5 resultados para index-based e-health environments

em Repositório Científico do Instituto Politécnico de Lisboa - Portugal


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Mestrado em Gestão e Avaliação em Tecnologias da Saúde

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A pesquisa sobre resiliência sugere que a criança que se desenvolve em contexto adverso, poderá usufruir de atributos relevantes, pessoais e do ambiente. Neste sentido pretendeu-se estudar, até que ponto, as competências de modulação sensorial da criança e a qualidade das interacções mãe-filho, influenciavam as trajectórias de risco e podiam promover as oportunidades de resiliência da criança. Participaram no estudo 136 crianças, 67 do sexo feminino e 69 do sexo masculino, com idades entre os 7 e os 36 meses. Analisámos a sensibilidade materna em situação de jogo livre recorrendo à escala CARE-Index e o processamento sensorial através do de entrevista baseado no protocolo de Dunn (1997) assente nos quatro padrões de processamento sensorial: baixo registo; sensibilidade sensorial; procura sensorial; evitamento sensorial, construto anteriormente validado. Constituímos, com base nas premissas do modelo de avaliação autêntica, um índex de capacidades, que nos serviu como referencial para a avaliação do risco e da resiliência. Os resultados indicaram que a resiliência infantil em ambiente de pobrezaestava associada a indicadores de elevada sensibilidade materna e a índices adequados de processamento sensorial. A discussão dos resultados enquadrou-se nos modelos actuais e emergentes das influências neurobiológicas e ambientais nos processos de risco e de resiliência.

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Nanotechnology is an important emerging industry with a projected annual market of around one trillion dollars by 2015. It involves the control of atoms and molecules to create new materials with a variety of useful functions. Although there are advantages on the utilization of these nano-scale materials, questions related with its impact over the environment and human health must be addressed too, so that potential risks can be limited at early stages of development. At this time, occupational health risks associated with manufacturing and use of nanoparticles are not yet clearly understood. However, workers may be exposed to nanoparticles through inhalation at levels that can greatly exceed ambient concentrations. Current workplace exposure limits are based on particle mass, but this criteria could not be adequate in this case as nanoparticles are characterized by very large surface area, which has been pointed out as the distinctive characteristic that could even turn out an inert substance into another substance exhibiting very different interactions with biological fluids and cells. Therefore, it seems that, when assessing human exposure based on the mass concentration of particles, which is widely adopted for particles over 1 μm, would not work in this particular case. In fact, nanoparticles have far more surface area for the equivalent mass of larger particles, which increases the chance they may react with body tissues. Thus, it has been claimed that surface area should be used for nanoparticle exposure and dosing. As a result, assessing exposure based on the measurement of particle surface area is of increasing interest. It is well known that lung deposition is the most efficient way for airborne particles to enter the body and cause adverse health effects. If nanoparticles can deposit in the lung and remain there, have an active surface chemistry and interact with the body, then, there is potential for exposure. It was showed that surface area plays an important role in the toxicity of nanoparticles and this is the metric that best correlates with particle-induced adverse health effects. The potential for adverse health effects seems to be directly proportional to particle surface area. The objective of the study is to identify and validate methods and tools for measuring nanoparticles during production, manipulation and use of nanomaterials.

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This paper presents the recent research results about the development of a Observed Time Difference (OTD) based geolocation algorithm based on network trace data, for a real Universal Mobile Telecommunication System (UMTS) Network. The initial results have been published in [1], the current paper focus on increasing the sample convergence rate, and introducing a new filtering approach based on a moving average spatial filter, to increase accuracy. Field tests have been carried out for two radio environments (urban and suburban) in the Lisbon area, Portugal. The new enhancements produced a geopositioning success rate of 47% and 31%, and a median accuracy of 151 m and 337 m, for the urban and suburban environments, respectively. The implemented filter produced a 16% and 20% increase on accuracy, when compared with the geopositioned raw data. The obtained results are rather promising in accuracy and geolocation success rate. OTD positioning smoothed by moving average spatial filtering reveals a strong approach for positioning trace extracted events, vital for boosting Self-Organizing Networks (SON) over a 3G network.

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In this paper a new PCA-based positioning sensor and localization system for mobile robots to operate in unstructured environments (e. g. industry, services, domestic ...) is proposed and experimentally validated. The inexpensive positioning system resorts to principal component analysis (PCA) of images acquired by a video camera installed onboard, looking upwards to the ceiling. This solution has the advantage of avoiding the need of selecting and extracting features. The principal components of the acquired images are compared with previously registered images, stored in a reduced onboard image database, and the position measured is fused with odometry data. The optimal estimates of position and slippage are provided by Kalman filters, with global stable error dynamics. The experimental validation reported in this work focuses on the results of a set of experiments carried out in a real environment, where the robot travels along a lawn-mower trajectory. A small position error estimate with bounded co-variance was always observed, for arbitrarily long experiments, and slippage was estimated accurately in real time.