Optimal control and numerical optimization applied to epidemiological models

A relação entre a epidemiologia, a modelação matemática e as ferramentas computacionais permite construir e testar teorias sobre o desenvolvimento e combate de uma doença. Esta tese tem como motivação o estudo de modelos epidemiológicos aplicados a doenças infeciosas numa perspetiva de Controlo Ótimo, dando particular relevância ao Dengue. Sendo uma doença tropical e subtropical transmitida por mosquitos, afecta cerca de 100 milhões de pessoas por ano, e é considerada pela Organização Mundial de Saúde como uma grande preocupação para a saúde pública. Os modelos matemáticos desenvolvidos e testados neste trabalho, baseiam-se em equações diferenciais ordinárias que descrevem a dinâmica subjacente à doença nomeadamente a interação entre humanos e mosquitos. É feito um estudo analítico dos mesmos relativamente aos pontos de equilíbrio, sua estabilidade e número básico de reprodução. A propagação do Dengue pode ser atenuada através de medidas de controlo do vetor transmissor, tais como o uso de inseticidas específicos e campanhas educacionais. Como o desenvolvimento de uma potencial vacina tem sido uma aposta mundial recente, são propostos modelos baseados na simulação de um hipotético processo de vacinação numa população. Tendo por base a teoria de Controlo Ótimo, são analisadas as estratégias ótimas para o uso destes controlos e respetivas repercussões na redução/erradicação da doença aquando de um surto na população, considerando uma abordagem bioeconómica. Os problemas formulados são resolvidos numericamente usando métodos diretos e indiretos. Os primeiros discretizam o problema reformulando-o num problema de optimização não linear. Os métodos indiretos usam o Princípio do Máximo de Pontryagin como condição necessária para encontrar a curva ótima para o respetivo controlo. Nestas duas estratégias utilizam-se vários pacotes de software numérico. Ao longo deste trabalho, houve sempre um compromisso entre o realismo dos modelos epidemiológicos e a sua tratabilidade em termos matemáticos.

Soft-computing techniques applied to artificial tissue temperature estimation

Tese dout., Engenharia electrónica e computação - Processamento de sinal, Universidade do Algarve, 2008

The LOICZ biogeochemical budget approach applied to Ria Formosa lagoon, - Algarve, South Portugal

Dissertação mest., Gestão da Água e da Costa, Universidade do Algarve, 2007

Bacterial algorithm applied for fuzzy rule extraction

This paper presents a method of using the so-colled "bacterial algorithm" (4,5) for extracting a fuzzy rule base from a training set. The bewly proposed bacterial evolutionary algorithm (BEA) is shown. In our application one bacterium corresponds to a fuzzy rule system.

Electronic Journal of Applied Multivariate Statistics

The ecological sciences have experienced immense growth over the course of this century, and chances are that they will continue to grow well on into the next millennium. There are some good reasons for this – ecology encompasses some of the most pressing concerns facing humanity. With recent advances in data collection technology and ambitious field research, ecologists are increasingly calling upon multivariate statistics to explore and test for patterns in their data. The goal of FISH 560 (Applied Multivariate Statistics for Ecologists) at the University of Washington is to introduce graduate students to the multivariate statistical techniques necessary to carry out sophisticated analyses and to critically evaluate scientific papers using these approaches. It is a practical, hands-on course emphasizing the analysis and interpretation of multivariate analysis, and covers the majority of approaches in common use by ecologists. To celebrate the hard work of past students, I am pleased to announce the creation of the Electronic Journal of Applied Multivariate Statistics (EJAMS). Each year, students in FISH 560 are required to write a final paper consisting of a statistical analysis of their own multivariate data set. These papers are submitted to EJAMS at the end of quarter and are peer reviewed by two other class members. A decision on publication is based on the reviewers’ recommendations and my own reading the paper. In closing, there is a need for the rapid dissemination of ecological research using multivariate statistics at the University of Washington. EJAMS is committed to this challenge.

New methodologies for directed functional connectivity applied to brain neuroimaging research

Tese de doutoramento, Engenharia Biomédica e Biofísica, Universidade de Lisboa, Faculdade de Ciências, 2015

Linking Research and Teaching: an Applied Soft Systems Methodology Case Study

This paper links research and teaching through an applied Soft Systems Methodology case study. The case study focuses on the redevelopment of a Research and Professional Skills module to provide support for international postgraduate students through the use of formative feedback with the aim of increasing academic research skills and confidence. The stages of the Soft Systems Methodology were used as a structure for the redevelopment of module content and assessment. It proved to be a valuable tool for identifying complex issues, a basis for discussion and debate from which an enhanced understanding was gained and a successful solution implemented together with a case study that could be utilised for teaching Soft Systems Methodology concepts. Changes to the module were very successful and resulted in significantly higher grades and a higher pass rate.

An heuristic approach for optimal location of gas supply units in transportation system

The best places to locate the Gas Supply Units (GSUs) on a natural gas systems and their optimal allocation to loads are the key factors to organize an efficient upstream gas infrastructure. The number of GSUs and their optimal location in a gas network is a decision problem that can be formulated as a linear programming problem. Our emphasis is on the formulation and use of a suitable location model, reflecting real-world operations and constraints of a natural gas system. This paper presents a heuristic model, based on lagrangean approach, developed for finding the optimal GSUs location on a natural gas network, minimizing expenses and maximizing throughput and security of supply.The location model is applied to the Iberian high pressure natural gas network, a system modelised with 65 demand nodes. These nodes are linked by physical and virtual pipelines – road trucks with gas in liquefied form. The location model result shows the best places to locate, with the optimal demand allocation and the most economical gas transport mode: by pipeline or by road truck.

Long-term price range forecast applied to risk management using regression models

Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

A short-term risk management tool applied to OMEL electricity market using particle swarm optimization

Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

Genetic algorithm methodology applied to intelligent house control

In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers’ preferences. The proposed approach is compared with a mixed integer non-linear approach.

Particle swarm optimization applied to integrated demand response resources scheduling

The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches.

Efeitos imediatos de um Sustained Natural Apophyseal Glide na atividade muscular dos eretores espinhais, na dor e na amplitude de flexão, em indivíduos com dor lombar crónica não específica

Introdução: O Sustained Natural Apophyseal Glide tem sido sugerido como uma técnica com potenciais benefícios na redução da dor e no aumento da amplitude de flexão lombar em indivíduos com dor lombar. Contudo, não existe evidência sobre esta técnica na atividade muscular dos eretores espinhais. Objetivo: Avaliar os efeitos imediatos de um Sustained Natural Apophyseal Glide na atividade muscular dos eretores espinhais, na intensidade da dor e na amplitude real de flexão lombar, em indivíduos com dor lombar crónica não específica. Métodos: Estudo experimental com uma amostra de 20 estudantes universitários, com dor lombar crónica não específica e com dor à flexão da coluna lombar, que foram aleatoriamente distribuídos em dois grupos: experimental - Sustained Natural Apophyseal Glide e placebo – intervenção placebo. Foram avaliadas a atividade muscular dos eretores espinhais recorrendo à eletromiografia de superfície (bioPLUX research®), a intensidade da dor através da Escala Visual Analógica e a amplitude real de flexão lombar pelo método de duplo inclinómetro (Universal Inclinometer®), antes e após a intervenção, pela análise do movimento de flexão-extensão do tronco. O nível de significância foi de 0,05. Resultados: Foram encontradas diferenças significativas, em ambos os grupos, para a fase de relaxamento (Grupo experimental: p=0,013 e Grupo placebo: p=0,047), assim como para a fase de extensão, no grupo experimental (p=0,037), verificando-se uma diminuição da atividade muscular da baseline para a avaliação final. A Análise da Covariância revelou que, relativamente à intensidade da dor, verificaram-se diferenças significativas entre os dois grupos (p=0,002), sendo que o grupo experimental diminuiu mais 2cm na Escala Visual Analógica do que o grupo placebo. Pela análise da amplitude real de flexão lombar, embora o aumento não fosse significativamente diferente entre os grupos (p=0,086), o grupo experimental teve mais 1,7º de aumento do que o grupo placebo. Conclusão: Os resultados sugerem que, a curto prazo, o Sustained Natural Apophyseal Glide parece produzir um efeito significativo na diminuição da atividade muscular dos eretores espinhais, durante o movimento dinâmico de extensão do tronco, assim como no alívio da dor. Embora não se tenham observado alterações significativas no aumento da amplitude articular, os resultados no grupo experimental foram superiores à diferença mínima detetável, sugerindo um efeito positivo da técnica aplicada.

Accounting for the dissociating properties of organic chemicals in LCIA: an uncertainty analysis applied to micropollutants in the assessment of freshwater ecotoxicity

In life cycle impact assessment (LCIA) models, the sorption of the ionic fraction of dissociating organic chemicals is not adequately modeled because conventional non-polar partitioning models are applied. Therefore, high uncertainties are expected when modeling the mobility, as well as the bioavailability for uptake by exposed biota and degradation, of dissociating organic chemicals. Alternative regressions that account for the ionized fraction of a molecule to estimate fate parameters were applied to the USEtox model. The most sensitive model parameters in the estimation of ecotoxicological characterization factors (CFs) of micropollutants were evaluated by Monte Carlo analysis in both the default USEtox model and the alternative approach. Negligible differences of CFs values and 95% confidence limits between the two approaches were estimated for direct emissions to the freshwater compartment; however the default USEtox model overestimates CFs and the 95% confidence limits of basic compounds up to three orders and four orders of magnitude, respectively, relatively to the alternative approach for emissions to the agricultural soil compartment. For three emission scenarios, LCIA results show that the default USEtox model overestimates freshwater ecotoxicity impacts for the emission scenarios to agricultural soil by one order of magnitude, and larger confidence limits were estimated, relatively to the alternative approach.

Uncertainty analysis based on sensitivity applied to angle-ply composite structures

This article describes a finite element-based formulation for the statistical analysis of the response of stochastic structural composite systems whose material properties are described by random fields. A first-order technique is used to obtain the second-order statistics for the structural response considering means and variances of the displacement and stress fields of plate or shell composite structures. Propagation of uncertainties depends on sensitivities taken as measurement of variation effects. The adjoint variable method is used to obtain the sensitivity matrix. This method is appropriated for composite structures due to the large number of random input parameters. Dominant effects on the stochastic characteristics are studied analyzing the influence of different random parameters. In particular, a study of the anisotropy influence on uncertainties propagation of angle-ply composites is carried out based on the proposed approach.