930 resultados para dyadic data analysis


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A thesis submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Information Systems

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Objetivos: O objetivo deste estudo é descrever o quadro de inovação no setor da saúde em Portugal, identificar os fatores críticos de sucesso da inovação, investigando os impactos da inovação nas organizações do setor da saúde. Metodologia: Na concretização da presente dissertação, recorremos a uma abordagem quantitativa, combinando a análise documental com a estatística, ao nível da análise do tratamento dos dados recolhidos através do Inquérito Comunitário à Inovação, efetuando assim um estudo de caso exploratório, descritivo e transversal. Principais resultados: As organizações analisadas operam sobretudo em mercados locais e regionais, de onde provém, maioritariamente, o seu volume de negócios, 80% do qual é composto por produtos pré-existentes. A maioria introduziu inovações de produto, processo, organizacionais ou de marketing, revelando potencial inovador. A maioria dos produtos novos ou significativamente melhorados foram desenvolvidos internamente, privilegiando fornecedores, consultores, instituições privadas de I&D e instituições do ensino superior como parceiros de cooperação, localizados sobretudo em Portugal e outros países europeus. As razões que motivam estas organizações a inovar são a melhoria da qualidade dos produtos e da capacidade de resposta a clientes e fornecedores, a diversificação da gama de produtos e o reforço da capacidade de desenvolvimento de novos produtos. Conclusões: O setor revela dinamismo na introdução de produtos novos para o mercado e para a empresa, apostando sobretudo num processo de inovação fechada. A cooperação externa é muito orientada à I&D e há um reduzido envolvimento dos agentes de mercado nas atividades de I&D através de parcerias. Contudo, estes são considerados importantes como fonte de informação e as organizações procuram responder às suas necessidades. Diferentes tipos de organizações adotam diferentes estratégias de inovação, conforme o seu mercado e a sua situação atual, o que traduz a materialização de políticas de inovação contextual, em linha com os desenvolvimentos teóricos da atualidade.

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Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

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Dissertação de Mestrado Apresentado ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Contabilidade e Finanças, sob orientação do Mestre Adalmiro Álvaro Malheiro de Castro Andrade Pereira.

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Prescribed fire is a common forest management tool used in Portugal to reduce the fuel load availability and minimize the occurrence of wildfires. In addition, the use of this technique also causes an impact to ecosystems. In this presentation we propose to illustrate some results of our project in two forest sites, both located in Northwest Portugal, where the effect of prescribed fire on soil properties were recorded during a period of 6 months. Changes in soil moisture, organic matter, soil pH and iron, were examined by Principal Component Analysis multivariate statistics technique in order to determine impact of prescribed fire on these soil properties in these two different types of soils and determine the period of time that these forest soils need to recover to their pre-fire conditions, if they can indeed recover. Although the time allocated to this study does not allow for a widespread conclusion, the data analysis clearly indicates that the pH values are positively correlated with iron values at both sites. In addition, geomorphologic differences between both sampling sites, Gramelas and Anjos, are relevant as the soils’ properties considered have shown different performances in time. The use of prescribed fire produced a lower impact in soils originated from more amended bedrock and therefore with a ticker humus covering (Gramelas) than in more rocky soils with less litter covering (Anjos) after six months after the prescribed fire occurrence.

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A new method, based on linear correlation and phase diagrams was successfully developed for processes like the sedimentary process, where the deposition phase can have different time duration - represented by repeated values in a series - and where the erosion can play an important rule deleting values of a series. The sampling process itself can be the cause of repeated values - large strata twice sampled - or deleted values: tiny strata fitted between two consecutive samples. What we developed was a mathematical procedure which, based upon the depth chemical composition evolution, allows the establishment of frontiers as well as the periodicity of different sedimentary environments. The basic tool isn't more than a linear correlation analysis which allow us to detect the existence of eventual evolution rules, connected with cyclical phenomena within time series (considering the space assimilated to time), with the final objective of prevision. A very interesting discovery was the phenomenon of repeated sliding windows that represent quasi-cycles of a series of quasi-periods. An accurate forecast can be obtained if we are inside a quasi-cycle (it is possible to predict the other elements of the cycle with the probability related with the number of repeated and deleted points). We deal with an innovator methodology, reason why it's efficiency is being tested in some case studies, with remarkable results that shows it's efficacy. Keywords: sedimentary environments, sequence stratigraphy, data analysis, time-series, conditional probability.

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Relatório da Prática Profissional Supervisionada Mestrado em Educação Pré-Escolar

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Relatório da Prática Profissional Supervisionada Mestrado em Educação Pré-Escolar

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This paper presents the project of a mobile cockpit system (MCS) for smartphones, which provides assistance to electric bicycle (EB) cyclists in smart cities' environment. The presented system introduces a mobile application (MCS App) with the goal to provide useful personalized information to the cyclist related to the EB's use, including EB range prediction considering the intended path, management of the cycling effort performed by the cyclist, handling of the battery charging process, and the provisioning of information regarding available public transport. This work also introduces the EB cyclist profile concept, which is based on historical data analysis previously stored in a database and collected from mobile devices' sensors. From the tests performed, the results show the importance of route guidance, taking into account the energy savings. The results also show significant changes on range prediction based on user and route taken. It is important to say that the proposed system can be used for all bicycles in general.

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Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

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O crescente reconhecimento das limitações das crianças com multideficiência e deficiência visual, quer nas interacções com os parceiros quer de uma forma geral nos ambientes em que se inserem, motivou este estudo, que pretendeu analisar o nível de participação destas crianças em actividades na escola. Considerando a importância de contribuir com informação para orientações na intervenção educativa de crianças com MDVI, realizou-se um estudo que analisa o seu comportamento e envolvimento em actividades da escola. Para a realização deste estudo, observaram-se os comportamentos de três crianças com MDVI, com idades compreendidas entre os 9 e os 10 anos, em três ambientes da escola, nomeadamente a sala de aula, o refeitório e o recreio, e em três actividades (pintura, jogos, almoço, saltar à corda, andar de baloiço e subir escadas) de forma a analisar o seu envolvimento e limitações nas actividades. Na análise dos dados das observações foram identificadas quatro categorias de participação: Inicia, Perde Oportunidade, Inicia com Apoio e Comportamento Potencialmente Comunicativo, registando-se valores que permitiram encontrar características dos comportamentos das crianças observadas, assim como o seu nível de participação em actividades na escola. Os resultados do estudo permitiram verificar que a participação das crianças em actividades está condicionada pelos ambientes em que estão envolvidas, e não pelas problemáticas que cada criança apresenta.----------------------------------------ABSTRACT: The motivation of this study is the increasing knowledge and awareness of children who have multiple disabilities and a visual impairment (MDVI) and the limitation with their peer interactions and in general. The purpose of this study was to analyze the participation level of children with MDVI in school activities. Considering the importance of contributing with guidelines for educational intervention with children with MDVI, we did a study that analyzes the behavior and the level of participation of MDVI children in school activities. In this research study we observed the behavior of three children with MDVI, of 9/10 years old, in three different environments at school; the classroom, the canteen and the playground, and in different activities (painting, playing games, having lunch, skipping rope, etc), in order to analyze their participation and their activity limitations in the activities referred. Data analysis identified four categories of participation: Initiation; Missed Opportunities; Initiation with support and Potentially communicative behavior. Results of data analysis allowed us to find out characteristics of children´s behavior, as well as their level of participation in activities. The main findings of this research allowed us to verify that the child’s engagement in activities depends on the environments where they are located and not on their disability.

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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.

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Human mesenchymal stem/stromal cells (MSCs) have received considerable attention in the field of cell-based therapies due to their high differentiation potential and ability to modulate immune responses. However, since these cells can only be isolated in very low quantities, successful realization of these therapies requires MSCs ex-vivo expansion to achieve relevant cell doses. The metabolic activity is one of the parameters often monitored during MSCs cultivation by using expensive multi-analytical methods, some of them time-consuming. The present work evaluates the use of mid-infrared (MIR) spectroscopy, through rapid and economic high-throughput analyses associated to multivariate data analysis, to monitor three different MSCs cultivation runs conducted in spinner flasks, under xeno-free culture conditions, which differ in the type of microcarriers used and the culture feeding strategy applied. After evaluating diverse spectral preprocessing techniques, the optimized partial least square (PLS) regression models based on the MIR spectra to estimate the glucose, lactate and ammonia concentrations yielded high coefficients of determination (R2 ≥ 0.98, ≥0.98, and ≥0.94, respectively) and low prediction errors (RMSECV ≤ 4.7%, ≤4.4% and ≤5.7%, respectively). Besides PLS models valid for specific expansion protocols, a robust model simultaneously valid for the three processes was also built for predicting glucose, lactate and ammonia, yielding a R2 of 0.95, 0.97 and 0.86, and a RMSECV of 0.33, 0.57, and 0.09 mM, respectively. Therefore, MIR spectroscopy combined with multivariate data analysis represents a promising tool for both optimization and control of MSCs expansion processes.

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Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods. This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.

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Dissertação apresentada para obtenção do Grau de Mestre em Contabilidade e Finanças, sob orientação de: Amélia Ferreira da Silva José António Fernandes Lopes Oliveira Vale