Análise de Resultados de Simulações não Estacionárias Aperiódicas e Cíclicas

Tese de Doutoramento, Matemática (Investigação Operacional), 23 de Setembro de 2006, Universidade dos Açores.

Anomaly-free U(1) gauge symmetries in neutrino seesaw flavor models

Agência Financiadora: Fundação para a Ciência e a Tecnologia (FCT) - PEst-OE/FIS/UI0777/2013; CERN/FP/123580/2011; PTDC/FIS-NUC/0548/2012

Principal components and generalized linear modeling in the correlation between hospital admissions and air pollution

OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children. METHODS Ecological time series study. Daily figures for hospital admissions of children aged < 6, and daily concentrations of air pollutants (PM10, SO2, NO2, O3 and CO) were analyzed in the Região da Grande Vitória, ES, Southeastern Brazil, from January 2005 to December 2010. For statistical analysis, two techniques were combined: Poisson regression with generalized additive models and principal model component analysis. Those analysis techniques complemented each other and provided more significant estimates in the estimation of relative risk. The models were adjusted for temporal trend, seasonality, day of the week, meteorological factors and autocorrelation. In the final adjustment of the model, it was necessary to include models of the Autoregressive Moving Average Models (p, q) type in the residuals in order to eliminate the autocorrelation structures present in the components. RESULTS For every 10:49 μg/m3 increase (interquartile range) in levels of the pollutant PM10 there was a 3.0% increase in the relative risk estimated using the generalized additive model analysis of main components-seasonal autoregressive – while in the usual generalized additive model, the estimate was 2.0%. CONCLUSIONS Compared to the usual generalized additive model, in general, the proposed aspect of generalized additive model − principal component analysis, showed better results in estimating relative risk and quality of fit.

Assessment of the ensembles regional climate models in the representation of precipitation variability and extremes over Portugal

A new data set of daily gridded observations of precipitation, computed from over 400 stations in Portugal, is used to assess the performance of 12 regional climate models at 25 km resolution, from the ENSEMBLES set, all forced by ERA-40 boundary conditions, for the 1961-2000 period. Standard point error statistics, calculated from grid point and basin aggregated data, and precipitation related climate indices are used to analyze the performance of the different models in representing the main spatial and temporal features of the regional climate, and its extreme events. As a whole, the ENSEMBLES models are found to achieve a good representation of those features, with good spatial correlations with observations. There is a small but relevant negative bias in precipitation, especially in the driest months, leading to systematic errors in related climate indices. The underprediction of precipitation occurs in most percentiles, although this deficiency is partially corrected at the basin level. Interestingly, some of the conclusions concerning the performance of the models are different of what has been found for the contiguous territory of Spain; in particular, ENSEMBLES models appear too dry over Portugal and too wet over Spain. Finally, models behave quite differently in the simulation of some important aspects of local climate, from the mean climatology to high precipitation regimes in localized mountain ranges and in the subsequent drier regions.

Probabilistic models for mechanical properties of prestressing strands

This study focus on the probabilistic modelling of mechanical properties of prestressing strands based on data collected from tensile tests carried out in Laboratório Nacional de Engenharia Civil (LNEC), Portugal, for certification purposes, and covers a period of about 9 years of production. The strands studied were produced by six manufacturers from four countries, namely Portugal, Spain, Italy and Thailand. Variability of the most important mechanical properties is examined and the results are compared with the recommendations of the Probabilistic Model Code, as well as the Eurocodes and earlier studies. The obtained results show a very low variability which, of course, benefits structural safety. Based on those results, probabilistic models for the most important mechanical properties of prestressing strands are proposed.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Algebraic structure for interaction on mixed models

Binary operations on commutative Jordan algebras, CJA, can be used to study interactions between sets of factors belonging to a pair of models in which one nests the other. It should be noted that from two CJA we can, through these binary operations, build CJA. So when we nest the treatments from one model in each treatment of another model, we can study the interactions between sets of factors of the first and the second models.

From weak Allee effect to no Allee effect in Richards’ growth models

Population dynamics have been attracting interest since many years. Among the considered models, the Richards’ equations remain one of the most popular to describe biological growth processes. On the other hand, Allee effect is currently a major focus of ecological research, which occurs when positive density dependence dominates at low densities. In this chapter, we propose the dynamical study of classes of functions based on Richards’ models describing the existence or not of Allee effect. We investigate bifurcation structures in generalized Richards’ functions and we look for the conditions in the (β, r) parameter plane for the existence of a weak Allee effect region. We show that the existence of this region is related with the existence of a dovetail structure. When the Allee limit varies, the weak Allee effect region disappears when the dovetail structure also disappears. Consequently, we deduce the transition from the weak Allee effect to no Allee effect to this family of functions. To support our analysis, we present fold and flip bifurcation curves and numerical simulations of several bifurcation diagrams.

Aplicação de um modelo de programação linear inteira mista (PLIM) na expansão da rede de distribuição de energia elétrica em Angola

Em Angola, apenas cerca de 30% da população tem acesso à energia elétrica, nível que decresce para valores inferiores a 10% em zonas rurais mais remotas. Este problema é agravado pelo facto de, na maioria dos casos, as infraestruturas existentes se encontrarem danificadas ou não acompanharem o desenvolvimento da região. Em particular na capital angolana, Luanda que, sendo a menor província de Angola, é a que regista atualmente a maior densidade populacional. Com uma população de cerca de 5 milhões de habitantes, não só há frequentemente problemas relacionados com a falha do fornecimento de energia elétrica como há ainda uma percentagem considerável de municípios onde a rede elétrica ainda nem sequer chegou. O governo de Angola, no seu esforço de crescimento e aproveitamento das suas enormes potencialidades, definiu o setor energético como um dos fatores críticos para o desenvolvimento sustentável do país, tendo assumido que este é um dos eixos prioritários até 2016. Existem objetivos claros quanto à reabilitação e expansão das infraestruturas do setor elétrico, aumentando a capacidade instalada do país e criando uma rede nacional adequada, com o intuito não só de melhorar a qualidade e fiabilidade da rede já existente como de a aumentar. Este trabalho de dissertação consistiu no levantamento de dados reais relativamente à rede de distribuição de energia elétrica de Luanda, na análise e planeamento do que é mais premente fazer relativamente à sua expansão, na escolha dos locais onde é viável localizar novas subestações, na modelação adequada do problema real e na proposta de uma solução ótima para a expansão da rede existente. Depois de analisados diferentes modelos matemáticos aplicados ao problema de expansão de redes de distribuição de energia elétrica encontrados na literatura, optou-se por um modelo de programação linear inteira mista (PLIM) que se mostrou adequado. Desenvolvido o modelo do problema, o mesmo foi resolvido por recurso a software de otimização Analytic Solver e CPLEX. Como forma de validação dos resultados obtidos, foi implementada a solução de rede no simulador PowerWorld 8.0 OPF, software este que permite a simulação da operação do sistema de trânsito de potências.

Relação entre a catastrofização da dor, percepção da intensidade da dor e incapacidade funcional em utentes com cdor crónica cervical

RESUMO: Introdução/Objectivo: A influência dos factores psicossociais, e nomeadamente da catastrofização da dor, na percepção da intensidade da dor e na incapacidade funcional, auto-reportada por indivíduos com dor crónica cervical (DCC), tem sido alvo de estudo em vários países, evidenciando o constructo multidimensional da DCC. Neste sentido, esta investigação pretende estudar se a catastrofização da dor, é uma variável preditiva relativamente à percepção da intensidade da dor, e à incapacidade funcional. Secundariamente procurou-se averiguar se as relações encontradas se mantêm estáveis antes e após exposição a uma intervenção em fisioterapia. Metodologia: Neste estudo participaram 40 indivíduos com DCC de origem músculo-esquelética e causa não traumática, que foram expostos a uma intervenção em fisioterapia no Centro de Medicina de Reabilitação do Alcoitão e na Clinica AlcaisFisio, que cumpriram os critérios de inclusão e aceitarem participar livremente no mesmo. A recolha de dados realizou-se em dois momentos distintos, antes e após exposição à intervenção em fisioterapia. A catastrofização da dor foi avaliada por meio da Escala de Catastrofização da Dor (PCS), a intensidade da dor pela Escala Numérica da Dor (END), sendo realizada a medição da incapacidade funcional através do Neck Disability Index versão Portuguesa (NDI-PT). A análise estatística incluiu duas fases: fase descritiva e fase inferencial. Foram desenvolvidos modelos de regressão linear com vista a testar o poder preditivo da catastrofização da dor sobre a intensidade da dor e a incapacidade funcional. O nível de significância para o qual os valores se consideraram satisfatórios foi de p<0,05. O tratamento dos dados foi realizado no software PASW versão 18. Resultados: Observou-se que existe uma relação moderada, positiva e significativa, nos dois momentos de avaliação, entre a catastrofização da dor e a percepção da intensidade da dor (p<0,001), apresentando um poder preditivo de 27,9% e 46,7%, das pontuações da intensidade da dor, antes e após exposição à intervenção em fisioterapia, espectivamente. Observou-se que a catastrofização da dor tem uma relação forte, positiva e significativa com a incapacidade funcional, nos dois momentos de avaliação (p<0,001), predizendo 51,8% e 61,8%, das pontuações da incapacidade funcional, antes e após exposição à intervenção em fisioterapia, respectivamente. Conclusão: A catastrofização da dor é um factor psicossocial que apresenta relação moderada com a percepção da intensidade da dor, e forte com a incapacidade funcional auto-reportada por indivíduos com DCC de origem músculo-esquelética e causa não traumática, antes e após exposição à intervenção em fisioterapia. Os resultados do estudo sugerem, assim, uma importante influência da catastrofização da dor sobre a percepção da intensidade da dor e a incapacidade funcional em indivíduos com DCC, realçando o constructo multidimensional da DCC. ------------ABSTRACT: Background and Purpose: The influence of psychosocial factors, particularly, the pain catastrophizing, on pain intensity and functional disability in individuals with chronic neck pain (CNP) has been report among recent research literature. The first aim of this research was to verify the predictive value of pain catastrophizing on pain intensity and patient’s functional disability. Secondly it aimed to verify the stability of these relations before and after a physiotherapy treatment. Methodology: A sample of 40 subjects with CNP of musculoskeletal and non-traumatic causes was recruited from the patient’s list of two private clinics in Lisbon district following verification of the inclusion criteria. All participants agree to participate in the study and signed a consent form. Data was collected immediately before and after a period of physiotherapy treatment. Pain catastrophizing was assessed by the Pain Catastrophizing Scale (PCS-PT), patient perception of pain intensity was measured by the Numerical Rating Scale (NRS), and functional disability was measured through the Neck Disability Index (NDI-PT). Data was analyzed through descriptive and inferential statistics. Linear regression models were developed in order to test the predictive power of pain catastrophizing on pain intensity and functional disability. The minimal level of significance established was p<0,05. Data analysis was performed using the software PASW version 18. Results: A positive moderate relationship between pain catastrophizing and pain intensity was founded in both moments, before and after physiotherapy intervention, of data collection (p<0,001) with a predictive power of 27,9% and 46,7%, respectively. A positive strong relationship between pain catastrophizing and functional disability was founded in both moments, before and after physiotherapy intervention, of data collection (p<0,001), with a predictive power of 51,8% and 61,8%, respectively. Conclusion: Pain catastrophizing is a psychosocial factor that is correlated moderately with the perception of pain intensity and strongly with self-reported functionaldisability for individuals with CNP musculoskeletal origin and non-traumatic causes,before and after a physiotherapy intervention. The results of this study suggest that pain catastrophizing has an important influence on the report levels of pain intensity and functional disability in CNP patients. These results also emphasize the multidimensional nature of chronic neck pain.

Probabilistic models for mechanical properties of prestressing strands

This study focus on the probabilistic modelling of mechanical properties of prestressing strands based on data collected from tensile tests carried out in Laboratório Nacional de Engenharia Civil (LNEC), Portugal, for certification purposes, and covers a period of about 9 years of production. The strands studied were produced by six manufacturers from four countries, namely Portugal, Spain, Italy and Thailand. Variability of the most important mechanicalproperties is examined and the results are compared with the recommendations of the ProbabilisticModel Code, as well as the Eurocodes and earlier studies. The obtained results show a very low variability which, of course, benefits structural safety. Based on those results, probabilistic modelsfor the most important mechanical properties of prestressing strands are proposed.

A score test for non-nested hypotheses with applications to discrete data models

This paper suggests that a convenient score test against non-nested alternatives can be constructed from the linear combination of the likelihood functions of the competing models. It is shown that this procedure is essentially a test for the correct specification of the conditional distribution of the variable of interest.

Kalman tracking linear predictor for vowel intelligibility enhancement on european portuguese HMM based speech synthesis

The recent developments on Hidden Markov Models (HMM) based speech synthesis showed that this is a promising technology fully capable of competing with other established techniques. However some issues still lack a solution. Several authors report an over-smoothing phenomenon on both time and frequencies which decreases naturalness and sometimes intelligibility. In this work we present a new vowel intelligibility enhancement algorithm that uses a discrete Kalman filter (DKF) for tracking frame based parameters. The inter-frame correlations are modelled by an autoregressive structure which provides an underlying time frame dependency and can improve time-frequency resolution. The system’s performance has been evaluated using objective and subjective tests and the proposed methodology has led to improved results.

Modelling and Forecasting Mortality Patterns

Thesis submitted in partial fulfillment of the requirements for the Degree of Doctor of Statistics and Information Management

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUCTION: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods. METHODS: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a Bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. RESULTS: The model proposed here suggests that deforestation rate, the number of inhabitants per km², and the human development index (HDI) are important in the prediction of malaria cases. CONCLUSIONS: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the Bayesian paradigm is a good strategy for modeling malaria counts.