The differential role of alcohol expectancies, drinking refusal self-efficacy and coping resources in predicting alcohol consumption in community and clinical samples

This study explored the relationship between coping, alcohol expectancies and drinking refusal self-efficacy in predicting drinking behaviour in both community and clinical samples. These variables were found to have differential effects in their association with frequency and volume of alcohol consumption across the two samples. Generally, drinking refusal self-efficacy was a more salient factor in relation to frequency and volume of community drinking, while coping and expectancies were more strongly associated with frequency of drinking sessions by problem drinkers. The interaction between expectancies and drinking refusal self-efficacy was related to volume of consumption in both groups, while coping and expectancies interacted in their association with frequency in the clinical group. The findings are discussed with regard to the different patterns of cognitive variables governing the decision to drink and the amount consumed in each drinking session, which may differentiate community and problem drinkers.

Kangaroos and feral goats as economic resources for graziers: Some views from south-west Queensland

Fifteen years ago it was proposed that the conversion of kangaroos from a pest to an economically valuable resource would allow graziers to reduce the numbers of domestic stock and thereby lower total grazing pressure. Since then, little progress towards this goal has been achieved. This is believed to be due mainly to the low prices obtained for kangaroo products. A survey of graziers in south-west Queensland was carried out to discover their opinions on kangaroos as a potential economic resource. Questions on the harvesting of feral goats were also included in the survey because of the contrast this industry provides to kangaroo harvesting in terms of grazier involvement. The results of the survey are discussed in relation to resource ownership rights; kangaroo product prices and marketing; and competition within the kangaroo harvesting industry. They show that while low kangaroo product prices do act as a disincentive to graziers, other administrative, legal and institutional factors are also important impediments to their entry to the industry. It is concluded that until the focus of attention widens to include consideration of these as well as just market factors, little progress will be made towards integrating graziers into the kangaroo harvesting industry.

Tecnologia assistiva e computacional : contribuições para o atendimento educacional especializado e desafios na formação de professores

Neste trabalho, analisamos aspectos relacionados a como a tecnologia computacional é utilizada no Atendimento Educacional Especializado (AEE) e como se deu a formação de professores para utilizar esses recursos. Para tanto, delimitamos os seguintes objetivos específicos: investigar a utilização da tecnologia assistiva (TA) computacional no âmbito das salas de recursos multifuncionais (SRM); problematizar as tensões, dificuldades e possibilidades relacionadas à TA com ênfase na tecnologia computacional para o AEE; analisar a formação do professor de educação especial para o AEE tendo como recurso a TA com ênfase na tecnologia computacional, visando à mediação dos processos de aprendizagem. O aporte teórico deste trabalho foi a abordagem histórico-cultural, tomando por referência os estudos de Vigotski e seus colaboradores. Trata-se de uma pesquisa qualitativa que fez uso de diferentes instrumentos metodológicos como, os grupos focais, o questionário online e a entrevista semiestruturada. Para desenvolvê-lo, realizamos a coleta de dados em diferentes contextos, começando pelos grupos focais da pesquisa inaugural do Oneesp, que serviram como dispositivo para esta pesquisa, seguida da aplicação de um questionário aos professores participantes da pesquisa-formação desenvolvida como um desdobramento no estado do Espírito Santo da pesquisa inaugural do Oneesp pelos integrantes do Oeeesp e da aplicação in loco de entrevistas semiestruturadas com professores de educação especial, de uma SRM do Tipo II. Foram oitenta e nove professores participantes na pesquisa do Oneesp, trinta professores na pesquisa-formação do Oeeesp e dois professores para aplicação da entrevista semiestruturada in loco, respectivamente. Esses dois professores participaram tanto da pesquisa do Oneesp como da pesquisa do Oeeesp. Analisamos esses três momentos, dos quais emergiram os apontamentos que nos proporcionaram pensar, com base nas narrativas orais e escritas dos professores: sua formação para uso da TA computacional; seu entendimento sobre sua formação para este fim; seus anseios por uma formação mais direcionada; a forma como utilizam a tecnologia na sala de recursos; seus entendimentos sobre as dificuldades e possibilidades relacionadas a TA com ênfase na tecnologia computacional para o AEE. Após essas análises, concluímos que poucos professores que atuam nas SRM tiveram uma formação que possibilitasse a aplicação das tecnologias computacionais em sua mediação pedagógica, aliando teoria e prática, com momentos de formação que privilegiassem os momentos presenciais e em laboratórios, onde possam interagir com os computadores e suas ferramentas simbólicas. Sem essa familiaridade com os recursos computacionais, os professores acabam sentindo-se inseguros para utilizá-los, deixando de potencializar, pela via desses recursos, os processos de ensinoaprendizagem do aluno com deficiência. Faz-se necessário um investimento nesse tipo de formação e, mais do que isso, que se viabilize para os professores que atuam ou que pretendem atuar nas SRM. A partir de uma formação apropriada é possível fazer com que os professores utilizem os recursos computacionais como mediadores dos processos de ensino-aprendizagem de seus alunos.

Competency Management Articulation for Human Resources

In recent years the approach to competences has gained great popularity due to process and organizational reengineering need. Taking opportunity on some recent work in this area dealing challenges that human resources face to develop planning training, I intend to identify several guidelines to develop a future architecture in a practical implementation. At this article is presented the concept development of competency management.

Chaos and crises in a model for cooperative hunting: A symbolic dynamics approach

In this work we investigate the population dynamics of cooperative hunting extending the McCann and Yodzis model for a three-species food chain system with a predator, a prey, and a resource species. The new model considers that a given fraction sigma of predators cooperates in prey's hunting, while the rest of the population 1-sigma hunts without cooperation. We use the theory of symbolic dynamics to study the topological entropy and the parameter space ordering of the kneading sequences associated with one-dimensional maps that reproduce significant aspects of the dynamics of the species under several degrees of cooperative hunting. Our model also allows us to investigate the so-called deterministic extinction via chaotic crisis and transient chaos in the framework of cooperative hunting. The symbolic sequences allow us to identify a critical boundary in the parameter spaces (K, C-0) and (K, sigma) which separates two scenarios: (i) all-species coexistence and (ii) predator's extinction via chaotic crisis. We show that the crisis value of the carrying capacity K-c decreases at increasing sigma, indicating that predator's populations with high degree of cooperative hunting are more sensitive to the chaotic crises. We also show that the control method of Dhamala and Lai [Phys. Rev. E 59, 1646 (1999)] can sustain the chaotic behavior after the crisis for systems with cooperative hunting. We finally analyze and quantify the inner structure of the target regions obtained with this control method for wider parameter values beyond the crisis, showing a power law dependence of the extinction transients on such critical parameters.

Short-term wind forecasting for energy resources scheduling

This paper proposes a wind power forecasting methodology based on two methods: direct wind power forecasting and wind speed forecasting in the first phase followed by wind power forecasting using turbines characteristics and the aforementioned wind speed forecast. The proposed forecasting methodology aims to support the operation in the scope of the intraday resources scheduling model, namely with a time horizon of 5 minutes. This intraday model supports distribution network operators in the short-term scheduling problem, in the smart grid context. A case study using a real database of 12 months recorded from a Portuguese wind power farm was used. The results show that the straightforward methodology can be applied in the intraday model with high wind speed and wind power accuracy. The wind power forecast direct method shows better performance than wind power forecast using turbine characteristics and wind speed forecast obtained in first phase.

Real-time energy resources scheduling considering intensive wind penetration

The use of distributed energy resources, based on natural intermittent power sources, like wind generation, in power systems imposes the development of new adequate operation management and control methodologies. A short-term Energy Resource Management (ERM) methodology performed in two phases is proposed in this paper. The first one addresses the day-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. The ERM scheduling is a complex optimization problem due to the high quantity of variables and constraints. In this paper the main goal is to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixedinteger non-linear programming approach. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units and 1000 electric vehicles has been implemented in a simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

Residential energy resources management by a SCADA system in case of excess wind generation

The end consumers in a smart grid context are seen as active players. The distributed generation resources applied in smart home system as a micro and small-scale systems can be wind generation, photovoltaic and combine heat and power facility. The paper addresses the management of domestic consumer resources, i.e. wind generation, solar photovoltaic, combined heat and power, electric vehicle with gridable capability and loads, in a SCADA system with intelligent methodology to support the user decision in real time. The main goal is to obtain the better management of excess wind generation that may arise in consumer’s distributed generation resources. The optimization methodology is performed in a SCADA House Intelligent Management context and the results are analyzed to validate the SCADA system.

Real-time energy resources scheduling considering short-term and very short-term wind forecast

This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process it is necessary the update of generation and consumption operation and of the storage and electric vehicles storage status. Besides the new operation condition, it is important more accurate forecast values of wind generation and of consumption using results of in short-term and very short-term methods. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented.

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.

Particle swarm optimization applied to integrated demand response resources scheduling

The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches.

Energy resources scheduling in competitive environment

The increasing use of distributed generation units based on renewable energy sources, the consideration of demand-side management as a distributed resource, and the operation in the scope of competitive electricity markets have caused important changes in the way that power systems are operated. The new distributed resources require an entity (player) capable to make them able to participate in electricity markets. This entity has been known as Virtual Power Player (VPP). VPPs need to consider all the business opportunities available to their resources, considering all the relevant players, the market and/or other VPPs to accomplish their goals. This paper presents a methodology that considers all these opportunities to minimize the operation costs of a VPP. The method is applied to a distribution network managed by four independent VPPs with intensive use of distributed resources.

ANN based support for distributed energy resources scheduling in smart grids

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant. Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players’ resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means.