Reabilitação no internamento hospitalar: prospectiva na dinâmica de actores - Implementação da governação clínica num hospital E.P.E.

Mestrado em Intervenção Sócio-Organizacional na Saúde - Área de especialização: Políticas de Administração e Gestão de Serviços de Saúde.

: SiC Multilayer Photonic Structures with Self Optical Bias Amplification

Characteristics of tunable wavelength pi'n/pin filters based on a-SiC:H multilayered stacked cells are studied both experimental and theoretically. Results show that the device combines the demultiplexing operation with the simultaneous photodetection and self amplification of the signal. An algorithm to decode the multiplex signal is established. A capacitive active band-pass filter model is presented and supported by an electrical simulation of the state variable filter circuit. Experimental and simulated results show that the device acts as a state variable filter. It combines the properties of active high-pass and low-pass filter sections into a capacitive active band-pass filter using a changing photo capacitance to control the power delivered to the load.

Short-term electricity prices forecasting in a competitive market by a hybrid PSO-ANFIS approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn. (C) 2012 Elsevier Ltd. All rights reserved.

Fuzzy linguistic model for evaluating the risk of neonatal death

OBJECTIVE: To introduce a fuzzy linguistic model for evaluating the risk of neonatal death. METHODS: The study is based on the fuzziness of the variables newborn birth weight and gestational age at delivery. The inference used was Mamdani's method. Neonatologists were interviewed to estimate the risk of neonatal death under certain conditions and to allow comparing their opinions and the model values. RESULTS: The results were compared with experts' opinions and the Fuzzy model was able to capture the expert knowledge with a strong correlation (r=0.96). CONCLUSIONS: The linguistic model was able to estimate the risk of neonatal death when compared to experts' performance.

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

O currículo funcional no desenvolvimento da autonomia e da comunicação oral e escrita

Os alunos com necessidades educativas especiais, mais concretamente os alunos que apresentam deficiência mental, com limitações acentuadas no comportamento adaptativo, necessitam um ensino mais individualizado, com uma vertente mais funcional. Os currículos funcionais, planeados de acordo com os contextos de vida atuais e futuros em que cada aluno se insere e se irá inserir, permitem desenvolver competências com significado e úteis para a formação pessoal, social e laboral, possibilitando uma vida adulta com mais qualidade e com mais autonomia. Este estudo incide no desenvolvimento de atividades funcionais para a promoção da autonomia e da comunicação oral e escrita em duas crianças, com 11 anos de idade, com défice cognitivo. Delinearam-se três objetivos para o estudo: i) caraterizar o nível de desenvolvimento e aprendizagem de duas alunas com défice cognitivo, nomeadamente no que respeita à autonomia e ao desenvolvimento da linguagem; ii) desenvolver conteúdos, estratégias e atividades funcionais que facilitem o progressivo aumento da participação das alunas em contextos reais de atividades de vida diária; iii) contribuir para o desenvolvimento de competências de autonomia e comunicação oral e escrita de duas alunas com défice cognitivo. Organizou-se um projeto de intervenção, desenvolvido na lógica da investigação ação. De modo a se elaborar o plano de intervenção, avaliaram-se as competências das alunas ao nível da comunicação oral e escrita, autonomia e funcionalidade, recorrendo à análise documental, a entrevistas às encarregadas de educação e à docente de Educação Especial, à observação direta e à elaboração de diários de aula. O plano de intervenção foi planeado no quadro dos currículos funcionais e centrou-se em atividades de vida diária, selecionadas a partir da análise das necessidades atuais e futuras das alunas. Após a implementação da intervenção, concluiu-se que a aplicação de atividades de um currículo funcional melhorou as competências de autonomia e de comunicação oral e escrita das alunas.- Abstract: Students with special educational needs, more specifically students who have mental disabilities, with significant behavior limits to adapt, need a more individual and functional education. The functional curriculum, planned according to the current and future life contexts in which every student is and will be, allow the development of meaningful skills, useful to their personal education, social life and labour, enabling them with an adulthood life with more quality and more autonomy. This study focuses on the development of functional activities for the promotion of autonomy and of oral and written communication in two children with 11 years old with mental disability. Three goals were outlined for the study: i) to characterize the level of development and learning of two students with cognitive dysfunction, regarding particularly the autonomy and the language development, ii) to develop contents, strategies and functional activities that increase the participation of the students in real daily life activities, iii) to contribute to the development of autonomy and oral and written communication of two students with mental disability. We organized an intervention project, developed in the logic of action research. In order to develop the intervention plan, we evaluated the level of oral and written communication, autonomy and functionality, through document analysis, interviews to the guardians and to the Special Educational teacher, as well as through direct observation and preparation of diaries of lessons. The intervention plan was outlined within the framework of functional curriculum and focused on activities of daily living, selected trough the analysis of current and future needs of the students. After the implementation of the intervention plan, we observed that the application of the activities of a functional curriculum improved the skills of autonomy and oral and written communication of the students

Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn.

Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

Scheduling maintenance activities of electric power transmission networks using an hybrid constraint method

In this paper we present a Constraint Logic Programming (CLP) based model, and hybrid solving method for the Scheduling of Maintenance Activities in the Power Transmission Network. The model distinguishes from others not only because of its completeness but also by the way it models and solves the Electric Constraints. Specifically we present a efficient filtering algorithm for the Electrical Constraints. Furthermore, the solving method improves the pure CLP methods efficiency by integrating a type of Local Search technique with CLP. To test the approach we compare the method results with another method using a 24 bus network, which considerers 42 tasks and 24 maintenance periods.

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.

Symbolic Knowledge Extraction from Trained Neural Networks Governed by Lukasiewicz Logics

This work describes a methodology to extract symbolic rules from trained neural networks. In our approach, patterns on the network are codified using formulas on a Lukasiewicz logic. For this we take advantage of the fact that every connective in this multi-valued logic can be evaluated by a neuron in an artificial network having, by activation function the identity truncated to zero and one. This fact simplifies symbolic rule extraction and allows the easy injection of formulas into a network architecture. We trained this type of neural network using a back-propagation algorithm based on Levenderg-Marquardt algorithm, where in each learning iteration, we restricted the knowledge dissemination in the network structure. This makes the descriptive power of produced neural networks similar to the descriptive power of Lukasiewicz logic language, minimizing the information loss on the translation between connectionist and symbolic structures. To avoid redundance on the generated network, the method simplifies them in a pruning phase, using the "Optimal Brain Surgeon" algorithm. We tested this method on the task of finding the formula used on the generation of a given truth table. For real data tests, we selected the Mushrooms data set, available on the UCI Machine Learning Repository.

Tratamento de cor em pós-produção televisiva: o caso da produtora de conteúdos SP Televisão

Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Audiovisual e Multimédia.

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.

Quase-verdade e pragmatismo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A hybrid recommendation approach for a tourism system

Many current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulnerable to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality.