Jornalismo best-seller: o livro-reportagem no Brasil contemporâneo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise da robustez e aplicações de controle com modos deslizantes em sistemas incertos com atraso no controle

Pós-graduação em Engenharia Elétrica - FEIS

HLA-E coding and 3' untranslated region variability determined by next-generation sequencing in two West-African population samples

HLA-E is a non-classical Human Leucocyte Antigen class I gene with immunomodulatory properties. Whereas HLA-E expression usually occurs at low levels, it is widely distributed amongst human tissues, has the ability to bind self and non-self antigens and to interact with NK cells and T lymphocytes, being important for immunosurveillance and also for fighting against infections. HLA-E is usually the most conserved locus among all class I genes. However, most of the previous studies evaluating HLA-E variability sequenced only a few exons or genotyped known polymorphisms. Here we report a strategy to evaluate HLA-E variability by next-generation sequencing (NGS) that might be used to other HLA loci and present the HLA-E haplotype diversity considering the segment encoding the entire HLA-E mRNA (including 5'UTR, introns and the 3'UTR) in two African population samples, Susu from Guinea-Conakry and Lobi from Burkina Faso. Our results indicate that (a) the HLA-E gene is indeed conserved, encoding mainly two different protein molecules; (b) Africans do present several unknown HLA-E alleles presenting synonymous mutations; (c) the HLA-E 3'UTR is quite polymorphic and (d) haplotypes in the HLA-E 3'UTR are in close association with HLA-E coding alleles. NGS has proved to be an important tool on data generation for future studies evaluating variability in non-classical MHC genes.

Análise de desempenho de redes de comunicação wireless em aplicações de Smart Grid

Pós-graduação em Engenharia Elétrica - FEIS

A neural network approach for robust nonlinear parameter estimation in presence of unknown-but-bounded errors

Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. This paper presents a novel approach to solve robust parameter estimation problem for nonlinear model with unknown-but-bounded errors and uncertainties. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Network reconfiguration and loss allocation for distribution systems with distributed generation

Distribution systems with distributed generation require new analysis methods since networks are not longer passive. Two of the main problems in this new scenario are the network reconfiguration and the loss allocation. This work presents a distribution systems graphic simulator, developed with reconfiguration functions and a special focus on loss allocation, both considering the presence of distributed generation. This simulator uses a fast and robust power flow algorithm based on the current summation backward-forward technique. Reconfiguration problem is solved through a heuristic methodology and the losses allocation function, based on the Zbus method, is presented as an attached result for each obtained configuration. Results are presented and discussed, remarking the easiness of analysis through the graphic simulator as an excellent tool for planning and operation engineers, and very useful for training. © 2004 IEEE.

A malware detection system inspired on the human immune system

Malicious programs (malware) can cause severe damage on computer systems and data. The mechanism that the human immune system uses to detect and protect from organisms that threaten the human body is efficient and can be adapted to detect malware attacks. In this paper we propose a system to perform malware distributed collection, analysis and detection, this last inspired by the human immune system. After collecting malware samples from Internet, they are dynamically analyzed so as to provide execution traces at the operating system level and network flows that are used to create a behavioral model and to generate a detection signature. Those signatures serve as input to a malware detector, acting as the antibodies in the antigen detection process. This allows us to understand the malware attack and aids in the infection removal procedures. © 2012 Springer-Verlag.

Human face verification based on multidimensional Polynomial Powers of Sigmoid (PPS)

In this paper, we described how a multidimensional wavelet neural networks based on Polynomial Powers of Sigmoid (PPS) can be constructed, trained and applied in image processing tasks. In this sense, a novel and uniform framework for face verification is presented. The framework is based on a family of PPS wavelets,generated from linear combination of the sigmoid functions, and can be considered appearance based in that features are extracted from the face image. The feature vectors are then subjected to subspace projection of PPS-wavelet. The design of PPS-wavelet neural networks is also discussed, which is seldom reported in the literature. The Stirling Universitys face database were used to generate the results. Our method has achieved 92 % of correct detection and 5 % of false detection rate on the database.

A novel approach to robust parameter estimation using neurofuzzy systems

A novel approach for solving robust parameter estimation problems is presented for processes with unknown-but-bounded errors and uncertainties. An artificial neural network is developed to calculate a membership set for model parameters. Techniques of fuzzy logic control lead the network to its equilibrium points. Simulated examples are presented as an illustration of the proposed technique. The result represent a significant improvement over previously proposed methods. (C) 1999 IMACS/Elsevier B.V. B.V. All rights reserved.

Collagen type VI is a component of the extracellular matrix microfibril network of the prostatic stroma

Stroma-epithelium relationships are of great relevance in prostatic morphogenesis and physiology, However, little knowledge exists about either stromal cells or extracellular matrix composition and arrangement in this system, Ultrastructural analysis revealed the existence of a microfibrillar system which occupies large areas of the rat prostatic stroma, In this work, we have applied immunocytochemistry and an ATP treatment for the ultrastructural identification of collagen type VI microfibrils, aiming at examining its participation in the prostatic microfibrillar network. Immunocytochemistry was also extended to a human case of prostatic nodular hyperplasia, Both methods succeeded in identifying collagen type VI in the rat ventral prostate, Collagen type VI is evenly distributed throughout the stroma but mainly associated with the basal lamina, collagen fibrils, and around the stromal cells, the use of ATP treatment allowed for the discrimination between collagen type VI and elastin-associated microfibrils, and demonstrated that these two classes of microfibrils establish an extended, mixed, and open network. The same aspects of association with the basal lamina, with stromal cells (particularly with smooth muscle cells), and with fibrillar components of the stroma were observed in the human tissue, We suggest that the collagen type VI and elastin-associated microfibril system may be involved in the control of some aspects of cellular behavior and may also play a structural role, maintaining the organ integrity after the deformations occurring under smooth muscle contraction.

Combining artificial intelligence and human problem solving: Proposing a new architecture for ITS's

This paper shows a comparative study between the Artificial Intelligence Problem Solving and the Human Problem Solving. The study is based on the solution by many ways of problems proposed via multiple-choice questions. General techniques used by humans to solve this kind of problems are grouped in blocks and each block is divided in steps. A new architecture for ITS - Intelligent Tutoring System is proposed to support experts' knowledge representation and novices' activities. Problems are represented by a text and feasible answers with particular meaning and form, to be rigorously analyzed by the solver to find the right one. Paths through a conceptual space of states represent each right solution.

Perceptual Information Integration: Hypothetical Role of Astrocytes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Astrocytes and human cognition: Modeling information integration and modulation of neuronal activity

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Fuzzy distributed artificial intelligence systems

In this paper, we introduce a DAI approach called hereinafter Fuzzy Distributed Artificial Intelligence (FDAI). Through the use of fuzzy logic, we have been able to develop mechanisms that we feel may effectively improve current DAI systems, giving much more flexibility and providing the subsidies which a formal theory can bring. The appropriateness of the FDAI approach is explored in an important application, a fuzzy distributed traffic-light control system, where we have been able to aggregate and study several issues concerned with fuzzy and distributed artificial intelligence. We also present a number of current research directions necessary to develop the FDAI approach more fully.