SPAnDX: a process-based population dynamics model to explore management and climate change impacts on an invasive alien plant, Acacia nilotica

This paper describes a process-based metapopulation dynamics and phenology model of prickly acacia, Acacia nilotica, an invasive alien species in Australia. The model, SPAnDX, describes the interactions between riparian and upland sub-populations of A. nilotica within livestock paddocks, including the effects of extrinsic factors such as temperature, soil moisture availability and atmospheric concentrations of carbon dioxide. The model includes the effects of management events such as changing the livestock species or stocking rate, applying fire, and herbicide application. The predicted population behaviour of A. nilotica was sensitive to climate. Using 35 years daily weather datasets for five representative sites spanning the range of conditions that A. nilotica is found in Australia, the model predicted biomass levels that closely accord with expected values at each site. SPAnDX can be used as a decision-support tool in integrated weed management, and to explore the sensitivity of cultural management practices to climate change throughout the range of A. nilotica. The cohort-based DYMEX modelling package used to build and run SPAnDX provided several advantages over more traditional population modelling approaches (e.g. an appropriate specific formalism (discrete time, cohort-based, process-oriented), user-friendly graphical environment, extensible library of reusable components, and useful and flexible input/output support framework). (C) 2003 Published by Elsevier Science B.V.

Fatores e estrat??gias que impactam a aplicabilidade de organiza????es virtuais no setor p??blico: a percep????o dos gerentes-executivos do PPA

O trabalho apresenta e analisa os resultados de uma pesquisa de campo realizada junto aos gerentes-executivos de programas do Plano Plurianual (PPA) sobre a possibilidade de aplica????o do conceito de organiza????o virtual no setor p??blico. Em uma organiza????o virtual, os parceiros compartilham informa????es e infra-estrutura de maneira sin??rgica, incrementando a efetividade para um n??vel que nenhum deles poderia alcan??ar sozinho. Nesta nova Era, tradicionais conceitos s??o abandonados ou questionados, e o pr??prio conceito de ???organiza????o??? est?? mudando, de forma a refletir os desafios inerentes ao novo ambiente. O trabalho descreve e analisa o contexto que molda essa nova abordagem para o processo de planejamento governamental, e os resultados de pesquisa de campo, na qual foram avaliados os fatores e estrat??gias que impactam a coordena????o interorganizacional requerida para o adequado funcionamento de uma organiza????o virtual. Entre as conclus??es, destaca-se a possibilidade de aplica????o, no setor p??blico, do conceito de organiza????es virtuais, as quais operam necessariamente a partir do compartilhamento de recursos, informa????es e de objetivos de organiza????es formalmente independentes, o que requer lidar com diferentes impress??es sobre autonomia, poder e controle e diferentes culturas organizacionais, alterando, radicalmente, conceitos e pr??ticas acerca de fronteiras organizacionais, propriedade de recursos, gest??o da informa????o e processo decis??rio.

Modelo de gestão municipal baseado na utilização estratégica de recursos da tecnologia da informação para a gestão governamental: formatação do modelo e avaliação em um município

São desafios constantes da gestão efetiva dos municípios a estruturação e disponibilização de informações confiáveis, oportunas e personalizadas para apoiar as decisões da administração pública municipal e para elaborar e controlar o planejamento estratégico municipal alinhado aos anseios dos cidadãos. A adaptação de modelos de gestão da iniciativa privada para o ambiente público é uma alternativa para enfrentar esses desafios. Este artigo propõe e avalia um modelo para a gestão governamental. O modelo é baseado na utilização estratégica da tecnologia da informação, que proporcione ao gestor público monitoração e controle da execução estratégica, informações executivas para a tomada de decisão, gestão dos relacionamentos com os cidadãos e o domínio sobre os processos da gestão municipal. A metodologia da pesquisa enfatizou o estudo de caso no município de Curitiba, utilizando um protocolo de pesquisa elaborado a partir da pesquisa bibliográfica exploratória. A seguir, são analisados diferenças, similaridades e resultados da aplicação de elementos que compõem o modelo proposto no município estudado. A conclusão evidencia que a utilização e adaptação do modelo proposto nas gestões municipais podem contribuir significativamente na evolução de seus modelos de gestão.

A informação médica e a tomada de decisão em cirurgias refractivas : o uso de recursos audiovisuais a dar suporte à escolha dos utentes

RESUMO: Neste estudo investigou-se a influência dos meios audiovisuais na tomada de decisão pelos utentes em cirurgias oftalmológicas, especialmente nas cirurgias refractivas. A metodologia escolhida integrou métodos quantitativos e qualitativos, com o objectivo de abranger a máxima amplitude da descrição, explicação e compreensão do objecto a ser investigado. Procura-se evidenciar e analisar sentimentos, motivações e atitudes individuais, como escolhas e tomada de decisão, bem como, perceber a relação entre o processo de comunicação médico / paciente e a tomada de decisão. Foram usados: um questionário, material digital e vídeos com as principais cirurgias refractivas apresentadas aos utentes, com uma amostra de n= 150 participantes do Serviço de Oftalmologia da HOSPOR e SAMS Centro de 01 de Julho 2008 a 28 de Fevereiro de 2009, com a faixa etária de 20 a 80 anos, com diagnóstico escolhido. Os dados recolhidos foram analisados pelo SPSS 18. A fundamentação teórica está baseada no estudo da captação e disfunções no trajecto da imagem, observando-se os componentes da aquisição do conhecimento: sensação, percepção, pensamento, consciência, memória, imaginação, linguagem, informação, bem como bioética, comunicação médica e a tomada de decisão, na qual se valoriza a educação do Utente para decidir. O resultado desta investigação aponta para novos paradigmas nos processos de informação / decisão consciente, indicando a necessidade de se investir na educação e na informação médica humanizada aos utentes para haver maior conhecimento, participação, satisfação e eficácia na terapêutica a ser escolhida. ABSTRACT: This paper analyzes how information and communication technologies, in particular the media of some ophthalmologic surgery, can help better decisions meaning new ways of information and new relationship between doctor and patient. This study investigates how doctors take hold of technological resources and discuss the client`s decision. We used the quantitative and qualitative structured interview of client who are visually impaired, especially myopia / hyperopia / astigmatism, presbyopia and cataract. We used a questionnaire, material and digital videos with the leading refractive surgery presented to the clients, with a sample of n = 150 participants of the Department of Ophthalmology, and SAMS HOSPOR Center from 01 July 2008 to 28 February 2009, with range 20 to 80 years, diagnosed chosen. The data collected were analyzed by SPSS. The theoretical study is based on the capture and routing of image and perception, observing neuro-psycho-social components: sensation, perception, visual perception, consciousness, knowledge, memory, imagination, language, information, bioethics and decision-making, in which values education of user to decide. The result of this research points to new paradigms in information processing / conscious decision, indicating the necessity of investing in education and humane medical information to the Users in order to archive a greater awareness,participation, satisfaction and effectiveness in the treatment to choose.

An architecture for an effective usage of data mining in business intelligence systems

Business Intelligence (BI) is one emergent area of the Decision Support Systems (DSS) discipline. Over the last years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. Therefore, a lack of an effective usage of DM in BI can be found in some BI systems. An architecture that pretends to conduct to an effective usage of DM in BI is presented.

ANN-Based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

Multi-criteria short-term maintenance outage scheduling in smart distribution systems

Effective legislation and standards for the coordination procedures between consumers, producers and the system operator supports the advances in the technologies that lead to smart distribution systems. In short-term (ST) maintenance scheduling procedure, the energy producers in a distribution system access to the long-term (LT) outage plan that is released by the distribution system operator (DSO). The impact of this additional information on the decision-making procedure of producers in ST maintenance scheduling is studied in this paper. The final ST maintenance plan requires the approval of the DSO that has the responsibility to secure the network reliability and quality, and other players have to follow the finalized schedule. Maintenance scheduling in the producers’ layer and the coordination procedure between them and the DSO is modelled in this paper. The proposed method is applied to a 33-bus distribution system.

Balancing market integration in MASCEM electricity market simulator

With the restructuring of the energy sector in industrialized countries there is an increased complexity in market players’ interactions along with emerging problems and new issues to be addressed. Decision support tools that facilitate the study and understanding of these markets are extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent simulator for competitive electricity markets. It is essential to reinforce MASCEM with the ability to recreate electricity markets reality in the fullest possible extent, making it able to simulate as many types of markets models and players as possible. This paper presents the development of the Balancing Market in MASCEM. A key module to the study of competitive electricity markets, as it has well defined and distinct characteristics previously implemented.

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).

A simulation tool for demand response programs implementation

The design and development of simulation models and tools for Demand Response (DR) programs are becoming more and more important for adequately taking the maximum advantages of DR programs use. Moreover, a more active consumers’ participation in DR programs can help improving the system reliability and decrease or defer the required investments. DemSi, a DR simulator, designed and implemented by the authors of this paper, allows studying DR actions and schemes in distribution networks. It undertakes the technical validation of the solution using realistic network simulation based on PSCAD. DemSi considers the players involved in DR actions, and the results can be analyzed from each specific player point of view.

Meta-heuristics self-configuration for scheduling

Scheduling resolution requires the intervention of highly skilled human problemsolvers. This is a very hard and challenging domain because current systems are becoming more and more complex, distributed, interconnected and subject to rapidly changing. A natural Autonomic Computing evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference. This paper addresses the resolution of complex scheduling problems using cooperative negotiation. A Multi-Agent Autonomic and Meta-heuristics based framework with self-configuring capabilities is proposed.

Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach

This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G user´s profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

Metalearning in ALBidS: A Strategic Bidding System for electricity markets

Metalearning is a subfield of machine learning with special pro-pensity for dynamic and complex environments, from which it is difficult to extract predictable knowledge. The field of study of this work is the electricity market, which due to the restructuring that recently took place, became an especially complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotia-tion entities. The proposed metalearner takes advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that pro-vides decision support to electricity markets’ participating players. Using the outputs of each different strategy as inputs, the metalearner creates its own output, considering each strategy with a different weight, depending on its individual quality of performance. The results of the proposed meth-od are studied and analyzed using MASCEM - a multi-agent electricity market simulator that models market players and simulates their operation in the market. This simulator provides the chance to test the metalearner in scenarios based on real electricity market´s data.

Virtual power producers market strategies

This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Producers (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. The paper detail some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study.

Complex market integration in MASCEM electricity market simulator

The restructuring that the energy sector has suffered in industrialized countries originated a greater complexity in market players’ interactions, and thus new problems and issues to be addressed. Decision support tools that facilitate the study and understanding of these markets become extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent system for simulating competitive electricity markets. To provide MASCEM with the capacity to recreate the electricity markets reality in the fullest possible extent, it is essential to make it able to simulate as many market models and player types as possible. This paper presents the development of the Complex Market in MASCEM. This module is fundamental to study competitive electricity markets, as it exhibits different characteristics from the already implemented market types.