Contributo do iPad® para o desenvolvimento de crianças com necessidades educativas especiais

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção do grau de Mestre em Ciências da Educação, Especialidade Intervenção Precoce

Maximização da produção de paraxileno

Pretendeu-se, com o presente trabalho, efectuar um estudo sobre a possibilidade de aumentar a produção de paraxileno na unidade processual existente na refinaria da Galp no Porto. Foram estudados o conjunto de colunas T-0306 e T-0307 e a coluna T-0303, recorrendo ao uso do AspenPlus, na realização da simulação do processo nas condições actuais e para a realização da análise de sensibilidade, que permitiu verificar se o equipamento existente possui capacidade para satisfazer as necessidades exigidas pelo processo quando se pretende aumentar a produção. Com a simulação do processo, foi verificado que este já se encontra a laborar em condições óptimas. Na realização da análise de sensibilidade foram variados para o conjunto T-0306 e T- 0307, o caudal de alimentação entre 62780 e 95000 kg/h, o caudal de destilado da T-0306 entre 15833 e 23911 kg/h e o caudal de destilado da T-0307 entre 139 e os 235 kg/h. No caso da T-0303, foi variado o caudal de refinado entre 201240 e 304600 kg/h, o caudal de destilado e o caudal da corrente lateral, variam entre 77290 a 116930 kg/h. Pretendeu-se obter soluções que conseguissem cumprir as especificações relativas aos produtos obtidos, nomeadamente paraxileno com uma pureza de 99,6% e uma recuperação de 99%, tolueno com uma pureza de 97% e uma recuperação de 90% (T-0306 e T-0307). No caso da T- 0303, obter paraxileno com uma pureza de 2,23% e paradietilbenzeno com uma pureza de 99,0% e ambos com uma recuperação de 99%. Para as simulações que cumpriram as especificações anteriores, verificou-se se a energia transferida em aeroarrefecedores, fornalhas e permutadores excedem em 25% o seu valor de projecto. Em caso afirmativo isso significou o acréscimo de uma peça de equipamento idêntica à existente. A análise de sensibilidade permitiu concluir a separação é possível para os caudais actuais de extracto e refinado e para um aumento destes de 32, 40 e 50%. Foi ainda possível concluir que para os caudais actuais é necessária a colocação de novos aeroarrefecedores para as colunas T-0306 e T-0303 e de uma nova fornalha para a coluna T-0303. Para um aumento dos caudais de 32% para além das alterações referidas anteriormente é necessária a colocação de um novo aeroarrefecedor na coluna T-0307 e de uma nova fornalha da coluna T-0306. Se aumentarmos os caudais em 40% será também necessário o acréscimo de permutadores para a coluna T-0307. Para o aumento de 50%, os calores de permuta são excedidos em bastante mais do que 25%, em todos os aeroarrefecedores, permutadores e fornalhas. Uma vez que todas as colunas se encontram a funcionar acima da sua capacidade, concluiu-se que em ambos os casos seria necessária a montagem de uma segunda linha em paralelo e igual à existente. Implicando com esta alteração um investimento total de 10.439.574,36€.

Surveillance of mother-to-child HIV transmission: socioeconomic and health care coverage indicators

OBJECTIVE: To identify clustering areas of infants exposed to HIV during pregnancy and their association with indicators of primary care coverage and socioeconomic condition. METHODS: Ecological study where the unit of analysis was primary care coverage areas in the city of Porto Alegre, Southern Brazil, in 2003. Geographical Information System and spatial analysis tools were used to describe indicators of primary care coverage areas and socioeconomic condition, and estimate the prevalence of liveborn infants exposed to HIV during pregnancy and delivery. Data was obtained from Brazilian national databases. The association between different indicators was assessed using Spearman's nonparametric test. RESULTS: There was found an association between HIV infection and high birth rates (r=0.22, p<0.01) and lack of prenatal care (r=0.15, p<0.05). The highest HIV infection rates were seen in areas with poor socioeconomic conditions and difficult access to health services (r=0.28, p<0.01). The association found between higher rate of prenatal care among HIV-infected women and adequate immunization coverage (r=0.35, p<0.01) indicates that early detection of HIV infection is effective in those areas with better primary care services. CONCLUSIONS: Urban poverty is a strong determinant of mother-to-child HIV transmission but this trend can be fought with health surveillance at the primary care level.

Dependent component analysis: a hyperspectral unmixing algorithm

Linear unmixing decomposes a hyperspectral image into a collection of reflectance spectra of the materials present in the scene, called endmember signatures, and the corresponding abundance fractions at each pixel in a spatial area of interest. This paper introduces a new unmixing method, called Dependent Component Analysis (DECA), which overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical properties of hyperspectral data. DECA models the abundance fractions as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. The performance of the method is illustrated using simulated and real data.

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.

Estratégias de implementação de plataformas de e-maintenance na indústria: desenvolvimento de uma aplicação para um sistema logístico de viaturas

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Efeito dos parâmetros técnicos na dose e qualidade de imagem num sistema de radiografia computorizada

Mestrado em Radiações Aplicadas às Tecnologias da Saúde - Ramo de especialização: Imagem Digital com Radiação X

Feature selection for clustering categorical data with an embedded modelling approach

Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.

Categorical data clustering using a minimum message length criterion

Research on cluster analysis for categorical data continues to develop, new clustering algorithms being proposed. However, in this context, the determination of the number of clusters is rarely addressed. We propose a new approach in which clustering and the estimation of the number of clusters is done simultaneously for categorical data. We assume that the data originate from a finite mixture of multinomial distributions and use a minimum message length criterion (MML) to select the number of clusters (Wallace and Bolton, 1986). For this purpose, we implement an EM-type algorithm (Silvestre et al., 2008) based on the (Figueiredo and Jain, 2002) approach. The novelty of the approach rests on the integration of the model estimation and selection of the number of clusters in a single algorithm, rather than selecting this number based on a set of pre-estimated candidate models. The performance of our approach is compared with the use of Bayesian Information Criterion (BIC) (Schwarz, 1978) and Integrated Completed Likelihood (ICL) (Biernacki et al., 2000) using synthetic data. The obtained results illustrate the capacity of the proposed algorithm to attain the true number of cluster while outperforming BIC and ICL since it is faster, which is especially relevant when dealing with large data sets.

Clustering and selecting categorical features

In data clustering, the problem of selecting the subset of most relevant features from the data has been an active research topic. Feature selection for clustering is a challenging task due to the absence of class labels for guiding the search for relevant features. Most methods proposed for this goal are focused on numerical data. In this work, we propose an approach for clustering and selecting categorical features simultaneously. We assume that the data originate from a finite mixture of multinomial distributions and implement an integrated expectation-maximization (EM) algorithm that estimates all the parameters of the model and selects the subset of relevant features simultaneously. The results obtained on synthetic data illustrate the performance of the proposed approach. An application to real data, referred to official statistics, shows its usefulness.

A new MILP based approach for unit commitment in power production planning

This paper presents a complete, quadratic programming formulation of the standard thermal unit commitment problem in power generation planning, together with a novel iterative optimisation algorithm for its solution. The algorithm, based on a mixed-integer formulation of the problem, considers piecewise linear approximations of the quadratic fuel cost function that are dynamically updated in an iterative way, converging to the optimum; this avoids the requirement of resorting to quadratic programming, making the solution process much quicker. From extensive computational tests on a broad set of benchmark instances of this problem, the algorithm was found to be flexible and capable of easily incorporating different problem constraints. Indeed, it is able to tackle ramp constraints, which although very important in practice were rarely considered in previous publications. Most importantly, optimal solutions were obtained for several well-known benchmark instances, including instances of practical relevance, that are not yet known to have been solved to optimality. Computational experiments and their results showed that the method proposed is both simple and extremely effective.

Leilões combinados

Mestrado em Controlo de Gestão e dos Negócios

O percurso escolar de adolescentes com síndrome de asperger

Dissertação apresentada à Escola Superior de Educação de Lisboa Para obtenção de grau de mestre em Ciências da Educação, Especialidade em Educação Especial – Problemas de Cognição e Multideficiência

Ambientes favoráveis à prática de cuidados de enfermagem: factores determinantes

Mestrado em Intervenção Sócio-Organizacional na Saúde - Ramo de especialização: Políticas de Administração e Gestão de Serviços de Saúde

Avaliação de fiabilidade de sistemas elétricos e de automação em instalações de frio industrial

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia/Automação e Eletrónica Industrial