tag:blogger.com,1999:blog-82638822458122730182024-03-16T02:09:55.253+01:00Mohamed Amine Chatti's ongoing research on Knowledge and LearningA research oriented blog about Web Information Systems, Data Science, Learning Technologies, knowledge Management, and me...Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.comBlogger586125tag:blogger.com,1999:blog-8263882245812273018.post-50234001189166589532018-06-25T12:44:00.002+02:002018-06-25T12:44:40.099+02:00Open PhD Position at the UDE Social Computing Group<div dir="ltr" style="text-align: left;" trbidi="on">
<span style="font-family: Arial, sans-serif;"><span style="background-color: white; font-size: 12px;">In the frame of the DAAD Graduate School Scholarship Programme (<a href="https://www.daad.de/hochschulen/programme-weltweit/promotionsprogramme/gssp/en/23570-graduate-school-scholarship-programme/" target="_blank">GSSP</a>) starting in 2019, t</span></span><span style="font-family: Arial, sans-serif;"><span style="background-color: white; font-size: 12px;">he <a href="https://www.uni-due.de/soco/" target="_blank">Social Computing Group</a> at the University of Duisburg-Essen offers a 3-years fully funded position </span></span><span style="background-color: white; font-family: Arial, sans-serif; font-size: 12px;">for a PhD Student in data science with a focus on user modeling, personalization and learning analytics. </span><span style="background-color: white; font-family: Arial, sans-serif; font-size: 12px;">Detailed information can be found <a href="https://www.uni-due.de/soco/people/open-positions/overview.php" target="_blank">here</a> and in the full job advert on the User-Centred Social Media (UCSM) graduate school <a href="https://ucsm.info/open-positions" target="_blank">Website</a>. </span><span style="background-color: white; font-family: Arial, sans-serif; font-size: 12px;">Application deadline is July 19, 2018. Please consider and distribute the job opening.</span><br />
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Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com5tag:blogger.com,1999:blog-8263882245812273018.post-90054319421230079712017-10-20T11:11:00.000+02:002018-01-16T14:45:44.537+01:00Appointment as Professor of Social Computing at the University of Duisburg-Essen<div dir="ltr" style="text-align: left;" trbidi="on">
<img alt="Logo" src="https://www.uni-due.de/imperia/md/images/cms/ude-logo_en.png" /> <br />
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As of April 2017, I have left RWTH Aachen University. I am now at the University of Duisburg-Essen as professor of computer science and head of the <a href="https://www.uni-due.de/soco/" target="_blank">Social Computing Group</a>. See my <a href="https://www.uni-due.de/soco/" target="_blank">new home page</a> there. </div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com1tag:blogger.com,1999:blog-8263882245812273018.post-26285857353435765022016-10-11T15:10:00.000+02:002016-10-17T14:14:56.843+02:00The Open Learning Analytics Platform - OpenLAP<div dir="ltr" style="text-align: left;" trbidi="on">
<b>Open Learning Analytics</b><br />
<br />
The aim of <a href="http://mohamedaminechatti.blogspot.de/2016/08/what-is-open-learning-analytics_25.html" target="_blank">Open Learning Analytics (OLA)</a> is to improve learning
efficiency and effectiveness in lifelong learning environments. In order
to understand learning and improve the learning experience and teaching
practice in today's networked and increasingly complex learning
environments, there is a need to scale Learning Analytics (LA) up which
requires a shift from closed LA tools and systems to LA ecosystems and
platforms where everyone can contribute and benefit. OLA refers to an
ongoing analytics process that encompasses openness at all four
dimensions of the <a href="http://mohamedaminechatti.blogspot.de/2014/11/learning-analytics-challenges-and.html" target="_blank">LA reference model</a>.<br />
<ul class="bulletpoints">
<li>
<b>What?</b>: It accommodates the considerable variety
in learning data and contexts. This includes data coming from
traditional education settings (e.g. LMS) and from more open-ended and
less formal learning settings (e.g. PLEs, MOOCs).
</li>
<li>
<b>Who?</b>: It serves different stakeholders with very diverse interests and needs.
</li>
<li>
<b>Why?</b>: It meets different objectives according to the particular point of view of the different stakeholders.
</li>
<li>
<b>How?</b>: It leverages a plethora of statistical,
visual, and computational tools, methods, and methodologies to manage
large datasets and process them into metrics which can be used to
understand and optimize learning and the environments in which it
occurs. </li>
</ul>
<div class="col-md-6">
<b>The Open Learning Analytics Platform (OpenLAP) </b><br />
<br />
<b> </b><img alt="" height="227" src="data:image/png;base64,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" width="400" /><br />
<br />
The Open Learning Analytics Framework (OpenLAP)<br />
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<img alt="" height="147" 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" width="400" /> <br />
<div style="text-align: left;">
OpenLAP Architecture</div>
<br />
The Open Learning Analytics Platform (OpenLAP) provides a theoretically sound conceptual framework for open learning analytics along with a mature reference implementation.<br />
<br />
OpenLAP
encompasses different stakeholders associated through a common interest
in LA but with diverse needs and objectives, a wide range of data coming
from various learning environments and contexts, as well as multiple
infrastructures and methods that enable to draw value from data in order
to gain insight into learning processes.<br />
<br />
OpenLAP follows a user-centric approach to engage end users in
a flexible definition and dynamic generation of indicators. To meet the
requirements of diverse stakeholders, OpenLAP provides a modular and extensible architecture that allows the easy integration of new analytics modules, analytics methods, and visualization techniques.<br />
<br />
More information about OpenLAP is available in <a href="http://mohamedaminechatti.blogspot.de/2016/09/toward-open-learning-analytics-ecosystem.html" target="_blank">this recent publication</a> and on <a href="http://lanzarote.informatik.rwth-aachen.de/openlap/index.html" target="_blank">the project Website</a> (with use cases, detailed architecture, souce code, documentation, related publications etc.)<br />
<br />
<b>Citation:</b><br />
<br />
Chatti, M. A., Muslim, A., & Schroeder, U. (2017). Toward an Open Learning Analytics Ecosystem. In <i>Big Data and Learning Analytics in Higher Education</i> (pp. 195-219). Springer International Publishing. </div>
</div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-46780348205871490982016-09-13T13:34:00.000+02:002016-09-22T17:34:23.309+02:00Toward an Open Learning Analytics Ecosystem<div dir="ltr" style="text-align: left;" trbidi="on">
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<div style="text-align: center;">
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" 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The Open Learning Analytics Framework (OpenLAP)</div>
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Our chapter "<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-06520-5_12">Toward an Open Learning Analytics Ecosystem</a>" has been published in the book "<a href="http://link.springer.com/book/10.1007/978-3-319-06520-5">Big Data and Learning Analytics in Higher Education</a>" by Springer. A preprint version of the chapter is available <a href="http://lanzarote.informatik.rwth-aachen.de/openlap/uploads/publications/BDLA_OpenLAP.pdf" target="_blank">here</a>.</div>
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<b>Abstract:</b></div>
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In the last few years, there has been a growing interest in learning
analytics (LA) in technology-enhanced learning (TEL). LA approaches
share a movement from data to analysis to action to learning. The TEL
landscape is changing. Learning is increasingly happening in open and
networked learning environments, characterized by increasing complexity
and fast-paced change. This should be reflected in the conceptualization
and development of innovative LA approaches in order to achieve more
effective learning experiences. There is a need to provide understanding
into how learners learn in these environments and how learners,
educators, institutions, and researchers can best support this process.
In this chapter, we discuss open learning analytics as an emerging
research field that has the potential to deal with the challenges in
open and networked environments and present key conceptual and technical
ideas toward an open learning analytics ecosystem.</div>
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<b>Citation:</b><br />
<br />
Chatti, M. A., Muslim, A., & Schroeder, U. (2017). Toward an Open Learning Analytics Ecosystem. In <i>Big Data and Learning Analytics in Higher Education</i> (pp. 195-219). Springer International Publishing. </div>
</div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com1tag:blogger.com,1999:blog-8263882245812273018.post-19753639700230048642016-08-25T14:59:00.003+02:002016-09-28T09:22:19.635+02:00What is Open Learning Analytics?<div dir="ltr" style="text-align: left;" trbidi="on">
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" 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The Open Learning Analytics Platform (OpenLAP)</div>
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Driven by the different perspectives on openness as discussed in the literature on open education, OER, OCW, and MOOCs, several suggestions can be made as to how ”open” should be interpreted in relation to learning analytics (LA).<br />
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– <b>Open learning</b> by providing understanding into how learners learn in open and networked learning environments and how learners, educators, institutions, and researchers can best support this process (Chatti et al., 2014).<br />
– <b>Open practice</b> that gives effect to a participatory culture of creating, sharing, and cooperation.<br />
– <b>Open architectures, processes, modules, algorithms, tools, techniques, and methods</b> that can be used by following the four R’s ”Reuse, Redistribute, Revise, Remix” (Wiley, 2009; Hilton et al., 2010). Everyone should have the freedom to use, customize, improve, and redistribute the entities above without constraint.<br />
– <b>Open access</b> to learning analytics platforms granted to different stakeholders without any entry requirements in order to promote self-management and creativity.<br />
– <b>Open participation</b> in the LA process by engaging different stakeholders in the LA exercise. Daniel and Butson (2014) state that in LA, ”there is still a divide between those who know how to extract data and what data is available, and those who know what data is required and how it would best be used” (p. 45). Therefore, it is necessary to bring together different stakeholders to work on common LA tasks in order to achieve useful LA results. Further, it is essential to see learners as the central part of the LA practice. This means that learners should be active collaborators, not just mere data subjects (Sclater, 2014) and recipients of interventions and services (Slade and Prinsloo, 2013). Learner and teacher involvement is the key to a wider user acceptance, which is required if LA tools are to serve the intended objective of improving learning and teaching.<br />
– <b>Open standards</b> ”to reduce market fragmentation and increase the number of viable products” (Cooper, 2014a). Open standards and specifications can help to realize the benefits of better interoperability (Cooper, 2014b).<br />
– <b>Open Research</b> and <b>Open science</b> (Fry et al., 2009) based on <b>open datasets</b> with legal protection rules that describe how and when the dataset can be used (Verbert et al., 2012). Sclater (2014) points out that datasets ”from one environment can be connected to that in another one, not only across the different systems in one institution but potentially with other institutions too”. <br />
– <b>Open learner modeling</b> based on user interfaces that enable refection, planning, attention, and forgetting and that can be accessed by learners to control, edit, update, and manage their models (Kay and Kummerfeld, 2011). This is important to build trust and improve transparency of the LA practice.<br />
– <b>Open assessment</b> to help lifelong learners gain recognition of their learning. Open assessment is an agile way of assessment where anyone, anytime, anywhere, can participate towards the assessment goal. It is an ongoing process across time, locations, and devices where everyone can be assessor and assesse (Chatti et al., 2014).<br />
<br />
The concept of open learning analytics covers all the aspects of ”openness” outlined above. It refers to an ongoing analytics process that encompasses diversity at all four dimensions of the <a href="http://mohamedaminechatti.blogspot.de/2013/01/a-reference-model-for-learning-analytics.html">learning analytics reference model</a> (Chatti et al., 2012):<br />
<br />
– What? It accommodates the considerable variety in learning data, environments, and contexts. This includes data coming from traditional education settings (e.g. LMS) and from more open-ended and less formal learning settings (e.g. PLE, MOOC, social web).<br />
– Who? It serves different stakeholders with very diverse interests and needs.<br />
– Why? It meets different objectives according to the particular point of view of the different stakeholders.<br />
– How? It leverages a plethora of statistical, visual, and computational tools, methods, and methodologies to manage large datasets and process them into indicators and metrics which can be used to understand and optimize learning and the environments in which it occurs.<br />
<br />
<i>References:</i><br />
<br />
Chatti, M.A., Dyckhoff, A.L., Thüs, H. & Schroeder, U. (2012): A reference model for learning analytics. International Journal of Technology Enhanced Learning (IJTEL). 4(5/6). 318-331.<br />
<br />
Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U., Greven, C., Chakrabarti, A., Schroeder, U. (2014). Learning Analytics: Challenges and Future Research Directions. eleed, Iss. 10. (urn:nbn:de:0009-5-40350)<br />
<br />
Cooper, A. (2014a) Open Learning Analytics Network - Summit Europe 2014. Retrieved from http://www.laceproject.eu/open-learning-analytics-network-summit-europe-2014/<br />
<br />
Cooper, A. (2014b) Learning Analytics and Interoperability - The Big Picture in Brief, Learning Analytics Review, March 2014, ISSN: 2057-7494.<br />
<br />
Daniel, B., & Butson, R. (2014). Foundations of Big Data and Analytics in Higher Education. In International Conference on Analytics Driven Solutions: ICAS2014 (pp. 39-47). Academic Conferences Limited.<br />
<br />
Fry, J., Schroeder, R., & den Besten, M. (2009). Open science in e-science: contingency or policy?. Journal of Documentation, 65(1), 6-32.<br />
<br />
Hilton, J., Wiley, D., Stein, J., & Johnson, A. (2010): The Four R's of Openness and ALMS Analysis: Frameworks for Open Educational Resources. Open Learning: The Journal of Open and Distance Learning, 25(1), pp. 37-44.<br />
<br />
Kay, J. & Kummerfeld, B. (2011) Lifelong learner modeling. In Durlach, P.J., Lesgold, A.M., eds.: Adaptive Technologies for Training and Education. Cambridge University Press. 140-164.<br />
<br />
Sclater, N. (2014) Examining open learning analytics - report from the Lace Project meeting in Amsterdam. Retrieved from http://www.laceproject.eu/blog/examining-open-learninganalytics-report-lace-project-meeting-amsterdam/<br />
<br />
Slade, S.; Prinsloo, P.: Learning analytics: ethical issues and dilemmas. In: American Behavioral Scientist, 57(10), 2013, pp. 1509-1528.<br />
<br />
Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-Driven Research to Support Learning and Knowledge Analytics. Educational Technology & Society, 15 (3), 133-148.<br />
<br />
Wiley, D. (2009). Introduction to Open Education. iTunesU. Lecture conducted from BYU, Provo.<br />
<br />
<b>Source:</b><br />
<br />
Chatti, M. A., Muslim, A., & Schroeder, U. (2017). Toward an Open Learning Analytics Ecosystem. In <i>Big Data and Learning Analytics in Higher Education</i> (pp. 195-219). Springer International Publishing.<br />
<br />
A preprint version of the chapter is available <a href="http://lanzarote.informatik.rwth-aachen.de/openlap/uploads/publications/BDLA_OpenLAP.pdf" target="_blank">here</a>. </div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com1tag:blogger.com,1999:blog-8263882245812273018.post-19067922573590743892016-08-25T14:59:00.001+02:002016-09-13T14:19:03.545+02:00What is Open Learning Analytics?<div dir="ltr" style="text-align: left;" trbidi="on">
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IarxrHooLupz2IKc9Oo2U/yg1Rz7ixi3PkKsf1q0PrfVdr/DDn1md3pBTH3rZrrawZZAwhgoxgy6jxFWyfzimSq/VQfuathnQxBEAQJG0zJ0Kdh+GZ7L2EY9vTpU1lZWXMzs4b6+t5eJqe74kWyYVfgXuzhzn6PP2mGswLO/p/G/p/UL0lbmpn6+Aakp2Y219WxWipan1nU2i/rtZ2BOH3DtPyyUGW6x5Hp9udXPbxnlpWeXFNbw+x6wRlo66Wn1ISeabT6ieM4k0P4ql7/y/Bzn1vIzPK3UaksKejvZ/JS8qh7Vou2Ti7eB4ouAKrdVB8lqrBOhiAIgoQNpmTo0zCmo8UwLCcn5/z581qammWl5I4OBruz5EWiRqfPRk7gGqbr7DKdL31PTzM9Nc/LRi01Ka6muqqvtxtF2ADpZlICGr229DrNRu792mH1dYbSPx1kvnC4vj32gUdlRXlv3yCGYQCw0f6KF8laLa6LWcTZrLvf1935MvziZ5ZHZ/qZXynNT2cyOxF0TEoe7rxFI3cJKFEVfQj+20ExAukzRDZLEARB0KcBpmTo0zCmo8UwrLKyUl9f38TEhFxc1MKgszqKO5NUO33XcIJX9Nz7maLzT99T02zOLAgnGtCohf29PQDDMIBiaE8v7WFL0L5+z0WIz4IXdt+m3fyH49EvnJV2pEZ5MxqqWKxBBMMwwMUGG7qzLNrvb2T7LWN7/tpg9mWUwjQr2Rm+FviSvNTu7k4E46IYggH01WmN6bwnVXsEyF0NqLZvNWhac/FEW5KWFI1qC6haBDEcnvi67UlaUjSiPOktjzJq5O8EDbYimyUIgiDo0wBTMiQi5RdAMk64o2jHqCOOrpMxDKurq7OysrKxsSkhl7QwGtmdRd0pqsyANdiDZQMesyiG//A5i7M+vyDM8w6tsqS/vx/BABcDGNbTV/Wg5eHBvoDlSPCiDqdv0lQ/Jxz/0unWrrSYgNbmei6bjWEYABjGau7Ks2kP3MIJWcHx+Y1u+d8oxWlWJ2f6WOGL85K7ujtRFMN4dxXhGVskT/4s5S4BJbcA1ebNB40oT5DdgNc6RJDF4XBz8VryNKoNSesQQXYugXiIQLQhyfK+tGdDksWJa2mSZHE4WXnez291oKFBMQCZPwF0UCgfSwiCIAgaBlMyJAq9hSB7Pqh2F+7IXz+qlx1dJ2MYRqVSb9++bWxsTKWWv2hr4XQVdaWrdYWsQSL+6Ls/q9Tknz4XcNYXF4V5mdIqKQN9/QDFMAzFsO6+2tCWKKne0NXIw6Wdrt9maHzucPJfDqp7kmNCmpubOBwuhmEYQFB2XfdzixcPNnLClrED5tfbfR15dbrV6Zk+1vjivOSuzk4EwcDIlMxXJItqlnJXA6r1mw8a8RKNegkvxkvJGwhac/FEa0DcIK51iSB7iEa1Hk7J1iStQzSqNUnrEEF2A14Wh5O99FYHGhr5O2CRDEEQBE0CmJIhUSBLgpKroMpduIOqz39D4xF1MoZhFApFRUVFT0+vjFLW3sZgdxZ1pat1PlyNPFra6/djifmXPvKf2VxaHO5lWlVRMtDbBxAUYAgAzJ768ObHR3ofrkIilrwgfpum/Q8HuS+d1HanxQS3NDdzOAiGoQBwMVZ9V6HVi3AJTtRyVuhv9Q5fRV3/3Epuho/N5eK81K7OThTh3VQEA0BQkSyqWcpdAkqUAdVqKg6KHsj8ERbJEARB0CSAKRmadL2FIGs+qCJOxshbN16djGFYUVHR1atXtbW1ycVkBqOR1VnUlanRFbYGiV7aF/BTqeW/fRWm28ovjvA2raosHejrAyiGYQgAzN76cEb0kb6wVUjkkhce36Xq/sPhzL+c1fekxgYzmpvYbN4X8rgYq6G72KYjcgv30UrWg/n1Tv+LUppudYaXklO6OjtQBH11/eaxRbIIZyl3FSiznIojfzsskiEIgqDJAVMyNOmGKtJJyX9UvfHqZF5Kvn79ur6+PrmY3NxMZ3UVdWdrdEWsRp4s7Q/8mWL1bz/8dFv5xZFeptUVJQN9fRgKUAzFALO3PozxRLovfCUataTDa0aa3j8dzvJSchCjuZHN5g6lZHZDd7FtR+Q27mMxVtj8epf/i1L+zOrcDB9bfHF+clfXCxQd/vbeuEWyiGYpdwkg3xR9JuYbpbqwSIYgCIImDUzJ0OQaqkjdJm+MUydjGFZTU2NqamphYUEmlzQz6Oyuou4s9aGUHPQLxeo/fvjP7OQX8VLyYH8f7woXGGD2NjxsIR3uC1+BRC3u8JqZpv+Fw9kvndV3p8YEMpob2BwOyrvGBZveXWzXEbWNGy3GDpvf4Pq/RyrTrc/P8LHFF+Ul8VIyhiEAoOMUySKdpdxVoMxiag1YJEPDKIVVEf4+jx/evXxO3sHCoqGKLuozgiDoIwRTMjS5yJKg5Mqk5j+qrsA6GcOw9vb2gIAALy+vsrLyllYGu7u4O1u9K3I18uSP/qBfyqz/48vrkr3NqitLB/v7MQygGIYBZi/9YSvpcH/4CiRycYfXzDS9LxzOfOmsvis1NoDRTGdzeF0yirEbu4ttO6I2c6KXs8Lm1bv8L0p5utXZmT42l4vyeF0ygmEo6H0uqEgW9SzlLgFkJVBmPlVGqTYskj9BDbWdvB8wBMT5k4gm5r4War56110uSxJvihOVVt29utgev8T52sH7xrrZT1Mn/wxLKTlJ8X5BPnf01SSN1A54uWnGkrzSMx7V0WkCt8cwDMVQgU9BEDTVwJQMTaKhivTeZA9BdTKGYRwOp6Wlpbq6ur6u/sWLNnZ3cXeWWnfEavTJHwNBv5RZ/9cXP91mKCXzVlxgKIZiWHc//WF7zOGBiBXooyW8lOx45l/O6rtSYgIYzXQ2m4vyrqzMojPJ1l2PNiHRyzhh8xpdvnp883PrszN9bC8X5SW/XJc8TpE8BWYpVwyUmU2Vkb8NFsmfGjabo3Lq0AM363BXc4LiIYLcprsX17hfFCNeWuVzc62f+obA65t9r4i7Kay2PrfY6NhcHek/Y/29Ju30+gb6Arz1XfQPxDpeTHRVfOqikOh8MeHuxQT3i49s5VyNTjx4RORwOHyvwgDvXyMwKEPQBwCmZGgSkSUBWRHQ7k32KNMZWydjPc95X5sbHBzs6Ojs62MizJKuDNXWgCXdAb+0EL/LvfMF8eI0y0u/R3iZVFeWDPTzUjKGoczeuofNUQde+C7s8p1d7/j1U41/2p36t7Pa3rSYoBbeumQUAxiKsRq7Ci0Zoau6An994f1DueV/HihOtzg908f6cnFualdnF4qggCmoSJ4is5S7BJCvgzJT0Y9STVgkf2pYbCwzOUVp929aknOMjy60Of27C36F57W1HtfW3VVc56q41lFhueP5ZdZnl5ieWnBHdq7B4Tlqf32vsmt+TIj/JJxeZ3eHl6v6A8LJp3cvJN/DJ7vhn96Vj3O5FO90MdHpUoLDhSTHS6FGMh6Oaj0DfWNfzvt3ySScJwRB7wOmZGiy8CpS2l3RjPFXJyNcLofNQRAOh0mpjbuRYTrjqcb0sOvTiOdxhkdwhhcXhXub1lSW9Q/0YRgCMIBh/Z1Vj4q89iTrffXk1j+CFac5n8bpSP/bXm1PGim4pYnBZiMYAgCGoYPNjZkW2YSF8dqfR97E+crjrI5O05f90cdGsSg/qbOrE0HHKZKnzizlioEyE9GP/K2wSP7U9PSydeT2qOyYaXBwjpH0ArOzC63P/251dqHp6fl3Tsw1lJ2jLfOrpvQvmod/0Tr4i+aBn9X2/3Rz96zL4v8h3JQX9rk10GucTOUjLKXjnM/F3VWIc5WPsT/3yPJ4uJl0kIGk1+0dDywPPrbZn2Z5POCOtIOd0thAjGEYG2HBpRcQNMXBlAxNFrIkIF8GNFfRjDLt8VYnYxiKoSiGcQeY1bmPDNyUfjc79Y2ezDfaR2beOvKjLl7iwX276gpq/0APCjgYxsFAbxMtNopw2P7SzDsnv9I5+q3mkW+Vj/1opSmVRApubqKzOWwMRQGGIewXlNR7vjrrLc9+b3jsG52j36gdmaFxerGHnVJhflJXdwfKLBBUJE+lWcpdAsjXRByRYZH8SWINDtySXnl710zd/T9pSc7WOPjLLckfr/81U3Hbd/gt316U+PqcxFf4bd9e3v6d/OZvr+34/vqe7xX/mnl56381Dot3t/UI78Q6ul5Yah2JtJKOt5GNIZwh2ZyINj8SqHvA6eYOh5tbnZQ3m1/601V1s6PaFieNrf76uz1vSUQG2I/dD4zIEDT1wZQMTYreQpD1G6h0FeXIWytwdTKKoijKRtABLqe7q7G0JDk0LsQu8r7JIy+TKG/zR4GOaQmPa6srBwZ6MIyDYRwUGxjsozeVx+fFuDwJsAz3toj0toy4b0164JmfldbCaOZw+wHGRlEOF+nvaaugZYcnhTtH+VhG3DeJvG8a5WsT/yiotLSYyWSCkrFF8tSbpdyVoMxYlAMWyZ8kVj/z5qEV13d8f3X7d/jN317bOUPnyBzri3+43hDzVFvnqbHB8cZyvZNz/XTWBehvunlwJv6vbxV2zpDf8h+l3fPo5dXCO7HgQFsvnZ3RFvsfWx+NMj0ceWefh4qEs9ruKI9bz+PtypOdSmItciNNMiItn3irumjscr+2zvjS2qKSDEE7wzgoW3inCkHQe4IpGZoUZElAxoNKF1EOita4105GURThIigL5Qz0Mjubm+rptbSmGlpjXWVDfXljY113dweXOwAwDoYiKMJFuFwua6Cno7WZ3kCvrWusrWuorWmoq21pbuvr7UcQNooiCIYiAEHRwYG+jpYWekN9Fb2+gl5b1VBdR69rbmvtYnfkCSqSp94s5S4BxVcB5Y5oRokGLJI/TVw2V/OoxDmJr9UOf++ruezZ3a0lYUern+LpqUrN6Teb0pToyVfKSWep0afoSec9NZed3/TVhS1fnd30r+t75jDqy9/z6OMtGe7sajNV2vZIf3+kyd4HRrvD9LYTVTa4qO8qeUpoKPSre+5NL/ZpoQa1l0e0VZAY5RGlyc5OSjucrqzxvKcl8DhsBH62IWjqgikZEr6hilSk4Y83BNXJvB8xDEMxBMG4XIzDRTgIF0U4KBfhclE2gnAwDEExLoYhGIoCBMFQFEMBhgCUi6EcBOFyuAiLiw6iCAcb2ox3kQsMwRAuxuViXC7CQbhchItyEYTL5SIogpUcFlQki3qKBM5SzkpAMRLNyNsCi+RPE5fNUT64/vru757Zr6oM2lLx6GBl3OmaZHx95q2GbI36TNW6FKX6DKW6tKv1yefiHDef2fh/cpv+c3rjvxR2/VRPzX6fQ2MYigHByyESkx86Ka19pLcnyOCvUE0JT+V19srbiuNt6rLu1Wfdped7NxYFNJWEtlAiGKVhNUWBdQV+2aGGtgqrLXVk4df1IOiDA1MyJHxkSVCMBxXOoh+lmuPUyQjAuBiGoUMdEgYACsDw34b+imEY7z+fHIBxAcAAQADgAsAFAAEAAYDDGxhgA8ABAMEwFAUYClAMIACgAAMAoACwAWBjPbkgf8UHM0u5i0GxIqAYTvYoUYNF8icL4SCKezfclvmuwE2sxFeCEn6gIuZUTdKVhgx1erY2PUurLvlmYZh0cZh0bdKZwtCDV3fPkF375bn1/5Xb9L/7duZCOiufu9r+6uvDtXYG6W70VxW3UdyY8lCfXuRbm0Ok53oyigJbSh+2UB+1lke3lEU2lobW5N2vSncNNjthdFMGpmQI+uDAlAwJGa8irXCaKiN3zXh1MgAAVCqCZNzQqFQU4rQIuLTF1J6lnBWAYjDZI2/z1CmS86o6RHXoxNIWUR1alDBgqoRXl/76ufuKYh+J/MCdlEcyVYkKdekqDZka9EyNuqQbpY9OUB+fqnp6rDL6kOGZuUdWfXFW7L/HVnzuaqojnDMCvtZXQ26tCVPfGqop5nB51QMHhcpM1yd+GkkRJg3FwYzSsNaKx6202NaqeHppVFKY2dNgHWoa4ZmPsubVIygKv64HQR8YmN1+BWQAACAASURBVJIhISNLgmIF0ce+l6P09nirk0Fv4aiFwi8fn3B8B/ogZil3MSjGA4o+oOiDfIlXv0vkSww9OOGj5NbUKZJrWnsPW4vgvm48F+9mf5pBOdzTU3nPjCyn5bHWf8RY/1EUuIsWK1eVdKUmVak2VakhVYWRp1qTeo0WK1cde8BFZYHUyi9Orfjv4d9x5jeUhHE+XBQl6Bz1VxYLVtvorbLBQ3tPeaJttKdSiDM+L9a6uSSkvTzqBY3UVhXXVhXXVBJelOAU66uWE6mXHKB6W/0k7JIh6IMDUzIkTEMVqaOgoU/QVuT9SYvXH3pEDId359+SpC1Dq3CkuSvSBO9naAzv5A3GeHUyX7/LVzNPIMFF8pud/KQNwXWyHihWALmLXz2euxgUKwCK3sSPPIkpUiSzuOh+i5Sdxs9EdQJnnbM26sa397BEdQKikpuQoSYzL9VlZYbbmhy3reSgQxWPT9DiLtQkXKYn3yyKOGej9Eeyx6Gqp6eq4g4EGi6T+vOfBPxirYO/uBlZCuN8uBhqq3nUR2lZoMZqU7m14S4K1CQLcoxZbfa95mJfRklIe/mjjqqY9uqnbbTYlpKwpkL/6izXqmfWT31VXDysBO0SQzBEGKcKQdCEgCkZEibyPlAsP24Oc1ckxSuS4h1J2jKE4zhxbX1eFCZpy9DiZQjujjTteTjcJvyIlEw6jsPh5uGPz8PhNuGP48S19UnHcTgcDu8+tJOhn4/jcLhNpPGOO16dzFcej218J4SAIvm1sySqMXaWchaDIgWQs0JAei7Vm+BBVpk6RbJZBGWvmYhT8mGrtLMu7/WNtA9RTzfTVmlTqsMfftoLlSR/SL77Fy36JI10rjpWvuHZDXLUhRDzbeQIuYrYI7S4A49s155Y/yXJXsz0hlh54fte40IgDACiuYK30gKvW3/Ib/ud5KtCjtereGZRm+nIKPRkFAe2lEW0V5JaaXGMikcMcjCjOLAqw6nqqUm8n0pmXqKAHWIYF4NXgoOgqQumZEho/r4iVcSLyfBiMYEXhbXnDWdcHN5dnyCGe5WStefhxHg5eJ74iMdfRuqhncTLENz1CWI4ce3XVssC62S+8piv8Z0oH0SRPN4s5awAmXP4f5fInANKdSd4TJkiObG0ZathollElWhTsk0UTdom3SWuUlTnICrxrudTbBalWv8Zbbo6P0CyLPIY7cm5qjiFmoSrtSlKjTk3q1IUymOPUUkHUr23eqj/nuy01slEQXj1rJerjpPiPMNT828cWpkTqUeO16EkmBXGWOVE2dXk+LZQwtoqYlsq41oqIypyfKL9LDLCzWqemT70VqbVUIR2UhAECQtMyZDQkPeBInlQ7jDpQ48ghsPhxEmv2aZEA+QuGXW2vCL5+YZXK26fb5j4ORFYJL/rLJGO43BiR2ijH6TF6Q09q83/1FuPsbOUsxjk7QLFe189UrwXFBwFpToTOcg3p0iR3N7D2mKQYBBaYRtdK/KU7J1E36AbX1zfJarTmHy93b3h5nuLicuLPNZQ/HaUBkmWRRwtCDlEjZevTbpWk3y1Ou1KVZICLfZUWeShkrDdtIhtRQ/O9na+EN4pxcUG39OTMlTYiZfcVBZrQ4kxpKU4EvTOrfr91wySS3t5RGt5TCsttq0qJi/ee9PSuQ7aZxuzXPy9b7NYn9yCGQj6CMCUDAkHryItJ0zd8fqLXQiJwCL53d8FHi8mPbTOhKhLEMPhxKQJ2tIk7bk4nDheW5omjFnKWQ6y5r36XSJrHijVnuCRt2mKFMlnnLPkiQVuCY1TISU/yGoxCS//y/hZzwBHVGcyydiDnEjCqVyXpc+9Vxff30oJ2Ut5fCzBY2tpjHxd0vW6pKt1qVdrkuQrY06URR6ihGzNdttQmOD2ngdFMOQ1X7NLz4x99lA9PlDJ7rYUmaRVGqtWkWyaE2vm736LmuPVUhrWVh7bSotjVETUk4MzYwjFCVZVKSbBPgbveVYQBIkETMmQcJD3gaJLoo/CfKNgx6t4l4wD2XNGnfPIIllIg6+ffq9Z0uVV5vihlEwgHcfhcOJ4bXE87/F3S8l8U5S3cUzpLib0Wcr8eSoUyS5xlQet0u4lNE6dlPwwq0XRveCGV76ozmTyZUY5ZTmtyPdeUewjURK8qyTqcMy9TWWxl+ue3ahNulqfcrX2mXxlzInyqMOUQPFUr0P9A+/7yelhd70mJaemP04KVsyJuk7yUnz+SL00Vo/6zKo6y6G52JNe5NNCiXhRGd9eHd9Ke9xU9rChJKA8w74qyTTUXe1FR/t7nhgEQZMPpmRICHgVKdX+TQZNay4Ot5HE92Cczhu+fMTQIYjh8MTxNyhRG7N+YAGg24lojt5uliZpjJ2i3CUge4lQ1mdPbcX1XRL6T60f10y1lBycwdhlkhSe2yCqk5lkPS2MdPd9Bd7Linw2koO2kcMkHzutzw48XP30ck3SlbqkqzWJl2ixJyseHaKGSGQEnHj/a62xXnvL6OyC2JSQm0UhN7LClZ9HaZbGGJUn21RlONfleTSTQ9uppO7apJL0QGp2aEtlTF1hUEWaEy3B5sG9W/TGT+UfGQR9TGBKhoSgeB8ougCodm8yeCkZL8srRLUJslIkLSm8lhRBFieupU0aetyO7wccDoeTVSDJ4nC4ueJiQ0/RiAq01xwrdzV/2muPAFk/imiO3m6WJmkInKKcZfzR+WPXM8DZafxMI5ByL6FxqqXkh1ktbk/rNujG17T2iup8JtlT/8s5LssKvdcXB24mP9xTFHqw+IFUdczFuniFuqRL1YlnKmKOUiP3lQduzPCURYR8444Cckq0m0KJ382MkKuZkbfIMQblqVZVWc61+V6NxQ9aKdHMuqSsGGJe4v12WmzD84CKFEJFopWf262OLiGuloYgSEhgSoaEIHcJKFF9u5SsJUWLkyIQ7QBxo7iWNklLiqAlRaOOCMfDG5C0pGhxCqQ4bYIWLyW/ei1Nay6eOP6xsubx3yWktxCkfyuiOXq7WZqkIXCKsuaC9BkimiPRuOX7/LRT7t2ExqmZksOyW/RCKNI2aSzuJ3Evt2pqRtpd8SLP5YW+6wuDd1IjDlU8PlIceeR5hGz+A2lq+LHyx0coYTsrAtfmBykK+/Z2HC6nuPBZhNflzLAbeQ9VyE/0KMkWtAyn6lzPhsIgRklkR1Vcdpxb2hOn9oro2lyvikTr0qfGQUEmwj0tCIKEA6ZkSAjaI0DuakC1nVqj6AIo3jfqPIt3AeoFEc3RO84STWsunmhL0pKiUW0BVYsghsMTX7c9SUuKRpQnvfsU7QPZy6fId+kmR3huww7jJKc4+lROyWHZLScJmRZRZaI6pfGwBwYGuts7a8ooQfcHGPXsnom5rXfBI6NMl4XP3f8s8d+U4bUt2lsyLV6FWugY630m+74UNfpISeiW0sA1DUVBE3K410tOi/azk8sMuZEVeqMwWqc00bw8zYGWTazN96EXh9QUhET6GRUkezaRQytTXctiTdMe3EzLiJyEE4MgaMLBlPxpqFQU+jeuKhVHHTF3CSi5Bag2U2tkLRh1zunfjPqWWPkFoc9S0Y73nCUaUZ4guwGvdYggi8Ph5uK15GlUG5LWIYLsXALxEIFoM9y+25BkceJamiRZHE5Wnvfzu0xR1m8g8yf+79JN/sdpstS09m7QjTeLrLr7tJEvJW/UjXeNrxTJ2GXybGxKDkht3G6UKPI7V7MHBumU7A5qdm9dfs1Th2IriVKrlSUmC0sN5jw3+j3faEUJ8TwjL+k9j8LsZDy7u7/AZVmJ1+oU1w2PnY+mR+qnPbyS4SddFnGyNOpwWfCmdOL2BtrE3HgFwzAMCC6lI4Ls1E8s8tDanet7LTX0am60RulT47JU+/Ist9rnfg3FwXXPA2i5Po3Pg6uyPclJllXx5qFu1xktcFEyBH2QYEr+BAjpHnJ8+G5cN1SUWk/dkb9jVEXaWwiy54Nqd+GO/PX8F1Z7y1miES/RqJfwYryUvIGgNRdPtAbEDeJalwiyh2hU6+GUbE3SOkSjWpO0DhFkN+BlcTjZSxMwS7yJypk/KtG2CqHA4/s4TQoWF5WySbvuXXz3aSNfSnaKbZB3K+ANBbcCBeKrgR89LhMLLrsPDcXR44p7wRWP5y/HVd7wfDWu8Q2v59eHh0dCPV9KDstucYyp3mqYIMI7V9c8C0jQE0/SmFt657cm5z/aPVc0OC+gWc8rN51dYTa7zOLnAp0ZeZrfpGssbHwe857Hqs5/lmu7p8RxeYnXttx72zNdJIoD99GiZKujjhX47YmxWBxqdQR9/+/uvVZUtKfzjbVOZ+bfwf+Z630lOwif++hmWaJxUqg20fJiKFE9ylsr+7F1Q9792gzPsmSH0jijvAjdgAD71+wTA0I+aQiC3gNMyZ8AId1Djs/Y6w3nLgElyoBq9W6DpnWQRL1N0LoIqLcJWhdpxIs0qhVNaw4Ot4GgdZD0rrsdGhQ9/vtWkCVByVVQ5S7cQdXnv+3z+82ScMfYWQIAlBwEWT8BNmPor2wGyJ478Rdum5zLV49mFkE5YpvhyovIo1MyMbGJmNjkntjkntjkkdjk8azJc3h4PWvySmryHh73k5ruJzf5JDf5JDf58kZKk19Kk19Kk39Kk39qs39qc0Bqc0Bqc2Bac2Bac1D6qxGcwXg5QjIYIZmM0ExGaCbjAW+MScnhOS037xeedxXNnaspJJcM4znFxt/RbOfUEZbRXTY0OK9vcNlQ7ypR57yu1v6PWoelDaYLyzR+Sdb4Jll/GYOS855HrM5KTLLb/txzHdlnO9VvT2nAwez7e6MsVz80WR5lsSjRTWZC3td46I1VVpo7IjXX3z07W0tmdsy9CznBl/MeXiuNNcgIMySaKkT7GCYEGeU8tq5OI1KTHUtiTWmxpl6uVxoZja/ZLRuFdxuBoKkLpuSP3eQUyTwC6uRVoMzy3QZJ6yCtzBK4XSTFXiTFWtLcLtLKLGlac3C4dbxH3nnPoMwS5G/nL5Kz5oMq4mSMvHVj6uR3nyXhDr5Z4k1U9mxQpTzqwSplQH9dWzYxHychSy9v22KYSCDVf1gpOTyn5YitCO5cXRrnlKs1q8jihzqHP+u9Vzf772UEH2nw3Ub32lHv+1fD/e0NHpvqPTY2uK6vtfyTbPxrlsaMxFsrme2vy4tvorEyiWS16rHukliTdfFWEk/MxZ86bXsefLAkZHu8zdqnDjZtpbT6Gsbf7+jteRI1ntzZ90Bjlafi77cPztZX2ZEafK0g+EbxI+3yJOv0CMMAB/nkUL2aTNeKZzalT02psYYJIaoJqQ+EcTIQBE0OmJI/dpNTJPMIrJPJN98rJZddxIsdHMrHYgdJvJTsdpDwPim5VHecInlSUjJVT0Cd/K6zJMQxdpYAAKVSIPMnMFg76sHBWpA9d0I/SQCASa2TeXei1g2pcH3aOEkp2VpuoVLGhKRk3xT6FoOEybxzdU8VOVVjfr7WD3WEFfVeW5pC91f5HqoJPFITLE0mrG8K3N/os6fJe0+j7+5G7z109+21LsvLjGfnqn6XdU/1PQ/dVl+USdyWbbcm11Wi0H8XJfxIxRO58ujT5REHM10krPbP9Tm9KeK6bPH9wAl5py9lZye46e6PN9zvf3u1n9Kf5vLbtIzOOhtI5gfeJD/RpcQblqeYVqZYUZ6alzy1qIg3q4zTL3lqFBpiwuVyJ/ZMIAiaTDAlf9Qms0jmEVwnW7zLcDtAiBX8FEnrAO3d9skbgotkt8kbguvk93hHwhgCi+T8lQKaY7o9f7s8USarTj7jnHXJrcDlaeM7pGSLa3In/UXZJYfntJhFVuwymaQ7V/cxu1KMdufpfEez/51O3Nbsd6TGTzbbXqIrw7gr1y7NdGOly5bmgCONfgcb/Q42+R5s9Nvf4LO1nvBnqfkPuQYL+lvesfZuolYG3tEhKonn3N1U6rOD4rerLORAWeSRiuhTlU9OV5COZ93d4a60kPwAz7SVpW6SqAwm9Q8O9rAHegb6e3r73uctNzRX376yJUxrb6jB1hC1lS7X/oyJ9PIMsCIaHbyvvy/Z72Zlonl1klVeqFbqfXym/41U31vPYwz8fXRa25vf57gQBIkcTMkftcksknkE18lKoMx8qoxSbUFF8pVJTclUXUF18tSeJTD8ceJbiIwOguy5r5YpT6xJqZPdE2kHLdNc4hvfNyX7Gy7A4XA43HbLJh9LORwOh8OtPh3Q5Gslh8PhcMsM1W/ILVyGw+HWnAlqDrCW22ndbK8k9/syHA635mxws73SGt7dcn6/mfm2KTkit/WKR4F2ULGw5woAUBPjQVb7sdR8Tp3zykavfQy/M9nmuzsyzJH6MLT+SXe2S5rVlgq3HYwQGXqAFN1fih5wqMl/T4O7OM1uLtnkhwIiHnn7axp311cn39jndnqp/eV5z+9tLvHaWuq3hxJ4mBIhXRF9rCriVPYDqQS3I9W60p26+wfsjTqcCXFSO55e2pNgffKZw8X7V/emBzpy2e/yWwSdUW9mq3zh2DJtxW1WSluISms9HNQAAP72KrmhKtHu8p4Ghx85XyoI1/MyOeGluTbeXNxZb19E1L3+/v53OBwEQVMKTMkfr8kvknkE1MlioMxsqoz8bYKK5HuTPQTUyVN4lsDoj1ON5qhrXNRoCu/TJOw6mXcnaotHNe+fkm8dGL4r5AE/nwDDhcOJ2ddKbodVk19Kk+2N1Tusm/2DjHbcyBhOyWt22jQHBRvtVMoYTslymm/fJUfktj7IbtltmhSZRxfeXAEAABvJstzzXP/7apulDcQtLSGyFLedjY9uofRYpDERaUoDjBxmkV/B3d0lLpuaQ483Bkg3+h9qDjxAv7+zzmlpofH3WdorB7p63vawyZpnqcrrqp0vBF9e+Nxpdam3RJH3Tor/AeoDSepj6ULL3dkOBzsrfDmm2u2SUq22hEI/A8qpVYwDv3Rora5TW/lM5pecs8ufWSlyOANve+gXXe1P4sL0DK+mZSdcvbzP2VodAFBQmEJyvVgYfiv/sVZRlE6Ct2Kk42ntcxucr4sFq/9x58aOrp7utz0QBEFTEEzJH6/RRTKGCfF6Q6N2LrhOvg7KTEU/SjUFFMlkRUC7N9mjTEdQnTxVZwmI4v9L8AizTubdiVotgOIS3zgRKXn1Kf+hFReqB+RUk5t8LeVGp2Q5uSC+lCx3NngoJd9WMnJ41xUXEbmtEbmt7glCv3M10tObcUcs33RWvcPq5vv7yz0PZLjv59KCEXoC0pyOtuShjHzQls+qCi/1O1Fgu67+/v7WYJlGv0N0n70NbmsrrOZStBeUhvm9ybGYL3qKUuKKPY1z9C9kXz3QePlgm5lClvHBUIPFhf6bs5xX53tsLn94IMV/f1G4OlP/Rof03m4XU1Zefr6+UmOcda//jTbng+xC42aHA2VXVxSeXRKjMD/twf23f9NYdFJkf39/W3tbTGJ0f39/Smakv+vZomil4mhVSqxOZaIBLcmwKtnI1/ikxtGFphf/tNOWTEoMePsDQRA05cCU/JEaXSSjKAoAqGtsvR9dZOWRbuWWOmp4pJq6Jes6J2q6JJq4PbP3Tb0XnOEWkmHvm2bilqzpkqjpnGjq9szKI5XvhdZeaf4x5KaW1peHGCK4TjYR/cjfKqBIpt0VzRBcJ4t6isbO0piP02QTWp2sGVh8yjHXmReR3z0lr+YtrjhlMbTiYsH1jOEVF7g3T8nDXfKacyHvmJIjclv1QyknCBnCu3N1X2tDtslCis2v9Xc3NPvJ5truaEjRxxrisOZMlJGDthRgLUVoWwHCSEPpcYx4g2d31qUbb2jyOdTkc5BOlKiyWkLW/CnfTO71R+ls6X5mb5uovCdLeV3ukdl5p+eUqB1u3C9ZoSwX43nR3WlnrNPaEs/1pQHbc73+8ri5sczXesDOuEvxItPVrtzVnOarhTVFdLpc7A6+hjC8X3gebXM5WO92IEtlaYzO0d6ut7sdIIoiXJTLYrMysmKjHtp4uV59SDxXGK1S/ESlLFarPF6/ItGgMtGwItGw4qlhlNPZELsz5Dgtfze5jKz3vUQ0BEEiB1PyR2pE84dhGIZhlfXNUk55vxpSf9Qqm6VJmaVJmaVZNkuTMlOD8j+lkh80KDudK3RJzQ/z+1KqWYUtbHILO7WOHV44YBDH2OlSMUuD/L8bJTPVKbO0KLO0eC+n/KhFmX2HepKY19Dc+gZ18jURhz/BRfJlQHMVzSjTFlQnT71ZAqIrknmEUyfz7kRNiKW/Z0qeqCvBDXXJIUZ/vdO65JfjlGOW8O5cXRHjTrH5tc5hSfO9LVVEmXijDQ2R51vjVXpLfNDWQrS1CG0vQtsK0PYctD17sNSjyPtorPmuAiuxCkfxJuL2evsVFUY/VXq+LiVjAKSG34k98VuB8tKWeIU2t3N0s0PllhfpGmqJx9YUx9wuqwtMCMGnOYiV39/4xEE8/Z5i2OFVlLvWSENjscJpyo7l3eaKPXaKrcfXd9w8wLQ72XT896YrSzp9jxefW5p0ekl5WuzbvmtGa8tNNRXN62cyglWKHlx+HqJcFH6L/Ph2Wcydsnij8nh9arw+NV6/NE637KlhWaxByWPV8nhNd8vzJvraTs5u9s4OXoFeZOpkrBqHIGhiwZT8MRpTJHM4HMegtJ/1yZ+p5E5TyZ2mzPszZ9rV7GnXcrfYU31z2us7WT0sdJALOAjgYoCLAQ4KWFzQy0bru9gBeS92OpRPu54z7WrW0GuVc6ap5Ey/lfeTfolbVA6Xy/27OnklKDMW5RBQJP8GKl1FOfLWjqmTp9gsvfw4YRjAAMAAhg393gXA0G9gKDriAQAwgKEYho74tQnDMBRDUQzFhv589QsVNvTsK2DUq4Yfmeg6ualzYL1uvHFElXN841RJyYdw798lR+S2Bqc37biTmF7eNoHT9VJRiG0tYXaj2/I2vz3pVjtyPU+BSiKo8Qf0R2CgEvRUgE4K6CoDL/JBTz5gPAM1kaAq+EWacYGnZLGbWKPrWprpzyWu0q85BLmsIcnxVOX1lQ2m+wcz9bsdTjVeEy+ykM+6q0E+vK7H8Apg5HZ2ZsS57k61WfbESZLFSG/wUE62PpMZ61Tra9jtrcEuC2XlurdaHu0lGbEz7Vp1d/U4n+pwOEo5szj/+h/FwZ5v9ZaZPV3qhjpqKnaa17WtNWWS/RULo24WPb5GfnKdEqNeFqdbFqdbFqtbFqtbEq9R/FSlNEGj9Kn2Y3e8kfJxLSUtHS2Clpal6k3TU2evGllZZOZlvt8/AQiCJhVMyR+j0c0fl8vt7OzUsX30vVrhZyq5nylnf6acPU05G6eY8YvOc0JCc3MPm8VFX7NuGcMAi4u29nKcU5vn6BbiFLOmKWdPv5n9mXL2Zyo536oV67rGM5lMBEFevUZgnVx8FVDuiGaUaAgqkvGg0kWUg6IloE6eUrPE+zi1hQMUAygACMBQgGIYgiAol4tyOSiXy+UiXARDEBThchEE4WIoG0U4CMIaHOzp6u5l9nBYLC7G5aJcLsrhoBwuwmUNDjK7unuZTA6bg2AYB0MRFMUQFOWirIEBZldXf28viiAcFB36XE5oncziorL26Ve9ip3iG6dOSn6fK8GNTMmRua1OsTVCunN16QN7ut08OnElw29/qsX2qlB5coRa5kP9zFCTJP875c+8BqsT67LudVeEliRaPnGSbSIptyaqFAbLRZnu9LiwMlp9QZXlr1m22zBE8JoQDAMJLu75p8Vqjv5OO72kze4wU/9o26l1JcYXSvx1mHa3OlyVOa3JADTSS709b/9R9NiAkx/OtFRod1epDzfvD7PsItwcSLrXG2bcory/96HWQJJVyx3J3vAbnT5nOnzPUUyWp5kpc97mTpH3vO9qqxqZartbmvnq6GhrKp/3d1Uk+SumheCfh10vfaJFidUtjdEmP9HMDVdNCrkZdV+ZcOey6nW8vu4dPU2D2yq6Gjf1tK+ZaF2zu3nFREbmxKM40f1vGQiC3hJMyR+dMUtI2Wx2U1OTutnD71ULP1PJna6c/ZlyNu5K5nITchKts4+F8MVjNhtNoDFjKroHBvmvhz/AQdKqusXMS3FXMj9TzpmunD1dJfdbtSINwuPW1lYOZ/SFlsbWyTkrAcVINCNvi6AiWaQRmTfG1slTZ5aGP04IhqFgqEsGGBgYHKiglKbGxz/PynrR0oKhWA+zj0ImJz+NLyoo6Ors6u/vq6SUPQ4Nu2vv6OV691lMLKOxCUW4KMod7O+voJRFPnjgQnDwdLmbFBPHaGrGMBRFUdbAILW0NDww0MXO3tfDszAnu4fZ/apdnrg62SyCIm2T4RTX+LGm5Mi8VmWfImHcubo6woVhs7DZbVOly16vq6vvKyyyP/GjDX5VmsfVrCBDTwVJF419ds6LHQgr7jvsjXaSNZebVxlxqp9iU+CrY7xxie+1uXU2c/JstmFcROD+WQiWZHqFcnh+jdT8dsPD7LowVnko00kRqX0CQPlghn/XA3NO1ROkO5vR/Cg+TGWwOflFoEGbxvE+H8P+1Pvd/gZdDrd645w6vdXbr+3rcVLsND3VIrm045p4m+WO3mDFarWlmce3tJTVv+H7LSzLk79+w0zX3d7c3cHuvgOBaGZqqa2ppKmmoK8hb6Qhb6J9yVD9rKn2JXM9BX3VS0YaN0x0NSzumBJsnC1MLA11dHRU1TWVb6sraateN1K9YqakYHRa/lJ7p1CafgiCJhxMyR+dMUtIWSxWfX296p2QH1QLp6vkTVfOwV3JXGZCzqphssc0OvQOln9+u9YTumY03T2rraqN/8JJHATNre9Zbl6Cu5L5uXL25yp536kVq9pGNjc3s9nsUZsKqJMXg2JFQDGc7FGiJqBILsaDCue/HTTteTgcDu/+91u+HCTto7Q33hiUao6pk6fMLAEASqWw9ggOwLgY1t/X39HaxuzskE8OXAAAIABJREFUqquiaSrfnDNz5q5Nmx4/fAgAKH5eeOG03M8zZ544ciQnPSMtKfm09JEFP/702+zZ8376adWChUaaWvSa2sGBgYyklNPSMr/N+vG32XMW/Pzrqt8WGmnrMBobWYODGSkpxw4c/G3GzAXz5s395ReJ1asCvb0GBwaGVmJMXJ18xjlLgfh8klKypdzCGxmjUrK13MIbGa9Sso3c70oZE56SHZ5U7zJ5NuFf4+tlVNS5L21xF6e67rZSnJfuIRXtLnNW9lfQEABAW01KmJvGtobGiCji0RR/eaQm3ub61pYkM1AfVRBlce/y4hr7tdVWczKst6Go4P911dfPLHmgSb0m3qAq2epxi92V11P4oF7nZIefUXe0K9PL8oWeKtPVrCclsLbwfnaOdWW5L4ca/yLQmlOVCNhVvUUPe1J8MRZ1gBrW6XWb2xTFrgxstjw7EGvT5niWdmpRxek/ylUOdFaXv+H7NbI10tOzszHzdnb0vufq7eToamdvb2NrY21jY2VtY25pZWp2584dfUNDXWNjfQtzM1sbG2cnR1cXR3s7K0sLY0N9fW1N7dvqOmoqeqo39W/dML6BN1G4oGnlbD5x/0wgCBIimJI/LoKuRTAyJf/jVt60q1kzbxfEUztYXBSM+E8Vm41kVvdYP2vWeUw3iKYbRNN1HjWYJjTGV3T3s0aVymwEfVbZ+aP282nXsv5xa/yUDATWySsAxWCyR95mAUVyhdObDJr2PBxuE6nCiXQch8PNExfDiWsbkI4P/YnD4fDuTi9/4G2M15ahvdnOh0bumjF18hSYpeGPEwIwBEMHegdyElL87F1igx4kP3osf/z4v6dNm/PddwRTE4TNjn30eP2fy7/A4Q7s3Bnm56948eLP381QOHHSz83d0dxiz6Yti+bOszc1pxQVXzx5+qcZMy+fkgtw83A2t9q1Qfy3X391tLSsoFDOyx7/8bsZ186df+jnZ6qnN/+XX1cuWvQ8JwcAMLSYZ4Lq5PYe1o47iXqhlROTkv0MF/xhaMmXkgMMt1/PeF2XHGS0c4LuUD02JfulNe68kyiMe1YPdDZXuCxp8pSg3z9Q4HHkvuaGlMDrSQ+u0zLcGgrco12PlycSAFLNoidG2R0Otz2W7nMt3l4hSlc23HJn7eMbNQRxuu1CiqfMhJ+YMDS3NOGv37Ix9Llr7+XhHuhO9HF1vevs5Ojs7Ojk5ODo6ODoSHB0snNwtLUnWNsTrAmO1s4utm5ERzd3R2cXe1s7SxMTYz09Ax1tAy0Nvduq+uoqRreu31G5ZH7yzIXahhpRvz8Igv4eTMkfF0HXIniZkmepFU5XyZt2LceYRO8ZHPV/PBtfsALy2/VIjTqPG/Qf0/WjXwVl7egG9+z2ytYBMGLlcu8gYhnf9NmNnM9V8maoj5+SBdfJeEDRn7xRcktQkazwpinZ/Qov8pKO414mYNJxHO74lVcpWVuGFi9DcDcgiOHeJSWX3hZUJ4t6loY/Trxv21WVUG2UtdUOn7JV0rBUunVk46Y5X/5r9r//pXoZ39rU6Et0Xzrzh5//8Q/pzVtMVNXW/vHHSZkjNeVUDos12Nf/5EH46vm/H97xl7uDw/K5804clmqqreOw2YN9fbGRUUvnzJbcsjnQjbj0hx9l90q2NTMAhnV1dN61dzy0d394yAMMw4ZS8sTVyenlbVsME+1j6e+fklUOyJ28Jndq9O33eBZez/C1lNtulSG3TE4tpcnfSm6HdbO/tdwO64wzy3A4HG6hUkaQjRzvxiK/43A4HO4vW0awrRzvDnzvnJKPEzI9EmkTMlF8kP6eGqIE3X1TDfEvZoZqU5pJur9qUYRxSYxFbRqhq/IBtzWe3ZwAOvK4jfFtea6d+cTuXM/ewkDQFNaaalhksqLZeml94ClhnNuES8tJVrulc9fuwX2fkMCAsPvefkSiG5HoSiTeIxLvubu7eXl5ePt4eft5e/vd9/a77+PvExAcEPwwJORhSECwv6ePu9NdF1uCo4WVrYmxiZGBib62qc5tM00ls8t4Tf+wQFG/PwiC/h5MyR+RcS5qy0vJ6iYhP6oXfnY9Z5VVad2LwZeBt4+NJFT2GMcxNB43GD5pMCI1GJHoRqRGI1KjEYluRKIbPaFrPqbrxzVHlzCZI1YqN3axxO0o067lzNQoUrcbJyWD8epkvckbeRKCimTHqTUE18mim6WXHycMYBhAAaA+JxN1zKwvKDnd0LS8oiy9YdPKn39Z8fMvJw8dSk9KtNLRXf79j2K/zt6zZi1eWmbV3HnWhobM9jZ6ZWV7U3Nxfv5pKakVixbJyx7789dfbQwMBru725uaBnp7y8vKZPbtWT57jvKZs7/NmGmto8se6OeyBlEEYQ8MtjW3MLu6eZdPmfCLXZhFUE445rx3SvbbesDPK8lv6wE/76SmWwfkbvF1yZZy262abG7IyQU2qR+UU0/lpeQRXbKN3E6blxe4wOEOBTjcXIM7FPDOXbJmQIkwViTzoBwWxXFbk9taquumlkRlUBMMWhNASypoSQMv8gGztLXYeqDhCdqaiTYmgsZ4UPMEq4pCy/0B7V5JwKWYW/NabZdWep4R0ulBEARNLJiSPyLjXNT2ZUqepVb42fVcu6dNfexXqxV72WgpY7Cc0V/bMlDbMlDbOlDbOlDJ6Kts7uP9zHu8nNFf3DgwMiX3s1HnZ4zpSnnfqRe+LiWP7f9yFoMiBVCqNxmDrDKmSN4HiuVFH4v5xtg6WbSz9PLjhGEowLgY2sfsifcJ0tgno7pH2lvf7KrsyS0rVsps235w2zZ7M7ObZ87uXrP2wPbtW1eKnd27f+28+UR7QntLS8bTBHJuPpVcrHhGbvHcOUf/2rFqzhyilXUPg9He2Mjq76+urLwge2zxrFln9u1fMOtHF1OT5rrK9pY6FGVjKMplc7hcLoKgCIJgE32xC96VLm7cJ79XSjaXG463crfGT8l+gYY7bhjuOOjnP25KXnM2eMSKC1s53KGAd0jJTrE1fxk/E8bVLYYmjcXKJuxp95KocJFoS7jJoXojVWFITQxSH4swUjnNiY1ZBhxGKsJIQRrikNoobvVDbnkQp9ANJdskWG/3ufRdh8MfBZ7XJ/zEaAQ8QSjtOQRBnzSYkj8W498d7WVK/lbl+Wy94iJ6z8irvr3o5TR0Drb0sBhMFoPJamayGjtZKBfFMKypg9XcxWL0sBhMVmsPq75zsL3vVUpGMYzS1DfXoOR/t16bksE4dfL/s/feYW1c+f7/5Lv3efbu93e/e+/e7Uk26ySb3Phu1omTbDYhiXs2m8Tdie3EuMRF7riBbYpNMdg0G9F7770PTSCKCkigLookQCBENcVgUJuZ8/tDdESz6T6v5/MQMTozczSeJ37p7TPniKyWolZFkDxDnLwsV2nkdiJwnVY1qHr6RKMefNyqpCekhdy44/DjaY+rt098u3vXps2WFy/t3b59z5Yt323bfunosZM//LDlw4/OHfh+47rXnWxt25XKskIKv7JSVFX1w86dH77zP2cPfvfhq6+6mlt0NjRIBPzO9rZqPv/A9h0frHv92omf3vzNb+5dN+nqlDY28ocGe5rrZdS8QplUhuHYTFNxPwcNHQP/sMp/kNHwzJZ8dafxNf3Te47Gmx2VoY56af7wSHTk1rERF8rIErrxu8g2Z+WYJZdGbTc04uLtK4zbu5FnG3GRWN7+T4fiQlH7glyc6WhKufI49OMGvy+6KWc1Qi9tbQxWn4nJUay1SNdW3Mf31zTl4G0lWHMO1piqk8VpagNVfNchpnWWxduRpN8qnd+WF0TM54RSspH+e4jRDB4sJZPIZBIJfe6Pt6BAd4dAVjvQktcK06+ONmrJ/2nC2RUg7RiYMF/b/fzWP5tzX7fjvWHHfcOOt86a8/odPk8x0NyrftdW9JoV5417XP1br93m3i9oG79v14DuQIj0l9dms2TDcfJpILJc3BJcMRAk806DWve5lzTgnHTmBnl35nXAaUt4E7DWr4irNHI7qZ70t9bUSlnszia5qIzue/Ou1ffHbI+cvnvszI6/vX9gx5dB7u7H9u///S9+8eUnn/g4O188ffrLL76wN7v56YYNh/furaSVCdhsYVVVpH/AxtffOPDVl0HuD//xlzeP7NzJo9G5zHIBuzIuNOxvf/rzgW3b48NC31u37pvP/sFnUdqUslqR6OrZs+++/U5yXKJGo5kwyeCCzp2cwmr+5kGJe65iDcwEd8qPvXir7o3SVpHY7r6BZvMWJ3QPLnykEfjpauOxxgxcUYC3lYDHHPCYBdqLMXmGThanrQ1TCTwwkYs87Uzujd/mmb7S6PRuR2nGfE+KkkjomC+TUJSk12ZUv4WESslGCAlFSUZk6VgzMtloZreey4mR4VM+y94r090hEMjcgZa8Jpg+SAYjlmxqn/ifl7jmGc1PJj63Z5HW9POT9JcuMF66QH/pAv2lc2UvnS2vkPdLu1Q/N2G/RCp76QLtpQv0ly7Qfn6CbpnaPH7ffjV2N7vll5e5N2a2ZGAwTt4ARBaLW+zPDATJteR5lTSAJK0lo4cQBFlHOrQOQYxIhxAjC6vhR/cCyKjFPvLo60MIghih8zoF7wQoRoar/O0pcfKSX6Vxt1OXQikoZVYWlshrJbIqgefNu7cOH/e2sPG6Y7dr05adO7ZnJydZm5r+18/+7eDuXdlJSed+OvnNjh1p0THXL116+89/Pr5/v99DNztLq88+/OjdN//i5+JSJ+KfP370L6+9duK77/zJ7vduWxq9v/F/33rLz+1RY0PD1Yvn33j5j8d2feNPfmRylvSnX/9m35f/klTXyGSy3t6J0zUs6FJ81yM4x73Zq92S7yaI9z8sW/Cp36YiY5cLbr5TbPGH8IuvPCm6ruG4aISBWkk83pipa84eqA5rpzkK40/0ljtj0lgV313Hd3tafi/P4l2a6a8Lb/1RQN4OBp/M96R6S0bJw8Y77MFGZPLYFhJZOtwCnbTxeRi25AmOTiINB9vTWLsRibTI7g6BQJYKaMlrgumDZDBmyfH/dZXrVNg2oJ5gyTYZzf9+loGYMBETJnKegZyiIyfLmY1PZJ1DP7tQiZwoQ87TkUtMxIT58zMMm/QJlvxUg7sVd/zyOn92S56a/1VuHLPDRSrGK1OC5FPztmSLdchGvQevM9qIIIgRyWKftJaMWuyT5u0j6y15+LUVeSNiZGE1j+PXuAD6H4CmDQAANG2A/jvAemeZr9K426m9pY1fxa+ks5gldGZ+cYyrV1pgmLCCzS4usb5pSjr2YxWjNC0ifM/Hn7jcucMrL79zw4x0/AS3glXJLD9rfHTjX95c//rrb7/5l82ffOpmd0/Z2NDVrmRQck8eOrR+3etvv/zK26+++umHHznesRZyeVVVHHpJ8bWfTm586823//TK//7lzaN795cXlvZ0Pi4uKhKLRBMGXSxonNw/pN1+r8gqrm71WnJosWKTNaWhY2ChrskM6NQaAXk3x+qViDP/yXT+q6rcUs1zVQl9dJJYTW1se7FFc+pRWfx3KlGAVuyPCT37i82yLf6WdvZXVZZ/5Ni8XPlgM66Z97BpvSWPOiuJhEzJkscsebQZeQEsmUyWTnZ0AKRkI6OJOm400h+SAU1fcHeHQCBLBbTk1c+MQTIYZ8m/vsF3L+kY/+ge0FsyiYFcoP+PA9eF0uKSo7yf29rcq+4d0j0qbHdEleQS5d/dBcgF+s/PMCdZ8pAWD6Z3zjRf8ngWNP+bN/ogucZtMcuSvBFBkE/Rue9StR9ILo51UnIRMNbN8IVnKRh3O6mfDnU/7lbU1tfQKxnpKPnqbZ8bd0pjU3mFVF5ZIZuS1SBkN0l4tezSJiG7rYYnoOaxC3NquKw2uUxayURD/dKCfDIjw6qoBa11gu7m2hYxu03EaeBVJvj4Bdo6BDs6FqenywTiQK+AS+cuJcbEdjZKCxPDYsj2aJivQsBpEosk/KrcjBRqXs5g/0QFXNDbid/U+/ndApcs+Wq05ARm2x7n0jS2YqGuxqwoi4NqbddV2b+abfarMtcP1GxLndhDIw7Q1UThDclqga+G74lLQnWCR9L4E7GXX8469/9Kbvy26u4rdXZ/qHAwGhx4umRdfV70bislk9HJjk6asmXUkqWL7e4QCGSpgJa8+pkxSAbjLPm/b/DdDFhy07+T6AiJ9qV7jcF/rtVixIlIGUKiTbXkQQ3uT+v8lekcsmSwwPnfvOF/DXg/gZpHK6vovweqxrFOqhoB/beTRycvMeNupzZFC4NGK0VRCZPZwOcEWd278vd/Xvv4q6Cbd+pYzHpOeVV+eh2nsEPCFMX50V3Mmf621Sn+wuQAZqhrifvdcrJVXYR7TbRPZbBrkevtKn8HXrCzKD64RcAoy0igJ8WXpyaVpaVUFBR6OLkeOfC970OXZl65rDhZQUuVlybJylKk5VlNQlpBWkxOatxAb8+Efi707eSdJ9nlXLYaLflSCPdySOUCXopZ6ZPxxHZvSx++yrF/OcPkl8X31rfnXtAIHmlrfDTVPppqL5XgUSfFpDZoa/rlX2Vd+H8F1/+79PZvmVa/Tzv/XwzLz1X9S5F5QyAQyPMDLXn1M1uoNvz03v34X13j3c9vNTDigsRASLRNbtVPNbqpu2sx4nCAFCEZyJIHNLhLUft/XufP8vTecOtZMu/FhbUeCG8svxZPqsrtE8YENz8E7E2A9ptlu0pgwu30tK+/qUHKKcgSU7LqeRVx7l62h05Z7PzB77Y1r6y0gcutLSyqys3gZMczHaxLrpJK7a+VOZmXOtwqvmfKdLhJu3W+zPIK0+VOpZdDmd11tuNthp0ZzctVxKDkZSVnxcTkRMdSkhI4pSXBHl6nfvgxITioqaqiJjupOj1OlpfESQyQFsfLKnPR+CBKZqJGNTihn4twOx31Yl4I5q0uS36UJfvSvqh/SDv7x1tACFwY9F2d4x9qnF4tt/5Dtsl/5pv9pjH6X+3ZP3aih5Xpe5oSdwjDNnVkHi66uS75zP+XafarUtPfMc1+mW7+qTwjAqgML0+9KD0dAQcYRgACJwhco8XVGI4DjMAJDYHrCB2BYwAjNDihJTBiwtgeCATyYgMtefUzW6imt+Sb9+P/+wr3coq8d2iCCttkNP87iYmQ6JvdxNNacqBhS+5TYTcymn95jX9rLpY8W+a9uHSlAtaHoObhrCU1X4cgn6KTNuaZz2XfiWVO3oiQAmZsI7YBjJeHhwXjKsB42cAy0Ut/oUZuJxzDVU97RAXJ+d4PKrISmNloaXJmtKuXyw3z+MDgokw0Lzk9OT4uLjw42sUxwu5OvLtTUWwYD03mpEczogKYUSECNFXBYciYRayUaHp0oDgrWVZCLcvLSYpLSI9Jzk5IpRUU1AkEni6PXO0dKKlp4pLS4oiYgoDwotBIalR4Vx23WViOxoejiXFtTRPuvcW4nbr61ZusKdZJkqmW7FPQYp0osUmU2CRKbBMltokSu/GVJLk3UvZJEvtkicP4SpHcT5HcT5E8GClHfaVKHFMlTmlj5ZwuHS2XceWaLo0saZlqyRElii+sKWxZ98Jeh7kgLgyUeaxr8FjHtHk55fx/FFque5xxuC1lV2vyjvbM3T2UkyrmbQ3Lti31OP2hUYXn+op7b2Ravfrw6gc17bJ+9ZKOuCAIggAABwQgtIDQ4gRGEBoAMAAInNDihAbHcQInCKAlCB2BQ0uGQCBjQEteE8wYJ+st+db9+N+Z8j73qFX0Tnh05nksua1Pu81H8qtbvFtus1ny1OSv9qdFfy6Nt23CGVnrgfA6qHGduaTm6xDkE9JB/bQVt8kHd6Pmu0nmu8kHESPz2+jwdtdJLxAEQQ6eRg+OPuSHkAJcpQGnpbOdDlR9Ndbhyq1j0qxHcm7Rr5Lk3HS3k2poaPBJp5yWUeRlR43wT3Lz8DG57Xj8oqvJreSI6PTU1KigoEhPj2hPr0CyW7ivd2ZCAqO0tIySmxETmhzsn+DrlxEZRU1LL0xMygoJyQ4IzA8PK81ILUxPy4tNYKRk1bOrGsTiChrj5tWbe77e7X7fRcbj1rHZNWUsWnpuJaWokc+p41QWZmaU5uS1y1tmup0WiEJR+2abQs88xSRLfpjV+OHt3KNezCWuY17MY17MT63yXdOlUy35oBvDO0+yGNdhdgggDfiGde13OWavsT0+7sw6q6mwU7PuqapsVZU26sp7Go6Llu+BiTxxnqOKcwswHBT5Jz8J+s2rma99TjFKbUpa0s4SOE5oMRzDCQIHBABA2t0nausBAOAAV+NajMAJXAcIHUEAgli6qBsCgaxwoCWvCWaMk0csOfG1m/xfW/Hya3u02NhfA9YjlrxpBksOkiJnJ1uyDieKZU9+Z8X/4y3Bbbf0WSx5UvI3wAXlb4L6wMWtyn9MnliN9SGocZm5hi3ZfLc0bzc5wAUEfGJkfgs130023y2tcRmRYxd0pAFqvluadwrNu0U2P4UeRMbvKzVfRwqY5XQTqnLb5KnrKt8HHbFjRtsRO8ud8AxM/Yo1cjv1dHS21goFWTGsGN/6iuI034Ar23bd3WdcFJUgZleKBFxJZQU3LZURFlESHiFAUTm7srGmpjAzE42KTg8ITvH0S/D0yQgOKQiLTHB1S3hETvX1SwgMCPfySvILyAsKq86hyKq4KdHRx/b/8P3uwy72TuXUHEZWAj05MT8+rjQ9lZmRWpqVSUlLo+fkDHaNS0wX898lrBOFP3pUTLXk7feKFumMs3LMiznVkm9FLeJK1LPCaWSWBu5SxB3qLTqPcax1vEdavq+WH6gVB+hEfhqhl0borhF6agXeOrEfkMR3VdndTnrrz9Rfv1L669fo//VhxVuKgaal6SpBEDjA1QSOYQQAQAdAGLNm/YVHb5x08aVwnwAAAAG0OI4BnMAIAoeWDIFARoGWvFaYPk4eseSktywFP7taeSG+sXdwbGjy8ExwZ+mb3Kqfqg1b8o9BUuQc/ednmLbjLLlvCDNNbvq3q5XrLEWzWPLU5E/wDRBeALLAxa2au5OXfWatB8KroMZ5JZb4zuTVPUR7QWcCKF83NlVc+brJs7Y9Pwa/YlW+Dwa4g929DZzKisxYMTWjnkWLdnS1PX7W5qezIfccaxjlNVVVsSGBGTHh2ZHhAQ72lfk55fm52cmJmQnxQgaDnpFNS8usoBRUFhdVFuQXxMZSU1MrCgoKUlKdre/GhYSUZ+fSE9NqmeVZ8bFmFy8fPXQ8iExuYVIkicHKvERxShiPElNfnCKOC6lNiqqKDm4XjDygtsgD3NU6/FvHkivhgpVsye5o/WYbirJnaOk709PffbH86HvZf7qR+Vddgz2Q+oGaEEwUjgnDtFz/viLT7mLT1oyDnZk/9KFnAMMBSDzZrHOHCtb9uvq1N8Tr/sb443raK3+k/dJb4r3YXR0blEwQOIEDAJr71ef9sn+x3w7Z54gccP63vXYHXeL47X0AAABwAtcROK7fZXT3xe4kBAJZyUBLXitMHyePrr23zkLw76aVv7fgFkv7dPjw//1NU+S/OEr72bGyvzuKBtQGngHSYMROT8n/OVL2C2P6zSS5fiOGE4zG/lfv8H9+g/2qhfDWoxkteWqQzHwTyAKWotgfT4mTPwDVTiuxKrcaCJIVbkB2dWyj7CpQuM3hbpgn08TJPW2d1Iz0/JSIuqri4phonyu3C2KT0NhEsplFSXwyr7iU7OwYGxOREBV5cOe3JqQzZpcvXTl9+s61az6uLn6urpZXr1w9RzK9fOnW5UvWN65bXr1idu6cranZj3t3nz910tXB3sXGuigj86G9nbOTg6eHR1lqcqnvA4abRd7NE+XupiVOF3NNjxTeOlFofrrsoQUvNsDw7bQINHQMfGKV75zZsDItOZ7RtgQrUU/HQ5HjG1W/fbn8129Sf3+Z8r/Z7ENtHHsgCwE1caDSR5Nr0pd2HGPagcqHKqZledFhF+oHX5S/8m7jX99XfPC/0vXvCl79H9afXqH/+p3UxZrLZViL9WA4jmMAAAKA5ErJ+6b+yO4HyCH3nx0hv2TsiRz2Qnbb//n0I+ccdhdOAAAIHcAwDB8BWjIE8oIDLXkNMU2crLdkm0fJX7hJ3ncRv2LDuxgj6RkYHp0cznj8nY9kr0+dVXqzSoNN3V2H4045bXu86r7zkkQyHus39g5oriRIX7Xhvu8s/tRNaueXNa0lTxskL4kl19wxECcLriy/E08qkZWBILkrFZSvmzxVXPm62e+E+TJNnKx7XNEhlzXWMOhZkbmB3oG37lifOmdx6mzwfedaRgWXWuLp7Bzo5RXi6f3jtzvJ9g4ejk4/7tlz19T08plT508ev3X9ytmTJ86d+unyWVJKfNwDa+sT3x88snuP8YH9Lo4Otncsz544Fu7vb3XjmqvT/ePGR6MePayI8GYHOhZakirdzdjut0rsTBie5mW+VrX5UaKMKACWbqaU8JKGbxxLvPIVK9CSf/JhWcTyl6UnQyrVZ5SPXhP97g3xq29K170tW/dR+W//mfa7G2V/92B9E1u+k8LYm8v6Po21N6Bilxnto3/S/vSebP0Xyk/3tmz+rnnz143vf1L/xl9q173Gf/mVuD8uUicxAsMITEfgGIHrI+SWfvWNSMp/HLRH9jkhxh4vHfVEjnohR32RYx4vHfFE9rm9tMt+1/2o0oZWAgAACBzD9aZMwCf5IJAXG2jJa4hp4mS9JfuEZN3I6CAlKE5F1+/3EYSXNA7ox1eMz0pmyU0IfYNBtS62rPGAL/9UlOxsYvPVjA7/6LxpLdlwkOy/dGU4Tnacb0nNd6LVjlLzPyMIQvJ3lPr/JB3+9WOy+U50/gecUAaDZDAlPJ4ULS8g08TJQ/3tjxtZFRnB7Oy4oriYR6a3Hpqal6K5LfX1Uj4vOznxzk2zB1aWQQ8f5SQkJkRGON6zSUuMDwvwDQ3wQ9MFO99RAAAgAElEQVRT7a2sfMnu8VFR5aWlkX4BFpeu2N686Xrf/q7FLYe7d+wtzElHja+TzgSSPWws7QLc3Dl5GTVoiiwlqsTlbm1+QjONWp2e3EijdMtEnXUiAJZ0ppRLwZUnfNgrzZKt4sQ7nUqWYCVqg6i1qm/Zm1+t+f3bsvUfNrz7sfxvf1e8+17z+v+Vvvm32nUfCV7/kvPWNt7//FP87heyTzY3b93TsXNf664Dyp0HW3f90Lpnr+LLLY3/+ED+wWuy13YUbV+kTmoJgiA0GKbVX6MsUeMnZgHIPnvkEPn/HPH+P0fIiLEPYuzz0hEf5KgHcsQd+dEf+cEP2W33ytH7Npn0boIAAOgIXIdjOL60U+xBIJAVBrTktYWhOFlvye5BWeeTOo5EK4yjmr4Plu5y5ybQmwZU2nn9iyIBwFO1Lo3dtNej6kCg5Eik3DhaQUpq94rINWzJhoPk80tqyTVWhuJkE1D9YF6Fmu+UVj/QazFa/UDqf2Ls19wTaO78jjahRBaGg2QwcSwyrhobo7zgTBMnD7XmtNWU8HLC8kPdvO/cRqNj0bgEtzvWyYFBpenp/LJSaloqJSGeVVBATU/NjAynpCRWFVGriqjV7EoOi01Bc9oaGjukNRJacXURhZef38CplNeImcVUWgEl3NPr+rmzXo4PIr39Xe+7uj5wigz1Ky/MrinO5+VkoWTPrPvumU6elMDIVrG0q0m5xFNu9w9pN9lQ7ibUrRxLDips3mRNqVU+Wa6eDA4N7Snb9o+mv22SbttT/9WhxoPHFKdJStL59nNnO86e7Txr0nbhSutF89Ybd9ssrNvv2HXeudV+7VzLqZ9ajp9U/HRCYXxAvvPbpm83NG8I7wxbpE7qcEDgOoBrB7T4vYTi/z5sh+y9hxzx+JmxJ3LE5yVjz5eMvRBj35eO+CDGfoixF2LsjhxzR466IwedkP13v7kfwW3pxgHQ4TqAQUuGQF5ooCWvLQy5jt6SH/pnnIjp2BfWsjekeV+I/Gufuu0uHDIq7eob0mFzMmUdRnT3D3nnNfzrIedr77p9IU17Q5r3hbYYx3S4hU2TJRsOkv2WugzEyRtB9f15FWr+rbT6vtT/hLT6Pqi+LzX/M7LxW9T8zwjyd9T/W3Lu/I42oSq3GA6S9TTcHpvjouH2nG6DZ8NQnEwIdnY2CcSlmTn+nl6mt7PDYtPDYhwu3Xhw4cq9M+ddrt1wOH/Z7vRZn7vWHrduO5w8Tb56xeP6DU8zS0pSegm1NDUtvae9pYFZIEyPkOYkc9PiGakJfFopo6CQV0qrQHNKMtLFLGZWfLSLjWV5UX5WdGhZYnRFHtpSJ6mjM0XZ2ZXp6azcglZpU39H79JPuc2WdX9+t8AtW74SLDmW3rbbqTS8pGG5urFawHENgesAAFlVEmTHVeQbB2S/K7L3AbL3PnLAGdnngux3Qva7IHudkP0uyD4nZJ8dsv8+ss8R2e+I7HNBtpu9ecSqXNYCAAF0Bh5ohkAgLw7QktccU1xHb8kufhk/RLR/HaT4OqDpmwD5NwHy7V7Sj+9zSSFCiqC9X6XRYThuKFgmCEKH4QMqTZGw43yY8OMHnG2ekm8C5F8HyL8OkH8TqPg+vP1RoKEs2WCQLDgHpH5LXdWWhuLkS6DaYR7l/y051/BbqPm30nkdanyJbk8bJC8xhr5iEZXvq7poT/t7HyuUqV4ht/cctTxwIs0rWEill2ZkFWWmFiTH0zNSa5g0dnZGfnAwPTk5JzTMz8Y+Jz4lP7cgE83o7GxsEDEUIoZSXFlbwcxJSEgIDU0ODxdQqaVxUbS0OGpaTHKwR0l6bCO/vCAmrCojg12QL6hkoelJ2SmxaFJMcnxMenpGU03msqzdqF+5eiVY8sUlX4l6lYIDFQ60BCAqm7tOuiV875r4nUfO924ZB8kpBzyzvidnfO+ecdA9+3ty8gGPrO/c0YPk5O/c8w6TMw+R0w+6537/MOGocyRH3gYIDGALOLJFSjZCjMjS2TYabAaBQJYHaMlrjimuo7dkR9+MA6Ht2/0U23ybtvvKt/s2bveTb/Wu/9BJ9LFTlUmMOIutlLX1D6g0QxqdSouptNiQBnuq1jS2D6C81ivx1Z84V33gKNriJdvmK9/uK9/uK9/mK9/h37wnuN3V35AlGwySpb6zljToknTmBhTbuRxnQhmOk+2Xvyo3zxQkLzGG4uR+5raK0iJWSVFVTh4tJKwqOlZRUtZRwWouK20oKmwuKW2iM7rqJa2NElZhAS03tyA1LTsuUczh0UtKczJTHyvrRazSwtTE/KQkSkZ2JaOivrqaV1YsphYUR4UUJ4VLOcVyXlljJVXKLMiJDqkXCQQsppBNTwzyTfD1zI6JyElLKS+vUFV+vSxfHtQ6/BCZftyHvbyWfCmEs9mG0tWvnr31CgElL5fpEQRBEBiBa3QAEAAoceJ8MBpd1VTR9cTYPaGieyiCWWsSnCvFwZ2YIi8KjzegOeaRQFU8TuLXX/RNbdHiBAA6QKgxbEGHf0NLhkBWH9CS1yITXWfYkv2y9gS3bfZTbPZp2uzTtMVbvsVbvsVHvsVb/pm79H1H4YcOnK8fcUnh4nuZta5o/UO03j6z7kKE6Fs37kcOnPce8I3cJcO7+Mg3j9QW3+ZvQzqcg6aMuDAcJJ8FUp9ZSxp0USr1QQ8jCPIG6fAbCPI56TBiZGmDHkYQBCEF+aCW35NHXx9GEORzdNbDVlsYipMvLrMir5wgWY/BOJm9QcGP59KK+MX5FQlRFD+PQl8vipdXsa9/kbdv+n3nXN/Aer6gXlaflpTi7+3j5vIwPia2WiBkltFy09Lq+TxGTm56eGSkp09BalY1X1RJK6th0yUsWh2jVMqmt8qECjFHXEzhF6AMNEPRIONXlNRXlUlL84XZaaKSwv7ODqJ/Ob88KHuGPrHKX15L3mCGLstK1NOBkhAEQYzIUpSEIAgJnfwuCZ3ekg3usoAMz3NM6DAcBwCIuvp+uefmhZD84PIaZPvZ6CrZBd/MPx62KlL2/fWM4177sAR+A/LleXJB+fWovP/Yc53X0QMAwHBcjYPRGTMhEMiLCbTktchE19FoNEql8lFg2u4gxRZ/5WZfxRZfxRYfxWYfxRYfxRb9r75Nn3s1/uORdKNL9XsPRBvsBe/ZC997INroWvPxI8kXXo1bfJu3+DSPtt88+tO/9eugZvfw9La2tgmWbCBIfgNIfOZSUss3kI3fkw8hCPKG0UYEQT4nWX4vlfiglt9LC74n6y15+LUNeSNiZGkzpyOz/z4lTn4fVN9bzlpRQbIeQ3GyqvJfjQ2yOhGPTy8Vs8rpRYUJMTFpSUn56al5CXH5iUmCSk7P416loo3D4RVSiwVCYVdHB4vOyMnIErC5FTRmJb28nFrSJJE+bm9n0UpqKulCRrFcKKjlVtXyqp60t7XVSWrK6NXFtNoyegu3oolTUkNDZSxqt7JRq1YRwmX98gBAGlvxTwfqcp39hE+5W3bdcp3dMCgJ0SeeKJksBVIyGQWjr41IKEpCjPSvEQRBEBJ5+MXYLosHTuj0U1rqf4o6+/903OVyLM2vSv6L763DeY3nwqivnSPnKHvfveGz3y0hga/4v/tsPAsqb8YW/9cRB05HDwCAwDEC08Kn9yCQFxxoyWuUca6j1Wrb29szMrOPOqZ+5SvZ4du43ad+u/e48qnf7tOgrx3e9Tt8xmq799hbY41HdvzSV/6Vd81xp4Sc3LyOjg7d6JMuhoNkEpB4L3TZkDciCPI5OpfGYnMDcTL/AhDbLU8Jby5UkIxhmFqtHr3+OI5jGKbT6XQ63byXRTAUJ2uZf81Ndk6MjY8ODC1EC5oamhpr60RMOqcQ5RZl84tzRfRiZa1ooKvzyZMnNdU10jpJp7KNVUYvysqt44ma6+XNTc1CoWBgoJ8gCBzHBweedHd0aNVqQBBAv6W/v1XeRKdQy9Dcrqamvo62/scdmsEBAAAY4Cz/lwcAfPIly3VqMlq3XFO/zYjehydb8rADj38NgJRMGvPjRbZkDBAAEAAH+ntf1PP0LbPIq5l8f1HH7y4Gh9W2XUiuesc8Lrvj6Uf2CQeDC2Lr2n9zxsudVn0rk/2HywGVXf0AABzDMAInYJYMgbzYQEteo4xzHRzHe3p6BAJBWFjo9bsuJ645/GhiZ/zc9aOJ3U/XHUxtHkZFRIhEor6+Pnx0Bn7DQfKCK/L8a2qcXPE+ENsuT7E3zRwk4wSh1Wq1Ou140zW4Hlhra1tJSYlYLFKpVGq1ur29o6m5Wd7U2FBf39vbq9FoBgcHnz59qlKpMMzAwjGTMRQnY/xdqiGVakg9pNZqcVw1ONAiEdWUF1UzKdWMQm5xHqcot6lG1N6i5FVxZLWS7q7HgipeRQmtjivo7+7DMEyj02L4tGfX27Nao9FoNBg20U2WdxTKCmAFKrJ+xAVCQodDZTJpNCqWToiNSSNZ8nhLHsmhFwf9yiCAGJ7fnf9EvcGZcqtIHlzX+4ZNTkRDz7U86Yeu1Mxu9Rc+1GMJnGh532tWyR5VTeaFdW/aplf2DAIAcAzHCACX3oNAXnCgJa9dRlyHIAi1Wt3W1sbj8QoKCpKSkmJiYqKfm9jY2OTk5MLCQqFQ2NHRodFohu3NYJDMJ4E6r+Uv0e0pcfI7gH8OiG2WuoSmswbJvX19PCG/uqa6t68Xw3QYhj19+rStvb2zq1OrnfAPwWV02snTp6wsLQsLC+kMenJKSmxsbEZmRkJCQl5eLrO8PDM7OzU1taiwSKFoxqdX1WGmmTuZ6OcAQBAAYDiB4zpAaHS6/oH+9sH+jp6OlnqxQCYQ1PAFXFZVU6McwzCVWq1WqTCVmtDhBE7oMGySJQ+PHwWAIAgMwzAcJwDAAKHFMR2O4wDggCBWwigUyOqCAATAR8Nk7oDu4xDBbdbj4KbB9b7CMMXADXr7Z2Hi1CfYjljRmZymyFbN/3iUe4o7LZntG7zL2U9UAAAMx7GR+xMCgbywQEteu0yMk9VqdW9vb0tLi0wmq10IWnmPxubxLUaI9pjh8xoMkus8ZyipxeukQE9Qd5608YB0xpYzljV5I0IKnK0Z66MpcfJ7QGy91MX+YpYgGSc4XK79fQdfXx9KUWGdpLautiYnJ8fLxzskIlwgEqrVapVKrVKptDotg0F3cnZ2dnYxvWF669Ytl0cPHz18lJaW6RcQcOnSxVu3brt5ePgFBNrY2OWiOU+fDmi1WnzmpXenWYqPIHQEgeMEIAgcEFoCqHGgIYAOAAwQOMCJgb4n8kb5467HBAD46GKOBCBwgsBndw4cAB0gtIDQAQLTjyt94YNkyLwhAAFwHAxHwbyn+Cex0tvc3sBW1V8jZGGtg6ZVPZuS6lMG8K/TG88Xd0R26N4Lq/aS9NzldX8cJWb1awAAOEFgetmGQCAvMNCS1zTjXEcf12m1WrVarXp+BnsJ5p/H1oEbXSLOcJB8ZhZLDjxPtjiAWhwgB55HLV5HkM9IhxAEQUiBntKxX/U/XzfaiAxz6Dw6bgsp0FMaeH52yRbdMhQnk4D47tKV8PpcRiTXy+r9/Pzs7e1tbWzv3rEyuXr57LlzZjdv3bawjImJrarklpWWFRcXS2V1jfUNNeLaKi43NDzMz88vJCw4KiYmMwuNCo/28fIODQ1JT8/IRtGAoKCk5BRmeTlfwO/u7sZwHMNwnU6n02GTB3JMFycPsAmgj9gIHOA4wIbllwAEAXCcwHBcf6iReQYIAgBiJDae1ZIJAHBAjBTAYZAMeQbGLBkAALiDmFF0rWVVb3CL+m+h9ZFK1U127xcJDen94Ot0xdnizoh23fuhdZ6Sfkt+3ydRUvaABoxkyWAOA5QgEMgaBlrymsag6ywICjcguzphi+wqULhNEyR7zFzSwHPSwM+MLO5KAw+QNiII8hnJ4oA0/wA58C5547AHG1ncRQ+NvnUOzb9Ltjg3bssBcqCH1OJ1UuAs5wJ1HtPEyXeWrtifTxckYxjW39//+PHj7p5udiXrEdnVx9snKirG09Pb5p6d1d279+7ZBwcHU6mFPB6PzWLV1tY2tyiUbS00Bi0+Pp5SWBCXEJ+UlCKXy1taWuSNTQpli6yxvq6upramtk5WzyxnhodHhIdHlpczFS0tbW0dtTUSiUTW3t4+pFJNsNhp4uSFuoPmBAySIfOHIAAgxmXJA1qjMIElq9tPMfTXwJowxeCNih6jWFnmE+KfKY0/UdvC23R/CxSRa/vMeN0fhtewnqgBADiG4zi0ZAjkRQda8lpnqussCOXrgKpxwhZVI2C+OiVI/hrwT8+urbOU3pU/Q5/3OCM1NU6ueAfwzgDRnaUowbUZguT6hgZLS4ttW7/46l87dmzfetXEhMPhKNtaq6try0pp9DJaS7OCx+MmxMUK+dwmeUN7a1t7e4eipbW4pCwmOjopKeG+o2NwSGhmdpatg63LQ5egoGAfP19PLw8HB/voqKi05FRvX5+o6ChfP9+MrExpvVQgEmbnorl5uW1tbTg+HAUDMG2cvCi3k0FgkAx5JvCRAT4AxwEAcjV+LJHrL+zI6RraG80q6Bry4rUezRBw1eASKnYobyrtww5Elac3P/Gt6/whgVOn1qsxjmMY0MERFxDICw205LXOIuV/+uR4PAo3wP7fZwiSl6cMxskiq6Wo6YNkAEBrW1twaOie3XvfWPeXHw/9GBsVx+fwuRxuk1wub2iQNzYODg6y2KzY+HilUpmXl+cfECCVySoqKkKCQug0+pPeJ9RCqn+An7enh/N9x0cPH3l5e6akppaVlURFR0bHxGRmZaUmJ6elpLp7eIZHRNLK6MXF1LKSUiaT2dHRMdA/0NqqbO/s0Gg1ACx3nAyDZMgzgeEYThAAI4YwokNLNGhBcZ+qQqUTa/H83iGejqh6qqH0qcQYUTKgYg5qxToi98kgX4NXqTBq31CdjmjQEe1aXIXjxMzD9yEQyFoHWvILwGLkf6MDkfXgKsD8E2D/dUIbwdeAdxrUuq/EEt4ErPUTelvxDuCdBiLLxS3BlemCZP3gXf22/IKCo8eOF1KLe3p6BEKhvKmpQVbP43LlTXIOh1NTUyuTNoiF1RfOX/zX119nZGUGBAV9semLGzeuSiV1sjrJQxdnZxcnvlAkFIgzM7K8vL1TUlKbFS1d3d19T/rVKo28Xh4eFu7l6UnJz29tbe1+3FVcRKUWFVexOeERYXGJ8W3t7QAsa5wMg2TIs4LrdABXA4BX96pvZFaeSudeQMUXskTnMoWXsmvOZQrPZ4kvZdeczeRfyBFdyBZcyOBdyBGey+RfzhRcyBGQMnmn0zi2+YJmNQbgkAsI5MUGWvILwCLlfw23x89xASr/ZiBIriWv3DIQJ28AIovFLfZnBoNkDMcHh4aePh1QqVVarS4vL/fHIz/m5RdwuJyr100cHjywtb134qfjgSEBpjdNnZ1dbG3tMtIzTp489fEnH/t4+Tx0dv1g48ZjJ46XlZU9euS2Y8eXn33++QMnp6iYmF17d39q9MnVK1dioqLi4mPDIsOioqOTkxN4XE55BSshKTk0LCw6NppKpRYVUnPy8+qkdSrV0OgjeMsWJ8MgGfKs4ASunyilU6Mrau3OaemjtDwpaOkfLuVAQctAfsuTAuVAvrI/X9mfq+yntPTnKPvzhrc8yVX0Mtv6enU4gcO19yCQFxpoyS8GnE8mGO1iFOeTCWcUfA3YOyY04J2YqqroIQTZuE86tsWKvBEhBczuuFKLfWgtWRpAktaSpRbrSAFkUEsibdxHPoQgG/ehFvvQWS1ZaDY5Tq7cuOhXifHKpCAZa0vEcVyj1iqVbUKxsLKKzRPwUlNTjI8a5+bndXZ2+fr6HTE+uv/AgQ/e33j8+PH7D+7HxsbcuWNZUy0KDA68Y32np6enuKzkzPmzUTExDAbj4KHDJ06eOnTo8KmffvLy8rh162ZhYWF/fz+HyyktKWXQaZVsNp8nbGtr7+7pqa9vEAj4HC6nmFpcVFSYkZ5eXEhls1myelm9vFHe3KRWxhtw4qW/nSCQOYPhOMABjhE40I3ORjjyYnTLrPpL4DiugzPBQSAvNtCSIYvAABcwXwf0P0yYKo7+B1DtAmrcJhaJtPFT0sF9qPk+0kHEyNxSGkCS1rihBxEEWTc26dtBEnoQMTK3HN0RNd8nrXHTN5YGkMjm+1DzfeQAkrSGRNq4T5pHQvPcppxrSrE+XM7AcoCrK383IjKypKSYL+CxqyorKlgVzIpaSV16euZRY+Pw8LAcNNvF0WnLF1/84+OPP/joow1/ey80JDw9Pd3Swlwk4Pt6+96xvNPT00MppJw+fTo2JraAQjlw4MDZcyT/IL+iIkpqajKJdMY/IIBZUVFYVExnlFdWVfH4vHIWMy4uJisrq4pTxeHxOjo6lAqFXN5QV1tNK6Olp6VTKYVSmbSjs12j0SzpE3sQyHODEzqA6TBCpwEaAmgBgeE4huMEjgGC0OI4juMAx7W4fiZDgsAIHOhwHMd1BA4wXAcIHdDiQIMTGozQLPengUAgywm0ZMgiwP8aVHwKJBcnbJRcBFX7Qc2jcWVB3oggyKdowKdG5hboQQQ5eEZqvo4U8Ag9iCDIpyTzvdK8M2ieBdn8jP7d0X1R873SmkdS83XIxr1owBlpwKdG5hbSgL2k4QPuJec9mnguQyW8MTlOXkpEe/sbInJy85KSUhKTksJCwyPCI1JTUiW10tycnAPfHYiLi2uUN+Tk5Hz7zTfffffdlStXPvvMiEotTExMMjEx4XK53j4++7/7Ljcvr7S07MzpMxYW5nQG3dT05tWr151dXKIiI2Nio2+Y3SjIz6PRyqKiotDsrLS0lJCQ0MTExKzMjKyMjPi4uPjYOB6X19raJhSKi6jFLHY5jUHLzsrMzs6iM+g9Pb1E55JPAAeBQCAQyAoAWjJkEWCtB/TfGpgqjv57w7aadwadi9SOrxk9WO/Qsx9EbAtov1meSwSGH4MjxiGVSq2t796zv5eekebt7S0SiXp7ewRcnr+PX1JCIovNDgkNYdBoYeFhIeEh8iZ5WWnZ3r17L5tcFldXk909vvrqq5SU5GJqyf793/3zy3+mpaRRi6jBwSHs8vJqsYjLrXr8uOvJk77CwsKkxOTMzAxKQUFjQ0NsTMzdu3dDw0L8Av1vW1iYmppyKqvYFezIiMgiKrX3SR/Rz4EP0kFWFiiZLJ1jSxSdQyspmbQAB5zbuSAQyCoCWjJkEehKBcw/T3hMDQDQ/BBUbgc1D1dQVW6f3MmlZOJjcO0d7YFBAXcsLVNTUsrKylpaWsvLKzjcqrycXBab3T/wRCqrE4urKQVF+fl5LYrmmpqahsYGDo/DYrPojLKSkhKBUFRbW1NAKUhOSS6g5JeWUKMjImKioouo1CJqcTaaUyeRtLa1UUuKE5KS8gsodDqjisNJTUuPi0/k84QCPj8rOzspMaVFoVRrNBqNRj95MnyQDrL8oCQEQRAjshQAlERC52DJKAlBENIctXWCJU9z8PEHnHTweZ0LAoGsIqAlQxaHircA4w8TpopjvAzE1qDGdaWU2BowXp7wLN3SM27Ib0NDfWRkeHxCfEh4SExMtI2d7WUTE0cnxxvXr182ueLgcP/atSs3zK57+3jHxsbY2lofO2ocEOAvEvFpZWWJ8Qm0Mlpzi4LD5aanpWdnZefm5eUX5JWWlebl58fHJcRER6elpjc2NmnV2jZlG4vN4nG5QoGIyWTyeFVNTfKBgf7u3m5Fi7Kto0OtUY92EH8Cg2TICmDEklGyEQlFSYgRWQqkZDIKhqVWSjZC9KY66tPD20lj7w7vNfZ63GEnW/JoMyOj0cZjBxx5PdIBsoFzkUjjTgSBQFYn0JIhi0NXKmC+PmHWgqqvQI3LCqrKbcsZJI9epZE4uUWhCPQPcHFydXMnX7522dbW1sPdg3TunIWV1aNHbqQzZ01NzcwtzJ1dnP38/O/ctbaxsY2LjmOU0nLR3NT0tEIqNSEu3tvdMyY6OiEhITUtLQvNTkpOptMZXD6fJ+BLpLXN8oZ2pbKxvlHR1FItrk7LTIuPjy+ilnZ2PR4/8GNCD2GQDFkJjOS7w/+dJKnSsfx3WFInWvLwu1IyGZ2kuUbTW/JoMynZaB6WPHouktFYSwgEskqBlgxZNFjrgfAqqHFeiSW+M3l1j+ViJE7W6nSlpaU//vDDuXPnEpOSnJycT546GRkRaWFx9+LFS5kZGS6uD0+ePOXh4ZWZmfXggeN1U1Mmk9H9uLuttUOhUMgkddyqSk5VZRWnKiIqMjgkhFFeno1ml5WVdvf1yGT1tJJSWllZbZ2kpaW1qbmZw+WkpaWnpaUVUgqamxSYztDqCXBpD8gKYVyWPDkJHp8Qk9CxbSgJQYxIJNK4liQUTNDc0cYzWLLRSEgNJh/QiEwe2Z9s4FxkMrRkCGTVAy0Zsmh0pQLWB6DaaSVW5dblD5L1dKUC0V6tVtvV2dXa0trT09PZ2aXRaAYGnnb39HR2dpaXMwVCgbyx0Z3sZn/vnkQi0WjUcrmcySgXVYs5vCo2q1Iiq2cwyytY5Q1yGYvNTk1NjYuLjktIiI2NCQ4OolKpfX19ff39snqpQqGorhZFRUUkJifTaHROVWVZaYmkrlqtGjLQNxgkQyAQCOQFBloyZDFhrQeCK8vvxJNKZLVSgmQ9le/3NBelpqVHRcfy+UKhQMjlcPlcjoBTlZ+X5+Hl4R/on56R7unhERocxGDQCwsLAwODbt66lZaeLhKJUTSnrrZO0dJcwWbR6PQCCiU9LSMyPNzLyzM8PKyYWiKpkw4+HSQIAsMwHMeVypaiIopEKtFqtTqdTqvVYphu8kALAINkCAQCgbzoQEuGLCbDcbLjyqqVEyTr6UrVcr/NzMj08PB0cnJ0cXXx8vEJj4hwcLhvbW177579NdMbtja2pjdM7R0cvH28vTw9zExvHD582CCnufIAACAASURBVMLCMjYuJjDIv7S0tLOzg8/jNTU1dbS1S2ulDfXyGlF1rVjc19eHYdh4CcZxXKPRYJihIRbjgUEyBAKBQF5soCVDFhnWeiAwAdUPVkqJLFZWkAwAAIBgv9ckymCxWHW1NQ0ymUQqa2pqrqmprShnllFLS4pKuFyeUCgUikS1tbX5eXk3rt345ptvbW3tsrKzg0JCg4KDzS3NzW7dzEjP8CR7XL16JSI6Kig02MLSqqiQqtXMf/0wGCRDIBAI5IUHWjJkkelKBayNoPr+SqnKLSsrSNbTlUoI92g0GgzTYRiGYVhPTy+TyYyOjvLz8/Pz8w0MDExJTk3LSA8JDfXy9jYzu3n+4gU7e7uggEBfb59r168/eODo6elJOkuytrV1feTm7uFhbW1jcvUKtYSq1Wrn3R8YJEMgEAjkhQdaMmTxYa0Hgkug2mH5S3R7BQbJw4ybOxkA8HTwqbhaXEYvo1eUl9LKiouLaXQ6i80qLStNSEw0NTU9c+bMZZPLdrZ2ZHf3S5cvWVhY2NvZX7x48cLF88eOHbW0tHJxcTE+ftTDy7O7p2d+PYFBMgQCgUAg0JIhS8FwnGy//FW5eSUGyXomLsWHE7hGo1GpVGq1WqVSDQ0Njb5WtCg8PT33H9j/0NWZTqMVUCgZGen29g53rW3KykqzMjLOX7hgaWnp5OhkYmKSmZmlVqtnOK0BYJAMgUAgEAi0ZMgSwVoPBBeXWZFXcpCsZ2KcPB0YhvX09DQ3N3d3d6tVqsHBwadPn3Z1dXV2dra2tUdERf30009hoWEtLS3t7e1Pnz41MH/FDMAgGQKBQCAQAAC0ZMgS0ZUKWO+D6nvLWSs5SNYzMU5+BjQaTWVVZXZWZlOjHMfxZzkEDJIhEAgEAgEAQEuGLB2s9YB/AYjtlqeEN1d6kKxnbnHydBAEoVKpnj59iul0z7I/DJIhEAgEAhkBWjJkqehKBRXvA7Ht8hR700oPkvU8d5z8XMAgGQKBQCCQEaAlQ5YQ1juAfw6IbZa6hKarI0jW83xx8rMDg2QIBAKBQMYBLRmyhHSlgor3gNh6qYv9xfMFyVKyEWJEls620WCz+bNccTIMkiEQCAQCGQe0ZMjSwnoH8ElAfHfpSnj9uYPkpbVksBxxMgySIRAIBAKZCLRkyNIyHCffWbpif746RiSPZ+njZBgkQ9Y0Xf3qoELpMW/mo+w6Zc/QcncHAoGsDqAlQ5acincA7wwQ3VmKElxbTSOSx7OUcTIMkiFrF1pt5/UIziZryuVQLjm73iyKv82u8LQ/K4fbutxdg0AgKx1oyZAlRx8ni6yWolZjkKxnKeNkGCRD1hz68HjHvaLDZMa9pNo0Vsf4csmQHPeu2GxDgdEyBAKZAWjJkOWg4h3AOw1ElotbgiurNUjWszRxMgySIWuLsfA4hBtarEhldUxXkaUtppEwWoZAINMCLRmyHHSlgooNQGSxuMX+bLUGyXqWJk6GQTJkTdDVrw4slO64V3SIzLiXVDuDHE8tGC1DIBCDQEuGLBOVG0ExsrjFeGUVB8l6OJ8s+lXifLLcHxICeS7mHh7PXDBahkAgk4CWDIFAIJDVx/jw2C6pJqWifaHKOb3uuHc5jJYhEAi0ZAgEAoGsJvTh8RfWlEsh3BBq8wL68fiKKFGYRvBgtAyBvMhAS4ZAIBDIKmDxwmMYLUMgEINAS4ZAIBDIimZ8eBxc1Jxc3r70FV6suAGjZQjkBQNaMgQCgUBWIuPDY9vEmmWR46nllF53DEbLEMiLAbRkCAQCgawsRsPjiyHcoMLmJGb7SqswquJGOIyWIZA1DrRkCAQCgawIJoTHCTXLrsJzKce0kWg5C0bLEMhaA1oyBAKBQJaZSeFxIrN9dVUoVXE9nLcVRssQyNoCWjIEAoFAlofx4bFNQnUio221l2Nq3VEvfbRcC6NlyHKBkhCEhC53L+aOlGz0HP1FSYgRWbqQ/RkDWjIEAoFAlprR8PhCMDeQ0pTAaFtLFVLUPBwt+8FoGTJvUBKCPJ/lzmrJk7QUJSHzF00p2chQP/W9n+1oEzsALRkCWbUUitqvR3AuBlfmcFvVOny5uwOBrGJGw+ODZIZ1QvWy6+xi1wMYLUPmDUpCjEik59HGeVvyMyElGyFGRkaTDBUlISTS7NoKLRkCWd00dAzYJAo321CMPZn3kmsfpNUd96nYbEMxj+Hzm3qXu3cQyOqDjNZtMEM3mKFbbAsPPKRNV+eDquIZbaurTCN4M3yiL+2p+g9uFsVd7j8EyIpH73yTvVH/uz6nnRjVDke6E2LdUUueqMtSshFiREbHdhg50IRmUw44bsN4Gx3t0qTOTNo2bu+pxxs+4vSfzsDukzYbkcljp5umq88OtGQIZAJd/erI0sZvHUu+ul9sGSeKLG1JY3WMVhxNaZ0g3utSuuNekXeeBIZDEMjcaegYYMu6Z60NZmg8vW111Vbbwhxu66wfrVb5ZLn/ECArnRHD1BvtJBce3jBOalHSeEEceT3WYLz/jmthYMTF6AGRcYcx4OujDL8xXomnbprm/AayZEOfTko2mrj7OM8ef4bhXxYiIZ8EtGQIBAAA1Do8h9t62p+1yZpyPqgqkCJPZXXMUKHFiqvhvG12hYfI9DS2on9Iu9yfAAJZI2wwQ+PobaurttoWwu/MkAVgkshOTGTHBNDgGINxTSZZ75hajjdOg5Y8dazGBFM1dL4JOxuRpeO6N0n1x/V7phEXE3Yff+bx3yAm9n6s/QJrMrRkyIsOW9ZtHsPfZEM57l3hkiGZWY6nlmduwyk/9iZryvUITqGofbk/DQSy6oGWDHlhmWCVU34xaMkThi8YsOTRPSccYSZLNuzfUwcxjB5jZO/RfSdq60TmY8mTlX2ql0++GtN09dmBlgx5QVH2DHnnSXbcK/ruEf1OvDimrCWlov2ZK4HZZpdUc8SDudmG4phe3dAxsNyfDwJZrUBLhryoTC+Vc/FIw1nyyPYZH5ebKUsGBlpNOsbk0cjThsHAwM4zfLr5ZsnTdvXZgZYMebHoH9ImMJsOkWnb7AqvhHFDqM3PI8dTK6JEcTNK8NX94m8dSyJLG7v61cv9iSGQVcYGMzSW1rq6CloyZAGYMh/bpJG4M1sySjKcJY/J9yTDHXeqmcYlT+yOQUuePCncxIEVBmPduY0nmTIuedzQ5fFNppxjwUYoQ0uGvCjoJ3TbZE0548/2yKlfWDmeWgEF8vNBVV9YU+AUchDIvICWDHkxMZR/jm6bdsTFuIkhyCTDljxskhOOPXFkwjRzXIwMMjY0ycTUcc4TptgwPMcFMs3QiBlGXY87veGpL0iolGw09jEMdvXZgZYMWeOMTuh2xINpm1gTz2hLLm9fynJIqT3mXb4JTiEHgcwNaMkQyAKz2tbiWzlAS4asTfQTun3jWPKVQ7F5jDCMqkhiti9jRZW2WMWJ9zjDKeQgkFnYYIbGlLWuroKWDFnJQEl+ZqAlQ9YUoxO6fWFNORdY5ZcvX145nlpBhc0modxtdoWH3GhwCjkIZCrQkiGQBQVK8rMDLRmyRhie0M2actS73DGtLpHZvsLLLbv+pC8LTiEHgUxigxkaXdq6pOVi/NZl+vMcAVoyBLImgZYMWd2MTuh24CHNMlYUUdKSwGhbRRVDa7VOqP5RP4VcmhhOIQeBzMmSXYy3uUzcEmu7bb6m+wy7QEuGQF4koCVDViXDE7q50bbaFV4O5QZSmpbdd5+zQoqab0QI/ulQ/A2cQg7yYrPBDI0qbZ2lXIy3ubQ+vGz81rsIgnxkHEs3fhdBEOSty/SoWNu3EARBkG0urVEuxgiCIO/aPtS/QD4yjm0dbaDnrct0/dHG7zjuyLP1pLQ1CloyBLJGgZYMWX2Q0bqPbuduMEP/YZl/4CHNYDmm18Uz2lZseeQ0TNfzz+8WbDBDN5ihd+IFy32lIZBlYIMZGlWinKWcjbc5Kx9e/nCbszIqxmbbZfrwzxKl2a4R/90VqW8WVaKMirEZNmBnpdkuY7OS4Y36XfTNzHZ9aByjjCqJ3LYrcsKRZ+1MiRJaMgSyJoGWDFl9NHQMsGXdMxQZrfvJhxVPb1uxdTGYcydeMPOnqFU+We4rDYEsAxvM0MgS5SzlbLzNWel62dg4RhkZY7PtMn34Z4nSdNeHxjETmkWWKE13GZuO/Dr8ukQ5usvIdv2Okdt2RU448qydgZYMgaxRoCVD1iBpbMUJH9ayL1o7Q10M5vjkS5b7OkEgK5ENZmhEiXKWcjbe6qx0uWx8JEYZEWOz9TI9oiRyK4K8dZkeMRIbv3WZrm+mb6/Pl7c6j77+8EjMyC76ZsM7fngkZtKRZ+sMtGQIZI0CLRmyBlkiS35o/PYVBrRkCGRh2WCGRhQrV1dBS4asZHACwwlsuXuxKoGWDFmD6C159hWz4mzfftfWbeY2rsbbXSfvtd2E/pwrdV2AlgyBTMMGMzS8WLm6CloyZIVDb8jvHGhd7l6sPqAlQ9Ygc7TkW7uNj5kYH4trjaW1upkYv/0ugiAfHYtrjXUdfhxe/3q7a+ut3ca3ho2ZfuxdBEGQt03owwIdZ/u2vrGJ8fB2aMkQyHMALRkCWXAaHtdkiWJYTdQh7dPl7stqAloyZA2Sxlac8GbNtlxW1PbdUSM/W91MPtru0hoTZ7vdhB4TNzwh1HaX1hgX49GfN3cb3ywbaVM2cWNZq5vJR8juqDmu1HUhCFoyBGKYDWZoWLFydRW0ZMgKZ1D7NEsUnSWKRsVx0k4xHIAxR6AlQ9Ygc7Lk4flTEQQxvlnW6mZifDRu2ICHxXecH8eURW3fbaz36eksefiYcxNlaMkQyHRsMEPDqMrVVdCSISuZnsEuqiQjUxiVKYrWu3Jlc8lyd2p1AC0ZsgZJYyuOe7NmXivr5i7jm+MW8Xp02fho7MhaXCMCvc1lbImvm7uQkbW+orYhyFuX6cNvjaxW8NbIogZzWanrPLRkCGQaNpihoVTl6qot0JIhK5Xadn6GMDJDGKWvTGFUpjBK3FY1l32HtC/6crDQkiFrkLlY8nxrzKoXoqAlQyDTAS0ZAlkQBtR9pbKcdEFEuiAiXRCZLogc1eWWvsbp9uoebK+Q54eU21lmfm+aurOlV7aEXV5xQEuGrEHS2Irj3hVzWTFrzkU33mXzcOEOeD6wCloyBGKQDWZoCFW5ugpaMmSlIekQZAoj0wTh6cMVMarLGcLIQc3kkHhIOxBX5XY3+0errEOPiq6EMBxS+cGh5Y7+NKtl6f8KAVoyZA2it+S5rJi1XHUOWjIEMg0bzNCQIuXqKmjJkJUDRmCMhvxUQWiqIDRNEDZS4emC8MK6tExhNCqOm7pXhTz/Qf6ZRG5AlihmfN3PPy3t5C/9p1ghQEuGrEGgJUMgq5cNZmhwkXJ1FbRkyAoBIzB6Q34KPySFH5LKD03lj7lydXsVTmB8ZTlLTp26I6eZ6lp0OWvk8b7Riq30vJ9/Zsk/x0oBWjJkDaK35LmsK7tcBS0ZApkOaMkQyLOBERitIS+ZF5zMC9aLsr5yxHHt/S36Nj2DnTXt3Kn7Sjv59/NPT7XkLFG0M+WCQElf2o+yUoCWDFmDpLEVx7wqnmV1AKeR6eH+auM86a1om62X6Au1BsFZaMkQyDRsMEODipSrq6AlQ5YdDMdo9blJvCC9JY8Wq6lYi2nGt+xX903dXW/JmaLoqRXP8bmff1o38SAvCNCSIWuQ57HkrU7DL966RB+R5g+PRNOP/BVBEGTiRmjJEMg8OOlbscEMnbU22xQGFSpXV+1xoc3lo/3kW77cfwiQtQmGY2X1OYncwERuYBI3KIkXpNfl+sc1czzCsCWPTBU3qdypplRJ0jN0rKO/2YVyntGAPsO+KwFoyZA1iN6Sn2UNLSfjrU7615Fbd0aGRdv8BUEQBNnqpAyLttl6iR5WrJyw8VlX6oKWDHkBUevwNLbioBvtS3vqlTCBV25zYKFybZdvQcv1COFOp9JvHpQkMJv6h7TL/YcAWYNgOFYqQxM4AYlcfQXqS949j79lWnpltjnHRqdVnlRJ3KC7WT/MK04e0g6kCwKss49EscluRVdXqShDS4asQYYt+RnW0HI03uo4/OIvF+nXdxpfH90YZbP1Ij2Mqpyw8VlX6jobAC0Z8uLS0DFgnSjcZE0x9qy4lyxddpddjHJOrz/mzf7CmnI7hs9v6l3uSw5ZywiUFQkc/5EK0OtyY3fdvA7SPdhulXUoQxg5XfmWWZmm7nSjXvGjWeVUR9Lrs+Td0wbVAiXdNueoT6nF6O6rVJShJUPWIGlsxVGvimdZHcBxbFyy07hfNzsqQ6mRmxHkLxfpEzc+Y5GgJUNeeMZHyyZhAs/c5oBC5Wovn4KWazA8hiwhvUNdibyAeI6fvvSuLGqtnO9xugfbrTIP6Rcfma5SeWHh5a5BjPvkYrNHRf8/e28aFtWV7//ue1+cV9fnuUPn3Puc7nO6Y4ZzOt36tJ1O/w2JOZ2hO6cTYzQqGiWCiESNAyqoDCIoMs8FMs+jzAgUUBQzBRQFxTwWMxSTDAJCATXs+2JDuWtk17gL+H2fz2Oqdq299kRqf1isvZaVS9FFx4JzJd3JS7x5cT3TS6NBlTYe9Kvp7Aip1X13oCiDJUN2YTBLJn2iASWAJUMg4mBNy0ce0c9TmM6ZfWH08Z2Ie27/T0EN0HgM0XNKejNT2c9S2c9S2SGp7JDn7NCOCZYa9WxZcrxKPGeHBFbY2OWdjK5/3MZlFHTGPi4yjal3V1R+x4kyWDJkF2bTksmeaEAJYMkQiFTETctfuZTfiGn1LRgOpY8bPgGFI1ZxbUfdq/7pVgGNxxA9hzPTntIUlNIUnNIUjLly42iVelXltoV70K+qasliIhiPPehXKZUPsltilBRLagjYWaMvgyVDdmEwSyZ9CFUlXAZLhkAURNxr+WxgnV1qN+kerAjnzL7zFCY0HkPIyurGSkZLeHITJbmJktIUlNIUlN0Wsy5YU7WeJd68X/mtoCrb3DfTWesK58IL4wv9ujgbOgpYMmQXBrNk0odQVQJYMgSybQqbJy6FMv/mXHolku1TMBxCHzcEAgpHrse0fuVSftqvJoc1tsYXkn2eIHs0NUNFSU2BSU2BmCgnN1FUGtQCC2em9Unxxdh6r5y2OF0TXGWf0Ryki1Ohu4AlQ3ZeiIy6eiWCTfoQqkq4E98OQ6tCIETCnV/1ye/57BH9bECdbUp3SMk4WThlbDYeP0pvH5xeJvvEQPZ0uK+GExsDEhsDEpsCMFcu5+SqWkntIPUp7VIaOxSbwlqnpLFDVR1LzhAClgzZeVnjCwubJ65Fsj51pF8MYXnmDpA+6pNW8M0fsgxn/8259KeguhzWGHRwhEDwETct/xzB9s4fflYyrh/8qdB4DDG45HclJjT6JTT6JzT6JzYGpLCDXq8vqlTD8Fy3K+1yVkuUHhQ5py3Wg351J05zDZYM2cF5ubSWUDX0jVvl313Kr0W1+FOHSR8HSg0oRaM3Y9uOelR9+aQsuLgP5rmFQJREqmlZp378KKPvHDQeQwwvcyvTcY2+8Y2+8Y1+8Y1+CY3+7ZOqjWuxxJt3p11OYQdlt8XogYjaJ6E19jo6GzoNWDJkN2Rwetktp/OzR/STPgyruLZg2hjpw0IRwTqh42xg3WEH2qP0dnj6BwJRKeKmZcsItlf+cHDJuLbwo478Ao3HEANOeX9OXKN3XKNPXKNPXKNvemuYQMhXqQa/8lsx9R7ZrTF6IKs50iH/DH5M5R0UsGTIrkpNz8yD5FZs4FXHtF7Sn4KXi0s256eghk8dS6zi2IXNE2SfMwhkB0fctHwmoO5BcncwbVwTHNP7zgUyjzyiP0qDxmOIgWZ143VCk18syzuW5Y25Mnu8WqrM+EJ/YVeCImKYLpTK+1mt0frBr/x2WV8GKedK84AlQ3ZhllY3clhjJkF1f3MuvRTa5JbbT7oZh9LHvfIGr0Syv3Ip/96rKq1u5OWSyuP1QCAQRSlsnjDfalr2zBsOoo0Tx7dg5JdoaDyG7Iz0TDfHsDxjWZ6xLK9Yllcsy/sVb06qTHyDmyvN0q/8rlwolQ/0pshZrdHuJVd2Yo9kLGDJkN0c7vxqRCnnn24V/+NaeT2mNaBwRP/jRlFoY7fi2r73qvnySZlPfg80UEEgugu+afl+cve2fvwQGo8hOy2FPcnRLPcYlkcMyyOG5VnQnShbxrHgx6TGwMyWKEMgus7dr/yW/k+UVgKWDNkT6eEuPkpvP+xAOxtQdye+Qz+PxtumdJ+jMA870B4kt7L6pX/Xh0Aguou4aflyBNsjb5hCG8fjUzByDRqPITswqxuvo1huUSy3aJZ7NMs9muXRM90sVWZuZcohz1hTuw36DMFyykq80OEUgiCfOahem1PhTztrMhFxwJIheyuFzRNWcez/5UAzCWp4lNGnCzl+mtNv+oz1qWPJpVAm3IAhEBKDb1q+l9xNKR5/mAaNx5AdnAXebNlATnyTb1SDWxTLLbbJS3ayPcZAvk/ZTY0t+ezFPMyMP3PAjPng2SBsofgt4dqCKu1Sm3xJOWMaBiwZsheztLqRVjdy2q/mK5dyywi2e96g5s/Fe+UP/xLT+rVr5T/dKhKqhqDbMQRiOClsnsBmI4LGY8juyOzKVMtErexzeyiKhtbYhzGcM1oiNSLo7MW8yIyWSHubs/Y272JaTAk6ezEvkiJ+S7i2dHaYXd7J1Y2d93spWDJkT2dwetknv+fLJ2Xfe9XcjG3zKxhR9aH4gKKxO/Edp31rP3tEf5TW3sNVbVx3CAQCgUC0mPu5x1ObnmU0R2rEVo+LvwdF2p9Etiz5MwR59z8Pbr1VpUIv+nXmMI3sc6NywJIhEBRFUVb/HDaEHMGHfvDP/VjFsUs7psg+AggEAoHs9fAF637lt9xol2PrvdKbIwyHgIp7mS3BZJ8elQOWDIG8CTb3tXko81PHkgvBrCfZ/VIP/VBo424vBs3Dmv7mXIr96RbmkYZAIBCIQaV7iuVKs3AvuZLICiDdjzGi6z286FfJPjEqBywZApETbO7rf7pVfOVSbhnR4pE37JM/ci269Vt3mEcaAoFAIDsgbVyGY8GPXvRfkhsp6c3h5JLI8ncs+JHsU6JywJIhEGURz3192IH2KA3mkYZAIBDITgpzmPYw/6xPmVUSi5LGDleNwJt22y4hxvOmEOvsb8k+GSoHLBkCgUAgEAhk14YvWC/ry3DIP5PQ4JfGDlNI4BEEOWLHDktjPzE7iHwVqLikWlhnfzs81032yVAtYMkQCAQCgUAguzy+5TcjGC7P2aEKCTQ2tT7yVWDo80Bj00Bj08DQAOsjprmhAdb7EQRBkP2muXKW2GIjYCBHbJXUzA59zg6NqnN7XGgaWmO/g2YYAUuGQCAQCAQC2eUhZMm5j02tr9taGwfk4i35iGku9qncJfsRBHnf+rFyRRYTUu3oVPhTfIPb3MoOGBsKLBkCgUAgEAhklye0xv5ZtWMqO0QhgadNc0MCrPe/b+2cmnvaNDAkwPpT09zNf7c+lV2yH0EQ5OR1ZTXLQKm0tc8zTm8OWuLNk31ilAUsGQKBQCAQCGSXp4KT5VV6QyWXJYCzKTbJCPLpA9VXD6i0ccg/k9MWRva5URiwZIj+srIuWFkXbAhEZO8IBAKBQMiPUCTC7gtCuC3oPpyZVpfii6nsZwZFShPFPu+0wfZUBkuG6DAr68IlnqBz4nVm00xm04xL/rBL/vDluJ7zEZ0AAADAHscsugu7L6Q2TGc2zTAHF+dX+KsbQpBmXYQvWH+Yfza+wTelKdhwSGT53889Tva5URiwZIj2s7IuWOIJSrrm7bIGSP8WBgAAAHYQNun9qQ3TM0sbvA3h3mlkHlsYXNR9D9305iD/CuuUpiDD4Vm1Q2iNva4PXO2AJUO0mZV1AWd69VHuIOnfswAAAMCOxiq1j9H/anVDSPadTbcZWxgo68vN70hu5dbreltzK1P2ecakmzEez9JrjIF8XR+42gFLhmgnvA3hzNKGS/4w6V+sAAAAwK7BJr2fM726si4g+y6n/YwtDJT15eR3JGFQO1PX+Ku63mhojX1wtX1yE8VAsM8zNuQh4cCSIVrIIo8fVskl/csUAAAA2JW45A/PLG2Qfa/TWtb4qzWDxXkdSVJwZjp0vek2LsOVdlmfHhzD9AxlOEkRWeuCfeRY8KOuD1mTgCVDNM38a75Vah/p36E7Ck7uAoouzNiSvye621X9HKNmW8mYmdj8KV4MJv9UAwCgjMtxPYMveev8Hd8BY3JxtKgrLa89URZad6ZQpNtW8yXevO2LH5KaAvVDNNPDiXret/ymFK40C6vMr60yvy7sTNDp8WoYsGSI+hEIRWPza2qNWTHWKFlVY6l+v3BLF1F0LTeDrK97HRpk8BCKoig6NEb2rmrvGEsXpX7wJpo52tgKJ3dBiycKAACdYxbd1TSyxNuxPZWFIkHzeN2L9kS5VHIKJhdHCVblRD2PWSae+7kntp2kI7TGLqjaLrEpQA8kNPq6FJsPz3VrfOZIC1gyRM0IhKLOiddm0V2qfs3ZNq+hEqKzpUH69BWSLVl3jDWi6MTCmvYaRwlrqO5OqVTNEj8tmuzeWKPUzyEAADsBetf8yvrOE+WV9eXS3he5bQmyVHDyJwj7MYqi4wv9zkWmBZ3JUviW3WIO05SsiHW3SGwM0A8e9Kt1Q0UanzkyA5YMUTNj82tqKDL2N245alK6iOqzRXm3WnLpIoouBmfMTGjtZBqeJW+2l2O/Bmiwe4p+FAEAMHhax5Z31iBxK+vLJT1ZuW3xUlA7U0fmVZ5Qo4KT5V9und+RLEU440lCg7uitbDxkmOYHgmN/nogsPJ+LNNVs9NGfsCSIepkkadmX2Sc30ghoTu2zWvowowtrmOGtM3g/wqPa4TefsU3qytQOgU1nxd3ZpCp1rZ5Teym4o+U7olqB4vfzE++WQAAIABJREFUrrKDetOFQE5fApW2IndX5Vy7zdPIyV2Q3D/xPuD9Vf6Jxa+roP1b5mLZNq9tLZHeiryjkLN70qdUvD9veiqjqORvGnKvcvAQii7M2OLWwlYR1w8WDgC6wCy6a2pxXfqb0VCzsr5c0pOZ2xYnBWOwWL1BLaLrH0fVuogHxxCTxn72pMhU0VrpzUG+FXf0o8jB1Q4eJT/zBTvmGikKWDJE5aysC9Qd8U1Zyx9ewrBeGW/cqHQRxQsHvuFZskVwa0WJeuS0qiqyZMU1Bw8p2AHZvd1+T2QsWfE+y5wWxe21uCZk2ZLbbkXB0eF2Vaa9f9MR5Z9SyWstuW7wEPZa0uZLF+X3uiHclqzkGhFqS1byY6bgKm/Z8OZZFcvx5lp6/gsJAOwlrFL7Fnl8bd7bdJOV9WVaT2ZOWxyevPbEvuk2tet0LDiX3hwma8n5HUn3co6vbizLrjK+0P+IahLf6Kdr4lg+XmU3ntXYyt2NHRewZIhqWeMLM5tm1P1eG2tEFfY/lrVkBUIm3VC62cj35jXeYxT0PZVvycpqVnIgcnpab7MnspasqKTU/ivrSiuxtzIKSPTMSB8dflelzo9kDcosWdFzcsR6BkvWLHm2lfzeJfnDtr0ly9lJ/K8Bcq+ydPu6lGcr/YEHAEBDfGmjBj6O8oZgndaTkdMWi6egM/nV6pzadc6tTDnkn5EdRQ7Dk36tjcuQXcuVdims1im+0VenRNY/dSk2L+1N0+CcGVbAkiGqZXlNoE535E1UsmQJPX0jebKdbnECJLOiAj+Ta8lKa5ZEolq5TbxK90TWkhWVJG7JUr441ohK6CPRM7P9ruIb+KUeqlNgyQp7AG/2hdhGlKXHuFDcYq34Gm1vyfJ2En+8cq+ytCVL/wjBMBoAoFsMfBDl+uHS7LYYPPmdSZooMoqizGGaT9lNuQPJ5bUnBlfZZ7YES61S1pfhWfpLXKOPEqKYbl5lN6OYbsqLKeEZw+FJ8cUdPaKFbMCSISpkjS9MZk5p8I2mTBpk2u2UWbLsrmnLkhXVLLd7q/S+Se+w5pZMuMeFjOQFD6EyTbBKzoyio5PUUNxWJLpbyDml0pasqOMBrosw0X7J0j9Rb3ZD4TUiaMnSOyn9CxhYMgAYFnZZAwY73sXwXF9WazSevI5EDRUZRdHMluDgKntFw8klsvy96FelVkljB/pW3FYiuD7ltx7mn81oCbbLO+VWYhlUbauGJT8uMuPMtGp4dIYWsGSICplaXNegIbnzvLKn92T7MMha8mJwxDbKpeW2ZKl6pGxMH5YspX0KOyVv9ZqVjmSPbUL7s/1HW08BynTqVbUtWfrky//ZIGrJyq4RtCUDwG6lc+K1bu98amVp7VVuWxxekXPb4uZWZjSv2ZV2OYkVqMiSc1rj7ucel31sLrc90rfidlyjtxTBNfYPC87mtIWJV2GPllMqre3zTnuUXguusQ9hPJRdSy4uxebdUyzND9CgApYMIZqVdYEvbVTTbzQFzzNJKYiskeBaLgn3zY3oVM2SldSstA+rDi05Y2aC0PBqcjseqLQVRUcnXfPmwTbLnEBl/ZKJdc9V9kglAUtW3s9YO/2SwZIBwOAwwOZkgUhA68nIbInCo5XZp1c3lu/lHH/RnqAEV5qF3DZdTJRjWd4YkfWu7iU/+5bfnF6SM1Tz3MoUvSfVt/ymJ/2KVebX93K+f1xkGlh5X7y6LG4llspHa96JAUuGEM3qulAr32jYX9hlxtiSfdQMlRrjQmq0AYnhC4j1XniDAvFSXLOE50nNb6f7HhcSUTBkh6LfPfCjXijr/azg6GT9e2ssOSkplyudko++SQ+pgbtwynoYE21LVnaN5DQVy3/AUeGPGVgyABgs3AX5f0wjK63ceilFrhuia1jn6sZyeV+GY8G5Z1UOcqcmERPJeGqfd+pFe8TcypRUJVui7OVXfseJep74lB+rG8ucmdaH+Wcj65/GsrzkApYM2dNhDi5q7UtN+nks6b+zS41Ni8rvMLoVNZ5Rk5n0WGrMMtma8b2WJ5o5wUOkPL1HzNWk9nlobPszo/Do5Mir7O85uH1DUVT+eMkSfUIkhweRc7alLxaxfsmKr5HM7inoByLxg6FokGbFZx4sGQDIIKf5peHMMcLjr2a3xWS0RIrJ70ziqTUuMpbppdH4Bje7vJP+5Xefs0NkpyaRJbMlMqjygUPemaBKG6leELntkXZ5p5MbvdUYqa2sL8ODfjWG5SkXV7BkyJ6NdrpbEEbxEGx7CznnQZvz6qmJ4s7lAAAAJGBQnS5axuvSmyPwzCzLezKcQFY3lkNr7B4VnAutccpuiSHix1LE1Lu7035+UmiK71Yh28ZMPE9pl54xHGJYHrK4lfxcwclSu2bDDFgyhFA2BCINn9tTCbDkTaS7UmAP85F7ZmAMYAAADI4lnkEMnMzbWM1qjcYrcu1giVSZ8YV+6+yjoTX2jIH8Jd68oqqml0Y9Siyj69xkJ+1TlZRGimvxpdbxakXbWt1YZo+WR9c/vp97QkkxFEXbuAwX2qVolocsbvQrhV0Jqp4xAw9YMoRQONOr+vy+A0t+g1TnEEXdEvR6aYg8UAgAAKA/qvpekXR7lEjLeF0aOxzPwuqsVJmgSpvoevfIWhffslt2eSddaRb0nlSpR+iaRkofF5kmN1KkJu1Tm4yWCE/6tfyOGPxW5lamGAP5QZU293KOe5VeD2c4JzdSHPN/VD6z9FPapWCGfTTLXQqwZMjejQbz7QEAAACAbgmr5PI2SO50IRAKsltj0thhYhiDxVJl2rgMD/pV/FR8iQ1+/uV3HxWccyz4MbMluHuKlc6meJddz2qJkpq0T3OCqh6E1thxZlqpXfGuNAuHvDO+Zbei692kykjJtFTGF/odqeejWG5SuNJ/VsmSZ19P98204+G+Gp59PS37ewWJAUuGbJ91vjC6ZoL0L0EAAAAAkItH4Qjps1VPLo4+Z4fikRI+vmD9ceGFVHawXIVNZQcHVdm60yxDqh207sdioutcn9IuBVTYJDb4yS2Q1RLlkH9GSVcQFEVDauz8Ku9GNbhFNbgFVt13o//sXGhinf2t3MmxFaX/ZWdBZ4oSGIO0Vm794GzP7OtpoYiciwuWDNk+q+vCoDLohwoAAAAYKI9yB0m35MbRqlR2iJgyTq5UgbK+DL/yO1JzVhsgkYzHUXXOSo50fKHfIf/MkyIzq8yvfctv0HtS1ZiYemxhoKAzmTjVA4X9LzsXleq71gOWDNk+K+sCl/xh0r8EAQAAAEAuVql9pPe4yOtISGU/E8N5KTGNyBJv3iH/TFZzZHZrjOHzpMhMufi2cRkazrTHfTWc35GsBmV9ue0TrOU1mRFddRCwZMj24W0IrVL7SP8SBAAAAABFkHujnH09ldIUjIe3sYIv8KI9wr/iLn7OakMmqu5pUKWNjs/YtHqWLIYxSBtbGNBpZwywZMj2EYr0OgwcAAAAAKgKuTfK9glmSlOQmNK+bKkCqxvLz6ofxNS7k27A2xLf4ONJv6rJsMpEssibrxuiS1HWl5PfkaQSxd3pvdNtG0rH5VA7YMkQQiH96w8AAAAAlEDuXbJqoCC5iSKmb6ZdtgxfsG74ohzf4ONdel3XirxtZl9PDc/1dU421Q3RCbry4Gy31tuVwZIhhKKVr7CLMd0WcT0WcT2kf5kCAAAAhoNFXLdFXI95TPf5iA61KyH3LpnXmZDUFCiGuzgstxhfsE6ptA5jOGe2RBkgcUxv79LrakxeretML3M7J5vKOXl5HUlKoPVkcV/JP/PqBSwZQiiafP1diOo0i+6yzx7Ma5ltGFpsGFpcWOEDAAAAAEbzyHLD0GJh+5wbdcQ8plu9Pn7k3iXxipzUFLgq2SlZKrHMpwYoytH1boapyPgsry22TTQUdacrcWXmcPkaf1UrmwNLhhCK+i0Esd3PysdH53ikfwsDAAAAhs/LpY242smfE1T+qyOJt8hF3nxiY4CYtObQbVeJZT4NqXYk3Yzxivys+oGuFbmNyyjsSpCCOUxToyruq6HawZK89kS5FHWlDbxUeXA62YAlQwhFPUW+ltjbPv6a9O9cAAAAYGfRP82785xjGqVCozKJt8jRhf6ERn8x+V1JRNZKbvSiVD3IaIkkneh61+DqB8onptZKrLOPBlbel8L2xUmpCbqJZ/b1VO1gyYv2RLlUDxRp2KgMlgwhFPUUuX8ampABAAAAdZh4tabSIKQk3iJ7Z1rjG/3EVA1SCa6YzqYEVdllNEeSSEStixf9qh4UeXpp9BH1vOx0IZ70aypN2ieb2ddT1QNFckW5uDtzfuWl2jWDJUMIRVVFtozvgVZkAAAAQBP6p3nXEnsN35L7ZzviG33FMIaLCa6Y2RIcUHEvvTmCRCJrXf3Kb+n0/GBp4zI86ddkhz2mVD6gdsVrXn/fTEd+R4qsKOd3pAzN9alXJ1gyhFBUUuQLUZ3PysdJ/3oFAAAAdjrpjdMXY7oN3JI5sx1xjT5iGMNFBFf0ol+Nrvcg15JTmoLv5x7X6fnBQu9JDay4J2vJMXXuYTUOWtnEGn+1frj0RXuCLD3TrWpUCJYMIRRVG5KJP673cmmjsH0usnrC+cWQGv06AG3xMHsgqGw8q2kGHrUEAMBweLm0YRlv8Jb8siOW5S2mZoioJVtnH01jh6pjt0E37QguJIBjwTm1ewYTT1iNQ0ydm+xQx2nsZ0+KTLW4oaHZ3rz25Ny2BCl6plQWZbBkCKEQl61z4Z3OeUMEv/6aR5YfZA6EVnIL22e7Jl6TfZR7Opzp1crehYS6Keu0/sqeBdJvjQAAABihFVwi4yiT+f35sj2W5SWmZqgQRdGZZe7gnLJhFviCdevsb9Obw+VidxJBEOR9Gxe7kwiCHLGTWP7mrZKFxHEpNufMqNPUqlKeFJmmsZ/JnRPEOvuodjtGz6/MFHWl57bFSzE026tSPWDJEEIhbsnmMd2F7XNEvviy2TOOOYPchTWyDw4ikZV1gS9tNKySS/qtEQAAYGGF3zC0SGQ6KhK/Njkv22NYnmJSW4JjWJ7pbWHLa6+UrDW3MmWfZ5zGDpdP4BEE2W+WG54WeMYsF3uLIAfPBAaeMcsND7Q+YpaLyfGbMtjCQOv9CIIgJ2+++VTRJrZwLb7cPcXS6SniC9ats48qGuHYuejC+EK/dre4xl9lDNJkRXlkXoUNgSVDCIW4JVvE9RCZN6R5ZNkxZ5Dsw4IoTGgFt4jYbzsAAAA6pX+aZx67/ZBw+v+eFAj5k0sjzdya3M6YGJYHniS2/yverPLVtyw5TDFPzA4esQs0NssN23Tfg8aBm2+PmOViBfab5Yal4RbaWRsHvll9v1mukvo38aRfIzJo8erGckqTr3p9M8YX+p2LLiiyZL/y24yBfDWqXd1YTm3yVTSftlAkqBuiS1lyXnvS9BKXYP1gyRBCUaEtObZr2wHgXi5tWKf1zyxtkH1YEIVZWRdYp/VDH2UAAAwBw+yXvLrxms2tTmT7R7Pc8SSy/edWprddXbklYz0ukJM3sEbl9w8iW5Z8BEH2f3XyiFnuZpmvAsPScAu32pJvvPl0O0v2oF9lj5Zvu7c+ZdfDapxciy8R756xxJtnj5anNvk+KTINqryvaBKQcIazY8G5+AZ35jBNkfLKZnpp9GmxeWi1oxvNYlhx55bmMUZuWxyevPbEl68niWwCLBlCKCo9B7bt911W00xCHdH/DSBkpbB9NrJ6gvS7IwAAgGFaMhaBkN89zX7e9iyK5RbFcott8hp7NUBkxSXevO2LH56zQ0ln237Jw3PdT4rMkliBL9oTs1qjXWkWVZwcJeXFZmyXd9K79HpItWNqU7CiuT8w0ppDwxjOPmU3HfLPiI1ZSWflrkmma/GltObQF+2JGc2RbrTLTSOligrLijK1M+XV6ty21wgsGUIo2rXkoLLxyt4Fso8Jsk26Jl47vyD6ICYAAIDuMGRLFmd4vregJ4mgImOxyvyadEV+zg59XGiqpC22dpDqRruc1RqNl1r/irvJjd6yFsscpjkWnCNoxtsas2P+j+V9GbLzZpf2pnuUXMHvUk5rnE/ZjezWMEVHwR5j5LTF4SnoTFlZ32ZGbrBkCKFo15IfZPYPz/LIPibINllZF1jE9ZB+d9QibM+Pkc1YpO2QmnV3Kj7y7CZ9NwCAIDvCktWIK80iqs49lR1CLtbZ3ypqtU1nU/zKrHJa42RdNpzxmFJhjSksX7CO+bFP2U2sfVcrZDRH+pdbP8w/W9AZK95QcqN3YIWN3PIh1Q9Da+zlHotQJKgZLMppi8VTM7jNmH1gyRBC0a4l79Cvsz0YIldzp8D2/Bg57MPG3mZbIOZ5hl+zTs8GWDKwg9itlhzf4EaptE1lPyORqDo3V5qF3N3jC9ZdaZdSm4LkztPxoj0hjun9tNi8pDvZMf/HLT+WX1ITslqjKFUPMFemVFiHM5wVlcxpjX2Yf3aJNy/3cDYE66W92VKi3DfdpuQCgSVDCEWvlswNQVu+QBm/QisQQIdU70PZRuiYHypU2K6/iyw5zwz52LVNZnmbz0dbjcBm2djCbtfDH7t6WiAIsum7csqoV3OeGWKRlm2BLfzIs3th67XCAsSqerNXxEqmmSOIZDHxEnkHCADks1stmdoZ71NuRa4lB1fZRdU5K9rDNi7Dg35VdoYOMc/ZoYEV99LZEUrKaIXMlihK5YO4em8lZSiV94u7kpSc8JX15YLOZLwl57UnKOmgDJYMIRQ9WTJvCG38M9r0JdpxC+31RvsjAB3SF4B2WqPsr9GG36PLzYquO+l3R+2QbfGmufcNeWZiL8y22Oos0e16GMEVxpVp8/lItkOFCjXnmSFbNWfjLPxNDbgCbT4fbcq34qrEq+MWyuyq3JKSbcltPh/JOQQAMCB2pSV3TjI96FeTGwNTmoLJxav0emlvmqL9DKq0ial3lx142NBIZ4c75v+47ewkI/Oc7LYYPGWcXIFIILcwWDKEUPRkyY1/Rjvvov3hgF7pfozW/Q7ly3mecpdbssTCbtfDmGKKXygpo17N+FZn3GsJoxUX2FqLeFWqbFTakuU2hwOAwbD7LHlLkQNSmoIMAZ+yWy/ao+Tu6vhC/9Ni8+yWGNI9WDke9KttXAaRk88aqchujcHTPyP/5wcsGUIo+rDkYRe0+Tu0PwwggZYzKOe63OtO+t1RO8hzWbbnx/g+xGnmWCcHGUuWDBFLVlCzSpa8uZYKVcnfVQKWLH76EFqUAUNll1kyY5DqSrMwHEXGCKiwUSTKGc1BwVUOpHuwEhIafL3oVwmefx5/ldqZktUaLYbamcLjr8qWBEuGEIo+LJn9Mdp5l3xf3Jt0O6P178i97qTfHbWD3OZSlduSNaxZn23JYghZsrhyeKQPMEx2kyUzBqlepb8kN1EMEP8K6zR2oOw+L/HmXYovprPDpEYdNhycC1Wb43psYQBvyVmt0W0TTNliYMkQQtGHJVfvQ3t9UU4IQA4Vcr4Ndo8lYw+oiSWyzecj8zwl/ZJxDca4MprWTMSS8Y8MKu+XLFuV3F1VaMmyY3FsNVSTf7EAQIpdY8mVnByDVWSMgEqb2HoX2T0v78vwL78rNeSwgRBa7ZjS5KPqtSjty8Fbcm5b3NLaK6kyYMkQQtGHJVcgKOeZrqCfNtr6M7SRvZOqq3Ps92PrWkbKfBr5qWXk1r8E6lFp69Sz2GY/peruzIjZ7Za8IDG2w9ZDeG96Kcg04opXxA0coWg4ZGI1E2tL9pQZuUK1qqT2ZJuSZtk7b7BnYA+yOyz5RXukd9n1pKZAAye42k5WlPmCdfu8Uxkt4VIjqRkCjwrOTS+Nqno5Xq3OSTUnN41VS5UBS4YQip4suS9YV5ScMjpzDe0LRvuc/A8hlpGKPlWw7qFTHGzdM9gLHJGfWkZu/bv91lXimuWhU5y+YI79p/4lOjszYvaAJRs8CgaVA7TE2Nyqb37PeUrdQRsqoGvOU+qcMzsGpl9r5drtAksu7U3zLrtJugETJKDSRqrrRX5HTFDVA9KFWC7Rda6hNXZqXJS6IXpmS5SYnLZYqd7JYMkQQtGXJQfpik1PDUL7gtDIT4zsnTj2byMIgiBv+5c4+R9CEARBDp2ivln4Zl2O/duWkfjarlluNrx9Qu0LQiM/sYzc+rfklNFmk/ObYkb218T1c7BiMjUYHXr7zds+iU1vNn7r7syIAUsmH7BkHVLeOfM/rhVWcS3++QM5DdOArvHPH7if2P61a0VR84Tml28XWPL4Qv+jQpPEpoAdgVfp9bK+DPHOc2Zan9IukW7DSvApu1k/tM1EerJZWJ3FW3JmS1TXJBtfACwZQih6suReiq6gnTQ6c3XzdcQnRnaPJF7jP5Uq0Evh2L1tGUGR8xYrFvGJZQQF+5d6ZusP12euSqwlrj/iE8sIOTUgZ67Kbghbi3oGsYygcOw+8afp7ORggCWTD1iyrhiYfn3UoyqmYiy7YRrQJyk13G/cK1tHXml4BXeBJaMoGlnnFFRlm9gYYOCE1z5xpV0S7/bqxrJLsXkKO0hqmGGDIqsl8nGRqRr9LqSakws6k/FjJ4MlQwhFX5YcqCtoJ43OXEF7A9FeR/9Db/vTAjl2b292U7ZzFH8qsVC8bsQniHjdMyepdm9bRmwuN7JzRCM+sYwIxP6lnnnbn7a5FkdcDL/1iE8sI3AfbdWAbU5iFdxb6hkEOXSSo7uTgwGWDOxenDLa7ye1ZzGnAP3zOKP7RkyThldwd1jyEm/eIc84nuWT0Ogvl6h6N7/yOxF1LooKaE5sg5dX6Q3lm3hSZMaZaRXvdkiNXVTtU6kBhg2QeKa3b9mNbWcVkcrM8oRUc/LwXJ/4U7BkCKHsBkveaufF1FNSiK9YIghy5op8S8Y89c26V970l9gS381/t7ayad4y9W8Wk6lBriXj9vkTf7u3dS7KYMnA7uWYZ1Vw0WBm/RSgf5Kqxg870DS8grvDklEUzeuI9i2/ndDohyes1tmr7MYjqoljwY8pTb4uxeZBVQ+kymiFUIbTI+r5vPbIx4UX7PNOe9CvPat5KFWGUvUA38e3tDfdv/wu6QZMkGfVDulsiqoXhd6bndESKaZm8E3PDbBkCKHoyZJ7AgDSAEsGdi8HbagZdVMAWRy0oWp4BXeNJfMF6w/zz0YzPeIb/YKq7T3oV+3zTrvSLlE748TD/fIF65F1Tl5lN+JYPvGNftoioPKeT9n1Jd48tpW5lakKTpZv+c17Od+7l/wcUHkvjuUTx/JxLDg3tzKFlVnizTvkn8lsjpAaC8KQUXXgZBRFh+f68Jac3RazsdUgrSdLploiiCVVP9tSEI6/Edm7sJOjL0v2B0gDLBnYvRy0oabXTgJkAZaMTxuXYZd3+l7O95RKa8ZAvlhJpVLam+ZSbB7NdI9v9NWQOJa3V+kvSY1ecnsjrG4sM4dpUXXO1tnfOlLP5bSFiT+idSf7V9wlXXxV4lm1Y3yDm0pXZEOwnt0ak9EcKWZ0fgD7iJAlUy0RRDPL3daSpRyWaokgRv4cNbbE8Rf/YR2RqEOhJau3La3t8A6Jniy52w8gi1cF/1tVVZXsdSfdbwBAcw7aUNNqJwGyAEuWyvBc9+rGMpFij4tMvcpuRjHd4hp91CCW5eVbcdep8KdKTg6RHcN3R+YL1h/m//i86Rnp4qsSmc0RDvlnxE3mBFM9UJjeHCGmfrgMW07EkqmWiJGlpUYtsapasgabwVXD8Td6465abkvea03T+rJkH4AseDTk3Xff3bdv34cffnjixAlXV9e8vLzvn2p6bwMAQ+CgDfU5YxIgC7BktbPEm8/vjLXLO+VWYhnCcCTuxxH1Lu70K7YvfohrcFNj5AcURZnDNK/S61JPtu0Igqps05uDVDrYoblevCVntUZjywlYMtUSMfLnSFsh9p5qKb/RdutRJ+qbOrA3krrM8TdCjPyp+OZfrCKJYjIV4hbg22+x2iTbc99UpHCHJY8M3xaNO16JfeAo2mF5R8dRVK2CozDQ6MuSvQHSqECCg4P3SeZf/+P9+3YPp+aWSbccANCEgzbUVMYkQBZgyZqHPVruSb/ysOBscI19XKO3EkJrH7kUmz8s+JExkE+kxVpRXGkWcUxv0pVXDZ6zQ+zyTqk02MWGYB1vyenNEQursygRS8YkWcZBMcfbXICzQ6olXgy3Xr8pgBdJXAk5HRjEFSK4auT4On6PZHVza/cld5jjbyRhq1v1Kdg7mX1QtMNK1pdavNPaovVlyZ4kUPydEYJYhuPf/ta/mIw9IZcKhMfjYc3JWItyQ0MD9LgAdgcHbagpNRMAWYAlayvDc932eadjWd5KeEqzqOBkabih7imWG82SdN9VG9/y2/hZUYikqDstvTlcTP/LLnR7S5YSWUUtsGIXlV4Xp7oyL/FVKLRk2b4aeMNVsDlcxOvLbkFG0KU9e6uMvP4iCnZY8uje+LlMtVQFR2Go0ZMld3mQQNF3lsZ/NTI2x95SjX9rafxX/yISdkO8D+RQgaAoijUn/+Y3v/nkk08uXrxoHMgk3W8AQHMO2lCTqycMAMZPfzS5t/k68cs/Ot+7+Zf3bjKIru5l8qXXhJ9KqxgGYMlazMP8s+H1T2JZXoq4l/M9wSZkzkyrVebXcrHO/ja6zp102VWb1MbgeznfKzq66v4XsmejYaQijR0upmm0Gt3WkiUET+aNXEuWfHxO1pLFa0rUoMyS5fu3TE8FAm3JcrcgaclS2bJkmYoVVbf1Ave5omrlHoWhRl+W7E4CRUctbW2oxn+ldrmjXRctjS9ybP/qX+TOsf0tgiAI8lv/Inc07K9Gh7C3f6V2SX+0dVn/Si06ujlDdRhuFeO/WooLyy2A/JXaZbM1i/VRDiknocsds2SsOfmTTz7h8Xj379//1/94v6yqjnTFAQANOWhDTaqaMAR8b/zlC6+JpKqJJC+T924wVFvdy2Rz3Z1NTIlDAAAgAElEQVQGWLIWE1JjF1BpE8vylEt4/eOH+WcJVkXtiveruIsfBG0vEMZwDq2xlz0bvdNteEumdWei21myIr9DFVqyPB2WWry5XLY7MMG2ZFROKXlvpZYRbEuWty0V2pLlHd02nSsMYJQ8AtGXJbuSQNG3lrZ30bCPjGzvcmx/axnmyrH9yL9o69Owj4xs76JhHyHGZmiXK1ZA+qNN8XWlGm/9f2Jstvlp113/Qx9Ru1zRsI9kC0jUWfStkbEZOWcAY2skuODg4G+++QZ7/fe7UfvfefepuxfplgMA29LeM+jo7CKX/7wQkFjJNQiSnd77LiGxkutzw8QkmZvoafKFJzfR0wT7YsBev3eDkVi5+ZHPjQ8RBEGQD/GFcctNbLA6cau/WW4wgCVrMRWcLLeSKzEsT7n4VNyOY7oSrCq0xj6M4Uy6tuqZhAa/x4WmsmdjYXU2jR2GB93GkmWGN8N1dtjekvEDTsg+1oZraN5ahNuUsn7Jkrsj/UShTN9pyTZuOb1HpFp95bXsKuiXLH+HOf5GiJGR9K8AihuMd0YPZX1Z8lMSKPrW0vYO2mVqeegjS+NvOV1PObYf+Rc93WowRoxs76BhHxnZ3kG7nnJsf2sZJvERx/Zb6lZVVOPf+hdtVRv2kWXYU7Trjr/xt5ytt1IF8HWiRd8aGZuScwYwtiyZx+NdvHhRfN37hsY/+9vnR48d7xsaJ12DAEA5NUz2P77+p9RDqH869OcDt3PiK7mGAeP8H02sKxnnv3PyquTGe5p87smN9zRBkA/PJ3PjK7nxnibv3WDEiz+qxC3cKry53NME+S4hvpJr/d3Wr9/fJUisZTCAJWsx4wv9j6jnY1gecnEtucwcphGs6nGhaUKDH36cYO3w4ux/Iu9efKH6ikG37LW+M/Kwzv5W9tk+gVAgZcmv15eUWbLStlmFPS5wo0j4W8q35E1jlahbsvuBgjEuxL2EtyK/H7JMw/dmLfjhNBQ1YysYb1lqH5TusJyjk61W+VEYXvRlyS4kUPSNpe0dtMuFY/vvRpsvPvIvcuHY/ruMJbtwbP/dMkziI/FrxPgCWvSNEW4VyzAXtOuOv/E3nC6XzbeSBfB1ol0XLLFKSDkJXS74WUV6e3vF1x27ZI7OLvvfeZdaUk66BgHAtsQlpooV+VdvvdXY2nXQhhpXwTUUPEw+v+70+XXG5msPbDnj/B+Q964z4pIkPvK8jrUZI+9dZ2wWxv5NcnrvaAJW4d2jH55PwlXuQfYBygCWrN3cyz0ewXwSzXKXxS7vtKJpSqTCF6xbZ38rNbCDOgSdMXshscTuJIIcPENRpRK7kwiCfGan+c4Qw6nwJ7nz8+W2xz9nh4qZXuaSNEO1IfUy2BltuWRHT5bc+XjHQT39F2rnY7TTyv9P2Isdy3Zz75VV1e1/5937dg9JdyAAUMTU3LK3H2X/O+/+/oMPMEsOj45fWOEftKHGVnANhoTPkQ/PJXFjK7ixHiafe3DvHN38XftzD+zTN2/FlvzudQZWWHKVD88lcWOTnN6VKkP+MUoAlqzd+JbfCKx+IKvIEcwn93KPE6xkfKHfqfAnbVkyxWY/giAIst/shZwlFJv9CLL/7yf349oLP7NrjkgP+gxBEOTgGcrmKp/JW3GrWu1ZsnfpDcZAvuw5ofdm4y15ZJ5DjiUbkiTLfz4QIhV9WHLlv6AdDmin086C82CrXfnBTdJ3RiMIzFA9Mjl79Njxz/72eXvPIOk+BAB4xH58yvhsDZPt6Oyyb98+cwtL7NODNtSYci5AFmDJ2g21M8699GoUy00Kv8q7ITV2BCthj5Z70K/hxz5Tk6AzZi/CKTZHzF5svsb+tbM5Q2kOT39xxixo81N5/+7HWfLmQtkVN6vVfFe3CKiwyWwJlj0ndUN0vCX3TreRYskGI8mb/SAMYl8MPPqw5Ib/Qlt/RjsfASTQcR+t/j/kXnfZy4e5SGpGDuliBAALMn6MLbx6/eafDv1ZPCHOQRtqdBkXIAuwZO2GM9P6uOhCVIObFG4lPxMfKZkz02qfd8q95OeQ6odp7BCtW3J60BEEQRDkiF2zQku2O4lsWfIRBNn/95NH5K6oRUuOqH3iRf/lXs7xxpFS2XPSyq1PZYeIaR5nkNTjArLTog9L7vsFbfwftPMhQALN59Dmv8m97nKvYA2T/adDf756/SZMyweQyMjkrKOzy69//Ru8H2OcMj7b2NolfnvQhhpZxgXIAixZu8G6FDsXXZDiXs73crvbKkkblxFd52yXd9KT/ksE4yl+KLTdREydp3fpTdsXPwRV2jCHaYqm5WufYOEtmTlSBpYMIRR9WPL6JFr/LtpmhXY4AHql/QFa/x663Cz3uisRFHMLyz8d+jPeRQBAPyjxYwypcb4P2lAjSrkAWYAlaz3jC/2cmVYphue61attdWOZOUzzK79p++IH79KbyY0UqdEedijPm54FlFs7FvzoSrOo4GQt8eaVn4f2iYZU9jMxYMkQotGHJaMoOpuP1v072nQCbb+PdtgD+oB9Fq17B+WGK7ruyi9leHT8/nfexR6QAgA9sK0fy+WgDTW8lAuQBVjyTskSbz650du79DrpgqsV/MruhFbbTi+NEjx8sGSImtGTJaMouj6JdpxAK/8FrUAAfdD8N3SxTsl13/ZqNrZ2Hf7Y6PwFs5HJWdIVCtjFiP3Y3MKSuB9jHLShhtLHAbIwcEtuHVlQe11NNrrGF+p/u9uGM9PqUmyujpUG3rDbrkyg9RGz3O1KEqiHID5lVoVdCcSPvX2iIaUpWEz9cClYMoRQ9GfJEEMKkau5sMKfmlu2umvz+w8+gOmsAV2A92P1xlf54nGpZ95gSMk4oH8oxWMfPijS8GdAd5YcVzn4jNan3W9OIslhjTmktul/u9tmeK77caEpfqgHJdiePGLLDn3Ovv7VyetEygdYHzHNVVIbgiBYhdrBk36N+BwrKFgyRO2AJe/NELRkjJz8IpjOGtAumvsxxvXopjvxHc9KxgH94/C898fAWg1/EnRkya0jCx8+KCLLkg/aUHNYY/rftPLMrUzZ5xkTNdHAI18Fhj4PNDbN3TRg25MIguw3zQ0NsDa2ZW9+9DzwCIIgyEFjW+sj4pIB1vuxkuJPA3D1BFhvjpQs9VolS3aj/dzGZRA/dtZoRUpTkJhWbh1YMoRQwJL3ZlSy5IUVPkxnDWiLvqFxrfgxBmtg/lv3Kv+isSDaOKBnjnszilsnNbyCurDkNb7wG/fKv7tUkGXJR92rPntE586v6n/rSrK6sXwv53v8UA9KcTa1vv7A+nQAOyTA+lPT3JBUtrPpwf2muSEB1qcfsENSA0+b5oZsau7B0w+sPzXNFZe8bmrtnMp+82nAZuFPTXNDsDpTc0+bBm6VDzxtmktwrzZxKb7ImWklfuz1w6V4S+6cbARLhhAKWPLejKqWjAHTWQOa0Dc0bnXX5ldvvaUVPxYTUtL/jXuVXWovhTYO6AenTM5xb4Z7brfml08Xlmyb0mr2jHUprIksSzYPaXBI7TzrzzC0DspWmV8TN9EA6/3vb8rup6a5IQ9OIgiCfBUYkhr46VYD8OZCKUsWlxR/GhD4KYLs/+ok5sTY6p8+YKtvyY4FPxJ/dA9F0dK+rOQmipiB2S6wZAihgCXvzahnyQswnTWgFjryYzGM3tkfA2sP2lAB/fCdZ5XmrcgYWrfkHNbYl0/Kg2hj5FpyRt3UT0H1vgW9+t8BJbHPO5XA8sOP9rBDsc8znluZIn7g+Z2JeEueWBwGS4YQCljy3ozalrwA01kDqoD5sbb6VwC7D+1aMnd+9cgjumtufyh9nHRLTqoa/9y5lNU/p/99UBTf8pvhjMekO66GJDYG3M89TvyoBUIBXpGTmyjzKy/BkiGEApa8N6OJJWPAdNaAcsR+bHXXBrqzA4rQoiWv8YXGfjW34tpC6OOGYMmZ9VMB1IGvnpS9XFrT/27IDa072afsFn60h51IUKVdVJ0z8aOeX5lJagrEIxAKwJIhhAKWvDejuSUvwHTWgALAjwHiaNGSffJ7TvrUhpSMG44lZ9ZP3YptNntWr//dkJvxhX6nQhPSNVdDPEquqjQM3PBcL16R8zoTUBQl35I5HE7SHkteXh7ZZ13lgCXvzWjFkhdgOmtAEvBjQFW0Zcms/rn/dir1p47gLfnh8zZW/5ye8af24i05izn1g09NXOWgrr/VCcY+71R8gw9+wIcdh+2LH7adkhqfprEqvCXXDBWhurBk0VaEIpEIRUUoKkJFigpzOJz8/PzKPZakpCStn3ZdByx5b0ZblowB01kD7T2D4MeAGmjFkl8urX32iP4oo+8ZNuMJfTyUPm4V13bcq+a4twQntuMHKXzkc1IZjLtxLXhLjq8c++wRnZSJAGUT3+AWWHmfdNNVm6jap640C5UOuaA7ObEpQEzHJAvVoiULhcLh4eGKyura2ob+gZHV1TWhSCQUiYQCgUgoROWJ8t5U5EqwZLDknRPtWvICTGe9h2nvGTS3sPzVW2+BHwNqoBVLtghruBTWhM11IrbkUPp4GH08vJQbXsqNKOVGlnIjy7hRZdyoMm40Rjk3ppwbU86NLefGVnDjKrhxFdz4Cm58JTehkptQyU2s5CZWTSRVTSRVTSRXTyRXT6TUbJLKmBTznDH5vHYyrXYyrXYyvXYyvW4qo24Kb8lZzKmn2T1/dykzhIHh2KPl7vQrUk+z7SC8y25RO+OJH++6YC2xMQAP99UwqhVLFolEY+PcwNBIJ68A16Aw1+Bwn/DYrKKSvuHR1bV1oVAoFApQkQBFJa768PBwQUFB1Z4MWLKyrzP+AjrkiLKN0AoE0DlsI5RzA12fVHLdtX63g+ms9xqYH0P7MaAJmltyXOXgdx7VwVszAhqmJWcxp65FNdmlqDARho4yvTRqn2dMuuyqjSvtMnu0nPjxji70JzT6i0lmBwmEAlRzSxaJRC2trQFhkYHxaS5hiS7RKT7Ps/xSM2JyC3Lolc1dfUuvV0UikVAoQEUSlry6utrQ0EC2r5ITsGSFX2cL5Wjd2yj7e7TTGu2PAHROpzXafAat+x06W6DouuvongfTWe8FxH7s6OwCfz0ANEFDS24dWfjUscQrb9jwLTm9fuofT8vJnbl6fKHfjWYRWetCuuyqTQzT05Vm0TJeTfCQmSNleEsu5eRgyzW15O7unuDQ8KTcgtSyupRqdvXIDGt6voE7xRqbKm3tKWKw2vsGXq+tC0QioVCIiiT6XfB4vLq6uuq9F7Bk+V9n65Mo8320xw3tDwf0Sp8/Wv8uutIt97rr7rYH01nvYsCPAe2iiSWv8YVfPSmzTuoMpo0bviVnN0xH0IcPO9DImrm6ZbzalWYRzfSQGhZtxxHP8nErsSztTSNy1BmtEXhL7ppiY8s1suTXy8vh4RHhMfHNQ6OtM4tdS/yeV7ye+deDKxt9q/yqoannFcz8qvpB7tQGny8SClGhxIN8IpFofn6+Zu8FLFn+1xnnOtpyBu0PA0igzQLt+EHuddf1zU/RdNYwbNwOBfwY0AWaWPKD5NZzgQ1BtPGdYsnZDdNO6V2kzFxd2pvmVmIZz/Ih3XG1hXfZzaRGL75gXclRc18NxTf64VleX8Q+0siSq6qqbe0eZuUXcpdXBxZX+xfX+hd5fQtr7bOr9ZOvSkfnMtoGY+n11ezupeUVoVDwZtCLrfD5/M7OTrKtVd8BS5b/ddbwAdptj/aHAiTQ54tW75N73fVw/5M7nTXlWTiIsmGi6LqAHwO6Q21LzmoY/eJJuX/RGGmW7GPy/q1aVS05u2H6XGCde26Xdm/lSsIXrMfWu3iX3cSP87A7oFTeD6y4s7qxrOjYqwapeEXO70oUf6SJJYsyMrJu3LpbWlUzt8YfW1obX14ffb0+sLTRPrvKnF6qnlwqGX2Vyux6UcMem34pFAlEIpFI0pJRFOXxeIw9FrBk+V9nFQjKCQFIo0LOt4F+LHlB3nTW5haW3n4U0u/NgCxSv88srPBrmGzwY0CnqGfJ3PnVww60J9n9QbRx9S050endPzh5KLdkL5MvvLTZlpzdMJ1aw/3isZ5mrl7dWA6suBNUZSc1zsOuIaTm0ZPii3MrU7LHvi5YS2IHxjf6iumaahJ/qlFbckFBoc0Dh7ySsvHFlanXG7NrgmnextDSWtf8SvPsa+b0SvXE64yWgec1jf0T06hIhIpEIlQkNXwyn89va2sjW1z1GrBkhZbc9wwgDVItGQM/nbW5heX+d96F5mRDIzw6/h9f/1P8tobJPmV8dv8774IfAzpFDUvGZqK+Ft1KoY1rYsm3j5qc+8XkXCI3tpzred3kvT8gCPLh+SRuvKcJgiAI8qFJ8qYl3/vO5F71RLKXyZfeEyneJgiCvH+rNtXH5CufyVSfzbfELTmnYZpSNPjfTnQ9zFzNGMh3K7Ek3WV1in+FTXyDm+yxd041xTX6iElkB6wL3pxwjSy5q7vX8bHbWbPL0c+zp5dWV4SihXX+2PJ63yte5/xK88vV+um1vN5JSn5pUX3j6toaKhKJpPomo6hIJBofH68lkrRbf0QQ5LiP5II/3kojtHZtrc9x/Mq6WmX7gCUrtuRggDQMwJIXcNNZ/+Prf+7btw+akw2KxtauX731lrmF5QLOj739KPDLDKBr1LBk1+zOE94MSvG4Zpac8N9HE2LKE/77aEJsOdfz+oefe3Djkpw+v86IT3Z6D0EQBPnCc6st2cvkS6+Je8dM7tdM+N38C3IsEetx8ZXPZMCtvyDHklRqS85pmM5hTVvFtehh5mq+YN258Keoejf8E2y7iXiWj0OesexUfAIhP601FG/JjOEifAE1LRnrOvF6hZeZW3DsjMldR5f+0YkNoWhxnT/1emN4cb13gdc+u8aaWS8cnL1Nibnv5tPbP4ii2JR80r3R19bW6ogkzerAgQMHkOO+Eu8PWKURWluyInElJAQsWaEl9wYBpGEYlrywwh+ZnMUUed++fXKbkwemX2fWj7nndJ+n1B20oQLa4jylzjmzI5kx0s2VY70jk7O//+CDffv2nb9gBn4M6Bk1LPmnoLrL4WxNLdkNazBGEMTkTjnX87rJ+aRNS7b+zsS6kpvgafLGkqsSvzxm8uWxxM0eF94myLGkzbZkxuRzHxPkeJKqlhxQMKCfeUbauIyntMtKRDOG6UG67CrfvXiWj6JPfctv53VEyx61VENyXKPP3Mo0voCaliwUiURCkUCAdnAGH7p7X7O2K6mqX+Txl9dFsysC7tLGwKu1zvm1prn13N7JWwGxjn4h/UMjIhQViYSojCULBAJCdplmdeCAla/YcH2PI8etrLYs2ff41k8y9nGa1YHjx4/jl9T5HkeO++JKYmsqXHGz7HFfhbUhEhWBJWvBkikAaRiAJY9MzlKehX/2t8/34SLVnJxUM/y1a8WF4Pq78S3+BQM5rGlAW/gXDNxPajcPbfjatSKwqFfq6pwyPiu+KE/dvcCPAX2ihiW/XFr74nHpw7Q+TSz59lGT29jTe+4mn7tLWPJWjwtcW3LVxL1jyJdeEyk1E/ePIfgeFw+23qpkyQnV418+LtXbnNW+5TeDax5KjfYQUefiQb9m++IHJ6pJWK2z1KcGQmS969Nic9sXP7iX/Cx7CNFMj4f5Z2WHuRAI+WmtIXGN3mLK+3OkyqjdloyiQlQkQrkzs8GxiebX7wRFJfaOTLziCeZXRRNL/KFX6z0LG6yXa7G1nbf9Y+NzaAvLK0JUzqjJWOqJJOP2gQO3MzJuHzjhV19f73cCe3PgdkZ9vd8J5MDtjPr6+vp6vxPICb/6+ozbBzYXbZXZ+qS+fquKekUrbn0qXkW2Nr8T4vfi0gQDlqzYkgMB0jAAS45LTHV0dvnH1//c/867YiETNyePza1efMa8FMJKreHmYA0tgG5Ir5+6GdNy2rdG3Kjs7UfB/+ry+w8+wLqPA4B+UMOSURRl9c/9t1OpX9GYRk/vqTLGxb1jJve09PReTsP0Gf/ayFKO1m/oijK+0P+IaiL2Tu+yWw8LfnxceKGCk7XEm1/dWPYqvWaAohxZ7+pe8vPcytTqxjJzmOZbftM+77RX6Y2IOhesgDv9SuMIXfZ4G0bLY1neeKQaklEN+yWLRKINvqC6vvHy9bsXfr6VW1I9tbQ2t4aOLwsGX613z62X9U0Hv6j0inlezmx+zVsXiVCRUCgSyvnDgQqWvPkfzFIlFskptam6Ci1Z+YoSliynNiyqSjJYsmJL7gkwRIp/MNq81kZU3HKO3dtGdg/J3z1tYQCWjGdqbplaUk55Fn7f7mFOftHCCv/iM+aTjB7xLQTQNZSiwe88q6YX1/uGxo8eO/6Pr/8phaOzC7QoA/pBPUtGUdQ1u/NsIFNflsz46Zizn5Ys+UFSux56JEslvZnypPjikyIzx4IfM1qCp5dG8Z9uibITfkQIcolmuj8tNpcavGJuZYrWnfy48MLDgrPu9Kue9CuyRzq3Mh3L8sJTJtOQjGpoyUKRSCRCV3hr6Tn5fz968qujp6KTs0ZmXy2K0JcbaO/L14WN3XEFlfE5NGfPwMDQ6NHxSZFIjiULhUImkWTePnDgdiaTmXn7AIIgWy8P3M5kMv1PICf8sVKbL7c+YTKZ/iewV+JCEp8pWfHNxzK1Zd4+sLWeygFLVmzJ/oZI8QlLOwe0xx8NNzLCXuxKFFhyDZNN+g1yYYWfVDN8KYRFujjuNWwSWl2zu0i/+gCgtiVjI13ciG3bWbOKUIoGv3pSpofRLaSyurGc0RLMmWlVUsCr9Fpo7SOp7rykEN3g/pR2aXyhX9HeTi+N5rVHyh0A7kVnnJQlyzYko5q3JQuFIqFItLq2XlpRc+ac6Tv/+cd/HD3+yNUrMjkjIasgMDrpzsOnl24+OGdx69gZs4DgyJez8yKRSLTZ6WKz68XGxkYDkWTdOXDgThb2AjkRILGoIWCraVfmk4aAE9irgBObn20WFi9VtOKbVeTXttUtWVycWMCSFVpyt58hUnzC0tYe7fZDwz82srVHwz9GEARBPqaGf2wZ7kc1/pja7Yd2W1oeOkG1/R2CIAjyO/9iP7Tb0hJrf7a1x5fhkH44itiyZB6PV1JS4urq+s033/yf//evwqPjSb9BDky//tq1Irl6XHwjAfRDWt3k915VrIF50n8GgD2O2paMoujg9PJhB5rbi8GdYsnp9VNfu1bU9Mzo6IauYQxElKOYbsoVWUl6ppulFLlmqFBuSY0sGUVRkUgkEAiFQpFIJJqdmy8uKfPw8n305GlweFRVHbOKUXfqzPn/6//77f/64rsTF64Zm12NT0lfWeWJsFEyUCGKClFUND8/z9ppCfjhwJ1smZfEApas2JJ9DJHi45s9Lg4d53T7oOEfW4b7oOIX4R8b2dpu/tu9udzI1pZj+7vNYt3yyhgevYn/u5WV1SeffCLue/rWW299cfMZ6XfHhRV+XOWQRRiLdGXcm9yJa3HP6Sb9ZwDY42hiySiK5rDGvnGrDCga2xGWfCmUpc9Z99TIEm/+Kc3cp/wWWYocWHXfiWqiniK/4s3FN/nGsDzFpLYE48dIxkdTS0Y3W5QF2ATUIpFIuBWRSLS0tBRICfrde//1//zH+0eOGp8wvfqjxfWMF9R1Ph8ri6JCgUDQ19fXuOMS8IN4iIsfAlRbFSxZsSV7GSLFxyxt7795G37YMhz/4pLloWP+xoep3V6czbZkxMj2Psf2d5vFuiXKkH84iqhAIiMj8U9oXbx48ahTNul3x4UVvl1Km11KO+m+uDfxzO29+IxJ+s8AsMfR0JJRFH2Q3HoxpMnwLdkpveusP0MPQ79pGL5gPactzLnwp/A6Z/wwEbomusHNnf5zZN0jJTNOy2b81SD2Yl2wltUeGcPywDM4p/B3Ei1YMoqimPKKNqfW21ooFIqEwrn5eTdPr1+//f7/+84fv/jhp+8vXDW/blNeXStCUaEIFYlEfD6fzWbrxmQNNGDJii3Z0xAp/s7S9v6bt+GHLcMlXlCNEcTYHO325Nj+VmzJaPF3RuLX2EfG5uQfixIqEBRFS0pKfvOb32CWfOXKld/814e//vVvThmfferuVVZVR9YN8phnVXDRIHYvAfRMUtX4YQca6ZIE7HE0t+Sl1Y2vnpQ5PO81ZEuOLh/97BF9cFoF/yM3w3PdLsXm/hXWUoNF6AhK9YNH1HN1Q0Xb75lkaH1pnJdtKIqW9mdJKXJpf5aSFTXucYEhEWw0ZaFIKBAIBEKhaG52ztXd69fv/P7f/vPDfxhbHP/p6tW7dnUsNipCBQLB+Ph40x4LWLJCS+7y2K1wbH9rGUb+bihjq19yb2/vH/7wh3379iUmJp6P6ByZnM3JL7pv9/Czv33+q7fewkY2oJaU6/MGedCGmrF1RwH0z0EbKumSBOxxNLdkFEVbRxb+5lzqnT+Mt2THtN5LYU0WW1wOa7ocvomlJD+HN/0csckVHFcjmq5GsjGuYURt8osU0ezrb2h2yerBW3Ja3eT3nlU5rDHt3r51Hb5gPanRy6XYPIrpKtXZV4vENHi4l/xMqbSWnT+PSJijpdEs99L+rGiWO56UFsrqxmslK2qxLVkk+RabeEQoFG6IRIKXL186P3X7t3f+8NsDh78+a3HM5Gc7F6/hMe76+hrZykpCwJIVW7LbLsXa/9BfqeTvhlJwY1y8evXqiy+++Oabb2SvJrWkHBvSGDNmbJg2XQ8HBpYMlgzscbRiySiKRpRyjnsz8JZ8KazpUijzGa1Pz1jFNpmHNOAt+WZM890Etrbu2npOaI29f8Vd3VlyCMPxceEFtXeve5ot5cfRLPe4Ju/JpRHlK2rHklEURVHx03hC8eAV4rZmFBVxJ6buOTz+19998M6hI6Y3bZ08AxZeLXZ1dbH3XsCSFVvyU4A0JEeC4/F4Fy9ePOlTpeQ6llXVPXX3OmV89te//s3hj42s7lvtndQAACAASURBVNqkZuT0DY1r/QZ50IaaXjcFkAVYMkA62rJkFEVNg+uvRbXgLfkZrU/zu6SqyWGN4S3ZN7//7y5lS6sb+t8TreQp7VII42Esy1NHxDS438v5Xnb+PIKZW5mWteSOKda2K2rBkoVCoUAg4PM3NjbWNjbW1tbWeLy1lRXe69e85eXVpaWVpcWVpcWVtbWN7r7By9fvvXPg8A8/WfYPDg8MDDTvyYAlK7ZkF4A05I2XbBxI9LGtGibb249y/oLZ/nfe/dOhP1+9fjM8Or69Z1ArN8iDNtS0rf58gP7ZPZY8P79QFDwfemHu4SFA18yHXpjPcV+YmdLKtdOiJb9cWvvycZlTRp/hWHJcxdgXepyJWhexzv42usEdP3CE1nlEPa/eoBYoigqE/CiWG57ygVwiK2pnjAsej7e4uDg7Ozc9PT02NjHQP9zZ0dvU2MZgNJTQq3JfFKekZsfEpUZEJz946HbyR/Nx7kRHR0dVVVXLngxYsmJLfgKQhvbm3mts7QqPjje3sPz9Bx/8/oMPzl8wozwLb2wlNDnFyOSsbGs0WDJYsubMd9fPe32zFH39dYYrj54A6JrXGa7LsVbzXv9caK3Q/PJp0ZJRFC3tmPrKpZxCGzMQSz4XWBdcTMI+EMkSb545TEtocLfPO53c6C23zPhC/yPqeamn4sQEVt+zyvzaKvNr50KTx0WmriWX3ehXxLjTLR8XmT4uMr2b/a1V5te+FVaK6nEtucweLVf7QDLawqMa3MRML3OJrKW1fsl8Pn9+foHLnR4aHO/tGWxr7WbWs8vKGXmFpWmZeXGJaaER8RExSe0d3evrm39QqKmpqaqqat17AUtWaMmdzgBp6GaG6r6h8bjE1KvXbx7+2AgbLsPbj6J8uIyjx45LTfh30Ib6vHYSIIvdYMkzUwt+36/mB/Ho8YBeKYpY8P1uYaxfwyuoXUtGUdQtp/M8hWkIlnw/kYSZqLfN+EJ/ZkvwkyJTu7yTvmW3ImqfZLdGOReZym3NZY+Wu5ZcjmZ5yBLV4Pa46AK21vBcN2emlT1aXtiVIIYxkM+ZaeXMtPIF63zB+sP8M+H1T+RW5UG/ltceqfYRtUzUFvQkxTR6Rja4Rja4xjf5zsqbk08qWuyXjKIoKhQK19c2Xi+vLi6+Xphfnpt7NTs3/3Lu1eLyyvoGH/+EH5a9KcpgyfK/zmr/DW27hXY+Akigwx6t/Be5112LpjIyOZuakSMeLuPoseNyh8twdHb51VtvpWbkiJcctKE+x8ZRAshgF1jyfLbrUqwVjxYL6J/lJLuFhDsaXkGtW/IaX3jCu9oqro1cSw4maSbqbRNd/9iTfi2NHZLfkSwmps49rMZBtnBee6QH/apsx99olrtX+c30Zgrx7dYNFbmVWMqtyq/ybkiNneaHtrrxemJppGWiljVWvi7gKS+sZUtWIzU1NUl7LGlpaWSfdZWjD0vuOIE2nUQ7HQESaLmANh2We911ZC1Tc8s5+UXYcBn79u3DD5fR3jOIDdjs7UcRWzJ+2FFAz+wGSw74YSXLk0eLAUiAGjr35BNDs2QURQenl488ohv715NlyT8F1f/jaUVpx/YtmvpPQoN7OONxfkeSFM5bDcP4hNTY+VXelfXa8PrHTtTzKk3/gaLoU9qlYIa93Nrs8k5q7xAJhXxLhuyI6MOSl5vR+vfQ9gdohwOgb5h/QOfkzGKvO0uWglpS/tTd6+ix4796663DHxv96q23MFE2t7CcmlsGSwZL1pC5h4dWi6MBsph7eEjDK6gLS0ZRNIc1dtCGSpYlH7ShuucY6EzUiiw5pZHiSrssVfhh/tmQ+kdSj8f9/+y9eVwT18L/P/e5z/N9/X6/7+W5S+vtovde69Le2uJTa597lVbb2lbbuhSqVVtRESm1rqigghtaV3ZlUaOibILsEAgEQhJCQkISAoQlQNhJ2CGQEBKyze+PSULYNECSAXI+r/eLznLmzMkMnXl7mMx5zLp5i3CIWJs41V039fKuZDmOr+0x6+aZ1O+m6twzDLBkEKNiCUuGYbjZF6YvhUsc4YrzAAtR6gIXvQfXn53svFteaK7f8jUcKHvjpm/eP5GoH8sKYHnmiSVnPwagxay1ZBiGT0dx0LLkrT6UWTsSdSTzFoZ2FVsRMx4fwmGukGZY+Gya/c3cX8JoFw2/HhdCPX895+D09v6Qfjko/7Rhbffol33yjpxJ/a7XiIeJTRhgySBGxUKWDMPwAB0u/jdMhgAWouifE/Yi68+7hW0GEx6p9+MPVn3o7OKKCY9ccSgCdVO0ZuaJJWc9BqDFbLZk8ZACleHuskraZvNI1HHFAfcLLmHLo8fzjHV3THeyWNZH5idfzdp7MXP37bwjD+hXHjNvXst24o17ITGpNrGwAffSvYtlfeexOx8VXccwfvMjnbiave9ixu60ckynuMWUH9KIAEsGMSqWs2SQ2RQLWzImPHLC0UlWeuCeFbQB0GKeWDLuEQAtZrMlg0yYrKqoO+Qz6eXREzK+OxlJp7glsTTEC7v9YubuMd+0E4jqfAm/BpHcA4luxogytiL8Iu5HL+z2iKIb/K4yk32wKQZYMohRAZZsnbGwJU820jWw5Nlpyc3tPajrr5FN6r24aigTA0ALYMlzLi+25BjW3UsZPwaSTgSSTiSVhhY15Yz5Sh+vg6V/gFipGk7jYq5k7YtmBSGbGyPKStUwV0ib9mB7pgqwZBCjAizZOoPKc8njWemBi6G0AdBivCXjcklbttmbamxFE9Lc3rNlm73hawRFekvOeABAC2DJcy5ZVVF3yB7p5VGTEVccElHkF1HkF0rx8icev5K171LmT7m8Z2O+XcfrYF3N2hdG8RqzuZE9yqgHWDKIUQGWbJ2xPkuO3gB9tDf2ZcV8HTf4Trwq4NhHy4/RXr4jX0dIG8czkxcztjYLWjIul4S8vC84DIP6L8aExCWm2tjYrFlrFxEdp1/Ye3GVFHvf7Hivc/aedC335yXO3ve5Py9Z8/NVk+xL9yu0LmXyYibb3cwAljznklUVFUT2SONGGs9zzr27ZA8v7PZI5k2BqE4s6wtnXL2V88tzzr0JywfMBVE2iyUrlEqFUqlSqVVqtVKpUiiVLyjM5/PRfn+xpYPFYs1x2M0aYMnWmdljydH5Qkvg47hhq+PyY7SXF/MZveSZ9/KtUVPbkY/RhdEGsWS9HyMvHkH9t+JFV5h9Tkg7312xAhMeKdJa8j2z4/2Js/eka7k/L3nBWi2YH9Z8fXTm+5ptAEuec3ladC2E4jUlS0ZIKX1yn+p9JWufF3b7Q9rVFxf2wm4Xy/rQ/qwviiktWa1Wq9Tqtu5uCqeYwiyurW/iN7UwK3iMCp6gs1ulUqlUqjGb8Pn8jIyMfCtLDBh7D1zO5khmjyVH5QstgPtWR/f8qA3ve/vlC6N8HJe/vxrp7nXPF/odQ6ZXOz4TRvk4bvBBCguj8qM2vO+44X0IgiDofW8/H8cNPsKo/KgNEARB0PJjtCgfbbfxBh+DfekWIgUMd6RfBUGO7khtYwo8814+vkJz8v6JRL0f618/snHTN7OWNWvtDFv77ooVZSdspelhZsf7E2fvMKn3J2v+uQSCIAj6JCU9TJp+xFl3RpG1zt4jC9e4eHNdkMJLfB54+/wTgiAI+ucP3JGtPklJD5N6f2JQoXZfkK6GsXvUrYKgT1ImbNKDH9bo22OBw5IeBix5DmVIIQnJ9wiheKZxI8xKKOV8YkkI2h/3JTGNJStVqmGFoqalJZmYH43FxaSlZ5PyOaXcwqLiDEphIrEAV8hkV1U1tDYPyWRKnStbpyLnA0sGljx3YmWWHLVha1RUvtB9q1aFoa1RUflCv2OrR3zUxxHxWq28Io57jBb1zHv51iikwAafsZto3dpwX1qZ1hUw3JGhVet2ZFjAfatOgZA9mp+VHrjy6gZnF1e9d+7YuRuXS5q1GDbV2cW1vLqh9+IqaVqI2bn8sfPlEOnlj6FNh6VpIVyXt5wva3/qZ5Ey+oWG265x8Zbe37HGYNuR5UjN4/elmzbco/Tyx1opvzxxk1I26X6FNh22xGFJCwGWPFfS1Mu7hncOZ9xK5UaYlXjOg0sZP6L+5byXxgSWPKxQVNTyc2iF2dTCFAIlIR2PwxFKmexqNodeQMsmU+MzCElZeYUlJSVVFVkFtOKqmmGFsqmpKTMzk2KVAZYMLmdzJbPHkiPzhWZnpBMXWn6MFunjuPwYLTJf6Hts9ec+Ql9tX7J21ec+wsj8qM/f996z1dE9Xxj5zHv51iikEqTw5z6GldP2vA8hten3NVJg7I683ccUG13AfevqPc/MfzQM0D+XrHflVxcsoBZxUP/FmJDaRgEyduOefU767xf2XlwlTQ02O5c+dr4ULL308ZqDl6WpwdyDbzlf0v7UzyJl9AuR5dpe4YOXpfe2r9n460jhVF1tSM3j95VqUGZkj9tTXtiklI1v+dwz/9EwAFjynEhhA+5Gzs9xnNBU7lNz40c8Wtych/YnfnlmasnyYUUujZHLKCKz2FkFhel51ORUHCEhseRJOMvfryj8MTUjOzklKwGLL2CxWFxuHoOVRMgnMzl9IhGTyUTbV9EJsGRwOZsrmT2WHEEWmpvTW1bviUGmoz6HHE/fdlx+lBZBFvocXf35baHPUZ0lH6VF3Hb8/LYQWQVtidJtAkFborSrYrQPRSw/Sju9RWveyCZabo88VjHZjkZqG13AsHILHJYIsnDMOy4QV16z1m6yN/ehy5Zt9jt27h4j8b0XV0lT7pqdix87X7wrvfjxGudL0pS7XOe3nC/elaYc0j5c8c5byFrni3elYduRZx7WOF/iOuss2fmStvDGQ/qtIOjjFH3No/cF6QuM3qO+QmjjoYmbZLB3SxyWlLvAkmd/+F1l13MOJpc+toAihzNuhuR7oP2JjcqMLFk2PJxLK8qi0ull5Vn51OTsvJQsYtLzRNy130jfbcP/72rcd9sIt/3waVm5VDqLW84qKycUFqUQ8p9n5hJoDLFEQqfTC6wvwJLB5WyuxKoseRpotdXEsu54miyMINP2vIdMoM+E70sur26Yhd3Jk7Wq9+KqoeQgKyF1o11qctBQ8gWfd5AJ9AGWPMsjlvX9hj8Qz7mXwn1ibpJLH13N3m/5UfSml+lbskqlLuXVPElKJTOL2dzK5MzsqITU+BRc6pPoNOefc5f/s+CNN1KXLsEedKGl49hl5VxeDaeiKjufFo8jxGLx8ZnZvLq67u4eqvUFWDK4nM2VzB5LfkoWzj5oP73neMrU1er7kpcdpaH9AbXMk7H3kgKthPIDi7Wd0wfOo94YBGDJszyBpBNPGLdTyp5MmdBTFw1mw8586ox9ySYh+edSuRi0P7Gxmb4lS6RDUanYiMQUAoVWQC+KT894GJsQk5SaExufftA1bcky6ptv5r+zvOCgCzMugUMvKi+vYJdX4AsYGSQajkLPyi8oLOYMDAxUVlaiba2WDrBkcDmbK5lFlkwSAtBinlhyoj8ALYAlo5shhYTfVWZIr7RDvxZb8SQ4/2xyWfiEXNgBQRD0zplbkxUwJPTMp87YFxV4Vhx8NWvf7P/Snj7TtGSFUkmkM+MzsnIptFwSJTUjMyEt7enzhMhnz4vwuWX3HuRt2Zy3ZDHtnXdom7fknXZnxiVUsUtYJdxsSmF6Xj6BwaaXlZdU8mrr6gcHB2lWFmDJL7qcKUVwvQfM/hAmQwCzw1wB17jAUt4LzjvqfiOSKld64J6QhAC0mB+WLEvwBaAFsGR0E8m86Z3leB1/UM8F7E63pE0IN3NcX+S+oZ9C0DJnbHjomWXIX7lGpnec1Gpx6KcQBEEf/HjhZZYcRHbHVUWifTymkOlbcnQyNptUUFZZQytip2CxqRnpiYnJ0ffDi+KSWp5Gs/btJyxbTln4d/LSpWl2/y687VvD4HCKK/KojMTs3AwKjc6tKOXVVFTXSAYHuVwu2uJq0QBLnvRyJiLB9Lfgkh1wlSdc9xBgdniX4FJHmLEMbns42Xm3pMoEh2HiElOb23vGLF/pgQsnCQFoMV8s2QeAFsCS0c157I7nxWHGdAZPwi3nDz7deObH0LLwZOyPzqHhF5BpXeexVpqNsORIpv+NHBe0j8cUMk1LbmnvTM0h0tmlZeW8fGphcmrK87i4xKjYRJ+A7KNHqd875H+4irJoUeHrr1NeW5C+4h2a92V+EbuUVZZPLcKSC9LIBdnUQmoxp7SS1ypsb21tLRwVf3v9C5nePxFfaIH420P2/tqfU91qygGWPPHlbLgdLnobrr4J12EAFoUfChf9Ex6gT3jeLakyze09yHgQ6z/7/PotXyKFjixf6YELJwoBaDFPLDn+NgAtgCWjGIGozjtrb1Lp4+mBPHEB7XBLClkPQRAErb9Q+jjEA+lLdgvxWH8AqyuzcvcFj/UHsC+p8Ldsp6beSf+AOdsyHUseVihzC4vS80ilVdUMRnFiQnJUZOTTR+FxDx5nX72WseFT3KLXKQtfo7/5Gm3h6/hXX0le/jbT27uFwylhczKyCYk5eSnE/KQcArGQweFWciurBwel9FEJsIfsA8ZMmTfG7yfezXamLQKWPPHlrPogXOYI1z0AoEDlaZizdsLzbmGb0YsykoULF+3Z5/TOgbDHRCEALeaDJd/6aij6guz5TYDlGYq73uf9vzM8g8CSpx0yP9mf6DZtSzY59wsuhzOuon1UjM30LFmRmJ0Tm45llnEpVEbk05jHmEdPHz16dh+T+9u1nE/XZy54lfjaa9Q33ih4c2H6K6/ELV3GuuQt4HCK2azo+ERMXMLz7NxEfA65kMEpLeeUlisUismcVT8V72ar7V3Wz9vb22tnA3R9z7Zu8dqtJi5suAQyLIPsR7u3kZW2bvGjS47ek7b8y3cHLHnc9W6CfTBXwLzzcN19AArwg+H8/zPhebeYxzS39+BySZeuXBszGLJfYPBKD9wjohCAFvPAkkWRbpIHv8ribgAsz+Bjt757e2d4BoElTzv3qecxtKuoy7GeRA7mQsYusawP7QNjVKZjyUqlMhWfm4rPZZSWURjMp1HP7oXee/roUVpUTL5fYNbnGxJeeSXtL6+SXnuz4M2/pS1YEL1sCfX8hQZGURGdFvEs9kHM88QcQgYpn8YqLi2rYBeXwDDMGJVAB72YOgQyGAxG4klbZILBCHSwPZnIYCSetIXGLDLYWjcf6AA5BCKFkUWJJ21HldVVjRTUFtevGF0UKWnQFKT81HbHYDAYwJInvpyRIZh/D4Aa5AmuBma15NpGQWpG9qUr17Zss1+ydNmrCxZs3PTNWa+LfoHBiB8vWboMl0sSSZUrPXCP8oQAtJgHltzXUCny3yJ7dlUeew1gYUQB9qJyCrBktHI2zT6uOCyx5NHswS/vGK0+A+0DY1SmY8lqtZpZUkYv4bIrayhsTmxKatj9Bw9CwwjJ6ZwHTzK++CLm1T8n/vmV3NfeIC5alPT6a+HvLM87f55XWFhIoyelZabmkojMYgqLU1JeVcnjVVRVwhNY8oitjtFmrTmP9s/Ek7Z6pTZco502WKQzau0WuvrGWfLI5JiSYy35pFG7A5ZsnCXXhgFQw/yWzC6riktMRXqLFy5ctHDhoo2bvrl05VpEdBy7rEpfDLHk9Z99XtsoQJas9MA9zBMC0GIeWLJIqhQRH4t8v5U+PC5/9hvAMkjDT4sCvhNlBM789AFLnl56pR1nUr97yvBNKHloMYI91julv6hAKOVCJPMm2sfGqEzTkmsbG4q4lVROBYlVkkmhPI2JDQq8mxGfXBoRg9v8zbMFryb+6dWsv76W/ebrae/+M2PnLjoGU1pIpdEYBHIhiVVSUFJBLynnVlZX8njVtdUqlapoVIIcIIegFy4oSjppa3syqWiiZQaFtZMGhYMcbE8m6f+DbOQQpC2o29Kw8rElR6+DHIKM2d3oAEue3JJDAKhhBkumFnEw4ZFnvS5u3PTNqwsWLFm6bMs2+0tXrqVmZJdXN0y21Zq1dm6nPQxHP17pgcPkCQFoMU8sWaoU1Zb0hTn2Xlw152g88z+ot2EaiAJN0Iusv2sAS55e+F1lN3JcbhMOR7PuGGu6IeshaL1XyUOv7eu9DJZ7bYeQ5UZZcvr14EkKPCq8EUg6MaVPIVfKzHR8XpzpWLJKpapraqSwS/AFRbkMFonNScrIunf/UWpcIvtZLG7njsjX/hr351dSFvw1ecErmWvXcgODq8mU4sKCQho1l1SAJdPwdDaNU84uq+SUllXXVMtkMuao3HGAHO6MW6SL7alkJjP5lK3tqWQmk8lkJp/Sd/ZqtxopjCwwKHzHAZnSF7G1tXW4o92jdr+jKxxTUjtveypZ304jdjcqwJInteSaYABqTGTJW7xTpnQnI1LowWEYt9MeyLPF765YsWPn7ktXruFySfqO4ZfWEBEdN2bhSg/cA4IAgBbzx5LnLIZ/WrFOgCXPMJwW0qXMHwOIbs/YwQklmJcQssspZNdXHte8tq/zKsEEeyyBIAiC1nmF7HJKx3htX+dVcvwraJ1XyfGvth/XrV3ilI6UXPLV9nVOHuug7ccnqz+aFXQp80fjG1/Tyc2oiKlsL1ZYfDiSab4JrrOnp7CsPKuAkVlAz2WwcERyamo6EZvJSkrNPXMu+t//++TN1yNfWRD+pz8nfWxX8fAxv4hdymAWM9lkGiONSMEW0InMEiqLU8Qqbmpq6unpYVlTgCVPbsl3Aagx2pI7Ojo2bNjwUkvG5ZL8AoOdXVzXf/a5jY3NmrV2e/Y5Xb/li8slGXYGz5CVHrj7BAEALYAlo0tcYqqNjY3baQ/UW4IiwJJNEmJt4hWcYyDJ/XnxvXgOZlLu7nJKw3htX/fl9nVeHIyX+667Bsvj76770v24l/u6L913Od3VrU3b5XQXc9d9nVMa5q77Egha5/WC+jkY95TNxozAp1ANM5qI2PJobEVMRkVMbnVyl6TNAgdKn+mPKkItq8iiMtPJtKScvLQcAiWfzCLmFWflsOKTSNd/i9v01cO//ePun/78bO3asvsPapmsUlYxi11KKy7FM1gp+bSkXFJOPo3JKh4YGKitrWVbU4AlW9SSc7a7el2a3rZ8r7fsjN82Z7sdBLk+NJx9KyjnRZWPFEZ4+LHrwynu1DyWTKFQFi1atGHDhjFns6NXgsslXb/lu2efk/7Fxs4urn6BwcjX7MwEsGRgydYM8v/aqwsWWHN3MrBkU2VIIcmsfHohY9f9gkvxnAcTc3enU9qDeM6xL6F1XpwH8XfXafuS766DoCVOacjycWs5D+66r3NKQ34e+3LlzruT1c95cCnzx05xy4ub2j/Um1udkl4elV4erRfljIpn7BaKxR7AmKYlK1UqcnFpKpGWTqLFpGXGp2cU5JPZZBKTQCihFvIK6cxHD5/Z2996/Y1H/1rLuBNcSSlgUWlECi2dSEkj0xLz8p8mp2PxeaUlXLlczuFw0BZXiwZY8qSWXB1kevAOrl4XzFLz+B3tsrPb5YrM4nYtdt1lF4SftDzfa7ErZvRCjN3YJZZEZ8k3btxAXjGBxWK3+1OQt7Pt2Ln73RUrbGxsNm76xu20R3AYRj/khwXYcDXPB9twjyAAWJ7gnNbV57JRlySrBelIRmLN3cnAkk0bsazPLWnTpJZsfoLzz13N2s8V0iZroUDUgC2PTuNGpnEjDUWZ3kigN+aVt7HUGpUFDtQ0LRmG4Y6e3piM3LS8gqgUbExSMplIZFHyaWQijV7I4ZaX0xmp164FbtwUuXcfOeweKxNHzyVk5eTFpGbGYnMSsokRSWnYbEJHe0dra2uxlQVY8uSWHGh68A6uXuf103YQBEGQKyaQ77UYgiAIWhyED4QxayEIgqC1OMxau1WLtdPVgTBmrSsmEB63UPcc+lrcuB3hdiELXV13ufK91gbhA+FqV9dR5fWzkCtmVJP0u5vCTk0IGerv73dwcNDfkhctWvSnv7yKvJ0NEx5JLeKgdYM8Gl58KrIiLFcAsDyXE2u3+1NRlySrxXCQHWvuTgaWbPJcyvwxkhnwnHMfLZ4W+d7M+SWAdHz8UHwqjap7sL17sF0gahgjyhY+StO35GGFklDITMomxWKzkjIyqfmU4kI6nUoj0ah5dDqZSstJTs148DDnISb/WQw9LY2ejc/G50anYGNSs5KySClZBFZxyZB0CG1lRSHAkie1ZF6A6cHbu3p6IdO4nTrV3Pmzdi1mrZ2nF4xZ64rRziKr+J6LXTEB2uXjFuocd6IdYdbaeXohJfmea4Pwuq10+9LPIhOjmqTb3RR2ajr6M3+3bNkyw+E8Dh06ZMzZtAAYQv3+UBbqvmidHHpU6p1YjvrvgHWCdCRv2WZvY2Pj7OJqzd3JwJJNngDS8Ye0a9MT3Dvu6/anTbTq7lHPKVb1kHbtatb++9TzvdKO8Y3sFAvSuBF6Ucbzkix8lKZvyTAMS4dkCThCTDqOWFDAYbG4bE4pu5TKZOMKqDgKLZ/OohYU0nBZ9PR0RmYmA5+Dz86OSUl/koiNTc0mFtAHBwerqqo41hdgyZNbsp/pwW9z9TyHTON2Lg7Ca5fzPZGeWsjO8xyMWeOK8YN5fjBmjZ3nOWStK8ZPu3z0Qr7nNtyLduTiumqN685tfJ4f33NNEF5Xla5y/SwyYdgk/e6msFMTQoZkMhkWi3Vzc3vvvfcQUf5ox0nU744iqZJV37fxBjk0VwCwPDsCCtOLhaj/DlgnGzd9gwmPFEmVNjY2IqkyNSP73RUrrLM7GViyyRNXHHCHfCaOc29Czm2HoO1HJ1t7x/2T/WkTr5oeYQWXzmN3xhb7jxmTr667MpX7VC/K+XxLj0UyI0uGYXhAInmWlkmmFZZySso4ZWXcKkZZZTaDnUljYkm0DHweNYfAys1h5uQwcnIJ2VnxqdiHsckRiemdXd319fUlVhlgyZNbsq/pwW9DHmmALKSVUgAAIABJREFUVm3j66btPM8aWPJZGLPGFeML83xhzBo7z7Mwz5fvudgV46tdPnqhfkNo58ExO3LVFdOVXxOE94V5B3WPWKzB8UZm7VYtdsWMNG+kGVPaqQkZ/Y6Lmpqa0NDQt+22XL/li/oNUiRVXk+uOvSwJDRHALAk7jGVrg+YqJ9966S2UaAXYsSSRVJlc3uPJb8SMHsAlmzyEKrj/IjH4zhhE3Fkv7v3HfcfznHC7rhrX/S2Py0s7u4nEARBK3845/7J/rSwc9s/Ocfx3r/9h/3btQXuaJfryk9c+aQE55+7kLHL8AGMUkFhKvepXpTpjQQLH6WZWjIMw9IhGYNVzGKymEw2tYidy2BnFLIy6cUJ2aTnaZn5eUR2PolJJDLy8kh4fGp6Jo5EHRwaqqyspFAopVYZYMmTWnLV7dkPbue/cFW34aozQauQifmy08lHFTHhO92mTefA8JbblGspdSE5AoBlCMpu/exKXn3nIOpnH6C3ZKsFWLLJw2kh3Sb8OrGwIjYMQW+7eyPiG3f3h/1pOmPWWXLc3U++dP/hS3fvOI73/pUjlqyfnaolx3HCruEP8LvK9I0k87EpZU/0olzRxrLwUTKBJcMwrFQqRSJRMYeTQymIxxPicogZ1KLnuNyY5DQSiVRcSGUXFLAolCIqVSBsU6m0X0ukUqkUCqXM+gIseXJLvjX74Xv+Q9cJ7TGvdmr+EaqnQXN7j35EElZ938br5OMR5cE5AoC5OfOM99mVvKySNmoR59KVa+O5fsu3ub0HdXmyEoAlA0s2eZSqYS/s9mhWUGxx6BjOnt4RWBwaW+y97/sdZ09/si81NPbujn2poWe/hyAIglbqFhZ771v5ln75F3dDA09/Yjg7vuYXE80KOptmb9hIbEV0clm4XpQFogYLHyXTWDISlUrV299fVF7xPJuQRipIwRNTs/BUGrWcw2qpqxP3i1QqlUajMdzEOkUZWPLklnwDgBpoW3JtowCXS8KERyIvnkNG7xvzvf7W3qFTkSX2frTD4WUX42uD8QKAafFO5B97wt0ZRN8fytD3IuvPhT5bttlb59OxaAEsGViyOZJQEhJEdo8tDpkuh7/4/vAMNh9LWMGF+9Tz+ubJlENJpY+Ty8L1oiyR91v4EJnSkpEoVSqlatRL7DQatVo16WvtqFRqjJUlPj7e5Ifd3LGUJV8HoAbaliySKokUurOL66sLFhgK2fgv9ePL2j2flW33p670wM1+3j+RiHobjGerD+VUZGkSo9XwgDe39yBvy0aCfJ8MYEmAJQNLNkcEojrvLMdnxcGzBF/CUTI/Wd+8+p6qpNLHelHOqEChh9H0lgwyL2MpS/4NgBqzwJIRmtt79O+IXbJ02Vz/s/76zz6fB92uRAod+dfLB6s+XLhwkbOLa3l1A+qtsh6AJQNLNlN8CYceFv6Guh8jeGc5CkR1+rbl8zMTSx/pRZnZTLb88QGWDGJULGTJlVcAqDE7LJldVrX+s8+3bLNHRNkvMBj12/NMQN53Oz/ecYsJj7Sxsdmzz6m5vef6Ld8lS5ft2eeE4lgzVgWwZGDJZkpRU86t3EMxxXctw4PCy7/hD9zI+dkn79h4zhg8lDw4LE4oeZhY8kgvyq2iessfH2DJIEbFEpZMfQUuPw1XXgagwyywZL/A4CVLlyF/0N+46Zt3V6yYDa/XmAmI68+bIdN+PXp8zVo7ZLqjVxIchnl3xYot2+xxuSTU2za/AZYMLNlMSeU+CCKdsowihxfd/g1/gN9VxhXSsqqixmM4YHVFGzuhBKMX5VTuU4Vq2PLHB1gyiFGxhCWXfQ1zdsKVlwAowD0MM96a8Lxb5hZY2yjYss1+/Wefs8uqkCUbN30Tl5iK+r15JiAdyZM9XT0X6eiVbNlmP2YhJjzyg1Ufrv/s87l+vmYzwJKBJZsjAlHdb9n7o9gB0cV3zE0UO+Aa3nn8YNSTJbPiWTwHoxdlakO2WQ/FZAGWDGJULGHJA3SYvhSuuABAAZYdLAid8Lxb4P6HCY9csnTZmOFL5oFW6p+unk/dyeXVDRN28Mclpq7/7PM1a+3Ad/vMAbBkYMnmSADp+D3q5Wj2HQvgRzyeV2Psqws6JcLnnPvxnAd6URb2N5n1UEwWYMkgRsUSlgzDcOMVuOg9uOwQXHEeYCG4J2D2Z3DlrsnOu1nvfM3tPTt27v5g1Yf6LmQ9c90pkY7kLdvsbWxsnF1c50138ovB5ZK2bLN/d8WK4DDMXH9aZlYBLBlYssnDbs67RTgUxQ6yAKEFF+5RPY1vG7Uh+znnvl6UsRXR5jsOLw6wZBCjYiFLhmG4NwtmvAWTIYCFoL4CN/72gvNuvtteakb2kqXLznpdnJc6tXHTN0ivKuI3qRnZ765YMdfV30ioRZwdO3cjfx+Y668omSUASwaWbNoMKSTeOMcnRbej2IHmJoRy7nau65BCYmTb2gaa4zhhcZx7elGu6eSa9Wi8IGa0ZI0uLy7G5/PRfn+xpYPFYs132M0Uy1kyyGyKmSy5ub3H2cX1g1UfEil01O++5qC2UaAXYr3fNLf3zNfPOyHl1Q3OLq4LFy66dOWalfzzwLSwy6rG/xZ19ErG/+HFGgCWbNoklAQHkE9GsgPNSgTL3zfv6CO6t9LoL96p1Kr08sjY4lC9KKeVR8oUUrMejRfE9Jas0WiGFUrpkFwiHeoXD4pEA32ifplMPqEu8/n8jIyMfCtLDBh7D1zO5kjMYclECv2DVR86u7jOyy7k8Vh5L2Bto8DttMfChYvcTnuAVyxPiY5eyQerPjT8iwQul7RmrZ11/pMDWLIJIxDVXcY5RrIDzMojxvVreOd8fuqU2lbcSnlWHBxbHKIX5YaeajMdB2NiSktWazSyYcXAoKxvQNrbL+kVSbp6+9s6exqbBTW19fy6xj5Rv6ErW6ci5wNLBpY8d2JaS+7olZz1urhk6bLUjGzUb7oWw8otGaG2UXDpyjUwHMlU8QsMtsKn2ycEWLIJcz3n4P3CyxFs/8l4yvL1Ix6/RTiEoV99QbEXgKFf/S3byfg3WiDpHmxH3hmnF2UiP81MB8HImMaSNTCsVKmH5EqxdFgsHR4cUgzJFUMyxeCQXDIoEw0MdnT28OubKnm1zS0C+fAwDMNNTU2ZmZkUqwywZHA5mysxoSWzy6o+WPUhMiYF6ndcSwIsWU9zew/ySuwdO3eD4UiMoaNXsmTpsvn3ppRpACzZhEkrfxRAPjmZ4IYUeF7K/BFfFU2rz7iate8a3jko332qlhxAPh3BvDmlVqnUyoyqmGj2Hb0ox5fcG5D1mekgGBnTWLJSpZbKlYMy5ZBcJVeoh5VquUI1JBseHJJJpEPiwaEBibS7V9QiaKtvaGkVtsvl8qGhISaTibavohNgyeByNldiKktGhmqzzneEAUseQ0evBBMe+e6KFRs3fQOGI3kpSHfyfHrr9vQAlmzaIKL8lOVnyCPGjRu5rsH57r3SDn1JXgfrMf3KeewPtwmHg8juDwqvjNlqQgLIpyKKpmbJtCZ8FDswih2kF2VuW5GpP/eUYwJLVqs1smGlVK6UDavkCtWwUiVXqGRyBfJoslgiHRBL+sWSvv6B7l5RV7eoubWts7NbqVTKZDI6nV5gfQGWDC5ncyUzt2T9iNNW2wcGLHkyMOGRa9barVlrB4YjeQH67mRr7kgWAUs2Q3Si7IsQSDrljdvDaSFNWLhX2kGojntMv3I1a59b0ia3pE0eqd89KPTWbz6GkAKvANJx4xvDaiVFsP0j2QF6UcZWRqnUKtN81Blkppas0cDDCpVUppAPq2TDSrlCNaxQyeTD0iH54JBMPCgdkAz2i8X9Yomof6C7p6+nt7+zu7dV0D4wIFar1X19fVTrC7BkcDmbK5mhJWPCIxcuXOQXGIz6LRZFgCW/mLjE1I2bvtF/TQ0wHqQ72Zo7kkXAks0TRJQfFHpfydr7jO1n/MvaYBjmCmnXc1xMYsklQtpTll8E289QlDvErdP6TCbOTC1ZpVJL5YohuXJYoRpWqJRqjVKllg8rpEMyiVQqlgyKJYMDksEB8WD/gLi3T9TV3dvdIxIIO7u6ehQKhVKprKysRNtaLR1gyeByNlcybUseP+K01QIs2RhwuaQdO3e/u2KFX2Cwlbz8xHg6eiXW87LtyQCWbKbEc+5czdo31a/ZIbmeczCMdvEJy2c8wQWeRlpyiZD6hOXzlOVrKMqlQvrLtuMH2UF2QfyXLZyw2BQyU0seVqgkQ8OyYdWwQqVUadQwrNJohpWqIdnwoFQmGRxCEA9KB8SSvv6Bru7ezs6etvZuYVuHdEgGw7BMJqNZWYAlg8vZXMn0LDkiOm78iNNWC7Bk46EWcfbsc1qydNmlK9fM8S3P1t6hm6m8HwKoKz1wc4v3jjxDvQ1TZZsPxSuWW9bcb5JzByx5FoYrpF3POfiEdXs8wQWeAaRjL62htpsbzrr1hHXbUJQZzQQjdj4XLFmjgWXDSrFUjliySq1Rw7BKrRlWqIbkCumQfFAqH5TqvsAnHhT1i7t6ets7uto7ulsF7WLJIAzDSqWSy+WiLa4WDSqWnFfR8fJCkwdYsnVmqpY874cLmQbAkqcKMhwJMi6jCTtQSZVdX98gu0eVBWc3pDI7Aebmfk7j+biKzbfyo6lNMz99wJJnZ4Lz3e8UnAln3RpDCNXLh3DopZsT+EmPWTcNRZnamG2BZhufGVmyWqMZkiskQ8PIQ8kKlVqp1ihUKplcIZXJB4dkg4MyiXRIZ8lSUb+4q7u3rb2zrb2zqVkg6h9ABucTCASFU4u/PaTL+yfip7Bh/In3kQ387SF7/5fv5aVlphNULPnAPcZMRBlYsnVmSpY8v0ecnjbAkqeHfjiSX48en/krlus7B7fcpjwht6YwOwGWJIHRsc2HQquZ6V8GgCXPzghEdZdxe8Zb8n2G98WM3S/dXKVWpldF6EWZXD/rRiaekSWr1Grx4FC/WCpXqGVypQ6F/h1wEsmQWDIoHhyUDA6KJYOi/oGu7h5hW0dTc2sVr7arp0etVsMwLJfL6VNLgD1kHzBmyqjEu9nausW/uMBU6ptWULHkvSH0NRdyhH1D09scWLJ1xkhL7uiVuJ32WLJ0GehCHg+w5JnQ3N6jH45kJs+4ez7jno+rSC7qAFiegIy6PcEzvTIAS561uUf1Csw//Zh105AQ2nljLBmG4SHF4POy0Mesmzm18Sq10tytnWpmZMlKlaq7V9Te1YdY8qBUPiiVS4eGpUO6By0kgwNiyYBEIhkcFEskOktu51XXMtmcjq4uZCg+lUo1Rc8cZ8nxbrb29vYQhCyOd7PV9jTrjDdA2/lsa6u15FE16Mvqp23d4vVl9Au1W+j3ZVD/nLDk3XcYuwKpcqV6GptbzpKVIrjxEsyxg8kQwOxw7GD+MXi4/QXn/aVnUz/itLUNF2IkwJJnTkevRD8cyfT+JbbNhxKa3ZDE6ABYnueF7R+dm+lwm8CSZ20EorpLuD2PmTcfFV2/Q/G4mfvLJdweL+z2rMpII2sQyXqI9amzUJHhmT5xoVZ394pahJ1DMoVMrpRIZRKpDHHlQalMMoi8KVk8IJZIBgcHJJLePlFHZ1erQFhaVl7E4vT09uqrYkwtgQ76Jy4cAhkMBiPxpK12isFIPGmrmwx0sD2ZyGAEOuhWBjpAticTGSOLRlaN2xhZp99CXzbxpK12UeJJW926qQQtS76aULvnbtHlhPJpbG4hSxaRYPpbMOc7uNIdrnsIMDuV7nDJLpi+GO7JnOy8v9hdrHDE6akCLNmE6Icjmeqv3EoPXCK9Y4ac1/WYvOPGmMbmwW4fTW9DZNuvAnSzAXtHpicpOZN9mYOVHrgZnnpgybM596he3rg97imbg/NPk/nJAlEd2i0yWWb6josBsaSppa1PJJEPqwaH5OLBIfHgkEQ6JBkcEkuk/QNi0cBA/4C4Xyzu6+/v7u5ta+9obGxmMNll5ZWSwUF9PVO35FFuO8pXDRQasWiDlfrJEeMd7bljLfmkQQFtYYNttBY+xaBoyfdzBV/8RkplTfk1hJaw5OF2uOhtuPomXIcBWJTaIJixDJZO8CagF5xNMFyIkQBLNjkR0XFTHY5kpQcuobB9Rvg7QvYxyLSX/UdO8UZvGH/lbd2G06XQyf5KsEFLvvKftHDwiY9esNZ0TZoawJLnd4YUEn5XGdqtMEtmasly+XCrsL25tW1wSC6VDYsl0gHJoHhQKpYMDkgk/QPi/oEBUf9Ar0jU3dPb0dElELRVVlXT6EVNLa1KlXZUFbVaXTS1BDlADkGGC5JO2tqeTJpkpcGSIAcIKadbNLbwuIoMCmgnDYoEOegLTyEoWvKjPKFPWv2aCzkNnVN4eThsGUvmH4VLd8F1DwAowHWBK76f8LxPeAatecTpqQIs2UykZmRv3PTNuytWTPh7GJeYavgt0pUeuOeF7TPhzgnH/fG6WX/Ht08Uan8Wtj/3d/zSv/15/JW3IQiCoC/925/7O0La6cL97yNfNL9yBylWGPOltg/H0ROp6v2PDGchfSWFo3dnMIvsYtSGI9VqG2C4r7dPFN45gRT+aH+8QZPGNUY7PWEzZgCwZJA5mplaslqj6RX119Y3Ctu7JFKZWCLtF0sGBgfFg9rBRAbEg6IBcU+fqKOrWyBs59c1MJjssvIKsXjE0hQKBXNqueMAOdwxXJB8ytb2VLLBal2QpcmntA8qOzg4IEtGatAXRuaRWdtTyfoyI7UhJQz2dcfBYK9GB11LfpgnPBlZvvlW/pQeULaEJTNXwLzzcN19AArUBsAFNhOe9zHnrrZRsP6zzzdu+gZ0IRsJsGSzQqTQd+zcvWTpsjHDkQSHYQwHq1vpgYujtc+EOycc9z/Xzfo7vn2iUPuT1h7n7/ilf/u5bbo7xbaYOH9HCPpIW/75lbe3xeiL3Tnx0Zf+oyqBtsXE0XTLDTfUcW6b4znDxvg7aksabKivVl/PqH3RRrdc16TxjdHPjm/GTACWDDJHM1NLhmFYoVAK2zuqqutahZ19InHfgLivf6BXJOrp7evu6e3q7uns6mnv7BII2+vqm9icMjantKOrC3m7BZK+vj6WNQV1S8bkCX+6W+QZO4W/j1jCkskQzL8HQA3yBFeDMWcTEx4JhguZKsCSLQC1iOPs4rpw4SL9cCRECt3GxkbfzbzSAxdLbZsRfo7QtuhYalsslbbv/Y/2xbXFxl354jgNWfWFX9vZbR/tizPchLbvfejt47TYuCtvIxv6OX7h1xZ4/KMv/LQVvn2cpv1JNViu31BbT/QX2v2OtOQLv7YxG+o3106M2Re1LfA40pc8qknjG6MvP64ZMwJYMsgcjQksGYZhuVze2NRMZzJz83LTM1JxWSlkCraAhiVRUnPykrKyE7PwKQQiLjcPX8hgCNvaFcqRbzKqVKra2lq2NWU2WHJoTuvmWxTjH1C2kCXXhgFQw8CS+/v79ecdOWVgxOlpAyzZYpRXN/x69PiSpcvcTnuwy6psbGxeXbCAWsQRmcSSqW1n9b3FkONZxF9181/4tcXGaZ+4ePs4TV/yCz9dsW3ROgfVb+V4ljpWdkdvaGC9L7NkfbVvvz9iyYZNGmXJ+iaNawyyr/HNAJYMYp0xjSXDMNw/IMrKSfa8+OuR4z+d8Txw/dZhn4Ajt/1/ve7zy9UbrleuH7p++3hI2DVOaZFCoTDcUKlUcjgctMXVopnQkll1vWbF3q/A0JIfEAS+2IZ1lwllzSJjzq+lLDkUgBoGlnz27Fn9eRcZDBeCugbNRYAlWxhkOJIlS5fZ2NjY2Ni8u2JFc3vPSg9cDKXNdERvgD7aG2vCCieDtnfrlQCz78XsAEsGmaMxmSWLJf1ZuXEnz/7g9POmw8e/P+r2/S9HNh869u2xU9tOnP7u6Mmtp8/tDAr24pTQh4fl+q3UarVAICi2skxoySs9cDsCaBOy3R+BOobvx+OnxWEc3/lSb6XWGVryfYLgzLPKL68RxUOK8e0ZEwtZck0IADV0ltzc3LxgwQJkeufdIjDi9AwBlmx5UjOyP1j1oY0uGzd9s9IDF01pA6AFsGSQOZoZWbIGhmEYVqvVXV3deHy2X8CF0+d+OHziu3MX9p/y2L3XaYOj06euRzb9cnTTgZ83nPXcE+B/NizYj0Ii9ff3I+OJDA8Po62sKGQyS05ldabqRvVEhixCXsmeSO9IoHckFLbHF7Y/Lxz5PgTyl6xnBdp/rEdT2qLyhVH5wkiyMIIsjCALn5KET0jCJyRhOFH4mCh8TBSOseR7BMH+MNbRx+yXnmtLWXIwADV0lvzjjz/a2NjAMEyhUBYsXOx22gOMOD0TgCVbktpGwZ59TjbjstBuF3J5BKACsGSQOZqZWbJGI5ZIy7mVD+5jHH/86fP1//ryi1Vbt6zdt3fz7l1ffvXF/3yxwXbLt6u3fPvhxg0rv9/88d4dX/+47etTh355FvG0taVZoVBUVVVxrC+zypKD8a3f3syPyG948bm2lCXfBaAGGYJhmMlkIlZx+fLlZcuWfXX6MereM9cBlmxJqEUcXC4Joby6Qb98pQcukiwEoAWwZJA5mpk+cSGTyf18Azdu+HK17cp33npryZtvvvP3RbbL33pv6d/f/tub7/ztjRWLF7731iLbxX9buXjRv/+5/Ou1/9q87uMDu3Z0tLc1NDSUWGVmlSXfyxXcxjZ8cuklDyhbyJKr75gJvtdbyJdRXDFmqB//vZ1hzfjv7aC3gvAvaszYZmDsXDF3+F5v2XldNN9BeAlkCIbhjz/+GLHk1atXd3R0GHM2AS8GWPJsYKUHLkJ3YQRYHmDJIHM0Jnguua+vb+u33y5+880lCxcuefPNJW+8vvSN15e+8fqyN15f/ubrby9845+L3lzxt4Xv/X2h7T/+tmrZ4u83bmgTCioqKigUSqlVZrZZcliuwD2m8svfiN1i+fiGIbGUJQeZBbyD3SoHfnUQXH0haBcy8cLCu1ynVgDv4LrLTr8Qt2ux6y67IPykNfC9FrtiRi/E2I1dYnnIUEJCguEfqRctWmS7aT94qcUMAZY8G1jpgXuiux7OEm4fWf3pLd1stPcyCBo9u/qn6JfXsOwIDfUPYgzAkkHmaEzz7T2xWEzMy/vXqlWv/elPC/7433/9o83rf/zD63/8wxt/sln45z/+7S9/+vsrf17y2qufrPqfpLhYqW5gaiqVSqFQyqwvs9CSQ3MF+0NZTmGMyU6xhSyZF2gO+J6LXTGGS1xdta85WovjBcKYtXarFutmzwetgiAIglY58PEOdtruZ10NmLV2nq4jBfQV4h1cPc/jdq7FIZXvdOV7rg3Cj9vRyCzkigmEDeqHMWsNfxq0JxDGrIVGVWIuZDnQsmXa1wK899573377rZub20c7TqZmZCMvoAVMD2DJs4GVHrhwknA2Qftxi/ct/Wy096dbHJdtiUJmT25Z/ekWxx+jR28S7a0vMOcAlgwyR2Oyd1zAMCyXy6t51U8eP/7loPOO77Zu37p5x3dbdn9vf8jZ6eG9sKrKCrlcbjiYCGytojxDS9aP8IS8LHNiS77t+PntqVnynezWb2/mh+JrJzy5lrJkf3PA91zsiploFrPWztMTxqyFdrqMLMfb2+10gXn+uJ06O93pAvM8g1ZB0Cp7Pm+kwAh4e1dPT6Q2pBK+59og/Ngd6WeRiVH1Y9a6Yvz1P0e1R2fJhh/BHNRE/wfyP+OY846638x1gCXPBlZ64B7rLoOzgpuOy47QRmajvNcfobltcXQjCh8To9ZvibpxxHF3lPBxlPcyCIIgaP1N2u73IAiCoPe8b9zUjiPtdtNx/U3hY2LUegiCIGjZEdpj7Spo/U20P+BogCWDzNGY0pKRqFQqpcGgITAMq8ctMQyVSo2xssTHx48/DkZa8p0TH2nHREX6kuOuLIcgCII2+LZF+yLXx9V7YnSWfMsRuXQaY8mhOYKb6Q3/vpDDqusd3zxLWbKvWcCsgXYehHm+MO9s0M5tOM/FrhjtcjvPs9qfPF8+shy/zW7nQZjni9u5OAivr+Rs0CoIWrWNzxspMAJ+m6vnWZh30HXVGted2/g8X77nmiC8rkLdjvSzyMSo+jFrXDEjPw3bw/fchjPTYRmDEWPvAaYBsOTZwEoP3KM84ezhxGbHE4ZLIr3XH6Y9uum47DDt+uHV628Krx923B0pPLFZ92/pzVGPIr2XbY56lCd8dNNx/U2hfgIpr63npiMErd4dif4HHAOwZJA5GtNbMsj0YqQle9o7nqO13znxEaQd4emjvXFtMZToDVujo2O1xvz5ba0l+xxZDW2JMrIvOTRHEJIj8Iqr+dSbMP4BZUtZso+Z0HfcumJ8YJ6z7smHNTiej85Kffie/xhZu9MZxm9Fnoiw8zyL2wm5YnxgzBpop/NIAX39+K2uuhp0Va0Jwo/bkW7WbtU/XDE+hvXDmDVI/chPw/bwPf+h63J2NuEBmQBgyeYBWPJsYKUHDpMnnDVErdscNWpJpPe6wzRMXtS69xzXbfa+lie8dthxV6Tw+ObVuyJHyixBtrrpuO6mUD9x7fBq7awW2q73oCWHaWh/xlEASwaZowGWPFsy5b7k51e+GG3JHlsd3fOFUT6Oekt+ShI+ueUIbYky3pKDcwQuDzj7xz2gbCFLrroNGANu579wVbfhqjNBq5AJswEs2TwAS54NrPTA3ScIZglXD69ed2P0wgjvdYepyKol2ok9OyME9yO8l0AQBEFLDlPvE6LWQRC0Oer+jT3azZEJgzJHdX3PY+tHG2DJIHM0wJJnS4x+Lrlw//sGzyVrn7j4aG+s/omLkb7kU1ugKT1xgVjy6ejKr8YNyGcpS74JGIO+L9nO0928+wKWbB6AJc8G1l0m+Gc03csVzAKoP2z2voJ+MywKsGSQORpgybMlqL/jArHkm+kN6y5P8O5kS1nydQBqAEs2D8CSZwOuD5jYx2NvAAAgAElEQVQeMZVhuQKA5bmeWvf1DfIMzyCwZBBUAix5tmQ2WHJQduu3tyhRlMbxzbOUJV8DoAawZPMALHk2QKvp+fYWJSRHALA8jiHMJ+SGGZ5BYMkgqARY8mzJbLDk/fdYx8LZEzbPUpb8GwA1gCWbB2DJs4QgXLW9H+23lLrgHAHAMtzGNh24zzkZUTLz0wcsGQSVAEueLUHdkt1jJngcWR8LWXLlFQBqAEs2D8CSZw/4svZNN8grPXAAy7DuMuFOVo1Jzh2wZBBUgr4l8/l8tN9fbOlgsdjxxwFdS76FnfhxZH0sYcmFb8LcE3DlZQAKVJyH8//PhOcddbOZ6wBLBgBmDrBkEFRiSktWKpUqlWp4eFiDBNb+R6XRqDXqCTfh8/kZGRn5VpaYWTZC9V186+ZJHkfWxxKWXOEAF2+HKy8BUKB0H1y8ZsLzjvrdca4DLBkAmDnAkkFQiWksWaPRqNXq2tra4ODgvLy8oaEhjQZWwbBKo1GrNUq1WqVWapTDip4u9aBELZMhW1mnIufPPkt2usfyjC0b3yTDWMKSJSUwYzlcfg6uuACwNEXvwb1ZE5531O+Ocx1gyQDAzAGWDIJKTGDJGo1GKpUKhUIejxccHOzv78fn8zUaWK3RqNUqjVqhVqvVMpmkvKw14CZ306c1Vy4MD/Q3NTVlZmZSrDKzypLPxFRuvpUvV07c2a+P8YrsFF7V0it78fXu16ia/qGJBi1v9oXpS+ESR7jiPMBClLrARe/B9WcnO++o3x3nOsCSAYCZAywZBJXM1JIVCgWTycThcHg8nkAgxMXFPXv2rLq6WqVSadQajUqlUSuVg5I+GrX2jBtj9XuZi17L/varpuxMqUTCZDLR9lV0Mpkl7wigTch2fwTqGL4fj58Wh3F850u9lVo3xpJvpNWtu0xo6JS89EQbb8kuT3nlgsEXX+98s5tZjeKJ9zRAh4v/DZMhgIUo+ueEvcgwDDf1yM4l1aF+d5zrAEsGAGZIW798/+MqYMkgls+MLFkul5PJ5ISEhISEhMTExISEhOTk5JSUlISEhBJOiVw2rFFrVGJxFyGn7Gcn8tv/SHl9Ae47+0ZcpnJQrFGrZTIZnU4vsL5MaMmsul6zYu9XcDWh1tCSQ3Jav7xGSmW1GnOup2DJETxytejFl7xnjI6k4q6Z/O6BWCD5NaIQogD1G+RcB1gyADBDygWDLk95wJJBLJ/pW7JKpaqsrHz+/DnSfxwbG/v8+fPUtDQcDhcQGHj79u3y8gq1dLCLkM3Zuytn0Wvxr/w5096hOSdHJZVqVCq1SqVSafr6+qjWlwkt2dzZG0IfY8l7gou8XvY4sj5Tei75bl7riy951e1Sz6R6hUpj1o8MMsP8hm1iNg6gfoOc6wBLBgBmSGxRB+hLBkEl07dkkUgUGBiYlpaWRyTSaLSSkhI+n9/e3i4Wi8srym/duvk4LKwem8Zx3pv1xqvx//0H7NdfN+FwqiFEkZUqtVql0SiVysrKSrSt1dKZDZZ8KrJ88+2XP46sz5Qs+ZfI6pde9VI4XZH0DrN+ZJCZJLeq7z5ZiPrdcR4ALBkAmCFnE+vAc8kgqGSalqxWq6OjowMCAlpbW9VqNazRwBq1RqPWaDQwDCuVygIi8d6p00mbv836x6Jn//X7xH/9b+2zWKVkUKNWq9UqlUat0qhVGo1arZbJZDQrC+qWfDutfs2FHGMeR9ZnSpZ88CkvldP90gvfjcymWGYn6FGehSHXiC6m1HeLFajfHecBwJIBgJlQWNdvzOMWwJJBzJHp9yXfv3//6tWrVVVVMAwbWrJarZENDTWxOVjng5Gv/CXmv/4zavHfmbduyLq6NWqNWqVSq9VqjVoNa9QajUajUSqVXC4XbXG1aNC15Hu5gi+vkfIqptaPOyVL3vOw8lBUTVu//KWXv3hWp2dSfX6NqKlHZqYPDmJ8usQKVqP4Fq45hCgw5vQBjAFYMgAwbbrFCre4WiPvO2hfQUHmYaZvyXg8/syZM3l5eTK5fGBALGxrr66uKaTTMzIz4yOi4s55Jnz0Ydz/819P/mCTe8C5r6oS1mjUKpVKa8laRdZoNBq1RiAQFBqX+BPvQ++fiDey9GwNupbsGFx0M3XKV5OpWvL+x1UXU43qjKxul4YQBeeSjPqDGsCsnIit9c1uLqzrR/3WOJ8AlgwATJuAnBan8Jc/kYxgjlsniJVnmpas0Whqa2s9PT3Pnz8fFR31LPxRtL/vkxs3HgQEPLh///Etn5AvvsT8f//v0//43fNVq+viEjSKYY1ape1I1gVRZI1aLZfL6UYl3s3W3t4esg8wrriFEu9mO7UWoWjJ7lHlu4Joxj+OrM80lMspvMoruR50SQKsHGDJAMD0CMhp+Tmi2vibjjlunSBWnulbslgsjo2NvXDx0t27dyKuX4758buk774meHpU52bz0lISv9r48Pe/D/3P3wesWZMUGtrYUKdWqVRKtUonyoadyiqVyii7DLC3dYunB+g1OcAeQmLrFj9+lh7vZqtd8NLyAYh+I4XdtNtpqxldT7xhyYAJ9jqLLdktgrv+MkHYNzSNzafXN7n/cdWRmJqXvhgOAJjHAEsGAKYKs3Hg1HO+8b3IwJJBzJTpW7JKpero6KiprW1ra6vLy87ctTVh6aLsjz6g/eKCdXZ6sHjx/d/97sHCN32/++7IAaegQP92QZtKqVYqFGqVSqPW6PuT1bAGhmGGEQl0sD2ZyGAwEk/aOgQazI9ZrY2u1MiaF5RPPGkLIaUDHSBkKvGkre3JxHH1BJ60hfTNGN0eo4OWJa8+lzXVx5H1mcnf8V0ieIdjauJZndXtUtQvvgCAhQGWDAAYSV2nDFvacz6lwWUqXcjAkkHMl+m/40Kj0SiGFZWVlWlpqQ/9bl/f9o3f0sWY/7bB2Pwh6I9/DPqv/wr9/e9TvvyqJOZZaFCQk9O+57Gxg2KJSqlELFn3TLIGeS2GUZIMjQTR0sSTtiMzY2ZHFX9Z+RHRHZnSu/Woek7q3Hi8tc9uS3YKY0zjcWR9ZmLJCHsfVR007nvKAMB8wsbGBvU2AABzAuenVQeeTP82YcI7JggIkun3JcMwPDAwEBQU9NNPu729PBPuh+YH+aXZb7v35z+H/Md/hP3uP4L/7//N+8VVXFXJK+ee9zx3+JdfGIV0lVKpfSLZIGq1uuilCXKwPZmknU46aesQpF+TdNJ2ZJV+NsgBMihjmAnKOzjoKhypWrvDMfUYbKxr0Zj6Xh5ULDkIVzONx5H1Qf3qCQDMUYAlAwCWwYR3TBAQJDOyZIlE4ufnt3evY2piYregRdzS2JickLR1c7CNTfDvfnfnrwuIXudENby+NmFWevqB/U5XrlwVCoV6M9br8vDwMPNlueNgeyrZYA6ydXCw1ffw3mEyk0+NmkUK6WJ7KnlsgVGzyadstRuNTOn3OKqeO6dsdQ0ZU8CwfS8OKpY8w6B++QMA5hDf+xB23i1Cpg0t+XsfAuptAwDmK2jfJ0HmYab/JjiNRqNQKtPT011dXKKfPu1obu5ubu2sqCi6G3Tvww+9//M//ZYvJ/r4dFVW9re2COr4YXfu7vlpT0x09KBEov/2HvLIRV9fH8uaAiwZAJjf7A7jLPrn6q9OP96js+TvruPeWvXpdn8K6m0DAOYraN8nQeZhpm/JyOAgJSUlri6uVy9crC0r62xqaqysLMHh4k+fDvpyQ/RBZ1ZcvKCM291Q19/WWlrEcD950sX5IINWqBxWKIYVKqVSo9aoVKra2lq2NQVYMgAw71m796KNjc2KDbttbGz+tfvsn/7y6v9s/hn1VgEA8xi075Mg8zAz6kvWaNRCofDsmXOuBw8W5OZ2NTc18qoqihhFGVhqbBQnLZVPLxLwqnqam8SdHf0d7SkJ8bt+2HnR63xTQ6NyWKFSKDVqtVKp5HA4aIurRQMsGQCY9+wO4yxYuNhGlz/95VXQkQwAmBW075Mg8zDTt2QYhtVq9aBkMDQkdNfOXc8iojobmzrq61p5lS2VFYKqynYer6Omtq2+tqGKV0Jn4rEZAb4+32zatPmbb7NxWUqFUq1Sq1QqgUBQbGUBlgwAWANIdzIS0JEMAJgbtO+TIPMwM7JkjUajVCqzs7J+2v2j783brbV8SUd7v6C1o76+trS0iEjMjI9/GHL36oXzx389cvDAARfnA8ePHvH38y1msRTDCrVaLZfL0VZWFAIsGQCwBvTdyaAjGQCwAGjfJ0HmYWZkyTAMq1QqHo939MiRY4ePUAiEcjY7OzXt/t27F86e+dXloNOen5z37T1x9PBvV73DHz/Mzc6qKud2d3bIpINKhUKhUFRVVXGsL8CSAQArAelOBh3JAIAFQPs+CTIPM1NLhmG4r6/v9u3bX2/c5OLkdOLwkQN79zrt3XPkkOulC173QoMzsencspL2tlappF81LIeVClil0KiUw8PDDQ0NJVYZYMkAgJWwO4zz17+/DTqSAQALgPZ9EmQexgSWrNFoaDTazy4u+/buveDl9eB+WHZWRlkJR9jSLBaJhoeGVHK5aliuHpZrlApYpVKpFGq1urKykkKhlFplrMqS9z2ucomont5wowDAPAC8IxkAeCkuETyXiOr9j6tmUgna90mQeRgTWDIMw2q1ur+/v7y8vLW1RSIZUCrkaoVSo1CohxUquVwtV6jkwzKxpKers6+vT6lUIltRqVQKhVJmfbESSz7whHc4piaK3sFsHGA2DoikSgAAAAAAxlPSLGE2DiQXdx2PrXWJmOYg1WjfJ0HmYUxjyQbRqNUqlUKhkg8rZHK5dKi/t6++tq6CWy4QCORyuUqlMixtnaJsDZbsGlkdw+hA/coLAAAAgLlFRlmPa+R0/vyI9n0SZB7G5JY8QZB3WUy2lkqlxlhZ4uPjLXDYTZspXaoORdWUCwZRv9QCAAAAYC7S0is7m1i379HUHsBA+z45ZzI8PCwWi7t7etra2pqam+sbGurq6+vr6xubmpqam4VCYWdXV09vb//AgEQikUqlMplMLpcrFIoxHZ3WEEtYMsg8iPHXqZ8jq6vbpahfZAEAAAAwd2nplf0aXQMs2SRRqVRSqVQgFFRUVdLodFxWVlxCwpPISEx4+INHj55GRsYnJmZmZZHz89nFxVU8XkNjo0AobO/oaG9vb2lpaWxsrK+vr6mtra6p4dXU1PD5zS0tfSLRsEKB9icze4AlgxgV459FBg9aAAAAAGDmZJf3/jyVb36jfZ+cdVGr1VKptLu7u6m5qaqqisfjVVRWsjkcCpWahccnpqREx8aGP336MDw8PCIi9vnz9IwMUn5+cXExr7q6qalJ0NraKhA0NTfz6+qqeLxSLpfF4TBYLCqdTiCR0jMz8Xl5zOLipubmIZkM7c9qrgBLBjEqRl6kjsfWon5hBQAAAMD84Aq28ScMsOTpRCwe6Ozs6ujo6O/v7+3tbW9vb21tbWpsqqur4/F4pWWldAaDQCSmpafHxMaGR0Q8iYx8npiYk5vLZrNramsbGxvr6uoqq6rYHA6FRsslEjPx+LTMzBQsNiU9PRWLxeJw2bm5BBIJTyCQKZSm5uZ5+TwGsGQQo2LMFWr/46rYItCRDAAAAADTQKjsPfjU2O5kk9zsWHW9JqlnGsmr6DBJPRqNpqurq6WltaOjs6mpubGxsbEReeS4ubm5paWlpbWlpaWlpam5ub6+vorHYxezSWRyOhablJKSjcczWayKysoyLreQwcgjkbJycjKysjKysjKzsrKys/E5ObkEQh6RSCKRKPn5lIICGo3GKS4uKy2tqamZf6IMLBnEqBhzhXKJ4JU0S1C/qgIAAABgftDSK3MKN/Y7fDO/0zV0Sr7zpcy8nunF+R7DJKI8JJOVcbk11bWlpdzSMi7ys6yMW8YtLysr55aXl5dXlJdXlldUVlRUVlRUcrnlJSWlRUxmAa2QnJ9PJJGIJBKZTKYUFNAKCxl0OrOoiMVkFrPZnOLikuLiUg4HoaykpKy0lFtayi0t5VVWVldV9ff3z7z9syrAkkGMijFXKOenVXWdMtSvqgAAAACYN1jsuWS5Ur35Vv4XvxFNctOcRvaF0NdeyOkWT/pOMCPT09NLzqfQ6UxyfgE5v4BELiCRKERifh6RTMgj5eQSc3II2dm5Wdk5uCx8VnZObi6xgEovKyuvqeEzWay4588D79wJunMnIiIiPT2dSCLR6XQ2m13C4ZSVlZVzuQgVBpRzudVVVeVlZcK2NpMcitkTYMkgRsXIixTq11MAAAAAzCcsZsk3Uiq/voGyJX/nU7AvlDHDejo6OhOTUygFVEIeOQOHT8/ISk/HpaVnpqZnpKZlpKZlpKVnYjOysvEEEpnCYnFqavkdHZ0DAwPd3T3ccm5CYuKNmzdPnDx55Ngx9zNnfrt+PTg0NDIqKik5OSs7m0giFVCphXR6UVERk8lE7LmioqK8ogKbkcmvqzPJoZg9Qd+Sh4eH09PT0X6FsUWDxWLRPupTDrBkAAAAAFgey1hyXkXHp955N1Lq0LVkvzS+gx81FF87k3o6Orti4p7nEvKKiphUamEugZiNJ+CycjJx+Kzs3FwCkVJQyGJzqng1LS2tvb2iIZlMoVBIpVKhUMgpKUlLTw8NC7tw6dKRY0edXVwOuroePnr0tIfHJW9vHz+/0Hv3nkZGJiQmZmZlkchkGp3OLi4uKS0lUygxz2KBJRsTjUaj0WiUGo1UrRIp5T0q5aBGo56w6PDwMA6HIxAI+daUmPk79h7q11MAAAAAzCcsYMndYvn6y4TL8TX+GY2oW/ITUuuaCzllzaJp19PR2fXseXw2PodKLSwooGXjc/E5hAJqIYvN4f7/7Z13VFtXvu81yZ1b1qy31nvvlvXWvXetVyaTuTMB35mXeMbJm4nj2MkkGVsRcktsbBMb4xbT7MQdsMEYbAQIjEuw6U0yIHoREhIggZAoxqaZZjAIAzK9qev9cYqOCiAdlUPZ3/Vd4ZS99znn573P+Whnn7Oft3a+6O4feDUyOjY1Nb24uKhSqdVq9eLi4vj4eG9fn0QiKS4pSUhMvBMRceny5e+9vU+cOul18uSpM2f8z5+/Fhh4Kzz87r17ScnJDCYzLz8f6l2urqnhVFZmMBiAkpeVVqfR6TQ6uVbZq5osnXp5b0hyta/S91VNkOwFc3GmT6tVYZNvTESuApQMDLyWXOhB+jD0GeGnYctJrolLAAY2bydQ8qG7dZ4/NT7kDK0GSn5SNxKS27kjuHJmAeecHRAll1dUCIS1VVU1efmFT7Jz8wuKK3nVjc1Pe3r7Rkdlc7NzCoVSpVKrVCq5XD4zOzM6OtrX19fy7FmNQFBcUpKZlfVTfHxUdHRoWFjQjRtXAwKuXLt2+cqVHy9evHz16o3g4MioqMSkJFZeHofLrREKuTxeJqDkZaXVarUa7YJqQbz4MkDG+7Qj5Z0G+n8X3/mFhPaPTY9+/7Lq0sKb5zqtRqfV6TCIXL3xBCjZrMdn5E0tbSWlZQwGk+AxMQ5TZmZmSWmZuPHp2JRN0xOCWFnpjtAtJBLJk4kn73KI2XT7w823O+DVZ7TNeGAUOjeSB8tw+zPaZhKJtIXWZPNJAkpebQbt1yo7mpLjyrt23a55wBlaPZScXTdyMr7xbEIDvnJGYUrmCIS1/Kqa/IKi7Jy8gsLiCk6lSCRua+8cHJJOTc/I5QoUkWUy2atXrzra2yUSCbeyMq+gIDUtLe7+vbDbt68GBPj6+584dcrj2LFDHh7fHTv2vbf31YAAWnR0ckpKXn4+t7JSIBRW8vmZWYCSl5NGq1Or5lvmu3xkJf+rM+5vG27+TBJCarz1VlPY2/UhPxNH//urmuvKeSmUenZ2ls/nE82rxCgdULKJB4Zl+fkFbDa7tra2qampZZ2qubm5tra2gsPJZbEGhmUgVsvILrGC/Yy2eYunxxYTErXIFlMyTneEbiFt3vIh6WghdjvzKGnzlg8BJa8/g/ZrrR1KyS0Dk38KqLhT2LfaKDlL+PqzEF6u+BWOckawlMyvyc8vys7Jyy8o5nB49fWS9g4sJSvlcvnMzMyYTNY/0N/W1lYnEpWz2U9ych4nJETR6cEhIRcuXTrr4+N18uR3xzw9jh49dvy4t69vQGBgJELJHC5XIBRW8gAlryCtViWTv4yaLvtV/09vN4S9LQn9u+ob/yX/h18U//h3dTd+Lg5+uyXhwzc9hdC4C61WOzMzU7MhBSjZyOMzchYrTywWE31jd54aGhry8vNHxmdArFYU7lihhli26TaWRAs9SJ5MlieJZNiPC/XgkkgkPVbCiMk8SjLsNvb0OEpC5cFSGcCovhy4A5t5lGR8LNgdoVtIHiwjkEVOD6VkfYGYEpDz33ybhmQv9EC7zJ/RNsPLZk9MX87S5wZsZ4P2i8OOo+SZBeX24MofM9oecIZWGyVn1408rOjfcpXdNzprbTkjo6MZWQyIkquqIEpm5RcUc7i8+npJO9SXPDWtUChUKqVCoZidnR2TyfoHBtra20X19eyKiuycnITExOgYevDNmxcvXfL29fU6dQqiZE8vLx8/P4iSkzB9yVweLyOL0QMoeRkpJ8VzAup40t93RL5Vd/PtBvr/acg4lBz01a39/zXH5+/EwX8jpv3zy9oQtRIek65Wq4eGhohGVgIEKNnIInEjh8N5usHEr6riVdWAWFkifLFC3BG65cPQZ1hqVE3OF3qQSDA0szxJKE0iVNp0G+3HRRATw6wocBv2JRdiUBUhTpbn5tsdk89om5fsFYYo2YDC4fL1RzQoUH+2yEbmURTrl6dkTDno3uXODdjOBu0Xhx1Hyf4pTQdjxfc5Q6uTknNEI9cYbW4RNXKV+e8fLKWR0dF0BqOcje1LZuUXFFVweKJ6SXtH59CQdGpqWi6XK5UwJctksoGBgba2NgNKpiOU7OPrdfIUti/5WmBgFGbERY1AwOXxQF/y8lLNDzDHWb8buvtWS/hbtSE/f86gvhmokVSmR578ffx3bwuvvy0K/4eu0lPymX40j1qtFovFgg0mQMlGLioqrq2tbd5gkkgkT7KzQawsEb5YwTaHlYaDEMwNSDCPmNACzLWTS1Eytg9YX9pSYx6Q0vS5EKxHtxgUaJre6CSXpmSz5eAcTg2Mx6D94rCDKDlX/OrTYF5s+eBqpuQc0ci30bVh+e1WlfN6ZDQ9M6u8giM0oORiDpcnqhejlKxQKJRKpVwux1JyvTElh0CUfOLkqaOemL7koCAsJQsEgkoej8F80tUNKHkJabVzb57Fdj/8n8/DfyYO/Vld+D/28QKVi7I3r5pKor7OOPmWIOgtcejfduS4z8kM/r2npqaEG0yAko2cmZnZ2NjYtLzyz28ikXbHYFc3nc9fOdPKiSxOZqHyz2/Sn+eySk9Pd0isYMXstiBEBMryQDXhihVk5lESOtACs2yekptuf6gfRWEyXAHu7sX0SVtKyWjJZnptUeY2wV9kwXCsCHIa+CjZUCjrL3FuwHa2Fe03//wm+F9p5VYCNaVl7mP2vcU1Wdl4m2xov5OOoeS+0dktV9mheT33K4aMKPnDq+x77C5C/FlIpSklp1cPbbvOtWrm6umZmRxWHodbWVsrqoLf3mMVFOpHXAzBfcn6ERcymWzg1UB7e3u9yYgLo3HJnl5e0LjkKMyIi9ra2uqamkwG8/Xr19bzxaqW3ShZo5kbfs4UxX9dcv3d7Ev/xI78YKg5XatRzEgbJQm7uJfeFge/XX/z5x15HvPjXYYZNWKxmGhwdaoAJRs5PT3dkrvy7t270RtzzO5Nu3fvtvS2b+0d3TY5lJItihWkmN27d+9ezZjsFEou9DACQ9OhuugydkiG2UG9LE/SFhoTw6yWU/LkvAp+Uc/4bT+DnmnS0UL90AvH9iVbcm7AdrYV7Tf/PHyDi1m5FS/ZlBx261vTlCxXaSgRNd8nttyvGDKi5NiyV54PGyEff9h4/Ce9vQx94qfGE/GwTxr6VHzjqUdNqE9Dfqz3GSMnNH2P+BFnwIiSc0QjMaW9W4M4Vs1c3dHRycrLr6kRCIV1xSVlObn5BYUlFZxKZMTFMELJKujtPbgvub1dVC9iV1Rk5+YmJCVBIy4umIxLRik5JTW1sKiouqamRiAoKimpE9k6a+AqlB37klWzk0P9bdVN3ORq1u2nVfGTYx06rXymK7s3efPz8Lcbb71VG/aLLs555bzBTyKNRtPb21trnWgU9JFHoS2RhunjAu2nUZZOtOwhcOSySICSTZ8cjSsq7xz1XF4MlRrT2NjYGEOlxuSdo57La2xsjKHCVYEa09jYGEPdtGkTdpUak3cO2rDpXJ5JYngZKtckLwmbGD4LfVnG6c1mWFb4KNmioqFLb4yhImcK/UGDRKUaXAh81sjlmYmG4S6S2WWDfy57BgpfrCbnscMtIKODLsxRMiYx8+iSPL0Z860MkzcClxiXjJyAwSuAsPWUjLxah5wwznHJ2I1Lj0s2sblzA7azLW+/jcgNDm69ZhupUVOC74/wxk3nYvSt0HCX+bulwxov/vY7r5p0ACWHstqoNOE9CJENKfknrvQnrjSeK43nSh9xpY8qpY8RJ1RKE3jSRMRJPGkSX5rMlybzpSmQq6SpVdLUKmlalTStejiteji9eji9ejijZjijZjhToHeW8DVqhvA1o/Y1s/Y1s/b1E8gmlJxbP+KT1Oxxz1IGVSqVAoEgOSWFFhkZezcuJTU9O4dVUlLOr6ppbHra1d3z+vXozMysUqlUq9VyuXx6enpsbGxg2bf3Tpw6ddTz+HfHjh0/ccLv3LmgGzfosbHJKSlp6elxcXHh4eH34uIKCwrm5+fxUsYqlT2/caHTaXQ6nVqlUsgXlIoFjUaumn4+LTw9lvgvPdFvN9z8WV3U/37VcF+jNg7i7OyslZxJo7j4MGtra2uZPi7wkrH0eyznXaaPi6PIGCtAyaZPjoYVxTpHPXP0aCAAACAASURBVMdqoFM3nWOxzm2i0uEN0HJDQwO0q4FOJVHpDQ3IdjqVSkdXzCRGV6HERnnhhwHd5GTMHQstXH+UlYSPki0qGrk26EyRuJ2jUql0ZJtpHOj6ByDd6OqMd5GQIrDL5s7CLoHCF6tJc+SHvJZnfsQF+rWHzbdpHmYoGYueqsl5/Scjlv/GBWYgh+k3mzGUDH07GcVubNevfrAEZgyx/hsdhh3e+o3LfePCgnMDtrMtbr8NDSyYVeHmZa6RGjclOhXbvgzamNEus3dLhzVe3O0Xsn0pWdA59nEQN7pkYG1Rcm79CJVm6czVMpksOTn13r373j6+FIoblbr7+PETV68GxN17kJ9fKBZLBgZezc3Na7VweqVKOT0z/fr1667u7samRh6fl19QkJqWdvfevVvh4VeuXfM7d+7EqVPfHTt26MiRQ0eOHPP0PHnq1OnTpz2PH9+3bx/Vze3M6dOxdDqTwRh4hefTdatZ9p2hWqPTadFlzcLL+bYbk6x3hx/+vPXOz+pC/uFp+t7JQTEmDSyVSlVnnSIprr7Murq6Oqavq34JvndQIuvqIin6lUgKiRJZZ5oGKsggHSRXX2YdJhe6nUSJrGP6ulIoFMNCrBWgZDxPDgiKG+jUTVQq9RyrYUlKhp4p+CgZm5d17pzpU8DgyWWcfjVRsv5ZStqEjRudSqXCvzSwF4I8STG8a3h1BruQSCCbsMsOCRS+WDnCRkOEgYGtslWUTMW2KXON1HZKNrhbOqzx2th+7UjJ0EzUVxkv7lUM4aHkMHf4rvpe0G0jSk4P2nZWuAQlCw+5kEgkEomcZgslp1QNfhzIsWTm6tGxscTklNjYuAsXL3l4fPf115Qdn33+ybZPt2/fsXPnroMH3b29fW7evPno0ePc3Nzy8nJo8goen19eXs5isVJSU+/Gxd0MDb1w8eLJ06fdDx1yo1K//Oqrzz7/fMeOHTt27Pjs88+/+MtfvvryS/KuXfv37Tt18uSNoKDER48YWVl9L1/aAThWk+xLyVotYrVqcqHn8VTx74cf/21bxFuim38jebhZ2pyqWpwxl01rNSVjgRbiXz34uvoysfyM8K5pGj0J1xmXgu6LpKAHiaSQKL6+rvC6/gCAkm2nZMmKYvlT/VkSiYTl7+oKL0AbUB6k0iUSCZ2K7HWl0iUSOhXeCu83TkylSyTogmFelr8rNi1yFvBGV3+W8bGQwl1dXTE5lhM+SrakZOTUoLOCr9zVn4X+NboQ+ISRrWauDrMLG0TDgGL/uewZKHyxcoCx/b7AwFbbwvYLNSEq0oahdmymkRo1JSiNaQIq3eBOuNTd0mGN18b2a0dKPnS37tjDxriKIdyUvDVMmsiTJoa5v3NGmBQOQfP7B9OFB98jkUikX50Vpt6GNn7gnolSctp2clpmhLuNlJxbPxKa9+KzkJVnrobm3stl5SUkJt8MDfP3P+/ldfLQoSN79uzbuZP8l798+en2HZ9+un3HZ5999de/Uihu+/fvP3To0JEjR9wPHtyzZ88XX375pz//6f++//57Lq6/fe+3v33vvU3/+Z8ffPDBn//8588+/3zXrl179+w5dOiQl5eXv79/YGBgbEzMEyazrKw8i/kEfAluOWmhXmKtVqvTLk52DfNO98f/txeRb4lD3hbF/sfLWppi9jVM0SYSWacoN1e/bJEo28+V5BYFbSBh5BYlEmX7ubr6ZcP73KLMpdEngZXt5woVp8+FTZPt5+rq5oauwydhvQAlmz45xKtPdDc3ulgsFuf6u0IL9hc+SnbMuax2rQpKBh9NA7bNoP3isL0oOZ7bvSu8Jo49ZAdK5qVu25malB70KxKJRCJtC8f0JWfAGz+9Y9iXTE7LFKRtJ6fZQsks8eipR42XM1uWv1Jo7r0KDre2TsTl8jIzGQ8e/BQVRQ8NDQ8KunHtWuC1gMCAwKCgoOvXr9+4cSM4OPhGYGDg5cuX/fz8PI977t+//6uvvvx468d/+OMf3//ggw82b/7Dli1/+vOfd+zYQf766wMHD548derCxYu3wsIePHjAZDI5XK5IJOJXVWeA7yVbIK1Go5mbnx/samouDKilu4pCfl5/55+62H4LU706rU6n1UDDl41Ub52i3Vz9curr6+tz/Fxd/XLq66PdSG7RBkmQPfp9pmmMt+jzmM0V7UZy89MnQU/CWgFKNn1y4Imjg5Xjh3So4PtntkD4KNlBJ7PKtSooGRjYNoP2i8N2oWRoJuqwgj77UHKY+ztnhP473c/xpcnh7lhKPr/L/Ycqadodd5SSo7zdD2UNZ0a4v0t23+4ttJGSn4hGPr/Jy5MMLnOxI6OjmQx47r2aGmFJaXlhUUlpGZvDreTxqni8Kh6PX1nJ43IrudxKDodTXl5eWFjIZDITExNjYmKCQ0J++PHHk6dPHTpyeP833+zZu3fv/v0HDh485unp7eNz+cqVm6GhkdHRPz16lMVgFJcU83g8gUAAZqi2UFqFUtHT08vj8liMR7n0b9jX/0UUt2mknaHTqnRarVan0Zobl2xli9MDKgq20fqeYle/HHOUbJoGswVKAa0acbc+l1s0FqQBJa9vSnaCACVbLkDJwOvAoP3isO2UDM1EfT69LY49ZCMlwzAAjUuGR1yQtoVLU/ip2wxGXGD6ku8guUgkEumDw1k2UTJLPBrPWWHmaniGanaFQFjLR2aoLigsruRVNTQ2d3X3DA+PTExMTk9PT05OvnnzRiqV9vX1tba21ovFlbzKgsLCzMysRwmP6bGx4bdvB4eEXA0IuHjp8vkffvD29fXx8zv/44/QDNXwrCJcLkzJDCag5CUFD7fQaZVKxbNnrekZjIcP7+UmhfB+2l+feexNf51Oq1uKkhcXF4n+30FOFaBk0ycHnpEra1/4KJnosyZGgJKB14FB+8Vh2yn5YkbLgRjxXQiRbaFkQr9xgVIySzwawGz7Jlq41MzV0IgLPSUXFGXn5EGziojqJe3tnVLp8MzMrFKpUqvVSqVyfm7+zfibV4ODnZ2dkoYGbmUlKy8vOSU1Ni7u5q1bl65c8fb19Tp58sh3Rw8dOfLdsWNnfXwMZqjmcqEZqjMYoC95acEMrNNqtJq+gf4nOdnpKYmVJcy22vTX3aULs0MIH2uMvnGh1WqHh4eteAtg7QtQsumTA89bkGtf+CiZ6LMmRoCSgdeBQfvFYRspGZqJOrpscD1RMks8eiCmbqmZq0dGR9Oz4BEXfMO590SYufcUCoVKpYRmFRmTYb6XzGabfi8ZO/ceNEN1JGbuPYFQyOXxMrIYPYCSl5RWq9NqdFrt3Px8a0drOaesoCCvkBHXUnRV1hg5O1qn1S6YdCLrdDqdWq1ubW214osya1+Akh3/5MB8mmTZVMt8zQ//R0wsFmGUjH4T0cwVQvsokcsGB5va0VGCBCgZeB0Yf/u1rD3aIoe2ZaIoWTqx8Mer7GBWz1320Dqj5CyB9NMbXEHnmOlVwyMuKjgCYW0Vpi+5glNZL5a0d3QOwjNUy5VK/QzV/QPmZ6hGKfmop6fH0aPHjh/39vW9ho64yMvjcrkCobCSxwPjkpeXRqtTaXUaqXS4vKysoDCfmc1IvONVEfLLxphfvaqP0KgmdMioDGw2uVxONLU6W4CSTZ8cdp64hUahUChLzDdjmMwJs8gsLXyUbIcDM30oPsxazPQ8GCFBWT44TpqBRy9AycDrwPjbr9n2aJdm6JS2TAgly1WafVGC0wktseyh9UfJeeLR2LI+szNXj4yMZmQxyysqTCkZmaFaOj09A1GyXC6fmZ0xT8n0JfuS0REXrLw8DkzJ/MwsZldPt7OoxEmy4wzVOq1Oo9Gou7t6SopKiouLs5mpyaHU4su/EN76x15+oFrxRqvTIYMuYKnV6s7OTiumuVwXApRstyfHEqJRKDSUAGkUFxfok+4UGjpxOcnFh4mmQ6jQxYeJzH1OoSGPJewWe2u1UDISFAoNuVwXHyZ0+ZhdtfqdNH0U4fChk8bDa9iY20WAkoHXgW2lZIOWhbmZYdsp3BYpFOydzTCB+UIc2ZYJoeRQVptbhDC2fGi9UnKeZNQ3+anpzNUjo2PpWQw2NOKCj1ByQRGHy6uHKFkKUbICpuSZGXiG6rY2lJIfJyZGQZR8+fIylIyMuBBU8quY2bk9Pb3OohInyW6UrFQqFxcXFxcXnz9vLS0pq2Czi/MysmmUsmt/L7z9P3qrQ9XKCZ1Op8NQslarnZ6ebtp4ApRs+uQQ2lM0MpkmFAppZBcfhlBII5PINKFQyPBxIdP0SVx8GHBCZBn6L4nk4sPAFIPdYm/ho2Q7HJgBPxmhgNDIyOvX0AUjW8k0g10GAURXaGQyDRNbNIymMbdNgJKB14Hxt1/0doRtWUgDM2jCSAvG3tmMEpgtxKFtmRBKPnS37vhPTfak5DB3EsndH6bk1G0k0rZwc5ScEfTpWaEBJd9x3x7hEEqmF/d+FlJp9BrfwuJiYXFJObtCJBJXVwvgcckFxXpKHpJOTU0vLsIjLrCULDKlZH1f8jEsJaPfuKjk8erq6vhV1U9ycicmJpwIJs6Q3Sh5YmKis7Ozu6dHLGkoKSmtYFcUszJSwqgZP/4zN+rDvvp4lVym0yp0WhVKyUqlsqWlhWhkJUCAku325DAr/QOBpMdf5C6P8CFJT8lCGtnFx4eMPgIYPi7YvZgt9haBlEzG/IBAIgTJiJL1u6yiZGzM7SJAycDrwLZTskHL0lMypgnrb1z6O5tRArOFOLQtE0LJshn5pze415hddqTkrWeCtp4RJvKkSWHu23a6bwuXJqNzi9yWop+B+9VZYVrmdWj79jsIJUe4k0ikd72F9qLk1Jqh7Te4Zuesbmtvf5yQWFpWXl0jLCll5+UXFhWXcisNKFkuV6hUKoVCAY24GOjvb8OMuHgMjbgICTEdcQGNS46m09PS04tLSnh8fjmbnZSczOPzncIjTpV9KFmr1Y6MjLDZ7PT09CfZ2QUFBfl5rMT4WHqQR0IwlZcZ1NdSMfPmlXJxUqNe0GpVOp1OLpe3t7c3b0gBSjZ9cgjspwiyi3cWskgiRyDrWd4u5AhBljdCyd5ZgggyOUIA7SKRI6AMcIdLhADaa7DF3sJHyXY4cJY3GYmJi3cWGhQX7ywBGhToL3aXUTIoKHBywzgZxtwuApQMvA6Mv/1CDc24ZSHNENs2kRYM79bf4/QJzBfiyLZM1Nt7gs6xj4O4UWWDdqPkMOGBnUHhPOGBnUG3w923hUvP7Xz/YLo0hZ+6bVfq+V3vu2fAfck/7EL779MgSo7y/oBk89x7WEreH137iGtmHPDCwkJ5WXl8/KNr1wICAoIiaFEJiSn5BUVV1cLmp896evtGRsdmZ+dUKrVGo1EoFLNzs2/evBkw+/ZeaOily5d9/PxOnDp17Pjxo56eXidP+p07d/3Gjajo6Hv370dHRwcGBl66eDEqMpKRlTU5aQbZ17TsQ8kajebNm3GhQJiSkhwfH5+UmJSU+Dg9NaE0L01SXdDTWjfyqmPmzaB8flKtXNBqNDqdbnp6uri4uLGx8enGE6Bk0ydHDaHK8nYh3yHguPgomYATXQUClAy8DgzaLw7bQsk6nS6U1fZNTL39KFkafub9rTvdt4VJk8xQsvv5KpSSPziUaTLiIsKdRE6zCyVfTH9uOiIZ0phMlpiUHBsb5+vrR6G4ff75X77+muLp6RUQEBQf/6i0tOxpS4tUOjw/P6/RaDQajVwun56efv36dU9PT1NzM7+qqqCwMDUtLe7evdCwsMtXrpz19T3m5eV++PA3B7795ttvD7q7ux86dODAAaqb21+/+orq5ubj7R0XG8vMyhro73ckiRAgO769p52anOrr62tpaRHXi582Nw/0901Nji3MTy0uTCvkM2rlnEat0Gn1H7iYmJgoLi5uampq2WAClLzKnhxEQTKgZCsEKBl4HRi0Xxy2kZKhL12cTXpmL0pO5KVuJbn782BKRkZcvH8ww/yIi195CyFKvkAm2WvERWxZ347gStOvW0AaHZOlpGXcf/Dw2rXAEydO7d//7c6du7Zv37F16yfbtn365Vdf7d237/hxrx9++DE4JCQ6Ovrhw4cJCQmPHj968OBBdHR0SEjIxYsXT585c/jIkd179vx1587PPv982/btn2zbtm3btu2ffvrZZ5998cUXu3bt2rd379HvvvP387sdFpaWmsJgMHp6wdt7K0mr1WphFEbnENHqzH4qeaOCMqBkI+flF3A4nOoNJj6fz2AwQKwsEb5YAQOvNoP2i8M2UrJOp+sbnf3jVfatgr718Y2LJ6KRz2/yua0jS13vyOhoJoNZVFL65ElO3L0HgYHXfXz8vbxOHT7y3TffHti9e+/XX7vt2kXeuXMXmfw1lbp737793x448O233+7Zs2fnX/+69ZNPPvjgg9++996v3n33V++++5vf/OZ3v/vdH7ds2frJJ1988YUblfrtgQPHjh37/uzZS5cv3759Oyk5ubi4mMPlghmqHaKJiQkmk5m+kcRkMomOutVyKCXzqmpKS0uJvpM7W2w2u7ikFMTKEuGLFTDwajNovzhsOyXrdLpc8auvblXRywbXASUfeyBZatY9SNAM1RUcbp2oXiCoLSwqTs/Iin+UEBd3n06PjYyMptEiIyOjoqKjY2JiYmNjY2Jjo6Kjw8PDAwIC/P39jx3z3Ltv3xdffvnx1q0ffvTRHz/88MP/9/+2fvLJX778cvfevd8dPerj5xcQGEiLjExKTi4qKqqurq4TiSr5VRlgVhGgDSuHUvKwbDo7J4fP51dtJOWyWC8HR0CsLBG+WAEDrzaD9ovDdqFknU7nn9J0JE6y1ik5gNlGpdUYffrNSCOjY5kMJhuZe6+wqKSgsKSsvIJfVV1XVy+RNDY1Nzc3P21qbm5oaKivrxcKhbzKypKSEiaT+fjx4+jo6ODg4AsXL3p7e584efL4iRMnTp783vusn7//jxcuXL5y5Xpw8B0a7cHDhxmZmQVFRdzKSoFAAM1QDSgZaIPKoZQ8Oa9q6+zJzc0tKysj+n7uDLHZ7FwWq6WtE8RqRdkYK2Dg1WbQfq21vSh5ZkG5PbjyataLtUvJifzBjwM5faOzy1/p6OhYBoNZzq4QCmv5VTX5+UXZOayCwmJuJV8saXzR1f369ejs7JxcrpDL5fPz8xMTE8PDw709vc+ePxeJRBwuJy8/PyMzM/7Ro2g6/VZY2LWAgPM//PC9t7fXyZNeJ096+/peDbgWGRWVlJycl5/P4XIFAgGYoRpoQ8vRlDw5rxoanSiv4BA9HMYZKi4ptbFnFMQKGHjtGrRfq2wvStbpdC0Dk38KqLhT1L8WKZlZ9/rr29V5ksEVL3N0dCwji1nORmaozodnqOZweaJ6SXt7x9CQdHp6RqlUqlQqpVI5NzcneyN79epVe0eHWCKp4HBycnMTkpKiY2KCb968cPHiWR+f4ydOeBw9etjD49jx49D3kqOiowElAwHBcgIlAwMDAwMDG9mOlKzT6eLKu3bdrlmLlPx9QvPZhIZlLk2r1Wo0Gp1ONzg0lJ7FKEdGXMBz7+kpuXNoaBidoVqhUMzOzspksoGBgbb2dpFYzGaz4e8lLzGriI+fX0BQUKTBDNVCaMRFR2enTqfTaDRarflvNqw5AUoGskiAkoGBgYGBnW/7UrJOpzt0t+7046dri5Iji3o+C6mcWVCavSKIj9VqtVwuV6lUz54/T8vILGdzBAKUkjF9yR2dg8jce0qlUi6XQ5TcPzDQ3t4uqq9fnpLRvmSIkll5eRwuF6XkOpFIpVItLi6qVKr1wcqAkoEsEqBkYGBgYGDn2+6ULJuRfxzICczuMqXkexVDgU+6gp50BT3puv6k6/qTrhtYZ3cFIw7J7grJ6bqJdW5XaG5XaG7XLcRhkFldYayu8Dy9b+d3o76DcUR+d2rVkCklp1QN/jmQI+kZX+qKtFqtSqVaWFiYmpoaHR0tLi5JSUs37EvOKygsruBUiurF7R2dQ1Lp9PSMQqGn5DGZrH9goK2tTYSZey+KTg++eROl5KOemL5kaMQF1JeMjrhgMJ9kZw8PD09NTc3PzyuVSkDJQBtFgJKBgYGBgZ1vu1OyTqfjto5sDeLGlg8aUTKt6OX7F8sO3a1zsg/frTt8t+7Dq+yI/G5TSt4XVRtX3rXM5cATTc/OymSy7u7uxKSkhJRUDpcnENby+TX5BcXZuXkFhUUcLq++XtLe0QmNS5bL5RAlz8zMjI2NIZQsgij5MUrJly9jKVk/LplOT8ZQMr+qipmdc//Bg7a2ttHR0enpablcrlar1zooE0/J3d3dRL9g4GwVFhYSHXWrBSgZGBgYGNj5dgQl63S6wCfPD8TUm1Ly9uBKBz1GV9Thu3WmlHwhbcmZqCFBwy2gWaZHRkY6OzszMjIiaJFsdkVjY7NAUFtQUGwwLrmjc2hoeGpqGkvJMpmsf6AfS8kJiYnRMUhfso+P2REXUF8yj89vaGioFggiIqMeP37c2toqlUonJycXFxcBJduq7u7uoqIioj9W42ylg7n3gIGBgYGBLbCDKFmu0vw1rMon+dlqpmR6Se/WII50YmH5jNi+5JcvX9bX1z988ODKlasJCYlVVQJ2BbewuLSktJxbyReJG9o7OgcH4XHJKpUKzdg/MNDe1l6PGXEBf+Pi0qWzPj7YERfevr4BQUExsbFZDEYZm83j89PS068HXY+KihIIBL29vWhf8joYmkwkJW9MRK4ClAwMDAwMDGyZHUTJOp2ub3R2y1X27cK+1UnJjNrXy89EjUqr1UKv7sFjJ/r7nz17VlxcfOfOnes3btwIDomgRWVmPeHxq589bxt4NTg+PimXKyB8VavV8/Pz4+PjrwYHOzo668XiCg4nJ5eVmJREj429eevW5StXfP39T585c/zECU8vr1Nnzvxw4cL14OCw8PCQ0NAbwcHXr1+/FRaWm5vb3Nzc19cHITL0At9aR2QdgZTc399fXFxM9MSZxAhQMjAwMDAwsCV2HCXrdLrkqr6vwqrusgdXISV/d098ObPFkoxarRYCZYVCMT8/PzU1JZPJpFJpd3e3SCTKzs6OiooKCrp+40ZwdDQ9LS29vJzd1NQslUqh1+zm5uampqZev37d09v7tKWlqrq6oLAwJTU19u7dm6GhFy5ePP3994c9PPbs20fds2fP3r2HDh8+febMhQsXIiIiMjIyBAJBZ2fn4ODg2NjY5OTk/Py8QqGAEBlQMn4tLCyIxWKieZUYAUo26/EZeVNLW0lpGYPBJHbguOOUmZlZUlombnw6NjVv45OjpbWznF3xJDub6GtylBgMZklpWVNL2/iMHATKobJXqDdCtEG1tFB2rFQOpWSdTvf94waPe5LVRslXs9p2hlctPxO1kSAq1Wg0KpVKLpcvLCxA+PvmzRupVNrb29vc3MzhcLKyspKSkh49ehQfH/8YUXx8/P3792NjYyMjI2+FhQVdv37l6tWLFy/+8OOP0KzUgUFB4bdv379/PyMjo7y8vLGxsbu7WyqVymSyycnJ2dnZhYUFaKAzNBZ5HfAxJCJHXCwuLtbV1dVsPKUDSjbxwLAsP7+AzWbX1tY2NTW1rFM1NzfX1tZWcDi5LNbAsAxfrIZl00XFxWVl5QKBoKGhgehrcpSamppqa2vZbHZ+fgG+WG2QQNku20O9caINqqWFskulcg4lzywoPw7iBDBfrB5KfsR99XEgp1M6jbsolJih7yhD44/lcvni4uLc3Nz09PT4+Pjo6KhUKu3v7+/p6eno6GhtbW1paXn69OnTp09bWlqeP3/e3t7e3d3d398/NDQ0MjIyPj4+PT09NzeHArFKpVKr1Wq1Ghp/vG7IGCsiKVmr1U5MTAg2ngAlG3l8Rs5i5YnFYqJv7M5TQ0NDXn7+yPgMjnAVFRfX1tYSfQXOk1gszmWxcPS+b7RA2S7cod6A0QbV0kLZUqmcQ8k6nU7SM/6ngIqo4v7VQMmZwtfk8Orkqj4nHBHiWi0iDUZajJxwJqtWBH/jQqVStbW1EU2tzhagZCOLxI0cDufpBhO/qopXVWNtrMRNTys2Xqy4lZXCunoQKCcIR6g3bLRBtbRQ+CqVMylZh8xcvRoo+cxKM1EDOVPOpmTT3yWLi4vCDSZAyUYuKiqura1t3mCSSCRPsrOtjVVJaZlQKCT6qeds1dfXs1h5IFBOEI5Qb9hog2ppofBVKidTslyl2R8tPHJPQiwlf5/QtDWII5uRE3UOQEayDyVb3icPdeljE6tUqmfPnhGLrU4WoGQjZ2ZmNjY2Nm08paenWxsrBoMhkUiIPnECZG2sNmygbBeolpYLVEsLhaNSOZmSdTqddGJhy1U2sZS86YeSZWaiBnK+7EPJGo3mxYsX2dnZKSkpqUsoJSWFwWC0traqVCpsXq1WOzQ0VLuRBCjZyOnp6dbdcfPPbyKh2nQ+3+rcVudZtrTdMTjz4nhyWBarmN3Wh4UoWRhAa2NlQ6XC/e+5hGJ2746xtNbZt3KiZVp1SQ6rlutQDq+W60VrgpJ1Ol2eZPDzmzzby8Enj3uiqOIXRB0dyKzsRskpKSl/+MMffvnLX77zzjvvvPPOL81p06ZNcXFxcrnx/0qQy+V1G0mAkk2fHI04FEOlxpjbnnduk/kdDpEtR8OHIyuXG0OlUqmbzuXhPC3cwhULCzPhwBFrz4MKRSzG3qFbqqIaHt1xldbash1VLdejHF4t14vWCiXrdLp77C67lIND0SUvrPr0G5ATZDdKLioqOnz4MIVCcXNzo5qTm5vbN998w2AwTClZrVYTDa5OFaBk0ydHAw7RqVQ6vLDpHAvZwjoHdQluOsdqoFPh3kEqvaEBWaPSoYx06qZNm0z2IqsNDQ0NDdiyjNObzWClHIQjMVRqDMp6MfBpUmMMl/PgazPebrgMXzKVSoXikGecEY1JDLoDw5gGmwwSkVhmWQAADkxJREFUY84MWbfvU9ZWSjYTH2oMNjj684fXsFdndOFQIZgYGaXBLMJIvVzhhmdttwjjDjWAPxCoFbWGKJlAAURehbIbJb969YrH45WWlpYvodLSUg6H09vbazTiApJoIwlQsiMpuaGBdW4Tld6g/4skwKSHKJmETYZArxnkRbJj06OF649ivfDhiIVxgaNCp8KU32CwbMD4S6RBikAukU6F02IzYmO4VCzsEUAcOLJyoVghP4lIaNgwl4nWHDQ4prWLZFTrGgwrHrYQ7AVDadBsdCo2OMsVjpVdq6hjquX6lMOr5XoRoGSgNSq7UTKfz//xxx9PnDhxcgmdOHHC19e3pKREoVCYlkA0uDpVgJJNnxwSHKJTqXSJRCKRsPyp/iz9Fpa/K5Uu0f+VSOhUV3+WPj20AG1EkrH8/ekmR2D5u0KkBGU3TA8Xrj+K9cKHIyuGBeE7EnTC0HUYLSNXY3CtxunhiCHxpVOpdIlBRsOYmMbCjgHEgSMrF2p4rlRMRIwuE3OGcHBMa5fR1WEvHKpvRiEySINup1OxwTFbuIMijMoh1XKdyuHVcr0IUDLQGpV9KFmtVicnJ//+97//13/9139fQv/2b//2m9/85u7du4uLi0bZNRpNvaOV4+dKcou2cNXBApRs+uQQ4xDdzY2OLCFc6EZHVt3omO3wZjg9tEB3c/XPFYvFuf6ubnRxLkIbJKRQaBeCILlG6dHCXV1dMTmskyNwBIN3dCoyVIJEIlHpEswAFD1eufqz6EulMUfJ2Iwm9EanwjlhLcNwKM67uq5GSja6TIRgMcExWbOEkpE8MBhjfswgoYOPtVzhhmdttwjjDjWAPxCoFQUoGWiNym59ya2trUlJSffu3XuwhO7fv5+QkNDU1KRUKo2yK5VKnJSxNgUo2fTJ4axfKEsq2g36mZTj5+rU30tO+kWx9oUDR4g+5bUqgqol3Y0E/Q7VC/lFilO5/q7GJdpboFpaKEDJQGtU9vwS3JMnT1JSUtKWUGpqKoPBeP78uSklT0xMEP1D16kClLwKKTnHD+mT88tx2kHx4YjTTm9VCQeOEH3Ka1XEVMtoNzc3N6PWt/JvVrMpnPhTF1RLCwUo2Znqjv4I/n9RXiXolo+iu4k9qzUqu1Fyenr6Rx999O677/7617/+D3P69a9//f777z948MBoxIVare7q6iL61QKnClCy6ZOD6LHixAgfjhB91sQIB44QfcprVYRUyyg3tyhRlJurXza0ho6hihJlw79gXf2yRaIoN1dXdFW/J8rPzMbsKDe3KGxp8BpSgluU02O1YasloGTnqTv6IxKMxCVe8AKgZNyyGyWXlpZ6enru3bt33xLau3fv4cOHc3JyjL4Ep1KpmpqaiAZXpwpQsumTg+iv8xEjQMmWC+CI00REtUR51tUvW5Tt5woBLLogQvdhQNogKTYNujHKDYJsOAlSAoTHRlnxCVRLCwUo2Xnqjv4I7UPW6XQ6XYkXCRW8Q9/bbNjdHO2l36RPgwFsbFnodjOlrR/ZbYbqiYmJFy9etLa2ti+h1tbWjo4OmUymVqvRjBqNZmhoiOgvOTpbgJIBJUPChyNEnzUxwoEjRJ/yWhUB1TKSgj54XX2ZTF9XSmRdXV0dtMD0dUV31UVSXH2ZUBZXX2YdktQgDZo/kgJlh9bgLEgJ+u02CFRLCwUo2Ykq8TIkW51xX3KJF4qz+sXu6I+wudBuaMO+aXgJS+KY0kwAfR3IbpSs1WpxpFQoFEQjKwEClGz65LB11m+mjwuJRKFhV118mLYWaWsRK4lISka5xAJSgHkCBZSVSrYdPkzlbBwxwC5itAzGMX1dHXdezq+WmJoVSSFRIpHa6epqhpJJBhU3kkIikSiRhv9c8EakKhrW9Q1FyVA4za4a7XKwACU7V0jvrtlxySjrIim9SnTLES42CQadjclZt2wha1b2oWR8UqlU7e3tRE8vT4AAJTuCkikUiguCyTSKC4VCWQFxmT4ueqwmRvhwxD7HplEs/w0Ah8rCLDSKI+KKA0dsOh7TB6pA1sTJzjJTQ51SaYmsliuKwH8Pc3J2tVyzApRMgLqjP8J0FKMoa0SyCOUaAy5mGAWC2/okmMQGYzDW4aALwihZoVD09fU1b0gBSjZ9cghtFMOH7MOgkck0oVAopJHJNIYP2YchFDJ8XEgkEolEpgmFNLKLC7qG7HDxYQhpZLh1k2lCIbJGpkHFGeQS6tNCq7aJWEpG+tdoUE88tGiwrE9EgigZWXPxYaIJXWCYRFdoFLhIu/IcwZRsLiwuPkzDONbW0ihwbaFQKJgkZjZiC0QTwCEzCDsmzphF+KfIEke3LfSAki0XoGQLBSiZEGFHSFjXl4xdM0hiwsIGpa1DEUbJ7e3t1dXVTzekACU7iJKFNLKLD4Ph40KmoRswTEsjQ2QLJUD+oH+FUHYhwtpChJINciEl2oGR8VKyHQ4sRC4WXtRHCbuMRga9djgLJi825kJMcfoEdhIOHLHpeNjfV4Yhwl6saeVxgX+cwb+pMHEz2LhMzTQOuxBTPrqJRsamRI9OMqrSuERktVxrcna1XLMClOw0dUd/ZDpi2BCBlxyXvGQSaLnEy1xHMSbpehSRIy4EAkF1dXXLxhOgZNMnh8BGZXmTvbMEggiyC5lM9s5CNkSQXbyzkDTISpa3CzkC/YP+RRJEkNFVcoRxrixv7whbz1UvfDhin2NjQoONEnYZjQy8gO6LILt4Z2V5u8Ady95Z+hgKoLgZBN4uwoEjNh0PrVEkg0sXYCvMkpUHzoxWIdONy9RMo7Bj46zfF0HGpkSPblDD8YrIarnW5OxquWYFKNmJMvdxCqOByvphEksNxNAn+Sg62gv7hp9JTsPN6w2YiaRk3UYFZUDJpk+OGhuV5U32zqqpqcnydnGBF8jeWTU1CGK4eGfV3CEju1zId2pqau6QSSQS+Q68QIJXau6QyXdqatAFw1wospDgRDZplVCyABMlg2VBBBQZFxeEkpHBJxECE3pDlyEotJHUTEQQJQugGrViiKCYWE7JRhEzpFuDsBvEGdpFjkDKND46oGQnC1CyhQKUvA5kwNGYL1+sbxFMyTqdDmo/G0pMJpPoqFut1U7JzhKC0Ahp26Z0XDhi82HXpKyN1YYNlO0C1dJygWppoXBUKkDJq00GQyvW4dcszIt4SgZaE3IoJeflF3A4nOq1oMyzSMfe2Uwbi+Lz+QwGw9pYZWZmVldXE/3II0DWPmU3bKBsFw6g2bDRBtXSQgFKXhfCfs9iQ3Qk6wAlA1miRaXGN6vLcZTMq6opLS21mWDXmNhsdnFJ6Tr+RWFH4fhFsTEDZbvw/XjbmNEG1dJC4atUgJKBVoMAJQOtrHmFOjC/z3GUPCybzs7J4fP5VRtJuSzWy8ERa2MlqK0vLikh+qnnbJWXl5eVs0GgnCAcod6w0QbV0kLhq1TWUvLptBcKlYbopyXQehOgZKCVNa9Qh5cOOI6SJ+dVbZ09ubm5ZWVlRLOrM8Rms3NZrJa2ThyBGhmfYbHyKisrib4I54nP57Py8gaGZSBQjha+UG/MaINqaaFwVyprKflybu+8AlAykJ0FKBloZS0qNQ+rpA6l5Ml51dDoRHkFh4jXKZ2t4pJSHL3IqF/0DOTk5JaWlm6E3vfy8nLcvyg2VKBsly2h3mjRBtXSQtlYqayi5JCi/nmFmuinJdB6E6BkIIuU0zjmaEoGttwj4zOcSn5mZibRwO9wlZWzbflFsXECZbtsDPWGijaolhbK9ko1aTElP6ySLipBXzKQnQUoGcgi1fdNA0oGBgYGBnayLaTkvGaZRkv0kxJo3QlQMpBFWlBoACUDAwMDAzvZFlKydFJO9HMSaB0KUDKQRbLkMxceCe2vxhcJv6UCAwMDA68bW4LI4AMXQA4SoGQgi6TR6vKaZcvfpzyTOp4PzRF+SwUGBgYGXh8enpIfedwOBiUDESVAyUCWamxGufx96mhiR+nzccLvqsDAwMDA68Pil9OeyZ0rUnLb8BzRT0ig9SlAyUCWal6huZzbu8x96sBPbdcLXxJ+VwUGBgYGXh+O4Q5aMtxiAXwpGcgxApQMZIXahudWGHSR3Cl+OU34jRUYGBgYeK278/X88ZSVO5IrOybU4PMWQI4RoGQgK7Rid/LB+LZTaS9kM0rCb6/AwMDAwGva/oxu0JEMRKwAJQNZpxVHJx9+3HarZACAMjAwMDAwbj/gS79L7AAdyUDEClAykHWaV6gj2a+Wv215JLSfzehqHpgl/D4LDAwMDLy23Pl63p/RbQki+2Z1zYFZqYEcKUDJQFZrakF1Ou3Fivcvz+SOGO4gGKYMDAwMDGyJnw/NPeBLLRmLDHXHDE6AmUSAHCtAyUB4NDgh90hY+RuW0Pt8liQDBgYGBt7g9kzqOBjfamHiut5pMJMIkKMFKBkIjxRqDf/FJOG3VGBgYGDgDei0upE5ORhrAeRwAUoGwql5hTqtboTweyUwMDAw8IZySFH/zCJAZCBnCFAyEH7NytWc9gnC75jAwMDAwBvE93lDAJGBnCZAyUA2aVGpaRmctXCMMjAwMDAwMG5z2ifAQAsgZwpQMpCtUmu0gxNy36wuwm+gwMDAwMDr0seTO1sGZxeV4HU9IKcKUDKQfbSo1FR2TBwHX7QABgYGBrafPRLacxrH5hVqMHsIkPMFKBnIblJrtPMKdU7jGBiAAQwMDAxsu+9WDk4tqOTgi29ABAlQMpCdJVdpFpWa+r7pFafoAwYGBgYGNnVgfl9F+8TMonpBAfgYiEgBSgZylOYVaoVKW983nVr3OqSoP6Son/A7LzAwMDDwKnRgfl9IUf/DKml119SCQjMP5p0GWh0ClAzkcKnU2nmFGtz1gICAgIDMCnpGgJfzgFab/j9FIncG+D22rAAAAABJRU5ErkJggg==" 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The Open Learning Analytics Platform (OpenLAP)</div>
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Driven by the different perspectives on openness as discussed in the literature on open education, OER, OCW, and MOOCs, several suggestions can be made as to how ”open” should be interpreted in relation to learning analytics (LA).<br />
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– <b>Open learning</b> by providing understanding into how learners learn in open and networked learning environments and how learners, educators, institutions, and researchers can best support this process (Chatti et al., 2014).<br />
– <b>Open practice</b> that gives effect to a participatory culture of creating, sharing, and cooperation.<br />
– <b>Open architectures, processes, modules, algorithms, tools, techniques, and methods</b> that can be used by following the four R’s ”Reuse, Redistribute, Revise, Remix” (Wiley, 2009; Hilton et al., 2010). Everyone should have the freedom to use, customize, improve, and redistribute the entities above without constraint.<br />
– <b>Open access</b> to learning analytics platforms granted to different stakeholders without any entry requirements in order to promote self-management and creativity.<br />
– <b>Open participation</b> in the LA process by engaging different stakeholders in the LA exercise. Daniel and Butson (2014) state that in LA, ”there is still a divide between those who know how to extract data and what data is available, and those who know what data is required and how it would best be used” (p. 45). Therefore, it is necessary to bring together different stakeholders to work on common LA tasks in order to achieve useful LA results. Further, it is essential to see learners as the central part of the LA practice. This means that learners should be active collaborators, not just mere data subjects (Sclater, 2014) and recipients of interventions and services (Slade and Prinsloo, 2013). Learner and teacher involvement is the key to a wider user acceptance, which is required if LA tools are to serve the intended objective of improving learning and teaching.<br />
– <b>Open standards</b> ”to reduce market fragmentation and increase the number of viable products” (Cooper, 2014a). Open standards and specifications can help to realize the benefits of better interoperability (Cooper, 2014b).<br />
– <b>Open Research</b> and <b>Open science</b> (Fry et al., 2009) based on <b>open datasets</b> with legal protection rules that describe how and when the dataset can be used (Verbert et al., 2012). Sclater (2014) points out that datasets ”from one environment can be connected to that in another one, not only across the different systems in one institution but potentially with other institutions too”. <br />
– <b>Open learner modeling</b> based on user interfaces that enable refection, planning, attention, and forgetting and that can be accessed by learners to control, edit, update, and manage their models (Kay and Kummerfeld, 2011). This is important to build trust and improve transparency of the LA practice.<br />
– <b>Open assessment</b> to help lifelong learners gain recognition of their learning. Open assessment is an agile way of assessment where anyone, anytime, anywhere, can participate towards the assessment goal. It is an ongoing process across time, locations, and devices where everyone can be assessor and assesse (Chatti et al., 2014).<br />
<br />
The concept of open learning analytics covers all the aspects of ”openness” outlined above. It refers to an ongoing analytics process that encompasses diversity at all four dimensions of the <a href="http://mohamedaminechatti.blogspot.de/2013/01/a-reference-model-for-learning-analytics.html">learning analytics reference model</a> (Chatti et al., 2012):<br />
<br />
– What? It accommodates the considerable variety in learning data, environments, and contexts. This includes data coming from traditional education settings (e.g. LMS) and from more open-ended and less formal learning settings (e.g. PLE, MOOC, social web).<br />
– Who? It serves different stakeholders with very diverse interests and needs.<br />
– Why? It meets different objectives according to the particular point of view of the different stakeholders.<br />
– How? It leverages a plethora of statistical, visual, and computational tools, methods, and methodologies to manage large datasets and process them into indicators and metrics which can be used to understand and optimize learning and the environments in which it occurs.<br />
<br />
<i>References:</i><br />
<br />
Chatti, M.A., Dyckhoff, A.L., Thüs, H. & Schroeder, U. (2012): A reference model for learning analytics. International Journal of Technology Enhanced Learning (IJTEL). 4(5/6). 318-331.<br />
<br />
Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U., Greven, C., Chakrabarti, A., Schroeder, U. (2014). Learning Analytics: Challenges and Future Research Directions. eleed, Iss. 10. (urn:nbn:de:0009-5-40350)<br />
<br />
Cooper, A. (2014a) Open Learning Analytics Network - Summit Europe 2014. Retrieved from http://www.laceproject.eu/open-learning-analytics-network-summit-europe-2014/<br />
<br />
Cooper, A. (2014b) Learning Analytics and Interoperability - The Big Picture in Brief, Learning Analytics Review, March 2014, ISSN: 2057-7494.<br />
<br />
Daniel, B., & Butson, R. (2014). Foundations of Big Data and Analytics in Higher Education. In International Conference on Analytics Driven Solutions: ICAS2014 (pp. 39-47). Academic Conferences Limited.<br />
<br />
Fry, J., Schroeder, R., & den Besten, M. (2009). Open science in e-science: contingency or policy?. Journal of Documentation, 65(1), 6-32.<br />
<br />
Hilton, J., Wiley, D., Stein, J., & Johnson, A. (2010): The Four R's of Openness and ALMS Analysis: Frameworks for Open Educational Resources. Open Learning: The Journal of Open and Distance Learning, 25(1), pp. 37-44.<br />
<br />
Kay, J. & Kummerfeld, B. (2011) Lifelong learner modeling. In Durlach, P.J., Lesgold, A.M., eds.: Adaptive Technologies for Training and Education. Cambridge University Press. 140-164.<br />
<br />
Sclater, N. (2014) Examining open learning analytics - report from the Lace Project meeting in Amsterdam. Retrieved from http://www.laceproject.eu/blog/examining-open-learninganalytics-report-lace-project-meeting-amsterdam/<br />
<br />
Slade, S.; Prinsloo, P.: Learning analytics: ethical issues and dilemmas. In: American Behavioral Scientist, 57(10), 2013, pp. 1509-1528.<br />
<br />
Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-Driven Research to Support Learning and Knowledge Analytics. Educational Technology & Society, 15 (3), 133-148.<br />
<br />
Wiley, D. (2009). Introduction to Open Education. iTunesU. Lecture conducted from BYU, Provo.<br />
<br />
<b>Source:</b><br />
<br />
Chatti, M. A., Muslim, A., & Schroeder, U. (2016). Toward an Open Learning Analytics Ecosystem. In <i>Big Data and Learning Analytics in Higher Education</i> (pp. 195-219). Springer International Publishing. </div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-41442213994407412652016-07-05T12:46:00.004+02:002016-07-05T12:46:56.715+02:00Video annotation and analytics in CourseMapper<div dir="ltr" style="text-align: left;" trbidi="on">
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" /> </div>
<br />
Our paper "<a href="http://slejournal.springeropen.com/articles/10.1186/s40561-016-0035-1">Video annotation and analytics in CourseMapper</a>" has been published in the Smart Learning Environments Journal, published by SpringerOpen.<br />
<br />
<div class="Para">
<i>Abstract:</i></div>
<div class="Para">
</div>
<div class="Para">
Over
the past few years there has been an increasing interest to investigate
the potential of Video-Based Learning (VBL) as a result of new forms of
online education, such as flipped classrooms and Massive Open Online
Courses (MOOCs) in order to engage learners in a self-organized and
networked learning experience. However, current VBL approaches suffer
from several limitations. These include the focus on the traditional
teacher-centered model, the lack of human interaction, the lack of
interactivity around the video content, lack of personalization, as well
as assessment and feedback. In this paper, we investigate the effective
design of VBL environments and present the design, implementation, and
evaluation details of CourseMapper as a mind map-based collaborative
video annotation and analytics platform that enables learners’
collaboration and interaction around a video lecture. Thereby, we focus
on the application of learning analytics mainly from a learner
perspective to support self-organized and networked learning through
personalization of the learning environment, monitoring of the learning
process, awareness, self-reflection, motivation, and feedback.</div>
<div class="Para">
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<div class="Para">
<br /></div>
<div class="Para">
We released the first version of the CourseMapper platform. It can be accessed <a href="https://gomera.informatik.rwth-aachen.de:8443/">here</a>. CourseMapper is a mind map-based collaborative course annotation and analytics platform that
fosters collaboration and interaction around pdf/video learning materials, supported by visual
learning analytics. More information about the platform can be found <a href="https://gomera.informatik.rwth-aachen.de:8443/about">here</a>.</div>
</div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com1tag:blogger.com,1999:blog-8263882245812273018.post-60167754665108190692015-05-20T12:09:00.000+02:002015-05-20T12:09:49.880+02:00PRiME received "Exzellenziegel des Deutschen Bildungspreises 2015"<div dir="ltr" style="text-align: left;" trbidi="on">
<a href="http://www.deutscher-bildungspreis.de/fileadmin/templates/img/logo.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img alt="" border="0" src="http://www.deutscher-bildungspreis.de/fileadmin/templates/img/logo.png" title="Deutscher Bildungspreis" /></a>In addition to the <a href="http://mohamedaminechatti.blogspot.de/2015/02/prime-received-elearning-award-2015.html" target="_blank">eLearning Award 2015</a>, <a href="https://prime.rwth-aachen.de:8443/confluence/display/prime/2015/04/30/Deutscher+Bildungspreis+2015%3A+PRiME+mit+Exzellenzsiegel+ausgezeichnet" target="_blank">PRiME</a> received the Quality Seal "Excellence in Education and Talent Management" of the "<a href="http://www.deutscher-bildungspreis.de/preisverleihung/preisverleihung-2015.html" target="_blank">Deutscher Bildungspreis</a>".<br />
<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgnRVPniBfIUhBe5itMb6zlM8vfopJGb9iCIDYp3Oq5L_Z3PflBVyGuNSZ6hvaHbgzp0hMu4waLAVyGbsWpuTVhbADzSqgeU7H_JhlPQ0kyx5K40zXR09ekWFlAghQzYygHl4EovT9gzsRD/s1600/20150422_163903.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="180" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgnRVPniBfIUhBe5itMb6zlM8vfopJGb9iCIDYp3Oq5L_Z3PflBVyGuNSZ6hvaHbgzp0hMu4waLAVyGbsWpuTVhbADzSqgeU7H_JhlPQ0kyx5K40zXR09ekWFlAghQzYygHl4EovT9gzsRD/s320/20150422_163903.jpg" width="320" /></a><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj6TCkSjroXmNPyivUGv3vUE-66Hb5eHEbdklVfbFOdSZ1pRdQ2CGUetusQkbalS9tTMaCMlfgIMEpcwjzjMv-GATbaRgb7AG5o5m0cuoKMWBZFH_-2iZlO8Pd16PcHZdeVy7pqyzWdWABQ/s1600/20150422_182513.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj6TCkSjroXmNPyivUGv3vUE-66Hb5eHEbdklVfbFOdSZ1pRdQ2CGUetusQkbalS9tTMaCMlfgIMEpcwjzjMv-GATbaRgb7AG5o5m0cuoKMWBZFH_-2iZlO8Pd16PcHZdeVy7pqyzWdWABQ/s320/20150422_182513.jpg" width="180" /></a></div>
</div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-52010371287957136142015-05-11T11:37:00.001+02:002015-05-11T11:39:42.604+02:00A Usability Evaluation of a Blended MOOC Environment: An Experimental Case Study<div dir="ltr" style="text-align: left;" trbidi="on">
The paper "<a href="http://www.irrodl.org/index.php/irrodl/article/view/2032/3270" target="_blank">A Usability Evaluation of a Blended MOOC Environment: An Experimental Case Study</a>" with my dear friend Ahmed Mohamed FahmyYousef was published in <a href="http://www.irrodl.org/index.php/irrodl/issue/view/68" target="_blank">IRRODL</a>.<br />
<br />
<i>Abstract:</i><br />
In the past few years, there has been an increasing interest in Massive
Open Online Courses (MOOCs) as a new form of Technology-Enhanced
Learning (TEL), in higher education and beyond. Recognizing the
limitations of standalone MOOCs, blended MOOCs (bMOOCs) that aim at
bringing in-class (i.e. face-to-face) interactions and online learning
components together have emerged as an alternative MOOC model of
teaching and learning in a higher education context. In this paper, we
present the design, implementation, and evaluation details of a bMOOC
course on “Teaching Methodologies” at Fayoum University, Egypt in
cooperation with RWTH Aachen University, Germany, provided using the
bMOOC platform L2P-bMOOC. In order to gauge the usability and
effectiveness of the course, we employed an evaluation approach based on
Conole’s 12 dimensions rubrics, ISONORM 9241/110-S as a general
usability evaluation, and a custom effectiveness questionnaire
reflecting the different MOOC stakeholder perspectives.<br />
<br />
<i>Reference:</i><br />
Ahmed Mohamed Fahmy Yousef, Mohamed Amine Chatti, Ulrik Schroeder, Marold Wosnitza (2015).<i>The International Review of Research in Open and Distributed Learning</i> (<i>IRRODL</i>),
<i>16</i>(2)<b>.</b><br />
<br />
<b><img alt="Figure 1" src="http://www.irrodl.org/index.php/irrodl/article/viewFile/2032/3270/16935" height="275" width="400" /> </b> </div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-50035525618106652142015-05-04T11:14:00.002+02:002015-05-04T11:15:37.174+02:00Learning Analytics Workshop at DeLFI/GMW 2015<div dir="ltr" style="text-align: left;" trbidi="on">
<a href="http://arbeitskreis-learning-analytics.f4.htw-berlin.de/arbeitskreis/" target="_blank"><img alt="http://arbeitskreis-learning-analytics.f4.htw-berlin.de/arbeitskreis/" src="http://arbeitskreis-learning-analytics.f4.htw-berlin.de/wp-content/uploads/2014/05/logo-akla.png" height="83" width="400" /></a> <br />
<br />
The <a href="http://arbeitskreis-learning-analytics.f4.htw-berlin.de/arbeitskreis/" target="_blank">Learning Analytics Working Group of the German Informatics Society (GI)</a> is organizing the 3rd workshop on "Learning Analytics" in conjunction with the joint <a href="http://www.delfi2015.de/" target="_blank">DeLFI/GMW conference 2015</a>, September 1-4, 2015 in Munich, Germany. The call for papers is available <a href="http://arbeitskreis-learning-analytics.f4.htw-berlin.de/call-for-paper/" target="_blank">here</a>. Please consider submitting a paper to this interesting workshop. Submission in English is also possible. </div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-39068515897647183452015-04-29T16:56:00.001+02:002015-04-29T16:56:09.637+02:00Learning Analytics at GOR<div dir="ltr" style="text-align: left;" trbidi="on">
On Friday April 17, 2015, I had an invited talk on "<a href="http://mohamedaminechatti.blogspot.de/2014/11/learning-analytics-challenges-and.html" target="_blank">Learning Anaytics: Challenges and Future Research Directions</a>" at the annual workhop organized by the <a href="http://www.analytics-gor.de/" target="_blank">GOR working group "Analytics"</a> at Bayer in Leverkusen. More information about the workshop is availabe <a href="https://gor.uni-paderborn.de/index.php?id=357" target="_blank">here</a>. <br />
<br />
<img alt="Teilnehmer 2015" height="165" src="https://gor.uni-paderborn.de/fileadmin/user_upload/Arbeitsgruppen/Analytics/Sitzung2015/Teilnehmer2015_web.JPG" width="400" /> </div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-21819611807905371302015-02-25T15:04:00.000+01:002015-02-25T15:27:50.232+01:00PRiME received the eLearning Award 2015<div dir="ltr" style="text-align: left;" trbidi="on">
<a href="http://www.qualifizierungdigital.de/uploads/RTEmagicC_prime-logo72_200.jpg.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img alt="Logo: PRIME - Professional Reflective Mobile Personal Learning Environments" border="0" src="http://www.qualifizierungdigital.de/uploads/RTEmagicC_prime-logo72_200.jpg.jpg" height="93" title="PRIME - Professional Reflective Mobile Personal Learning Environments" width="200" /></a><img alt="Den eLearning-Award schon in der Tasche und für den Deutschen Bildungspreis 2015 nominiert: Das Team vom Projekt "PRiME". (Bild: BMBF)" src="http://www.bmbf.de/_img/images/Prime_540.jpg" height="240" width="400" /> <br />
<br />
<br />
<span style="font-family: inherit;">Our BMBF Project Professional Reflective Mobile Personal Learning Environments (PRiME) received the eLearning Award 2015 in the categories personal learning environment and knowledge management.</span><br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">An overview on the project can be found on the <a href="http://www.qualifizierungdigital.de/projekte/laufende-projekte/prime.html" target="_blank">qualifizierungsdigital portal</a> and more details on the<span style="font-family: inherit;"> </span><a href="http://prime.rwth-aachen.de/" target="_blank">project website.</a></span> <br />
<span style="font-family: inherit;"><br /></span>
<span style="font-family: inherit;">A recent report i<span style="font-family: inherit;">s</span> avaible at of the German Federal Ministry of Education (BMBF) <a href="http://www.bmbf.de/de/26098.php" target="_blank">Website</a>.</span><br />
<span style="font-family: inherit;"><br /></span>
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<div class="MsoListParagraphCxSpFirst" style="line-height: normal; margin: 0cm 0cm 6pt 18pt; text-align: justify; text-indent: -18pt;">
<span style="font-family: inherit;"><span lang="EN-US" style="font-family: Symbol; font-size: 12.0pt; mso-ansi-language: EN-US; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font-feature-settings: normal; font-kerning: auto; font-language-override: normal; font-size-adjust: none; font-size: 7pt; font-stretch: normal; font-style: normal; font-synthesis: weight style; font-variant: normal; font-weight: normal; line-height: normal;"> </span></span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-ansi-language: EN-US; mso-bidi-font-size: 11.0pt;">Provide an innovative professional learning approach, where informal and
network learning converge around a self-directed learning environment. This
approach is grounded in the <a href="http://mohamedaminechatti.blogspot.de/2013/01/the-laan-theory.html" target="_blank">Learning as a Network (LaaN) theory</a>.<span style="mso-spacerun: yes;"> </span></span></span></div>
<div class="MsoListParagraphCxSpMiddle" style="line-height: normal; margin: 0cm 0cm 6pt 18pt; text-align: justify; text-indent: -18pt;">
<span style="font-family: inherit;"><span lang="EN-US" style="font-family: Symbol; font-size: 12.0pt; mso-ansi-language: EN-US; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font-feature-settings: normal; font-kerning: auto; font-language-override: normal; font-size-adjust: none; font-size: 7pt; font-stretch: normal; font-style: normal; font-synthesis: weight style; font-variant: normal; font-weight: normal; line-height: normal;">
</span></span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-ansi-language: EN-US; mso-bidi-font-size: 11.0pt;">Design a work-integrated framework that links mobile job activities and
self-directed learning in context.</span></span></div>
<div class="MsoListParagraphCxSpMiddle" style="line-height: normal; margin: 0cm 0cm 6pt 18pt; text-align: justify; text-indent: -18pt;">
<span style="font-family: inherit;"><span lang="EN-US" style="font-family: Symbol; font-size: 12.0pt; mso-ansi-language: EN-US; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font-feature-settings: normal; font-kerning: auto; font-language-override: normal; font-size-adjust: none; font-size: 7pt; font-stretch: normal; font-style: normal; font-synthesis: weight style; font-variant: normal; font-weight: normal; line-height: normal;">
</span></span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-ansi-language: EN-US; mso-bidi-font-size: 11.0pt;">Develop and evaluate mobile learning applications to support mobile
learning in context.</span></span></div>
<div class="MsoListParagraphCxSpMiddle" style="line-height: normal; margin: 0cm 0cm 6pt 18pt; text-align: justify; text-indent: -18pt;">
<span style="font-family: inherit;"><span lang="EN-US" style="font-family: Symbol; font-size: 12.0pt; mso-ansi-language: EN-US; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font-feature-settings: normal; font-kerning: auto; font-language-override: normal; font-size-adjust: none; font-size: 7pt; font-stretch: normal; font-style: normal; font-synthesis: weight style; font-variant: normal; font-weight: normal; line-height: normal;">
</span></span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-ansi-language: EN-US; mso-bidi-font-size: 11.0pt;">Support continuous reflection at three levels:<span style="mso-spacerun: yes;"> </span>(a) the personal learning environment (PLE)
level where professional learners can annotate learning materials on their
mobile tablet devices; (b) these materials can be shared, commented, and rated
by peers at the personal knowledge network (PKN) level; (c) the new generated
learning materials can then be shared and used within the company at the
network of practice (NoP) level.</span></span></div>
<div class="MsoListParagraphCxSpLast" style="line-height: normal; margin: 0cm 0cm 6pt 18pt; text-align: justify; text-indent: -18pt;">
<span style="font-family: inherit;"><span lang="EN-US" style="font-family: Symbol; font-size: 12.0pt; mso-ansi-language: EN-US; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font-feature-settings: normal; font-kerning: auto; font-language-override: normal; font-size-adjust: none; font-size: 7pt; font-stretch: normal; font-style: normal; font-synthesis: weight style; font-variant: normal; font-weight: normal; line-height: normal;">
</span></span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-ansi-language: EN-US; mso-bidi-font-size: 11.0pt;">Develop and evaluate learning analytics tools and methods (e.g.
dashboards, recommendation, intelligent feedback, context-based search) to
support reflective learning at the workplace. </span></span></div>
<br />
<br />
<br /></div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-52142909294822479232014-11-07T15:25:00.000+01:002014-11-07T15:25:01.198+01:00Learning Analytics: Challenges and Future Research Directions<div dir="ltr" style="text-align: left;" trbidi="on">
<div style="text-align: left;">
<img src="http://eleed.campussource.de/archive/10/4035/dippArticle-1.png" height="292" width="400" /> </div>
<div style="text-align: left;">
Learning Analytics Reference Model</div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
<img src="http://eleed.campussource.de/archive/10/4035/dippArticle-2.png" height="98" width="400" /> </div>
<div style="text-align: left;">
Future Research Directions in Learning Analytics</div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
The paper "<a href="http://eleed.campussource.de/archive/10/4035" target="_blank">Learning Analytics: Challenges and Future Research Directions</a>" has been published in the e-learning and education journal (eleed). </div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
The paper presents a revised version of the <a href="http://mohamedaminechatti.blogspot.de/2013/01/a-reference-model-for-learning-analytics.html" target="_blank">learning analytics reference model</a> and highlights future research directions in learning analytics. These include:</div>
<div style="text-align: left;">
- Open Learning Analytics</div>
<div style="text-align: left;">
- Big Learning Analytics</div>
<div style="text-align: left;">
- Learning Analytics in Open Learning Environments (e.g. MOOCs)</div>
<div style="text-align: left;">
- Mobile Learning Analytics</div>
<div style="text-align: left;">
- Context Modeling</div>
<div style="text-align: left;">
- Privacy-Aware Learning Analytics </div>
<div style="text-align: left;">
- Personalized Learning Analytics</div>
<div style="text-align: left;">
- Lifelong Learner Modeling</div>
<div style="text-align: left;">
- Learning Analytics for Open Assessment</div>
<div style="text-align: left;">
- Embedded Learning Analytics</div>
<div style="text-align: left;">
- Learning Analytics Design Patterns</div>
<div style="text-align: left;">
- Learning Analytics Evaluation</div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
<i>Abstract: </i></div>
<div style="text-align: left;">
In recent years, learning analytics (LA) has attracted a great deal of
attention in technology-enhanced learning (TEL) research as
practitioners, institutions, and researchers are increasingly seeing the
potential that LA has to shape the future TEL landscape. Generally, LA
deals with the development of methods that harness educational data sets
to support the learning process. This paper provides a foundation for
future research in LA. It provides a systematic overview on this
emerging field and its key concepts through a reference model for LA
based on four dimensions, namely data, environments, context (what?),
stakeholders (who?), objectives (why?), and methods (how?). It further
identifies various challenges and research opportunities in the area of
LA in relation to each dimension.</div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
<i>Reference:</i></div>
<div style="text-align: left;">
Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F.,
Wahid, U., Greven, C., Chakrabarti, A., Schroeder, U. (2014).
Learning Analytics: Challenges and Future Research Directions. eleed,
Iss. 10. </div>
</div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com1tag:blogger.com,1999:blog-8263882245812273018.post-15383305689637694802014-09-04T17:38:00.002+02:002014-09-04T17:38:50.959+02:00LaaN: Convergence of Knowledge Management and Technology Enhanced Learning<div dir="ltr" style="text-align: left;" trbidi="on">
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEia4_jynVy47pofYc3gHhAI4G69DKfMuB82wVrHXqdcwVf0Tyd3FAVc8VNyUyWASHg0OkXTA5-1BfAqdz2i2epH_k5yfLPduUih5WiqiK5QmDT6fXVcVDqlFiVKNxIt7GexUGOBZZC09XQ0/s1600/KM-TEL-LaaN.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEia4_jynVy47pofYc3gHhAI4G69DKfMuB82wVrHXqdcwVf0Tyd3FAVc8VNyUyWASHg0OkXTA5-1BfAqdz2i2epH_k5yfLPduUih5WiqiK5QmDT6fXVcVDqlFiVKNxIt7GexUGOBZZC09XQ0/s1600/KM-TEL-LaaN.png" height="236" width="320" /></a></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;">The paper
"LaaN: Convergence of Knowledge Management and Technology Enhanced Learning" has
been published in <a href="http://www.computer.org/csdl/trans/lt/2012/02/index.html" target="_blank">IEEE Transactions on Learning Technologies</a>. T</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;">he paper has now free access <a href="http://www.computer.org/csdl/trans/lt/2012/02/tlt2012020177.pdf" target="_blank">here</a> (pdf) and <a href="http://www.computer.org/csdl/trans/lt/2012/02/tlt2012020177.html" target="_blank">here</a> (html)</span><span style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-ansi-language: DE; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;"><a href="http://www.elearn.rwth-aachen.de/dl1142%7CMohamed_Chatti_PKN_JKM_2012_preprint.pdf"><span lang="EN-US" style="color: blue; mso-ansi-language: EN-US;"></span></a></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;"></span></div>
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<i><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;">Abstract:</span></i></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;">Knowledge Management (KM) and Technology-Enhanced Learning (TEL) have
attracted attention over the past two decades and are meanwhile
considered as important means to increase individual and organizational
performance. There is, however, a wide agreement that traditional KM and
TEL models have failed to cope with the fast-paced change and critical
challenges of the new knowledge era. In this paper, we propose a vision
for future KM/TEL approaches which aims to fulfill the needs of the new
knowledge landscape by introducing the Learning as a Network (LaaN)
theory as a new learning theory characterized by the convergence of KM
and TEL within a learner-centric knowledge environment. We further
discuss a possible implementation of the LaaN theory based on the
personal learning environment (PLE) concept. </span></div>
<div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;">
<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;"><br />
<i>Reference:</i><br />
<br />
</span></div>
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</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;"><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;">M. A. Chatti, U. Schroeder, M. Jarke (2012) LaaN: Convergence of Knowledge Management and Technology Enhanced Learning. IEEE Transactions on Learning Technologies, 5(2), pp. 177-189. </span></span></div>
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Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-89837145039096227762014-08-18T17:13:00.000+02:002014-09-04T17:15:19.029+02:00Two papers presented at ICALT 2014<div dir="ltr" style="text-align: left;" trbidi="on">
<div class="articlebody clearfix">
On July 7th, we had two papers presented at <strong>ICALT 2014</strong> Conference held in Athens, Greece, July 7-9, 2014.<br />
<br />
<strong>Learner Modeling in Academic Networks</strong>
<br />
<br /><strong>Authors:</strong>Mohamed Amine Chatti, Darko Dugosija, Hendrik Thüs and Ulrik Schroeder<br />
<br />
<strong>Abstract</strong>—Learning analytics (LA) deals with the
development of methods that harness educational data sets to support the
learning process. To achieve particular learner centered LA objectives
such as intelligent feedback, adaptation, personalization, or
recommendation, learner modeling is a crucial task. Learner modeling
enables to achieve adaptive and personalized learning environments,
which are able to take into account the heterogeneous needs of learners
and provide them with tailored learning experience suited for their
unique needs. In this paper, we focus on learner modeling in academic
networks. We present theoretical, design, implementation, and evaluation
details of PALM, a service for personal academic learner modeling. The
primary aim of PALM is to harness the distributed publication
information to build an academic learner model.<br />
<br />
The paper can be downloaded<a href="http://learntech.rwth-aachen.de/dl1210" target="_blank"> here</a>. <br />
<br /><br /><strong>What Drives a Successful MOOC? An Empirical Examination of Criteria to Assure Design Quality of MOOCs</strong>
<br />
<br /><strong>Authors:</strong>Ahmed Mohamed Fahmy Yousef, Mohamed Amine Chatti, Ulrik Schroeder and Marold Wosnitza<br />
<br />
<strong>Abstract:</strong> Massive Open Online Courses (MOOCs) have
gained a lot of attention in the last years as a new Technology Enhanced
Learning (TEL) approach in higher education. MOOCs provide more
educational opportunities to a massive number of learners to attend free
online courses around the globe. Discussions around MOOCs have been
focusing on the potential, social, institutional, technological,
relevance, and marketing issues and less on the quality design of MOOC
environments. Several studies have reported a high drop-out rate in
average of 95% of course participants and other pedagogical problems
concerning assessment and feedback. Thus, the quality of MOOCs design is
worth additional investigation. Although several studies identified a
large set of criteria to the successful design of TEL systems in
general, not all of them can be used in the MOOC context, due to some
unique features of MOOCs. This study is a first step towards identifying
specific criteria that need to be considered when designing and
implementing MOOCs. The results of this empirical study are based on a
large survey targeting learners as well as professors, both with MOOC
experience. As a result, we identified and rated 74 indicators
classified into our two main dimensions of pedagogical and technological
criteria distributed over six categories. From these, the learning
analytics and assessment categories were found to be the key features
for effective MOOCs.<br />
<br />
The paper can be downloaded <a href="http://learntech.rwth-aachen.de/dl1209" target="_blank">here</a>. <br />
</div>
</div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-28236953047213553692014-04-16T10:47:00.001+02:002014-04-16T10:59:16.661+02:00Tag-Based Collaborative Filtering Recommendation in Personal Learning Environments<div dir="ltr" style="text-align: left;" trbidi="on">
The paper "Tag-Based Collaborative Filtering Recommendation in Personal Learning Environments" has been published in the <a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6547629" target="_blank">IEEE Transactions on Learning Technologies</a> (Vol. 6, No. 4). The paper can be downloaded <a href="http://learntech.rwth-aachen.de/dl1189" target="_blank">here</a>.
<br />
<br />
<i>Abstract:</i><br />
The personal learning environment (PLE) concept offers a learner-centric
view of learning and suggests a shift from knowledge-push to
knowledge-pull approach to learning. One concern with a PLE-driven
knowledge-pull approach to learning, however, is information overload.
Recommender systems can provide an effective mechanism to deal with the
information overload problem in PLEs. In this paper, we study different
tag-based collaborative filtering recommendation techniques on their
applicability and effectiveness in PLE settings. We implement 16
different tag-based collaborative filtering recommendation algorithms,
memory based as well as model based, and compare them in terms of
accuracy and user satisfaction. The results of the conducted offline and
user evaluations reveal that the quality of user experience does not
correlate with high-recommendation accuracy.
<br />
<br />
<span style="font-style: italic;">Reference:</span> <br />
Chatti, M.A., Dakova, S, Thüs, H. and Schroeder, U. (2013) ‘Tag-Based Collaborative Filtering Recommendation in Personal Learning Environments’, IEEE Transactions on Learning Technologies, Vol. 6, No. 4, pp.337–349
.</div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-64034621729958356242013-01-21T12:17:00.001+01:002015-09-14T11:25:07.001+02:00A Reference Model for Learning Analytics<div dir="ltr" style="text-align: left;" trbidi="on">
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<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOgeGCi8-lR22XU4ZM7zxkhenVGS_8xYtPnf7ZBR8WpXbbBRCTU5nEJFYDtLr_CZTBkYYr5Bj0pGc4llWzo293NIK0Ts2oXTHDZLFVQZ-JOcph880u5iho6EVX8yO4w7-YHtDTgbKxIiXJ/s1600/LA_RefModel_Chatti.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="300" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOgeGCi8-lR22XU4ZM7zxkhenVGS_8xYtPnf7ZBR8WpXbbBRCTU5nEJFYDtLr_CZTBkYYr5Bj0pGc4llWzo293NIK0Ts2oXTHDZLFVQZ-JOcph880u5iho6EVX8yO4w7-YHtDTgbKxIiXJ/s400/LA_RefModel_Chatti.png" width="400" /></a></div>
<br />
The paper "A Reference Model for Learning Analytics" has been published in the <a href="http://www.inderscience.com/jhome.php?jcode=ijtel">International Journal of Technology Enhanced Learning</a> (Vol. 4, Nos. 5/6). The paper can be downloaded<a href="https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&cad=rja&uact=8&ved=0CE8QFjAEahUKEwiz-qDFmPbHAhXMuhoKHYacCOA&url=http%3A%2F%2Flufgi9.informatik.rwth-aachen.de%2Fdl1139%257CCDST12_IJTEL.pdf&usg=AFQjCNHQcUV7D1MpLjQl0tt7kxTENrwaAg&sig2=T3O0NaaWOPAJ8l4oEexi_Q" target="_blank"> here</a>.<br />
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An updated version of the LA reference model as well as an overview of future perspectives in the LA research field are available in the paper "<a href="http://mohamedaminechatti.blogspot.de/2014/11/learning-analytics-challenges-and.html" target="_blank">Learning Analytics: Challenges and Future Research Directions</a>".<br />
<br />
<i>Abstract:</i><br />
Recently, there is an increasing interest in learning analytics in Technology-Enhanced Learning (TEL). Generally, learning analytics deals with the development of methods that harness educational datasets to support the learning process. Learning analytics (LA) is a multi-disciplinary field involving machine learning, artificial intelligence, information retrieval, statistics and visualisation. LA is also a field in which several related areas of research in TEL converge. These include academic analytics, action analytics and educational data mining. In this paper, we investigate the connections between LA and these related fields. We describe a reference model for LA based on four dimensions, namely data and environments (what?), stakeholders (who?), objectives (why?) and methods (how?). We then review recent publications on LA and its related fields and map them to the four dimensions of the reference model. Furthermore, we identify various challenges and research opportunities in the area of LA in relation to each dimension.<br />
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<span style="font-style: italic;">Reference:</span> <br />
Chatti, M.A., Dyckhoff, A.L., Schroeder, U. and Thüs, H. (2012) ‘A reference model for learning analytics’, Int. J. Technology Enhanced Learning, Vol. 4, Nos. 5/6, pp.318–331.</div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-90276205472609651072013-01-11T14:45:00.000+01:002017-11-14T09:43:23.939+01:00The LaaN Theory<div dir="ltr" style="text-align: left;" trbidi="on">
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEicBlotwfFhzvamJ7ReiJI-hKM6sml1TvJVKvtik-DFVBGt-URiFc7AIvhMMHa3SYyKU802KakIEJdpiAzTBWeuUba1hXW0caq_2UHO-28zbcNeFMH4v96zUNMHceeB9zzeAYoMC3U1Zaou/s1600/LaaN.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="300" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEicBlotwfFhzvamJ7ReiJI-hKM6sml1TvJVKvtik-DFVBGt-URiFc7AIvhMMHa3SYyKU802KakIEJdpiAzTBWeuUba1hXW0caq_2UHO-28zbcNeFMH4v96zUNMHceeB9zzeAYoMC3U1Zaou/s400/LaaN.png" width="400" /></a></div>
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Reference: <br />
Chatti, M. A. (2010). The LaaN Theory. In <i>Personalization in Technology Enhanced Learning: A Social Software Perspective</i> (pp. 19-42). Aachen, Germany: Shaker Verlag.<br />
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<b>The LaaN Theory</b></h2>
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<b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-family: "cambria" , "serif"; mso-font-kerning: 14.0pt;">Mohamed Amine
Chatti</span></b></div>
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<span lang="EN-US" style="mso-font-kerning: 14.0pt;">RWTH Aachen University </span></div>
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<span lang="EN-US" style="mso-font-kerning: 14.0pt;">chatti@cs.rwth-aachen.de</span></div>
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<b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="mso-fareast-font-family: Calibri; mso-fareast-language: DE;">ABSTRACT</span></b></div>
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<span lang="EN-US" style="mso-fareast-font-family: Calibri; mso-fareast-language: DE;">One of the core
issues in Technology Enhanced Learning (TEL) is the personalization of the
learning experience. This implies a need for new TEL models that start from the
learners and satisfy their unique needs in order to achieve a personalized
learning experience for everyone. </span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">This paper discusses the Learning as a Network (LaaN) theory as a
theoretical foundation for self-organized and networked learning models. LaaN
builds upon connectivism, complexity theory, and double-loop learning. It promotes
a theory of change, movement, dynamism, self-organization, emergence, and
effectiveness, which puts the learner at the center and represents a knowledge
ecological approach to learning. In many ways, LaaN overlaps with other
learning theories; yet it is distinctive enough to be treated as a separate
perspective on learning.</span></div>
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<span lang="EN-US" style="font-size: 11.0pt;">Keywords</span></h2>
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<span lang="EN-US">Learning
Theories, LaaN, Personal Knowledge Network, Knowledge Ecology</span></div>
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<b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Introduction</span></b><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="mso-bidi-font-size: 11.0pt;"></span></b></div>
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<span lang="EN-US" style="mso-fareast-font-family: Calibri; mso-fareast-language: DE;">In the past few years, the discussion about
technologies for learning has moved away from only institutionally managed
learning management systems to the use of personal and social tools for
learning. Central aspects of this discussion are the concepts of the personal
learning environment (PLE) and connectivist massive open online courses (cMOOCs),
where the learner is in control of her own development and learning. </span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">The PLE concept represents a
significant shift in pedagogic approaches toward a self-organized learning
model that puts the learner at the center and gives her more autonomy and
control over the learning experience. </span><span lang="EN-US">cMOOCs combine
MOOCs with PLEs and refer to open-ended, distributed, networked, and
learner-directed learning environments where the learning services are not
predetermined, and most activities take place outside the platform</span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;"></span></div>
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<span lang="EN-US" style="mso-fareast-font-family: Calibri; mso-fareast-language: DE;">While </span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">pedagogical and</span><span lang="EN-US" style="color: black; mso-fareast-font-family: Calibri;"> </span><span lang="EN-US" style="mso-fareast-font-family: Calibri; mso-fareast-language: DE;">technological aspects of PLEs and cMOOCs are
increasingly discussed in the TEL literature, the discussion of a theoretical
framework for these concepts lacks behind. This missing theoretical framework
hinders PLEs and cMOOCs from being more widely understood and adopted. This
paper </span><span lang="EN-US" style="color: black; mso-fareast-font-family: Calibri;">discusses the Learning as a Network (LaaN) theory as a theoretical
foundation for self-organized and networked learning models. We start with a
brief introduction of connectivism, complexity theory, and double-loop
learning, which build the backbone for LaaN. We then provide a detailed
discussion of the key characteristics of LaaN that distinguish it from dominant
learning and social theories. These theories are behaviorism, cognitivism,
(social) constructivism, situated learning, activity theory, and actor-network
theory.</span><span lang="EN-US" style="mso-fareast-font-family: Calibri; mso-fareast-language: DE;"></span></div>
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<b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Connectivism</span></b></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Siemens
(2005) argues that knowledge and learning are today defined by connections. The
author stresses that “know where” and “know who” are more important today that
"knowing what" and "how", and introduces <i style="mso-bidi-font-style: normal;">connectivism</i> as a new learning theory
that presents learning as a connection/network-forming process. He suggests that
learning which resides outside the individual learner is focused on connecting
specialized information sets and the connections that enable us to learn more
than our current state of knowing. According Siemens, the main intent of
network creation is to enable learners to continue to stay current in the face
of rapidly developing knowledge. He further points out that the half-life of
knowledge is shrinking and argues that learning networks can resolve some of
the challenges of the rapidly diminishing half-life of knowledge. </span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Connectivism
is also the assertion that “the pipe is more important than the content within
the pipe” (Siemens, 2005). That is, the connections that enable us to learn are
more important than our current state of knowing. As the author puts it: “Our
ability to learn what we need for tomorrow is more important than what we know
today [...] As knowledge continues to grow and evolve, access to what is needed
is more important than what the learner currently possesses”.</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">A
similar view of connectivism is provided by Downes (2007) who writes: “At its
heart, connectivism is the thesis that knowledge is distributed across a
network of connections, and therefore that learning consists of the ability to
construct and traverse those networks”.</span></div>
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<b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Complexity Theory</span></b></div>
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<i style="mso-bidi-font-style: normal;"><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Complexity theory</span></i><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;"> refers to the study of complex
systems. A complex system is an open, dynamic, and constantly evolving network
of many interacting identities, where the behavior of the whole is far more
complex than the individual behavior of the parts. The term complex adaptive
systems (CAS), or complexity science, is often used to describe the academic
field that has grown up around the study of complex systems. Complex adaptive
systems are special cases where a complex, non-linear, interactive system has
the ability to learn from and adapt to a constantly changing environment
(Holland, 1992, 1995).</span></div>
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<i style="mso-bidi-font-style: normal;"><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Emergence</span></i><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;"> is central to the theory of
complex adaptive systems (Holland, 1998). The term “emergent” was coined by the
pioneer psychologist G. H. Lewes in 1875, who wrote "The emergent is
unlike its components in so far as these are incommensurable, and it cannot be
reduced to their sum or their difference" (Lewes, 1875, p. 413) (as cited
in Corning, 2002). A more recent definition of emergence was provided by
Goldstein (1999) who refers to “the arising of novel and coherent structures,
patterns and properties during the process of self-organization in complex
systems” (p. 49). Holland (1998) stresses that emergence must be the product of
<i style="mso-bidi-font-style: normal;">self-organization</i>, not centralized
control (as cited in Ryan, 2007).</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Drawing
on complex adaptive systems theory, Snowden (2002) developed the Cynefin
framework, which is made up of four domains:</span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Simple, in which the relationship
between cause and effect relationships is well known.</span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Complicated, in which the
relationship between cause and effect is knowable but some effort is required in
order to analyze the system.</span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Complex, in which the relationship
between cause and effect are intertwined and cannot be known in advance.</span></div>
<div class="MsoNormal" style="margin-left: 36.0pt; mso-layout-grid-align: none; mso-list: l0 level1 lfo1; mso-pagination: none; tab-stops: 28.0pt 56.0pt 84.0pt 112.0pt 140.0pt 168.0pt 196.0pt 224.0pt 252.0pt 280.0pt 308.0pt 336.0pt; text-align: justify; text-autospace: none; text-indent: -18.0pt;">
<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Chaotic, in which there is no
perceived relationship between cause and effect.</span></div>
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<b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Double Loop Learning</span></b></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">The
concept of <i style="mso-bidi-font-style: normal;">double-loop learning</i> was
introduced by Argyris & Schön (1978) within an <i style="mso-bidi-font-style: normal;">organizational learning</i> context. According to the authors, organizational
learning is the process of detecting and correcting errors. It takes account of
the interplay between the actions and interactions of individuals and
higher-level organizational entities such as departments, divisions, or groups
of managers. Each member of an organization constructs his own representation
of the theory-in-use of the whole. Organizational learning then occurs when
individuals within an organization experience a problem (error detection) and
work on solving this problem (error correction). Error correction happens
through a continuous process of organizational inquiry, where everyone in the
organizational environment can inquire, test, compare and adjust his
theory-in-use, which is a private image of the organizational theory-in-use.
Effective organizational inquiry then leads to a reframing of one’s
theory-in-use, thereby changing the organizational theory-in-use.</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Argyris
(1991) asserts that most people define learning too narrowly as mere “problem
solving”, so they focus on identifying and correcting errors in the external
environment. This is what Argyris calls <i style="mso-bidi-font-style: normal;">single-loop
learning</i>. But, in the words of Argyris:</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">if learning is to persist, managers and employees must
also look inward. They need to reflect critically on their own behavior, identify
the ways they often inadvertently contribute to the organization’s problems,
and then change how they act. (p. 99)</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">This
deeper form of learning is what Argyris terms <i style="mso-bidi-font-style: normal;">double-loop learning</i>.</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">To put it simply, single-loop learning differs from
double-loop learning in that the former aims at efficiency (i.e. doing things
right) and the latter focuses on effectiveness (i.e. doing the right things).
Argyris & Schön (1996) define single-loop learning as "learning that
changes strategies of actions or assumptions underlying strategies in ways that
leave the values of a theory of action unchanged" (p. 20), and double-loop
learning as "learning that results in a change in the values of
theory-in-use, as well as in its strategies and assumptions" (p. 21).</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">In other words, Argyris and Schön differentiate between
learning that does not change the underlying mental models of the learners but
merely revises their application scenarios (single-loop), and learning which
does affect such changes (double-loop). Double-loop learning starts from a
learner’s mental model (i.e. theory-in-use) defined by base norms, values,
strategies, and assumptions, and suggests critical reflection in order to
challenge, invalidate, or confirm the used theory-of-use. Double-loop learning
also encourages genuine inquiry into and testing of one’s actions and requires
self-criticism, i.e. the capacity for questioning one’s theory-in-use and
openness to change the same as a function of learning. The result of
reflection, inquiry, testing, and self-criticism would then be a reframing of
one’s norms and values, and a restructuring of one’s strategies and
assumptions, according to the new settings.</span></div>
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<b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">The LaaN Theory</span></b></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">The <i style="mso-bidi-font-style: normal;">Learning as a
Network (LaaN)</i> theory draws together some of the concepts behind
connectivism, complexity theory, and double-loop learning. An abstract view of
LaaN is depicted in Figure 1.</span></div>
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<span lang="EN-US" style="mso-bidi-font-family: "Trebuchet MS"; mso-bidi-font-size: 13.0pt; mso-fareast-font-family: Calibri;">Figure 1:
The LaaN Theory</span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;"> </span></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh197rRVUE2tQQLFZtWidBU2k9pKRHYs7WLXF4HtgbYK9P8LiZSNve-fNJRSnj_B-kdTEJCAcSofR5ufRgJvk5QnWzzjc1MWkJY2pPHGbDU2lJapWWYQe4BotunsPEw05Qf5jp1w8LtfWXe/s1600/LaaN_Chatti.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh197rRVUE2tQQLFZtWidBU2k9pKRHYs7WLXF4HtgbYK9P8LiZSNve-fNJRSnj_B-kdTEJCAcSofR5ufRgJvk5QnWzzjc1MWkJY2pPHGbDU2lJapWWYQe4BotunsPEw05Qf5jp1w8LtfWXe/s320/LaaN_Chatti.png" width="320" /></a></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;"> Connectivism
focuses on making connections (at external, conceptual, and neural levels) and seeing
patterns. However, it misses some of the double-loop learning concepts, which
are crucial for learning, such as learning from failures, error detection and
correction, and inquiry. On the other hand, double-loop learning aims at
detecting and correcting errors by changing the values, strategies, and
assumptions of the theory-in-use according to the new setting. Double-loop
learning however, does not recognize the power of connections and networks that
can help us operate in highly dynamic and uncertain knowledge environments,
characterized by increasing complexity and fast-paced change.</span>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Within
LaaN, connectivism, complexity theory, and double-loop learning converge around
a learner-centric environment. LaaN starts from the learner and views learning
as the continuous creation of a <i style="mso-bidi-font-style: normal;">personal
knowledge network (PKN)</i>. A PKN shapes the knowledge home and the identity
of the individual learner. For each learner, a PKN is a unique adaptive
repertoire of:</span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Tacit and explicit knowledge nodes
(i.e. people and information) (external level)</span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">One’s theories-in-use. This
includes norms for individual performance, strategies for achieving values, and
assumptions that bind strategies and values together (conceptual/internal
level)</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">In
LaaN, the result of learning is a restructuring of one’s PKN, that is, an
extension of one’s external network with new knowledge nodes (external level)
and a reframing of one’s theories-in-use (conceptual/internal level).</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">LaaN-based learning implies that a learner needs to be a
good knowledge networker as well as a good double-loop learner. A good
knowledge networker is one who can:</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Create, harness, nurture, sustain,
and widen her external network to embrace new knowledge nodes.</span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Identify connections, recognize
patterns, and make sense between different knowledge nodes.</span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Locate the knowledge node that can
help achieving better results, in a specific learning context.</span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Aggregate and remix. </span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Cross boundaries, connect, and
cooperate. </span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Navigate and learn across multiple
knowledge networks. </span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Help other knowledge networkers
build and extend their networks.</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Furthermore, a good double-loop learner is one who has
the ability to:</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Build her own representation of the
theories-in-use of the whole.</span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Reflect.</span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">(Self-) criticize.</span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Detect and correct errors with
norms and values specified by the new setting.</span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Inquire</span></div>
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<span lang="EN-US" style="color: black; font-family: "symbol"; mso-bidi-font-family: Symbol; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Symbol;"><span style="mso-list: Ignore;">·<span style="font: 7.0pt "Times New Roman";"> </span></span></span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Test, challenge, and eventually
change her theories-in-use (i.e. her private image of the theories-in-use of
the whole) according to the new setting.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">LaaN-based
learning also implies new roles for the learning institution and the teacher.
In LaaN, the learning institution needs to act as a hub connecting third
parties providing personalized learning experiences to the learners. And,
teachers need to step back from their traditional role of instructors and experts.
The new role of the teachers is to act as co-learners and facilitators of the
learning experience. Their major task is to help learners build their PKNs in
an effective and efficient way, by providing a freeform and emergent
environment conducive to networking, inquiry, and trial-and-error; that is an
open environment in which learners can make connections, see patterns, reflect,
(self)-criticize, detect and correct errors, inquire, test, challenge and
eventually change their theories-in-use.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">At
the heart of LaaN lie <a href="http://mohamedaminechatti.blogspot.de/2014/09/the-paperlaan-convergence-of-knowledge.html" target="_blank"><i style="mso-bidi-font-style: normal;">knowledge ecologies</i></a>.
A knowledge ecology is based on the concept of PKNs, loosely joined, and can be
defined as a complex, knowledge intensive landscape that emerges from the
bottom-up connection of PKNs. </span><span lang="EN-US" style="mso-fareast-font-family: Calibri; mso-fareast-language: DE;">The definition of knowledge ecology is highly
compatible with complexity</span><span lang="EN-US" style="color: black; mso-fareast-font-family: Calibri;"> </span><span lang="EN-US" style="mso-fareast-font-family: Calibri; mso-fareast-language: DE;">theory. As with complex adaptive systems, a knowledge
ecology holds</span><span lang="EN-US" style="color: black; mso-fareast-font-family: Calibri;"> </span><span lang="EN-US" style="mso-fareast-font-family: Calibri; mso-fareast-language: DE;">emergent properties, includes self-organized entities,
and can evolve in ways that</span><span lang="EN-US" style="color: black; mso-fareast-font-family: Calibri;"> </span><span lang="EN-US" style="mso-fareast-font-family: Calibri; mso-fareast-language: DE;">we may not expect or predict. Knowledge
ecologies blur the boundaries of learning and harness the power of PKNs. They
house self-directed learning that occurs in a bottom-up and emergent manner,
rather than learning that functions within a structured context, of an
overarching framework, shaped by command and control.</span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;"> </span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Based
on the PKN and knowledge ecology concepts, the next sections outline the major
differences between LaaN and different dominant learning and social theories.
These theories are behaviorism, cognitivism, (social) constructivism, situated
learning, activity theory, and actor-network theory.</span></div>
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<b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">LaaN and Psychological
Learning Theories</span></b></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Learning
has traditionally been the province of psychological theories: <i style="mso-bidi-font-style: normal;">behaviorism</i>, <i style="mso-bidi-font-style: normal;">cognitivism</i>, and <i style="mso-bidi-font-style: normal;">constructivism</i>
(Wenger, 1998). </span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Behaviorist
theories focus on behavior modification via stimulus-response pairs and
selective reinforcement (positive and negative reinforcement). Their pedagogical
focus is on control and adaptive response (Skinner, 1974) (as cited in Wenger,
1998, p. 279). Behaviorists define learning as a change in behavior in the
learner, and see the mind as a "black box" in the sense that all
behavior can be explained without the need to consider internal mental states
or consciousness. The goal of instruction for the behaviorist is to elicit the
desired response from the learner who is presented with a target stimulus
(Ertmer & Newby, 1993).</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Cognitive
theories focus on internal cognitive or mental structures and view learning as the
transformations that occur within them. They address the issues of how information
is received, organized, stored, and retrieved by the mind (Ertmer & Newby,
1993). Unlike the behaviorists, who were only concerned with behavioral
responses and what learners do, cognitivists are interested in what learners
know and how they come to acquire knowledge (Jonassen, 1991). The goal of
instruction for the cognitivist is to make knowledge meaningful and help
learners organize and relate new information to existing knowledge in memory
(Ertmer & Newby, 1993).</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">The
class of behaviorist and cognitivist learning theories has been referred to as
objectivism (Jonassen, 1991). In the literature dealing with learning theories
and instructional design, objectivism (i.e. behaviorism/cognitivism) has often
been contrasted with constructivism, which holds that "knowing is a
process of actively interpreting and constructing individual knowledge
representations" (Jonassen, 1991, p. 5). Constructivist theories focus on
the processes by which learners build their own mental structures when interacting
with an environment. Their pedagogical focus is task-oriented. They favor
hands-on, self-directed activities oriented toward design and discovery
(Piaget, 1954; Papert, 1980) (as cited in Wenger, 1998, p. 279).</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Objectivism
(i.e. behaviorism/cognitivism) and constructivism, as psychological theories,
assume that learning occurs inside a person (Siemens, 2005a) and view knowledge
as a property and possession of an individual mind. Sfard (1998) points out that
the objectivist and constructivist processes can be conceptualized in terms of
the acquisition metaphor, where learning is mainly a process of acquiring and
accumulating desired pieces of knowledge. The author further notes: "The
language of ’knowledge acquisition’ and ’concept development’ makes us think
about the human mind as a container to be filled with certain materials and
about the learner as becoming an owner of these materials" (Sfard, 1998,
p. 5). Instead of knowledge residing only in the mind of an individual, LaaN
focuses on knowledge as both intrinsic and extrinsic. In LaaN, knowledge does
not only reside in the mind but also in a distributed manner across a network
(Downes, 2007, Siemens, 2005). In other words, psychological theories emphasize
knowledge as a thing/object that can be acquired or constructed, and the
individual mind as a container, whereas LaaN emphasizes knowledge as a personal
network, which provides the learner with the means needed to deal with
ill-structured topics/problems and to explore complex knowledge environments.
In LaaN, learning is learner-initiated knowledge networking rather than
knowledge acquisition, internalization, or construction.</span></div>
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<b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">LaaN and Social Theories</span></b></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">LaaN
shares a core proposition with dominant social theories, such as social
constructivism, situated learning, activity theory, and actor-network theory in
that knowledge and learning are fundamentally social in nature. However, the
LaaN view of learning as a continuous creation of a PKN is quite distinctive.</span></div>
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<div class="Subhead2">
<b><span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: Calibri;">LaaN vs. Social
Constructivism</span></b></div>
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<br /></div>
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<i style="mso-bidi-font-style: normal;"><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Social Constructivism</span></i><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;"> is a theory of learning based upon
the learners’ social interaction and collaboration. Social constructivist
theorists (e.g. Vygotsky) have extended the traditional focus on individual
learning to address collaborative and social dimensions of learning. Whereas
Piaget’s cognitive constructivism focuses on the individual mind, Vygotsky’s
social constructivism conceptualizes learning as more socially constructed.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">A
central concept in Vygotsky’s social constructivism is the <i style="mso-bidi-font-style: normal;">’zone of proximal development’ (ZPD)</i>, which highlights the
potential for future learning which can be achieved with appropriate support.
Vygotsky (1978) distinguishes between two developmental levels: The <i style="mso-bidi-font-style: normal;">level of actual development</i> is the level
of development that the learner has already reached, and is the level at which
the learner is capable of solving problems independently. The <i style="mso-bidi-font-style: normal;">level of potential development</i> is the
level of development that the learner is capable of reaching under the guidance
of teachers or in collaboration with peers. The learner is capable of solving
problems and understanding material at this level that they are not capable of
solving or understanding at their level of actual development. Vygotsky (1978)
further defines the zone of proximal development (ZPD) as "the distance
between the actual developmental level as determined by independent problem
solving and the level of potential development as determined through problem
solving under adult guidance or in collaboration with more capable peers"
(p. 86).</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">LaaN
differs from social constructivism in four different ways. First, the theory of
social constructivism suggests that learners co-construct knowledge. However,
the usage of the word “construction” implies that knowledge is a robust and
durable object. As Latour (2005) states: "using the word ’construction’
seemed at first ideal to describe a more realistic version of what it is for
anything to stand. And indeed, in all domains, to say that something is constructed
has always been associated with an appreciation of its robustness, quality,
style, durability, worth, etc" (p. 89). Robustness and durability however,
do not apply to knowledge. Knowledge is much more varied and uncertain. As
Siemens (2005) stresses: “In today’s world, knowledge life is short; it survives
only a short period of time before it is outdated”. In LaaN, knowledge is a
personal network rather than an object that can be constructed.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Second,
although social constructivism takes social interactions into account, it still
sees learning as essentially intrinsic (i.e. in the learner’s mind). In fact,
the primary focus of Vygotsky’s social constructivism is to determine the state
of a learner’s mental development by clarifying its two levels: the actual
developmental level and the ZPD. The actual developmental level of a learner
indicates her actual mental abilities and the ZPD represents her potential
mental development. As Vygotsky (1978) puts it: "The actual developmental
level characterizes mental development retrospectively, while the zone of proximal
development characterizes mental development prospectively" (p. 86). The
focus on the mental development of the learner makes thus from social
constructivism a learning theory with a primarily psychological perspective.
Unlike social constructivism, which views learning as internal developmental
processes that result in mental development (i.e. intrinsic), LaaN views
learning as both intrinsic and extrinsic; it occurs through the continuous
building of personal knowledge networks, at both internal/conceptual and
external levels.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Third,
Vygotsky’s social constructivism has centered on the role that adults play in
fostering children’s development. ZPD, which is closely related to the concept
of "<i style="mso-bidi-font-style: normal;">scaffolding</i>",
emphasizes that learning occurs best when an expert (either an adult or a more
competent peer; aka the More Knowledgeable Other, MKO) guides a novice from
their current level of knowledge to the level of knowledge they are capable of with
assistance. However, nowadays the lines became blurred between the expert and
novice roles. At each moment the novice can move into the expert role and vice
versa. In LaaN, everyone is a knowledge networker and can thus act as a novice
in one context and step into the expert role in another context. The bridge between
where the learner is and where she is going is through her personal knowledge
network, rather than in the hands of a teacher or a more competent peer.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Fourth,
the notion of ZPD enables learning, which is oriented towards a level of
potential development, embodied in the adult or more competent peer. Knowledge however,
is complex and the level of potential development can thus never be anticipated
or predicted. In LaaN, learning takes place in a knowledge ecology rather than
within a ZPD. Unlike a ZPD which is characterized by a rigid, restrictive, and
unidirectional development (i.e. training the novices within the ZPD towards a
level of potential development), a knowledge ecology is open, multidirectional,
and without an end point of development. The boundaries of knowledge ecologies
are less fixed and can easily be bridged and merged. And, the development
within a knowledge ecology is never fully predetermined and occurs in
unpredictable directions.</span></div>
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<br /></div>
<div class="Subhead2">
<b><span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: Calibri;">LaaN vs. Situated
Learning</span></b></div>
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<br /></div>
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<i style="mso-bidi-font-style: normal;"><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Situated learning</span></i><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;"> is a model of learning first
introduced by Jean Lave and Etienne Wenger in 1991. This model proposes that
knowing and learning involve a process of engagement in a community of practice
(CoP). As Lave & Wenger (1991) write: "Knowing is inherent in the
growth and transformation of identities and it is located in relations among
practitioners, their practice, the artifacts of that practice, and the social
organization … of communities of practice" (p. 122). Rather than looking
to learning as the acquisition of certain forms of knowledge, Lave & Wenger
(1991) explore the participation metaphor of learning in which learning is a matter
of legitimate peripheral participation (LPP) within a CoP. According to Lave
and Wenger, in a CoP, a newcomer learns from old-timers through participating
in certain tasks that relate to the practice of the community. Over time the
newcomer moves from peripheral to full participation.</span></div>
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<br /></div>
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<span lang="EN-US" style="mso-bidi-font-family: Georgia; mso-bidi-font-size: 13.0pt; mso-fareast-font-family: Calibri;">Wenger (1998)
revised his earlier work (Lave & Wenger, 1991) to offer a social account of
learning through the negotiation of meaning and identity formation within CoPs.
While Wenger does not ignore legitimacy and peripherality, it is participation
that he extracts as being crucial to the revised notion of a CoP showing it to
be the key constituent in the processes of the negotiation of meaning.
According to Wenger (1998), participation refers to "a process of taking
part and also to the relations with others that reflect this process. It
suggests both action and connection" (p. 55). Wenger stresses that learning
is social participation. He further explains that any CoP will then produce
artifacts, which reify some aspect of its practice, and refers to this process
of giving form to the experience as reification.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Within
LaaN, the notion of legitimate peripheral participation (LPP) - which is very
close to Vygotsky’s ZPD - is absent. In LaaN, role models are not strictly
defined. There is no distinction between "newcomers, novices, or
peripheral participants" and "old-timers or masters". Every
participant is equally treated as a knowledge networker. Unlike CoPs, which are
characterized by a single movement from the periphery to the center, in a
knowledge ecology, the center does not hold and the movements occur in
unpredictable directions.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Moreover, in Wenger’s social theory of learning, the
emphasis is on the CoP. As Wenger (1998) writes in the introduction of his
book:</span></div>
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<br /></div>
<blockquote class="tr_bq">
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Communities of Practice presents a theory of learning
that starts with this assumption: engagement in social practice is the
fundamental process by which we learn and so become who we are. The primary
unit of analysis is neither the individual nor social institutions but rather
the informal "communities of practice" that people form as they
pursue shared enterprises over time.</span></div>
</blockquote>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">In LaaN, by contrast, the primary focus is on the
individual learner and her PKN. Knowledge development in LaaN is driven by the
learning demands of the learner, rather than the community in which she
belongs. In contrast to Wenger’s learning theory, where learning for an
individual is "an issue of engaging in and contributing to the practices
of their communities" (p. 7), LaaN views learning for an individual as an
issue of continuously building, maintaining, extending, and restructuring her
PKN.</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Furthermore,
the social landscape is quite different within LaaN. A strong distinction can
be made between closed, bounded, structured, and hierarchical CoPs on the one
hand and open, distributed, diverse, emergent, and self-controlled knowledge
ecologies on the other hand.</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Wenger (1998) discusses three dimensions of a CoP (p.
73):</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">1. How it functions (community): A mutual engagement
that bind members together into a social entity.</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">2. What it is about (domain): A joint enterprise as
understood and continually renegotiated by its members.</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">3.
What capability it has produced (practice): The shared repertoire of communal
resources (routines, sensibilities, artifacts, vocabulary, styles, etc.) that
members have developed over time.</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">A
knowledge ecology differs from a CoP on all these dimensions.</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">According
to Wenger, "the first characteristic of practice as the source of coherence
of a community is the mutual engagement of participants" (p. 73). It is
mutual engagement that binds members of a CoP together as a social entity and
enables them to define themselves as members of the CoP. Unlike a CoP, a
knowledge ecology is a social entity which has no clear boundaries and
membership criteria. It involves an emergent network of people not so tightly
bound as a CoP. A knowledge ecology is driven by independence and autonomy
rather than membership, mutual engagement, and belonging to a community. Rather
than being forced to interact intensely with other members of a CoP, within a
knowledge ecology, everyone can rely on her PKN. Often, people turn to their
personal relations in order to learn and get their work done, rather than
trying to get access to a well established community of mutual engagement.
Wenger further stresses that the kind of coherence that transforms mutual
engagement into a CoP requires work and asserts that "the work of
"community maintenance" is thus an intrinsic part of any
practice" (p. 74). In a knowledge ecology, however, people focus on
forming and maintaining their PKNs and sustaining dense relations with nodes in
their PKNs rather than maintaining the CoP to which they belong.</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Wenger
states that "the second characteristic of practice as a source of community
coherence is the negotiation of a joint enterprise" (p. 77). According to
Wenger, a CoP involves organizing around some particular area of knowledge
(i.e. a shared domain of interest) that gives members a sense of joint
enterprise and shared identity. Membership in a CoP implies a commitment to the
domain and a continuous negotiation of a joint enterprise. A CoP is thus a
homogeneous social entity consisting of members with a joint enterprise and a
shared domain of interest. Unlike CoPs, knowledge ecologies are not bound by a
shared practice, a joint enterprise, or an overarching domain. They are open,
flexible, heterogeneous, and multidisciplinary social entities. In a knowledge
ecology, people continuously create their PKNs which shape their identity and
knowledge home, rather than create a shared identity through engaging in and
contributing to the practices of a CoP. Wenger further notes that
"communities of practice are not self-contained entities. They develop in
larger contexts - historical, social, cultural, and institutional - with
specific resources and constraints" (p. 79). Consequently, the practice of
a community is profoundly shaped by conditions outside the control of its
members due to external efforts to maintain influence and control over the
practice. In contrast to CoPs, knowledge ecologies are not positioned within a
broader system and are not bound to the control of any external force. They
emerge naturally without strong predetermined rules or external authority.
Knowledge ecologies are thus self-controlled and self-contained entities.</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Wenger
notes that "the third characteristic of practice as a source of community
coherence is the development of a shared repertoire ... The repertoire of a
community of practice includes routines, words, tools, ways of doing things,
stories, gestures, symbols, genres, actions, or concepts that the community has
produced or adopted in the course of its existence, and which have become part
of its practice. The repertoire combines both reificative and participative
aspects" (pp. 82-83). In contrast to CoPs, knowledge ecologies lack a
shared repertoire and are thus open and distributed knowledge domains. The
knowledge resources are distributed over different PKNs within a knowledge
ecology. Unlike participation in a CoP, where the result is the development of
a community’s set of shared resources and practices, the result of
participation in a knowledge ecology is a restructuring of one’s PKN, that is,
a reframing of one’s theories-in-use (conceptual/internal level) and an
extension of one’s external network with new knowledge nodes (external level).</span></div>
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<b><span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: Calibri;">LaaN vs. Activity Theory</span></b></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">The
cultural-historical theory of activity (<i style="mso-bidi-font-style: normal;">Activity
Theory</i>) has grown out of the work of Vygotsky, Leont’ev and other Soviet socio-cultural
oriented psychologists. Activity Theory approaches human cognition and behavior
as embedded in collectively organized, artifact-mediated and object-oriented
activity systems (Vygotsky, 1978; Leont’ev, 1978; Engeström, 1987). According
to Engeström (1999b), an activity system "constantly generates actions
through which the object of the activity is enacted and reconstructed in
specific forms and contents - but being a horizon, the object is never fully
reached or conquered" (p. 381).</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Activity
Theory develops from the work of Vygotsky, particularly his arguments that
human development and learning is mediated by artifacts, such as language,
signs, and symbol systems. The classical representation of an activity system
is a mediating triangle comprising three central components, namely subject,
object, and mediating artifacts (Vygotsky, 1978). Activities are social practices
oriented at objects. An entity becomes an object of activity when it meets a
human need. The subject constructs the object using mediating artifacts
(Engeström, 1999b). Leont’ev (1978), drawing on Vygotsky’s foundational work,
points out that there is a crucial difference between an individual action and
a collective activity and extends Vygotsky’s original model into a model of a
collective activity system. Leont’ev’s conceptualization includes division of
labour, which helps to differentiate between what is accomplished collectively
or individually. Leont’ev further adds a distinction between activity, action
and operation, as three different levels of human practice in order to
delineate an individual’s action from the collective activity (Leont’ev, 1978,
Section 3.5).</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Inspired
by Leont’ev’s work, Engeström (1987, 1999a) also notes that the problem with
Vygotsky’s classical representation of an activity system is that it does not
fully explicate the societal and collaborative nature of the human actions. He
then graphically expands Vygotsky’s original mediating triangle with a social
component by including three contextual factors, namely community, rules, and
division of labor. Engeström uses this expanded activity system model as the
basis for his theory of expansive learning, which focuses on the expansive
transformation of the object of activity in a collective activity system
(Engeström, 1987, 1999b).</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Engeström
(1987) conceives the notion of the ‘zone of proximal development’, initially
discussed by Vygotsky, as the cornerstone of expansive learning. Within an
expansive learning framework, Engeström (1999b, 2001) presents the notion of
’expansive cycle’ as the equivalent of Vygotsky’s zone of proximal development.
As Engeström (2001) puts it: “a full cycle of expansive transformation may be
understood as a collaborative journey through the zone of proximal development
of the activity” (p. 137). Engeström traces seven expansive learning actions to
be taken in traveling through the zone of proximal development of an activity.
Together these actions form an expansive cycle or spiral. According to
Engeström (1999b), an ideal-typical sequence of actions in an expansive cycle
includes (p. 383):</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">1. questioning, criticizing, and rejecting some aspects
of the accepted practices,</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">2.
analyzing the situation,</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">3. modeling of a new solution to the problematic
situation, </span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">4. examining the model, </span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">5. implementing the model, </span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">6. reflecting on and evaluating the process,</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">7.
consolidating its outcomes into a new, stable from of practice.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">However, in the new knowledge intensive era, it is increasingly
evident that knowledge is highly complex and that dealing with knowledge is
definitely not reducible to any sequence of actions. The actions which form
Engeström’s expansive learning cycle are not the only kinds of actions that
must be mastered and performed in highly complex knowledge ecologies. LaaN does
not postulate a predetermined sequence of actions; it rather enables a wide
range of learner-driven actions that are neither predetermined nor predictable.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">In
general, using Activity Theory as a framework for the analysis of activity in
complex learning environments has a major limitation. Learning as a complex
activity cannot be captured by an overarching activity system (or even a
network of activity systems) purposefully oriented toward the achievement of an
object of activity. Learning is multifaceted and dynamic, and activities in a
learning environment are fuzzy, varied, and often fragmented, which makes it
very hard to elicit a complete picture of the activity system(s) under
observation, encompassing, in activity theory terms, an evolving set of
subjects, objects, mediating artifacts, actions, rules, norms, and division of
labour. The solution to this problem is to understand the learning activity
from the learner’s point of view. Whereas in Activity Theory the prime unit of
analysis is an artifact-mediated and object-oriented activity, LaaN rather
focuses on the individual learner and her PKN. In activity theory, you are what
you do. In LaaN, you are what your PKN is.</span></div>
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<div class="Subhead2">
<b><span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: Calibri;">LaaN vs. Actor-Network
Theory</span></b></div>
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<br /></div>
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<i style="mso-bidi-font-style: normal;"><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Actor-network theory (ANT)</span></i><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">, also known as the sociology of
translation or sociology of associations, proposes a socio-technical account
that makes no distinction in approach between the social, the natural and the
technological (Latour, 1996, 2005; Law, 1992). ANT is based upon the principle
of <i style="mso-bidi-font-style: normal;">generalized symmetry</i> employing a
single conceptual framework when interpreting actors, human and non-human. </span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">ANT
has several limitations to be applied as a framework for dealing with complex
learning environments. ANT explores the ways that heterogeneous networks of
both human and non-human actors are constructed and maintained and focuses on
tracing the transformation of these heterogeneous networks (Latour, 2005).
Central to ANT is the concept of <i style="mso-bidi-font-style: normal;">translation</i>,
which is the process of creation of an actor-network and generation of ordering
effects Law (1992). The main problem of ANT’s heavy emphasis on the
construction, maintenance and transformation of actor-networks (i.e.
translation) is that it reduces all actors into black-boxes, and thus ignores
internal actions which are crucial for the creation of PKNs, and hence
learning, such as seeing patterns, reflecting, (self-)criticizing, and
detecting/correcting errors. These actions build the cornerstones of LaaN.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Another
limitation of ANT is that it does not distinguish between complex and
complicated systems. ANT, particularly in Latour’s work, refuses to make the
distinction between human and non-human actors i.e. between complex and
complicated entities (Williams, 2007). ANT’s strong emphasis is on the
heterogeneous nature of the actor-networks. Latour (2005) stresses that, in
ANT, non-human actors, similar to human-actors, are treated as mediators rather
than intermediaries. Latour makes a strong distinction between intermediaries
and mediators. He writes:</span></div>
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<blockquote class="tr_bq">
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;"><span style="mso-tab-count: 1;"></span>For intermediaries, there is no
mystery since inputs predict outputs fairly well: nothing <span style="mso-tab-count: 1;"> </span>will be present in the effect that has not
been in the cause ... For mediators, the situation <span style="mso-tab-count: 1;"></span>is different: causes do not allow effects to be deduced as
they are simply offering <span style="mso-tab-count: 1;"></span>occasions,
circumstances, and precedents. As a result, lots of surprising, aliens may pop <span style="mso-tab-count: 1;"></span>up in between. (pp. 58-59)</span></div>
</blockquote>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Treating
non-human actors as mediators makes from them complex entities where cause and
effect are intertwined and cannot be separated. Non-human actors, however, are
rather complicated entities - or in Latour’s terms intermediaries. All the components
of a non-human actor are knowable and cause and effect relationships can be
predicted. In LaaN non-human "actors" are just an enabler. At the
heart of LaaN lie individual learners and their PKNs.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Another
point where it also becomes clear that ANT does not make a distinction between
the complex and the complicated is the ANT’s concern with the way in which the
social is constantly reconfigured, or in Latour’s words ‘reassembled’ (Latour,
2005). Law (1992) stresses that the core of the actor-network approach is</span></div>
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<blockquote class="tr_bq">
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;"><span style="mso-tab-count: 1;"> </span>a concern with how actors and
organisations mobilise, juxtapose and hold together the <span style="mso-tab-count: 1;"> </span>bits and pieces out of which they are composed ... and so turn a
network from a <span style="mso-tab-count: 1;"> </span>heterogeneous set of bits
and pieces each with its own inclinations, into something that <span style="mso-tab-count: 1;"></span>passes as a punctualised actor. (p. 386)</span></div>
</blockquote>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Latour
(2005) uses the verb reassemble to describe the same effect. However, in the
new knowledge intensive era, the relationship between different knowledge nodes
or in Law’s terms ‘heterogeneous bits and pieces’ is becoming flexible and is
changing rapidly; thus, it cannot be captured through a reconfiguration
process. Reconfiguration, or in Latour’s terms reassembling, works well for
complicated systems in which different components and associated relationships
can be identified and managed. Reconfiguration, however, cannot work while
dealing with complex knowledge systems comprising many interacting identities.
In the latter case, networking is the solution. In complex knowledge systems,
the way the knowledge nodes network with each other results in unpredictable
movements in the knowledge ecology. Knowledge ecologies lie at the heart of
LaaN.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Furthermore,
Latour (2005) claims that "it’s possible to render social connections
traceable" (p. 16) and that the role of ANT is to trace actor-networks (p.
128). In complex knowledge systems, however, there is no chance to trace social
connections, nor is it possible to follow the actors or their actions. Latour
himself acknowledges that following the actors themselves is not an easy task
since, as he writes, "the actors to be followed swarm in all directions
like a bee’s nest disturbed by a wayward child" (p. 121). Thus, there is
no means to trace actors’ actions and connections because their actions are uncertain,
unexpected, and often hidden; and their connections are varied, ubiquitous, and
open. The role of LaaN is neither to follow actors nor to trace their actions
or connections. Its major role is to help learners build and nurture their PKNs
and to foster connections between different PKNs in order to form a complex,
adaptive, dynamic, open, and living entity; i.e. a knowledge ecology.</span></div>
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<div class="Subhead2">
<b><span lang="EN-US" style="mso-fareast-font-family: Calibri;">Learning
Theories Compared</span></b></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Schunk (1991, as cited in Ertmer & Newby, 1993, p.
53) emphasizes five definitive questions to distinguish each learning theory
from the others:</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">1.
How does learning occur?</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">2.
Which factors influence learning? </span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">3.
What is the role of memory? </span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">4.
How does transfer occur? And</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">5. What types of learning are best explained by the
theory?</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Based
on these definitive questions and other dimensions (i.e. focus, core activity,
learner’s role, underlying social entity and its characteristics), Table 1 and
Table 2 summarize how LaaN differs from the learning and social theories outlined
in the previous sections. The stress here </span><span lang="EN-US" style="mso-fareast-font-family: Calibri; mso-fareast-language: DE;">that different
learning theories cannot be considered in isolation. They complement and
enhance each other. They interplay with each other, but cannot replace each
other. In fact, many the assumptions that underlie LaaN are compatible with
those of other theories. LaaN is thus</span><span lang="EN-US" style="color: black; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;"> </span><span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">not a replacement for other theories that address different
aspects of learning but rather that it has its own focus and explains specific
types of learning. The primary focus of LaaN is mainly on the learner and her
PKN and complex network learning is the type of learning best explained by
LaaN.</span></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;"> </span><span lang="EN-US" style="mso-bidi-font-family: "Trebuchet MS"; mso-bidi-font-size: 13.0pt; mso-fareast-font-family: Calibri;"> </span>
</div>
</div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Table
1: Learning theories compared – Psychological Learning Theories</span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgBt5eINHNYmhO7Wrr0Ne0di9XGm4kVXG-RAxiYB2UFiPFc5qqq_AlQ_yAt7oDJ9zhgPla2ouHLosFWQqRIDBMC-QF36VShmlsQbijMLziax5y52Y9utBwglYTh5VNNdikznsEcWDm668C4/s1600/Learning+Theories+Comapred+-+Part+1.PNG" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="190" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgBt5eINHNYmhO7Wrr0Ne0di9XGm4kVXG-RAxiYB2UFiPFc5qqq_AlQ_yAt7oDJ9zhgPla2ouHLosFWQqRIDBMC-QF36VShmlsQbijMLziax5y52Y9utBwglYTh5VNNdikznsEcWDm668C4/s320/Learning+Theories+Comapred+-+Part+1.PNG" width="320" /></a></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">Table
2: Learning theories compared – Social Theories</span></div>
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<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhfYMCV-9x00_YoIHD1xnnyUaY75BQGcrCam7_2sPLkfPEANVQlyGVCSD8sifdU38XcjM17bogrO4Lei2Tjbmz7Oj23OiJqmIwu6LtNKbpPwKLShbPndVGSLwDytuC3j_7Djc3CmQbB8GC8/s1600/Learning+Theories+Comapred+-+Part+2.PNG" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="191" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhfYMCV-9x00_YoIHD1xnnyUaY75BQGcrCam7_2sPLkfPEANVQlyGVCSD8sifdU38XcjM17bogrO4Lei2Tjbmz7Oj23OiJqmIwu6LtNKbpPwKLShbPndVGSLwDytuC3j_7Djc3CmQbB8GC8/s320/Learning+Theories+Comapred+-+Part+2.PNG" width="320" /></a></div>
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<div class="Subhead2">
<b><span lang="EN-US" style="mso-fareast-font-family: Calibri;">Conclusion</span></b></div>
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<span lang="EN-US" style="color: black; mso-bidi-font-family: Helvetica; mso-bidi-font-size: 11.5pt; mso-fareast-font-family: Calibri;">This
paper introduced and discussed the Learning as a Network (LaaN) theory, which
represents a theoretical framework for self-organized and networked learning
models. LaaN builds upon connectivism, complexity theory, and double-loop
learning. It views knowledge as a personal network and represents a knowledge
ecological approach to learning. By comparing LaaN to influential learning and
social theories, the aim was to better explore the scope of LaaN. As is
obvious, LaaN has a number of points in common with other learning and social
theories; mainly that knowledge and learning are inherently social. However,
its focus on the learner and her PKN is quite distinctive. </span><span lang="EN-US" style="color: black; mso-fareast-font-family: Calibri;">LaaN provides a
theoretical foundation for self-organized and networked learning models, such
as PLEs and cMOOCs.</span><span lang="EN-US" style="font-family: NimbusRomNo9L-Regu; mso-bidi-font-family: NimbusRomNo9L-Regu; mso-fareast-font-family: Calibri; mso-fareast-language: DE;"></span></div>
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<div class="Subhead2">
<b><span lang="EN-US" style="mso-fareast-font-family: Calibri;">References</span></b></div>
<div class="Subhead1">
<br /></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Argyris, C. (1991). Teaching smart
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Goldstein, J. (1999). Emergence as
a construct: History and issues. Emergence: Complexity and Organization, 1(1),
49–72.</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Holland, J. H. (1992). Complex adaptive systems. Daedalus, 121(1),
17–30.</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Holland, J. H. (1995). Hidden
Order: How Adaptation Builds Complexity. Reading: MA: Addison-Wesley.</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Holland, J. H. (1998). Emergence:
From Chaos to Order. Reading, MA: Addison- Wesley.</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Jonassen, D. H. (1991). Objectivism
versus constructivism: do we need a new philosophical paradigm? Educational
Technology Research and Development, 39(3), 5–14.</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Latour, B. (1996). On actor-network
theory: A few clarifications. Soziale Welt, 47(4), 367, 369–381.</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Latour, B. (2005). Reassembling the
Social. An Introduction to Actor-Network- Theory. New York: Oxford University
Press.</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Lave, J., & Wenger, E. (1991).
Situated Learning. Legitimate Peripheral Participation. New York: Cambridge
University Press.</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Law, J. (1992). Notes on the theory
of actor-network: Ordering, strategy and heterogeneity. Systems Practice, 5(4),
379– 393.</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Leont’ev, A. N. (1978). Activity,
Consciousness, Personality. Englewood Cliffs, NJ: Prentice Hall.</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Lewes, G. H. (1875). Problems of
Life and Mind. London: Truebner.</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Ryan, A. J. (2007). Emergence is
coupled to scope, not level. Complexity, 13(2), 67–77.</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Sfard, A. (1998). On two metaphors
for learning and the dangers of choosing just one. Educational Researcher,
27(2), 4–13.</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Schunk, D. H. (1991). Learning
theories: An educational perspective. New York, NY: Merrill.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Siemens, G. (2005). Connectivism: A learning theory for the
digital age. International Journal of Instructional Technology and Distance
Learning, 2(1). Retrieved<span style="mso-tab-count: 1;"> </span>from<span style="mso-tab-count: 1;"> </span>http://www.itdl.org/Journal/Jan_05/
article01.htm</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Skinner, B. F. (1974). About
Behaviourism. New York: knopf.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Snowden, D. (2002). Complex acts of
knowing: Paradox and descriptive self- awareness. Journal of Knowledge
Managment, 6(2), 100–111.</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Vygotsky, L. S. (1978). Mind in
Society: The Development of Higher Psycholog- ical Processes. Cambridge, MA:
Harvard University Press.</span></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Wenger, E. (1998). Communities of
Practice: Learning, Meaning and Identity. Cambridge, UK: Cambridge University
Press.</span></div>
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<br /></div>
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<span lang="EN-US" style="color: black; font-size: 11.0pt;">Williams, R. (2007). Managing
complex adaptive networks. In Proceedings of the 4th International Conference
on Intellectual Capital, Knowledge Management & Organizational Learning,
(pp. 441–452).</span></div>
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<span lang="EN-US" style="mso-fareast-font-family: Calibri;"></span><span lang="EN-US" style="font-family: "arial" , "sans-serif"; font-size: 11.0pt; line-height: 150%;"></span>
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Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com4tag:blogger.com,1999:blog-8263882245812273018.post-10024230434905983712012-12-05T09:45:00.002+01:002012-12-05T09:46:55.842+01:00CfP DeLFI 2013<div dir="ltr" style="text-align: left;" trbidi="on">
====================================================<br />
CALL FOR PAPERS, POSTERS, DEMOS & WORKSHOPS
<br />
Die 11. e-Learning Fachtagung Informatik
<br />
DeLFI 2013
<br />
<br />
in Kooperation mit Mensch & Computer 2013 & Usability Professionals 2013
<br />
<br />
8. - 11. September 2013
<br />
Universität Bremen
<br />
<br />
<a class="moz-txt-link-freetext" href="http://www.delfi2013.de/">http://www.delfi2013.de</a>
<br />
<a class="moz-txt-link-freetext" href="http://www.interaktivevielfalt.org/">http://www.interaktivevielfalt.org/</a>
<br />
<a class="moz-txt-link-freetext" href="http://fg-elearning.gi.de/fileadmin/gliederungen/fg-e-learning/Newsletter/CfP/delfi-2013-cfp.pdf">http://fg-elearning.gi.de/fileadmin/gliederungen/fg-e-learning/Newsletter/CfP/delfi-2013-cfp.pdf</a>
<br />
=====================================================<br />
<br />
Seit 2003 präsentiert die DeLFI-Tagungsreihe dem interessierten
Fachpublikum aktuelle, innovative informatiknahe Ergebnisse zum Thema
e-Learning aus Forschung und Praxis. Ausgangspunkt für die DeLFI 2013
ist die zunehmende Vielfalt der Lernenden und Lehrenden an verschiedenen
Lernorten, durch unterschiedliche Lernvoraussetzungen und -erwartungen,
durch die Vermischung von formalen, non-formalen und informellen
Lernprozessen, die neue Anforderung an die Entwicklung von
Informatiksystemen stellt. Auf der Tagung sollen sowohl
Forschungsbeiträge als auch Praxisbeiträge vorgestellt werden. Damit
besteht für die Teilnehmenden die Chance, die Vielfalt der Informatik im
e-Learning aus beiden Perspektiven zu beleuchten.
<br />
<br />
<br />
Formate
<br />
================
<br />
Beiträge:
<br />
---------
<br />
Es wird um bisher unveröffentlichte Beiträge im Themenbereich e-Learning
und Informatik gebeten. Die Beiträge können in folgende Kategorien fallen:
<br />
<br />
* Forschungsbeiträge motivieren ein Forschungsziel, beschreiben die
Forschungsmethode und bewerten die Forschungsergebnisse.
<br />
* Anwendungs- und Industriebeiträge stellen aktuelle e-Learning
Anwendungen vor und werten Erfahrungen mit ihrer Nutzung aus.
<br />
<br />
Jeder Beitrag kann Kurz- oder Langbeitrag (6 oder 12 Seiten) sein. Die
Einreichungen werden vom Programmkomitee begutachtet. Angenommene
Beiträge werden im Tagungsband (Lecture Notes in Informatics)
veröffentlicht.
<br />
<br />
Demos und Poster:
<br />
-----------------
<br />
Demos und Poster stellen aktuelle Prototypen, Systeme und Anwendungen
vor. Sie sind als Kurzfassung von 3 Seiten einzureichen.
<br />
<br />
Workshops:
<br />
----------
<br />
Workshops werden gemeinsam mit der Konferenz Mensch & Computer
durchgeführt. Sie dienen der Bestandsaufnahme und dem Austausch über ein
relevantes Themengebiet der Tagung. Sie bieten größere Freiräume für
Diskussion und werden von den Ausrichtern eigenverantwortlich
durchgeführt. Workshops können als Kurzfassung (maximal drei Seiten)
eingereicht werden.
<br />
<br />
<br />
Themenbereiche
<br />
==============
<br />
Eine nicht ausschließende Auswahl möglicher Themenbereiche umfasst:
<br />
<br />
* Softwarewerkzeuge und Technologien
<br />
* Standards und Interoperabilität
<br />
* Kontextbewusstsein und Adaptivität
<br />
* Learning Analytics
<br />
* Usability und Accessibility
<br />
<br />
* Kooperatives / kollaboratives Lernen
<br />
* Non-formales, informelles und formelled Lernen
<br />
* Mobiles Lernen
<br />
* Innovative Lernformen wie Flipped Classroom
<br />
* Game-based Learning
<br />
<br />
* Didaktik und Wirksamkeit des e-Learning
<br />
* Assessment, Kompetenzmessung und Feedback
<br />
* Wissens- und Kompetenz-Management
<br />
* Evaluation und Qualitätsentwicklung
<br />
* Organisationsentwicklung in Bildungsorganisationen
<br />
<br />
* e-Learning in Anwendungsfeldern: Schule, Hochschule, Berufsbildung,
<br />
betriebliche Aus- und Fortbildung
<br />
<br />
<br />
Einreichung
<br />
===========
<br />
Alle Einreichungen müssen in elektronischer Form geschehen.
Informationen zur Einreichung werden zu einem späteren Zeitpunkt über
die Webseite <a class="moz-txt-link-abbreviated" href="http://www.delfi2013.de/">www.delfi2013.de</a> bekannt gegeben. Es ist das LNI Format
<a class="moz-txt-link-freetext" href="http://www.gi-ev.de/service/publikationen/lni/">http://www.gi-ev.de/service/publikationen/lni/</a> zu verwenden. Die
Beiträge sind in vollständiger Fassung als pdf-Datei (mit Grafiken in
einer Punktdichte von mindestens 300 dpi) einzureichen. Die
Einreichungen werden vom Programmkomitee begutachtet.
<br />
<br />
<br />
Termine
<br />
=======
<br />
19.02.13 Einreichung von Workshop-Vorschlägen
<br />
17.03.13 Einreichung von Beiträgen, Demos, Postern
<br />
19.05.13 Entscheidung über die Annahme
<br />
01.07.13 Einreichung der druckfertigen Endfassungen
<br />
08.09.13 Workshops
<br />
09.-11.09.13 Tagung DeLFI 2013
<br />
<br />
<br />
PC-Chairs
<br />
===============
<br />
Andreas Breiter (ifib Universität Bremen)
<br />
Christoph Rensing (TU Darmstadt)
<br />
<br />
<br />
Programmkomitee
<br />
===============
<br />
Andrea Back, Uni St. Gallen
<br />
Doreen Böhnstedt, TU Darmstadt
<br />
Torsten Brinda, Universität Duisburg Essen
<br />
Mohamed Amine Chatti, RWTH Aachen
<br />
Ulrike Cress, IWM Tübingen
<br />
Jörg Desel, FernUni Hagen
<br />
Jens Drummer, SBI Dresden
<br />
Wolfgang Effelsberg, Uni Mannheim
<br />
Stefan Göbel, TU Darmstadt
<br />
Jörg Haake, FernUni Hagen
<br />
Andreas Harrer, KU Eichstätt-Ingolstadt
<br />
Ulrich Hoppe, Uni Duisburg-Essen
<br />
Christoph Igel, CeLTech Saarbrücken
<br />
Reinhard Keil, Uni Paderborn
<br />
Michael Kerres, Uni Duisburg-Essen
<br />
Andrea Kienle, FH Dortmund
<br />
Ralf Klamma, RWTH Aachen
<br />
Dennis Krannich, Universität Bremen
<br />
Bernd Krämer, FernUni Hagen
<br />
Ulrike Lucke, Uni Potsdam
<br />
Johannes Magenheim, Uni Paderborn
<br />
Alke Martens, PH Schwäbisch Gmünd
<br />
Agathe Merceron, Beuth-Hochschule Berlin
<br />
Wolfgang Müller, PH Weingarten
<br />
Wolfgang Nejdl, Uni Hannover
<br />
Niels Pinkwart, TU Clausthal
<br />
Rolf Plötzner, PH Freiburg
<br />
Sabine Rathmayer, Fischer & Partner
<br />
Gabi Reinmann, Universität der Bundeswehr München
<br />
Holger Rohland, TU Dresden
<br />
Guido Rößling, TU Darmstadt
<br />
Nikol Rummel, Ruhr Universität Bochum
<br />
Uli Schell, FH Kaiserslautern
<br />
Ulrik Schroeder, RWTH Aachen
<br />
Sigrid Schubert, Uni Siegen
<br />
Till Schümmer, FernUni Hagen
<br />
Andreas Schwill, Uni Potsdam
<br />
Christian Spannagel, PH Heidelberg
<br />
Marcus Specht, CELSTEC
<br />
Emese Stauke, IFIB Bremen
<br />
Stephan Trahasch, Hochschule Offenburg
<br />
Michael Weber, Uni Ulm
<br />
Armin Weinberger, Universität des Saarlandes
<br />
Martin Wessner, Fraunhofer IESE Kaiserslautern
<br />
Karsten Wolf, Uni Bremen
<br />
Martin Wolpers, Fraunhofer FIT
<br />
<br />
<br />
Kontakt
<br />
=======
<br />
<a class="moz-txt-link-freetext" href="mailto:delfi@interaktivevielfalt.org">mailto:delfi@interaktivevielfalt.org</a>
</div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-89519284485100589452012-09-10T09:27:00.001+02:002012-09-10T09:27:10.214+02:00Design and Implementation of a Learning Analytics Toolkit for Teachers<div dir="ltr" style="text-align: left;" trbidi="on">
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The paper "Design and Implementation of a Learning Analytics Toolkit for Teachers" has been published in the Journal of
Educational Technology and Society (Vol. 15, Issue 3). The paper can be
downloaded <a href="http://www.ifets.info/journals/15_3/5.pdf">here</a>.<br /><br /><span style="font-style: italic;">Abstract:</span><br />Learning Analytics can provide powerful tools for teachers in order to
support them in the iterative process of improving the effectiveness of
their courses and to collaterally enhance their students’ performance.
In this paper, we present the theoretical background, design,
implementation, and evaluation details of eLAT, a Learning Analytics
Toolkit, which enables teachers to explore and correlate learning object
usage, user properties, user behavior, as well as assessment results
based on graphical indicators. The primary aim of the development of
eLAT is to process large data sets in microseconds with regard to
individual data analysis interests of teachers and data privacy issues,
in order to help them to self-reflect on their technology-enhanced
teaching and learning scenarios and to identify opportunities for
interventions and improvements.<br /><span style="font-style: italic;"> </span><br />
<span style="font-style: italic;">Reference:</span> <br />
Dyckhoff, A. L., Zielke, D., Bültmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and Implementation of a Learning Analytics Toolkit for Teachers. Educational Technology & Society, 15 (3), 58–76. </div>
Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-90360318854735104872012-08-17T10:07:00.000+02:002015-10-13T14:55:27.454+02:00Knowledge Management: A Personal Knowledge Network Perspective<div dir="ltr" style="text-align: left;" trbidi="on">
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;">The paper
"Knowledge Management: A Personal Knowledge Network Perspective" has
been published in the Journal of Knowledge Management. </span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;">The <a href="http://www.emeraldinsight.com/doi/full/10.1108/13673271211262835">paper</a> can be downloaded </span><a href="http://www.emeraldinsight.com/doi/pdfplus/10.1108/13673271211262835" target="_blank"><span style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-ansi-language: DE; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;"><span lang="EN-US" style="color: blue; mso-ansi-language: EN-US;">here</span></span></a><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;">.</span></div>
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<i><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;">Abstract:</span></i></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;">Knowledge Management (KM) and Personal Knowledge
Management (PKM) have attracted attention over the past two decades and are
meanwhile considered as important means to increase organizational and
individual performance. In this article, we review previous models of KM and
PKM and explore their failure to address the problem of knowledge worker
performance and to cope with the constant change and critical challenges of the
new knowledge era. We further highlight the crucial need for new KM models that
have the potential to overcome the shortcomings of previous models. In light of
these shortcomings, we introduce and discuss the Personal Knowledge Network
(PKN) model as an alternative model to KM and PKM that is better adapted to the
demands of the new knowledge environments. The PKN model views knowledge as a
personal network and represents a knowledge ecological approach to KM.</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: DE;"><br />
<i>Reference:</i><br />
<br />
Mohamed Amine Chatti, (2012) "Knowledge Management: A Personal Knowledge
Network Perspective", Journal of Knowledge Management, 16(5)</span></div>
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Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-26532781813752902632012-07-11T13:44:00.000+02:002012-07-11T13:45:36.691+02:00A Reference Model for Learning Analytics<div dir="ltr" style="text-align: left;" trbidi="on">
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The paper "A Reference Model for Learning Analytics" has been aacepted for publication in the International Journal of Technology Enhanced Learning. A preprint version of the paper can be downloaded <a href="http://www.elearn.rwth-aachen.de/dl1139%7CCDST12_IJTEL.pdf">here</a>.</div>Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-88812388275022180972012-06-06T17:39:00.000+02:002012-06-06T17:39:31.547+02:00A SWOT Analysis of LA<div dir="ltr" style="text-align: left;" trbidi="on">
In the "<a href="http://www.elearn.rwth-aachen.de/SS_2012">Advanced Learning Technologies</a>" lecture class yesterday, we conducted with our students a SWOT analysis of learning analytics. You can find the result below. Comments, extensions etc. are welcome!<br />
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<br /></div>Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com6tag:blogger.com,1999:blog-8263882245812273018.post-56967187369071882882012-05-09T15:27:00.000+02:002012-05-09T15:27:06.362+02:00Two i-com articles<div dir="ltr" style="text-align: left;" trbidi="on">
The papers "Forschungsfeld Learning Analytics" (<a href="http://www.elearn.rwth-aachen.de/dl1133%7CCDST12_iCOM_preprint.pdf">.pdf</a>) and "Formatives
Assessment in offenen, informellen, vernetzten Lernszenarien" (<a href="http://www.elearn.rwth-aachen.de/dl1134%7CHSSC1_iCOM_preprint.pdf">.pdf</a>) have been
published in the special issue "Forschungsherausforderungen des
E-Learning" of i-com - Zeitschrift für interaktive und kooperative
Medien. </div>Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0tag:blogger.com,1999:blog-8263882245812273018.post-15408103089127161492012-04-16T14:02:00.005+02:002012-04-16T14:08:21.019+02:00Two Open PhD positions in the Learning Technologies Group, RWTH Aachen University, Germany<!--[if gte mso 9]><xml> <o:officedocumentsettings> <o:allowpng/> </o:OfficeDocumentSettings> </xml><![endif]--><!--[if gte mso 9]><xml> <w:worddocument> <w:view>Normal</w:View> <w:zoom>0</w:Zoom> <w:trackmoves/> <w:trackformatting/> <w:hyphenationzone>21</w:HyphenationZone> <w:punctuationkerning/> <w:validateagainstschemas/> <w:saveifxmlinvalid>false</w:SaveIfXMLInvalid> <w:ignoremixedcontent>false</w:IgnoreMixedContent> <w:alwaysshowplaceholdertext>false</w:AlwaysShowPlaceholderText> <w:donotpromoteqf/> <w:lidthemeother>DE</w:LidThemeOther> <w:lidthemeasian>X-NONE</w:LidThemeAsian> <w:lidthemecomplexscript>X-NONE</w:LidThemeComplexScript> <w:compatibility> 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<w:lsdexception locked="false" priority="19" semihidden="false" unhidewhenused="false" qformat="true" name="Subtle Emphasis"> <w:lsdexception locked="false" priority="21" semihidden="false" unhidewhenused="false" qformat="true" name="Intense Emphasis"> <w:lsdexception locked="false" priority="31" semihidden="false" unhidewhenused="false" qformat="true" name="Subtle Reference"> <w:lsdexception locked="false" priority="32" semihidden="false" unhidewhenused="false" qformat="true" name="Intense Reference"> <w:lsdexception locked="false" priority="33" semihidden="false" unhidewhenused="false" qformat="true" name="Book Title"> <w:lsdexception locked="false" priority="37" name="Bibliography"> <w:lsdexception locked="false" priority="39" qformat="true" name="TOC Heading"> </w:LatentStyles> </xml><![endif]--><!--[if gte mso 10]> <style> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Normale Tabelle"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;} </style> <![endif]--> <p style="font-family: georgia;font-family:times new roman;" class="MsoNormal"><span style="font-size:130%;"><span lang="EN-US">We have two PhD student openings in our <a href="http://lufgi9.informatik.rwth-aachen.de/">Learning Technologies Group (Informatik 9)</a>. </span>The positions are fully funded for at least 3 years. Application deadline is April 30. More information about the positions can be found <a href="http://lufgi9.informatik.rwth-aachen.de/article71-Promotionsstellenangebot">here</a>.</span></p>Prof. Dr. Mohamed Amine Chattihttp://www.blogger.com/profile/00553355699924587660noreply@blogger.com0