Using remote lab networks to provide support to public secondary school education level

The advantages of networking are widely known in many areas (from business to personal ones). One particular area where networks have also proved their benefits is education. Taking the secondary school education level into account, some successful cases can be found in literature. In this paper we describe a particular remote lab network supporting physical experiments accessible to students of institutions geographically separated. The network architecture and application examples of using some of the available remote experiments are illustrated in detail.

Virtual Communities of Practice: Design and Development of Support Platforms and Participation Fostering

Mestrado em Engenharia Informática - Área de Especialização em Sistemas Gráficos e Multimédia

Profissionais de saúde mental enquanto agentes de estigmatização da doença mental

A doença mental continua imbuída de mitos, preconceitos e estereótipos, apesar da crescente aposta na investigação e na melhoria de tratamento nesta área da saúde. Como consequência, as pessoas com doença mental são discriminadas e estigmatizadas quer pelo público geral e pelos meios de comunicação, quer pelas próprias famílias e pelos profissionais de saúde mental que lhes prestam cuidados. Uma vez que os profissionais de saúde mental estabelecem uma ponte entre a doença e a saúde, espera-se que as suas atitudes e práticas contribuam para o recovery da pessoa com doença mental. No entanto, se os profissionais também apresentarem atitudes e crenças estigmatizantes face à doença mental, este processo reabilitativo pode ficar comprometido. Nesse sentido, e perante as lacunas de investigação nesta área, este trabalho tem como objectivo explorar e clarificar a presença ou ausência de atitudes estigmatizantes dos profissionais de saúde mental e, quando presentes, como se caracterizam. Para tal realizaramse 24 entrevistas de carácter qualitativo a profissionais de saúde mental que trabalham em três instituições na região do Porto, nomeadamente num serviço de psiquiatria de um hospital geral, num hospital especializado e em estruturas comunitárias. A análise do material discursivo recolhido junto de Assistentes Sociais, Enfermeiros, Médicos Psiquiatras, Psicólogos e Terapeutas Ocupacionais evidencia a presença de crenças e atitudes de carácter estigmatizante face à doença mental, independentemente da idade, formação ou local onde exercem funções, salvo escassos aspectos onde parece haver influência da idade e da profissão. Significa isto que é provável que as variações de atitudes dos profissionais sejam fundamentalmente consequência das suas características pessoais.

Gestão de dados pessoais em saúde

Na presente dissertação pretendemos averiguar da pertinência prática do actual modelo de protecção de dados clínicos, ou seja, se nele está devidamente consagrada a autonomia e a individualidade do utente; pretendemos ainda perceber a tendência evolutiva do sistema português de protecção de dados clínicos, nomeadamente a sua capacidade de inovação e adaptação aos sistemas internacionais, respeitando o nosso ordenamento jurídico. Concretamente, pretendemos perceber de que forma esta informação estará protegida, bem como até onde os utentes estarão consciencializados dos perigos que enfrentam. Embora este seja um problema mundial, o facto é que a Gestão do Sistema de Protecção de Dados Pessoais e Clínicos suscita polémica e interpretações diferentes, dada a sensibilidade ética do tema, a integridade humana. Além deste facto, estamos perante uma problemática que irá sempre envolver vários interesses e consequentemente um confronto de posições. Este trabalho procura ilustrar de que forma se lida com a gestão de dados pessoais no nosso país, de que modo se harmonizam os diferentes interesses e perspectivas, que prioridades se encontram na orientação governamental nesta matéria, quais as penalizações para os eventuais incumpridores e qual o futuro possível dos dados pessoais em saúde, tendo como objectivo comum uma eficácia e sustentabilidade dos mecanismos utilizados. Vamos encontrar interesses divergentes, compromissos permissivos ou restritivos de tratamento de dados, tendências que suportam interesses privados e públicos que se vão concretizar em escolhas eficientes de gestão de dados. Esta diversidade de comportamentos vai ser objecto de estudo e análise neste trabalho, procurando aferir das vantagens e desvantagens de um sistema de informação em saúde: universal com a população coberta, e integrado a fim de compartilhar informações de todos os pacientes, de todas as unidades de prestação de cuidados de saúde.

Analysis of ground reaction force and electromyographic activity of the gastrocnemius muscle during double support

O documento em anexo encontra-se na versão post-print (versão corrigida pelo editor).

Comparative analysis of the ground reaction forces, during the support phase, in a group of pregnant women on their 3rd trimester of pregnancy and in a group of not pregnant women

PURPOSE: To analyze and compare the Ground Reaction Forces (GRF), during the stance phase of walking in pregnant women in the 3rd trimester of pregnancy, and non pregnant women. METHODS: 20 women, 10 pregnant and 10 non pregnant, voluntarily took part in this study. GRF were measured (1000 Hz) using a force platform (BERTEC 4060-15), an amplifier (BERTEC AM 6300) and an analogical-digital converter of 16 Bits (Biopac). RESULTS: The study showed that there were significant differences among the two groups concerning absolute values of time of the stance phase. In what concerns to the normalized values the most significant differences were verified in the maximums values of vertical force (Fz3, Fz1) and in the impulse of the antero-posterior force (Fy2), taxes of growth of the vertical force, and in the period of time for the antero-posterior force (Fy) be null. CONCLUSIONS: It is easier for the pregnant to continue forward movement (push-off phase). O smaller growth rates in what concerns to the maximum of the vertical force (Fz1) for the pregnant, can be associated with a slower speed of gait, as an adaptation strategy to maintain the balance, to compensate the alterations in the position of her center of gravity due to the load increase. The data related to the antero-posterior component of the force (Fy), shows that there is a significant difference between the pregnant woman’s left foot and right foot, which accuses a different functional behavior in each one of the feet, during the propulsion phase (TS).

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Personalized sightseeing tours support using mobile devices

In this paper, we present PSiS (Personalized Sightseeing Tours Recommendation System) Mobile. PSiS Mobile is our proposal to a mobile recommendation and planning support system, which is designed to provide effective support during the tourist visit with context-aware information and recommendations about places of interest (POI), exploiting tourist preferences and context.

Agent-based simulation of electronic marketplaces with decision support

This paper presents a Multi-Agent Market simulator designed for analyzing agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. The system includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agents reactions.

A multi-agent system for the support of producer coalition formation in electricity markets

All over the world Distributed Generation is seen as a valuable help to get cleaner and more efficient electricity. To get negotiation power and advantages of scale economy, distributed producers can be aggregated giving place to a new concept: the Virtual Power Producer. Virtual Power Producers are multitechnology and multi-site heterogeneous entities. Virtual Power Producers should adopt organization and management methodologies so that they can make Distributed Generation a really profitable activity, able to participate in the market. In this paper we address the development of a multi-agent market simulator – MASCEM – able to study alternative coalitions of distributed producers in order to identify promising Virtual Power Producers in an electricity market.

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Knowledge extraction from medium voltage load diagrams to support the definition of electrical tariffs

With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.

Decision support system with incomplete and domain incoherent information management

Many of the most common human functions such as temporal and non-monotonic reasoning have not yet been fully mapped in developed systems, even though some theoretical breakthroughs have already been accomplished. This is mainly due to the inherent computational complexity of the theoretical approaches. In the particular area of fault diagnosis in power systems however, some systems which tried to solve the problem, have been deployed using methodologies such as production rule based expert systems, neural networks, recognition of chronicles, fuzzy expert systems, etc. SPARSE (from the Portuguese acronym, which means expert system for incident analysis and restoration support) was one of the developed systems and, in the sequence of its development, came the need to cope with incomplete and/or incorrect information as well as the traditional problems for power systems fault diagnosis based on SCADA (supervisory control and data acquisition) information retrieval, namely real-time operation, huge amounts of information, etc. This paper presents an architecture for a decision support system, which can solve the presented problems, using a symbiosis of the event calculus and the default reasoning rule based system paradigms, insuring soft real-time operation with incomplete, incorrect or domain incoherent information handling ability. A prototype implementation of this system is already at work in the control centre of the Portuguese Transmission Network.

An intelligent tutoring approach to support students and technicians in electrical installation design

Designing electric installation projects, demands not only academic knowledge, but also other types of knowledge not easily acquired through traditional instructional methodologies. A lot of additional empirical knowledge is missing and so the academic instruction must be completed with different kinds of knowledge, such as real-life practical examples and simulations. On the other hand, the practical knowledge detained by the most experienced designers is not formalized in such a way that is easily transmitted. In order to overcome these difficulties present in the engineers formation, we are developing an Intelligent Tutoring System (ITS), for training and support concerning the development of electrical installation projects to be used by electrical engineers, technicians and students.

Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.