Diversidade genética em maracujazeiro-amarelo utilizando marcadores moleculares fAFLP

Marcadores moleculares fAFLP foram utilizados para estimar a diversidade genética entre 36 acessos de maracujá-amarelo (Passiflora edulis f. flavicarpa Deg.) coletados em 18 estados do Brasil. Os resultados obtidos permitiram concluir que os marcadores fAFLP se mostraram consistentes na avaliação da variabilidade genética, detectando e quantificando a ampla divergência genética entre os 36 acessos analisados, bem como a não-formação de estruturação geográfica. Tais resultados podem auxiliar na definição de estratégias mais eficientes a serem utilizadas em programas de melhoramento de maracujá-amarelo.

Caracterização molecular e diversidade genética de diferentes variedades de abacate por marcadores microssatélites

Pertencente à família Lauraceae, o abacateiro compreende três raças hortícolas: antilhana, guatemalense e mexicana. Os marcadores moleculares são uma ferramenta rápida e eficaz para estudos genômicos, uma vez que detectam o polimorfismo diretamente ao nível do DNA e não sofrem qualquer tipo de influência ambiental. Com base nesse polimorfismo, é possível fazer inferências sobre as relações entre o genótipo e o fenótipo dos indivíduos, o que, em última análise, permite aumentar a eficiência dos programas de melhoramento. Diante o exposto, o objetivo foi investigar a diversidade genética entre sete variedades de abacate a partir de 5 lócus de marcadores moleculares microssatélites (SSR). Nas amostras de abacateiros avaliadas, encontrou-se um total de 18 alelos, com uma média de 3,6 alelos por lócus. O dendrograma gerado a partir de análise de agrupamento UPGMA agrupou, separadamente do resto dos genótipos, a cultivar Geada da raça Antilhana, possivelmente por esta variedade ser uma raça pura, e o restante foi agrupado em dois grandes grupos das raças, a Guatemalense e a Mexicana. Os genótipos das sete variedades de abacate apresentam diversidade genética nos cinco lócus de marcadores moleculares microssatélites (SSR) avaliados, o que indica que são materiais promissores para utilização em futuros programas de melhoramento.

Diversidade genética entre progênies e matrizes de rambutan

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A formação de conceitos no ensino de graduação em enfermagem à luz da teoria da aprendizagem significativa

The Theory of Meaningful Learning (TML) described by David Paul Ausubel offers a proposal for the teaching strategies to provide a more active and effective student learning. The projection of the TML practice is demonstrated through the development of concept maps (CM) technique, created by Joseph Donald Novak, which presents as a strategy, method or schematic feature, which is an indicator to identify the cognitive organization of the knowledge acquired by students. The survey was conducted in the light of TML in relation to learning concepts involving students of undergraduate nursing in a public university in the state of Rio Grande do Norte. Thus, the study aimed to compare the concept learning of students of undergraduate nursing, when subjected to different forms of education, to point approaches that promote more effective and meaningful results. It was a quasi - experimental study with a qualitative analysis, conducted with students of the Undergraduate Nursing of the Universidade Federal do Rio Grande do Norte (UFRN), approved by the Research Ethics Committee/UFRN Certification of Presention for Ethics Appreciation (CPEA) in 11706412.3.0000.5537. The study took place at two different times and involved content on complications mediate postoperative surgical wound in the same discipline with students who attended the 5th semester of the degree course in Nursing. For the viability of data collection, in the second half of 2013, we used the technique of CM, to represent the concept of complications mediate postoperative surgical wound covered in the classroom. CM were built at a different time from that of the discipline, with the support of tutors and preceded by a brief description and explanation about the form of preparation and application. In this study were subjected, 31 students of undergraduate nursing, registered in the discipline of Integral Attention to health I. In the first stage, 18 students participated in the survey, they had the teaching intervention based on TML, and in the second stage, all students participated in the lesson provided curriculum with the responsible teacher of the subject, on the same issue occurred. At the end of each meeting, the students 11 developed concept maps with the aid of software Cmap Tools®. Data analysis happened upon the technique of content analysis, supported by a conceptual map "glass", previously developed by researchers and aid in the preparation of the categories in which the concepts found were classified. The study found that the teaching intervention based on TML with the help of CM, managed to develop in students a more expressive teaching learning process than just classroom curriculum with the traditional teaching method, and also that the association between the intervention motion teaching with the traditional method and the use of the technique of CM encourages the student the ability to articulate the various acquired knowledge as well as apply them in real situations

Relações filogenéticas e diversidade de isolados de Guignardia spp oriundos de diferentes hospedeiros nas regiões ITS1-5,8S-ITS2

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Construção e implementação do Balanced Scorecard em uma organização educacional: o caso do Serviço Nacional de Aprendizagem Comercial do Rio Grande do Norte SENAC RN

The changes that have taken place in the organizational environment in recent decades have led to new performance measurement systems being proposed, given the inadequacy of traditional models. The Balanced Scorecard (BSC) emerged as an instrument to translate financial and non-financial assets into real values for all interested parties in the organization, allowing the introduction of strategies to achieve the desired goals. Research shows that most errors committed with the use of this method are related to the implementation process. Thus, the aim of this dissertation is to analyze the process of building and implementing the BSC in an organization. This empirical exploratory study is based on the classic case study method, which enables the researcher to work with a set of evidence, including direct observation, interviews and document analysis. The results show that the use of BSC in the company investigated posed problems during the process of building and implementing the method. These problems were caused mainly by the lack of involvement on the part of upper management and the team s scant knowledge of Balanced Scorecard. One of the gains obtained from adopting the system was the introduction and/or consolidation of a culture of strategic planning and participative management. The continuous implementation phase was highlighted in the monitoring program, created by the organization in an attempt to reverse existing problems, using the BSC as a third generation strategic management system, which led to significant gains, better use of the system and stronger management practices

Gestão ambiental na indústria do petróleo :um estudo sobre as práticas de gestão na perspectiva da aprendizagem organizacional

This work discusses the environmental management thematic, on the basis of ISO 14001 standard and learning organization. This study is carried through an exploratory survey in a company of fuel transport, located in Natal/RN. The objective of this research was to investigate the practices of environmental management, carried through in the context of an implemented ISO 14001 environmental management system, in the researched organization, from the perspective of the learning organization. The methodology used in this work is supported in the quantitative method, combining the exploratory and descriptive types, and uses the technique of questionnaires, having as scope of the research, the managers, employee controlling, coordinators, supervisors and - proper and contracted - of the company. To carry through the analysis of the data of this research, it was used software Excel and Statistical version 6.0. The analysis of the data is divided in two parts: descriptive analysis and analysis of groupings (clusters). The results point, on the basis of the studied theory, as well as in the results of the research, that the implemented ISO 14001 environmental system in the searched organization presents elements that promote learning organization. From the results, it can be concluded that the company uses external information in the decision taking on environmental problems; that the employees are mobilized to generate ideas and to collect n environmental information and that the company has carried through partnerships in the activities of the environmental area with other companies. All these item cited can contribute for the generation of knowledge of the organization. It can also be concluded that the company has evaluated environmental errors occurrences in the past, as well as carried through environmental benchmarking. These practical can be considered as good ways of the company to acquire knowledge. The results also show that the employees have not found difficulties in the accomplishment of the tasks when the manager of its sector is not present. This result can demonstrate that the company has a good diffusion of knowledge

Algoritmo Q-learning como estratégia de exploração e/ou explotação para metaheurísticas GRASP e algoritmo genético

Techniques of optimization known as metaheuristics have achieved success in the resolution of many problems classified as NP-Hard. These methods use non deterministic approaches that reach very good solutions which, however, don t guarantee the determination of the global optimum. Beyond the inherent difficulties related to the complexity that characterizes the optimization problems, the metaheuristics still face the dilemma of xploration/exploitation, which consists of choosing between a greedy search and a wider exploration of the solution space. A way to guide such algorithms during the searching of better solutions is supplying them with more knowledge of the problem through the use of a intelligent agent, able to recognize promising regions and also identify when they should diversify the direction of the search. This way, this work proposes the use of Reinforcement Learning technique - Q-learning Algorithm - as exploration/exploitation strategy for the metaheuristics GRASP (Greedy Randomized Adaptive Search Procedure) and Genetic Algorithm. The GRASP metaheuristic uses Q-learning instead of the traditional greedy-random algorithm in the construction phase. This replacement has the purpose of improving the quality of the initial solutions that are used in the local search phase of the GRASP, and also provides for the metaheuristic an adaptive memory mechanism that allows the reuse of good previous decisions and also avoids the repetition of bad decisions. In the Genetic Algorithm, the Q-learning algorithm was used to generate an initial population of high fitness, and after a determined number of generations, where the rate of diversity of the population is less than a certain limit L, it also was applied to supply one of the parents to be used in the genetic crossover operator. Another significant change in the hybrid genetic algorithm is the proposal of a mutually interactive cooperation process between the genetic operators and the Q-learning algorithm. In this interactive/cooperative process, the Q-learning algorithm receives an additional update in the matrix of Q-values based on the current best solution of the Genetic Algorithm. The computational experiments presented in this thesis compares the results obtained with the implementation of traditional versions of GRASP metaheuristic and Genetic Algorithm, with those obtained using the proposed hybrid methods. Both algorithms had been applied successfully to the symmetrical Traveling Salesman Problem, which was modeled as a Markov decision process

Mapa de Conteúdos e Mapa de Dependências:Ferramentas Pedagógicas para uma Metodologia de Planejamento baseada em Objetivos Educacionais e sua Implementação em um Ambiente Virtual de Aprendizagem

Furthered mainly by new technologies, the expansion of distance education has created a demand for tools and methodologies to enhance teaching techniques based on proven pedagogical theories. Such methodologies must also be applied in the so-called Virtual Learning Environments. The aim of this work is to present a planning methodology based on known pedagogical theories which contributes to the incorporation of assessment in the process of teaching and learning. With this in mind, the pertinent literature was reviewed in order to identify the key pedagogical concepts needed to the definition of this methodology and a descriptive approach was used to establish current relations between this conceptual framework and distance education. As a result of this procedure, the Contents Map and the Dependence Map were specified and implemented, two teaching tools that promote the planning of a course by taking into account assessment still in this early stage. Inserted on Moodle, the developed tools were tested in a course of distance learning for practical observation of the involved concepts. It could be verified that the methodology proposed by the above-mentioned tools is in fact helpful in course planning and in strengthening educational assessment, placing the student as central element in the process of teaching and learning

Uma implementação paralela híbrida para o problema do caixeiro viajante usando algoritmos genéticos, GRASP e aprendizagem por reforço

The metaheuristics techiniques are known to solve optimization problems classified as NP-complete and are successful in obtaining good quality solutions. They use non-deterministic approaches to generate solutions that are close to the optimal, without the guarantee of finding the global optimum. Motivated by the difficulties in the resolution of these problems, this work proposes the development of parallel hybrid methods using the reinforcement learning, the metaheuristics GRASP and Genetic Algorithms. With the use of these techniques, we aim to contribute to improved efficiency in obtaining efficient solutions. In this case, instead of using the Q-learning algorithm by reinforcement learning, just as a technique for generating the initial solutions of metaheuristics, we use it in a cooperative and competitive approach with the Genetic Algorithm and GRASP, in an parallel implementation. In this context, was possible to verify that the implementations in this study showed satisfactory results, in both strategies, that is, in cooperation and competition between them and the cooperation and competition between groups. In some instances were found the global optimum, in others theses implementations reach close to it. In this sense was an analyze of the performance for this proposed approach was done and it shows a good performance on the requeriments that prove the efficiency and speedup (gain in speed with the parallel processing) of the implementations performed

Algoritmos genéticos e processamento paralelo aplicados à definição e treinamento de redes neurais perceptron de múltiplas camadas

ln this work, it was deveIoped a parallel cooperative genetic algorithm with different evolution behaviors to train and to define architectures for MuItiIayer Perceptron neural networks. MuItiIayer Perceptron neural networks are very powerful tools and had their use extended vastIy due to their abiIity of providing great resuIts to a broad range of appIications. The combination of genetic algorithms and parallel processing can be very powerful when applied to the Iearning process of the neural network, as well as to the definition of its architecture since this procedure can be very slow, usually requiring a lot of computational time. AIso, research work combining and appIying evolutionary computation into the design of neural networks is very useful since most of the Iearning algorithms deveIoped to train neural networks only adjust their synaptic weights, not considering the design of the networks architecture. Furthermore, the use of cooperation in the genetic algorithm allows the interaction of different populations, avoiding local minima and helping in the search of a promising solution, acceIerating the evolutionary process. Finally, individuaIs and evolution behavior can be exclusive on each copy of the genetic algorithm running in each task enhancing the diversity of populations

Uma contribuição à solução do problema dos k-servos usando aprendizagem por reforço

Neste trabalho é proposto um novo algoritmo online para o resolver o Problema dos k-Servos (PKS). O desempenho desta solução é comparado com o de outros algoritmos existentes na literatura, a saber, os algoritmos Harmonic e Work Function, que mostraram ser competitivos, tornando-os parâmetros de comparação significativos. Um algoritmo que apresente desempenho eficiente em relação aos mesmos tende a ser competitivo também, devendo, obviamente, se provar o referido fato. Tal prova, entretanto, foge aos objetivos do presente trabalho. O algoritmo apresentado para a solução do PKS é baseado em técnicas de aprendizagem por reforço. Para tanto, o problema foi modelado como um processo de decisão em múltiplas etapas, ao qual é aplicado o algoritmo Q-Learning, um dos métodos de solução mais populares para o estabelecimento de políticas ótimas neste tipo de problema de decisão. Entretanto, deve-se observar que a dimensão da estrutura de armazenamento utilizada pela aprendizagem por reforço para se obter a política ótima cresce em função do número de estados e de ações, que por sua vez é proporcional ao número n de nós e k de servos. Ao se analisar esse crescimento (matematicamente, ) percebe-se que o mesmo ocorre de maneira exponencial, limitando a aplicação do método a problemas de menor porte, onde o número de nós e de servos é reduzido. Este problema, denominado maldição da dimensionalidade, foi introduzido por Belmann e implica na impossibilidade de execução de um algoritmo para certas instâncias de um problema pelo esgotamento de recursos computacionais para obtenção de sua saída. De modo a evitar que a solução proposta, baseada exclusivamente na aprendizagem por reforço, seja restrita a aplicações de menor porte, propõe-se uma solução alternativa para problemas mais realistas, que envolvam um número maior de nós e de servos. Esta solução alternativa é hierarquizada e utiliza dois métodos de solução do PKS: a aprendizagem por reforço, aplicada a um número reduzido de nós obtidos a partir de um processo de agregação, e um método guloso, aplicado aos subconjuntos de nós resultantes do processo de agregação, onde o critério de escolha do agendamento dos servos é baseado na menor distância ao local de demanda

Classificação de padrões através de um comitê de máquinas aprimorado por aprendizagem por reforço

Reinforcement learning is a machine learning technique that, although finding a large number of applications, maybe is yet to reach its full potential. One of the inadequately tested possibilities is the use of reinforcement learning in combination with other methods for the solution of pattern classification problems. It is well documented in the literature the problems that support vector machine ensembles face in terms of generalization capacity. Algorithms such as Adaboost do not deal appropriately with the imbalances that arise in those situations. Several alternatives have been proposed, with varying degrees of success. This dissertation presents a new approach to building committees of support vector machines. The presented algorithm combines Adaboost algorithm with a layer of reinforcement learning to adjust committee parameters in order to avoid that imbalances on the committee components affect the generalization performance of the final hypothesis. Comparisons were made with ensembles using and not using the reinforcement learning layer, testing benchmark data sets widely known in area of pattern classification

Roteamento em Redes de Sensores Sem Fios Com Base Em Aprendizagem Por Reforço

The use of wireless sensor and actuator networks in industry has been increasing past few years, bringing multiple benefits compared to wired systems, like network flexibility and manageability. Such networks consists of a possibly large number of small and autonomous sensor and actuator devices with wireless communication capabilities. The data collected by sensors are sent directly or through intermediary nodes along the network to a base station called sink node. The data routing in this environment is an essential matter since it is strictly bounded to the energy efficiency, thus the network lifetime. This work investigates the application of a routing technique based on Reinforcement Learning s Q-Learning algorithm to a wireless sensor network by using an NS-2 simulated environment. Several metrics like energy consumption, data packet delivery rates and delays are used to validate de proposal comparing it with another solutions existing in the literature

Estudo Comparativo de Métricas de Pontuação para Aprendizagem Estrutural de Redes Bayesianas

Bayesian networks are powerful tools as they represent probability distributions as graphs. They work with uncertainties of real systems. Since last decade there is a special interest in learning network structures from data. However learning the best network structure is a NP-Hard problem, so many heuristics algorithms to generate network structures from data were created. Many of these algorithms use score metrics to generate the network model. This thesis compare three of most used score metrics. The K-2 algorithm and two pattern benchmarks, ASIA and ALARM, were used to carry out the comparison. Results show that score metrics with hyperparameters that strength the tendency to select simpler network structures are better than score metrics with weaker tendency to select simpler network structures for both metrics (Heckerman-Geiger and modified MDL). Heckerman-Geiger Bayesian score metric works better than MDL with large datasets and MDL works better than Heckerman-Geiger with small datasets. The modified MDL gives similar results to Heckerman-Geiger for large datasets and close results to MDL for small datasets with stronger tendency to select simpler network structures