Avaliação da função ventricular esquerda e dimensão da aurícula esquerda por Vscan® e relatório de estágio

Mestrado em Tecnologia de Diagnóstico e Intervenção Cardiovascular. Área de especialização: Ultrassonografia Cardiovascular.

Projecto de fundações e estruturas do edifício administrativo do parque de materiais e oficinas de Ceira

O presente trabalho refere-se às fases de projecto da estrutura do Edifício Administrativo do Parque de Material e Oficinas, destinado ao apoio à frota circulante para a rede de transportes do Metro do Mondego. As instalações serão construídas junto à Estação de Ceira. Na escolha das soluções a adoptar foram considerados todos os condicionalismos existentes, em particular os inerentes à implantação geral da obra e sua utilização. Foram igualmente tidas em conta as acções passíveis de actuar na estrutura, bem como as propriedades dos materiais utilizados e os processos construtivos que venham a ser utilizados.

Solubility of Ethene in Water and in a Medium for the Cultivation of a Bacterial Strain

The solubility of ethene in water and in the fermentation medium of Xanthobacter Py(2) was determined with a Ben-Naim-Baer type apparatus. The solubility measurements were carried out in the temperature range of (293.15 to 323.15) K and at atmospheric pressure with a precision of about +/- 0.3 %. The Ostwald coefficients, the mole fractions of the dissolved ethene, at the gas partial pressure of 101.325 kPa, and the Henry coefficients, at the water vapor pressure, were calculated using accurate thermodynamic relations. A comparison between the solubility of ethene in water and in the cultivation medium has shown that this gas is about 2.4 % more soluble in pure water. On the other hand, from the solubility temperature dependence, the Gibbs energy, enthalpy, and entropy changes for the process of transferring the solute from the gaseous phase to the liquid solutions were also determined. Moreover, the perturbed-chain statistical associating fluid theory equation of state (PC-SAFT EOS) model was used for the prediction of the solubility of ethene in water. New parameters, k(ij), are proposed for this system, and it was found that using a ky temperature-dependent PC-SAFT EOS describes more accurately the behavior solubilities of ethene in water at 101.325 kPa, improving the deviations to 1 %.

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.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

Multilevel negotiation in smart grids for VPP management of distributed resources

A multilevel negotiation mechanism for operating smart grids and negotiating in electricity markets considers the advantages of virtual power player management.

Adaptation model for PCMAT – Mathematics collaborative learning platform

The aim of this paper is to present an adaptation model for an Adaptive Educational Hypermedia System, PCMAT. The adaptation of the application is based on progressive self-assessment (exercises, tasks, and so on) and applies the constructivist learning theory and the learning styles theory. Our objective is the creation of a better, more adequate adaptation model that takes into account the complexities of different users.

AI in power systems and energy markets

In a world increasingly conscientious about environmental effects, power and energy systems are undergoing huge transformations. Electric energy produced from power plants is transmitted and distributed to end users through a power grid. The power industry performs the engineering design, installation, operation, and maintenance tasks to provide a high-quality, secure energy supply while accounting for its systems’ abilities to withstand uncertain events, such as weather-related outages. Competitive, deregulated electricity markets and new renewable energy sources, however, have further complicated this already complex infrastructure.Sustainable development has also been a challenge for power systems. Recently, there has been a signifi cant increase in the installation of distributed generations, mainly based on renewable resources such as wind and solar. Integrating these new generation systems leads to more complexity. Indeed, the number of generation sources greatly increases as the grid embraces numerous smaller and distributed resources. In addition, the inherent uncertainties of wind and solar energy lead to technical challenges such as forecasting, scheduling, operation, control, and risk management. In this special issue introductory article, we analyze the key areas in this field that can benefi t most from AI and intelligent systems now and in the future.We also identify new opportunities for cross-fertilization between power systems and energy markets and intelligent systems researchers.

Personality, emotion, and mood in agent-based group decision making

Group decision making plays an important role in organizations, especially in the present-day economy that demands high-quality, yet quick decisions. Group decision-support systems (GDSSs) are interactive computer-based environments that support concerted, coordinated team efforts toward the completion of joint tasks. The need for collaborative work in organizations has led to the development of a set of general collaborative computer-supported technologies and specific GDSSs that support distributed groups (in time and space) in various domains. However, each person is unique and has different reactions to various arguments. Many times a disagreement arises because of the way we began arguing, not because of the content itself. Nevertheless, emotion, mood, and personality factors have not yet been addressed in GDSSs, despite how strongly they influence results. Our group’s previous work considered the roles that emotion and mood play in decision making. In this article, we reformulate these factors and include personality as well. Thus, this work incorporates personality, emotion, and mood in the negotiation process of an argumentbased group decision-making process. Our main goal in this work is to improve the negotiation process through argumentation using the affective characteristics of the involved participants. Each participant agent represents a group decision member. This representation lets us simulate people with different personalities. The discussion process between group members (agents) is made through the exchange of persuasive arguments. Although our multiagent architecture model4 includes two types of agents—the facilitator and the participant— this article focuses on the emotional, personality, and argumentation components of the participant agent.

Context-aware emotion-based model for group decision making

Involving groups in important management processes such as decision making has several advantages. By discussing and combining ideas, counter ideas, critical opinions, identified constraints, and alternatives, a group of individuals can test potentially better solutions, sometimes in the form of new products, services, and plans. In the past few decades, operations research, AI, and computer science have had tremendous success creating software systems that can achieve optimal solutions, even for complex problems. The only drawback is that people don’t always agree with these solutions. Sometimes this dissatisfaction is due to an incorrect parameterization of the problem. Nevertheless, the reasons people don’t like a solution might not be quantifiable, because those reasons are often based on aspects such as emotion, mood, and personality. At the same time, monolithic individual decisionsupport systems centered on optimizing solutions are being replaced by collaborative systems and group decision-support systems (GDSSs) that focus more on establishing connections between people in organizations. These systems follow a kind of social paradigm. Combining both optimization- and socialcentered approaches is a topic of current research. However, even if such a hybrid approach can be developed, it will still miss an essential point: the emotional nature of group participants in decision-making tasks. We’ve developed a context-aware emotion based model to design intelligent agents for group decision-making processes. To evaluate this model, we’ve incorporated it in an agent-based simulator called ABS4GD (Agent-Based Simulation for Group Decision), which we developed. This multiagent simulator considers emotion- and argument based factors while supporting group decision-making processes. Experiments show that agents endowed with emotional awareness achieve agreements more quickly than those without such awareness. Hence, participant agents that integrate emotional factors in their judgments can be more successful because, in exchanging arguments with other agents, they consider the emotional nature of group decision making.

An emotional and context-aware model for adapting RSS news to users and groups

The exhibition of information does not always attend to the preferences and characteristics of the users, nor the context that involves the user. With the aim of overcoming this gap, we propose an emotional context-aware model for adapting information contents to users and groups. The proposed model is based on OCC and Big Five models to handle emotion and personality respectively. The idea is to adapt the representation of the information in order to maximize the positive emotional valences and minimize the negatives. To evaluate the proposed model it was developed a prototype for adapting RSS news to users and group of users.

Demand response management in power systems using a particle swarm optimization approach

Competitive electricity markets have arisen as a result of power-sector restructuration and power-system deregulation. The players participating in competitive electricity markets must define strategies and make decisions using all the available information and business opportunities.

Tissue composition and density impact on the clinical parameters for 125I prostate implants dosimetry

The MCNPX code was used to calculate the TG-43U1 recommended parameters in water and prostate tissue in order to quantify the dosimetric impact in 30 patients treated with (125)I prostate implants when replacing the TG-43U1 formalism parameters calculated in water by a prostate-like medium in the planning system (PS) and to evaluate the uncertainties associated with Monte Carlo (MC) calculations. The prostate density was obtained from the CT of 100 patients with prostate cancer. The deviations between our results for water and the TG-43U1 consensus dataset values were -2.6% for prostate V100, -13.0% for V150, and -5.8% for D90; -2.0% for rectum V100, and -5.1% for D0.1; -5.0% for urethra D10, and -5.1% for D30. The same differences between our water and prostate results were all under 0.3%. Uncertainties estimations were up to 2.9% for the gL(r) function, 13.4% for the F(r,θ) function and 7.0% for Λ, mainly due to seed geometry uncertainties. Uncertainties in extracting the TG-43U1 parameters in the MC simulations as well as in the literature comparison are of the same order of magnitude as the differences between dose distributions computed for water and prostate-like medium. The selection of the parameters for the PS should be done carefully, as it may considerably affect the dose distributions. The seeds internal geometry uncertainties are a major limiting factor in the MC parameters deduction.

Molecular mobility of n-ethylene glycol dimethacrylate glass formers upon free radical polymerization

Quando um líquido evita a cristalização durante o arrefecimento, diz-se que entra no estado sobrearrefecido. Se a temperatura continuar a diminuir, o consequente aumento da viscosidade reflecte-se na mobilidade molecular de tal maneira que os tempos característicos se tornam da mesma ordem de grandeza que os tempos acessíveis experimentalmente. Se o arrefecimento continuar, o líquido altamente viscoso acaba por vitrificar, i.e. entra no estado vítreo onde apenas os movimentos locais são permitidos. Os monómeros da família n -etileno glicol dimetacrilato ( n -EGDMA, para n = 1 até 4, que constituem o objecto deste estudo, facilmente evitam a cristalização, sendo pois bons candidatos para estudar a mobilidade molecular nos estados sobrearrefecido e vítreo. A Espectroscopia de Relaxação Dieléctrica (DRS) foi a técnica escolhida para obter informação detalhada sobre a sua dinâmica molecular (Capítulos 1 e 2). A primeira parte deste trabalho consistiu na caracterização dieléctrica dos processos de relaxação existentes acima e abaixo da temperatura de transição vítrea (g T ), a qual aumenta com o aumento do peso molecular (w M ), sendo este resultado confirmado por Calorimetria Diferencial de Varrimento (DSC). No que respeita ao processo cooperativo a , associado à transição vítrea, e ao processo secundário b, observa-se uma dependência com w M , enquanto que o outro processo secundário, g , aparenta ser independente deste factor (Capítulo 3). Nos capítulos seguintes, foram levadas a cabo diferentes estratégias com o objectivo de clarificar os mecanismos que estão na origem destas duas relaxações secundárias (b e g ), assim como conhecer a sua respectiva relação com a relaxação principal (a ). Do estudo, em tempo real, da polimerização isotérmica via radicais livres do TrEGDMA por Calorimetria de Varrimento Diferencial com Modulação de Temperatura (TMDSC), levado a cabo a temperaturas abaixo da g T do polímero final, concluem-se entre outros, dois importantes aspectos: i) que a vitrificação do polímero em formação conduz a graus de conversão relativamente baixos, e ii) que o monómero que está por reagir é expulso da rede polimérica que se forma, dando lugar a uma clara separação de fases (Capítulo 4). Com base nesta informação, o passo seguinte foi estudar separadamente a polimerização isotérmica do di-, tri- e tetra-EGDMA, dando especial atenção às alterações de mobilidade do monómero ainda por reagir. Com as restrições impostas pela formação de ligações químicas, as relaxações a e b detectadas no monómero tendem a desaparecer no novo polímero formado, enquanto que a relaxação g se mantém quase inalterada. Os diferentes comportamentos que aparecem durante a polimerização permitiram a atribuição da origem molecular dos processos secundários: o processo g foi associado ao movimento twisting das unidades etileno glicol, enquanto que a rotação dos grupos carboxilo foi relacionada com a relaxação b (Capítulo 5). No que respeita ao próprio polímero, um processo de relaxação adicional foi detectado, pol b , no poly-DEGDMA, poly-TrEGDMA e poly-TeEGDMA, com características similares ao encontrado nos poli(metacrilato de n -alquilo). Este processo foi confirmado e bem caracterizado aquando do estudo da copolimerização do TrEGDMA com acrilato de metilo (MA) para diferentes composições (Capítulo 6). Para finalizar, o EGDMA, o elemento mais pequeno da família de monómeros estudada, além de vitrificar apresenta uma marcada tendência para cristalizar quer a partir do estado líquido ou do estado vítreo. Durante a cristalização, a formação de uma fase rígida afecta principalmente o processo a , cuja intensidade diminui sem no entanto se observarem modificações significativas na dependência do tempo de relaxação característico com a temperatura. Por outro lado, o processo secundário b torna-se melhor definido e mais estreito, o que pode ser interpretado em termos de uma maior homogeneidade dos micro-ambientes associados aos movimentos locais(Capítulo 7).

Distributed, Agent-Based Intelligent System for Demand Response Program Simulation in Smart Grids

A distributed, agent-based intelligent system models and simulates a smart grid using physical players and computationally simulated agents. The proposed system can assess the impact of demand response programs.