Submitted by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2018-05-29T14:38:33Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) VersaoFinal.pdf: 2882901 bytes, checksum: a35c6d27fe0f9aa61dfe3a96244b3140 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2018-05-29T14:38:46Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) VersaoFinal.pdf: 2882901 bytes, checksum: a35c6d27fe0f9aa61dfe3a96244b3140 (MD5) / Made available in DSpace on 2018-05-29T14:38:46Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) VersaoFinal.pdf: 2882901 bytes, checksum: a35c6d27fe0f9aa61dfe3a96244b3140 (MD5) Previous issue date: 2018-03-12 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The traditional estimation of mixture regression models is based on the assumption of normality of component errors and thus is sensitive to outliers, heavy-tailed and/or asymmetric errors. Another drawback is that, in general, the analysis is restricted to directly observed predictors. We present a proposal to deal with these issues simultaneously in the context of mixture regression by extending the classic normal model by assuming that, for each mixture component, the random errors and the covariates jointly follow a scale mixture of skew-normal distributions. It is also assumed that the covariates are observed with error. An MCMC-type algorithm to perform Bayesian inference is developed and, in order to show the efficacy of the proposed methods, simulated and real data sets are analyzed. / A estimação tradicional em mistura de modelos de regressão é baseada na suposição de normalidade para os erros aleatórios, sendo assim, sensível a outliers, caudas pesadas e erros assimétricos. Outra desvantagem é que, em geral, a análise é restrita a preditores que são observados diretamente. Apresentamos uma proposta para lidar com estas questões simultaneamente no contexto de mistura de regressões estendendo o modelo normal clássico. Assumimos que, conjuntamente e em cada componente da mistura, os erros aleatórios e as covariáveis seguem uma mistura de escala da distribuição normal assimétrica. Além disso, é feita a suposição de que as covariáveis são observadas com erro aditivo. Um algorítmo do tipo MCMC foi desenvolvido para realizar inferência Bayesiana. A eficácia do modelo proposto é verificada via análises de dados simulados e reais.

O fator de simultaneidade é fundamental no planejamento de redes de distribuição de gás natural. Trata-se de um multiplicador entre 0 e 1 que ajusta o consumo total teórico de um número de aparelhos de utilização em condições reais. Em 2005 o Instituto de Pesquisas Tecnológicas (IPT) e a Companhia de Gás de São Paulo (COMGÁS) realizaram um estudo no qual determinou-se o fator de simultaneidade em um conjunto de edificações residenciais. Um modelo de regressão foi proposto para expressar o fator de simultaneidade em termos da potência total instalada. O modelo ajustado pode ser utilizado para predizer o fator de simultaneidade em novas edificações. O modelo em questão é um modelo de regressão linear normal no qual a variável resposta é o logaritmo do fator de simultaneidade. Nesta dissertação, o objetivo é investigar outras possibilidades de modelos de regressão adequados aos dados obtidos pelo IPT e pela COMGÁS. Especial atenção é dada ao modelo de regressão beta proposto por Ferrari e Cribari-Neto (Journal of Applied Statistics, 2004) por possuir vantagens sobre o modelo de regressão linear normal. O modelo de regressão beta assume que, dadas as covariáveis, a variável resposta possui distribuição beta, sendo adequado para modelar dados observados no intervalo unitário. Desta forma, a transformação na variável resposta - o fator de simultaneidade - é desnecessária. Além disso, é proposta uma nova abordagem para a predição do fator de simultaneidade, diferente de todas as abordagens pesquisadas na literatura, utilizando a técnica de bootstrap. / The simultaneity factor is fundamental in planning gas distribution networks. It is a multiplicator between 0 and 1 that adjusts the theoretical total consumption of a number of devices to realistic conditions. In 2005, the Instituto de Pesquisas Tecnológicas (IPT) and the Companhia de Gás de São Paulo (COMGÁS) performed a study in which the simultaneity factor of gas consumption in a set of residential buildings have been determined. A regression model was proposed to express the simultaneity factor in terms of the total power of installed equipment. The fitted model can be used to predict the simultaneity factor in new buildings. The model they proposed is a normal linear regression model in which the response variable is the logarithm of the simultaneity factor. In the present dissertation, our aim is to investigate other possible regression models suitable to the data obtained by IPT and CONGÁS. Emphasis is given to the beta regression model proposed by Ferrari and Cribari-Neto (Journal of Applied Statistics, 2004) which has a number of advantages over normal linear regression models. The beta regression model assumes that, given the covariates, the response variable has a beta distribution, which is adequate to model data observed in the unit interval. Therefore, no transformation in the response variable, the simultaneity factor, is needed. Additionally, we present a new approach for the prediction of the simultaneity factor, that is different from all the approaches shown in the literature, using the bootstrap technique.

A Euterpe edulis (palmeira juçara) é uma espécie de alta densidade na Floresta Atlântica e sofre processo de extinção devido ao desmatamento e ao corte ilegal para a extração do palmito. Uma alternativa para conservação é o manejo sustentável, utilizando seus frutos para a produção de polpa. O objetivo do trabalho é fornecer subsídios de mensuração para um sistema de manejo, visando à produção de frutos e polpa, através de modelos preditivos e, avaliação da estrutura, dinâmica e a regeneração natural das palmeiras. O levantamento foi realizado no Sertão do Ubatumirim, Ubatuba-SP, em uma área de bananal e uma de floresta secundária, durante 2011 e 2012. Para a construção dos modelos, foram selecionadas matrizes produtoras acompanhadas durante o período de frutificação. Foram coletadas medidas dendrométricas e os frutos maduros foram colhidos, pesados, despolpados e analisada sua massa seca. Foi avaliada qualitativamente a luminosidade recebida pelas matrizes. Foram ajustados e selecionados modelos para a predição da biomassa dos frutos e polpa seca. As variáveis preditoras foram: DAP, altura, e as indicadoras: área, ano e luz, com efeito simples e de interação. As variáveis resposta foram modeladas por regressão linear, com e sem transformação logarítmica. Os critérios de seleção dos modelos foram o coeficiente de determinação ajustado e o valor do Critério de Informação de Akaike (AIC). Para o levantamento das populações, em cada área foram locadas dez parcelas circulares, com 14m de raio, sendo mensuradas e identificadas as espécies arbóreas com DAP >= 5cm. Dentro das parcelas instituiu-se subparcelas para a amostragem da regeneração natural da juçara; com raio de 4,5m para os estádios de desenvolvimento das varas e arvoretas e raio de 3m para plântulas e mudas. Foi estimada a taxa de mortalidade de toda população e das palmeiras adultas. De cada estádio estimou-se os indivíduos por hectare e taxa de mudança anual. A regeneração encontrada foi comparada com uma estrutura padrão, sem ações antrópicas. Foi estimada a biomassa de frutos e de polpa da juçara nas áreas estudadas através de modelos lineares. Os modelos com transformação logarítmica apresentaram bons ajustes sendo os mais adequados os que utilizaram a combinação da variável indicadora altura ou variável combinada (diâmetro ao quadrado vezes altura). Os resultados mostraram efeito de interação da variável combinada e variável ano, o que indicou efeito nulo no ano de alta produtividade. Independente do tamanho das palmeiras, a produção foi igual, e efeito positivo crescente no ano de baixa produtividade. Os levantamentos mostraram que a área de formação secundária possui maior riqueza de espécies que a do bananal, com uma área basal duas vezes maior e menor taxa de mortalidade. Em ambas as áreas, os estádios plântulas e regeneração não se assemelham à população padrão, resultados estes mais drásticos na área do bananal. Isto pode levar à redução da população, com risco de desaparecimento. Na estimativa produtiva dos frutos por matriz, verificou-se o aumento entre os anos de produção. Quando se considera a produção por hectare, ocorre diminuição da produção na área do bananal devido a elevada mortalidade. / Euterpe edulis (juçara palm) occurs in hight density populations in the Atlantic Forest, but due the illegal exploration of its heart palm is a extinction threaten species. An alternative for its conservation is the sustainable management of its populations for fruit production. This study aims to provide measurement and biometric information for the development of a sustainable management system for fruit harvest. Prediction models for fruit and fruit pulp production were developed and the structure and natural regeneration of two populations were studied. The study was carry out in \"Sertão do Ubatumirim\", Ubatuba, in the state of São Paulo, where a banana plantation and a secondary forest were surveyed in the years 2011 and 2012. In order to develop the fruit prediction models, producing fruit palm trees were monitored throughout the period of fructification. Height and diameter measurements were taken in these trees and the ripe fruits were harvested, weighted, pulped and the fruit pulp dry weight was determined. The light intensity received by each tree was qualitatively determined by a ordinal scale with 5 levels. Prediction models for fruit biomass and fruit pulp dry weight were fitted by linear regression. Prediction quantitative variables were diameter (DBH) and height, while prediction qualitative variables, add to the model as indicator variables, were: forest type (banana plantation and secondary forest), harvest year (2011 and 2012) and light intensity. The criteria for selection of the models were adjusted coefficient of determination and the value of the Akaike Information Criteria (AIC). For the population surveys, circular plots of 14m radius were located in the study area, and all trees with DBH equal or greater than 5cm were measured and had its species identified. For the survey of juçara natural regeneration, circular subplots of 4.5m radius, concentric to adult tree plots, were established to enumerate small trees and saplings, and circular subplots of 3m radius were established to enumerate seedlings and small seedlings. Mortality rate was estimated of the entire population and adult juçara palms. For each plant development stage (tree, small tree, sapling, seedling and small seedling), the number of individuals and the annual change rate were also estimated. Best prediction models for individual palm tree fruit and fruit pulp production were logarithmic models, and prediction variable was tree height, followed by the combined variable (squared diameter times height). There was a clear interaction effect of the quantitative prediction variable (combined variable) and the qualitative prediction variable year, as indicator variable. In the hight production year, the quantitative prediction variable was not relevant for fruit and fruit pulp prediction, while in the low production year, there was a positive relationship between production and the quantitative prediction variable. Study sites were very different in forest structure and dynamics. As expected, the secondary forest site had higher species richness than the banana plantation site, as well lower mortality rate and twice its basal area. Compared to what is expected to sustainable juçara populations without human influence, both sites showed lower number of regenerating individuals (seedlings and small seedlings). Over the years, this fact, combined to the harvest of fruits for fruit pulp production, can represent risk to the sustainability of juçara populations in the study sites.

The financial crisisand the European debt crisis wreaked havoc on many European economies and stock markets. Previous studies have shown that crises are associated with high debt and linked with lower growth. Studies also suggest that politicians underestimate the risks associated with high debt during economic upturn and that economic crises are usually connected with high volatility. Volatility is used as a measurement of risk since high volatility indicates larger market uncertainty of the valuation of the underlying asset. Previous studies have shown that volatility can be a good indication of a firm’s riskiness. As volatility and capital structure both relate to risk and are influenced by market reactions, investigating the impact that capital structure has on volatility during times of global financial market distress could provide insight and be an important tool for investors. This thesis will investigate firms listed on the Stockholm stock exchange, divided into seven industries, in order to find the impact capital structure may have on volatility, before, during and in the aftermath of the recent European debt crisis (2006-2016). The study will use a quantitative research method, with an objectivistic and positivistic research philosophy as well as a deductive research approach. By using multiple regression models, theoretical relations surrounding volatility and capital structure will be contrasted to the results of the study.The results of the study finds that capital structure does not play a significant part inchanges in volatility for firms during any investigated period when testing for all firms simultaneously. However, the same claim cannot be made for when each industry is tested individually. Empirical evidence showed that capital structure is a influencer for changes in volatility for the consumer goods industry prior to and after the debt crisis and in the consumer goods service industry after the debt crisis. Investors are urged to not be concerned by large debt levels, as long as they invest in largefirms and choose the right sectors. The financial sector is seen as the least risky, with low volatility levels. Furthermore, the basic material sector, despite outward appearances, should be avoided as it presents recent periods of unusually large volatility levels.

This thesis is about the creation of a simulator of football leagues and championships. I studied the problematics of football competitions and their systems and also about the base of machine learning. There was also an analysis of similar and existing solutions and I took inspiration for my proposal from them. After that, I made the design of the whole simulator structure and of all of its key parts. Then the simulator was implemented and tested. The application allows simulating top five competitions in UEFA club coefficients rating.

The master thesis is focused on creating a methodology for quantification of the nerve fiber layer on photographs of the retina. The introductory part of the text presents a medical motivation of the thesis and mentions several studies dealing with this issue. Furthermore, the work describes available textural features and compares their ability to quantify the thickness of the nerve fiber layer. Based on the described knowledge, the methodology to make different regression models enabling prediction of the retinal nerve fiber layer thickness was developed. Then, the methodology was tested on the available image dataset. The results showed, that the outputs of regression models achieve a high correlation between the predicted output and the retinal nerve fiber layer thickness measured by optical coherence tomography. The conclusion discusses an usability of the applied solution.

Although studies examining juvenile recidivism have focused primarily on violent recidivism, the factors that predict recidivism likely differ by offense type. To examine general, property, and violent recidivism, this study combined individual-level data (i.e., offender and case characteristics) from the Mississippi Youth Court Information Data System (MYCIDS) for the years 2009-2011 and contextual-level data (i.e., county characteristics) from the 2010 U.S. Census and the 2010 Uniform Crime Reports (UCR). Results showed that offender characteristics predicted only general and property recidivism, but case characteristics mattered for all three types (i.e., general, violent, and property recidivism). Contextual characteristics (i.e., the percentage of the population that is male aged 15 to 24) also mattered, but only for property recidivism. These findings have implications for policies and programs related to the treatment of juvenile offenders.

[pt] O gerenciamento correto das pastagens em regiões agropecuárias tem papel fundamental na própria produção, na prevenção ao desperdício da biomassa vegetal e a liberação de gases de efeito estufa (GEE). Além disso, é necessário evitar o movimento inapropriado do rebanho entre pastos, pois este é um processo demorado e pode ser estressante para o animal. O sucesso desta gestão requer uma avaliação eficiente dos recursos da plantação. Os estudos desenvolvidos com esta finalidade tem relação direta com a estimativa da quantidade de biomassa em uma região específica da pastagem, pois, na prática, ela não é realizada de forma precisa, devido à dificuldade de medição em toda a área delimitada. Este trabalho tem como objetivo desenvolver uma metodologia de estimativa de biomassa de baixo custo, baseada em modelos de regressão que correlacionem os atributos de entrada mais relevantes para a aplicação com o real peso da plantação, medido em g/m2 . Para os atributos, foi medida a altura da grama forrageira e calculados os índices de vegetação baseados em RGB a partir de imagens de veículos aéreos não tripulados (VANTs). Como metodologia, utilizou-se regressões lineares, não lineares, redes neurais artificiais baseados em perceptrons de múltiplas camadas e a combinação de todos os modelos gerados (stacking ensemble). Foram alcançados resultados satisfatórios utilizando modelos de redes neurais com ainda duas camadas e com a metodologia de empilhamento de modelos, alcançando um RMSE de 31.76 g/m2 , MAPE de 13.35 por cento e R 2 de 0.9. Portanto, a metodologia proposta pode se tornar uma solução promissora e acessível para a estimativa de biomassa vegetal para uma gestão eficiente e sustentável do rebanho. / [en] The correct management of pastures in agricultural regions plays a fundamental role in the production itself, in the prevention of plant biomass waste and the release of greenhouse gases (GHG). In addition, it is necessary to avoid inappropriate movement of the herd between pastures, as this is a time-consuming process and can be stressful for the animal. The success of this management requires an efficient assessment of the plant resources. The studies developed for this purpose are directly related to the amount estimation of biomass in a specific region of the pasture, because, in practice, it is not carried out accurately, due to the difficulty of measurement throughout the field. This work aims to develop a low-cost biomass estimation methodology, based on regression models that correlate the most relevant input features for the application with the actual density of the plantation, measured in g/m2 . For the features, the height of the forage grass was measured and the vegetation indexes based on RGB were calculated from images of unmanned aerial vehicles (UAV). Linear, nonlinear regression (MNLR), artificial neural networks (ANN) based on multi-layer perceptron (MLP) and the combination of all models generated (stacking ensemble) were the methodologies tested in order to achieve the best correlation. Satisfactory results were achieved using models of neural networks with two layers and using stacking ensemble methodology, reaching a RMSE of 31.76 g/m2 , MAPE of 13.35 percent and R-Squared of 0.9. Therefore, the proposed methodology may become a promising and affordable solution for plant biomass estimation toward efficient and sustainable herd management.

Air quality assessment plays a crucial role in understanding the impact of air pollution onhuman health and the environment. With the increasing demand for accurate assessment andprediction of air quality, advanced data analytics modelling techniques offer promisingsolutions. This thesis focuses on leveraging advanced data analytics to assess and analyse airpollution concentration levels in Italy over a 4km resolution using the FORAIR_IT datasetsimulated in ENEA on the CRESCO6 infrastructure, aiming to uncover valuable insights andidentifying the most appropriate AI models for predicting air pollution levels. The datacollection, understanding, and pre-processing procedures are discussed, followed by theapplication of big data training and forecasting using Apache Spark MLlib. The research alsoencompasses different phases, including descriptive and inferential analysis to understand theair pollution concentration dataset, hypothesis testing to examine the relationship betweenvarious pollutants, machine learning prediction using several regression models and anensemble machine learning approach and time series analysis on the entire dataset as well asthree major regions in Italy (Northern Italy – Lombardy, Central Italy – Lazio and SouthernItaly – Campania). The computation time for these regression models are also evaluated and acomparative analysis is done on the results obtained. The evaluation process and theexperimental setup involve the usage of the ENEAGRID/CRESCO6 HPC Infrastructure andApache Spark. This research has provided valuable insights into understanding air pollutionpatterns and improving prediction accuracy. The findings of this study have the potential todrive positive change in environmental management and decision-making processes, ultimatelyleading to healthier and more sustainable communities. As we continue to explore the vastpossibilities offered by advanced data analytics, this research serves as a foundation for futureadvancements in air quality assessment in Italy and the models are transferable to other regionsand provinces in Italy, paving the way for a cleaner and greener future.

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