Abstract
In this work, kriging with covariates is used to model and map the spatial distribution of salinity measurements gathered by an autonomous underwater vehicle in a sea outfall monitoring campaign aiming to distinguish the effluent plume from the receiving waters and characterize its spatial variability in the vicinity of the discharge. Four different geostatistical linear models for salinity were assumed, where the distance to diffuser, the west-east positioning, and the south-north positioning were used as covariates. Sample variograms were fitted by the MatSrn models using weighted least squares and maximum likelihood estimation methods as a way to detect eventual discrepancies. Typically, the maximum likelihood method estimated very low ranges which have limited the kriging process. So, at least for these data sets, weighted least squares showed to be the most appropriate estimation method for variogram fitting. The kriged maps show clearly the spatial variation of salinity, and it is possible to identify the effluent plume in the area studied. The results obtained show some guidelines for sewage monitoring if a geostatistical analysis of the data is in mind. It is important to treat properly the existence of anomalous values and to adopt a sampling strategy that includes transects parallel and perpendicular to the effluent dispersion.

Abstract

Abstract
Coal has been for centuries a central energy source to fulfill industrial and domestic needs. Its large scale extraction produced huge amount of debris that were piled in the neighboring of the mines, quite often going into combustion triggered by events like forest fires or lightning. When in this state it can continue for years, releasing substantial emissions of toxic and greenhouse gases with recognized impact in the environment and, more serious in the short term, in the life quality of the populations located nearby. Continuous monitoring of combustion temperature and emission levels of certain gases opens the possibility to plan corrective actions to minimize their negative impact. Optical fiber technology is wellsuited to this purpose and here it is described the main attributes of a fiber optic sensing system projected to gather data on distributed temperature and gas emission in these harsh environments.

Abstract
A interface de utilizador é essencial para o sucesso de uma aplicação, ora a sua implementação requer tempo e esforço. Metodologias baseadas em modelos suportadas por uma geração automática têm sido propostas como solução para reduzir os custos de desenvolvimento. No entanto, um elevado nível de automação nestas abordagens requer o uso de modelos detalhados da interface, originando complicações. Por um lado, esta perspetiva é contrária a uma conceção baseada no progressivo refinamento de mockups, típica do desenvolvimento de interfaces. Por outro lado, obriga uma distinção entre os modelos utilizados na lógica de negócio e na interface. Este artigo propõe uma abordagem para gerar a interface de utilizador baseada diretamente em modelos estruturais da lógica de negócio. A identificação do domínio de aplicação é um factor chave da metodologia, permitindo automatizar o processo de geração. Por sua vez a separação entre conteúdo e forma favorece o refinamento iterativo das interfaces geradas.

Abstract

Abstract