62 resultados para Multidimensional projection


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Dissertação de Mestrado apresentado ao Instituto Superior de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Empreendedorismo e Internacionalização. Os orientadores: Prof. Doutor José de Freitas Santos Profª. Doutora Maria Clara Dias Pinto Ribeiro

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In the current context of serious climate changes, where the increase of the frequency of some extreme events occurrence can enhance the rate of periods prone to high intensity forest fires, the National Forest Authority often implements, in several Portuguese forest areas, a regular set of measures in order to control the amount of fuel mass availability (PNDFCI, 2008). In the present work we’ll present a preliminary analysis concerning the assessment of the consequences given by the implementation of prescribed fire measures to control the amount of fuel mass in soil recovery, in particular in terms of its water retention capacity, its organic matter content, pH and content of iron. This work is included in a larger study (Meira-Castro, 2009(a); Meira-Castro, 2009(b)). According to the established praxis on the data collection, embodied in multidimensional matrices of n columns (variables in analysis) by p lines (sampled areas at different depths), and also considering the quantitative data nature present in this study, we’ve chosen a methodological approach that considers the multivariate statistical analysis, in particular, the Principal Component Analysis (PCA ) (Góis, 2004). The experiments were carried out in a soil cover over a natural site of Andaluzitic schist, in Gramelas, Caminha, NW Portugal, who was able to maintain itself intact from prescribed burnings from four years and was submit to prescribed fire in March 2008. The soils samples were collected from five different plots at six different time periods. The methodological option that was adopted have allowed us to identify the most relevant relational structures inside the n variables, the p samples and in two sets at the same time (Garcia-Pereira, 1990). Consequently, and in addition to the traditional outputs produced from the PCA, we have analyzed the influence of both sampling depths and geomorphological environments in the behavior of all variables involved.

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This paper discusses the changes brought by the communication revolution in teaching and learning in the scope of LSP. Its aim is to provide an insight on how teaching which was bi-dimensional, turned into a multidimensional system, gathering other complementary resources that have transformed, in a incredibly short time, the ways we receive share and store information, for instance as professionals, and keep in touch with our peers. The increasing rise of electronic publications, the incredible boom of social and professional networks, search engines, blogs, list servs, forums, e-mail blasts, Facebook pages, YouTube contents, Tweets and Apps, have twisted the way information is conveyed. Classes ceased to be predictable and have been empowered by digital platforms, innumerous and different data repositories (TILDE, IATE, LINGUEE, and so many other terminological data banks) that have definitely transformed the academic world in general and tertiary education in particular. There is a bulk of information to be digested by students, who are no longer passive but instead responsible and active for their academic outcomes. The question is whether they possess the tools to select only what is accurate and important for a certain subject or assignment, due to that overflow? Due to the reduction of the number of course years in most degrees, after the implementation of Bologna and the shrinking of the curricula contents, have students the possibility of developing critical thinking? Both teaching and learning rely on digital resources to improve the speed of the spreading of knowledge. But have those changes been effective to promote really communication? Furthermore, with the increasing Apps that have already been developed and will continue to appear for learning foreign languages, for translation among others, will the students feel the need of learning them once they have those Apps. These are some the questions we would like to discuss in our paper.

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The ecotoxicological response of the living organisms in an aquatic system depends on the physical, chemical and bacteriological variables, as well as the interactions between them. An important challenge to scientists is to understand the interaction and behaviour of factors involved in a multidimensional process such as the ecotoxicological response.With this aim, multiple linear regression (MLR) and principal component regression were applied to the ecotoxicity bioassay response of Chlorella vulgaris and Vibrio fischeri in water collected at seven sites of Leça river during five monitoring campaigns (February, May, June, August and September of 2006). The river water characterization included the analysis of 22 physicochemical and 3 microbiological parameters. The model that best fitted the data was MLR, which shows: (i) a negative correlation with dissolved organic carbon, zinc and manganese, and a positive one with turbidity and arsenic, regarding C. vulgaris toxic response; (ii) a negative correlation with conductivity and turbidity and a positive one with phosphorus, hardness, iron, mercury, arsenic and faecal coliforms, concerning V. fischeri toxic response. This integrated assessment may allow the evaluation of the effect of future pollution abatement measures over the water quality of Leça River.

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This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.

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Com o aumento do preço da eletricidade e o fim dos combustíveis fósseis, associados à necessidade de Portugal reduzir a sua dependência energética do exterior, provoca a necessidade urgente de apostar nas energias renováveis. Perante este cenário, e assumindo que o custo da fatura energética, é para as empresas portuguesas um fator cada vez mais determinante para serem competitivas, devido aos aumentos consecutivos da energia nos últimos anos, bem como, a subida do imposto de valor acrescentado (IVA) de 6% para 23%. Outro aspeto importante é a eficiência energética como instrumento para reduzir os consumos de eletricidade. Com estas duas medidas: utilização de energias renováveis e o aumento da eficiência energética, são extremamente importantes para a redução da produção dos gases de efeito estufa (GEE). Consequentemente, as empresas terão de investir na produção da própria energia a partir de fontes renováveis, de modo a proporcionar um desenvolvimento sustentável, associado à redução da fatura energética. Esta dissertação propõe o dimensionamento de um sistema híbrido composto por tecnologia fotovoltaica e eólica, com e sem armazenamento de energia em baterias, adequado para reduzir uma parte dos consumos de uma empresa enquadrada no sector dos plásticos. O dimensionamento deste sistema, foi efetuado com recurso à caracterização dos consumos da empresa através da recolha de dados e leituras no local da instalação. Paralelamente, foi efetuada uma pesquisa em diversos fabricantes, de modo a identificar qual o sistema mais indicado a adotar, considerando painéis fotovoltaicos, turbinas eólicas, inversores e baterias. Com base nos dados recolhidos na empresa e referentes ao potencial eólico e solar para o distrito do Porto, em conjunto com as características técnicas dos equipamentos selecionados, foi delineado o sistema híbrido utilizando para o efeito um software de simulação e otimização de sistemas híbridos, denominado Hybrid Optimization Model for Eletric Renewable (HOMER). São apresentadas várias simulações para as diversas configurações escolhidas e estudos comparativos entre si, com o objetivo de reduzir o consumo de eletricidade da rede. Adicionalmente, foram realizadas duas configurações apenas com tecnologia fotovoltaica, de modo a efetuar uma análise comparativa entre um sistema híbrido e outro apenas com uma fonte renovável. Os resultados apresentados focaram-se no desempenho diário, mensal e anual, bem como, a produção individual de cada tecnologia evidenciada. Por último, procedeu-se ao estudo da viabilidade técnico-económica das configurações.

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O texto retrata o percurso metodológico de uma investigação no âmbito científico da Ciência da Informação, com a aplicação do método quadripolar. Explica-se como este dispositivo de investigação proporcionou o conhecimento multidimensional do objeto de estudo. São abordadas em detalhe as componentes envolvidas nos quatro pólos desta metodologia: o epistemológico, o teórico, o técnico e o morfológico. Evidenciam-se as relações dinâmicas entre os quatro pólos, com o pólo epistemológico a enquadrar os referenciais teóricos, que influenciam o contacto com a realidade operado no pólo técnico, o qual determina os resultados do pólo morfológico.

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Num mercado globalizado, a procura contínua de vantagens competitivas é um fator crucial para o sucesso das organizações. A melhoria contínua dos processos é uma abordagem usual, uma vez que os resultados destas melhorias vão se traduzir diretamente na qualidade dos produtos. Neste contexto, a metodologia Failure Mode Effect Analysis (FMEA) é muito utilizada, especialmente pelas suas características proactivas, que permitem a identificação e a prevenção de erros do processo. Assim, quanto mais eficaz for a aplicação desta ferramenta, mais benefícios terá a organização. Assim, quando é utilizado com eficácia, o FMEA de Processo, além de ser um método poderoso na análise do processo, permite a melhoria contínua e a redução dos custos [1] . Este trabalho de dissertação teve como objetivo avaliar a eficácia da utilização da ferramenta do FMEA de processo numa organização certificada segundo a norma ISO/TS16949. A metodologia proposta passa pela análise de dados reais, ou seja, comparar as falhas verificadas no mercado com as falhas que tinham sido identificadas no FMEA. Assim, ao analisar o nível de falhas identificadas e não identificadas durante o FMEA e a projeção dessas falhas no mercado, consegue-se determinar se o FMEA foi mais ou menos eficaz, e ainda, identificar fatores que condicionam a melhor utilização da mesma. Este estudo, está organizado em três fases, a primeira apresenta a metodologia proposta , com a definição de um fluxograma do processo de avaliação e as métricas usadas, a segunda fase a aplicação do modelo proposto a dois casos de estudo, e uma última fase, que consiste na análise comparativa, individual e global, que visa, além de comparar esultados, identificar pontos fracos durante a execução do FMEA. Os resultados do caso de estudo indicam que a ferramenta do FMEA tem sido usada com eficácia, pois consegue-se identificar uma quantidade significativa de falhas potenciais e evitá-las. No entanto, existem ainda falhas que não foram identificadas no FMEA e que apareceram no cliente, e ainda, algumas falhas que foram identificadas e apareceram no cliente. As falhas traduzem-se em má qualidade e custos para o negócio, pelo que são propostas ações de melhoria. Pode-se concluir que uma boa utilização do FMEA pode ser um fator importante para a qualidade do serviço ao cliente, e ainda, com impacto dos custos.

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The objectives of this study were to compare how different frailty measures (Frailty Phenotype/FP, Groningen Frailty Indicator/GFI and Tilburg Frailty Indicator/TFI) predict short-term adverse outcomes. Secondarily, adopting a multidimensional approach to frailty (integral conceptual model–TFI), this study aims to compare how physical, psychological and social frailty predict the outcomes. A longitudinal study was carried out with 95 community-dwelling elderly. Participants were assessed at baseline for frailty, determinants of frailty, and adverse outcomes (healthcare utilization, quality of life, disability in basic and instrumental activities of daily living/ADL and IADL). Ten months later the outcomes were assessed again. Frailty was associated with specific healthcare utilization indicators: the FP with a greater utilization of informal care; GFI with an increased contact with healthcare professionals; and TFI with a higher amount of contacts with a general practitioner. After controlling for the effect of life-course determinants, comorbidity and adverse outcome at baseline, GFI predicted IADL disability and TFI predicted quality of life. The effect of the FP on the outcomes was not significant, when compared with the other measures. However, when comparing TFI’s domains, the physical domain was the most significant predictor of the outcomes, even explaining part of the variance of ADL disability. Frailty at baseline was associated with adverse outcomes at follow-up. However, the relationship of each frailty measure (FP, GFI and TFI) with the outcomes was different. In spite of the role of psychological frailty, TFI’s physical domain was the determinant factor for predicting disability and most of the quality of life.

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This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.

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This paper studies forest fires from the perspective of dynamical systems. Burnt area, precipitation and atmospheric temperatures are interpreted as state variables of a complex system and the correlations between them are investigated by means of different mathematical tools. First, we use mutual information to reveal potential relationships in the data. Second, we adopt the state space portrait to characterize the system’s behavior. Third, we compare the annual state space curves and we apply clustering and visualization tools to unveil long-range patterns. We use forest fire data for Portugal, covering the years 1980–2003. The territory is divided into two regions (North and South), characterized by different climates and vegetation. The adopted methodology represents a new viewpoint in the context of forest fires, shedding light on a complex phenomenon that needs to be better understood in order to mitigate its devastating consequences, at both economical and environmental levels.

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Complex industrial plants exhibit multiple interactions among smaller parts and with human operators. Failure in one part can propagate across subsystem boundaries causing a serious disaster. This paper analyzes the industrial accident data series in the perspective of dynamical systems. First, we process real world data and show that the statistics of the number of fatalities reveal features that are well described by power law (PL) distributions. For early years, the data reveal double PL behavior, while, for more recent time periods, a single PL fits better into the experimental data. Second, we analyze the entropy of the data series statistics over time. Third, we use the Kullback–Leibler divergence to compare the empirical data and multidimensional scaling (MDS) techniques for data analysis and visualization. Entropy-based analysis is adopted to assess complexity, having the advantage of yielding a single parameter to express relationships between the data. The classical and the generalized (fractional) entropy and Kullback–Leibler divergence are used. The generalized measures allow a clear identification of patterns embedded in the data.

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This paper examines modern economic growth according to the multidimensional scaling (MDS) method and state space portrait (SSP) analysis. Electing GDP per capita as the main indicator for economic growth and prosperity, the long-run perspective from 1870 to 2010 identifies the main similarities among 34 world partners’ modern economic growth and exemplifies the historical waving mechanics of the largest world economy, the USA. MDS reveals two main clusters among the European countries and their old offshore territories, and SSP identifies the Great Depression as a mild challenge to the American global performance, when compared to the Second World War and the 2008 crisis.

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Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2013

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In this paper we study several natural and man-made complex phenomena in the perspective of dynamical systems. For each class of phenomena, the system outputs are time-series records obtained in identical conditions. The time-series are viewed as manifestations of the system behavior and are processed for analyzing the system dynamics. First, we use the Fourier transform to process the data and we approximate the amplitude spectra by means of power law functions. We interpret the power law parameters as a phenomenological signature of the system dynamics. Second, we adopt the techniques of non-hierarchical clustering and multidimensional scaling to visualize hidden relationships between the complex phenomena. Third, we propose a vector field based analogy to interpret the patterns unveiled by the PL parameters.