A segmentation-based and partial-volume-compensated method for an accurate measurement of lateral ventricular volumes on T-1-weighted magnetic resonance images

Lateral ventricular volumes based on segmented brain MR images can be significantly underestimated if partial volume effects are not considered. This is because a group of voxels in the neighborhood of lateral ventricles is often mis-classified as gray matter voxels due to partial volume effects. This group of voxels is actually a mixture of ventricular cerebro-spinal fluid and the white matter and therefore, a portion of it should be included as part of the lateral ventricular structure. In this note, we describe an automated method for the measurement of lateral ventricular volumes on segmented brain MR images. Image segmentation was carried in combination of intensity correction and thresholding. The method is featured with a procedure for addressing mis-classified voxels in the surrounding of lateral ventricles. A detailed analysis showed that lateral ventricular volumes could be underestimated by 10 to 30% depending upon the size of the lateral ventricular structure, if mis-classified voxels were not included. Validation of the method was done through comparison with the averaged manually traced volumes. Finally, the merit of the method is demonstrated in the evaluation of the rate of lateral ventricular enlargement. (C) 2001 Elsevier Science Inc. All rights reserved.

Role of hlx1 in zebrafish brain morphogenesis

hlx1 is a related homeobox gene expressed in a dynamic spatiotemporal expression pattern during development of the zebrafish brain. The homologues of hlx1, mouse dbx1 and Xenopus Xdbx, are known to play a role in the specification of neurons in the spinal cord. However, the role of these molecules in the brain is less well known. We have used two different approaches to elucidate a putative function for hlx1 in the developing zebrafish brain. Blastomeres were injected with either synthetic hlx1 mRNA in gain-of-function experiments or with antisense morpholino oligonucleotides directed against hlx1 in loss-of-function experiments. Mis-expression of hlx1 produced severe defects in brain morphogenesis as a result of abnormal ventricle formation, a phenotype we referred to as fused-brain. These animals also showed a reduction in the size of forebrain neuronal clusters as well as abnormal axon pathfinding. hlx1 antisense morpholinos specifically perturbed hindbrain morphogenesis leading to defects in the integrity of the neuroepithelium. While hindbrain patterning was in the most part unaffected there were select disruptions to the expression pattern of the neurogenic gene Zash1B in specific rhombomeres. Our results indicate multiple roles for hlx1 during zebrafish brain morphogenesis.

Painel 4 - Experi??ncias Setoriais: o Monitoramento nas ??reas de Meio Ambiente e Desenvolvimento Social

Painel 4 - Experi??ncias Setoriais: o Monitoramento nas ??reas de Meio Ambiente e Desenvolvimento Social; Palestra 1 - Minist??rio do Desenvolvimento Social e Combate ?? Fome: ???Monitoramento Anal??tico do Plano Brasil Sem Mis??ria e de Programas do Minist??rio do Desenvolvimento Social e Combate Fome???, apresentada por Marconi Fernandes de Sousa; Palestra 2 - Minist??rio do Meio Ambiente: ???Monitoramento de a????es governamentais de mudan??a do clima e combate ao desmatamento por interm??dio de ferramentas gerenciais e satelitais???, apresentada por Carla Leal Louren??o de Miranda, Josana de Oliveira Lima Esser e Nelcil??ndia Pereira de Oliveira

Cem anos de Josu?? de Castro: legado permanece

No ano de seu centen??rio, celebrar o nome de Josu?? de Castro ultrapassa a mera sauda????o ao ge??grafo, intelectual, professor e diplomata pernambucano, autor de Geografia da Fome, seu mais c??lebre livro datado de 1946 e traduzido em mais de 25 idiomas. Seu m??rito est?? inscrito em um amplo e consolidado legado de toda uma vida e obra dedicadas ao combate ?? fome, verdadeiro libelo que denuncia um dos maiores flagelos da humanidade.

Laser scanned photodiodes (LSP) for Image sensing

An optimized ZnO:Al/a-pin SixCl1-x:H/Al configuration for the laser scanned photodiode (LSP) imaging detector is proposed. The LSP utilizes light induced depletion layers as detector and a laser beam for readout. The effect of the sensing element structure, cell configuration and light source flux are investigated and correlated with the sensor output characteristics. Experimental data reveal that the large optical gap and the low conductivity of the doped a-SixC1-x:H layers are responsible by an induced inversion layer at the illuminated interfaces which blocks the carrier collection. These insulator-like layers act as MIS gates preventing image smearing. The physical background of the LSP is discussed.

Sensor element for a metal-insulator-semiconductor camera system (MISCam)

We discuss the operation of a new type of optical sensor (MISCam) based on a metal-insulator-semiconductor (MIS) structure. The operation principle relies on light-induced changes of the band bending and barrier height at the interface between semiconductor and insulator. An image is obtained from the quenching of the ac signal in analogy to the principle of the laser-scanned photodiode (LSP). Lateral resolution depends on the semiconductor material chosen. We have characterised the MIS structures by C-V, I-V, and spectral response measurements testing different types of insulators like a-Si3N4, SiO2, and AlN. The presence of slow interface charges allows for image memory. Colour sensors can be realised by controlling sign and magnitude of the electric fields in the base and the interface region.

Systematic and liquidity risk in sub-prime mortgage-backed assets

The mis-evaluation of risk in securitized financial products is central to understanding the global financial crisis. This paper characterizes the evolution of risk factors affecting collateralized debt obligations (CDOs) based on subprime mortgages. A key feature of subprime mortgage-backed indices is that they are distinct in their vintage of issuance. Using a latent factor framework that incorporates this vintage effect, we show the increasing importance of common factors on more senior tranches during the crisis. An innovation of the paper is that we use the unbalanced panel structure of the data to identify the vintage, credit, common and idiosyncratic effects from a state-space specification.

Influência da actividade do tronco inferior na sequência de activação muscular proximal durante o passo (fase de apoio) - em crianças com quadro motor diplegia

A marcha assegura uma progressão do corpo, compatível com o equilíbrio dinâmico e adaptada a potenciais factores destabilizadores, de um ponto de vista antecipatório, através de sinergias coordenadas entre os MSs, o tronco e os MIs. O tronco inferior tem um papel preponderante na marcha, sobretudo na estabilização necessária durante a fase de apoio. Esta actividade implica mobilidade pélvica e alongamento activo dos abdominais para conseguir a relação comprimento-tensão muscular óptima entre quadricípite e isquiotibiais, permitindo uma correcta sequência, timing e amplitude de activação. Nas crianças com alterações neuromotoras existem alterações no controlo do movimento e na estrutura do próprio movimento, alterando todo este processo. Como tal, este estudo tem como principal objectivo determinar a influência da actividade do tronco inferior na activação muscular proximal durante a fase de apoio da marcha, em crianças com quadro motor de diplegia, caracterizada por uma dificuldade na relação entre os membros e entre estes e o tronco. Para responder a este objectivo realizou-se um estudo de série de casos, com 2 crianças com quadro motor de diplegia. Efectuou-se EMG dos músculos abdominais, quadricípite e isquiotibiais e análise de imagem (para amplitude da CF) durante a marcha, em ambos os membros e em dois momentos de avaliação, separados por 2 meses, nos quais se realizou um protocolo de intervenção terapêutica adequado a cada caso. Os resultados indicam que a variação de amplitude da CF desde a fase de ataque ao solo à fase média de apoio é aproximadamente igual em M0e M1; concretamente, a amplitude inicial é inferior à de referência (pouca flexão) (melhor em M0) e a amplitude final é superior à de referência (pouca extensão) (melhor em M1). Estes resultados são idênticos em ambos os casos. Na EMG verificou-se uma actividade mais global e sincronizada de todos os músculos, mantendo-se aproximadamente a mesma percentagem de activação em M1, sobretudo no caso 1. No caso 2 verificou-se uma maior eficiência na variação da percentagem de activação dos abdominais, em M1, e dos isquiotibiais, à direita. Em conclusão, pode dizer-se que, em crianças com alterações neuromotoras (quadro motor de diplegia), uma actividade mais eficiente e sincronizada no tempo do tronco inferior, nomeadamente dos abdominais, contribui para uma maior capacidade de extensão da CF, durante a fase de apoio.

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.

Drogas, sujeito e proibicionismo: uma discussão sobre os modelos de atenção aos usuários de drogas

Pós-graduação em Psicologia - FCLAS

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.