Diagnóstico e análise das tendências e condições para a implementação dum modelo de qualidade num serviço de imagiologia hospitalar: o caso da radiologia convencional

Mestrado em Intervenção Sócio-Organizacional na Saúde - Área de especialização: Políticas de Administração e Gestão de Serviços de Saúde.

Contributos para a implementação dum modelo de gestão da qualidade num serviço de tomografia computorizada: uma análise prospectiva

Mestrado em Intervenção Sócio-Organizacional na Saúde - Área de especialização: Políticas de Administração e Gestão de Serviços de Saúde.

O Planeamento estratégico nos hospitais portugueses num contexto de austeridade: o caso particular dos Hospitais E.P.E.

Dissertação apresentada para obtenção do grau de Mestre em Contabilidade e Finanças Orientador: Professor Doutor José Freitas Santos

Fatores influenciadores da gestão da qualidade e independência do auditor: um estudo de caso

Dissertação apresentada ao Instituto Superior de Contabilidade para obtenção do Grau de Mestre em Auditoria Orientada por: Doutora Alcina Dias

Alianças estratégicas: análise do sector exportador de produtos português

Dissertação de Mestrado em Finanças Empresariais

Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

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.

Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

Strategic bidding methodology for electricity markets using adaptive learning

The very particular characteristics of electricity markets, require deep studies of the interactions between the involved players. MASCEM is a market simulator developed to allow studying electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is implemented as a multiagent system, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. This paper also presents a methodology to define players’ models based on the historic of their past actions, interpreting how their choices are affected by past experience, and competition.

Multi-agent negotiation for coalition formation and management in electricity markets

Negotiation is a fundamental tool for reaching understandings that allow each involved party to gain an advantage for themselves by the end of the process. In recent years, with the increasing of compe-titiveness in most sectors, negotiation procedures become present in practically all of them. One particular environment in which the competitiveness has been increasing exponentially is the electricity markets sector. This work is directed to the study of electricity markets’ partici-pating entities interaction, namely in what concerns the formation, management and operation of aggregating entities – Virtual Power Players (VPPs). VPPs are responsible for managing coalitions of market players with small market negotiating influence, which take strategic advantage in entering such aggregations, to increase their negotiating power. This chapter presents a negotiation methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using MASCEM, taking advantage of its ability to provide the means to model and simulate VPPs. VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself.

O Balanced Scorecard no sector de controlo contabilístico

Mestrado em Contabilidade e Gestão das Instituições Financeiras

Conhecimentos sobre VIH sida nos clientes de um hospital de Lakes State, Sudão do Sul

Enquadramento: O VIH/Sida exige uma ação direcionada na vertente da prevenção, cujo suporte integra a transmissão de conhecimentos promotores da adoção e manutenção de comportamentos seguros, em conformidade com as características sociais e culturais dos indivíduos. Objetivos: Validar, para a população do Sudão do Sul, a Escala de Conhecimentos sobre VIH/Sida, The HIV Knowledge Questionnaire: HIV-KQ-45, de Carey et al. (1997); analisar de que forma as variáveis sociodemográficas influenciam os conhecimentos sobre VIH/Sida, dos cidadãos de Mapuordit Sudão do Sul; verificar se a frequência de formação sobre VIH/Sida influencia o seu nível de conhecimentos. Metodologia: Estudo quantitativo, descritivo-analítico e transversal, com 232 clientes do Mary Immaculate de Mapuordit Hospital. Foi utilizado um Questionário de caracterização sociodemográfica e do contexto de formação sobre o VIH/Sida, e o HIV Knowledge Questionnaire (HVI-K-Q) de Carey, Morrison-Beedy e Johnson (1997). Resultados: Amostra é maioritariamente masculina (74.6%), com uma média de idade 22,83 (±5.793 anos). A análise fatorial confirmatória do HIV-K-Q permitiu apurar 5 fatores, cujos valores médios mais significativos foram nos fatores preconceitos/medos (média=80.60%), conhecimentos sobre os comportamentos de risco (média=76.58%) e vias de transmissão (média=70.36%). Os sudaneses pontuaram maioritariamente com razoáveis conhecimentos sobre a Sida (média=68.08%). As mulheres, os participantes mais velhos, com companheiro(a), mais escolarizados, profissionalmente ativos, a distar do hospital =<20 Km, deslocando-se num veículo não motorizado e com diagnóstico de VIH relataram mais conhecimentos sobre a Sida. Os participantes com informação sobre a prevenção do VIH/Sida e frequência em workshop na área demonstraram melhores conhecimentos. Revelaram-se preditivas dos conhecimentos acerca da doença as habilitações literárias (β=0.32) e o diagnóstico de VIH/Sida (β=0.14) revelou-se preditor dos conhecimentos sobre os comportamentos de risco. Conclusão: As casuísticas significativas do VIH/Sida justificam considerar as habilitações literárias e a presença de diagnóstico VIH/Sida como variáveis a avaliar previamente ao planeamento estratégico das ações de educação para a prevenção do VIH/Sida no Sudão do Sul. Palavras-chave: Conhecimentos; VIH/Sida; Sudão do Sul.