Predicting SO2 pollution incidents by means of additive models with optimum variable selection

The aim of this paper is to predict time series of SO2 concentrations emitted by coal-fired power stations in order to estimate in advance emission episodes and analyze the influence of some meteorological variables in the prediction. An emission episode is said to occur when the series of bi-hourly means of SO2 is greater than a specific level. For coal-fired power stations it is essential to predict emission epi- sodes sufficiently in advance so appropriate preventive measures can be taken. We proposed a meth- odology to predict SO2 emission episodes based on using an additive model and an algorithm for variable selection. The methodology was applied to the estimation of SO2 emissions registered in sampling lo- cations near a coal-fired power station located in Northern Spain. The results obtained indicate a good performance of the model considering only two terms of the time series and that the inclusion of the meteorological variables in the model is not significant.

Comparison of different numerical methods for the solution of the time-fractional reaction-diffusion equation with variable diffusion coefficient

In this work we perform a comparison of two different numerical schemes for the solution of the time-fractional diffusion equation with variable diffusion coefficient and a nonlinear source term. The two methods are the implicit numerical scheme presented in [M.L. Morgado, M. Rebelo, Numerical approximation of distributed order reaction- diffusion equations, Journal of Computational and Applied Mathematics 275 (2015) 216-227] that is adapted to our type of equation, and a colocation method where Chebyshev polynomials are used to reduce the fractional differential equation to a system of ordinary differential equations

Instrumentos de políticas públicas do turismo: uma análise empírica dos municípios portugueses

Tese de Doutoramento em Ciências da Administração

Extreme value Birnbaum-Saunders regression models applied to environmental data

Extreme value models are widely used in different areas. The Birnbaum–Saunders distribution is receiving considerable attention due to its physical arguments and its good properties. We propose a methodology based on extreme value Birnbaum–Saunders regression models, which includes model formulation, estimation, inference and checking. We further conduct a simulation study for evaluating its performance. A statistical analysis with real-world extreme value environmental data using the methodology is provided as illustration.

Qualidade de vida, morbilidade psicológica e stress familiar em idosos residentes na comunidade

Este estudo procurou investigar as relações existentes entre morbilidade psicológica, stress familiar e qualidade de vida (QV) da pessoa idosa. A amostra foi constituída por 126 idosos. Os instrumentos utilizados foram: The Lawton Instrumental Activities of Daily Living (IADL), Quality of Life (WHOQOL-Bref), Geriatric Anxiety Inventory (GSI), Geriatric Depression Scale (GDS); e Index of Family Relations (IFR). Os resultados revelaram a importância da idade, estado civil, escolaridade e número de patologias assim como o género na capacidade funcional, morbilidade, stress familiar e QV. Ao nível dos preditores, a depressão foi a variável que mais contribuiu para a QV. Não foram encontradas variáveis moderadoras no modelo. A discussão e implicações dos resultados são abordadas bem como a intervenção psicológica nesta população.

Study to determine the cardiovascular risk ofthe population of Guimarães/Vizela, includingthe prevalence of arterial stiffness and early vascular aging syndrome

Tese de Doutoramento em Medicina.