Interest rate exposure of Spanish banks: A nonparametric analysis

Interest rate risk is one of the major financial risks faced by banks due to the very nature of the banking business. The most common approach in the literature has been to estimate the impact of interest rate risk on banks using a simple linear regression model. However, the relationship between interest rate changes and bank stock returns does not need to be exclusively linear. This article provides a comprehensive analysis of the interest rate exposure of the Spanish banking industry employing both parametric and non parametric estimation methods. Its main contribution is to use, for the first time in the context of banks’ interest rate risk, a nonparametric regression technique that avoids the assumption of a specific functional form. One the one hand, it is found that the Spanish banking sector exhibits a remarkable degree of interest rate exposure, although the impact of interest rate changes on bank stock returns has significantly declined following the introduction of the euro. Further, a pattern of positive exposure emerges during the post-euro period. On the other hand, the results corresponding to the nonparametric model support the expansion of the conventional linear model in an attempt to gain a greater insight into the actual degree of exposure.

Spatio-temporal alignment of pedobarographic image sequences

O documento em anexo encontra-se na versão post-print (versão corrigida pelo editor).

Optimal methodology for renewable energy dispatching in islanded operation

The management of energy resources for islanded operation is of crucial importance for the successful use of renewable energy sources. A Virtual Power Producer (VPP) can optimally operate the resources taking into account the maintenance, operation and load control considering all the involved cost. This paper presents the methodology approach to formulate and solve the problem of determining the optimal resource allocation applied to a real case study in Budapest Tech’s. The problem is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The problem has also been solved by Evolutionary Particle Swarm Optimization (EPSO). The obtained results are presented and compared.

Distributed energy resources management with cyber-physical SCADA in the context of future smart grids

In the energy management of a small power system, the scheduling of the generation units is a crucial problem for which adequate methodologies can maximize the performance of the energy supply. This paper proposes an innovative methodology for distributed energy resources management. The optimal operation of distributed generation, demand response and storage resources is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The paper deals with a vision for the grids of the future, focusing on conceptual and operational aspects of electrical grids characterized by an intensive penetration of DG, in the scope of competitive environments and using artificial intelligence methodologies to attain the envisaged goals. These concepts are implemented in a computational framework which includes both grid and market simulation.

Uma caracterização dos teores de azoto ureico no leite (AUL/MUN) nas explorações leiteiras da Ilha Terceira

Dissertação de Mestrado, Engenharia Zootécnica, 04 de Junho de 2014, Universidade dos Açores.

Interlaboratory validation of an environmental monitoring method for trace analysis of endocrine disrupting compounds

Environmental pollution continues to be an emerging study field, as there are thousands of anthropogenic compounds mixed in the environment whose possible mechanisms of toxicity and physiological outcomes are of great concern. Developing methods to access and prioritize the screening of these compounds at trace levels in order to support regulatory efforts is, therefore, very important. A methodology based on solid phase extraction followed by derivatization and gas chromatography-mass spectrometry analysis was developed for the assessment of four endocrine disrupting compounds (EDCs) in water matrices: bisphenol A, estrone, 17b-estradiol and 17a-ethinylestradiol. The study was performed, simultaneously, by two different laboratories in order to evaluate the robustness of the method and to increase the quality control over its application in routine analysis. Validation was done according to the International Conference on Harmonisation recommendations and other international guidelines with specifications for the GC-MS methodology. Matrix-induced chromatographic response enhancement was avoided by using matrix-standard calibration solutions and heteroscedasticity has been overtaken by a weighted least squares linear regression model application. Consistent evaluation of key analytical parameters such as extraction efficiency, sensitivity, specificity, linearity, limits of detection and quantification, precision, accuracy and robustness was done in accordance with standards established for acceptance. Finally, the application of the optimized method in the assessment of the selected analytes in environmental samples suggested that it is an expedite methodology for routine analysis of EDC residues in water matrices.

Consumo de eletricidade nas famílias na Região Autónoma dos Açores

Tese de Doutoramento em Ciências Económicas e Empresariais, especialidade em Desenvolvimento Regional.

Does the intake of LC-PUFAS during pregnancy affect the body composition of term neonates? Preliminary results

Background: The effect of the intake of polynsaturated long chain fatty acids (LCPUFAs) during pregnancy on fetal body composition has been assessed by studies using mostly neonatal anthropometry. Their results have been inconsistent, probably because neonatal anthropometry has several validity limitations. Air displacement plethismography (ADP) is a recently validated non-invasive method for assessing body composition in neonates. Objective: To determine the effect of the intake of LCPUFAs during pregnancy on the body composition of term neonates, measured by ADP. Methods: Cross-sectional study of a convenience sample of healthy full-term neonates and their mothers. The diet during pregnancy was assessed using a validated semi-quantitative food frequency questionnaire; Food Processor Plus® was used to convert food intake into nutritional values. Body composition was estimated by anthropometry and measured by ADP using Pea Pod™ Life Measurements Inc (fat mass - FM, fat-free mass and %FM) within the first 72h after birth. Univariate and multivariate analysis (linear regression model) were performed. Results: 54 mother-neonate pairs were included. Multivariate analysis adjusted to the maternal body mass index shows positive association between LCPUFAs intake and neonatal mid-arm circumference (= 0,610, p = 0,019) and negative association between n-6:n-3 ratio intake and neonatal %FM (= -2,744, p=0,066). Conclusion: To the best of our knowledge, this is the first study on this subject using ADP and showing a negative association between LCPUFAs n-6:n-3 ratio intake in pregnancy and neonatal %FM. This preliminary finding requires confirmation increasing the study power with a greater sample and performing interventional studies.

Modelos de avaliação a edifícios habitacionais na cidade de Ponta Delgada : abordagem hedónica

Dissertação de Mestrado, Gestão de Empresas (MBA), 19 de Fevereiro de 2016, Universidade dos Açores.

Modelo de otimização do espaço livre de armazenagem num silo de cereais

Este trabalho baseia-se num caso de estudo real de planeamento de operações de armazenagem num silo rural de cereais, e enquadra-se nos problemas de planeamento e programação de armazéns. Os programadores deparam-se diariamente com o problema de arranjar a melhor solução de transferência entre células de armazenagem, tentando maximizar o número de células vazias, por forma a ter maior capacidade para receber novos lotes, respeitando as restrições de receção e expedição, e as restrições de capacidade das linhas de transporte. Foi desenvolvido um modelo matemático de programação linear inteira mista e uma aplicação em Excel, com recurso ao VBA, para a sua implementação. Esta implementação abrangeu todo o processo relativo à atividade em causa, isto é, vai desde a recolha de dados, seu tratamento e análise, até à solução final de distribuição dos vários produtos pelas várias células. Os resultados obtidos mostram que o modelo otimiza o número de células vazias, tendo em conta os produtos que estão armazenados mais os que estão para ser rececionados e expedidos, em tempo computacional inferior a 60 segundos, constituindo, assim, uma importante mais valia para a empresa em causa.

Preditores da qualidade de vida na epilepsia

A epilepsia é uma das patologias neurológicas mais comuns em todo o mundo, com repercussões importantes na Qualidade de Vida (QDV) dos indivíduos. Deste modo, o objetivo do tratamento ultrapassa a remissão total das crises epiléticas, dado que também prioriza a QDV do indivíduo com epilepsia. A QDV tem vindo a ser associada a alguns fatores modificáveis, importantes para a sua promoção. Assim, pretende-se com o presente estudo identificar se a Adesão à Terapêutica, as Estratégias de Coping e a Espiritualidade são preditores da QDV de indivíduos com epilepsia. O SF-36 v1.0, a Medida de Adesão aos Tratamentos, o COPE-R e a Escala de Avaliação de Espiritualidade em Contextos de Saúde foram administrados a 94 indivíduos com diagnóstico de epilepsia entre quatro e 49 anos. A relação entre as variáveis foi analisada através do modelo de regressão linear múltipla. Os resultados revelam que a Adesão à Terapêutica, a Esperança/Otimismo predizem positivamente a QDV. Já as estratégias de Coping Desinvestimento Comportamental, Expressão de Sentimentos e Religião predizem-na negativamente. Estes resultados são importantes para os profissionais de saúde, na medida em que a identificação de preditores modificáveis da QDV sugere pistas para intervenções que promovam a QDV de indivíduos com epilepsia.

Hyperspectral subspace identification

Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.

Does independent component analysis play a role in unmixing hyperspectral data?

Independent component analysis (ICA) has recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2)sources are statistically independent. Independent factor analysis (IFA) extends ICA to linear mixtures of independent sources immersed in noise. Concerning hyperspectral data, the first assumption is valid whenever the multiple scattering among the distinct constituent substances (endmembers) is negligible, and the surface is partitioned according to the fractional abundances. The second assumption, however, is violated, since the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be statistically independent, this compromising the performance of ICA/IFA algorithms in hyperspectral unmixing. This paper studies the impact of hyperspectral source statistical dependence on ICA and IFA performances. We conclude that the accuracy of these methods tends to improve with the increase of the signature variability, of the number of endmembers, and of the signal-to-noise ratio. In any case, there are always endmembers incorrectly unmixed. We arrive to this conclusion by minimizing the mutual information of simulated and real hyperspectral mixtures. The computation of mutual information is based on fitting mixtures of Gaussians to the observed data. A method to sort ICA and IFA estimates in terms of the likelihood of being correctly unmixed is proposed.

Empresas familiares vs. Empresas não familiares : evidência empírica em Portugal numa ótica de desempenho e do órgão de gestão

Mestrado em Contabilidade e Análise Financeira

Longitudinal anthropometric assessment of infants born to HIV-1-infected mothers, Belo Horizonte, Southeastern Brazil

OBJECTIVE: To evaluate the growth parameters in infants who were born to HIV-1-infected mothers. METHODS: The study was a longitudinal evaluation of the z-scores for the weight-for-age (WAZ), weight-for-length (WLZ) and length-for-age (LAZ) data collected from a cohort. A total of 97 non-infected and 33 HIV-infected infants born to HIV-1-infected mothers in Belo Horizonte, Southeastern Brazil, between 1995 and 2003 was studied. The average follow-up period for the infected and non-infected children was 15.8 months (variation: 6.8 to 18.0 months) and 14.3 months (variation: 6.3 to 18.6 months), respectively. A mixed-effects linear regression model was used and was fitted using a restricted maximum likelihood. RESULTS: There was an observed decrease over time in the WAZ, LAZ and WLZ among the infected infants. At six months of age, the mean differences in the WAZ, LAZ and WLZ between the HIV-infected and non-infected infants were 1.02, 0.59, and 0.63 standard deviations, respectively. At 12 months, the mean differences in the WAZ, LAZ and WLZ between the HIV-infected and non-infected infants were 1.15, 1.01, and 0.87 standard deviations, respectively. CONCLUSIONS: The precocious and increasing deterioration of the HIV-infected infants' anthropometric indicators demonstrates the importance of the early identification of HIV-infected infants who are at nutritional risk and the importance of the continuous assessment of nutritional interventions for these infants.