Identificação em tempo real de modelo dinâmico de robô móvel com acionamento diferencial e zona morta

Several mobile robots show non-linear behavior, mainly due friction phenomena between the mechanical parts of the robot or between the robot and the ground. Linear models are efficient in some cases, but it is necessary take the robot non-linearity in consideration when precise displacement and positioning are desired. In this work a parametric model identification procedure for a mobile robot with differential drive that considers the dead-zone in the robot actuators is proposed. The method consists in dividing the system into Hammerstein systems and then uses the key-term separation principle to present the input-output relations which shows the parameters from both linear and non-linear blocks. The parameters are then simultaneously estimated through a recursive least squares algorithm. The results shows that is possible to identify the dead-zone thresholds together with the linear parameters

Problematizando a Reforma Psiquiátrica na atualidade: a saúde mental como campo da práxis

Mental Health, in the form of the Psychiatric Reform, and the Anti-Asylum Movement do not ignore the production of knowledge about that field, mainly due to the consolidation of Public Health as a field of knowledge. The article explores some authors who consider Mental Health as a new field of knowledge, introducing a new paradigm in the perception of health - Disease and Care -; however, the goal is to introduce Psychosocial Care as a means to enforce the transdisciplinary and multiprofessional practices. The possibility is that mental health produces developments in Health, consolidating the public policies. In practice, the hospital-centered and drug-based model still predominates, and there are setbacks to be overcome by taking advantage of loopholes capable of breaking with what is instituted.

Processos cognitivos que operam na configuração de narrativas: uma pesquisa exploratória dos fenômenos que subjazem à compreensão de textos

This paper investigates the cognitive processes that operate in understanding narratives in this case, the novel Macunaíma, by Mário de Andrade. Our work belongs to the field of Embodied-based Cognitive Linguistics and, due to its interdisciplinary nature, it dialogues with theoretical and methodological frameworks of Psycholinguistics, Cognitive Psychology and Neurosciences. Therefore, we adopt an exploratory research design, recall and cloze tests, adapted, with postgraduation students, all native speakers of Brazilian Portuguese. The choice of Macunaíma as the novel and initial motivation for this proposal is due to the fact it is a fantastic narrative, which consists of events, circumstances and characters that are clearly distant types from what is experienced in everyday life. Thus, the novel provides adequate data to investigate the configuration of meaning, within an understanding-based model. We, therefore, seek, to answer questions that are still, generally, scarcely explored in the field of Cognitive Linguistics, such as to what extent is the activation of mental models (schemas and frames) related to the process of understanding narratives? How are we able to build sense even when words or phrases are not part of our linguistic repertoire? Why do we get emotionally involved when reading a text, even though it is fiction? To answer them, we assume the theoretical stance that meaning is not in the text, it is constructed through language, conceived as a result of the integration between the biological (which results in creating abstract imagery schemes) and the sociocultural (resulting in creating frames) apparatus. In this sense, perception, cognitive processing, reception and transmission of the information described are directly related to how language comprehension occurs. We believe that the results found in our study may contribute to the cognitive studies of language and to the development of language learning and teaching methodologies

Structural damage detection by fuzzy clustering

The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

Screening and Production Study of Microbial Xylanase Producers from Brazilian Cerrado

Hemicelluloses are polysaccharides of low molecular weight containing 100 to 200 glycosidic residues. In plants, the xylans or the hemicelluloses are situated between the lignin and the collection of cellulose fibers underneath. The xylan is the most common hemicellulosic polysaccharide in cell walls of land plants, comprising a backbone of xylose residues linked by beta-1,4-glycosidic bonds. So, xylanolytic enzymes from microorganism have attracted a great deal of attention in the last decade, particularly because of their biotechnological characteristics in various industrial processes, related to food, feed, ethanol, pulp, and paper industries. A microbial screening of xylanase producer was carried out in Brazilian Cerrado area in Selviria city, Mato Grosso do Sul State, Brazil. About 50 bacterial strains and 15 fungal strains were isolated from soil sample at 35 A degrees C. Between these isolated microorganisms, a bacterium Lysinibacillus sp. and a fungus Neosartorya spinosa as good xylanase producers were identified. Based on identification processes, Lysinibacillus sp. is a new species and the xylanase production by this bacterial genus was not reported yet. Similarly, it has not reported about xylanase production from N. spinosa. The bacterial strain P5B1 identified as Lysinibacillus sp. was cultivated on submerged fermentation using as substrate xylan, wheat bran, corn straw, corncob, and sugar cane bagasse. Corn straw and wheat bran show a good xylanase activity after 72 h of fermentation. A fungus identified as N. spinosa (strain P2D16) was cultivated on solid-state fermentation using as substrate source wheat bran, wheat bran plus sawdust, corn straw, corncob, cassava bran, and sugar cane bagasse. Wheat bran and corncobs show the better xylanase production after 72 h of fermentation. Both crude xylanases were characterized and a bacterial xylanase shows optimum pH for enzyme activity at 6.0, whereas a fungal xylanase has optimum pH at 5.0-5.5. They were stable in the pH range 5.0-10.0 and 5.5-8.5 for bacterial and fungal xylanase, respectively. The optimum temperatures were 55C and 60 A degrees C for bacterial and fungal xylanase, respectively, and they were thermally stable up to 50 A degrees C.

Uma estratégia dirigida a modelos e baseada em linguagem de descrição arquitetural para linhas de produtos de software

Model-oriented strategies have been used to facilitate products customization in the software products lines (SPL) context and to generate the source code of these derived products through variability management. Most of these strategies use an UML (Unified Modeling Language)-based model specification. Despite its wide application, the UML-based model specification has some limitations such as the fact that it is essentially graphic, presents deficiencies regarding the precise description of the system architecture semantic representation, and generates a large model, thus hampering the visualization and comprehension of the system elements. In contrast, architecture description languages (ADLs) provide graphic and textual support for the structural representation of architectural elements, their constraints and interactions. This thesis introduces ArchSPL-MDD, a model-driven strategy in which models are specified and configured by using the LightPL-ACME ADL. Such strategy is associated to a generic process with systematic activities that enable to automatically generate customized source code from the product model. ArchSPLMDD strategy integrates aspect-oriented software development (AOSD), modeldriven development (MDD) and SPL, thus enabling the explicit modeling as well as the modularization of variabilities and crosscutting concerns. The process is instantiated by the ArchSPL-MDD tool, which supports the specification of domain models (the focus of the development) in LightPL-ACME. The ArchSPL-MDD uses the Ginga Digital TV middleware as case study. In order to evaluate the efficiency, applicability, expressiveness, and complexity of the ArchSPL-MDD strategy, a controlled experiment was carried out in order to evaluate and compare the ArchSPL-MDD tool with the GingaForAll tool, which instantiates the process that is part of the GingaForAll UML-based strategy. Both tools were used for configuring the products of Ginga SPL and generating the product source code

Uma análise da aplicação do modelo de Rede Neural RePART em Comitês de classificadores

RePART (Reward/Punishment ART) is a neural model that constitutes a variation of the Fuzzy Artmap model. This network was proposed in order to minimize the inherent problems in the Artmap-based model, such as the proliferation of categories and misclassification. RePART makes use of additional mechanisms, such as an instance counting parameter, a reward/punishment process and a variable vigilance parameter. The instance counting parameter, for instance, aims to minimize the misclassification problem, which is a consequence of the sensitivity to the noises, frequently presents in Artmap-based models. On the other hand, the use of the variable vigilance parameter tries to smoouth out the category proliferation problem, which is inherent of Artmap-based models, decreasing the complexity of the net. RePART was originally proposed in order to minimize the aforementioned problems and it was shown to have better performance (higer accuracy and lower complexity) than Artmap-based models. This work proposes an investigation of the performance of the RePART model in classifier ensembles. Different sizes, learning strategies and structures will be used in this investigation. As a result of this investigation, it is aimed to define the main advantages and drawbacks of this model, when used as a component in classifier ensembles. This can provide a broader foundation for the use of RePART in other pattern recognition applications

Edge detection and noise removal by use of a partial differential equation with automatic selection of parameters

This work deals with noise removal by the use of an edge preserving method whose parameters are automatically estimated, for any application, by simply providing information about the standard deviation noise level we wish to eliminate. The desired noiseless image u(x), in a Partial Differential Equation based model, can be viewed as the solution of an evolutionary differential equation u t(x) = F(u xx, u x, u, x, t) which means that the true solution will be reached when t ® ¥. In practical applications we should stop the time ''t'' at some moment during this evolutionary process. This work presents a sufficient condition, related to time t and to the standard deviation s of the noise we desire to remove, which gives a constant T such that u(x, T) is a good approximation of u(x). The approach here focused on edge preservation during the noise elimination process as its main characteristic. The balance between edge points and interior points is carried out by a function g which depends on the initial noisy image u(x, t0), the standard deviation of the noise we want to eliminate and a constant k. The k parameter estimation is also presented in this work therefore making, the proposed model automatic. The model's feasibility and the choice of the optimal time scale is evident through out the various experimental results.

The Origin of Nonmonotonic Complex Behavior and the Effects of Nonnative Interactions on the Diffusive Properties of Protein Folding

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

Jogo da Minoria: um modelo baseado em agentes aplicado ao mercado financeiro

Nos últimos anos houve uma contribuição significativa dos físicos para a construção de um tipo de modelo baseado em agentes que busca reproduzir, em simulação computacional, o comportamento do mercado financeiro. Esse modelo, chamado Jogo da Minoria consiste de um grupo de agentes que vão ao mercado comprar ou vender ativos. Eles tomam decisões com base em estratégias e, por meio delas, os agentes estabelecem um intrincado jogo de competição e coordenação pela distribuição da riqueza. O modelo tem demonstrado resultados bastante ricos e surpreendentes, tanto na dinâmica do sistema como na capacidade de reproduzir características estatísticas e comportamentais do mercado financeiro. Neste artigo, são apresentadas a estrutura e a dinâmica do Jogo da Minoria, bem como as contribuições recentes relacionadas ao Jogo da Minoria denominado de Grande Canônico, que é um modelo mais bem ajustado às características do mercado financeiro e reproduz as regularidades estatísticas do preço dos ativos chamadas fatos estilizados.

Increased corticosterone levels in mice subjected to the rat exposure test

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

Spatial electric load forecasting using a local movement approach

An agent based model for spatial electric load forecasting using a local movement approach for the spatiotemporal allocation of the new loads in the service zone is presented. The density of electrical load for each of the major consumer classes in each sub-zone is used as the current state of the agents. The spatial growth is simulated with a walking agent who starts his path in one of the activity centers of the city and goes to the limits of the city following a radial path depending on the different load levels. A series of update rules are established to simulate the S growth behavior and the complementarity between classes. The results are presented in future load density maps. The tests in a real system from a mid-size city show a high rate of success when compared with other techniques. The most important features of this methodology are the need for few data and the simplicity of the algorithm, allowing for future scalability. © 2009 IEEE.

Case report: Disseminated Mycobacterium tuberculosis infection in a dog

An uncommon disseminated Mycobacterium tuberculosis infection is described in a 12-year-old female dog presenting with fever, dyspnea, cough, weight loss, lymphadenopathy, melena, epistaxis, and emesis. The dog had a history of close contact with its owner, who died of pulmonary tuberculosis. Radiographic examination revealed diffuse radio-opaque images in both lung lobes, diffuse visible masses in abdominal organs, and hilar and mesenteric lymphadenopathy. Bronchial washing samples and feces were negative for acid-fast organisms. Polymerase chain reaction (PCR)-based species identification of bronchial washing samples, feces, and urine revealed M. tuberculosis using PCR-restriction enzyme pattern analysis-PRA. Because of public health concerns, which were worsened by the physical condition of the dog, euthanasia of the animal was recommended. Rough and tough colonies suggestive of M. tuberculosis were observed after microbiological culture of lung, liver, spleen, heart, and lymph node fragments in Löwenstein-Jensen and Stonebrink media. The PRA analysis enabled diagnosis of M. tuberculosis strains isolated from organs. Copyright © 2013 by The American Society of Tropical Medicine and Hygiene.

Analyzing the effect of homogeneous frustration in protein folding

The energy landscape theory has been an invaluable theoretical framework in the understanding of biological processes such as protein folding, oligomerization, and functional transitions. According to the theory, the energy landscape of protein folding is funneled toward the native state, a conformational state that is consistent with the principle of minimal frustration. It has been accepted that real proteins are selected through natural evolution, satisfying the minimum frustration criterion. However, there is evidence that a low degree of frustration accelerates folding. We examined the interplay between topological and energetic protein frustration. We employed a Cα structure-based model for simulations with a controlled nonspecific energetic frustration added to the potential energy function. Thermodynamics and kinetics of a group of 19 proteins are completely characterized as a function of increasing level of energetic frustration. We observed two well-separated groups of proteins: one group where a little frustration enhances folding rates to an optimal value and another where any energetic frustration slows down folding. Protein energetic frustration regimes and their mechanisms are explained by the role of non-native contact interactions in different folding scenarios. These findings strongly correlate with the protein free-energy folding barrier and the absolute contact order parameters. These computational results are corroborated by principal component analysis and partial least square techniques. One simple theoretical model is proposed as a useful tool for experimentalists to predict the limits of improvements in real proteins. © 2013 Wiley Periodicals, Inc.

Estudo do efeito da adição de frustração no enovelamento de proteínas utilizando modelos baseados em estruturas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)