Analysis of the domain of applicability of an algorithm for a resources system selection problem for Distributed/Agile/Virtual Enterprises integration

The process of resources systems selection takes an important part in Distributed/Agile/Virtual Enterprises (D/A/V Es) integration. However, the resources systems selection is still a difficult matter to solve in a D/A/VE, as it is pointed out in this paper. Globally, we can say that the selection problem has been equated from different aspects, originating different kinds of models/algorithms to solve it. In order to assist the development of a web prototype tool (broker tool), intelligent and flexible, that integrates all the selection model activities and tools, and with the capacity to adequate to each D/A/V E project or instance (this is the major goal of our final project), we intend in this paper to show: a formulation of a kind of resources selection problem and the limitations of the algorithms proposed to solve it. We formulate a particular case of the problem as an integer programming, which is solved using simplex and branch and bound algorithms, and identify their performance limitations (in terms of processing time) based on simulation results. These limitations depend on the number of processing tasks and on the number of pre-selected resources per processing tasks, defining the domain of applicability of the algorithms for the problem studied. The limitations detected open the necessity of the application of other kind of algorithms (approximate solution algorithms) outside the domain of applicability founded for the algorithms simulated. However, for a broker tool it is very important the knowledge of algorithms limitations, in order to, based on problem features, develop and select the most suitable algorithm that guarantees a good performance.

Development of a meso-microscale coupling procedure for site assessment in complex terrain

A procedure for coupling mesoscale and CFD codes is presented, enabling the inclusion of realistic stratification flow regimes and boundary conditions in CFD simulations of relevance to site and resource assessment studies in complex terrain. Two distinct techniques are derived: (i) in the first one, boundary conditions are extracted from mesoscale results to produce time-varying CFD solutions; (ii) in the second case, a statistical treatment of mesoscale data leads to steady-state flow boundary conditions believed to be more representative than the idealised profiles which are current industry practice. Results are compared with measured data and traditional CFD approaches.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion. © 2014 Springer-Verlag Berlin Heidelberg.

Characterization of the volatile fraction emitted by phloems of four pinus species by solid-phase microextraction and gas chromatography–mass spectrometry

Pine forests constitute some of the most important renewable resources supplying timber, paper and chemical industries, among other functions. Characterization of the volatiles emitted by different Pinus species has proven to be an important tool to decode the process of host tree selection by herbivore insects, some of which cause serious economic damage to pines. Variations in the relative composition of the bouquet of semiochemicals are responsible for the outcome of different biological processes, such as mate finding, egg-laying site recognition and host selection. The volatiles present in phloem samples of four pine species, P. halepensis, P. sylvestris, P. pinaster and P. pinea, were identified and characterized with the aim of finding possible host-plant attractants for native pests, such as the bark beetle Tomicus piniperda. The volatile compounds emitted by phloem samples of pines were extracted by headspace solid-phase micro extraction, using a 2 cm 50/30 mm divinylbenzene/carboxen/polydimethylsiloxane table flex solid-phase microextraction fiber and its contents analyzed by high-resolution gas chromatography, using flame ionization and a non polar and chiral column phases. The components of the volatile fraction emitted by the phloem samples were identified by mass spectrometry using time-of-flight and quadrupole mass analyzers. The estimated relative composition was used to perform a discriminant analysis among pine species, by means of cluster and principal component analysis. It can be concluded that it is possible to discriminate pine species based on the monoterpenes emissions of phloem samples.

Finding Aedes aegypti in a natural breeding site in an urban zone, Sao Paulo, Southeastern Brazil

ABSTRACT This is the description of how nine Aedes aegypti larvae were found in a natural breeding site in the Pinheiros neighborhood, city of Sao Paulo, SP, Southeastern Brazil. The record was conducted in December 2014, during an entomological surveillance program of dengue virus vectors, with an active search of potential breeding sites, either artificial or natural. Finding Ae. aegypti larvae in a tree hole shows this species’ ability to use both artificial and natural environments as breeding sites and habitats, which points towards the importance of maintaining continuous surveillance on this mosquito in all kinds of water-holding containers.

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.

Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.

Dimensionamento de uma central de miniprodução fotovoltaica para uma exploração agrícola direcionada à indústria de laticínios

Este documento apresenta o trabalho desenvolvido no âmbito da disciplina de “Dissertação/Projeto/Estágio”, do 2º ano do Mestrado em Energias Sustentáveis. O crescente consumo energético das sociedades desenvolvidas e emergentes, associado ao consequente aumento dos custos de energia e dos danos ambientais resultantes, promove o desenvolvimento de novas formas de produção de energia, as quais têm como prioridade a sua obtenção ao menor custo possível e com reduzidos impactos ambientais. De modo a poupar os recursos naturais e reduzir a emissão com gases de efeito de estufa, é necessária a diminuição do consumo de energia produzida a partir de combustíveis fósseis. Assim, devem ser criadas alternativas para um futuro sustentável, onde as fontes renováveis de energia assumam um papel fundamental. Neste sentido, a produção de energia elétrica, através de sistemas solares fotovoltaicos, surge como uma das soluções. A presente dissertação tem como principal objetivo a realização do dimensionamento de uma central de miniprodução fotovoltaica, com ligação à rede elétrica, em uma exploração agrícola direcionada à indústria de laticínios, e o seu respetivo estudo de viabilidade económica. A exploração agrícola, que serve de objeto de estudo, está localizada na Ilha Graciosa, Açores, sendo a potência máxima a injetar na Rede Elétrica de Serviço Público, pela central de miniprodução, de 10 kW. Para o dimensionamento foi utilizado um software apropriado e reconhecido na área da produção de energia elétrica através de sistemas fotovoltaicos – o PVsyst –, compreendendo as seguintes etapas: a) definição das caraterísticas do local e do projeto; b) seleção dos módulos fotovoltaicos; c) seleção do inversor; d) definição da potência de ligação à rede elétrica da unidade de miniprodução. Posteriormente, foram estudadas diferentes hipóteses de sistemas fotovoltaicos, que se distinguem na opção de estrutura de fixação utilizada: dois sistemas fixos e dois com eixo incorporado. No estudo de viabilidade económica foram realizadas duas análises distintas a cada um dos sistemas fotovoltaicos considerados no dimensionamento, nomeadamente: uma análise em regime remuneratório bonificado e uma análise em regime remuneratório geral. Os resultados obtidos nos indicadores económicos do estudo de viabilidade económica realizado, serviram de apoio à decisão pelo sistema fotovoltaico mais favorável ao investimento. Conclui-se que o sistema fotovoltaico com inclinação adicional é a opção mais vantajosa em ambos os regimes remuneratórios analisados. Comprova-se, assim, que o sistema fotovoltaico com maior valor de produção de energia elétrica anual, que corresponde ao sistema fotovoltaico de dois eixos, não é a opção com maior rentabilidade em termos económicos, isto porque a remuneração proveniente da sua produção excedente não é suficiente para colmatar o valor do investimento mais acentuado de modo a obter indicadores económicos mais favoráveis, que os do sistema fotovoltaico com inclinação adicional. De acordo com o estudo de viabilidade económica efetuado independentemente do sistema fotovoltaico que seja adotado, é recuperado o investimento realizado, sendo a remuneração efetiva superior à que foi exigida. Assim, mesmo tendo em consideração o risco associado, comprova-se que todos os sistemas fotovoltaicos, em qualquer dos regimes remuneratórios, correspondem a investimentos rentáveis.

Benchmarking de identificação de ano: criação de um site para benchmarking de técnicas de identificação de dano

Dissertação para obtenção do grau de Mestre em Engenharia Mecânica na Área de Manutenção e Produção

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion.

Materials selection for a set of multiple parts considering manufacturing costs and weight reduction with structural isoperformance using direct multisearch optimization

Materials selection is a matter of great importance to engineering design and software tools are valuable to inform decisions in the early stages of product development. However, when a set of alternative materials is available for the different parts a product is made of, the question of what optimal material mix to choose for a group of parts is not trivial. The engineer/designer therefore goes about this in a part-by-part procedure. Optimizing each part per se can lead to a global sub-optimal solution from the product point of view. An optimization procedure to deal with products with multiple parts, each with discrete design variables, and able to determine the optimal solution assuming different objectives is therefore needed. To solve this multiobjective optimization problem, a new routine based on Direct MultiSearch (DMS) algorithm is created. Results from the Pareto front can help the designer to align his/hers materials selection for a complete set of materials with product attribute objectives, depending on the relative importance of each objective.

Exploiting the bin-class histograms for feature selection on discrete data

In machine learning and pattern recognition tasks, the use of feature discretization techniques may have several advantages. The discretized features may hold enough information for the learning task at hand, while ignoring minor fluctuations that are irrelevant or harmful for that task. The discretized features have more compact representations that may yield both better accuracy and lower training time, as compared to the use of the original features. However, in many cases, mainly with medium and high-dimensional data, the large number of features usually implies that there is some redundancy among them. Thus, we may further apply feature selection (FS) techniques on the discrete data, keeping the most relevant features, while discarding the irrelevant and redundant ones. In this paper, we propose relevance and redundancy criteria for supervised feature selection techniques on discrete data. These criteria are applied to the bin-class histograms of the discrete features. The experimental results, on public benchmark data, show that the proposed criteria can achieve better accuracy than widely used relevance and redundancy criteria, such as mutual information and the Fisher ratio.

Autoconsumo fotovoltaico, um elemento de eficiência energética. Caso de estudo: ISEP

Mestrado em Energias Sustentáveis

The selection of senior civil servants in the context of the different public administration models: an issue of accountability

Comunicação apresentada no Congresso do IIAS-IISA no âmbito do IX Grupo de Estudo: Serviço público e política, realizado em Ifrane, Marrocos de 13 a 17 de junho de 2014

Experimental paracoccidioidomycosis in hamster: transmission electron microscopy of inoculation site lesion

Interaction between Paracoccidioides brasiliensis (Pb) and inflammatory cells in hamster testis was studied sequentially by transmission electron microscopy. In early lesions (six hours after inoculation), polymorphonuclear neutrophils (PMNs) were the major and mononuclear cells and eosinophils were the minor constituents of the inflammatory cells. PMNs were later replaced by mononuclear cells. Viable Pb cells were phagocytosed or surrounded by inflammatory cells. Preserved Pb cells usually had broad host-parasite interphases, whereas dying ones had narrow interphases. The outer layer of the fungus wall was sometimes broken by PMN in some focal points, broken pieces being peeled off and phagocytosed. Small Pb cells were uninuclear, and were often related to broad interphase. Large Pb cells were multinucleated with irregularly shaped wall, and sometimes had lomasome and/or myelin like structures. Different interaction patterns of Pb with inflammatory cells may be due to functionally different host cell flow to the inoculation site or due to the age of Pb cells or both.