Opini��o de m��dicos brasileiros sobre fecunda����o artificial do ponto de vista ��tico

Foi realizado levantamento da opini��o dos m��dicos que compareceram ao "XIV Congresso Brasileiro de Reprodu����o Humana", sob o ponto de vista ��tico, a respeito da fecunda����o artificial. Foram analisados os resultados, chegando-se a selecionar alguns itens comportamentais como preliminarmente aceitos. Prop��e-se a realiza����o de estudos mais profundos que objetivam abordar os v��rios aspectos aceitos ou n��o pela sociedade.

Duration of larval and pupal development stages of Aedes albopictus in natural and artificial containers

Aedes albopictus were reared in different containers: a tree hole, a bamboo stump and an auto tire. The total times from egg hatching to adult emergence were of 19.6,27.3 and 37.5 days, respectively, according to the container. The first, second and third-instar larvae presented growth periods with highly similar durations. The fourth-instar larvae was longer than the others stages. The pupation time was longer than the fourth-instar larvae growth period. The temperature of the breeding sites studied, which was of 18&deg; C to 22&deg; C on average, was also taken into consideration. The mortality of the immature stages was analysed and compared as between the experimental groups; it was lower in the natural containers than in the discarded tire. The average wing length of adult females emerging from tree hole was significantly larger (p < 0.05) than that of those emerging from the tire.

Encontro de Aedes scapularis (Diptera: Culicidae) em criadouro artificial em localidade da regi��o Sul do Brasil

Duas larvas de Aedes scapularis foram encontradas em um criadouro artificial, no Munic��pio de Sertaneja, Norte do Estado do Paran��, Brasil, durante atividade de rotina para o controle de vetores da dengue.

An artificial neural network approach for short-term wind power forecasting in Portugal

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. 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. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network���s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network���s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Rede neural artificial para detec����o de sobremortalidade atribu��vel �� c��lera no Cear��

OBJETIVO: Avaliar as redes neurais recorrentes enquanto t��cnica preditiva para s��ries temporais em sa��de. M��TODOS: O estudo foi realizado durante uma epidemia de c��lera ocorrida no Estado do Cear��, em 1993 e 1994, a partir da sobremortalidade tendo como causa b��sica as infec����es intestinais mal definidas (CID-9). O n��mero mensal de ��bitos por essa causa, referente ao per��odo de 1979 a 1995 no Estado do Cear��, foram obtidos do Sistema de Informa����o de Mortalidade (SIM) do Minist��rio da Sa��de. Estruturou-se uma rede com dois neur��nios na camada de entrada, 12 na camada oculta, um neur��nio na camada de sa��da e um na camada de mem��ria. Todas as fun����es de ativa����o eram a fun����o log��stica. O treinamento foi realizado pelo m��todo de backpropagation, com taxa de aprendizado de 0,01 e momentum de 0,9, com dados de janeiro de 1979 a junho de 1991. O crit��rio para fim do treinamento foi atingir 22.000 epochs. Compararam-se os resultados com os de um modelo de regress��o binomial negativa. RESULTADOS: A predi����o da rede neural a m��dio prazo foi adequada, em dezembro de 1993 e novembro e dezembro de 1994. O n��mero de ��bitos registrados foi superior ao limite do intervalo de confian��a. J�� o modelo regressivo detectou sobremortalidade a partir de mar��o de 1992. CONCLUS��ES: A rede neural se mostrou capaz de predi����o, principalmente no in��cio do per��odo, como tamb��m ao detectar uma altera����o concomitante e posterior �� ocorr��ncia da epidemia de c��lera. No entanto, foi menos precisa do que o modelo de regress��o binomial, que se mostrou mais sens��vel para detectar aberra����es concomitantes �� circula����o da c��lera.

Estudo do recife artificial a construir na Leirosa: aplica����o de modela����o num��rica para simula����o da propaga����o da agita����o mar��tima

A tem��tica dos recifes artificiais multifuncionais �� relativamente recente, sendo que o primeiro recife artificial multifuncional constru��do data do ano de 1999 (Perth, Austr��lia). A palavra multifuncional est�� associada aos m��ltiplos prop��sitos que se podem atingir com a constru����o de uma estrutura destas, sendo eles, a prote����o costeira, o aumento da biodiversidade local, a melhoria da qualidade das ondas para o Surf e a promo����o do turismo ligado aos desportos de ondas. Para dar resposta a um caso de prote����o costeira, na zona mar��tima adjacente �� praia de Leirosa, Portugal, foi pensada uma constru����o de um recife artificial que funcione como obra de prote����o do sistema dunar local e que, adicionalmente melhore as condi����es locais para a pr��tica de Surf. Este trabalho descreve a an��lise de duas solu����es de recife (em forma de ���V���, formando um ��ngulo de 45�� e 66��, entre si), atrav��s dos valores das caracter��sticas das ondas (altura, per��odo e dire����o) e par��metros de surfabilidade (linha de rebenta����o, n��mero de Iribarren e ��ngulo de rebenta����o), para uma gama alargada de condi����es de agita����o frequente. Para tal, foi necess��rio caracterizar a agita����o mar��tima, atrav��s do modelo num��rico SWAN para determina����o dos casos de agita����o mais frequentes na zona mar��tima adjacente ao local de implanta����o do recife e para, posteriormente, se proceder �� sua utiliza����o no modelo num��rico DREAMS, que permitiu a simula����o da propaga����o das ondas sobre o recife. A compara����o dos resultados do modelo num��rico DREAMS para as situa����es de com e sem recife (para as duas solu����es de recife) permitiu avaliar a influ��ncia do mesmo em termos de alturas de onda, linha de rebenta����o e ��ngulo de rebenta����o, tendo-se chegado a resultados satisfat��rios do ponto de vista do melhoramento das condi����es locais para a pr��tica do Surf.

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

Combining artificial intelligence techniques for the training of power system control centre operators

Control Centre operators are essential to assure a good performance of Power Systems. Operators��� actions are critical in dealing with incidents, especially severe faults, like blackouts. In this paper we present an Intelligent Tutoring approach for training Portuguese Control Centre operators in incident analysis and diagnosis, and service restoration of Power Systems, offering context awareness and an easy integration in the working environment.

Perspectives and progress of ecology and conservation science in the Azores: the possible contribution of Artificial Intelligence

EPIA 2013 - XVI Portuguese Conference on Artificial Intelligence Angra do Hero��smo, Azores, Portugal, 9 ��� 12 September.

Feasibility study on multifunctional artificial reefs for the Azores

Tese de Doutoramento, Geografia (Ordenamento do Territ��rio), 25 de Novembro de 2013, Universidade dos A��ores.

Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity markets

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.

Controlo e optimiza����o de sistemas AVAC recorrendo a t��cnicas de intelig��ncia artificial

Trabalho Final de Mestrado para obten����o do grau de Mestre em Engenharia Mec��nica

Environmental Decision Support: a Distributed Artificial Intelligence Approach

Decision making in any environmental domain is a complex and demanding activity, justifying the development of dedicated decision support systems. Every decision is confronted with a large variety and amount of constraints to satisfy as well as contradictory interests that must be sensibly accommodated. The first stage of a project evaluation is its submission to the relevant group of public (and private) agencies. The individual role of each agency is to verify, within its domain of competence, the fulfilment of the set of applicable regulations. The scope of the involved agencies is wide and ranges from evaluation abilities on the technical or economical domains to evaluation competences on the environmental or social areas. The second project evaluation stage involves the gathering of the recommendations of the individual agencies and their justified merge to produce the final conclusion. The incorporation and accommodation of the consulted agencies opinions is of extreme importance: opinions may not only differ, but can be interdependent, complementary, irreconcilable or, simply, independent. The definition of adequate methodologies to sensibly merge, whenever possible, the existing perspectives while preserving the overall legality of the system, will lead to the making of sound justified decisions. The proposed Environmental Decision Support System models the project evaluation activity and aims to assist developers in the selection of adequate locations for their projects, guaranteeing their compliance with the applicable regulations.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.