La programación neurolingüística, una alternativa metodológica para desarrollar estilos de aprendizaje Neurolinguistic programming, a methodological alternative for developing learning styles Yaimer Pérez- Charón; Roberlandi Hinojosa-Benítez; Josefa Brugal-MustelierUniversidad de Guantánamo, Cuba.Correo(s) electrónico(s)yaimer@cug.co.curoberlandih@cmrg.rimed.cujosefab@cmrg.rimed.cuORCID: https://orcid.org/0000-0001-2345-6789 https://orcid.org/0000-0003-3233-1376 https://orcid.org/0000-0002-9345-0064 Recibido: 19 de abril de 2021 Aceptado: 6 de junio de 2021Resumen El artículo aborda la programación neurolingüística como herramienta que ayuda al crecimiento personal y elevar la calidad de vida; con el objetivo de proponer una alternativa metodológica basada en la programación neurolingüística para desarrollar estilos de aprendizaje mediante la enseñanza de la Matemática en estudiantes de la carrera Ingeniería Forestal. Se emplearon métodos teóricos (histórico lógico, análisis y sintesis, inducción y deducción), y empíricos (observación, encuesta y la entrevista). Su aplicación ha sido comprobada en la práctica y demostrada su efectividad en el campo educativo, lo que ha permitido hacer recomendaciones para el fortalecimiento del aprendizaje en los estudiantes. Palabras clave: Programación; Neurolingüística; Proceso de enseñanza-aprendizaje; Alternativa metodológica; Estilos de aprendizaje.AbstractThe article deals with neurolinguistic programming as a tool to help personal growth and improve the quality of life; with the objective of proposing a methodological alternative based on neurolinguistic programming to develop learning styles through the teaching of Mathematics in students of Forestry Engineering. Theoretical methods (historical logic, analysis and synthesis, induction and deduction) and empirical methods (observation, survey and interview) were used. Its application has been tested in practice and its effectiveness in the educational field has been demonstrated, which has allowed recommendations to be made to strengthen student learning. Keywords: Programming; Neurolinguistics; Teaching-learning process; Methodological alternative; Learning styles.IntoductionThe circumstances of today's world, characterized by signs of economic and lifestyle globalization, accelerated change and the increasingly aggressive challenge to everything established, require individuals and organizations to thoroughly review their behavioral patterns, attitudes, values, knowledge and the way they are applied. The demand that is presented in all areas is more pressing in education, a privileged space for human development. In today's Cuba, it is indispensable that students of university education graduate with a system of skills that allow them to develop the capacity to deploy their potential, individually and collectively, for present and future life.The use, in the teaching-learning process, of learning styles through neurolinguistic programming (NLP), facilitates the development through strategies, which should be integrated to the learning activities, making them applicable to any pedagogical approach to achieve an innovation that allows giving students an integral education.This research is oriented to the development of learning styles through neurolinguistic programming in the teaching of mathematics. Students state that it is complicated and difficult to deduce and understand, that it is complex, mysterious and boring, thus generating frustration and anguish, instead of satisfaction for the achievements obtained. In this sense, NLP appears as an alternative for many aspects of the individual's life, and the case of mathematics teaching has an opportunity to design classes where learning styles play an essential role in the learning of students, increasing motivation and attitude change and thus reach a more significant level of learning.Several authors have referred to the study of neurolinguistic programming, among them Armendáriz (2004), Oropesa (2005), Camacho (2007), Gómez (2007), all agree that NLP represents a way of approaching human learning, They also recognize that learning styles are cognitive, affective and physiological traits that serve as relatively stable indicators of how students perceive, interact and respond to their learning environments.Hence, the objective of this article is to propose an alternative methodology based on neurolinguistic programming to develop learning styles through the teaching of mathematics in first year students of the Forestry Engineering course of the encounter course.DevelopmentNeurolinguistic programming. Background and applicationsAccording to Gómez (2007), "neurolinguistic programming is a set of techniques designed to produce short-term personal changes" (p. 25). In this, the brain is directed and the states and behaviors of oneself and others are managed. It translates into selecting a plan of action among several alternatives, in order to find the one that best suits our objectives.Neurolinguistic programming is a formal and dynamic model of how the mind and human perfection works, how it processes information and experiences and the various implications this has for personal success.Armendáriz (2005), for his part, considers that it is convenient to detect how each person internally processes information, both those who do it well and those who do not, in order to implement new ways that are more functional.For Oropesa (2005), NLP is the study of mental patterns. It allows knowing the mental processes used to encode information, and therefore, the way of thinking and acting. It explores how information about the world is transmitted through language. According to this, life experiences are captured by the five senses and processed as information by the nervous system, which helps to internally represent these experiences in order to give meaning to each of them; this is done at the linguistic level, through words, sounds, senses, sensations and smells. The neurolinguistic process is based on the foundations of the constructivist theory, which defines reality as an invention, not as a discovery.Fonseca (2009) admits that it is a psychic construct of Grinder and Bandler (1976), based on the fact that the human being does not operate directly on the real world in which he lives, but does so through maps, representations and models, from which he generates and guides his behavior.There are six (6) principles that can be modified through NLP and these structure a kind of mental filter, according to Fonseca (2009), they are: 1. Control: it is currently known that the conscious mind directs mental processes, so no one can make you feel less without your consent, as Blander would say, quoted by Gomez (2007), creator of NLP, if you do not manage your mind someone else will do it for you. In any case, the control of life begins in the control of thoughts.2.Pleasure: the objective of decisions and actions is personal benefit.3.Cause and effect: the causes are the thoughts and the behaviors are the effects, this obeys a universal law.4.Correspondence: if you believe you can do something, or it is believed that you cannot in any case, you are right. According to Fonseca (2009) there are three types of beliefs:(a) Of capacityb) Of esteemc) Of hope or expectation5. From the moment you are born, a mental map of ourselves and others is formed. The external world corresponds to your internal world of thoughts and emotions.6.The virtual structure of the mind facilitates the creation, together with the consequence in an interactive space, of new realities that one can design for one's well-being.Characteristics and utilities of NLPFor Camacho (2007), NLP has the ability to help the human being to grow, presents a practical approach, allows achieving personal changes and is conceived as a powerful tool for communication, influence and persuasion. The utilities of the process are:Improve awareness of what they do.It enhances communication or the way of saying things.Increases the potential of people.Guarantees the use of resources.Improves personal health.Develops personal habits.Increases brain utilization.Enhances interpersonal relationships.Develops creativity and self-esteem.Cures phobias and fears.Shows vision and personal mission.The human being absorbs and stores in the unconscious all information to which he has had access. This information is filtered through his thoughts, passing through his neuronal system and forming programming, which form beliefs that will lead him and keep him all his life. The human being is a product of his thoughts, about what he has lived and experienced, which is the responsibility of parents and teachers: what kind of world is presented to children, adolescents and young people, what example is being given to them, how are they respected as thinking and valuable persons, how are they respected as thinking and valuable persons? It is essential to remember that what is recorded are experiences and not theory. Teachers must be aware of the enormous responsibility they have been given; their job is not to inform, but to form human beings.Perception channels In Carbonell's opinion, (2007), the objective of all education is the formation of individuals with a high level of knowledge and skills that will allow them to develop integrally in society. These aspirations can be truncated if the teaching-learning process is not properly carried out in the classroom, and therefore, they are placed in the work context, in their academic training. Communication between teachers and students must flow naturally so that the data presented can be fully assimilated. This process can be affected if different channels of perception are used, which prevent the information from being received in the proper way and registered in the directed context.It is necessary to investigate whether the same perceptual channel is used among students and teachers to ensure that low achievement is not due to conflicts of interpretation that can be avoided by being aware of this difference.The mind perceives the external world using a preferred communication system. For Galván (2006), these channels can be divided in the following ways:External Visual: sense that is activated so that the external world.Internal: images stored in memory or that are a product of the imagination. External Auditory: sense that is activated to hear the outside world. Internal: words, conversations and sounds stored in memory or that are the product of the imagination.Kinesthetic External: sense activated to perceive tactile sensations.Internal: memory of tactile sensations and all emotions, both real and imagined, in the present or in the past.NLP in educationFor Carbonell (2006), NLP broadens the skills to know the importance of verbal and nonverbal language. With it, effective communication is appropriated, since the processes through which information is allowed, stored and received can be enhanced. Communication, the objective between what teachers want their students to know and what they want to learn, occurs in the adjustment that arises from the negotiation of the two objectives.The more that can be discovered about how students learn, the better the process can be designed to foster meaningful learning, since teaching and learning are parts of the same process.It is important to give students the tools to learn how to learn, and this is where NLP can help. This type of programming helps to discover how the subjective world is created, this is achieved by relating words, thoughts and behaviors with the objectives. This process encompasses behaviors, actions, values and beliefs.Advantages of the use of NLP in education According to Bandler (1976), NLP represents a way of approaching human learning. Although many psychologists and social workers use NLP to do what they call therapy. However, we consider it more appropriate to describe it as an educational process; that is, the co-creator of NLP, in addition to showing his conception of this model as an educational tool, supports the thesis of education and learning as a communicational process that represents, in essence, a complex, delicate and nurturing fabric of human interactions.To educate is to communicate, it is to bring about changes in the parts. It is known that the relationship existing in the teacher-learner-environment triad tends to condition the interrelation and, consequently, learning.The challenge is to make the school an adventure that keeps both students and teachers interested in continuous improvement. It is necessary to learn while enjoying learning, using simultaneously the logical mind, the body and the creative mind, in other words, the brain in an integral way.The close and efficient teacher is the best of the professionals, since he manages to be with his students, he learns to be with them and through them. Neurolinguistic programming provides all the tools to elaborate a teaching-learning process that any student can learn quickly and easily.When identifying the attitudinal components of students in mathematics in the first year of the Forestry Engineering course by encounter, it is concluded that the cognitive, affective and behavioral components are present. In the case of the cognitive component, knowledge is affected, students show difficulties in mathematics, they do not understand it, and beliefs are also a fundamental part of this process.On the other hand, when referring to the affective component, it was observed that the students' feelings are affected when they interrelate with the subject, they reflect fear when they study it; in addition, emotions are placated in the face of achievements, downplaying the importance of obtaining good grades.Finally, the behavioral component is reflected through behavior, which is oriented towards motivation for the study of mathematics.The theoretical and practical limitations faced by mathematics teachers in the teaching-learning process to enhance the knowledge and development of the learning styles of university students become a problem to be solved. In this sense, neurolinguistic programming appears as an alternative for the development of learning styles in the teaching of mathematics, they have in it an opportunity to promote motivation and change of attitude of students, and thus reach a more significant level of learning.Taking into account all that has been analyzed so far, the author of this article proposes the following methodological alternative.Methodological alternative for the improvement of school learning. According to the Dictionary of the Royal Spanish Academy (1984), the word alternative comes from the Latin alternătus and is understood as a choice between two or more things. This implies choosing, in correspondence with their interests, needs and possibilities, the option that is more beneficial and/or convenient. From the pedagogical point of view, the rationality of the option, the results it can produce, as well as the objectivity with which it allows to approach the study material, will also be taken into account for the choice.Matos (2004), when analyzing the concept of didactic alternative, states that "it is an instructional methodological option for educational practice; in order to optimize the direction of the teaching-learning process that truly stimulates the intellectual development of students" (p.54). Similarly, Hernández (2004), when defining what he considers a methodological alternative based on the circular reading approach, states that "it is defined as the option available to the teacher who teaches literature to implement a flexible, creative and dialogic reading, which facilitates the development of literary comprehension in students as a complex cognitive-affective processing" (p.59). It is necessary to recognize that these authors assume the alternative as an option, and that there are other variants.Similarly, Mendoza considers the alternative as:'An option for the didactic direction of the process, conformed by a system of general procedures for the didactic direction of the initial training process of the education professional, which responds to the current training conditions, where adjuncts and tutor teachers, as training professionals, play an essential role'." (2004, p.69).Therefore, the alternative is presented as part of a conception, especially as the one that concretizes in the educational practice the ideas and points of view that are supported within a certain pedagogical conception.The methodological alternative is carried out taking into account the need to guarantee a more personalized teaching-learning process for students using the tools provided by NLP.The objective of the methodological alternative is aimed at providing a methodological alternative based on neurolinguistic programming to develop learning styles through the teaching of Mathematics in first year students of the Forestry Engineering career of the course by encounter. The methodological alternative presented consists of three stagesStage 1. Orientation In this stage, an orientation is given to teachers and students about the syllabus of the subject Mathematics I for first year students of the Forestry Engineering course belonging to the course by encounter.Basis of the subjectThe subject Mathematics is part of the basic discipline Mathematics and Physics. It provides the future professional with knowledge that are quantitative tools, necessary for the study of the theories of both basic and specific disciplines, as well as those of the exercise of the profession, which can be applied in activities related to the profession and contribute to the sustainable development of forest ecosystems.Syllabus E presents the improvement of the study of mathematics in the training of the forestry engineer.The main problem of the subject is to contribute to the formation of students with a scientific conception of the world that allows the solution of problems present in the field of action and the sphere of action of the forestry engineer, making him/her competent for his/her performance (sustainable forest management) both in the national and foreign environment.Mathematical methods are instruments for the determination of the quantitative aspects of the qualities of things. Therefore, the study of the content and methods of the subject Mathematics are linked to aspects of the professional model, constituting the basis for the analysis of information and scientific decision making. The knowledge of this subject responds, especially to the design of research in forestry sciences.It provides the forestry engineer with concepts, techniques and mathematical tools that can be applied in activities related to the professional training.Its object of study is elements of differential and integral calculus and ordinary differential equations, and its general objective is to apply the tools and methods of mathematics to the solution of problems related to the profession, contributing to the development of skills and logical-professional thinking based on the work procedures applied in the course.Among the essential skills, the following stand out: solving exercises and problems by applying the concepts studied in Linear Algebra, Analytic Geometry, Differential and Integral Calculus and First Order Ordinary Differential Equations and First and Second Order Linear Differential Equations..Stage 2. DiagnosisDiagnosis of the learning styles of both the teacher and the students who will be part of the methodological alternative.The following characteristics should be taken into account in order to carry out the diagnosis:According to Carbonell (2006), in 40% of people the visual profile predominates; in another 40% the kinesthetic profile and only in 15% the Auditory profile predominates. In the remaining 5%, all three systems dominate and these people are called achievers.Individuals of the visual type understand the world as they see it, when remembering something they do it in the form of images, when imagining the future, they visualize it. They are characterized by their organization; they like to control things to make sure they are in the right place.CharacteristicsWay of capturing reality: through the eyes. Remember on the basis of images.Eye movement: upward in some people, slightly out of focus.Body movement: rapid movements. When walking they give the impression of looking for something.Conversation: use phrases such as "I see what you mean" and "I cactus the image".Breathing: slightly shortness of breath, at the level of the upper chest. They have high-pitched voices.Personal appearance: they dress very well, generally fashionable and their clothes are always clean and neat. Auditory individuals are rather sedentary, cerebral and have a lot of inner life. They generally prefer to talk about something that interests them rather than show it, are great conversationalists and have the ability to organize their ideas in the midst of a logical debate.CharacteristicsWay of capturing reality: through the ears. They remember on the basis of sounds.Eye movement: sideways and always at ear level.Body movement: sedentary tendencies.Conversation: use phrases such as "that sounds good" and "I hear you clearly".Breathing: uniform rhythm at mid-chest level. They have melodious voices.Personal appearance: prefer conservative and rather elegant clothes.Kinesthetic individuals demonstrate their sensitivity and spontaneously express their feelings. Physical comfort is important to them. They generally look down to gauge their feelings.CharacteristicsWay of grasping reality: through tactile sensations. They remember on the basis of feelings.Eye movement: downward.Body movement: they walk in a carefree manner.Conversation: use such as "I have the feeling that" and "it seems cold".Breathing: low in the belly. They have deep voices.Personal appearance: they dress loosely.Identifying the preferred communication channel of the interlocutor increases the likelihood that the transmitted message reaches its destination with the same intention with which it was emitted.Stage 3. ActionsIn this stage, actions are proposed according to the students' preferred channel to develop the different learning styles:Actions for each channel1. Visual.Use facial expressions and gesticulate concepts with the hands.Show drawings, plates, posters and diagrams on the blackboard.Visualize materials in interactive videos.Make power point presentations.Express yourself with key words.Always add a visual element to the information.Use eye-catching colors.2.AuditoryPractice speed of speech.Use different tones of voice in oral preparation of information. Experiment with tones of reprimand, emotion, decision, suspense and curiosity. Use music to focus students' study. Ensure that there are no noises that interfere with the information delivered.3. KinestheticPerform activities that require movement, especially games.Tell exciting stories. Ask them to participate in the blackboard.Conduct experiments.Use lived experiences.Relate topics to daily life activities.Example In the class, the teacher should take into account the diagnosis of his students not only in the content but also in the learning styles as expressed in the alternative.Class topic: Limit and continuity of real functions of real variable.For the application of the actions to be developed in the alternative, the teacher must take into account each of the didactic functions of the class in order to give an output to each of the actions proposed in the alternative.Assurance of the starting level: Use different tones of voice in the oral preparation of information, use facial expressions and gesticulate concepts with the hands, use lived experiences.Target orientation: expressing oneself with key words, practicing speed in language.Treatment of new subject matter: show drawings, pictures, posters, and diagrams on the board, view materials on interactive videos, ask for participation on the board.Fixation: use lived experiences, experiment with tones of reprimand, excitement, decision, suspense and curiosity.Control: different activities or questions that satisfy the learning styles of each student will be carried out in order to take into account the characteristics of each one of them.RecommendationsLearning to use the correct language that reaches each student is part of knowing how to use all representational systems with visual, auditory and sensory terms. Phrases such as "I see it clearly" (visual) "I want to say this loud and clear" (auditory) "I feel what you mean" (sensory).Good use of language includes always talking about what you intend and what you want to achieve and not using phrases that illustrate what you don't want. For example, "remember to bring your homework tomorrow" instead of saying "don't forget your homework tomorrow."Teachers must learn to read their students through their body language. It is impossible for a student who comes to class with a psychological problem to perform like the other students in the class without first being attended to. That is why the management of ocular cues, as well as physiology are helpful in the teaching-learning process.The alternative will be evaluated individually and collectively.Conclusions The methodological alternative is of importance, it is a subject that is little treated among teachers, besides its usefulness is not only in mathematics but in the rest of the subjects and in the different teachings. Its application is based on neurolinguistic programming, providing tools to teachers for the development of learning styles, and thus offering students a personalized and efficient teaching. The theoretical references allowed us to assume positions, elaborate the proposal and reach conclusions. Bibliographic references Armendáriz, R. (2004). Educando con el corazón. México D.F: PAX.Camacho, R. (2007). Manos arriba. El proceso de enseñanza - aprendizaje. México D.F: ST.Carbonell, R. (2007). Aprender a aprender. España: EDAF.Fonseca, C. (2007). Cambios mágicos con la PNL, PAX, México D.F.Galván, J. (2006). Aprendizaje integral en la práctica, Tomo, México D.F.Gómez, G. (2007). Optimicemos la educación con la PNL programación neurolingüística su aplicación práctica en el trabajo docente, Trillas, México D.F.Kasuga, Y. y Gutiérrez C. (2004) Aprendizaje acelerado, tomo, México D.F.Marzano, R. (2005) Dimensiones del aprendizaje, ITESO, México D.F.Oropesa, R. (2005) Aprendizaje Acelerado, La Revolución Educativa del siglo 21, Panorama México D.F.Matos, C. (2004). El Taller: Una Alternativa Didáctica para la estimulación del desarrollo intelectual de los escolares en el proceso de enseñanza-aprendizaje del sexto grado de la Educación Primaria. (Tesis de Doctorado). ISP “Raúl Gómez García”. Guantánamo.Hernández, J. E. (2004). Alternativa metodológica basada en el enfoque de la lectura circular para el desarrollo de la comprensión literaria en la Enseñanza Media General. (Tesis de doctorado). Camagüey. Mendoza, M. (2004). Alternativa para la dirección didáctica del proceso de formación del profesional de la educación. (Tesis de Doctorado). Santiago de Cuba. Diccionario de la Real Academia Española (1984). Madrid: Ed. Espasa-Calpe.S. A. right-37147500Vol. 7, No. 15, enero-abril, año 2022 PAGE \* MERGEFORMAT53-2952751714500lefttop00 Ciencia y Progreso Publicada en línea: Enero, 2022, RNPS: 2477, ISSN: 2707-7098 http://cienciayprogreso.cug.co.curight856297500 Vol. 7, No. 15, enero-abril, año 2022 PAGE \* MERGEFORMAT65Yaimer Pérez, Roberlandi Hinojosa, Josefa Brugal: La programación neurolingüística, una alternativa… iVBORw0KGgoAAAANSUhEUgAAAX0AAABqCAIAAADfvxaRAAAAAXNSR0IArs4c6QAAAARnQU1BAACx jwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAJZjSURBVHhe7X0HYB1Htfb7KSlWl2z1Lt3er4rj 2Inj9EICLwmBQBIgIUBCIAQIIUAI7dFDgEAIAdLcJUuy1buuyr3q5Vb13rvkGluW9X9nZu/qWrKd Au894Gn8+ejs7Ozs3NmZb8+Zmd39D6vdydFmc3Cl1WqHtDlcXF4IjjYbSbvLbnNCAk7oTLbbXS4m LwWbU0hjddAhzvYOB5OXhsO1NsbudEG6Ojq59EC7WzpdHS43oIvxlAwndXV0tXd2c9neyWVne2c7 kxzQPfeuB4/vYCkhOXgOiOdSPIWIix116XPx3NYfdd65nIjq7O7o6uGyo6u7oxOyq6O7gyQH6fjP 9iINElAaEV1sF1J2spSQnS4o3VB6Ort7mezp7Ool2d3d2dNFclURd7mVnk5CN/Z2caWD4ntxLhdO QKfupsypYLx4TOfxq6W6NIR82qmQXe1daBW8lni1XLpKIddUKVoU4t/zUR573w2Ey402iaPQSh0u tNK1DXUNcH3XxfBe4LI7Hc52pxvQkSF6CnWTS4D3PrE/AmIPdXdn6uxMOliXt4sksI4c3gGcVVZ5 hwPsA97hHMSlmNQDSEkSJWCSAwWCdHGJ4nLpUfS1QAL8Tkj8bDAIpAi0GC49cF4d8WT8KDFSBLtg JFH7uJas3gFcCZ5MSMyOwmXr4pJdQi5FFoPCdXHvegh7WUuF5ED782QQsXlBcR9FChXSfRau8/iL n+tdHMV/Ds7I5WoZulAwKgAAKiHlol1L3NXZ3oXfAtnpgoKj0MO7e0i6+/kqTawq4i5IBrAJEQpi wC+ksBx62zvBO+5zuU+0etJ1xUNWnpvng6fsonKCJTvpQgsV9c5Vir1ClbazKoUUfj5JEatncUtW TgFcX5sGZ+HyQuClWiUO8M76hsqBsnHJjuKbUOhYsReAd5ADz8ThAu+s5iOmWQ/ej8T+uKaTMil2 cFAB00USOI8ZLgqRSTirXMDeuTjdiBBOycgPkoNbPSjrhVnGE+Jv44pYKZfG+jQ8RrxsHsBekszM 4Y0PgC5eM0rGjqLGwaW7oYgNSGxMnnvX410d5T6FiHd11Dq8q6P4uXgXZd1etHfc7NDVRUbBenZY xTpOgQUBheiDTJj3bu/AzDkvDbN36FxUSH4u94lWT7queO5fdEEI+bCfRiSLC+2uqNXKWQex6tZU KQ4X967HRY/y2PtuIOTDioozoj1z2/y8hroG61q7EAN+YYxz3o2W2Tvv0Ll4P1rfMRm4InZwoh7q +O+Rd0RwVlnlnYu6V9jL5CrolCRtIu+ABVEs6CiuIAXYUG76DR46o1VI8Qc7RFYG+zK5HojHIUiP +uVSBI7l0gNIQxLXkkt3sxCbi3jhhV4KyXLjLQAZCu0Jt02mIA2XAnCTdOt0w2R7hUNE3R1PyVgx 0KPQc0i6O8lqr3brl+AC4L0cJZIO0pDOuYYd1d3lgk68wBmEXJ7zcUlO6RR2IX9hFzlQ5yniLrcC 36qzs6uLgDSkUNnYLg9uwmYPi+S6UAa4Y4gkiQvEftEFIfxM8gpRyZfkLw+4q85dOe3sKEhnO7++ l8bai74uAay5dTEiyEDmEk1azAoK9q5rqFQeSOo1kOcBt1WSnLPoLosuA91JPQ46lNWudAGgyzAp EJC7hzKInZp3cHR8KOAdbp0wjwfgtMB1T6zjJk4vxDswcIB34V4JQO5tdlaydfaOjREkk+1crgfi waaQ9Av5b3534JUiEs2lwewduopcXuJyeoDHIyU52260kx2O6y1IwvqjXMQySAnJgRzOOxcxGpSu Hi7bcVsWGISNuQidBI4MxZOkNNydcY+88O7RgUgwCFJCUieBzuPdXYhSioczrDsXGS9wRuD+9EJS l2Zwkw7r7YxcOkAcPWAodEsoOJD13k4alwFxcF44Pw0p4uGr+eBYN38JipjAIw3b63kuokUqJE7K pLv2Lojudqoc2DvnVQ5JHr82Pcfao1zsKEhnO78bQa4Hj4ckvnCD6+69dNGFBnNxsFZBjWQNf53X eMQmxOQF2z+PIa4ReId8TNza35W9I4J3MfRNK+uhDFxhRgZJp5UZGSIJkFFCnOBBERRPcjXGA6AX yLV+FqIuRjeiscNojOR63iFqJMNMMHBWdU+wMSAorFJIXgD0+9fqrFIAwT4Cwa+RTuYAk+xAGpLM VxclnHZcG3Z7IYnGQeAKi+F7cdXfuRF4gMfzlBc9inJmBgiXbo5A73oXlssq/kFHCU4T9XnBY1oP slOAC9g7XZyYLpnGbaqICuLdabgiHMvTcIXFn2fvCAnwK7h0G3EXhLsS1lbOe7N3xKPaacCb2VAX AI+H7OjohHnFwXVhL11oJi/ecgC2i1EGNTwB1FB5g+QNlcOFeyBJNPJ19o4QQ74VJCkuQVKveUd7 xw3qj2QciIOzItwd/Dw/i7PBOqJw8wNT2KbHXk4vxDuesZ7ALqDNZm+FQQQvrI2C1WqFneWw2/C/ zQHXzG5lkusOO3acJy+Yxm6zQTocoC76QyPkzHgTdToDJP0VdBsbTsdhbXYbz4eyOV+unoslQ2Ix p9V8Ln4uCiiO0wleEwkROi4q/pN06zyeJ3jXaSi6A62KS/YfbOhkPh28Oa7zeJKXToN2KqbBfe09 5cPgzoc3d2qz1GxxnySDH4YbErgg0Zc6xR6FpNjoYtNSbPQXCSCFFB4K7eLDw0hACp3IfS46PSQ7 kVgY0nn8+YFHeMp/mSBebtaULhz4Lid6MusO7zvww6kX2Hhv+LsC9YKLg3denAhUAEkK+99ms7WC JKCyDmhzIp3DdpE5r1V7hwNRXAKwgEDGi8eOzy0cnV88euyoOywSIOaPHl1wg+s8nie5VJpF2n8M gf1ZPH5s8XxJe86XC8eOYhfk/OLiuzoXOwcdyyXLZ81ZIMX8uTzOz4X8cDokOIozIgc6kiTVAek8 HvLdpmHlxymOH8cZmGT/jx4/vnicJAd0Hk/y0mnOlxdO867z4QqCZxqUGRLhxIkT7O/xE/jnsevo ieNHT544hjhKgX8Erhw7cYLtor3HeBpWt+K5INcUBpLH/9sE8XKjadLFvxB48yX97wtCI0dmrGf9 twZ+ioWFhfn5eUhS2H/IucV5dALcafhd/33aO61WO1z9lZWVs+dWlvFnXUDkOTe4vj68mzTvKVwi nzXnen9BzP8S+fxfSMOV9eESu95TWFOYvz/DjfC/G/h1RICn3Ar7x0ljQOt5h2M974hJycOCA7x8 7tyZs8tL+HPu3DKXJEgunTt31g2u83hP+U+VRowX06zJAeH/YBquIHim4cqaNJDvLw0PnmnEBJ5p +N6N8K8V+FWj68gIoqu7u8Vt71xsrv0/+PDyBWB3tVkdnV29yOnMWeS4Qifgks7yDk3nnzONGC+m WZMDwv/BNFxB8EzDlTVpIM9Ls3LxNGyXqPDgmWZNYXgavncj/GsF8ZqeZbxD9o7dauX2Do3vvHd7 p7O7B+YTaAzNgkwpLpk1BXmWWVYcXOfxnvKfKo0YL6ZZkwPC/8E0XEHwTMOVNWkg318aHjzTiAk8 0/C9G+FfK4BscOXo/gHDZGWlo6e31QZjx26lNT58QGct9fzHmm2r3T0E7bS32a0d3V1oCkvnzp5l DeMi4zyrQSAmKgcFHiVIEWyT9gqSIi66l21RJNPEBKsKk0IaLjfCRtgI/4MBvQ6g+wfrgJ09Ay1W xiE0VQXlXdo7NmIfq8PearPCZEJOF7N3/geCeEbxdALFrAs8lqcUwWM85XrweE+5HjzeU64Hj/eU 68HjPeV68HhPuR483lOuB4/3lOshxvMgxntCjBeV9RB3icoa8CDqYrwnePxG+NcL5GRBLiNA7+we aLU6rU6ydxiZvCt7x+1nOR1tdltXL/Ozzi2fZU0CZgY/0UbYCP8Dgbc2atIe+jsHz8O4shH+ccGj dkklO8BjhAd/unv6aZmfHbC/s5/FDZ82GxhHWKEIk0ewdzZ4ZyO898DaClmnzCsWwGKYyncKCvvr 3s0iVpPQBtvn3k/gUcL26gbT6e9qpKCw+I3w9wR3HVJNCxXMtXNsEAZ/lk5DGewbaG1pcVppEY9A KR5LBznPrLV32PQWLYWm6Xe7rXvD3tkI/w2BN1emrv5dH/geyLPr6ENQ3Cncf5m+urER/vGB1yuY hoaTsckNnXPnlpZoMGa0sNxZXEZr0G1tdrJ3nDRoY3fAi+KMw7HW3iHptndsDntPXy/y3uCdjfA+ Amsr1DYZFZwHCIpnQUi4Ef7pA79ORDJsCBmcQIHoZ3l56ezymZWVlq6Ka+6070nr7O2122Ho0HyW jYhllXHW2jt8IQ89L+6kB8Oc7N1lG/bORnjfgc19ouXw9rO0dG5pGW2VtVhqtBtt6V8t8MlssMwS 6WfP4rLSODJI59S500vnOofab/90bYhmfP/hrt4eJ40prz5fypR2kWfW2jvYzZM6NuydjbARNoJH QOfn9g3C2XNncRfBBm2ePbMyMdn7uac6w4yuKMPYnsyerl7wiMPZTi/NcJIpQ2sInUQy4B3Itbyz xt75t+IdbtKLkivCb6L/G2EjbISLBbGHgHkgGeHQOnN6hGrxRO/XnrVFaI/FpnbGGMb3ZvV2Dbbb OpwOGDhOUIqdGTTc3lnrZ3nwDtk75/HO+et3/hUDUbRIMO7/JNhfQW6EjbARzg/Ua4R/rAuxuzUP 8KHPLZ1eOb088pPfOTcrp2KNM+HKrgjN6J7Mjp5+cpfAKiATgvDOPze9tF/M3qH3Qtud8LP4+M47 rFdm64UEfSNshI3w7xqIbjgFoc+fObuytLLw69dsoYbpiKTZGN1UhKIzQjOyO6O9u8tmb4GnRWTC XvIn2DvuV8f/A+wdRFE5hK2NsBE2wr9PoJ7NiMY9rAPnCj4WwtmVsyuLf02zRRhnQvTz4frZGP1M uNoRqR3ee6i9u7PN0UKvRoWrJbyG9J3tHXLJnOz1zj19/cQ7NIjECnFhc2cjbISN8G8Y0PfZhBUY hpiGlHPLp5dPnz2zhKj5Nw+7InUT4eqFSO1smGY2Qj0XqasPVQ7uSWvv6WmzEd0IfpabdBi9XJR3 IOn9izCQevr6iHf+9cd3NsJG2AjvI1CPJ3uHjaUsnzsLLjjz9srZpTO7M+0JKUPhqrlI/UyYdjpM OxGuHY8yFPnH9b55oKurz2oFk7AXwjtAN6vUc2neOd/eoRkzdyE2wkbYCP+mgfdvRjX0lxTmZrF1 OuAesnZg6ZzOyHHKU6cjtNPRuolQ9XS4diZSPxxhsAbJ070ju9482N3Za7WyZyNcxC2ccTgE3uF0 48E7wvqdDXtnI/y9gc23UhM+e3blzJkV3CePH+V7/r0DDYS4+8t/X+D5sxP+wwORDQItRWbT5kvn ls+cOb0M9+rMysmMEpd8+0i4aj5SOx2umwxTTUYoJ6L1bZuVlT7SfT5xHW+l9/T02+3ws+h7OJ6T Wau8I5IOX8/DdmyM72yEjfC/Hzi/iEGIZUHcXBP/dwaWF/VydrtgWbNT05rkt0+tLC2dTs9r1+wa j0qaj9DNhmnAOzORmpFIddtmhSlAWeavPBAoc+3P7OwfBIlwY8fmQTqAla3o2bB3NsJG2AhCoNfo CE+yUG+HX3V2mZ6HoIU0p86dOpDjUF49Hq5diEqaC9MA0xGG8UiDc7O83Fda5K8s9VOkByra9x/u 6h9wOkAgzLq5oL3j8VgWSfZdLpLvaf3ORtgIG+HfIDDagXHBZ81hbZxZWj5Nb7c4s3J8T2GPZOd4 mGY+Sj8bCmjnInQT4QZnoLLGR1rsqwDpmHyVB/1kHfuyuvv7HXY4TB0Cq5xPPQDxDlk67vU8HvbO u12/sxE2wkb4dwjo3ATysdioDmydU8tnTmLP269l2OOvnohMXYxKmQk1TIcmzYYlTUQYXEFys09i hbe0wk9V7ieHfNNX4tqb3j3Q56CVOx3cpvEEZyLBzxK3Ge+Q/Dd8PmsjbISNcKlARg5JuFvQzi4t nzm1cvbM/Kt7bfEpU2H6uZiUmcikqTDDTETSWHiSK1Bp9o6v8E4s95FVBWgqAlQlfsq9vrKevZl9 vf1Ou4eftd7ecdONiA3e2Qgb4f9E4N2ZkQ1T2R+2RQPJ586cXTlxcuyFl2yh0olw3Xxk0ky4cSpc PxWhn4gyOIKUVV6JZZsSynwkFb7yMm9piZ+sNFD9pnfC8IEjQ1197LlQ7jy122kJz3n4DyvtI43L Dd7ZCBvh/1DgHZpJbuYQ6SydXT59dmVqevzbP2iLUo5FqBbC9XPhMHP0MxHG0cikliBlhbekxEta 7i2v8FWW+chLfaUV/sqD3rEHUnZNlJp6Orq4vSPMowvcsgrYO06wDNvY8LM2wkb4PxR4d3ZbOLQk Gcry0pnl06dXhie6H/+GK0K9EJ00G2mcC9fPh+vmIo0TUcmOzVqTr7zYR17sJ6/wl5f7yoCyAPke 74ScG+8eSUsfaWtzOZw0vkPrlV3rjR0AvGO3uxxsg+bV3w/vbHDRRtgI/4KBHrii7+3RG7xoxQ7+ nD5NU+iO9vZPPNwRrJ6OME5H6KfDjbMgnQh4WMltfooqH3mZr6LUV1HmLy31Tyzxi8/3iXvDO7rk 418YL6hoq7fYwDtgEmeHlT8hIU6ou0kHgJ8F0hEYh8sNe2cjbIR//0CdmRbGEOHA0jlL70hGxNmq tq5dH+8KUR+PTZ2L1M6EaeZC9TPh+uFwXXOAtNI7sdRHVuKrhI1j8pVUBCqK/BL+FpRQ/uSz4yaL vbau2lzT2trmdHVaXZ3gGvaFiPMYhwP2zj+Md2Cq0Q84d+7EiRMO+mSXTdixETbCRvgfDLyXordC YfRCnhTFsr9M0h/h1Rb0Wgv08HMrby8fzyp1pXykb7PuRGTyfLh6LkI1E66ZiTD0blE3+kvKvOPK vaXl/qrSQHWFv6IqUHnIO/71eKPz578fsjQ31tQ2NTR96UuPH8nO7e0faOMz6G7SWWvvMCeLD/EI kqVwnsc7Hut3qLxusN/GJf2lX8OSISwtLb399ttc/6cLrNrFX8KLvhE2wj9/GB8fb3e1d3d3z83O Umdjge3hf9zb7O9ahbV7Htx7yM9aPkNv3F85sTT9hzcciVeNhxmPRabO0wMQqulw1VSMsWOLpsor ocwnsdxXbvJRlPkpi4JUZVt0+6+My0i+of1ve1219Y21DU31tkc/+3h0ZPyRnPyewUErjfC0O/kT V+7HIwBOQJ7jO5yQRHtn3XplwShDmVFqUQgSQfw93OqBfhZcen5gCYWUG2EjbIT14djxY2fPnsWd G/0ICgKU48ePnz59miu4o6P/nCVjwB3oOLFTretmXAHtLAvPG1AEO+wcffRq+dz47PBTz7dF68di jLNRSXMRSdPhhpko/Wik3hogN/vIKnwVZWAcb3mpr8IUoAb17PaJL7zr02PZxT3m5pbapsqqqvvu fzg6Vi2RarKzC3pg77SDWNodzg6ni1Ytu5clE/UAsHfAQyK4vUP6hewdKikVnniS/xba5NE8MHuN uIbV2BKPZMk9U1Hgx6Eel8nOo/Rsi9UCy5ni4HKyDN2BHUkv5SBQChbBk+Ivz5OI8D2BCsfkRtgI /3uBNWUKfNPd3N1t2x0DRegb7ubP49hhtLEM+4DFwkY4I/RHGrzhz3afO3lm+e0lSoG4U2+j5S9Z Xd0PfLEtWDEdnTwToZ8MVU+HaWeik0Yik1oDlNVekipvZbmPqtwXkFcGqLM2SfaEGVqe+v50WU1z ZW1bk73gSOHtt98Zm6BW6bclSNTZR/LhZ7W67Pb2dgeop52IRiQdwd5hG9C41UPgFhBZRw5XT6/w PDoMMeqc9OqflSX+5BiNh9OvJXBmQc2Ao7BzhX0mCXaS8N4eVg+UjCUmQXVHeyBQHfQxJfxfgqSj EEX586iVMyw7OowdwY5mgSs4Mx1OCqtyzovvDpzlcDSTCGLeG2Ej/E8G1j9WJVc8gxhJjMPu6nwT TVfYSSpizxKpMGJCRzrNbv/Lp0+yfrIyaW7tM9WBd869fWb5xImVt5fncysc224bClZNxyRNhWmn Q9SzIaqZCM1IhK45QFnhA8dKWeMlr/CSmQIUZUGyjCtjDimu6/jt6/2WtmpTbUuzfe++Qzt23BAX L9doU9TaFIlMfTg7r7u/r9Vpc7S7YOzYIds7QECeDhd4h2wcxjjc5IG9Qzp/Hr27rw/lPUO/lCbc eEdlHZYPNrNvdxGxUhwk0RJ7LxlJqgoArEAJSBESIIJL1BjfSZkjkmeNQJ4dGT84LXbQWenkPCE7 GgdStf9DQFePyY2wEf5HAuOJ88KayPM2uUqNHw2fCIW5XuQmIBnrjjwN+uPSWXTWc6ep4yAHkNDp M8sgopVzpzr7G579We0vXzk1MX/2zBKOX5k9Mffia82SrQOh6mORSbOh+ukw3RRkRFJfmL7OL7HC R2ryVVTSmI7EvFlV5J2478rYvLseHMzI6aptsFTVNbc6X/zdKxptslSmVaqMSpUBeqJUeeRIbk9f v9Vhh6XjcHUwSQPMJNfZO+t5h9s7ffhBS/iN1P25h0MkxPiHwLiAAm0SS7B64ApAqcFaBGSzzOw+ VmMsA6pBVAF0fjztXCavlhiHkQ4uAMuEHcB9Ln5qfoQQPC8Z9PcAupB0DrZJF1ugIg4WsRE2wv9u oMZIDRMtHgoC9RDqHNR0mSQBubS8tHTuzAp1oaXlM2fOnYbBs7LSPdL/u7+9nnpj/fO/Xlk4vXLq DG7d57pHhp94rjlUOxWlWwjXzwZrFkIM8+HJ46EpPSHGGl9psXdCma+s3I+WBVYGKTO9Y3ZHamu/ /J3xsppWc211TU1Lq/3b335eJteBcXT6rQqlHopakySRqY5k58JPstntZLu4OiA97R2Rd863d2i+ 3QHpBB/ZXb09ffhRy2fY17noE8jsyfglWG74gUvgANArASYdka9HQJUwS4WZPzD9RCCSGT5UkeRk IVvmWrGU8NIoK2bVIBUcNRpbYv9wNliM4KmlJfqPLYAMS05GxEc4YlUh3S09FXGTYmh9AIDzsOKT VcbLzqRwXUkXNmgX6UKCjbAR/vsD71mQzHMgoOUxSY2T3zNZQAelu/jbp2nsArJnePoPb1m2fyw7 VN/3ahp1qJNvQx4vqmnfdV9PsHo2wjAbrqU3WoRq50ONE6EprkB9xZWJhV6JRb7SEh9JsZ8s2ztx z6b4w8k3Db76Vl9LS21NXVujtbqi5jMPPZKQqATRaHUpak2yWkNSpUlOlGkOZ+evsXcuyDvrxncc pDvaO2wOZ3dvH37sGfcQynmBGOfsytFT52aPL08dOzu2sDQ0faZ35LSr/7S151SD65TZdrKi8WRR NXCiqOpEvolQUHmioPpEfhXJQvPJ4rpT5Q2nalpONdjfbnWdcfSc6RpcGphYHlk4N35sZeb4yvzx lROnaIzHI6AwNMYkXgtuKBELQWOGEN0MBKbwBAlR4g8C32Qaj2Bb58e5d2yEjfBuAmcGhH9Iw6E2 SKyCfFmrBdjQBIuiSK7jFnwGd2Scc/nsmc6+47/5c/eNHy/aIs9PvuV0eSM6zPKJt8/NHhv/xSt2 1XXdEYa5SONiiAaMczIyaT5c3x2kbPCVVnrLSr2VRd6yIt/E4kBJzhWxe4OUVY9+Yzy3rMvSaDFb XM7OnKy82266Q5qoMCRtpTEdIh0GdZJamySRao9kF4B3QCOwdMA73N6BnwUJ0uETW+Adwb3ysHdI 0sS7o320vXtl9vhKz9iKs3+lyXrGZFnMKZvZe2TipTcnf/qH8Wd+1P/4066Hvmy/95G2Ox9svfm+ tmvvbLnq1uakm1p0u5pU1zZJtzUnprZItjYlpDTGJTfGA0mN8dBTG+NTmxK2tiZe3SLb3qja0ai/ rjnlxtbtt7ftvNN68732Ox903ftI54OPDz729MQ3vj/5/V9NvfjaxCv7ZvZknSyuWq5pWGlyrLQP rgxOrkwvrpw4Q1YSXSJ2zdlfuj7c82P+GvPUyPVDSkSxISN2ydg144JdTBxPVhYIDTnQvYRdXX65 KVN+9ekkgtwIlwi89v7tA2s91PRY6/uHNQx3PtRMuaTBBj7cSvfbc2eoRfNWjAbNDARn99iLLzlu /k9LvPGId0zj/V9acQ2snF4+d2ppuaW9/bNP1odoJiJTpyJoQGcxDL4VTZn3bFaafRJLvaQlvspi X0V5gLLIX/rWpoh0w86h3/1l0lzfVFVXW9dibXO+8KvfpSRfLZUqNdoklRZEk6zWpcDMUWtJeto7 a3hnvb3DLR1u9dAT6zbsc7TjqJ7u/tHCStunH++5/v6elNuH9Nf0SZJ6I9U9m6X9/tKBAFl/oHRo s3x4i3J4M6AYDVaNhWjGgtXjIZqpcN10uB6YgS0XaZyNNMxFGT2QBMnijUgwTdBPhunGQzUTodqx YBVyGwySc/QHSAcDZEOBpPRulvdFaYbkKUO6nV2pt3Xecl/3g491f+27vT/4+fDvXps/WLhc3XSu o+/cxNTK8eMrS0vkuNE1w1XDhYFveObcydPLb589d5p8RnIZGTHRSBTxCWccXKXT587BpaQZAUh+ xcl/gwQtMYnA5Ua4RCDaZoF6CluHguCOwh0avef9B+QhnkCI+t8I/61nR9aUPTVAKGdXlk+fPbe0 hDZLgwXLNDh6BpYMjSaftXUO/uCFnmvvbI9S1wTEHY5Uu57/9bmZY3RLnj8+81aWI/W2wVDdXHTK VLhxNix5JsS4GLttLCKlzUdq9paWeMuKfRSl/qriIFqF/IZ/guWTj07k5Tvra8wV5Xars8bc9NiX n4pLVMhVeo0+SaFJUoJrdClKbbJCm6LUbVVoUqEkyNVZR/LhJ1ntNme7S+QdEZyAwDt8ZIeN8oCK hE9ttTsc9p7+3rGDh82glWD9cJhhJEw9EaaZDtMuRBhORiUfj0xaDNcfi0o6Hp0MuRhh4FgI1y+E 02AVjDemM0WIBwRlPky3EEGSJdPNI9tw3WKk8agHkC2HEBNlXIgyTkdoJyM0o2GqwWDFwBZF12ap a4vcHqy0hajtkYb2xKtc6uu6d3xk6J6H5r/09ZM/+PWJP+8+nl9+wt61NDyxMje/cvptdiXpiqLV k2dGQ0dLtICKj3BTIEuWeIb4CBcbGhlDOIa2N+yddx3AMlyigyAw2lkNPAbVzSXVOwuivkZBuNhe yP+xgNJy6VkA/OGgTSb//sCzpqa3gvshbny0yI8Czr30tnDbO3HqZFXD3BPfaTfsao/W2CN1lf6K 3NQ7ZrNKV07ivrtyxjUw9dXnW8M1g6HahejU2Qj63BXMnMnw1PbNeou/qtJXafJWFvokVgYpqreo 0y6LOqDb1fP7v860NDabTbXVlS6bPePQ4VtuvSsmVqbSJGkMKXKVAWYOUQ+MHV2SEpaONlWpIQ6C vcP9LPY5CZrJ8rR3IPnElsg7zN6hlcrsNe+k23v7umYPZvZEpxyNTJ6PSpmNMMxHGecjjXORBpR+ DlQCLzFcPxumnQnVTIdoZuAuhulmw3SiBKewJ+gvAB4PKeaA9DxDisfh7r0cs5SM52lYjDQcjYY0 LoTpjkYYjkUmHQ03Ho0wHgsxngg2Ht+inw1SjQTK+gIkXcEyR5SmMdFYn3S9445PdT30+Mgzz82+ /LeFjPwzlfXkqc0cXTmFa8qvIa4yLvY5GrrGTQXtigwhsoTInCU7hy46iyMG4pIfI0jKgsmNwAJV EZPoKWJHFQOPEZP90waxkOKvECPfU+AHQIqKpxTA2xFyp5YlSHYXXD5Di0uw4T7x6MzJjJLBR552 qG/oC9VNxyZZQ1VpvrGNn/nmmZ5JymX26MKBbPuuezqDVEdhHETqF4LBONqpCHCQriWA3qFT6KMo 8VWZfOlhqyObYt7cLLF87isnC6uHG6yl1RWWprrmluaf/fwXemOKRKbS6pKVWpg5okyGvaOiSMit Sk0q8Y5ck51T2Nvf7yA64XRDjCOCE9D5vMPYCOYRfdLYZe/v7Zrbl9FDVox+Jgoek34mUsB0FFHP PNk1cBGJJjibuCFsCtTD+GINxL2eKT0V4iDwV6gWci5UB96ZD9MvhOjnYSsiDWSoYR4Oaih07Tyj rbnQpLmwpPnQlIXw5IWIJBDTYjS4Ug+fbipcP7pF3e+X2OMb1x8k6QxRdEmu6tpxl+Pjn+/4xvNj v//bsZzyc7a+5bmTK2+fw43iHPs+Ga1kPHOO1kPQ+62F604T/axtUAPgzYS1BNLOay0USwrtEyTb YtJ9xEb4PxL4pRYvPFMEiDprM7ST/nKFSzQ8mOSwuNEm315e7uwbeumNjjs+2xue2uMH12nnWMLO XO+oTNm24b+mrZygweWzHT0DX/++M9o4Eaw5GpkyG6aHd3ICvThc27VFZvFPKPSJz/eXFvgqygPV xT6StMujCpJuGX75zcnWtraquurS6ma7s7DM9OnPPBwnUShUOq0+Wa01gGXgTDGuIdLhHKTQQKYq YO9oUhJkambvDNmdDmbvgGtW7Z1V3rG54Ic5bPyJdWbpWB0uh9PV4ejo6+2b3p/VG550NAJEo5uG mROpm4kyzJC9Y4C9Q+wQ7sEOHmzC5bsEz4FYjOfpISlnpAklgH2QciYcdQca4gr2ambDVXPhSigz oYaZMMNcqBEpF4N18yGa6VDldJgKErblYoTxeDS8QjCRdjJSOxqpHQhVdYUqXGFKR6TWEZ/q0t/Y u+vjsw9+7eRzvznxetYJs/VM3zjNqTFriDcNEA8tuqLpe7rvsYbB2ghrIUK6Vcn+sAR8wwN8kwT+ s7h/w8B/OuQlgpjsnzaIhRSDGPkeAl1s9g8H4lAuaHNV58n4BksGxqHbHN+zPDF+vLB89ps/bE++ xRpm6A3Tj8dunYy/pv4yadaVCebPfvlteydlsHD8xGsHXDvusIYppqMNx9GtwnSL9A4d42CYptE/ rtw7utg/IS8gsSBAUuQvO3JZfFqYoe3pH56oax6sr68tLWpraHC22f748svbtu+MA5FojWq1QaU2 ajQpKhANbBw2suOGp72TCnsnJ6eor3/Y6XLRWhzmVXHGOY93mLHDLB3Bz6IBZ7ZaubOnZ3Bq/5Fu 2Dvh+mnYOPSWQz3MHJgexAVhTLqJgyvvGxfLAfH8XEQ97Izc4SIZrmVgvBNGvEORoax4yC1ES+5b mAaYDlXPhKnnyYkDwamnwzRTEdq5aMMCEAnoj0bR+NECrCpy0JQd/gpbhKFJdW3rjff0P/K1yZ/8 5ui+zLOm2pXO/pXFU9QsKNAgHw09g4poXoGGg8gHJz7Cf85KZBPRH+E/C2Ici0UQ8tsI/9aBXXHG JO4WgOaCbWouFLkM2xrNhhTE0GgjTGt6HHvl2OmVlvaZP+/uvfdzbZKUrmD5fKRhMSp1IX6HPVif 8eHYfP2tY387vLJwduX0ynKdvfcL32iO0o9skc9F6Y9GaObD1LD3RyONzs3ySu/IIiKd+BJ/SXmQ /LB33Gt+0WV3fHrxQO5si73eZKoylbusrWVFRY996UsSiVQiVWp0SUq1QQHe0SSp1HCvmIdFBo4I YYyZrB5NMtk7uYW9AwP2dlp7vMbMEZX/WMNGZOgw68hub+/qHpjce7gzQrsQiu5NozBz6Pno0sy0 QR8mIvi7GccTPDdPKULgOBAQQAoVg22y8lBhBNCBVDCBHFdzoF0Ug3jGXzoYnzwZdtHgUTjjONhZ kUYYdFPhuvEQ9WCgfMhfMhqi6E1Icu28y/rQE90/+PXcgfSzteZzo0Mrp07S2iJaic78rqXTy2fe XoZEw1mmGTAaCGStjf0hP52GCElBMyQpBtY0SW6Ef8tA15+uOt2XliBpKQdb00E3LjhRZ0/T9NTp Zfp7ZuU0fSfv3ODk4sG8rse+25t8S1+QeipQtxCeNB+dPBmT1BmiLrosJi1QbX38h0uuUcp65MTE r15zGW7p3qyixhyqmg1WojHPRCZ3BassfvFFPlH5myJL/OOrg1Ul/rK3PhRxRHNt/69+t9Dcam9o NFeYbPCwWtr+8IeXt199TWxsvFKtB+RKrUxjlIN6NEYOJaRW0Dlor9oo1xjAUPESVVZOfnd/fxvM mI52V3unq6MTxALDhzOMaO+QIeQJMpBoPqujG/bOviMdEbpF1mPhvAgd2N2TRazhiPeBi+VAZAGF 042bMjhBeCqU0g12IHJjHIQEVHhGRizeg6H4USylcBSLYRxE40fYjDIuRicfjYKhpB0L1/aFabpC NZ0Relu0tkO9Y2DXxyYfePz4T186eqjgZLPr3Og0LRJ1NzJ252LzNWwwEFtsXeNZNDg2t79M82Tk r3MreoNx/t0D2TuceYTmgEtPEbR44ywYB7cjSrZ8dnlk7ESJZfHrP++99uOuuNSeUPVIqPZYZMqx yK0TYcbeEEPlZdHpHwotuPn++QIzHbJ4+nhGYe9dD7SFqYfgncSlzsFQCIVRbxgKT23z15Zvii/2 Siz2lZQESIt9EzI+FHEoTGf+yrcXzQ2TVkdNmclSXdvu7MjLLfjMQ4/IpEqlSqs3pqo1BrlKBzaR q/QEtQGmDZdrwMiIS2OCVH04t6BnYMDaTs+Ww47xpBuAE9CF7R2SdvDOwNQ+2DvgHdg7HhYB6/+e fXU9xGTrQDkIVoanshYs2SXBaAXn4sThpg8e6SYpTi6XKJWQjOtEUu5dLB9yKsmv1M3AoYtkU3iR MIjAWdr5YPV0kKI/SG7dLGuK1jYn7+r8zwfHv/Hc5Muvny40rdh7V6aP0zouoTXh7kZPeZA/xlYL MWtIMHewicA3KDWXCBtc9L8XxCvgCR5zoSDuZMAVpC2muK8pLjPtIWvnHE1WIAItgRay4h5EO1aG xs4UV0394veuOz7ZnHB1e5BhMti4GGY4Gmaci0geDzf2BastPrH7Lg85knzD0Cv7VmbeRgM72+gY +fr32xTbekKVU5G62XAaaZ2JNA6FabvD9LW+8lJvWbG/ojxAbfJXHdmU8Lq/tPKeh0+k5R61uVot lpqqmrY2e1Nd609/8suUlKsTEuQ6bbLOkKrSwIQhriGLRm04z965FO8YzuOdjg7R3hF5hivgHdAP j2KSjz/Dz3K0k72zF/aOluwd6oo0xMuwakF4SnfvfTfg6ZEtFGZ9cEl8ISoXAQ5kks4FmjhvFxEH P5wXlccTm7ACs3K6Je1lv4vAD3TDvZcS8NzCaDgJkTRmFK6mqgjTzEVowUS0AJLWPWrHQxXDARIw UX9CSue22zoffKLnuV8e3ZexbO84N7OwcuosuWMsLL99dhkGNZnVfO0XmUVolCRZoGbLSIgEk0Lj ZbsoXth0R/A4LsU9QkYb4b0Gse7WAP/d8rzaFnZSHP0RNL5Fgv1htxealTiHq//2WT5bilZxbnLu eGFJ1w9/6rzt4wOS1KFg9WiQei5861zM9tmYrXORKVNhKYPhW82+0oMfCs6KUzm+96PTPf3Ic3lo avgXf+zS39S/WT4RoZuKTJqMoBvkTERSb4iuyV9m8oqrCJQW+iWWbFbl+ErfujLWtONjo6+mL7R2 dDQ1VRbT+HGn3bbnzb0f/dh98QlKhVKv0SYr1YxfmF0DhRs4brq5AOkAHrwDe0d1ODePeMfFeafD eT7vcFzI3qGB6A6HraO7e2By32H4WTTgir4HP4t1Qjd3sI7tIVkXFbq3CLKSLorzGOHdYzVPIgjO HaIigJGFkEAssGdpBXIR+U7IgaVnm5xuGB9p2Ww9TdsvhBiOBhuOhlCdzIeqaf4+VLMQqjkWoTsK wzBCOxuhG48yDERquoKljs0J7bF6u2an6/p7Zr/wrWMv/PVofs3p9qGVqQWY2Gh48MrANPSUKy0X oiWMRDG85VJTdYO1XCFG2BajBfDAFHGPKDfCuw1irXkC/wU24ZLH01+K4FskaFvQ3ReRBbJvadEf G9VjYeH4247eY1kls0/9qGPXPdbEVOsWWUewcizSuBiRvBiZPBeZNEGvUje2hyRVb5LkfCAsPUzd +JVnT1md1G4m5xb+vLfz+rvt4ZrxEM1CpGGGmmvSTEQKvDBLoLrcW1G+SVrqE28Kkhf5SNI/HHNI fX3nT1860WIbaLNVlVaY6yzNjta8otzHHn9UJldFREvV+qtU2lS5KkmhZgsCaQQHVCJCsHTcBHQe PHgnKUGqPJyb2zPQZ21fHd8ByayZ2Fpn7zg77FCcHU57e1dP/9T+zC7yszRzcDRoFokBhhwcSPRG 1vlFyfqzYZ7WExq5XIikpyJotaH7wYi5KMNsZNJsRPJsRNJ0RNJMOGTyVFQy5HRk8nRU8hRJI1sl hJSGWTqKbAq2XpFJmBiQOBExApfCbNdqSTjjCPEGWv7D4C4tzdkTPdEvovkv/Cg3xTCucW9STKh+ gVgGEvno50OM86E087VASwfUMzSJppkN09DbkkJV0Odh60bp52IMc+FqkNGxCPA1PfnRuVlqi9A2 xqXYrrq98xOfH//+r44ezF021a30jNCEBRqT0CRxYwT9oIW658jcjZcraMOMnIRN1soF8EjKh0vK i8mN8N4C1RqqEgISjvISvUjhHLtEQo2TW0RJWCoC6ezK0GjN8lnmR2FzCXaN+1rA6R6bOVtZN/vq nsknnm3bfmdjbJJzs2IkWHUsKuVk1Fbc2mlOI2brWPTWrhCdLUBu9krY94GQPeEK82cfO1Fes3Li 3MrC6WOZxZ33PNwcqRmN1i9G6BeCNYvhhulw43CwweGjrvGWFwWoi301Ji9l0aa43ZdF7Zde3fHt n54xNUw1tVlqaiqqq1vtjpr6hmeffz7l6m3RCfFkxehS5WxhjlyTLNemKMAm8LO0sHTAMgBXQDEX IB3Ag3eYvZMDe6ff6gKLXMDe4Qp4x+FkM15O9lowsnSYn+VwOLt7eqb3HeoJ1x4LVdHL5UEB0Ukg AnrwKkxHb2AF0UbSC1kh5yCjksfDDJPhhvFww0iIbjTUMBym7w/RDITq+oI13UHK7iB0P1VnkLKL yY5ARUeQwrVZ6QzSuIK0rs0a12atEzJI1RWo6A5QdAcqe4JUkL2b1f3Bmr4t6oEQ7VCofjBUPxxq GAdCDBOhxqmI5MkwMBd9YGyaPQ42E4FIXAy4RQaaWYeXRIphhhRYKGo2N0cExKhHMKDIoiGjhutE RpCgnsVQ/dFg3WIIxSMrWk/A+ZfGfXTTZNyKTMeyJVLjLIz02nk0DnrCI2kuGlWUNB6qGQiUDQZK h7co+xXbHTv/0/XI10df+PPx7OLljr5zi+zh+9NwymARnT135vQysPT2ubNvn+MDQ8KSeT5XxkAz I2dpnHqFGU20xadomTW1Ed5zoFqD14v6BG3QTNPKEnthKKMSQRIRCVTDLwKtqDh9bunU8tvHz50+ yWYOyI1aOXpm2TW0eLCo59lfOO/4dIc0tS9EMRIgH9uiQkOai8Ht1jAVapwNgY2TPBmu6wvVtWzR Ffso0y6P3RskbfjEZ6cKS+jhnjPLZ0rquh78mitMP7JZOR1jnIqES6WZj06aCNV3BKhqN0mqvOQV /qrSLYZcH9WBy+LTw/Sur/xgrsQy3tnebK6pKi6xttgaG1p+8/uXb7ztrpgEuUylY+sA6XEHhR4M kqzUJSn1yTRjpdYpNXoFg1KrU2j4cDKXa3G+vQM/C/bOgBW00tHpam93QDLq8ZzYAu+AdBjjMHvH AcZx0jy6m3cyOiMMx9izVLBHpqJTIMfDjJPhSUPhBmeI0hmqsgerWgJlbUHK5gBFvY+kwVdW6yOt 2SSp9pZWAT7SKl+ZyVtSfmV8uVdCmXd8OX0Ng8ELMrbUO67YO6HYJ76EZAL0Uq+E8k1AfIVXQqW3 hI71TizdFA9AqURuPpJqnMJbilOYfeUWf6XFT9EYILcGKa0BCkeQqiNY17FF2xOsHgpRDwarRsN0 k1HG8UjjVJRhLlIPc4lsqAi6vRBBhGnIk6KVPloiEXAH+VAgEWYukaWjh73DdbL1iF9wFC1QZFRl ZCsVyQ6aDyMskEHEdxFmQ3TAXIh2aotyBnenSCN/og18NB6mGQpTdwcrbVsUrrjUDt2NA7c/dOxr Pzj60uvHi2tOd7DHOE7z2yWZRIx4aDiIzZOhza+wFR80Q0LdABHU3AEiIOo8TG6Edx/Y5CLRCC2i YVVKo77E+Kxu6S9944DqHElRzfz1fmcQIZg1RFuzsydtrqMHso8996uR/3zYYbihJUpvDVb2hqjG aem/AfehuUg0JM1kuGoqXDseYegNNdiDNE2B6pJNCYf+X1hasMb06a/OFlbT1T+1fLSsdvgL33Jq ru/GbS/SeCI6aT5KPxWhGwrTODfLzD5xJu+ESn95RaA6d5Pk4AcjD0YYzY8+M5lnWnR2ttVUV1eU WluaHK3WN17fffc990vk2gSZWk2rjWl+inlVbvYh6oFEpJ7ZOxygnndp73jwDhypS9o75/EOgU19 OZyurt6+8QNZLVEwW7TdISpbkMIapGgNkDf6SIBaHzBCfIVPQoVPfKlXfJlXIkihDJThIwHKfGRl PtJSX1mZr7TUT1bqzyEvI9BXTRFZ7JtY4isp8ZUVEaRMEop9ZSU41kdS6iulT6Bi04/eQlTiK2WQ MF2CnOkU7pRlXpKKK8FWieVXJlR4JVb5yGq8E83e8Wav+FrfxHp/Wb2fpCVA6tqsat+s7gjWwJTt DNH0h+vGIpLApBPhSRORKRPwASPJPYS1QkYQoxg4SoQQ7Twb1llg8eS4gVDIFzNwewoKWIYkAzll tMZaPxMKt1Q3HUKPsE2HaulBNrd/R6YQgDOG68me2qIaDVY6AxNbw1RN8m1tOz46+OCTo9/7xfzf 9i4XVa44eldmTtCYEJq40D2WT6/QyxzPLoF52H2YXpPGOgvbZg6XJ9iRYhdxd5QLBzGNCL75TxjE songm+8YhJRizfBK45wOwYwaMjGhsxnIs0tLZ0+fPvv2GfhT7GjyvpZWVk6srPSOrVhawDVjP3qx 91Nfsl51iy3W0B4kHwqUzYZq5iOMi1Eps+FJM2GGmXAjHIXxcO1whKYnTOkKUdg2K5r8FKWXx+39 QNTBqOSGR59ezDOtLC6tnFo5a24b+dr3WtRXt4epZ6OT56OT4VuMR+r7IjTojGYfdBBJiV9imW9i ha9k7xXRb8Uk1X3hGydzSmdb7S215vLK0qbmOmtbc3pa+mc/87BcoY1LUOgMW9X6VHAEowxI4YkH kvSEJ3SjnMwc4H3wjvpwXg7zs7i9Q7zjae9chHfcfpbd4ezq6+s/kJm3WWL2jSvzii32hkmSSJ2c aIW6PScFyHI/SHkZSUmFj5QYwZfYB5tlfiQ5UDuIrPChlydW+ELKy33Yx5V9FGU+ylVJb1eUVMDM 8ZEyiQNxuLQcWfnhpDx/KSc4SunDs1UA5TjcV1niJy/xVRT5yYt9pcUgOD9ZoY+0yA90lljqnQDD in0MiH5FtS/4SNHgI28NUDuDdI4AbfdmzXCYejhMMxapn4g2jkcbpthzIaAJsoBg+4SASrSTYaqJ MOVUuGY6QjsVoYacDdfSdPt50MxGwBhGvGYmXD0ToZulNd869n17NwRPjdLwxLNROhreijBMhuhH A9W9fnG9wYm9Ccb2q2+z3fdI97M/nXntwFKFebmzZ3nx6LnTZ6jdwzWDZQMegj8A6qFXQbLVQoyO qEcJgXUv2iQp9DMWaJ87sHTsECZWA4+DZLvpeBbJY4QolkLcL4LiKYmgvNvAk1NmbuA/KWxjNW/P jNluFikGD51pPJ07A9JZjBD42BmtrzpDS0CX6BXF9MlweF0IMHPOLK28ferc/Nyys+t0XuXIb19z fumZzts+3ae5oTdEO+inGA5QTgVrFyOTjkUlH4tMWsBtCT5+OBztrdPRW2eirxoNTeoIUOIuaAmS lfjH53pHH7gy+nBssuvL31uobAXdrOCq1jT0PvWcU31t72bJVJRqJkINA2cqOrU/Irl1i7rSL67I K6HEX1vor8ryluy9MiorRud69Jtzh0tnOjubm5rMNZUtbfVttuYDaQe+8OijCqU2TqKQKfX8dTmw azyfdRCph/EIpJG5V+/B3uFgvKM9nMfGlS/NO+zxLVAPeIfYx852QFqdru7+vt59GYf8EqrBpuzj pMV+ylL0avo6sqrUT1HiLymFJeIPiwYWB3gXm0QuiC/xZ0YKM3OYTp0fknjKW0Z0w+EN0kHOtAvG EYwgKHQUS8kl6IzAOE4AERwjLKawSDCRvMSH3loEWeYN/kI53UBRmURpGU+BrYQCuO0yGE2yMm8Z ZLl3QpV3XI1vQl2grGGzArBt0XRt0XVu1g6GGEdDU8ZCkqfI3NVwupmJ1E4DYJYwDSQp9IlFLUiE eETYJDAmgqVDEkQzTTGaOZqYx1Hk4k3TS4vg6JGLdzQ8+XhE6vHI1ONxW+djjaMRmoEwRXeo3BUq s4bInQkpval3DNz+wPxj3z36q78uZBadarCf7R1dmaaZMh7Ql6BSNyMO4iAmYlGso4o9TwQ/bHXT Y5e4yf5z4Rn4NktE/7k8H/jPlAsEHiuk8zgXBJO0QRD+8bRC4Brfz3YKR7BoHs5LTMlYDQggnSwc WtRJ89zsCUyqOuEQFuZPLw/MnGztWsg2HX3hjYUvPNt36yfbk260xyXbw7VdoZqhcB3s5fmorYtR qfP0Bj8YxZrZMPV8mJqmgyOME1FJg5HG9hCdNUDb6KM2o5X6KTIui8nYFJeberPtJy+87egkX/r4 2aNFtZNf/JZLsbU9VD4erZ2PM0xF6+CgDYQa24IMlT6aYl9l+WZVRaA8/4q4gx+OOiTd1vjktxfK SqetVpu5trqqsqGhwWqzHczMfPiLX9Iak2MTpXKVXp+8TaJPlYFutKlKHXvMah3cBGSQq3WMbjig g31AMVyuBc21a5hUG+MlmqzcHFBHmxN+1qXsHdCNx/gOKSRtTmd3f3/3vkP7fWJhwqFn4tcW+arQ gUvRpX1AQPCVKB78wqwP6r3QqQ9D+pEnxaQHyCaSgyDK6dPuZOyAMkABLP0arD2c2VNyetG0W+eb boU4qBSESGchq4cZPtgkiXMxhewsspvIOgXpUD5UWua+oWzFfooifzn5g/ixPpJi70Ry6JDYW1Ll Ja3eJK31VjT5qJt8FLZAZccW+GvK7lBdX6SxL9wwGmWgZ/SjkmjonQ1pz4Wo54NVC2wqkJkzalDM Ag1U6+bJLyP3nmbWaCAJxhQcNzKpiJVouo2m4RZCDYs0YESzpJPhmqlQ9XyEZjFcczRCdzRMN71Z PbZF0x+scYZqGmMN1pQbnDfd0/vAY+PPvzDxp90ns0pWalpXOvpXJuZXTuBezToPehJaNutPNAHD /Ad0MW4XLVOAycRsJNrmvZEPmq6C4oX9FMR+K3ZjIYbnSaD8WTztEdNcBCjeGoWBn90zRsiTxaOE osKSsRPjtOITvGxykFwlYWgM2VOWLA8h0BkZ3l5amZpd6Rlaqbe+nVs2+dq+ke//evCz3+i+/aG2 1NsaY1NtmzXdAcrhQAVc5kXwC1tLMR9OzvhUsGoyRDEVppqMVE9GayeiNeORMJ813cFqW5C6zk9e 7SM1+cgKNsn2XJbwVnhy0Uc/P/yXgzSnifPOLJw4Ujz4xadbZNd0Mq+cTOMo/XC4tifc0ByoNPsq qnzV6H2l/sps74R9/pIMw000V1VYudhib6urrqmrqW9taWhp2b1n78OPfAHmSXgc/B69DDyiMci1 SRJ9ioyRiwJGCtk16wEegYTxIho7HO/V3nknPwv/1wA7wDt22Dt9/T37M/b5xcOBLPRLQJdmVgln DbfFAQpgMRXeIptQf2Z7uWTgFoogwRRcrvLIOqw7XISHvSOCcQqxD21y+8idWDyKxQtU5S4DsFoG bkm5sxJAv9RPXuGnKPcHl8EaksL0K/OSQIE0+UjpQx/e0kY/qSNA1hmsGgjVjYYbx8ONc5H0LrQ5 5lvNgpKi6dVCC2yAma0GYoPTREOG+RAjKIbN0NPD/fOhdJ9EI4ayQG/AZWNMcPHYBBl0PvBEKxUi jUejk4/GpMxFGqdCtRMh6tHNypFA2XCQojfS2K3a6dx+l+ueRzoe+9bgj16c+duhkzmVZxscZ7uH z03Orpw4SXx08gx1M5ARv7+7QQOlcNneXmbfWlo6d3qJ3nLH3upPFgEZBYyl2FAr+jFNqbF+jB7N +jkIgL2skT3Ajx2cIxBY7vw0HuECvZ/FIC3/R2CnIB5jhLGMYtPMHp2HFWlFGOJlH4fi3x04c3r5 7VPLp99GkejpFPFnQsGvPrm0cvzMyvzJc6PTSx2DS1Uti/uzh3/9cudT3+144FHXTXd366/vjE3q 2Czv95eNg2W2aCZxV4DfFEnvn2Ojcox0wjTzEdoFeMdkt6pmIjUzscbxWH1PqMKxRdIckGDxibX4 Jpr8ZHk+kkNeCW96xRySb3c+/oPJoqaVBfqZy5Pz8/szHZ/8vDPWMLRZMRdumItKmYpMGQ1P6g7W t/qrzD4Kk7+yLECd7y1NvyJmf2Bi1bV3Df/yLwslzcOtjubqmqZqs91mb7FaX9+z9/6HHpKpNHES OXwllSFZoQPFGCHlGqNMa5SzR6ugi2ThCcY4FyaXS4MeziIlJVGmO5Kf1zs0YAOZXNLewR/SRHuH 847T6eoF7xzI3OcfX+ovKfSDGyUr9odFQ0YKOIgMDR+yStAbK5jrxOJJsm5PHZvJtWCEwgE6IMlS rgFScrl6FMkLgcWzxNwOcucvbpLk8ZyABPA0rMBUBpYbS4NTU6kEwJqTlfvLiXewizl0lBUbpSIr z0tS6i0p80qooAm4uEqvhFpfSZ1fYkuQ0hmsbg9VdYGMYIRHJ01HJc9E0kdgZ8OT2Dg0uVSwZZhj JbAJYxYaSKIYxjJsht69NIkWOq7q9JIQ9iQOn7+n9z3yl6LRWxl1o2Ha4TB19xa5c7O8PVTdHq5v j051SLZ1JN00eMPdE3d/bvYzTx19+meLL/x18fVDi4dLFqqaj7d0v+0aOts3vjI+tzJ7bOXYqZUz Z+j7H+i3HkEkBg6BB7hVQU/E8ik3tlIOjAC2WmKP7NM7Zd2SwF0/Zo/QkC1ils6dPUOS3uR5hjMd fcKEvpNwhhZ3s730TSSWBpE4hpHSamFWAzZg5dGg75mVydmlwdFTzu6jdW0LJebFN9MWfvbbo9/+ 8fyj3xj76OcGd36803CzQ3q1NdpgC1F2BCu6Q5QDIZqRUMN4RBL6/0LU1qNRqUfJe+K1zaYacCHo dStaWvMVZZiK1MMXHo4w9IclOzdrGvwkFVfGlPvFVwbJqnyl0LM+FHogWJ5/54M9f/jL6e4eKuHy yinH0Nxv3uy76X5ntLFjs3QsUr0YYwCLDYWoHcHqel9V5SZVhY+6PFBd5CfLujzucHhS5b1fGPzr /oXapv66psZyc625ztZstVSZf/vyy/953ycVan28VCFTaTWGFBk9XaWXE+PA2DHCxoFOA8lqMNF5 rLEOAgedD4o/P5mAVdrSJAv2zuB7t3f49/3AOz39Az0Hs/b7x5f5S4v8E0uFURi3XUNAPxQ6IRst ZhSATd6HzwffJVDAeTiPX/5urM1ttVRuCJusVBcoGxSehu8CuXB7h8ewTa7Q0LgPm2JjVIWjSqlm FOSmeSeAjEzeiSavONOm2BqvhEZfCezzdprg1/WEGAcjkkcjcU8zkm/F5vXpdUIRelphxGyf2VDj bFjSHPwsWgTEVwMJ9g7pjHpAN6AegL+raCZUOxMCWwlSO8P6w9EII8yrYxHGo+GGE5EGeuNcuHY6 RDW2WToYkNjlJ+8IUtqD1S2RuoZ4fa08ud5wbes1d3Tc9onuez7T/8Djw1/4+vA3vzf+w19O//oP s39+c2F3+rGDh5eOlK0U165UN680OlasnSvt/SuD0yuj8yuTCyvzJ+g9Iejnp+idDGRWENjz+qKt AXm+bUWb4l4OHtbsgnLmHNkpJ06tLJxYmVokF3JkaqV3cMXRudJkXzG3nCusfPtI8dGDeTOv7pt+ 8fWJ7/926MkfDjzy9a77HnLc9rG2Hbc06XY0SlJbYwz2YAXouDNIPrxFMxGMmjQcCzeioo5HQqYc C09ZDE9ZiEhdiEiZp0mopLnwZHJ4gzVsZSmuiHEa8dEpU1FJw2G67hB122ZpU5C0zk9WfaWkBuaw V0LRpvjcTbF7Lwvbt1lStO1m67d/cry4ir6SgJ8zu3C6rHLw2R/Zd3ykKUw3GKw7HpG6GL11PMKA W5QtILHGO7bsilizLw1THrws/jXfxIyUG+u/+M35/Yfnapt76potlVX15trWpraSMtNPf/bL2279 SIxMHiuTwydS6UABepqQorV/ZOMweweWTpIM1AOmoLmnd6Se9wa3lfSu7R2imPPB7R2Xo723v7/3 QNb+gISyAGkRDeXAz6KRF+pgvEOSQkOzwlDxxbq05yYdxXvpPxiMBC8U43amROU89+o8nTYpmUAu PB8aDKpgJa+AyeOvAAeV+SnYmgAaTmJmII0TlfsqyryVpT6KEj8FPYmH9KxOiJGZMVjmjeYoNW2S VHpJLX7KBn+V3V/eHSDr36IaiTRMRKdMxKRMRBinwmAQJc+GJYN65kKMc2xIiOiGGAcKsQ+jIZIc 7gT88Q7cjWlUaAGcxWb94azNh6rgu9EoeLhmJhLOgnaB3uGSTIOgtODTMBmtnYxSj4UrR0NlI8Gy 4QDJsH/ikG/CgF/CYKB0aAu9yrpvi7w7VNEXk9Qv2danurbbeEPXVbe0X/OR1uvubbvtAcfdjzg+ 9VjHw091f/nbnV9/vvs7P+3/4QuDP/7NwH+9OPSL34/+7pXh3/155HevTr/y5vTLb0y+8ubU3/ZM /XXP9N/2LMD6eCN9/o2DU3/djU1Ejr7y+tgrb4z98bXRF/4y+ss/j/78lZGf/an/+d/2fPtnnU9+ t/1L33B+5iuOT3yh7c7PtN30Cce1d7VfdUuX4YZe9a6+xKv6ounppK5ARV+gciRI0+8t6feKHQmM nQxJnIlQ4ofTene4qFFJ81HJc9EpxB3RSTOoDVogSpW2EGpcDEuCnA0xzIboZ0J0M8GwMVOORl41 F5EyG5k6GZEyGpncG6p3BWtagxTwoSq94yt848sCpZVBClOgMsc7ft/lkXsCErMV1zg/97Wxt9JP 9Q4Tky6vLA2NL76VZr//C66E5K7AhNEw1aJk+2zctiEqs6bVX2Emjyy+2F+WdaUky0e6P0RVdP3d vT9+cSa/dKS5ubXJUlVdYW1usjU0Z6dlPfWVb9KruSTyOIlCqUtS6Wg8WKFl6/20BoVaJ9eSpQNJ 1MOdLNgsRDoXtlzeN/4xvAPZ7urs6x/shb0TlEi8E0B3dVQHrB7qRay70mQQsyDQtQBs0oIdNvrD OrxbYSCGos5PS3ioV7M+LCBAWXFB+JPE3nJ/kjySK56btHc1N3fO7ETCGTm4ISMUzBOrhcQmHcgT MCai3wt7x5+GpfkoT0kASEdRGiCHUuQvLyIzEIkZ8/qQSVjkJy/yJ93kJTd5y0zeqBxsQtJsWrmP BK5+mS9zzbziK71jq7xia/zY9FmQ3Bas7ApTD0boR8P1k7jBgnrI5DGQF8YHekTqYdIN9oSH2wUT fbe5MPZStHD5fLhsLkxN3ASbCCYPEKadDtVMhajZeiItX0+0EGWAnb8YQyNT7GX7OnpjP3VRekgF FhlN+UcbJ8GSEfqxUHgi6qFQDfrMQIi2P0TTF6rpDVH3hKh6wjRdYaqOUGVHiNK1Re7YLLNvkbcF K9uCFfYtStsWGFlKR7jGEaZ2hGk6wnUdEbr2cK0zVOkKUzlDFfZguT1E7gxWwBzrDlZ3h2g6g9Wd oZquEHVXsLI7RAX7AmfBufrC1MPhmrEI7USkHphE2eDyRLFyRugWo+jTAEcjDPzRuflIzUy4cipM NR2umgpTgojZ6D5NLy6E64+GGY7Sp120MAkXIzQLqBNydQ1zkcmzUVsnI7aORmztDUtxbNY1B2nM /nJa5roprtyLBm6qAnC/kR3+YGTGByIyveKOyLeVPfD40GsHz3QO0eMRCLOnFgurF779i77tH7NH 6HuC1dPRScfiYPDqe0JBN9Jq7zgTblRByiKfuOzLgzN8Jdma22sefbZ3d8ZglaW7tratqtLV1Ghr bjSVl/7xT3946KGHtBpjbLw8UaaWq/Raw1awCdwrJUhH7V5nTBxklLHxHfK2YO8wCT+L5D8U/wDe sTtph8vV0Tsw0J12+ECgtDxQXuwvYTPlxD50k6eZIFl5APU3NoclqfCVVQYoqgB/kpX+yko/uQnW gb+i0g9QVvooKrykpZsSC7wSCjYl5F8Zn3NFbO4VsdmXx2ReFnPoQ9EZH4qCkvHhmMwPk8y4DPGx kIcuj824PC7j8lgohy6Py4R+WWw6UgI46rKYrCviDl8Rd+SKhJwr4vKvBOLzN8XjFIVeCaVeiWVe 9KXnSm9Zlbe02kdWhVKhofjJK/3lKCokim1CgRmFEVWR5SJFPLd9aEAHfEE8S8AmXwNJChvwKvaT QqIGyvwSgVLAP5GWctHiI6JjRj3SSi8Zrb2mFQlsuSOtOcBRiSW0UDuuxDu+2CsBssIrrsqHFjo2 B8htQWpnkKZzi6Y/1DAWaZyKTJmJgPPFHkll1j4IiH8saAr9h61OZK9bpNk05rWxebQINd3nwwG4 YHQU7Qpjt3H6OoiaBixCQGpsQTa8PFqpRJzFh43osRJ6/yxZUvQIPrplJA2Q8xEl+joIrYhLpteG RycfjYQBlXQsyng8At4KuXXHIwynIpNORiUdj4SjZzweZYS7xwGP5gSlgTSeDDeeCIfUn4wwQJ5g OBkOnbI6Hm5gGZKC9DSMBfMkOmk+mj36Ry/STjoaRYPr+O34jfRgYDhDGNxPepQPLEwrNkN1U+Ga qQjNJBCmmqJH6tQLoZqjYZpjwerFYO0xHBtunI6AD5U8EWkcjNJ2havbg9VtQcomf4XFR0ar5NEe vOJwiU3B8qrN8mo/Wf5lsQcvi33dW3JEc2PLo98aeGX3Smv7ykl4Uyu0Eqe5e/7l/T33P2GV73AF KieDQe4pUzEp/RE6+xZFs5+s1ldW5ZWIfnH48sS3vCVvxCYV3vnJ9h++OH2ofKyq2WppMtfV1zU3 19c3ZGVmPvvMt2+44aZ4iTQuUSrT0FMOKn2yXEOGjDBUTFNXzLGiNcf0jBUzdkR7h5R/PnuHr1TG DheN8nQN9fccPHzQXwp+YePHibR+z19WtVldGaRG3yv2k5QEyXK84nJ94vN9E7I3xWV5EdKujN5z RdR+n4S9vvF7/GX7g5QHtqgPbtEcCtNnxqZkq3bmGa4vMN5UmHRT2VW3V1z70fIb7q248b6qmz9R c+v91bfeb77t09W3f9p8x6ctH3nQcueDkLV3PmS+86Gau0ia73zQfNdD1Xc8UHnL/VW3fqrqlvtN N36i5Lr/LNlxV8m2j+Sn3JKbdHOO/sYs9c4M6baMmOS0cN3BUFXaZuWBAMnBQElaoHS/XwLBJ36P V+y+TdEHvWLSvWIzfOIP+0uyA6R5fokFtARJWuSbUAITOkBeFQgmlVf6gVhlpgCQKaTMxAZ9ytjS JEbHtHap3DcRBAQJcOup1EcJS8fkI6sk20da6SuBLKellbSistQHvJNQ6ptQyldjUw2Ta1ZK3ETT +WVIvCmxepOkwUdq81d0b1YNhWqHg9UTIZrpKDARe6SWnIVk8AWxSZhhOhRWEtlHzOqhp8wAYcki JyMaQuJzZ2yUlJ6qJ9NmPoReeAJ3Y4GZTsLrB8K19GgeMY4GCs0W82l+ZmSBrehEYejPummm8PVH ZCkIj8XhFNgEf8G4ANgztILC45lZwUBkJ5hpdCzf9ATsMlpmGUofUyLF/RtpETBJI344zEN4SYvM VzoGGQqPiSpkOiwJxuNMqH4aKUFP4HE6hGyio+AykHWIbjxE17tZZ/dT2f10Lf6q6sD4Mr+4Er+E Qr8ENHU0+MpARXWAosQ3IdcvPss3+oBXxMEgSb7hpo4vPDO2O/tk+6Bg3Zw5d7Zv5PjBvI4vf7dz 6x39Ibr+QLi3W2eitw6GG9q3qBr9JdWsWxX6ybK9Eg96SfYHGwpT7rJ/+Ycjr2cNmursdc2NDc1N TS0tbbb84rKf/eqFez/xaV1yakyCNE6mVLKnqJgDxV0nsl8AhWbVqAGzMEuHWz16JV9/rHmHlTjv G/8YP4vm0ds7uwcHetMOpwVJKwIVJYFKADZO6aaEkk0xOZeHZX4oLONDMVle0iNB2swQfVZUcrZy Z8G1d5Xd+UDVp79Y88WvWb727aYf/Nz6mz9bX3q94/UD/Zl5XRk5HVm5HTn5vYUl/UVlvUUlQ2Wm oaqa/tragfqG/tr6vhpzn9kC9FtqByx1A+ZaiqmxAL01lh6zpafGDPSaLb3m2t6a+l5LfX9tw4C5 odtU01lR3VFSaS8ocRSWdRSV23OLHIdz29Oz7XsPOfak2d840PLqmw0v/qn2v140P/+riq//oOiL 3yr4zFOF93+h4GOfPnLDx7J3fCQ75ZZ06faD0Ulp4fqDQep0f1mmtzTz8oTDH4rN+XBs3uUJuZfF 5V4Wk8vsqcJNkkIvuFRwuNTlBHAQnDslXDD4XyWww9m4DwxDziBsiAdOFlsnzdYQweqhT1B7kyFJ oLVCUJgDy9iHmIvoXlHuJS/3AgHFV3jHVfkmVPvF1wdI2jYrbEHKzmDNQETSOL3OEuRCH/mgIWpa AA3qMU7T07Dw1EQ3DayhAdfQJvVY9ubpEPK/QCKggIVQ/bEQeBxk+PCxITJwQtjza/QGAr5QhXiB OIsRBHEE0RlJUWEeH5PCIBQkDqRFLgxc4fGro1QiUDZaZwCdf0GEUSEUlBN7efkRSdTDToeTTkXQ sm9yHhFJLzOglIuhOvyWRcoQRlzSLKzFiKS5yJTZiJTpMON4mHFgi4aeTN6isgeqGr1lZlr5JcdV qPCWVXhJ4PIU+kuKg5TFQZrSLfrSQFXOZZFp/xGcgWYQc1XeLZ9u/P6vjpZWnh2fWDkrvGfydNfA fFbx7Nd/0H3VrV1Ruq5Q+WiUYSFxx1Tc1a4tusYANZwAvua+9Ir43CviMzYrj+h3lj3yRNurbw4U lXdXWuzV9fb6lrZmW3VN3RtvvfX4V7+2dfu1iXJVvFQJ+0WtT9YYU1cZB8wCltExW0ZHYEYNSWbp kBTGegRA/2e0dyjKTvYOUrUP9A/27st+a1N87pWS/Vck/M07YV+kIU1x9QHjroLbP1792S83fvOH zh/9duQPe6bezDyeVXy8oGK+vHLWVD1XWz/b3DJZ3wDu6LDUOS31trq61oaGpvqGhob62ro6i8Vi NpvxH3+rLTUVgNlispirLJbq2jpzfUNNfX0NktU31Dc01TU01jU11ze31DW31La21LW01ra01LY0 1ba0WpqAFktji6WhydzQaGlsrAWa6xtwt2hqaISoq2+qrWtBAWobHHWNzrr6jrrG7rqmgdrmYUvz uKVlxtw8U2mZLDZN5pVOHi4aP5g7vu/I0OsHO3//musXL9t++JvGp39i+er3Kh55Ku++L2R/5MFD N967b9vtu3U7d8u2wSR+PVTzNx/Ja5cnpF2ZkHVl/OHL47Mvj8u7IrF4E602BNHA/azyY76nP3lz FbCSAmjCq8xHXuGvLIPH6kNrFNkCRVqpSJaOH7d3YGOCnmgRdqmvqtxPVYoYmsV385d3gunK+Gqv hHofWZO3xOontfvLO4I1PWHa3nDdaETyVHjKJAyiCFoJMh2ZjL6H3jvP3h/CbAowETws9uhZKL3S hL46wDoq+XHo9qAYtoZonhk7BG740PvPQAo0LEImCfiL+j8zPdyKuEtMTMcScDjLhySP5wncIAZk TMQLKZSEFZXZXxTJH/onGmVGUChZcDDl3MRHz7VMRRlmoun1CRMRuuEwbU+oridU3xVscAZqXJs1 jgBVi3dik1eiZVO86YroiiujyzfFlfrICnxkhYGKsi2qcn9ytKuDlCZ/ee4VdHF3eyfsjdRlX3tH 85PP9v5p94rFSa/741Nvb59Z6eg6kZXd+50fOm79eHNcavsW+QTIMcowGmVsD1Fbg5T1sJXo8WZJ 3qb43Zvi34hKyrz6zsrPPdX1m1dmswtHa2qdLa3WVmuL1WGqrN37Vtq3vvXdW2+7U65UxSZIE2Dg cPrQ6OUqrZtQGOmgwxPXsAlysnGIhgQQE0Ea6LFyYe0f8A5PPLxv/H284yTpcNGTpF0u12DvYPuh gjfUu6qvf8D60Lfav/Or3t/+bWT/kfHiqmGTpa+2wdXSYm1paq2vb2mE+1lbU1ddZa6oqC6rqCgt Ly2pMVXU1lTVW2rqay11dbV1xCENza2o4La21lbAZrU6bHaH3Wm3AQ6HzdnWam2oa0Jyi7mu0lRd UVpRVlRakFeQm5Ofn1eQl1+Qk5d/JDs3Oyc/O6cAyMkpyM0tKsgvKSooLS4qLSstq6qsNNdUg9Ea G+pbW1usVputzeZotdmbra5mm7W5rbmltbmlrbWpubmuqam2odFcD3qrs9TVm+vrLQ3QGxubmpoa 6+tqG+tqWxsbbE2NzpYWR3OTzWxuN1V3l1f1FpsGcwoG9qV1v/pW569e7v7x713f+pnj0W+1ffJL Nbd+qnz7x4oNt+RKrzkUpn3TN3a3T+wBn7iDmxLSrog7dGVM5pWxh6+My/eWsIc2lJUBquogdWUA 2reCj4XB2BEn5mD7kE3kqyj2U5fSw2tsHTa7J+OGySfLKDGtqJaVe7GBaopPrPCKa/FX2AOUnIk6 Q3Qwi8YikiYjkibCjTPRqTD4ZyJT5mkJXNI8TIBw4zwHPUCPLq2fZTPxs2xWnro3uIm6OjlZjAXo qVcmCbPwYsLJl2H+DmwrIR52FhlEpDCyYEyxyh3sRJ75APTgfqhhMZLefbUQkQRPin/wGuYbWXBk sBDmIpPALyQj6YHe8YhkYDhEPxJqGAS/wAzcrII749ysaPVLrPNNpHE9H3mlt6xsU2LpJgnqysQe G4SsBCP4yUyB8rItiuJAeUGALM9PlnllzL4Phe/eFH0oRF2ou9H26a+O/+71mZKa06MTtE6ah4VT S47eo/syHU8823bdR10xho7N0sFQ7XT01eNRKV1b1C0BMjNNO0gO+yQc3BSzxzt+f7CqOOnmxoe/ 6fjd6125xd3Nza2tzc1NLdY2R7Wl4a196c9854e33v6fKpUhJk6aKFWo1FqNnh7RpFkqxjs0RQUJ QmFz5ALXXBgwfCD/J3gHpMPB1+948g6xyru0d2iHq72zvb2zq89W32LNLxiobui1tPU0tLXXNlpr ahuraiyVlVUVFeYKk7mqEqirNTc01DU2N1rtVruLuAQd3gpuaW5FL4ZpU24y5RcVZ+bk7E9Lf+31 t/7w8iu/euHFn/zXz5797nNfe+qbX3zsK5/53Oc/+amH7rnv/o9+7N7bPvLRG269Y+eNN++47oZt 116XfPUOXepV6qQUdXKKKilZqU9SGpNU+mS1IVWtT9EnbUu56prUq67dun3X9utu2HnjrTj2lo/c ecfH7r7r3nvv+dQDDz78+S9+6Yknn/zm977z/E9+9LNf/fLFP/3pr2+8tnvfnoMZaUfyckFXlVU1 tQ0NrS2NbU0NLS3NVit+htXa2kqFb6gHK9Vaaiy1ZnNtdXV9VXWz2WK1WOzmGltNdVdDQ19TUy9k Q8NAfeNQXeOopWGwwtyZXdSVltGze6/1139o+d7PGr/y/Yr7v1p+6/2lV92SLb0qK1yXHag6vCkh 44PRmR+IOvLhqNwr4gq8Ekv4rB9ZRqqKQNx1lTShxh4uK/aFy0ZLFomMaEgbOsliOAL+9GQcQIpv YpF3fJFXXKkXvWakzCsWd3J638im2Fo/ab2fvN5P1hyobgnStgSqu4LVfcGq/mD1YJh2KFw3FKYf D6d3wUyHGmfCk2bCk2fCjbORfOkKZBIkzc3THHPKQhgik+dDBcy59Vm3shDCZGgSFHoxCD32wd4T Iszx80xEpAKLYSluJC8ik3CYaSmTUSkTEUljYQa4RSPh+oEIQ2+4tidM07FF0RmsdGxW1QWqaoM0 lkBVla+s2ldWCeb1k5og4cx6g4ITKsh7TSzzTqQBSloIIimjsRVZxWaZabO8PFBW6p+Y5xV78INh GR+MyrgyMSssueCaj5u+8O2Ol984aapbHh6n9ZM8wMAZmVqsbpz941tTn/5qp/FGR5TGFa7qjTP0 J6T0RBsdYbrmzboqH0XRFQm5H47NviwyfVNsWmxq4a331zz1vO3PewYLK/trm7raWrvQQZpbK6st f92z/+nvPH/rR/5TqjaEJ0hjpHKwjEqt1+qT1FoYOBpsytVaJnWwd+SIpMU4BpqiugS0BjbQQ+M7 JAX8d43vrFmvzNcN0pt3wCeMd8AqXPIXD64Z36EVg4h1EPM42zu72+xOmCvVdQ0mS2N1fVM1jIDG 5larw+popy8B2l0tzW319WCW2sLC4kOHMt98c/dLL/3xZz/75XPP/fArX33qM5955O57PnHbnR/d dePNV+3YaUjZBqKVyDVxiYqYeFl0nDQ2QQ4lMk4CPSpOGh0vxa4EmTpRrklUaBKVWomKIFXppBq9 REuQaQwytY6eOlEbpOw194BUqZPItYlKXaJCmyDTIAcgTqKMjpfFShSxifKYBFlsvCw+VhoXJ4uL k0olao0mOTl5+7ard+7cdfOtd3zsP+/+xIMPfO7xx5585lvf+8mPf/7ii7//y19e27cv7ciR3LLy SrMF7iEsoVar1Wl3drRabTDcGlqa6pthMNWaLNVwFcvM1WXVVRVVVVXVZHBZGxo6mxp76hvG6ltm GqxzjY4TNY2nSiqPZhcdzyiYeT2t74VXWr/zX7Vf+XbJ/V/IveWTh5JveysudU+wcq9f/Bsfjtj7 gfDMD8XkXZZQcrnUtElR5aOq8dXU+KqrfZQmP2VpAD1zX+wvK6IlDrJCWtgpLfRPLPRJKKYRaxhB klKfBPhu8NeYlcTYitw3OGiSMm9pmbekYlOc6cqYiitjanwTzb4Ss09ig7+0yU/a5Cu1BalcNJum ag/Wtm/Rd2zRdYYYukKMPSFJPSHG3lBjX1jyQGjyQFjKIL39N3kwInkwHArbDEsZCk8ZDjsPQ+FJ w2HJgzgqPGkgzDAQZuwO1vcEG3uCDVzp3qLvDNK0B6pdgWqHn9Lpp7L6KVr85QQ/WYNXYr13Qq1P gsVXUuOTAFR7xdd4xVWyuQ6wCX4djZrRz6SH8qhyYCrCUQ2AtSgpDZSZgmgGE6ZNKX8nwZVx6R8M e/1Doa/5x70Vo83cdmv5px6zPvfC2L6c09VtKyMLAtEgLC2vzB5daWs/kZYz8oNf9dz7SLPu+uZw Q09o0mhk0kgU6kTTFiCrY0Uq3hSfdnncmz7SN2JSD2y9HXk6vv+r4TcOTRVXDZkbu5rtDquztqGp oLzkz3/561e++uRtH7lLoTdExEpiExRo2HJDMjiF2rYKHIFubJTTpDh/rbobgplzaWMHEJLx1YPM wAHADv+t9g49n8XsnUE7rJv3aO84QUkO/O2gT1G0ttrtjo6GBmtNTUNJSWVmZu7fXtvzwm/+8Nzz //XlJ59+4LOPfuRjH99x3c2pV1+nM25FfcUkyCNjJKCPOOrwSmxCxktVRCVyDRLwj5zCVFEBOiip GsNWjX6r1ngVKUxX61I0+lQVffudVRB7Vwj9Qv5T3ZsikAxZ8UOQIUMKMiGFHrRN5mAxBBWvLLVR otDFg6Skalx4MCB4MDpOFgXESlFajSHVkHL11dfccMvtH73nvgcefvTxr379med++NPfvvTHN3a/ lZF9uKSstKqmuq6+vrm5ua2tzeFwWJkT1wbzub6+tsZcU1lVWVFugvdpgm1YVWuxNNU3NDY2wgdE ZXd3tg+2t085XEcb7YvVbTNFdTO7MyZffHnoez/tePSbzR97uGzbXQXynUciU9IDVPsui0+7LCHr 8sTDmxIzvRIzNyXk+MoLA5UVWzRlAQpTkLI6RF0RxJYC+LGljz7yUi9ZqbecljKyJ2bdY0NSesSM LTui9UcBbAmSj6LYV1bsl1jsC7aingmvjWwEIilai0QLAnwVJj9Fpa+i2lfJAEVR7aOo8ZXX+Epr fKRmH5nFWwZJio+sxocMEMgaWCI0A02yCj6OdyLA8k8o90ooZ+eCpPc3+SSAF2idgTe9Vokm+1AG L3rPCRsU48Nb4Bc5SkKrxmhRFdwlKVBBgzKK6gBVTZC2MkBDazsDFSVBsmyf+Eyf2EyfuLRNMbuv iN63WZ4eqS8x3tR0z6PdT/9k4o1DMzXNx7uHV47xuSgWTq+cmz9xpmfkVH756At/sj/4eFvqTZ2x SV1Bir4QXX9Y0kBEavdmY6OfEpSX65OQ5hW3e1PswRB1jvb6qo9+pvVr3+986fXeI8W9lsauNkdH e5fN6jTX1GVl5f78Fy889JlHtl+zK0GqiI6XJMqUCo1OrU9C4wQX8K4rNmn2cpwLgcdDXgJisv9+ rHkeHbzTNzzk6OwiA4fRjSfAMJBr7B3OO/SEOtins7vLUtfwzaefffSLT9xz76duuvkjqVuv1epT 5Qp9gkQdGy+HQRGXqIRMlGvRgWUqWCIgVOr/jAJQlVShnDvACOwR+2RG3qhfgSOxuR48nhKwiyEQ NjuKLo87kiQt+uYKLWEQjwXAcYBUoZWrUC/uQxhQHmzyWuOFJFbSp4D1wE0oJKO8JPwcHCtV6vED 8UvjJapYWGoJ0kSFWqnTq/T6lKu23Xjzrfd8/L7PP/rFp7/97E9//stXXnk1LS2joKDIVFFVV9dg bbM7HPSGa9A81KaW1jqYjRaYSOaKGrO5xmIqNZnLqppNllaTxYVm2tzab7ONO13zHZ1zDtes1T5f 3zBVWDyyJ73rN39ufeYXls99q+SmTxQbbsyJvyotUHlwU8Khy2IPfzDiyAdDsj4Yln15VK5XTIFv PN3kaYJfhr5XHkjrKtl0GyiJnrwv81aCaIp8lYV+imIaulaUeavKfODWScr9E5ikOReArWAi3qFB JRpgoofy6Fl/HwVIrcxbTqu0wWIgJjbYRETAlpKK4Js0CAXKAHGAUIhf4A3J3U+9kaQhdnplCi2P Qgz5m2w9Kh3Lns4ppjeZkM6yktPC0UBlJdyrAJUpUI3fWOgny/dJzNkUf/jy6PT/F3bw/0XQIq9N CfnhxiLdDSW3fcry5Wdaf/nSRFb+mTbn8vTsyrET9Da/swLVkD4+c7zFtXCoeP4Hv5+677Hurbc4 JVutEXp7hMERk2SPMNqCtXBUK69MLP5wXPYHorOuTDgUosrW7yq5+7PV33q+9ZXXunLz+syVwy2N g1Zrj9UBH6Egt/jlV19/4qlvfeRj9+mSryJbXoa7L3VUtTZJg1anNqK7oumy5s3sFNZWWcsU9PNA DVg0Zy4BIRm1fN59VkE5i43/74dYNpR/1d7pZMaOm3e4wuhlvb3jyTtOZ99Af25+QVh4dEycNCFR KZFqFEr6TLJak6zVpehgnhi3sreTUS+lN5hpBYqhGOGZel6mCxAK3yvGr4FnMpJ0PVbBN3kOQgIm +SEC2Oc4PPdSYs+7AW2ySHEvlZ82xcIDzFwicLuMfjUbVwJPwaeVKrWJMhUsu+gYSXSsJJ5qSaVQ aFNStt944+133/3JBx965KmvP/uTn/36pZf/si8tK6+w1FRV29Bia7K6cCOEgWlts7U0NjfXNzXU 1tdU1ZRWlBeWlVZUVxdXlJVUlldUV1rqaqyNdf3NLVOOjnlb17HWzqVW57nqxjP5FbP7s0de3d/5 k5dav/bdmoe+WHTXJw9fe/te9fZXw5Wv+Sa8tSnuzcui9nww8sB/RB7+QGzBh+NKroyn5+m9ZSYf eRW9VEFZ7a+qCdRWB2kq/VUVfuAdWTl7DQjctHJYEAHgFFhJsCmU4JoKXyRQ0Qi3r7LCX1XuR9IU oCLuIAYBKdDwE19ZSmsI/EEfFAlzjFiPVpbD4yOzq8RXWuQtKfKRFPpKinylSFkWCPME/qMK5EL8 AjsuSEkFwC4wkT+VweQHRwmKrMRbkndl7OEPR6R9IPzAB2P2Xh7/uk/iK4GSNxKMaak35t16b8Pn n7Q/81+dv31t9GDe2+V1Kx0D5CvBY1p9+OvcyqkzK/NHV+zdK4U1R187OPz0j3o/+SXb9o+0SHe0 RSbZQ9R94freEJ0jUNXgJy+8PPrQhyJ2Xxa3e4t6v2rHoes+WvipL9V/+yc9f3prPKtwotwy0tTa a7W77Lbmlqai0pK/vf7W97/340/e99D2HTfIlIboeHlsglKhMqp1qRpdqlqbqtKAcZLUqiSVOpkt IGYtFk2RYU0L/ycHdR92+4fkvEP2TgfsnS5ilfdi78DVcvUPDuTkFcoUGtg4Wt1WNXwZIp0kjTYF Veb51jLWXdFXBd6BFOFZvjUQE4iJRfBIUb4jLpbsYllBFyPPV9j9gSuIFOL5IaubHOwnMwkXD9CC jlOVKiMAkxA0nZCoio2Tg4zIj5OokDj16p3X3Xj7HR+778HPf+nrzz73y9+8+Orf/pqWnlZaUmKx WJobGmwt9H1Xh9Vhs9mbWtrq4K41NFRXV1dVmMzlJktZhaW0vLrSVGOpqW+ob21rc7lcA51dU91d Rzvb33Z1HGtomzXVThRUTu7JmXlp7/R//bnrsR84Hni68Y7PV+74zwLtzRlxW/cHq/cHKvf7Svdv ij1wZfSBK6IPXBm177KIvR8KTd8Um+kdn+UVn70pIc9LWuQjL/SWAVBK2HsYygPVMC4qAzWAyV9N XADq8Se7wz0fxzgCfBGoJO4IUIA+YIyAeohHglTlUAJVZUFKIiZ61kRe6CcF+xTQHLM0e5M088qE 9CviD1wRs+/y6L1Xxuzxit6zKXKPV9R+n9j9AYn7QlX7wjXpktTC1Jsrb7637t5HXV94buyHf5z8 W/pkUdVci+2Es2N5bHzl6Al6iFQMYJmlsyunTi/PLC51DJ6uqJ9/I63n+V/bPvcV5413dyh3dIRp ejar2wOU1gBVA0roL8/3l+zdFLXHN3bfZll6bHLRVXdUffRzzV/+fu8Lrw7szRgoM/U2NnTY7DZ7 e6vVUVvbmJdX+Oc//fU7337+E/d9ZseOm5TKlJgYZXycRq4CidCrtlSaFLUmRalKBtGo1CmAUp2s VoF9qNeIzczDHlkPce8/F6i/rOed9q72zm7iE4FxuOFDJAN5KT+L2TuFiVKlCjahJlmlMqhA2Ook jSZZgyrTpijgkri/0A724bzjrr7VMl0QnglEXQSP9JSXgJhsPcS9l8YF03hE8qyYJDPKoFAnKVUp kApqPVCSOdDCsMmlxrBNZ9yuM16tNV6tMlyl1G+Va1MkKmOcTBsn1cZKNbFSdVSCLF6GjFO2b7/u tlvvfOD+h5568ukf/+TnL/3hz/v2H8ovLq+ywC1rttrhJtO1cdho6UJdQ3NVbX1FVU1FZXVJSVlF eUVVGVipvK68srXK7DDXddQ39jZbxx1dC50Di4MT80OTiwMTR7sGjrf3v23vPlNrWyy0DKcVdPzp 9ZafvWh+9sfVX3627KEnCj/6UPFNnyzadmeheleJ5JqS6K1FYYaCzdpsf/khr/hMr8R0GjSNSv9/ ERkM6f8Rlv4f4Yf+X0TW/4s4/IHIrA9EZv6/8Iz/CCP5wYj0D0WmfzAi7YPhaR+KTPtgxIH/F3rw A/B9QtMYDnwwNP3DEZlXRB2+MibHOx6dvGizuiA4uTBqe4FkZ4H2pqJr7i657YHCex4p//zXq77x /dof/Mz10p8H96ZNFVacbrCd7eilaab5hZWT7C1Cp8/SQ96eAd7Twsmz/ROn7P1Hq1rn9+fP/ea1 uSeeG77joZ6rbnfKr22KSq0LNVo2q6vg3PnQk1YFl0fBhjriIzkcYTys2Jm96+6ihx43ffsHTS+9 aj9wuKvINFLfOtpqHWm1DbY57bXNlsqagwfSX/jN75/6+jN33/PprVfdoFCkxMfr46VJicpkmQau AH2Rit2i4O/rYIzjlsbmfagtCbc3BijUa1bb278Y2A8R5N9p77i4vQPekchUai1jGXQ5+KIauCH0 ilbysOCAMHvHk3fWlOnS4OlZvZ8Hcdf/OtYWg1iVKkEEazFrpZzoCXSMOqF39MthSNNoEZodjCbY R6mgIbURZJRCsxgqg0SulUg08XHyqOjEiJhE2EfIwZhy9c4bbv3o3Z/4wmNf/vZ3nvvVr2AcvXUo K5d8NXNDM1q/s9Nm67BaXS3NLY0NDY0N9bQes6a6uqa6sqrSZKqqqjabTJXlZTTJVldX29zYaG9q 7rU5Rzp7x/qHZ4ZGjo6Pn5yZXZ6YpU8Jzh1dmTxKL7XoGFpxDqw0us5WNZ8oqTuaXzOXXT52MH/g rayuV/e1/+Gtjj/s7njpLccvXrH/+CXH8y84vvVT5zd+4njqR61fea7ly99rfeK5lq9+vxl48vnW r/2w9akftn7jR7bv/dL2/RdsP/6d87d/c/72L47f/aXzT7v7X08f3Z8zfbh0oaDqRJHldEXLSq1r pa1vpWd8ZXRuZfbEyokl4U2JFwzgGuw9dWplappeD9jcvlLWcPZg4cLvdo9+/7fDjz/Xdc8X66/+ WKXq+pLIpLwAWaFPYsmVMaVXRhZcEZPnp87cotsfZdyr2Ja+47a8ux8o+/xX67/5g/Zf/HHwtYNj mQUTpdWTTS3DbdZeq62rvbu5zV5SUXnocNYfX3nlu8/9EO7z9TfcpjekJkhVMbGy+ERNolSv0mxV 63GP2a7QXSXXbFWCdHDp6S6lU2jEBThs+R+tGxYg1/LnGOBk8alo3t6grwGP/6foFGtAfYSa/Vre 4faOm3e4vQOGcUG+G95RwoMAv9CoLdXO6vcuwDUc75t3/mWBn7kKPm4nSoAvZ+DD1eyGRpsCoNMK i2SFjh6xoaPAViyxON2m1qfKQEYKXaJME5eoiqHBbBrFR6TWuPWqHbtuu/PuTz30+Se++s3vP/+z l/7w6sH0tJz87NKK0qbmhtbW5pbWJmtri72NVnW2NTeBk+prLbXm6lo4aKYKU3lpZXlJdXlpdSlQ XFNWUmsqb6iparCYW1qarHYrreJ0Orp6e4ZGRyemZ+aPHj95+szSsofbsj6AAmBicEBHWg6ESx53 gYDD6QtiyyunllaOnzo3v3h2em5pbPr0wMiprr4Tts4T5paFI6Xjb2R2/PLPjc/8V82jX6u456HS 6z5WoruhJDK5LFBd4iXNvjz2iFdC5qa4Q97xh8I0mfJtOcYb8rfdXnrzPXX3PdL2hadt3/p598// 3P+XfUMZ2UPFJX3V1X0tbf0OV197p9PpbLPbLE1NeQUF+w8c/N3v//D0t75z/4Ofvf6mW3VJqRKV NjpRFiNVxsnUUrVBZUjVGLfi/oF+QbcQXFD6IhX7GgwuvR73ZlxxPecOOSSlMch0JOWQGuh6xk1s aJlaCG8qfK0NX3TDdR7v0ZD+afBueIe5V1BoDAfy4rzjBO/QuPIF7R3hexd/t73zrwfiCMYUlwRV FKsTAHXiOeDNdGYzrtvlCZyLOEvwYWm1AVgJB0qVOlqRINPE404bJ4uOlSYkyhUqjTEl5drrdt11 110Pf+7hZ771zE/+67/+/OqrBw4ezMvPr6gwNTU2AdaWVofdYbPZ2KrItuaWlobGxrq6+sqqqkoY SJVVpoqKSlMlzf2XllWXV5grK2sqKixVlXU1VfXm6sZaS0tDnbW52dYCtDis1k6Xs6ezc6ivf3Rw cGJkeG5qCphlcm56en56an5mZn5memF2dnFxYXFubmFsYqTB2ldq6cgu7skv7C8oHMkrGE7PGtxz sP+1vd2vvOZ68WXHT1+0fu9nLd/4YdPjz9R/9nHz/Z833f1g0S33Zl97R1byjYelOzLjtqXFbt2f kLo3MWWf7OoM7Y1ZxluPXHVn3vX3Fd72QPE9ny/97Fct33iu7nv/1fyrl9r+8Jfmv75hy8hy5OR0 lJb0VNf01dYPNDUPtdqGbY4xV8eAzeGsb2iptlQWlmTsP/jqy69+/wc/fORLj33k3vu277pBrUuS KGghWEwCm7RVGzi5KA0pKkOKUp+sAOj+QaRDu3RGhT5FrktCPC0XZpIfcjHwd4+izZCkNiayj8BW bt1z7z8XqLlehHfYYkBhZEe0d8Az79beId5Z7Q8C0Yjgm5A8wQZYPQh1woBIgUS4TrzDpgLP3yWC pfGoWEgVSdQwWyjB7SNtskaTpIWVpE2Sq2DqK6JjE2JiEuLjpXHx0gSJ3Ji8FdbR9Tfc/LGP3vvZ zzzyjae//aMf//S3v/vjm2/tPZKdV1VtsdQ21Dc0t7ARJJvNBbS12hubmusbGpoampobm5gH11RX W0cP05mrzdU1Vabqqsrq6iooVdVMMYGn4NGZLXDwamqQrBY6FMhaHAhRW4cM6WGUakuTyWwpKKs+ kleTfaQqK7My7WD13n3Vb+6ufO3Nir++aXr1jZpXXq/70+v1L/2l4cVXml76U8tLf7S+9CfnH17t +ONrPX96fezVfRN/SRv9y8GxNzPG9x2eTMudyiieyiyZzClbKK+bq6ybMFnGLI2jDS0D9U0jVvug 3dFlteEu63DYWmytVfV1FRZLYUXFwYzMP73615/87JdPfv2Zhz7zyJ13fPSaq6/Va5MSEmSxCdKY BEWMRJGg0MJBVtHqim0q/VaVLhW+M80toPNzo4aDt3yKZ5eSX3RI3DaYZC4C9AuAjuISnYsOB3gP +jfhHSd9H50W8oBYOO+AXkQ/C3TDQVTk/qIWeGcwN79IQhNaSRu8857grgeiCc+KEkFN8/w6XA+W gJIhEy7ZJRCWF5ArpzaqVDTUrdIYNFqDRpek1SdrdUlQCEisgnGkipPIYxKk0fGS6NjEmNjERBiw +uTkq7anbLvm+ptvu/u+T33+i1/+6teffv7HP/v9y39+7a29aRlHCopKyyoqq2osdWAfemLR2Wa1 tdlsrVZ7E3vArbG5taGppamlrbm1ra6uob6uEUxVa6m3mOnZulrAAt+ONs3mWouFJD1dU20GQVWD jBoba5saLI215sbauuYmeo63pbmutbXBZm12OJrbHM0t1oa21npHW529ta61qbG1ucna1myzNdrt jQ5Hq9NldXTQtLXDabc7bVaYcPCN4FnaWpvb6usaKk1VpUWluYdzDu7d/9dX/vrLn//6O9/53mOP P3HvJz59820f2bbjuqSUq5UqI1uQpYqXKOMkqgSJSi6npy5pOSuneGrzyfTJcHWyjGSSUp0iJ0uW zFW6suzmQYmZ9UoQNz2lEH8B0CVmkjnmvM1w+W/COyAdJjsdcK5W7Z11fhZ9oViYz+K8UyiRcnuH OIW3eHeXcNf1Bu+sAzNJhDoRa+l9Q8yK58xdMzl6CC0AAemQFNaeuReh8YtFDxDqk+EUKNkCfBzO M5RpjImgJJUet/RYqSo6URGVIIeSoNBINQYkMCRfvX3nDTfd9pG777v/oYc//8Unnnzmu889/5Of /PzXL7z86quvvbV7X1p6Tl5+YRHNp8HeqQFD1dY3NzZbW2wOm9MOEmgFS9nsVruNRozoydwmq7W5 ra21pa2tBX4edrLxp1Zwl7W5tbWJnjtpbmprBbURx7Xamlqsdc2ttS2t9U0tdfVNNea6ympLebWl sNyUW1yamZ2fnnFk34G0P//1td++9Ief/vyXz373ua9+7Zufe/Sxez714C0f/dg119+oTdoqU+vj ZMo4qSI2URYTL42XKWG+g44VCr1ayaoLVcqg0NAYzVr/iL9cQicogIxJuhw0eHfeZXofgNHEJV1T ofGg5UCCaP49xnfI3gG38OWC4B3wDywb0d5hvhWD3enkJk//4BAf36EFOxv2znvBf189iFdBGJD2 HFRCAlJASfxdlvTmJ1CMTE3ryKk7gbkET4GNJujIiVDT2GeyxrhVbUhlTZ+5ANokqZK+TBCTIIuK S4yIiYeSIJUnypWAVKmWq7X6pJTUbdu379x1/U233H7HXR/92L1333PfJz756Qce/OxnP/foww9/ 4eFHvvj441/98uNPPvbEk499hfClJ77y2OOELz/x5Ne+8c0nv/6Nr379G9985jvf+NazwNeffuap bz7ztW9+68mvP/3EE9/40uNPPvTwFx/47KOfeujh+z7x4L33fOojd917/c23w0i76ppd+qSrVPiB Sq1UoYXBEpugiJMoo+IkEbDpEuXxMpUU5IKfiWrRJamN8JVStMZULVvzqdal8IXCchWROA3hK3VS lUFKnZ/8HTe4jkiATz8hRiSIfyQ8GgxXIEExkBxcF/f+c4EK/w72DqiHO1zn2TtgIP7VUGIcpov2 TpFECt6hFZZiixeJRsQG71wIvLqY3fH3QcxqzSk8+gYkrjpJAhLTgUQu1Ek4K4GPEEkT/O58WKQ7 sTvevQldhR5LxJSsMUDHJQZgcOkB9AS5WidRaBJk9Eoq9iyejLy5OAkcOigMsthEObw8SOwlkA6F 9tITJ7SKVw5Jj+8mKuKkSvYYijxeSs+jgErgB9GDOIlEK/QAHSMX9tgwPfvCWAOlPa/V8QlBYdP9 i5CAG4nEqtx7YmNkiJGp9JAccvaYMdJzyY7lFQJJU1HiJmXlPurvwcWu7L8W6Cdcknc8CGjt+A4Z OCAd9tli2mGn+ayB3PxiiUS9Ye+8V/z31YN4FRjR8I4hQKQYd2LWGjgTcQJyg3OTGOOhrBabnYjP tUHCzuc/CowDL4MABZTEkKQxJGuNACepZF1SKkD2BQG2BkWCswBsUkoDIq/iTwLz05ENok9VsWEv rWErPZWih3mylUt90jZ6dI498cubHMiFUyFRCZOMWVBIoYr4b8FPExR30wWIkbmvyh7ig+TgOg7h kleaWxF1Ai/zPxC8eAxcgVwPce8/F6jwl+Sdi9k7q2PJYCOuw9tivFPEeGfD3vnXxCoHvT+IbZ3s fNYAuNUg9G23gkgB/L0NDNjLD+EQ01CGnCDo8POzov7vNkA4EXAu4Hsh3QqtdWJNbnXAi8MzpSd4 JG+lrKEikqTnsRxi4g28S1B1rfIOfx590NHRKfKOBwER73B7hzQ2pkPbfBMKNvsGhmHvJErVak2K SpMiXmDxsnmANlljWncVPbBm1wb+dSqHtypBOR+IIXg2RIJwCFc84+kQ8SczxZ3YnQbNDFJBHLTK KWsUd3ujGJGb3LuEeE/wXbCncNSF75RcEYq99vBLgP8WjjW7/nfxLstzscKL8e8IWq3G/Hco8RJm 7wwNOQQDh3BB3iF7h7lXtI4Qxo6ro6O9k2a8+gdHyM+SqbTsIVpurzLjVrhsbtCT6EySLyZcxXeE 7iL6evC9F0zDTHcmYWyv23tBrE8m5s8zced5MTDb/vwcOLCXS5YP9wI4LnWUJ3gOa+DO1jMrKiFd CxbP07xbsGIIWQnFe99w/1KhqIIiSg+IiT2kCJ5GVJgjw6QcfIE7GWt4YtPiCjlctHKVNj3phqch ICsuWeYsT5R5lXG4XD3EowCC7pkJl2tKK+66NDzTvJv07x5rcrtY5oj/x573fFAdum8ziTLt4fy8 XrJ3iGjgXkG207t41vKO6F5B4fYOPTCKA3r7B3PyimLiJAmJClg9CVJ1vESVIFPj9iJV6mQqgB54 kyq1HBKFhiNRDqlNJF0rU+ikCq1UzhS5VoJdTCbKWDL2MjBBxyFMeoJ2iXCnEZMhMoHt4nLN3jWg 8uDUMpz9PNCBTCayd6OIuV0MbFyTFft8SCgflkZK+VB1MUDhm2vSJ6JUcg3qxF0tdCxXPMEjeQ5i VrwM/GUuCTwNKxuV4ZLAb2S58axIJuCaknxPUJFkOVABWGFQw1AS5BQjlodOxySdQkj/zkiQqriM T1TS0/wSNdUS8sRlYq2LI4FfNalQD2JdcYgxLA01XUjeCKUKei8lPYYipzILGbK6FdNDembizkpM s5pAKlxEkkIz402OSsvamAzNkm0KOrv0FwJ2rYnhWBMvbEKyMojgRaIzMsnbFZ2OlxmR7mPp/ZyQ Ch1N5PFOKhzFfgWTPBLy0qD0OC/qgT3WExkTfyQvt29wwE7+06XsHT6TRZ/r42t76AWmzEbq6esv Kinfcc311+y8fueum3dcd/M1JG+6aseubddwXL/tmuu2XbPzqh3XXbUDEriWsH3n1qsRc902pNy+ a/uOXTt2XL+d4ertFMOlqGxFymt2XX0tciO5feeNV197AwNiBOCkbBePv1FMg13boVxzvZiexwNI A+y4zp145w07rr3xmmtuJHkebthxzQ1cv/baG3fwo1ZxE4Oo3ITToRJ4VUBec93NO3bdfO11N+/c CXnLtTtvvmbnTdhk+dxAiXHUdTexkgiKoNNRN+287ubrdt0CeS2O2kmb68F3iQmQP0567fW34OzX 7rrlmutv3bnrluuuu4XkO+DWVf06dsglwROIibni3st+Ly8PioEy3HDbNfgh19963Q234US7dt12 3fVQbtnFJP8hlMO7AE8JicuxE7+aXxooVNWoc/zwW3gFoh5YtZ9XXRy8bLzScMVxia/diUxYhkI+ dAX5taBLxk7hPgQSmdzCcxbj0VRYJlDcRWIpCcKlFxobXeVrb7yaSbHJuZsfNUWxffL0HJ6t1xO8 C0BBi2LK9chhO1o+OhekCHcvA/B70XOv3rELHeeq7eiMTGHguVE+kOhT1+xCSmS17errtiElJClC 90QOFwTt3YGufT3qcxdd5Vuuv+G2XTfcvG37jqKSkp6+HrvT5iTPiZs8F7B3+EwWDejA0oHCU7AD QEAdoCFQkt3ZYXV2ODq6LQ3Nf3l99xt7DryxZ7+I13cD+6C8uffgm3sPvLUP8iDk7n1pexj27k/n 2Hfg0H6GAwczSO7P2HcgY8/BQ3sPZuxLy2Aya3/64f3pkFkHDh0+kEE4mHkEMi3zSHpWNkPOocO5 GUdyuQQys/MAppOSmZ2fmQMUZOUWHMktzM4rys4vyi4oyskvzs1j0gM5ecXZuYX5haWFxeWFRWX5 xeV5xeWQF0MeS1NQUsElKSUVOLaouKKopKK41FRSVllaVllcVllUbioqMxWXV5ZUVJWYqovLq4BS UzU2IUsra8pN1RWmmooqi6nKUlld60adG1wX4quqa6tr6syWhmpLfXVtfU1dY3VtQ01tY3Vdg7m2 0WJpIlnbZCF5MTRAIgfI2jpKWVvfVN/YUtfQDCmCNhtIMrTU1UNnEBRENkE2NLU2NLZSysaWWvqU UDNgrm+y1DcDtfUtpNQxpa4ZZaPi1TXhvO+EZrF4KKpwIGLopzVVWuqr8PMtDVXm+koma2rqasz1 qBlePyLYZn0Vq0lUb0WlGbK80ozaBujFIZCVZroQDOWmmrLyqvKK6vKKmtLyKlxEjuISE13T0kpI XN+iEi4JuOgFRWUAGk8+lMKyvMKSHKCgOLewJLeAdMj8/JI8yAImmZ5TSAmQDM3ySB7aZ+Hh3MKs 3EK0WCgistgmJIAmjbZ96DBaPjX+dMjMnIyMbJJuHMrEZnZGZnZ6xpE0AN0nPWtfeiZ6FnoTJMde 6m6H9h3M2EtdL2P/QaEz7tmXznvr7r1pb+05sHvvwQuB9jKQ/vqbe+sbW9s7ehzOTsDV0cPHbUAV zg47+AVk4uoEn5AdcwHeYfyCKCIagK1opkcqmN7V0dnj6uy2I4vu3sZW6+u79765d/+b+8AyRDSM ZQ7s3p+250D67v3pRCL08w7hJ+1NO3Qw/dD+tEP4bfsOpO8/mH4gjZSDaYfS0jMOHspIS89KO5R1 ICPzYEZWWuZhyPSsI4cOZ4NiIDOO5KQzmZkNQsnJzMnNygWnkDycl89kQXZ+4REgj5CdT5tATkFh Tj7jF7SAouK84hJCSUlBcWlhEcmCIrAMUAbApoMsLq0oLQdfmErKTcUVlSUgC+KLylITOAKsISik I7KyqqwKxEGyrLK6tKqqvLKmgvGIqcpcVW2pqjKbqi2mGguXleZagCmWKgt0FmOpqzbX1ljqzKAP kvUWYhBBIpIrtfWNjAjQgZtIR5cWvxTGYGmCbEZ8HSRIoZFIhEv+NENDM1fAFI0NTU1AI309pQVo bqX1w1y2Wq0tVpKtNiuXbTZ7q9XRZnNY7Q4rJFfsfBGyrc1OC4tbbbYWu7XZbmu2WVsg7dYWxFhx oL3FZifF5oBsbrW1tJEEmlqsQGNLWxM9byFIlJBLKnATdCit+AlsE+Vva8RmE/9QGqG2oQk6JGqG fjV9Jw1sRUxKqAPHCRVodisshuLNdfXVDFWWOrou7HJU1dRWmWurzXRRSK+2VOIi0tNnuDfgstLb jspNVZ4SKKsAKkvLTaUVlWXUZkxF5RVFZRXFaEVlpsKyiqLS8hK6G5WjmUEvJllWUIY7VllBSWl+ SSm1zyJqn1BycGss4JIUsFIOb975hbzB85Z/ODc/CzIn70h2LklS8g5DZ8g6nJORlQ0covv0EfQg SHSog5m4hWdBoqNBYcjcfygDPfQAiAnsk0Zg/ZTk3v0wF9LcykFgDywJ9HTCfq6/tWc/LmhHVx8Y x9XeA4WZNu2wXhxELDS44yTGuYi9A35xL2Smb01wxmG2DxGVq70TB9uhd/c0Wa1v7du/e//+3QfA OGlENwcP7T6QzjgVzHoI8gCo5FAmM1gy0zMy0thv4+B6ekbmocysQ5mHM7IOZxw+kn7k8KEjRzKy syEzs7OzckA0JA/n5nJ5JA/VnXskPz+7oIDLnMJCLnOLigCu5BeX5BcXF5SWchSWcZQVlpcXlZcX V5SXlFeUlpEsKSsvhc5QVmEqN1WWV1ZWVFZVMKWsqqqiurq8uqqiptpkNgMVNTVc4XqlmT4xyCUp 9PVBYhA0WXMtPf9YW9dQU1dXXV9nbmiohlJXZ2ZfIuR6TX29uQFAJ2mgrxI2NNaj/zTSFwq5RHxj c0tTa2tTCz1H0NTaxh4mAB3YiAvQ4R12q9NuczkAK80JsFuFxw2jq6enq6cXsru3F4DS29/fN9jf O9DXPzQwMDQ4ODzE5dDIyODwMDA0Mjw0OjI8NgpwZXRifGRsYnR8YmxikuT4JJSxiYmxybGxifHR yXEuRyfHRqbGRyAnx4YnR0dw1PjE6NgE5MjYOA6EHBodgxweHRsaGR0aGRscHh0YGh4aplND6R8c 6hsYhOztH+zpG4CDz2VnN35FHySaHzXI9nZ7Z6e9vcPmam+1O9ocTlQF1Q+rouZWTl4gqZb6JtBu M2RdIyqWG2hC9YK7axsazI2NNQ10RXBdqtmlMVvo2jEIFxH0JG7W0PUFK1mqasyQ1TUWkm4gsorJ yhoztRzWfgA0JN6u0MDQzMrQupiC21gJyYriigpqmeWCUlhGb7ktwA2ypLQQbbikJL8ErETILRQa OaGgEJu5+YW5eQUgJig5uAHn5nN5JAfsk0M4kp0JsN6EngWkHz586DDu6KSnZR0GDmZmpWVk8l4J mXYI1JMOHXL/wTSAK27eIcbZvXffW3v2Mn3f3v0HWqy29k6YOfCQeqEwV4k3QjJ2GNd4ylXecbA1 yrRQkDOOJ+9A0gHt3U5c7076AEKTtW0vygGkpe85cHBvWtq+9HQYONwtIpkBZs0+mJF9APZL1mGQ LigmIysz43BW1pHDh7OPcHkkJ5vLI7k5WXnZh/NyjuTnMpmXXZh/pCAvpzA/t6ggp4BkXjFqnGR+ SRGXBaXFXBaWlQBcKSovKy4vKwG/APSamQqOsirAVFZtqqiqNFWSrKC3YlVyVPLnq801NRZzjbmm ylJTWWuurrNUMVlTX8u+5CMoXK8mBQxCkmLqLeb62lr6/F+tpb6utgG31rraxoba5sa6lqb6lubG NnojDmRjWwsAuwBkYW13gjXsLifQ3tXZ2dMtogt0MdDfPzTYOzgwAGpgLCBwwfjY8AR179GpsbHp sbEp6vMT01OT09MkZ6Ynp6fGpyahA25lEsrUzNTU7CQwPjUOTExPcDk9MzkJiV0zSDyBDCemx7mc pBjKc3p2Zork9DTJqelZpKcXXtBRs1OTsxMTc1PjsxPA2Oz4OI6awfmmIPnhvCS8MMiK58YKJuxF mSemUAwhmQCWAPG0i2eCHzIzNTY1OTo5wSphbHhcqJbhMVDYECdToG9wQERPH27CnR1daPdo1TRj awdZtzusLker3cYuSmtjK6y/pkZcqWbQE24G9UySYqmvB0/hmrLrS5cYCt8kyUBffmTSzNpDVZ2Z N5XKWks1GpUF3GSuqqE2Vo2bVg199ajCXF1RU1VejZucCZIrJay5FpuE1gtZUlFWxCA2cgJXSoqL iotIlhQX4nZbVFhYXASZV1iQi16Tn5+bl5ft7k3oWUBWbjbAlcycI0BG9uGMI1nom4cyqYdCpmcc OpSVAZl2KD0tI50rB9PTgP1pB/cdPADs3b//APQDByBhGnfAkyKvqNdF1ANyAWm0o7ZR7YyGUPMX sHf4NNYqy/BBZQZS6EjmpDk7Otu7ukFv6TDV4BbBJ8okHMo6jJiMwyDXHC6zYOxl54Jrs7JzjgA5 udm5MAgh83Ly8rnMZR//zCsozC8syoepUlRUUFzMJCoUrlAxUFhSCqO0qLS0uKyspBx2SnlpRQVA Zgu7e5SZTLifkIVCSpWpugY3GRNdWrMJNx/6cmhNlZk+hYzLX4UWwO9dTIpAYzKDJtCM0MLQgFjr IeJgT1nXNTUQg7DmVd/U0NDcVA83hX3tFF5JG2MQWB/gDmdHuwMGZlcnWAMmBicOtHsQB93VR4YG R4eH0D1GR0bGx8gogLHgBvoSetrYJLiAOifveJ4K1ynB5OTYFNhhHDopJJm+DkJ63ocJ0D3BY5A5 dWweOeEJxAsgXvAAURjXp9wxHolFCHwnlpwXhh9Ie1cLJuwV06BCeCRVzjjVD0/MfrggwUFjLP+1 IHqiM7ICCPXGNyEpN5YhmAtyZGLMfVGG6TKNwPgaAk/19vfhCnZ0dwHtXcRW6CogLKuD3oiGi45L D6AZNLU0N7W2gLDQMCDRNqipNLrJqJ7uQJZa+gIuNusa0aLQxmBJ1aLJoe3BTCZLuZYe40ezhFJT i1ZqAUOZYGuDoUBJdKekds7NJWrwvNmbTGUV5WUmGOww22G/l8GKh0PHZFkR+g6MJupHrGcVFuUV gpIKYSvBRMrJL8hGTyTkHaG+iU6aA5mVDWsA3TY7C4bS4SPkjjDAdICPApBlRMMjGWkZGQfS09Oz strsjo5uGochY6eju6OLiIZxyDvYO6vGOaSYYj3QpTo6e6w2J/mQR1C+HDAI4xRBujmlgCxAohVS wCx5BUX5hcWQBUUg5hIuWaWQRB2VlMHxQd2ZuITDXEGeM2q2innXVfCuhbco1MCaFRTu10B6AjGW WtymmJNfD38el5xuULh3MdlY39RCFjhDXQPub3BnqN0QlaAxtbWibTlctJDS1dlBLa+rs7uXnJSe /j6QCKcS5p7ANxkaBomMjY2Mjo6OjQkYh/cBx2QVvK0jGRo670tCHyOHRQD1BHJP1qW5MCbBPgLQ qbhcB6Tk+YxPTUGK/XANsBcdledJVMIsJsj1QOfnCjdYPBVYN1xZAzFDXiR2utUSehbDE2JKVnjh t4gYZ/CMuRjEyuQVCzk8Ogrwa4HN89Kwa+d5+dhRcBhx1AhdaGwyzkIOvAGAquC69g8O4B6DRtLT B3+2GzceNBvc88FQuBuhOdGHuekT2a1oZk2taHI09NbA2iEaKjVF+Nr1ZGHRbY+aK9gKt0YLb71w 9OjD3eTr1aHZs+EnLs3s9Uc1MNiZ2c4dumpTVTUp6ESV6ESmEhq4NJUKYwvYJG4qLikrovHNUsiC ohJ0T5IFxXn5rKuybosuDK+N40gOjR9xmXUEHlxuZnZOxhF4JwVWV3t7T6+zq8vV1Q3DBx4+cQVx COwdziQX5h0ycy5i7xDoyI5uwd7p7LE5nOBOlAklA4NAh2ScUoKfwWURflVJGVcYrdBgCpeoCHFI BbXAh+gqq1F3YBYaxmN1ukouvJbhWpO/zb1uwWDBfQO0QhLAJi4hH5WEZCMj5PBzq4SopN1F7kwn 3Jmejm44Mn09fWgr/X0D/QNDA2g6NNjBWhJaFaMJAA0UzZSYhQ1SUGNFQ0QzZS14mO2iRsySUUqe GOCN2xMU4279NDLi7hie4AnEzuDZ8cTuB3ZgN/y14PFrEvN8YHdwiUguRQWSejhLQGmYPcKlJzh3 MIUDnAIXicUzBflwheBxoJghz59OzanHzS8XBE8JycsvHCjuZeAKxZy/1xM4HFLMh101gXd4JC6E eFHWX7L1cKcRrzW5ve42IIBiWJ50UpYzGAqJ2UgWWb59AwNdPb3dvX2Q6KW4raFZwphChySSYm8S QqNFG+ZUBRuqoZnewoa7Jm//7jFyKMRNZCVZzGYYSuTQ8XEozk20yWMYSVEX432toqoa/gFZT2y8 SeyYYlclblo1mjy4CXYTfJSCIj7eVFBWZgdpdIEfQLjEONzeYRYMXC3uZ61aM+/L3iEa6rY7XSgZ CAWFE0vMeYSPn4FHmEIGCxTQh9tOgRQUxKBS2Fgdt1OoatwVyuuUwKZyaCAQ94SmFhoyhGQjrDBL 7DZHO735Cb+DFRjXDzwCQoHkw5M0VDk02D84yE3owRHco0aHR8ZALcPCMCeRC2cQ3oyYJP18oGHx 9kpSjGHgrY0Uwai5kOQtm7dFAuLdrVPsFaLCu4rYed4HxMO5IloQF8TqLsYRopGyHmL8BeydSx4C yU/BFS7fsUiQ76kexKrjlQzFM4ZLqnNufhJfCJeGQ7hAHhfuQhCuuEdjEBWhtdDw/IjQosQYt47E vJnxTGiUHRLxAjeNDKPFornS0N5APw1O9fd19fZ09/V2dqNvUz91uDpsDmr2bXY76Ak81Qwvr6kR XYRNSpB00xPdp903Zupl6F88nm9yCOPi7E6PGz9kWUUVswnYWDi5HZVwREBDghNXBllRWErzcaWm SvpMDXgHdNPV00G8I9o7IBPOJJxxuDyPdwhs/JlMI8Y+qweIYEd22Z0dKBxnGRAne9cll0LRITmJ gFDEHyn+fjanQHO93DAh24RNozIqscGYarM5QCg4nbOdaI6fl1ydvgFGKLBNcMcYghwYGhExOAzT F4BO8Wy6hDbBNZx0cF2ZbYxGgGvsKbGLxiP5heeSNS/enri+2oawiTRoNGiaaDHUcN3gkZ4SQJNC axabPho3SdZwqRXyTSbFnvNesf4onAtS7MAXhLgLh4vsIPLF+k3PmHfDO+tjOHg8JM67piQixMKv /2mXqCL+qyGJUxiJYBPpAV75ogIp1D8zfHj9e+7i1+4S4Jceh7sbzGqbWQ+x2bBmRi1tXSOkQXHE o62675Hu8SYaKefj5cP9g0g2wtt/3wBNAvbDVGcMBXpivh7ZUJDo/Lyfg6Ssdid4qs1mZwsm2Nwf e10ku6NTl2RoBDhbQaL/gokgeddmID7ylGVVVaUmU6XF4gC/8PEdZul42DsCb3Ccxzsd7f8fyBHk 782d73sAAAAASUVORK5CYII=iVBORw0KGgoAAAANSUhEUgAAAS0AAABZCAYAAAEcIEIbAAAAAXNSR0IArs4c6QAAAARnQU1BAACx jwv8YQUAAAAJcEhZcwAAIdUAACHVAQSctJ0AAC8aSURBVHhe7Z0HmBVFuvdxd13vIqggCrqygoCg KAZkRRQTBkRRAdFddXENK3JRr7rrmhARA2Dgu2u4hkVFdJUwgWGYIQ2S88wgwwSQnJPknKyv/u/U 21OnTnU4c84EmP49T53TXV1dVV399lu5qtrI3/1O2HCzLy+q4Wd0kyYUETbJv/0tXaxIKMXM1EmR ETPtyhsnYjbDNPjDH9SREAcOHBD5Cxeqs2L27t2rjorZt2+fWLN6NR0fPnyY/vPy8ug/KyuL/sHu 3bvVkRDLly0TZ9WtS8fr1q6NPWK1Tj1VHUXS8fbbyTB/OOcc+u/bp4+YNnUqHYN6Z5yhjqJBxBqe ey75ExExoEeI2b1rl/jll1/ouJ56Kua8Bg3o/9z69enfBBH7/VlnqbMSTPdHjhyhiF3aogWdOxH7 RV7Ys2KFZwTLk6gUQ8os/eyzqIhltWypjoKxftIkdVQ6qqX+1385kZh9771iljSpJ50UETH9OEVe Y3AvAxXz4/PP0z/M4f37RdKvf+24gZ8A13DM9mm1aomDO3bQsQ7pMQQMFbGrqIgiBvatX0//gCOW dMIJEZHkwDZOmEAPxcAN+XniicqmJAz9fib70UfVUQlOxPgfZmanTnSuA3tELOvaa6NSYdMPPzgR 4zcAo0cM9raIkb8yZU0oYswYmQNUFqpNbt9eHVYunFepm8oAqYudUuh1KkPkXLMkNw4ePOj8I09k Jht6Kzc3l/JVsHz5clH/978XR48epXMwe/ZsdVTMQpWXFhYW0n/MEQOXXXJJVJaC/G3nzp3qLBpE DDSVH9jA99+nY2bDhg3qqNgf5M1OxAD+j6in1Plc5gR6Rq5HbMjXX9O/DlKyg/ZRoYSBiLnlpxxp sHXrVnIfFTHomsqAEzFWmoAjqrPw1VfVkT9b58xRR6UnIsXSatYUo2Wy2iKmM1xq6mSp1cddfLHY s2qVkwPwfXwOpspXumbECCePHXnyyfQ/5vzz6V9PEJ2IiM24806x8JVX6NiL5N/8hgLHfXtVSRWM 5KxKEwcEvG/NmojIFr71lpN1uWHNK010e/wjYmwXETHlRo8EIpD3wgsRKcMRA7pbHSevzH3iCTG1 c2d1VvFUm9utm/P0eIojmtJkUIZy3Lg8YVWBZN/GYa1mpTPCUnSqSjjKIhZjQ8+w/Li5XTt1VDbo GVxp4aq3SZknmG7/wb/+Rf+ck+s5er0zz6T/Zio7WCEruExdVXXfsnkz/eugNg30cMwEW7JkiToS 4rq2bdVRNLofnGDcnADatG7tnmColevomt6GW4LpzQkoKgE9oU475RRxZp066iw4XODkBPti0CD6 B5xg92nVSTD4yy/VkZ2JEyeqo5IE4xfJRCXYTlWq3SX/2S5JqxqGOkxLLDYMnxfJcgBAXVu/XhWJ SrDsHj3ogmmvGxsLZGELxY9EkqwV3sBsWQSqaKoly09MT4xftMqoDbcEM+2P7N9P/7o99KBOkjpH 6ZfrAiOqlbTvcBFdh/1DC9LMu+6iY1ucWI3wtZ9nznRqfmyXWqsW/YOgtcKIthQufg+VkWY73Xih F+UBmtCAfh8nWPKvfkX/meeeS/9IMHzuwGz3A8tkPfvg9u10zHZmkxvQwzITB6CKAdi9/kWkVq+u jrwpeZ0SeA4zSuZa3GgODm7bFlEHqsqQDgsKNTNbPpOqQoTCTzn9dGVdwqJ33qEEYjcHPdq8jnec T1FPEJtZPXSocll1qTb5xhvVYSRHjdI9OHr4sEiVCVdVKfkMVVOejTl/+lOJlAXMPY5HrKV6L4M2 WJP9RvP6OWefrY6imTZtmjqKnxtvuEEdlbBs2TJ15I1bPRfoFXOd2BPLMjjALbHQrw6mTplC/wA9 OCia6CMWwH5VoN24caPTKwN2GJ26k2S5jHt4uFeIwYgI9PowCxcuFAsWLFBnxSDc7HnzXBNrwoQJ UYk1UY20iDmxRmklYMYtsYbJTKH9rbfSsS5t5pAQBqMoGG5V0B/KbB3Q3a9bt47+bZLFftWpXZv+ gS2x1qxZQ/96Yu1QheI2V10Ve2KlawEyXonFvNa7tzoqSSxuS+LmHDwAD1+xJdbZ9eqJG6+/Xp2V JNYZWpz0xLqpXTtKYPZDT2wzsfQmJE4sfOYcHxhrYn2v1dUAckG+lu4xxobhiOiJ1ceSWDNmzKD/ Q4cO0b/5AMDLjhNLlxhOLF3q2D03MgLTX90PTqyXX3qJ/pmoxNpZUEAXkGBsFwQEjsa6kamp4tsh Q8jOL7HAJRdfrI4E6ZLpKgOYMnky/esPxaO2WHL1BEEDI1jy00/0j5bYXbt20TH70fbqq8XKlSvp 2EwscIYqlC9evJj+AY952rNnT3RiMTY7FFzXp6ers6pHTIk14je/iejdrGq4JpZe/WFwzM0sVRGr gt+jvuv92oAPvsbtVTYwHm1c06Zin8rGE82igQPVUcVglaxkWUo37dmMcEksXOOWUv5PJGaLa0UQ kVgMRjqOadiQWjRndekihqmRjTDjLrhAuSqh6M03xWJjGBIYrtU3uTkZYLiAPoQTx9Nuuy2irQxh cYvmyEaN6BqGsDBDpTpAq6juD7e3ccto9mOP0T/s1qakiHT4o7kHOE8KqIcjEkuPrI2lH30k9m/a pM5KMJX+YVU0SNYSyzaegsND2Awfp7hIUm737vSv3zPaGNLMYeVIt9xAoLtPVuVI3c4WP5OozxAJ Ahb/619i6s03i2wZ4NqRI8nODfPT5Ei4JRauI4HZHf8DPtYTK++VVyhhYdJq1CA7/cUilwamv0gs Rg+D7+X7gH7djWo7ZKUVDnXHfG4aL/TIs9vJWqsAJ5buDx/b7PTE0q97JdYk1XXPnRp+iWWLsxck j3gQOOaRjBiMjHPd+LFh7FirexwjUmy3d+3aYjsVJjDdgy2yKoRjdJZ8L/UT/IDxlSzNX7/EypT6 V4+bH04lEB2cFJj8P7h1q7ItxtQJVZXIGrMEleaJ11wjxl10kVhXhas2NqILpbVqUYKVli2yImx+ xvonE1J1cL5E9GTrAuFmoENZL3oZuKnoGkpIxRKl5pm9skqdIgUElQqb8OgGRdOCPn0iRkroQJuF VD0issUpLt3UsXJUlnIx+Mrxuwr30FZlostchkGb0gipvQpef53a4rdMnSo2TZwo1qSkiOkdO4rh qlpiu9cxHsMEdNC9ZutUYPR+yFgYPHiw6ySyigJa3utZddB7ZQ5tj4eg4ZqgB8xtPoENX+FKhElT vVZ+lJVwHeuEwuVhbMNLbMQqXOjWxaQSLBmB7tEWzZtTx7w+6QSgSxb+cncv5vCabhjTHoMI6ko/ H3v0UXFKjRpWDQi/Z82cSdf0OOK49mmnicf/9jdx6SWXRLyYk048kcLi+SQMuorhD9bruK9rV+pX hztTuPBMuNaje3d6ZtxTkJ+vrpaA+CNtrr/uOnH1VVfRPUGE6/ChQ+QnwkE8OF1N4cIQH7irKXOn J2Rc0HXNXdXlI1yW4Tg2SiNceDCTOzp0ENe0aaPOioE7fSzBbe3biytbtVJnxTQ+7zzxjRqPANzi gkTWx0DZ3G3ZssVX22BMgi5cPXv0iBJuEFRzmfGw+QX8hOvhhx5yxkjomJoLaeCmyRBGqYQLZSx0 V2GmA7q+cOxV7kq3jI62gUFxXg9uJhaEy1z4BmBwCs9aY0zhAhiahMn6AMsjmOPCcM/kyZOthgev AL+X9VC3bhR3M06mcLm9KJtwIcz7//xndVaMHo+/SgG56MIL1VkkfvG1LQwETOFqedllovWVV1rT ByawcO3SRuiAMU2bUgcLBGud0Ys257HHxNgmTcSW6dOVTXCgnQb9+9/qrIQf589PuHAB1ny2BEV4 uhC54feyGLjTNZ4pXO+9806UdgamcMHNu9KtiRkPt3j5xff9996j7NTEVubi9LPhK1xDLSMfbO7G NGumrsbPtm3baJweEgEG+T2PBtVBFoa83uSlF14Q5xsLC6EsgOFqNvTxgiZYwoTLbDB4sfhadXiI nA6XqXAPXsCpljHgZ8kX+EqvXuqshAfuv5/C4TDxos0Pa9asWfSi2Q0+jpPlezDBcGXEj93BnxoB m4a6dukSFY9GDRuqqyXgw9fjgvTEem2+wmWyWU2BNY1tch96R92uhRz/+ArXT2qJAh2bO314EMPl MMyuDal6BCpzmUOqMAlWv44llXSOymqsXsD3GoEZcvwSSLggKAd+/plu2C7z1xRZHlqpVdl3FBSI FJnPutUY3UasupH/6qtWv2B3LIABVYhvskWbVyU8hUsHY7ttboKYWIQL7iu7EGX98Y/WxSpCIqmG 5gRTGNgcdqldBQErB7I/q777Ttl6M1wKIdwHBQOCbe4xLMgcZ5922mli/jPPiMUDBohRUvPivgy1 nBVgbTPz3uJViCe1ayfSZC0Ldvqatz9cc03x0E0pXDiGYRbKmt/Ujh3Fov79RU6PHlSRMeNHzTfS P3xAaCOcduedzlDQ7drEUwxxxcqN+dLPAqnJh6vK0YrBg5ULNXpb2qHilCL/58owWeNnNG5MzUTT O3Wif9gtMcrPcAuT+9//TXFGfOAuEUtWgmpzH36YPPQ0p54qdi8LNl3ZBHMUgqKP5LaBa3r5LRbh YlZLQccympn160fcy8KV++yzyqaYSVdfHRUGyqBemmv30qVi5VdfiW3Z2XQvjzMG/DGb4CXro9OZ I7L8CoHakJlJ9+o1bxYucxlI2M02GlhZkBikkW16wriWLa3xKw00ngsvAh6aJqNRI3LEIHI2d6YZ J7ON0oBlT3E/JsXYwDVduJICCtcINXJDX78OE3T0e1m4ePkhBssQmWG4CRfcQUvowM4ULv0lM7DT hQvnZg3cTbhMYMcTeRj4p4eLY31iETOtfXurn6WhZAC9+qLY4EF0hivVHcQkxzF+68ennyY/8PCb tbURMEQF9mbNE3ZmXE3hwgvRExbEI1wQVtMOa+bDztQYsCuNcOG+FKMWnkjhWj5oELn7QeuDxZxL 2I209CuWhqiRqJz/s/npf/9XXSkhX2qYrFatRPqZZ4rxzZuLHCkQXJtMJNA0KCcUvv46ZQterE1N FQWvvSZWyESzrcEDUJbAuDS03dnie8CybCg4YMxWYTCubauxJDtY/tlnNDJ3XVqasonENpcT5Vvb 6nrwg57riy+scxusz2GxQ/aqLyWvs+zTT8Wifv3UWeJwHeaMtqxh6gv1MxDIIktfV0jVhpoiMLrh oFq1JVHMfeghUsPwP6TqEdHGhfw8nnl3yMImyKqz7idMSNXD2r4FTYOC8I9GddwG8v9kWeZynR3k MiYo5PiGylnrR492Gvm8DNUypABxFudlRkj//JZ8Djl+iSjAYydBmwYLaiBwacY4o5CqiWvNcOnn n1P/n02AdAMNhhb3XYsWqTtDQlQZS98c0ws0WLptkGQjv29fEr6QqodTK0RhPZ7OaJ3CN96I0Goh VY+oITUoxE+/5x51OTj7N24U6XXrWgv2IVUPz7FaEBJMxZ9y001idVISdWVgwRDUIov69xcp1avT 0BDbvboJCiYJZGZkqLNIMHHCbTqTH5j80O0vf1FnlQNMULDNnrER1F0Q7r7zTmcJ9liJJf09BStR JihlJViY2PrJxx+rs8rBvTJX6KR2tfIjFCwXE5SyEqxjnVCwXExQYhUsfQuR/Px8MWP6dGe7EQaz sjGxVJ9cam49omO7Nn/+fPLbNo/xgAyPt0NBrVnfxB7n83NzaTYxJqrqmHHSwWiEnJwcWk8C26bY BGu9rMnPmzeP9qjZZFnLWAdpg61OsJUAtGQsgpWTnU1zHhGGm2AhzefOmUOz0JljWrAwmRKgDIUF P3hHEH3Dihf++U9a2EOfxIpJnjYBKiwsFKdpE1H79+tH/tWX7h+8/36awWy+ZExGxTR42CMePJt4 xIgRdIzp6488/DBNxE2W5VQGk2bZrQ7CQPyann++uL1DB1pchJ+TgUDi/rs6dqSyI9LFJnwDBgyg MBA2NrzlRVGCCBbiAbcXNG0qOsiiRG1LPADcwG2XTp3Exc2bk5tPP/nk2Bcsc0bxJvmFm3ZY10EX rI8/+ihiuxkGgsl7/ritP4E4QGgYCBYSdrqxBAHs/DAFCwuc2KblB8kKcZ+uLbFLjS3+QbLCq1q3 joobMNN/8FdfRa2TAXDvMS9Yg7TtqBkzQU3BAraE04Xhumuvtb5QvLDTtZV3IFg2d3jRWFrACzMO NkEAQQQL2uLFF15QZ8VpaUubIILlFg8z/RH/VatWqbMSsPzAMS9YRyyjK4MIFs4//OADdSbEkz17 UtbCIC43XHddxIZZbPR4QLCQ3ZmgbIR4QMCyJkxQtpHogoVFTmIRrGxZ9mnapAlpDMQH9/7z+efV 1eI0QBnPxE+wsM2dWzxs6W9LH+QYpRIsNKJicCBa6/Hv13EdFGiCz2T+bAMLmpmro7glgGlvEyyg vzAz60JYWCnHXNIH5gdtjDwEq602NcxktfyisbAchIg3UWN0wcJaXG7PYwoWLwL3k9obDFx+6aVR gmXDT7Ag4G7ZuE2wsH+jLY0CCxYaS7FVgxdzu3Wz3hsUbJRmLlnEQKOg8KkTr2DpCYgCvs4pssCL JYD88BMs5oJmzaIWQtMFy0tTmIJlcwetaQqWrdbpJ1i4xy0eNsFyI5BgYYaKDubSsaYaZXlhWO+h oH//mMdj5eXlRRW8GdiPGjVKnRXj9mCmvZtgPfvMM+Latm1pbaul8pl0UEU3E9JGUMFCpQLZq44u WADPuNkyscMULPM+YAoWlkDCMpwmQcpY8N9stgFmeuBDf+yRR9RZJL6CpW/Bz0S5MaZnxQNUPGps a9eupXOUVaBNbOo5XsECeJm2FwXwQmH0sso6FS/GTbCKiorUkSBhQRjpxrBvM9wkWdtE3OcYM5RN wUJaPPDAA+qsGFOwAPzvcNttEfG/8447fAXrqy++oHvRNqVj+9Dg7nVjHVbE31ewTDLOOy+Qu3i4 Vr4ovHAkMiLeTJsir2NqAMa0R/mlppGNMr+VH849Xbqos2iw+KweFwi5/qK6dO4srrj8cnVWAuKA e2AgGKZGBLb4o32N27hwL8JGW5YJ2qbYDf5RPn333XfV1RIQth5/fGB/f+45ddWd7du3R8QD/2ZW znD7GMf3dyed5C1YyOpM3IYwY3kjnc3TplG5bHYl6/wNKR88Bcu2voCbYO3UVD84LGtxfC2k6pE4 wTKGJmOfTb4WUvXwFCxkZSYozNvcmqxNS3O9FnL841t4nykLpzpLPvwwyo1NAGHH10OqHr6CBYM9 Z3XQTqVfx6otOhlNmkRcD6l6BBIsmPQGDdQtJWzPy1NHxWBWdJJlMZHSgIVJ5j/1lBgjw8UW4RvH jVNXjg1WfPutWKz1RVY1AglWktpQHiTXqCGyLrssYkmenMcecy3Uw8SKWzlu1dChykXlJgnFAGmq 8kxwT8HSV6vjpRBLY2KBy2bzHn1U2VRODrqsCzZCFhP0BeCqKq6CBQ2kY67+F4sJCq1ULN0vquTr bSGOboIVUoyrYOnZH4MpYD/Ilz+qTh3afRVLDs558EExuW1bWgkZSzra/AoKtBWm7AcFK9xgu2Gd jePHkz2WFmfSzz2X7Nh/ZLXmKnnQNKlqVCk+KrjFPbn/8z9kx/CqOvADBtPjGHx8vGgK3GW2aKGu lAB7LPc0/7nnnDjBHxMs3cjX4adtKUfEs/DNN8XKr7923OIf0CpA6jnwb1tNEKv5sRvcN/bii9WV +HEVrHjUOZZmpMSQ/iDSQYH7NJfOYhsUT0OwNmVlkf1+bdmASTfeKKZ37iwWS02Y/eSTFCe40cHz OgJVvbqY1rGj8wzMuKuuEpNuuIHsJrZuXbxM97XXqqsyPjVrilzp/+J33xWZDRsWx0/6qQM7DC9C OD9cd50YdXrxUuCmu+HyHDuv/TRwoJjetSu5MdMSdqPr1SP7WdJPLOEJu7EXXlj836yZGHfRRdZ7 x7dqRfaj6tYVyz//nJYLx3mitrHxLGNNueMO5ax04EXZVup1A2GOt3zlAH2RaM3XgfsggmWCrxdu lmhzDSFYsINW1oEdXrIO7IJkhcNl3OBWB+fmS+a13b1Yk5pKbvQOcJyb93GRRSdJfiimHc6Ry+hg TVLYYw/xeKmGh4RnbgZf7QyP3n8/ZsmsMigIz02wZtx1F13XwXksgrUjP1+sHDxY7MjLIzcLXnxR XSkWLPOFA7gzu7Zg5yVYa2Ttdc3w4VScgFsdnI+XWkRnQe/eZG+rRW6Uz7NqyBCxedIkcqO3GeLc HCiAbZbNMA/v2xdhhzIszndrI1AZ2GP92Xip5rdUUcYfSsY24WtZN2qUyO7eXUxt107MkOoTa43m 9OxJyx5t//FH5bJ0ILw0y6wPEI9gTb31VrKD4GDjAv6YFmjTxEiwVPlEB+6CChZrPfjDGyTA6OB8 u7H3M1Zahr0uWLxhghlnU7BGGlsqc9ZqotvNuv9+OrctAgP75EQIFh6GImgx+gL8GClqc2Ma21cf FLrf8nKBm2Ahu9ExBYs7w7NkeUYHdokULGT7sNfTDBUL2Ong3OywNwUL5Sqcb5AVEWbHggVkZwqW uVd3EMHKVuvqH9wavdw47NHIHS/0VmxNCeb4dvO6l0FBsDRMuOIKun9mp07KpgQ3wTJrhaZgLVXl BhPYxSNYOwsL1VkxuNcsT5ZWsIZZ7kukYO1ZtYrOsa+OCezRAB4vzluBh7rBuHYd87qXOeIydTwI eEHwI1kavbA+/fbbyV6Hd4vYpw0XNgULmwDgfN5f/0rnDOziEawJxna/9HEa2rq0gpVRv3jvH73J JJGCBThr1UEfcDw5jo4jWIVSehEQGySUDtcYvAx2zzJHkpYG3h3MZnSQ7XAC4Z+PYfQyFgsgBIeM cldaweLds+AP7gNY6ZDtOBycw+jg3E+wQKrKRRy/VJwTJVhH5cfP6UBhqONt2s5k8RCRj8ySBXF4 zsbWVLANi1U88IDIlIX60WedJabccgu1g5QFWI8Lib70o4/E3jXRm5MzB7ZsobajojfeEFvnzlW2 0Sz9+GPa+mS9ZVIsGhSxxZvJoV271FEksMc6Yahx6WzPzRWFfftSOUnXOAziasNmD2FdNGAAbWL1 s2WLlUMuy3balvN0C3f3kiWUxoloYtCJLKBIdq9Y4UgvG6h0vAxb6y3AC9k4YYIYKt2GhIAowWIK 5FenV5m9DNo93L7skKqJq2DpYBuUqZ07006gYxo3FhOvvlrkPv54VH9bSAgT1aVDBVJZcBwty1Ao 4FUmsGkjFbKNrJrjHRIScvzj210Ig+IWKolo+1w3cqS6texAJQiV1FQZNjdq+xl9r/CQkJDjF6cI n9msWbGCCKDEdAOlhx6moSecQNsIYJdkbJS7YcIEmuPz88yZZHC8cdIkseSTTwT21McuzbiHtgcP qJh0Q02HtWqJtcnJ6glCQkKqAtZ2h5+lgkmSigTjVm0KoyIMlCO2G1vxzTeVrtoaEhJSfgRqLMVo gYV9+oi00093bVNKlIHfGCGJ6l52z55RIy5CQkKqNtUwxmzPypXqNHYw8mPxwIHFE0Vk9TC1Rg2q ZkLxoKQWYaQ9RoeknHyySD3jDJHZqJHI79VLHNy+XfkWO+iynNCqFVUXQ0JCjn+c3kOUcNBrqM+c q6wsfPllUo56iQ+N9iEhIcc/nrNYMVV2QosWYkNGRsRY2vIEU61n33efM0jbFk8y1aurO0JCQo5n PJWWzUBxYAhzijxek5JCk1v16eCxwvdjmEN6vXo0JYp6Bo1wfY2sciYSbDeXNnKk+P6772gt9Fg4 fPiwGIEZ0tq2dGUNNrBMSkoSQ7//XiwyJnGERDJh/Hjxn2+/dd0orDQsXbKE1umvU7u2sqk8tL/l Flr3H5uRlRfY/wnp4bZnQDzErLSCGCg2KB6nbQsG7VnSzrO0FI9JsNLaf+CAs/FErMKN3fGw01yQ bXoSBfarxMYRMH+JYSmNqgYySHzAeK/mLjPxgF2b4Z/bpmoVCbZWwvP67YKTSJZIJQ75NzfzSwRl orQqwqRpOz0ngmNNaYVULKHSiiRUWgFMWoKLoaHSComFUGlFEiqtAAar3SWSslZaaFPBpqC6IL3a q5c4o3ZtEnyu6uEY7gb066dcRYMd7tGWwgZ7DCIOOg0bNCB/alSvTu11QcCWw/AL9+l7ZesUFhaK q668kqpc+t6JiDfSD3tC+oXXtWtXCuOVl1+m8527dlEVFwIPP/4g/TNJS0uj/bjhhsOFwTHSHenQ /+23lesS8Px6WvV+9VV1xZs3+valZ4RiwvPpz4lN4fmd4d+N9evX096TZ2nu2R/EG/60atlS5OTk qDv8wUasne++m9LJ9JPTAekEu0QpLbzP3pBVGaZbeuB5cB5UaT395JMUV3w37B+nCTaCnT9/vnJ5 HCmtUceY0ho2dCj5fbl8IY0aNqQX1eKii0hR6EBALmzWjK7DT9s+48wu+bHDHT4ubFpvgk1kEeal LsvBmmBLbfgHpWgyY8YMR2Fg41wb2HyWhbD5BRe4dthgl2TEq5P8+BBHpBsUoW3z2kOHDolTa9Ys Ti+fVcbh1g1+tr59Incn1sG7wIa3iBvilJKSoq5Eg63G4y5pyfTBR4rwsPW3F0O+/rokbS+8kDbw deOWm24iP+NVWpBrNK7DL4Q9MStLXYkGmRnSzEtpQbbxHuAf0i7PstIWZObG668nOUParli+/DhS WsZSZfFSXkoLBh+hHxc3b04Cig/cDWy/DjduSgvXUaKBm6+l0HvR57XXyN01bdoomxLWrFlDQgRB 81KizC0330zPCQVtg5UWwnvzjTeUrZ3lshoGd1Bu8eCntHJzc+n94TnRE+xHoqqHR48epdI20mPw 4MHKNhK8E8Tdtqu4jURUD2fPmuWkx/gAW6sFqR6eKkvxiNdL2jrnbkB2ETZMqLRcKC+lhZe6c+dO ZevOP/7+d3J/2SWXKJto/JQWQDUPLx45+rZt25RtJD8tXuwIqA1UsRCXfpbqlxt4TpjdlqUJWWmh ROkHPurG551H7vGc+LC9SlRu+CktvD+EgQ8+CPEoLTyTTsvLL6ew//n888qmBJQ+EW+kJRR4EOJV WijtcAmrxxNPKFtv/JRWv7fect6h+fxunCIzd9xz3CitdKlgEgmqBqy00H4SC/gwgyot1OOD0Kd3 b3Ifr9ICzz37LLlDHM0qG87R7uL18UEQERf4EYvBR21T0Ky02rpUM914/733qMQF5cph4HxA//5R z2XipbS2/vyzU03z84cJorTwcV4iq7QcVxjcg7BgcMzPgrBtSqv744/TtWbnn69s/IlXaUGWYlXI fkqLFbOeFkFNmSotjMnCTm1jmzalJWmwSxy2fDkkBfeo1jiL9XT3b9hAo9+Xff65GCmVBUbj2/x0 M4lWWuAc+ZKQsI8Yy8r7UZCfT4kLpecGK616Li/VJJFKC6DNCP49ZuxRioZgfDjTpk5VNtHguRCO W+N8rJRWaZlgUG+vXr2cD//5f/xDXYnGS2lt376d0hBx+jnA/l3AT2lNmzaNwsNHPHnSJGVrBw3P CNumtJ5+6im6hsb1oMSrtPZJWUK83UreNvyU1nVt21KcrpDKK1YSrrSwpE3+a68p7+0ckIKwq6iI NhHeu2qVsrWDFSbSZYlAH5SKYxq4etJJYuINN4g8WXVa/f336o7EAaGHoOFlBc1xQQ0ZR7yQdjfe qGyiqWilBfjjzlNbzX/yf/9H5+jJ8eIK9VE1lCYRJEppMdtltRf+IR3Mjg3Gr3qIEhH8QCdJEPyU Fkq1CO/jjz5SNu54KS2U4vHe4B8UYRAS0aaF54IfTRo1Ujbe+CmtDbKQgufA9VU+OsAkYUoLe+ba tlJgsGtB0oknuo6IhwJa9c03ynU0UBoVsW54AyloeFlowN64caOydYdfLl6GV1d/ZVBaaAuC8ONj w7PhGL18fuBdcPUJYcVLopUW95Iibm6ZjZ/SOiJlDR8d3DSTNQU//JQW2usQp6ZNmigbd7yUFsie N4/ihY/+/w0cqGzdSYTSQikW7xzholPIjyAN8d999x3FC2kWtH0OxK20oIQKfEpWhW+8EXhF1OxH HlF3VR5Gp6cLbt/CS8OLgBLDS8E/v0xcx7UPP/hA3elOZVBaIH/hQhJ++I22rFhAlzfmluFeCB7S CD1bGDfUpVMnqgJAEfK4JDdiUVr3dOnipDtKQ7fefLO4rX17Gi7CPaN4Hr8GYz+lxXwzZAi1OyJ+ 8Bdpe83VV9Pzwdx8002kkBA2p4MbT8kSbH2ZHnAH96ii39u1K41Jw3Ph+XE/vw83pQWgRDrefju5 ZZlElRHzDO/p3Fnc1bEjxRPX8Q7gXzxKi/nyiy+i0qMt0kOG2Rnp0a6duEAqeU4PL6UF9svCTOtW regZWHaRcd7RoYPoKtOkg3y3OId/SBt8N3EpLSisha+8ooK3g7Yr2z6dbibIEjNYqjlJulspq4SH PManlBXo5p8zZ47IGD1azJg+3bUKUtVAqWbBggXU25olFRrG1JQlBQUF1P0+JjOTcvbyYrl8rnFj x4oxY8aIebLUEzSDMEEVCemUIdNrrpSnIL3IfiA+8A/+YtKy38DeRIAezbEqPXKys8U+Y0O9WMEg XMQ/U77X3JycqGE1cSktbHaBRnUvNk+dSu1PtvttJlm63avtuWoD17GCKt9DbVxSMYaEhBz/xKe0 fu2/Aw4a22NRWnC7Q1ZZvMAa8diTV78Pm2OEhIQc/8SltFDa8drzGWBHezTA2+63GZS0DroMemT2 rlsXUdKCSZHnISEhxz9xN8QPP+EE5ZU7Y5o2td5rM2MC9EyMsJTckn/1K3U1JCTkeCZupQWD0hH2 NvQCq53a7tXNcOnGC5TAUqpXt96bHEB5hoSEHPskRGmxQXtUbvfuyutojh46JDIaNxbJWFJZDYHA aqZJNWtSNdKLYdK92xgvmKRQaYWEVAkSqrRgMP1mbrdutLVXacEmGgtffFGkSmUWdL348lBa+zdt EpPbt6deU694kSKWbub8+c/qzpBEsGXKFEpXTuckmYkd2LxZXQ2pKsSttFD6wdiqNcbyHYexH+Gl l1p7DukeGPlxo9ePz013sZgRZai05jz8cET8cIwhFmPl883v2VOs+PJLsVwa7OE46dprxehzz6Xt /jGfMiSSw/v2iUMeMydsHNqzh+awIu0xc2LCH/+oroRURUqttFCi2mUZeo8SljkcoTxMWSitXYsW UXsdhwEFu3XePHU1JFZGn302ZVQHA05CBkekktu3dq3Yu3q12B9gGlXI8U+plNYwDwWBql1WmzZU Eom39BSTSfBuPHtWrIioiqTVqROxMkW8YEYchotgoGyQPSUxyRxx8hvMi1Hp23JyxMohQ0TR22+L wrfeEj8NHCg2jh0beO/Kg1u30q7j5lxPjI9bk5REfi7q31+sHzVKXfFmxp/+VCIL8t9rjqrJjrw8 seo//xGLBgygcBe/+65Yn55Oc1mDcHDHDnoWrCRisk7GH2lUJP1dK58LCjIoB7dvp1VLln78McUL ZsW//+07xpDBu8D7N0vjeH94V0X9+lHc1o0cSW3BNnYWFIjF771XHPagQaTcS8OO/Hyx7NNPi9+r TGeE+Us5jKQvLdViGfjJxktpJZr9W7aIWffcI1JOPZWqBm6KcE63buqOxDBUPiP7jdKWm+CUlmSp ZB3/fXpNN2VlOW1oGJ+2f/16dSUSrJPPfiKddMP2MNhb0k1xoETDY+CQ3lBcqeecE3G/aeB/nmX+ 6cpvvqHMy3aPbrARrzmxWU9/r2dBqR4lYjc4fAyERmfP2GbNovwwzViPSc0bMjOdJg8zXrq/OM6Q 6eZGTvfujtuJsrq7SCpjr3ZSvAsoF7w39LLb3LBBrcMrg8I7zWzePCq+umH7JFkt321Z9roiqRbL vEDdjG7cWHlR/iyVOZoe71R5vG3uXHU1MeBlsf8ZAVY+iJWyUFpHfJY+zn3mGUcgkX47i4rUlRJ0 pUUCLMPFh79ClnZ0xYKjKe3aOc8AM//pp4svWsDgX3In/fQbPAz8cvr5zz3nhAsl4lby0pUmngdu F770UlQpsuidd5w0hrvZDz2krkTjtdoISsJY8YTDzHRRgLrSonSWZvrdd6urxSANsqRC48UG2B2U /E6pwHQ2T5kS0X78vYtMYSUVXTlOuOyyqAwDoMQ4XHtnIwMuVlkeVBvXooXzALEaJCA+7rXluJOy znRZAkMcRgZYYz1W9BebJV9sLEy77TaRIgULH6pbqbQslFYQ0uvWJX+Qbvl9+yrbEnSlRe5k6c0m 1Iwj2NIMlwrCjQilFUOblhdDtRJHRv3oHXuArrQwVtCLmffe67gNMkXNja0yA+VwkTmg19kkQmnJ d7v0ww/VlWj0GSVJ0q0bK7/+OsJPE5qzq5USCyzv3wQ7ybOfWVdcoWwrlmo7CwuLP3wtcqU1KJZi BYYgOWmiyJO55trUVHWWODDCnp8r88ILlW0wZtx1l3Ov21CMslJaKHFAUUJp4r3aDIe7QKadSURJ S4a5wmVzBQZVd/YPVTU3SqO00N4zonp1eheIi9ezpNWooe6KRFdaaBvzIr9PH8dPPItXFSunRw9K ZygBM07sBwxKP0hTkwilJY0Xo2Qph93aMhodDhv/JjmyJMzX0V6LdlI/pt16qxM2esQrA/S1TL7+ eidiQQ09fK1a1GhoY/7zz9PLQE7DH1wsBv6TwJ1yCjVIljcQSI4LnsFv8KtORSitH2V1id0g7eCu sH9/sXroULEhI0OsGTaMhmXoH0AQpbXJZ0nl2VrpJFFKq+D11yNkBiW4vJdfFlidlp5l+HAaZqJn LH5KC2my06PtCxT4KC20a6JtlcOEQspq2ZLisi4tjTomUNrByr1cVQuitNJ81jEbdfrpjls0lnvB 8ce/yaz773f8wfMd3hO5N6aN7Ecfde6pLFPlnK9lbKNGTuT8zAgpCG4sGzTISbhEGgjxss8+U6GU PXuWLXM+XhgoLvSeBSGI0kqSHxm7QTXHCz+lhaEn7Bc+Eq9OA5SE2W2FKC1pbNUlZrMMD43OcId4 2NrdGH1qWHkorf9o4SXJEqAbOxYscMKtTEoLPYMsR3gfuwOsQTZeVgk57GGVTWmBybff7qtw8LB7 PNZ0Rs+U7b54DeKV8+yzKpTyAV3SEeO0ZBymeKz7zgRRWttycyP8RQnCDT+ltS4lxfEL172oKKWl b1SCkokbq7/7LuJZvbrxy1tp6W1oqAG4UVmVFkg97TTHL7/FDlZ88YVTYsQ7wVCIykBUFr9p4sSI XDHKyMSY++CDynU0OwoKYlqKxtfI8FCXrshu1x+ffjpCecHgJUKwR9WpQ1UE9PJkNmhQPMVHc+s1 vajQqAbhXlTV8159lT6gAvmPdogxTZo4wmhTWvi4Rp5xhuMP/JzYpg2NpUJvEQzGN6HKPlIr4ZWn 0tI/FnwIk6XyXyifb2GvXmKKzCz1qkp6/fqOWzw3dnPCWKpVQ4aIVd9+S+POfnzhBSeeMOWhtPZt 2EAlbg4T30mufD/LPvmEqq4YK1XQu7eY0bmz814rm9ICmMmB74rdZciMDP5S9Vam8VxZJcTzc5hI 582TJ6u7Kx7Xesk0jI3SPijToH57YMsW5drOuAsuoHYtnqpj84cNruNF4yWjx2rGncE2ySxvIJSp UlFRA6yMLxnEXcUfBtcoh6pXT+R5rPPNFEnllSJzbi8/8fHhw8cyPwc9Bpimn3deoLil1a0rit5/ X91VAnZHQs8Z3j3cIhPzgsbQSbcw6En0AiuBpMoPMCJ+CEc+m5kpUXd/69bF8sBuXZ4FCjtLfog2 kBHws3hVNQEa4slP6Z7GjhlKi0H7FZ7DiRvHS4sbvS9kEFIh2KZz5fztb3Q/wkLbsBdptWs7z1D4 5pvK1g7HH/9+LHr7bYpj1HPgfhn/DJlZeslaReHdmCLBPoT6yHDT4AGxxRcaessCjPrFdmMhISEh wFdp6WQ//jjlwtDGNgXGBtdRdMYgO1TtRstcfdI114hZ990nsp94gorUOU8+KeY89JCYesstYows HWC8F3I3tH3A5PbsqUINCQkJKSEmpWWChuoxDRqQcqIipipe2hSZbtgd7sG9Y2U18oBHj1JISEhI MUL8f+uJdEhk6+3rAAAAAElFTkSuQmCC28213427823530Microsoft Office Word019655falseTítulo1false27753falsefalse15.0000AchastyCP92022-04-01T14:32:00Z2022-01-07T13:26:00Z2022-04-01T14:36:00Z