Desenvolvimento de apoio à decisão operacional – previsão de curto prazo de geração distribuída de tecnologia eólica ligada à RND

No contexto da penetração de energias renováveis no sistema elétrico, Portugal ocupa uma posição de destaque a nível mundial, muito devido à produção de eólica. Com um sistema elétrico com forte presença de fontes de energia renováveis, novos desafios surgem, nomeadamente no caso da energia eólica pela sua imprevisibilidade e volatilidade. O recurso eólico embora seja ilimitado não é armazenável, surgindo assim a necessidade da procura de modelos de previsão de produção de energia elétrica dos parques eólicos de modo a permitir uma boa gestão do sistema. Nesta dissertação apresentam-se as contribuições resultantes de um trabalho de pesquisa e investigação sobre modelos de previsão da potência elétrica com base em valores de previsões meteorológicas, nomeadamente, valores previstos da intensidade e direção do vento. Consideraram-se dois tipos de modelos: paramétricos e não paramétricos. Os primeiros são funções polinomiais de vários graus e a função sigmoide, os segundos são redes neuronais artificiais. Para a estimação dos modelos e respetiva validação, são usados dados recolhidos ao longo de dois anos e três meses no parque eólico do Pico Alto de potência instalada de 6 MW. De forma a otimizar os resultados da previsão, consideram-se diferentes classes de perfis de produção, definidas com base em quatro e oito direções do vento, e ajustam-se os modelos propostos em cada uma das classes. São apresentados e discutidos resultados de uma análise comparativa do desempenho dos diferentes modelos propostos para a previsão da potência.

Solar Intensity Forecasting using Artificial Neural Networks and Support Vector Machines

This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches.

Constructing the National Canon in Ireland and Macedonia: The Function of Folklore in the Plays The Countess Cathleen and The Land of Heart’s Desire by William Butler Yeats, and Proud Flesh and The Black Hole by Goran Stefanovski and Their Role in Strengthening National Identity

Erasmus Mundus Masters “Crossways in European Humanities” June 2011

Ferramentas de gestão de redes e classificação de tráfego: estudo de um caso

A gestão de redes informáticas converteu-se num fator vital para uma rede operar de forma eficiente, produtiva e lucrativa. A gestão envolve a monitorização e o controlo dos sistemas para que estes funcionam como o pretendido, ações de configuração, monitorização, reconfiguração dos componentes, são essenciais para o objetivo de melhorar o desempenho, diminuir o tempo de inatividade, melhor a segurança e efetuar contabilização. Paralelamente, a classificação de tráfego é um tema de bastante relevância em várias atividades relacionadas com as redes, tais como a previsão de QoS, segurança, monitorização, contabilização, planeamento de capacidade de backbones e deteção de invasão. A variação de determinados tipos de tráfego pode influenciar, decisões técnicas na área da gestão de redes, assim como decisões políticas e sociais. Neste trabalho pretende-se desenvolver um estudo dos vários protocolos, ferramentas de gestão e de classificação de tráfego disponíveis para apoiar a atividade de gestão. O estudo efetuado terminou com a proposta e implementação de uma solução de gestão adequado a um cenário real, bastante rico na diversidade de tecnologias e sistemas.

Cavin 1 and cavin 2 roles on adipocyte cell function

Dissertação para obtenção do Grau de Mestre em Genética Molecular e Biomedicina

Microglial clearance function: dependence on phenotypes

Dissertação para obtenção do Grau de Mestre em Genética Molecular e Biomedicina

On the numerical computation of the Mittag-Leffler function

Recently simple limiting functions establishing upper and lower bounds on the Mittag-Leffler function were found. This paper follows those expressions to design an efficient algorithm for the approximate calculation of expressions usual in fractional-order control systems. The numerical experiments demonstrate the superior efficiency of the proposed method.

Molecular modulation of brain development and function: lessons from “old” and “new”

Dissertation presented to obtain the Ph.D degree in Biology

Using data mining with time series data in short-term stocks prediction

Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on short- time stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.

Analysis of Systems with Backlash and Impacts through the Describing Function

This paper analyses the dynamical properties of systems with backlash and impact phenomena based on the describing function method. The dynamics is illustrated using the Nyquist and Bode plots and the results are compared with those of standard models.

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees : the PredictAL study

Background: Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. Methods: A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score >= 8 in men and >= 5 in women. Results: 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). Conclusions: The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

Adrenal function in 23 children with paracoccidioidomycosis

Adrenal involvement by Paracoccidioides brasiliensis was described at necropsies and in many clinical studies, but only in adults. Therefore, the aim of this study was to evaluate adrenal function in children with paracoccidioidomycosis. Twenty-three children with the systemic form of paracoccidioidomycosis were evaluated and divided in two Groups: Group A (n = 8) included children before treatment and Group B (n = 15) children after the end of treatment. Plasma cortisol (basal and after ACTH test), ACTH, renin activity, aldosterone, sodium and potassium were measured. They were within normal range in all cases, except for renin activity and aldosterone, which were elevated in some cases. Group A patients showed basal and post-ACTH cortisol levels significantly greater than Group B patients. The results showed that adrenal function was not compromised in these children with paracoccidioidomycosis.

Adhesive selection for single lap bonded joints: Experimentation and advanced techniques for strength prediction

The integrity of multi-component structures is usually determined by their unions. Adhesive-bonding is often used over traditional methods because of the reduction of stress concentrations, reduced weight penalty, and easy manufacturing. Commercial adhesives range from strong and brittle (e.g., Araldite® AV138) to less strong and ductile (e.g., Araldite® 2015). A new family of polyurethane adhesives combines high strength and ductility (e.g., Sikaforce® 7888). In this work, the performance of the three above-mentioned adhesives was tested in single lap joints with varying values of overlap length (LO). The experimental work carried out is accompanied by a detailed numerical analysis by finite elements, either based on cohesive zone models (CZM) or the extended finite element method (XFEM). This procedure enabled detailing the performance of these predictive techniques applied to bonded joints. Moreover, it was possible to evaluate which family of adhesives is more suited for each joint geometry. CZM revealed to be highly accurate, except for largely ductile adhesives, although this could be circumvented with a different cohesive law. XFEM is not the most suited technique for mixed-mode damage growth, but a rough prediction was achieved.

Zero-Time Kidney Histology Predicts Early Graft Function

Our purposes are to determine the impact of histological factors observed in zero-time biopsies on early post transplant kidney allograft function. We specifically want to compare the semi-quantitative Banff Classification of zero time biopsies with quantification of % cortical area fibrosis. Sixty three zero-time deceased donor allograft biopsies were retrospectively semiquantitatively scored using Banff classification. By adding the individual chronic parameters a Banff Chronic Sum (BCS) Score was generated. Percentage of cortical area Picro Sirius Red (%PSR) staining was assessed and calculated with a computer program. A negative linear regression between %PSR/ GFR at 3 year post-transplantation was established (Y=62.08 +-4.6412X; p=0.022). A significant negative correlation between arteriolar hyalinosis (rho=-0.375; p=0.005), chronic interstitial (rho=0.296; p=0.02) , chronic tubular ( rho=0.276; p=0.04) , chronic vascular (rho= -0.360;P=0.007), BCS (rho=-0.413; p=0.002) and GFR at 3 years were found. However, no correlation was found between % PSR, Ci, Ct or BCS. In multivariate linear regression the negative predictive factors of 3 years GFR were: BCS in histological model; donor kidney age, recipient age and black race in clinical model. The BCS seems a good and easy to perform tool, available to every pathologist, with significant predictive short-term value. The %PSR predicts short term kidney function in univariate study and involves extra-routine and expensive-time work. We think that %PSR must be regarded as a research instrument.