Factores que influenciam o peso e crescimento de cabritos da raça Murciana-granadina, em aleitamento artificial

A exploração caprina de leite tem evoluído no sentido de alguma intensificação, com recurso a raças de elevado potencial produtivo, de que é exemplo a raça Murciana- Granadina. O leite constitui a principal fonte de receita destas explorações. Complementarmente, vendem animais para carne e, as de melhor nível genético, animais para reprodutores. Analisaram-se os pesos de 241 cabritos da raça Murciana-Granadina, numa exploração comercial, com o objectivo de quantificar os pesos e crescimento de cabritos, e identificar os factores que os influenciam. Os cabritos foram aleitados artificialmente, em regime ad libitum, com leite de substituição comercial, dispondo ainda de concentrado comercial, feno de luzerna e palha. Os cabritos foram pesados ao nascimento e, posteriormente, semanalmente, até aos 60 dias de idade. Calcularam-se os respetivos pesos ajustados, bem como os ganhos médios diários, a diferentes idades padrão. Procedeu-se a uma análise de variância com um modelo linear que incluiu os efeitos da época de parto, tipo de parto, sexo e idade da cabra. Foram registados pesos superiores nos partos simples e duplos, relativamente aos triplos, e nos machos, relativamente às fêmeas. Os ganhos médios diários, a partir do mês de idade, registaram valores inferiores na época inverno-primavera, comparativamente com a época primavera-verão. Dairy goat farming has evolved towards intensification, with increased use of high milk-yielding breeds, including the Murciano-Granadina breed. Milk is the main source of farm income. Secondary income sources are the sale of animals for meat and, in genetically superior herds, the sale of breeding animals. The weights of 241 commercial farms artificially reared Murciano-Granadina kids were analyzed with the objective of quantifying weight and growth and identifying variation factors. Kids were artificially reared to weaning, on ad libitum commercial milk replacer, commercial concentrate, lucerne hay and straw. Kids were weighed at birth and at weekly intervals until 60 days of age. Age adjusted weights and growth-rates were calculated. A variance analysis was performed with a model including the effects of season of birth, number of kids per kidding, sex and age of dam. Single and twin-born kids had higher weights than triplets, and males had higher weights than females. Average daily gain after one month of age was lower for kids born in winter-spring than for those born in spring-summer

Logic Programming and Artificial Neural Networks in Pharmacological Screening of Schinus Essential Oils

Some plants of genus Schinus have been used in the folk medicine as topical antiseptic, digestive, purgative, diuretic, analgesic or antidepressant, and also for respiratory and urinary infections. Chemical composition of essential oils of S. molle and S. terebinthifolius had been evaluated and presented high variability according with the part of the plant studied and with the geographic and climatic regions. The pharmacological properties, namely antimicrobial, anti-tumoural and anti-inflammatory activities are conditioned by chemical composition of essential oils. Taking into account the difficulty to infer the pharmacological properties of Schinus essential oils without hard experimental approach, this work will focus on the development of a decision support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks and the respective Degree-of-Confidence that one has on such an occurrence.

Soft-computing techniques applied to artificial tissue temperature estimation

Tese dout., Engenharia electrónica e computação - Processamento de sinal, Universidade do Algarve, 2008

Experimental study of the macrobenthic colonisation and secondary production in the artificial reefs of Algarve coast

Tese de dout., Ciências do Mar, Faculdade de Ciências do Mar e do Ambiente, Universidade do Algarve, 2010

Introdução à inteligência artificial com recurso à programação visual

Relatório da Prática de Ensino Supervisionada, Mestrado em Ensino da Matemática, Universidade de Lisboa, Instituto de Educação, 2014

Impacto da iluminação natural e artificial na disponibilidade de macro-invertebrados bentónicos para as aves limícolas

Tese de mestrado em Ecologia Marinha, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2015

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).

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.

Feasibility study on multifunctional artificial reefs for the Azores

Tese de Doutoramento, Geografia (Ordenamento do Território), 25 de Novembro de 2013, Universidade dos Açores.

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 optimização de sistemas AVAC recorrendo a técnicas de inteligência artificial

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

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.