJ4hSSCiqBiqh6HRmNJTq382gHKvCXmWzXzF8qPW61Fs+QpaD38OwXaReezdtyj1M/hyMCFg+RFYl0TlcHOULLDuDD3mTwiGQ7BOuFWJoQiEplE53Iw8y2Y+pqPUU4d7BnejYaxbtAVGjXKwfbE0zaxFq9xrQiA0zQtstmVlJEkg9AxJAhFFw1g37hncnVTTu382gGJ3DR/TEPl0iOO3CYtB5OK4CphXb0RF7bk2PkcCq2xYxeufDfBN4PcM7latDMngmxlXPCYhEJpGudcU1Wqgp9/dMNYd1d2RYy5Ew1g3P6fImkNbsibpEkkCoWdIEghFKPXURclBgaMsOtAsYAXsK+NLgv0K4INq1cvKZCHPsplvNWgY64Z1wo18a4nuuhyYJORZNiuyvcrh5ig5yLeW6HYiLyYLOeZCvmXov73zH3y5tWrpUBOSBELPkCQQUbDAwUSb3NlMhUZXhXifcefV0q0JPWsV+BXyYDLA0lCvs23nu0e8U6O6eVJlkmBoNCqyvcjkTHqdNCoWdq78swFcbi7EZc236zbBVKKQJBB6hiSB4IkMMJQjCZXDzVE3aclK9cx2oEMkHoHvclgNzHhT/SkJYZ1w80/WuS0b8ejJN5HbshHrbNt1EZ+ghCSYRu3It5bwv0cvApQo3qlRvlvor5puw48Hnkt3kVKGJIHQMyQJBA97wpSa2CkQmub7hg2NRvlPo0EfYMuJLwkdlwLDxan/mATxzYxHJXG6cv/3YWg0othdo3lZhMqWrCQEw6GovAWVw80qlFBbYq+/W12P4bFTb6e7WElDkkDoGZIEgocluYmXL9864eaf5HJbNibel937D9JdDvbVKf6S5GFJmSKXdDfJJysJA5MjfF6KHHNhVghCJJXDzVwcTNNtGZ2VkSSB0DMkCQQPG3om9vQcmf1unW17cgF+/lrp1gRHriI5E5LFOuFG5XAznz/hyv3fz7jm+ciUxmvbtmZMCupEGZgcwb/1PM6PeLD9+US6i5QwJAmEniFJIHhYEy5LdRyLb2Ycaw5tQbG7Jvm++nBQWhJUyJmQDMFwCJ8+8EMYGo24/9jL6S5OQgRC08i3lmBD386sGAEgRe25NtzR9yRyzIUZl76ZJIHQMyQJBE+ppw4FjrIliWoGJkf41xSpcOTkTLCvVDVnghyC4RBu7HxEF+PyY+eyEMM3M86PLlkOcsAz0YB65y0ZmWyJJIHQMyQJhDgXnRhouwq55u8ht+UHyuUPON8K2FfpImeCFMFwiA9oXN3yA5j9vWkphxxJYC09OebCrEtdLErAChy5jrte7CtR0fVDXhQypYuIJIHQMyQJBADuqdM64V68sYaD8Hb+D+Q2fZsLZjzwLwj+qV65HdpX6ypnQjwCoWlce/DHaU3gIyUJ/tkAn00yW5IMxWVqABi4gZODiGvmk+41GZeVkSSB0DMkCQSApRM7+YYexhrzeq7SMd/Izbdgv4K7MSuRx2C4WJc5E8RgT+npqniYAAjtm8UfsIpRrSm+dUHQBwzeBNhWCF8zthWYGd2NfGtJxnQ7kCQQeoYkgQCwOLHTPYO7gaAPxgNf4yqd5psXZ2pkeQxsOcDIQ1wQYrLMeHWbM0EM38x42p5M40kCG5WSSU3sCTPnBzx3ALbLuetCYnRMYPbPGSEIAEkCoW9IEggAwIa+nYvJdo7eiE2t1yGn6bvwvnul6BMbnH+d2lDFnrW6zpkgRut4P9a2bdW0Qo4nCWyoZlbGIcz5gaGfckIp1fIUOVHY+5UAuKnD860luh4CSpJAaMrgTfL+jhaW7/2LAd498j+Pjsu4v1uFIEnQCSw4r3W8Hzh6I2BfBb9VxlBF+xXAcWNyF8UH1brPmSAEm3Z6feeDmu3TeeFU3Dk1sq6LYT7AtVbZcrjWgwRuatx1eSUQDvKtLJEpqfUGSQKhKX35if89JfS3t4rrFlQIkgSdwOYuuPvo8yh21yAw+iJ3o5Vzg+64hPvswtObbOYDMnMm3KrOj06Gi074e/8JOU3fhaHRmNbZEyuHm7Ghb2d2CUI4yF1HthWAXSTuQM5iuxw4+yiC4RCfIZSftlxnkCQQmkKSIAhJQhwCoWkUOMpwdesP+Sj61vF+rhI/ead4kJhQq0Lvl4GLTvk7d98i76JLJf5BCaYGuGa6hemuKw/+LTd9c8sdmjyhFrtronJY+GcDfEZFseRXGccH1VzLUcyIheRFYQUw5+eDctl003qDJIHQFJIEQUgSZMC6HIyuiug3LjoB19/xFaSsm/PQT+UlQ5KTM8FxJZfOOR0EfVzAXEzFFWy/BPnmG7k01sdeUL0YBY6yqLgDFkOSby1Rfd+qM24COj8rfR0kfLNaCZzmJitjwyKXXNs6gCSB0BSSBEFIEuLgmxlH9VmL9NPW2Ue5bgWp6PJ2A9fc67iKqwCkkJMzoS9f2R8tBR8wJx5N72z7NHKavoty189UL06kJDgvnOJbfDIlgl+QiYYF+VRYDmKvwzk/fDPjyDEXppZSXCVIEghNyRRJmPMD7lsQPLwCvr0GoOsawFee+nZFIEmIAwvuktVvO+ePanaXdcFI5VYQyJkwY8vDWNcNONv9HZzu/jcc7n0S77x/Hq4/h3F2+hOcnf4Ek2rc6+cDXHlsObLiJfy2qzl5UhkmCaZRO9+KcM/gbtX3qwp8lkSZ11AqS8clwKm7Aeg3uJMkgdCUTJCEGS/Q+RmBba8E+tcpchhiIUmIw01dv4ah0YjLmm+XfyM938o1EcsJPGQn98x24diChZwJU/Zr4XIV463eOrzkqsdv2/fhwbZGlL6zDz8/8CpKOlx4oO8jlPYHUdofRJV3DnUj8zjs/xhjH32C8CcpHIRwkIumt18pr/JyXMVVdBedcF44hQ19O1V9QmWSUHuuje8WyrhWhKkB4Og3Eoo5CNquxqjzf2Os6waE2y+T/T3umlvBdX2NvcgXwTs1ik39VbrKJ0GSQGiK3iUhHARcX4hfl6jQokCSEAcW/b1vrDuxL4aDwJkHEohVyOHEImZI42QIONxfjRec9XjA8kfc3/wcihorly7N1ShyzSxZSvo+wqPHZvG6bx6nLoYxn4gtsGh6x1Xyoukdq7kLOGDlN7G2betijgmViJQEAMrNo6EFQR9wbL38ANh2A8a6bsAB11N4uacJv2lvRGVXK8Y7r5N5nV2+mPArJi6GxSaITYeeDkgSCE3RuyRMNHD347h/4ysUOxwMkgQRAqFpvqsh6SfhGe9CHn2Zfcv2lcDgTZgP+tEz8TGqvHP4ucONoqYqYTlgS9NzKLKfFhSFItcM7nNNo3xwFnvPzuPCnAxR8NcuBMzJeLK1r+L6zycalmymcrgZhkYjChxlyR0/GQxMjsA64cb+8T7V9qE4c/6F0TEysiQuLFP2a7G/9zk8Zm/FNksdihor8YDlj6jv24MZW17873dcyl1bQz8VzdvBRjqsObRF44MhDkkCoSl6l4RzFTJS9l/FtUwqCEmCCK++Z+W7GlJm3MSdPBm5FaYcf4N9PS/iwc6jKHLN4F5nQFoSGitxr6VJVBLYcn/PR6j0zuH4xbBwOScauGyRcqTGvooTiTijK3wz43zQp5pdDkzocls26i74Lor5wGKWxAQSIV3o/Hu80lPPy8F9TVV41PoGmvpqMGW/Vrr1wHOH5M0pGA7xQ0f10uVAkkBoit4l4f1K6fuG/QpFszoCJAmibOyrhKHRiP9yUKGbFF9BiDctT9mvRU3XHvzM/CKKmp5BkbkG99p9KGqtl5SEoqYqFHX/RVIUilwzKD86C+fEx4tlC1i51gCHjNEU9lVR6X2lYF0ODYl22cikYawb/9r9mOotFinBZ0lckVSWxNd7TSg5UIPilpfw2/Z9qOtrwPHuTZhvj9NNYVvBBdImMBEY63JQs3soEUgSCE3RuyRcdHL5UuLto+saxQ4HgyRBBBYEx/q6FeOik0usFBOvwAThvthWg6ZnUNT6JvdvXEl4FvdaB2RJQpFrBg8fnYXz/TNckKFUP1e7gSuvbQU3YiGBBE6lnjpV+7o39Vfx3ULlXhnDSrWExXXYV6eUJbG17znUHdmPlr5qvOf8JoK2q+M/SRy5Lqkmx2qfRVc5E0gSCE3RuyQAwPHbxLso7VdGxYQpBUmCCKo3vb5fuZDeOQehjtUwuV4RD0yU0d1Q1FiJopY98iTB8R6KWt/Cjva3cbrz1vgXdpxgNzm0jvej3GviMlWqQKQk6GoiJ3+tslkS5dyAer+c0k1iYHIEpZ66tKbUjoQkgdCUTJCEcJAThdgWhc6ruYkBSRK0wTs1CkPTbfirptvUHU435weOG9HV+xBKD+6RJwISrQlFXR+Ky0HXhyg62My3Smxteho1zjeEA99kBLvpga3HXuQlIRCaTndxuLgONbIkxrvxOP9aMHA00yFJIDQlEySBcb6Vuzf3r+OGPc75uXvAcaMy24+AJEEA64Sbr3jUTjQzMfsJnhocRZH5JekuBcllF4oOty+VA+cEig4dXGiReDrqOyUHamDpeSLmYl4pK9hNLrXn2lDsrlGlVaZo8AUYGo34zIE7Fd92QpxvVSxL4pnOb+Gw63cY7L5LPO7AvlIycDQZGsa6Ueqp08UU0iQJhKZkkiSI0ZVHgYta4J0a5aPl1aZ9PIxtfR9xQYeH2xdE4enkRSEiZ8K9zgCK3jnMyUGcLovnO+vxF8fnuYs4wWA3ObD4DjWascu9pvTGIyicJXG0859R2dWKn7e+giccZnzY9dWYG80Vi4GjKkzuxbJW6mFmSJIEQlOyQRLOVQAjpYpukiRBADZmXO1JgkJh4A/Doein/s4PUHTAxHUdJNXl8ByKbKcWhONZWa0Tv3ynHoODOxQfX8tgUfOKB4GCa/Up95q0j0fgZ7+UH3MQbr8M453X4T3nN0VbCDzOf8cj7WZss9Thtd43F4c4siyJIw+pOvPnPYO7dRMESpJAaEo2SMKcH+heo+g9giRBgP/Zvg2GRiO+/K66ExSNffQJHj02Kxw/0HGCq/CT6oLYJe97Tc+iaP+ruN9xBm+dm1ftd7LgQjUkgbUkbOqvUnzbggR9XL9fAlkS2WJzleG3tlbsaG9GW89jgp8JdaxGn+t+2FxlmHR8cTFw9MwDSQWOJkraW2YiIEkgNCUbJAEATm5StBuSJEGALy2M7b+95z9V3c+JyTC2HwmKBhoudhekGqsQvdzb9CyKzDWciCzs69WR+cTSNicAkwQ1Kp6fHXsZhkYjigZflP5wKiSRJTF2ebm3BVubnsY2Sx0O9j0d//Mdl3JyoHHgqObSFQeSBEJTskUSpgYUnR2YJEEAVqntOmNWdT+df1qIR5AzZHH/q1zlntLoh+dQ1LxbMJ/CC0MhTM2TJCwhySyJQstA9xbUHdkvnSlRZpZENSBJIJYt2SIJADfq4aJTkU2RJAjAKjW1M88d9n+MEjmSwBZrb3KxCk3PcoLwbrfotqu8c+pMMQ2gYqgeBY4yVbsbFE/WlGKWxKQWWw4X56BSbIgcas+1ocBRRoGLxPIjmyRh3AR4NiiyKZIEAbQK3pLdkhC52IbkJ1di3QvNL3CtEXG2q2ZLgppUDNUr++SbYpbEKfu1ONb9/9Dnuh+hDhlprtsNi1kSFTL/bIEkgdCUbJKEcJALYFSgq5IkQQCtgrekYhKWjHpofSv5+ISmZ7j4BpH5HdSMSWAzNaqR7Kj2XJtykpBilsRQx2q83mvC9nfewiPtZgx23yV903D9nSpZ0pLFOzUK64Rb9fwgciBJIDQlmyQB4JIs+cpT3gxJggANY93IMRdiQ99OVfcTd3SDSJbE1GISqhaGSJ5cMjtkw/sfSxc4SfKtJTA0GlVJ0OO8cAq5LRuxzrY9+Y3IyJLo6C7Bke6fItx+mehn/uL4PKq7m1Hc8hJ+b2uAt7tQ/Gah0yyJRleFajktEoUkgdCUbJMEhYZDkiQIwDIurnfuUHU/8+FPUDcyj61CcsBnSXyGG9Ko4OiGoqZnuFwMzgkUuWbw0MAsvGLTR6dIMBxSNW0ym456bdvWxL+cQJbEPx5pxu/t+3Gie2Pcz3m7C/lsiUuEwrZClSyJSpJn2QxDo1HddOQyIUkgNCXbJAHg4hLGU2sRJ0kQIBgOaTYPQGxcAjfs0SqZJVExWWizqxqP4LxwCoZGI/Ism1XZPoDEm8ZZlsQEch20u8rxy0Nv4CVXE5e/IJGbg+1yVbMkKoV/NsALXTCsUhRrApAkEJqSjZKgwHBIkoQ4aCELkyGgyjvHxQq02bmKW44cND3DdR2kKAo/t9Sh/chO1frFK4ebNZt+WFIWWJZEW07UH+9E51cw6fii+DwJ7QZ82PVVPOVowi8O7UWf636ZNwVtsiQqhVaZRuVCkkBoSjZKAvtdKYyYIkkQgVVuWgwF6z9zCL86LDMoselZFLW8hiL76YWsjMnnTrivqQqv9fyRi8JnkzopnLiH9XGreRxNo3bkmAtxz+Bu4Q+IZEmcb1+BA33P4AmHGb9pb0RDXy03h4XIH7m952GUWRvwet8+6ZYDWw4wXKxJlkSlKHbXwNBoFD+OGkOSQGhKtkqCv5bLwpgkJAkisKj5AkeZejvx1wKdn0XYfhXqe17G/eZq8WGMAlkSi7r/IlMSdgm2Tvyn7Q2Mdv7z4kXccSlgvxIYVS6RTrG7BmsObVG1j5vFkKw5tCX6DYksie/27ECZtYE/HttaX0VDX63oH3moYzVa+qpx6Mgzwp/puJTbl86n1xajYqgea9u2omGsO91FAUCSQGhMtkpCisMhSRJEYAFxOeZC5bscJhqA7s9HBcwFbVfD5HxhqSgsZEksaj8mPPrhUCskZ41segZFloaolorfvVsHt/P7IhfzSqDnv6c1qU8iBMMh5JgLYWg0wjs1yr0okSVxyn4tqrublxyr13rfTPIGcEXasiRmKyQJhKZkkiSMm4ATt3NlPvOA9My9I6XcDJFJQJIQBzZ0T7FMgQErV/mKRNMzUSg5ULMYc2DtlU7ZLNWa0FSFojY77rX7sNX8Mh7reEtcEKKazVcAp+9Lusm89lwbWsf7lTl2ErBujXKviSuvxPwKQdvVeMFlxn0RLSy/fvdNWHt+m9gfvg6yJCpBw1i3LoY9RkKSQGhKHEkwGBJbVJOEcBA4fhvgiEnU1vlp4MM94t9LYTgkSUIcWFxCSmPwAa4COXId94QuUekEHdfC0lOJHT0nsFUk8dGSxfyydJeD+QWU9H2EmuEQhof3cpWbnImKbJdz2QcTHNMf+XSvhSiwoDt+FMXZRyWP9ynn9/ByTxMebGvEE/Z6WHsfjxu8uKTlIIuyJK45tAWGRiOqfZZ0F4WHJIHQlExoSfBuFL9vO3Lj34+OG5PKzUKSEIdAaBo55kKss21PbkjYjJd7ypSTwc92ORcPMPIQMB/A6Yth1AyH8IsjQeE8CpGzRbY54wY9/sz8IipsTbD43scMmxF6zg8cvVGWuPAX+MDXZF/kTLCSyl+QJHmWzVjbtpUb5RAOcsdT4ndNOr6Is93fwVjXDfIEQYdZElOFCVZuy0ZdDH1kkCQQmqJ3SZga4IZSx9tH9+fFvx+wAkcLEt4tSYIESaWnDfq4/mk54/BtOdzFIxDsNjMPHDn/MfaMhFB2dFZ8MijnxBJJuN9cjV8c2oun7G+jubcaH3Z9lQvii+V8K+D4lPx5CuwruVSfcZqtguEQ1i5Mt632JFmRLDlX7z2h3ORMOs6SmCoFjjJ1JslKEZIEQlMSkIS0dDe8Xyl9P7NdHr97OInhkCQJMgiGQ6gYqod1wh3/g3N+4NTdXEUq1ZTPIuFP3ikZdRoKA2emwrB8MI89I/Oo8s7h0WOzKO0P8suDHe14sK0Ruxz1qHHtQ0PvH3Ck+6eY6PxKxEV6pXDlHg5ywS+2HKBDPO3w4nZWAF3XiD5Ns0mX8iyb0/JkWu2zcP3r4SDXBJeSHFyh+yyJqWAatfMBunqYryESkgRCU2RKQqQExAqBqCAoIQm+cunyOa6KLwEBa8KtoCQJMmCV3tq2rcKV3nwAOLM9bjR9tO2t4PqHkrhgwp9wCZjGPvoEZ6cjlg86cLbndlzo/HsEbVeLXECr46fonPECfV9ZGhQjetGvBNy3RElOMBxCbsvGtOX/Z5VenmUzV+n5a6Wb6ATPUU5GZElMFdbio4epoWMhSSA0RQFJiCsKqUrCuEn63txxqWKHg0GSIINgOMTntI+aGTIc5ALkbCvkyYH9CvUi4cPBJZkEBZf+ddLbYhWrnHiFjku5i/+9J/iv+2bGVZ9BMx5sVAo/M2S3eIIkwXOUQVkSU8U/G0C516SrWAQGSQKhKUlIQkKtCalKQjjIdQ3Huxefuluxw8EgSZAJC+7KMRdyY/Hfr+Si/uX05TtWc5Hwage7nbxTXiUoJ6nGfAA4uVn+tMn2lXB2/iN8Eyr/RhkMTI7wcxBYJ9xcHIFUfEiGZklMFu/UqC4mcYoHSQKhKXqXBGAhhkygZbTjMsD1BVUebEgSEmC9cwc3O6TlZnmVpyOXi4Q/36pNAS86pZ/+bZdzrR+JbNP1BclmrmD7JVjTfDNymm9TZUroRLlncDc/D0EwHOJ+g5h9Z3CWxGRZZ9sOQ6NRN9kVhSBJIDQlwcDFtEgCwN2Tu65BsG0VfG+t5u5hx29T7eGGJCEBfDPjyDEXIm//v8LfHmd6YcdqLtgtHZHwXddIX+Rd1yag+TsAAAscSURBVCS+3XMVXOCjSJdGces/cKmRD/5YF03XgdA08iybkduykXtiDliXGvgyzZLIhqfmtmzUXbBiJCQJhKYkOAQyVhTijmxQUhIW8J20o3z7j1TvFiVJSJDW8X4EL/YC9pUItl+ytNJx5KY3Ev7so9LxEVJJN8SY8wPH1i9pVag4+EW+eV+rDItysE64+ZTawXCIywvRbsiaLInJwAI79ZY4SQiSBEJT9J4nIQafz4fy8nLFticGSUKStPb9O/KavwXr4c9wcQn21fqIhJ/zywuiFMqZIJfzrUDX5wDHVag99F/5SiedwYrxGJgcQZ5lM/aPvAa4b82aLImJ0jrez2fB5IM6dQxJAqEpJAmCkCQkidFZzjXZNn0HAye26SvYrX+d9AXr+FRqQrOQW6H00D9ygjBYqVz5FYZNgZxjLtRVS4fWVPssGSMIAEkCoTESczcIdS1oPndDBCQJOicYDqGgo5jv29VVpPi4STqA0ZH4fAyR+GbGuf/MeGH11SlUcPXY0Ldz2YoCf64A6YRgOoIkgdAUakkQhCQhBQKhaX5M/ppDW6JuxmlFbs6Eo/+S1OZZs3WmPJECnNSx0Sm6kzoVGZgcQW7LRqx37tBFQGkikCQQmkKSIAhJQooEQtP8DHqKTSmtBLJyJlyZ8LC/yuFmvl870yqeYDjED/3Ta/yEkphG7Xz2y3xrCR/EmSmQJBCakmSeBNnvkyQsX3wz43xa22A4hMrh5vRXnnJzJkRkSoxHIDTNP4kbGo0wuirS/xuTIBCaRqlnsXukcrg54ypPKYLhEDb1V/Hnap1te0b+RpIEQlOSzJMgWyBIEggAKPea+Buzd2o0vYWRkzOh+29kbYo9geeYC3U/dE4utefa+Dk59JAASilY/EWmt5iQJBCakkSeBCYFkjkSSBIIRut4Pz/PQ27LxvR2QcjKmbA6br4AFmfhvHAK+daSrOrLd144xU9wlGMu1OUkR4nAzpVvZhxr27ZmVJCiECQJhKYkGZMgKQckCUQs/tlAVNP8hr6d6WnunfPLSx8tMCmIdcKNdbbti7MpZimB0HRU0/x65w79BKDKZGByBOudOxbnFckSSBIITaGWBEFIElSETTGdZ9nM999rfhOXkzPBfiWfM8E64UaBo4yvNHNbNmb8E6kcas+1IcdciBxzIS9Feq9wByZHYHRV8Ocqx1yYlum51YIkgdAUikkQhCRBZbxTo/y4fDaTpNFVoV0f+LiJuzgFL9oruKXvK0A4GNWXnduyEcXumqxuRYjFNzPOT3jEZpIscJTpMq9CqacuSg429VdlXAuIFCQJhKbQ6AZBSBI0hLUssKXAUYaGsW51RwmEg1xLQWwcQudnERx5BLXDb/CVYOVwM3JbNqLca8rIaHglYS0L7FzlW0tgGrWn9biYRu18S0HDWDdyzIVZLXIkCYSmJNiSQBkXCVXwzYyj2F0TVQGxCPSByRF1AgNP3sklV7KtgLP/DlS4K/nRCqwCBLihc8tdDiLxzwZQ7jXxuQYiUxp7p0ZVbw0amBxB5XBzVHxLZNdVtsoBgySB0JQkWhJkTxNNkkAkin82gFJPHda2beXFgAXQ5Vk2Y1N/FWrPtaXcL+6fDcB74Sgw0YDas5aolgw2VLPaZ8nInAdaEQhNo2KoHvnWEr47gg11zW3ZiA19O1Hts6QseIHQNL8N64R7ybnKt5agYqh+2YgcSQKhKQpIAnU3EKqyqb8q6qmVLZXDzQC4CXpKPXUo95qiFhZYODA5gk39VShwlPFD+1i3BsC1YjABSXfTeaZT6qnjh7pGLsXuGgBcd0Cxu2bJuWKS4ZsZxz2Du1HgKONTe7P03gDXqpNn2YwNfTtRe64t61sNhCBJIDQlyZgE2a0JJAmEUjgvnEKpp46vPFjMQGRgoVDFxJIDRS4sbz+hDgOTI6gYque7b1iiqcjAwsjF6KoAINxSkGMuRIGjjORtAZIEQlNIEgQhScggWsf7lzyZRj6dDkyO8OvLYdiinnFeOCV4rlhyLd/MOMq9JphG7bBOuKm7RwCSBEJTkhwCSZJAEASRBkgSCE1JIpmS0GgHClwkCILQAJIEQlNoqmhBSBIIgtAlJAmEppAkCEKSkCLF+a+gOP8VWZ9Tan9q7yMV9FAGIbQ+T6luS4lyJLsNvZxDkgRCU0gSBCFJSIHIm6nUjZUkIX2k4zylui2SBJIEQmNIEgQhSUgBsZspe2oVq5zi/V9oXWiR2qfUutD25b4eb996qWAi0fo8iW073n7FthOvnFKvJXIe9XgOSRIITSFJEIQkIUXiVTKR61KVj9RrQvsSKotYOaTKlMh+5fxevaH1eRJ6XaxciW4jkfMWu55I+dMNSQKhKSQJgpAkKEQiN3uxG7XQk2DstlKRBDnbj7dfqSdvsf3qCa3Ok9DrsZ+Rs43Izwq9J+c3Ce1T7r7TCUkCoQQGw9JqTug1kgRhSBJSIJWnSqkbv5zvyCmPGmWVKqPeSMd5EvtsouWRW0Y52xA7j3o9hyQJRKoIyoDYeyQJgpAkpIjQjVfuE7fU9+RsV2ybclsSxMoq9brYdvVSwcSi9XkS+67UvuOdo2TLLuc86vEckiQQqUKSkDokCcsYua0DBJEOSBKIVCFJSB2ShGUMSQKhZ0gSCCWITJkclT45liyQBKnfGE+axCBJIAhCl5AkEJqS4ZIgRwpIEgiCyBpIEghNyUJJiH2dJIEgiKyBJIFQArHuhmyLSZATf0GSoBFa9NnL2YfaMQVSQ/+yGbHfrrc4jlSuE71DkkCkithTdDbGJEgJgGgshgQkCUmgd0nQsgzLAT3Lkh6uE7UgSSBSJZ4YZFtLAvtNCY3okAFJggRyx61Hvif1/dj3Yz+XyFj5eGWWsx+x7yWTsyHR/ArpRKqlIN5xkMpRICfngNQxUvM60eP5EIIkgUiV5SYJakCSkABiN/DY16SePIUqonjbk7Of2M/Eq2SkXo/3mtxtiJVDT8Q7D6n8djmVdLzvyymf2LYTLb+eIUkgUiWeGJAkyIMkQQZST3lir0l9VuhJUux7Uk+vQp+V2me814XKF69sUq/rsXLSgyTIeU3p60Sv5yMWkgRCCYREIRtjEtSCJEGCRJ/64v0/ke0kU2FFvq6XloR463pATtn10pKQyH7lXid6hiSB0BSSBEFIEmQg1JIgduOWWzEKPR1KPTEm0pIQ+x25laHYeipPrHp+ck30uCghCbHbFPuMWDlSuU70fj4iIUkgNCXDJSHeUM+4mSYlIElIE2repDOhAiDksZzPJUkCkSoJVYwZLglSJCMIAEmC5qj9FJcpT4lEfOg8kiQQyiFLFrJUEpJtQeC/n/Q3CYIgVIQkgVCauBVmlklCqnLAbyflLRAEQagASQKhFtmYljnub0sBkgSCIHQJSQKhNNnekqCkHPDbVHyLBEEQCkCSQCjFcohJoNENBEEsK0gSiFSh0Q2pQ5JAEIQuIUkgNIUkQRCSBIIgdAlJAqEpJAmCkCQQBKFLSBIITRm8KaFKf/UVBtiqEpCEjsuAOb9ixSVJIAhiWUOSQOiZSy65BCaTKW37J0kgCGJZQ5JA6BmSBIIgiDRCkkDoGZIEgiCINEKSQOgZkgSCIIg0QpJA6BmSBIIgiDRCkkDoGZIEgiCINEKSQOgZkgSCIIg0QpJA6BmSBIIgiDRCkkDoGZIEgiCINEKSQOgZkgSCIIg0QpJA6BmSBIIgiDRCkkDoGZIEgiCINEKSQOgZkgSCIIg08o1vfAMGg4EWWnS73H333SgvL0/LUlxcjIqKCtX/DkkSCIIgCIIQ5P8DQvPsQO8DGHsAAAAASUVORK5CYII=" /></p> <p><strong><span class="underline_text">Objectives </span></strong><br /> In this project we aim to develop an universal theory of drying in particle beds that encompasses both drying under vacuum and with nitrogen convective drying. The new drying model will be based on mass transfer resistances in the horizontal plane (in analogy with Catalytic reactor bed models where this approach has been very successful) coupled to diffusion and flow in the axial direction towards the bed outlet. The impact of solvates/hydrates in the model is via a mass transfer resistance internal to the crystals, and depending on solvate content.</p> <p>Simple, non-toxic compounds that form hydrates will be identified from the Cambridge Structural Database&nbsp;and these will be used to test the model, and determine mass transfer resistances in representative drying equipment at both Leeds university and at AstraZeneca&rsquo;s labs. This will result in the crystals mass transfer resistance vs time/solvent load.</p> <p>The third leg of the project sees the development of a methodology to predict large scale performance from small scale development for instance DVS, loss of weight methods, or NIR observation of solvent content in small fluidised beds. The aim of these tests is to assess the internal mass transfer resistance</p> <p>&nbsp;</p> <p><span class="underline_text">Benefit to institution/student of AZ involvement; benefit to AZ &amp; fit to Strategic Areas:</span></p> <p>AZ has identified a need to further understanding in this area. Prediction of extent of attrition and agglomeration in a dryer requires knowledge of the drying time, which requires an accurate model. Therefore development of a drying model for solvates and hydrates is an important step in improving particle design and process efficiency.</p> <p>The student will gain a range of numerical, experimental and analytical skills. Working in close collaboration with industrial supervisors will ensure the project is relevant, and helps the student comprehend the nature of their innovations. The opportunity to demonstrate the techniques developed on AZ compounds during a stay at AZ will be an excellent demo station of the students work&rsquo;s impact.</p> <p>&nbsp;</p> <span class="underline_text">Proposed programme of research:</span> <p><strong>Work package 1:</strong><em> A universal drying model</em><br /> Starting with a review of drying models we will at first build a 1D resistance model to describe the drying of solvates/hydrates. The model will be solved using a solver that is available to both AZ and Leeds, e.g. dynochem, MATLAB or excel.</p> <p>&nbsp;</p> <p><strong>Work package 2:</strong><em> Proof of concept:</em></p> <ul> <li><em>&nbsp;Solvate selection and manufacture</em><br /> Simple, non-toxic compounds that form hydrates will be identified from the Cambridge Structural Database and literature data collected. The project team will rank the options, and a number of solvates/hydrates will be manufactured and analysed for structure and composition. (e.g. XRD, SEM, NMR, Loss on drying, DVS). Initial test system could be based on caffeine which forms a hydrates, and solvates with formic acid, acetic acid and methanesulfonic acid. Acetic acid and formic acid solvates lose their solvent readily<a href="#_ftn1" name="_ftnref1" title="">[1]</a>. Or the condensation product of para-aminobenzoic acid and vanilla<a href="#_ftn2" name="_ftnref2" title="">[2]</a> which is a methanol solvate.<br /> &nbsp;</li> <li><em>Manual determination of mass transfer resistances</em><br /> A small 1cm ID filter bed (based on the design of the Addopt filters) will be used to dry crystals by vacuum and nitrogen. The drying can be followed by weight and or sampling. The drying model will be fitted to this data, and possibly the DVS data so as to arrive at the resistances</li> </ul> <p>&nbsp;</p> <p><strong>Work package 3</strong><em> Automated determination of mass transfer resistances</em><br /> The manual proof of concept is labour intensive, and not very practical. We propose to build a small fluidise bed cell with integrated NIR and sampling port. Detailed drying curves may be constructed by interpolation of the sampled data using the NIR data, or by building a PCA model of the NIR response as was done for fluidised drying granules in PROPAT. This work will reuse measurement equipment and software designed/purchased on the PROPAT project (RH, NIR, ChemiView <a href="https://chemiview.leeds.ac.uk/">chemiview.leeds.ac.uk</a> ).</p> <p>&nbsp;</p> <p><strong>Work package 4</strong><em> mass transfer resistances for hydrates in controlled final humidity</em><br /> To ensure reproducible results with hydrates, the dryers will require a drying has with controlled humidity, or a temperature significantly above ambient. Here we propose to control humidity of the automated dryer (wp4). The drying of a hydrate under various endpoint conditions, or the response to step changes in RH can then be studied in detail.</p> <p>&nbsp;</p> <p><strong>Work package 5</strong><em> Observation of Solvate drying with XRay tomography</em><br /> This work package is a stretch in collaboration with the Royce Institute. The aim is to take radial scans of a solvate crystal during the drying process. A solvate crystal will be mounted on a needle, and using nano XRT equipment in the Royce institute a 2D cross section scan will be taken several times, with drying periods in between. The aim here is to get an insight into the physical reasons that can explain observed mass transfer resistances.</p> <div>&nbsp; <hr align="left" size="1" width="33%" /> <div id="ftn1"> <p><a href="#_ftnref1" name="_ftn1" title="">[1]</a> Acta Cryst. (2005). A61, c286</p> </div> <div id="ftn2"> <p><a href="#_ftnref2" name="_ftn2" title="">[2]</a> 4-[(3-methoxy-2-oxido-benzylidene)azaniumyl]benzoic acid methanolmonosolvate, Acta Cryst. (2018) E74, 1847-1850</p> </div> </div> <hr align="left" size="1" width="33%" /> <div id="ftn1"> <p><a href="#_ftnref1" name="_ftn1" title="">[1]</a> Cryst. Growth Des. 2018, 18, 1903&minus;1908</p> </div> </div>

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="http://www.leeds.ac.uk/rsa/prospective_students/apply/I_want_to_apply.html">University&#39;s website</a>. Please state clearly in the research information section&nbsp;that the research degree you wish to be considered for is &lsquo;A universal theory for drying pharmaceutical hydrate/solvate organic crystals&rsquo; as well as&nbsp;<a href="https://eps.leeds.ac.uk/staff/553/Professor_Frans_Muller">Professor Frans Muller</a>&nbsp;as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>

<h2 class="heading heading--sm">Entry requirements</h2>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<h2 class="heading">Funding on offer</h2>

<p>The projects are funded by the Engineering &amp; Physical Sciences Research Council Case Competition Award and will run for 4&nbsp;years. A full standard studentship consists of academic fees (&pound;4,500 in Session 2019/20), together with a maintenance grant (&pound;15,009 in Session 2019/20) paid at standard Research Council rates. UK applicants will be eligible for a full award paying tuition fees and maintenance. European Union applicants will be eligible for an award paying tuition fees only, except in exceptional circumstances, or where residency has been established for more than 3 years prior to the start of the course.&nbsp; The project is supported by a case top up from Astrazeneca</p>

<h2 class="heading">Contact details</h2>

<p>For further information please contact Doctoral College Admissions by&nbsp;email:&nbsp;<a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a>&nbsp;or by&nbsp;telephone: +44 (0)113 343 5057.</p>


<h3 class="heading heading--sm">Linked funding opportunities</h3>
<h3 class="heading heading--sm">Linked research areas</h3>