Innovative molecular approach to the identification of Colossoma macropomum and its hybrids

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A New Approach for Nontechnical Losses Detection Based on Optimum-Path Forest

Nowadays, fraud detection is important to avoid nontechnical energy losses. Various electric companies around the world have been faced with such losses, mainly from industrial and commercial consumers. This problem has traditionally been dealt with using artificial intelligence techniques, although their use can result in difficulties such as a high computational burden in the training phase and problems with parameter optimization. A recently-developed pattern recognition technique called optimum-path forest (OPF), however, has been shown to be superior to state-of-the-art artificial intelligence techniques. In this paper, we proposed to use OPF for nontechnical losses detection, as well as to apply its learning and pruning algorithms to this purpose. Comparisons against neural networks and other techniques demonstrated the robustness of the OPF with respect to commercial losses automatic identification.

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

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

Neural network based estimation of torque in induction motors for real-time applications

Induction motors are largely used in several industry sectors. The selection of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this article is to use artificial neural networks for torque estimation with the purpose of best selecting the induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Since proposed approach estimates the torque behavior from the transient to the steady state, one of its main contributions is the potential to also be implemented in control schemes for real-time applications. Simulation results are also presented to validate the proposed approach.

Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Development of a methodology for cost determination of wastewater treatment based on functional diagram

This work describes a methodology developed for determination of costs associated to products generated in a small wastewater treatment station for sanitary wastewater from a university campus. This methodology begins with plant component units identification, relating their fluid and thermodynamics features for each point marked in its process diagram. Following, its functional diagram is developed and its formulation is elaborated, in exergetic base, describing all equations for these points, which are the constraints for exergetic production cost problem and are used in equations to determine the costs associated to products generated in SWTS. This methodology was applied to a hypothetical system based on SWTS former parts and presented consistent results when compared to expected values based on previous exergetic expertise. (C) 2008 Elsevier Ltd. All rights reserved.

Boundary identification criteria and geometry analysis of Al 7050 stretch zone from elevation profiles

This article describes the development of a method for analysis of the shape of the stretch zone surface based on parallax measurement theory and using digital image processing techniques. Accurate criteria for the definition of the boundaries of the stretch zone are established from profiles of fracture surfaces obtained from crack tip opening displacement tests on Al-7050 alloy samples. The elevation profiles behavior analysis is based on stretch zone width and height parameters. It is concluded that the geometry of the stretch zone profiles under plane strain conditions can be described by a semi-parabolic relationship. (C) Elsevier B.V., 1999. All rights reserved.

Wear Particle Classifier System Based on an Artificial Neural Network

This paper describes a method of identifying morphological attributes that classify wear particles in relation to the wear process from which they originate and permit the automatic identification without human expertise. The method is based on the use of Multi Layer Perceptron (MLP) for analysis of specific types of microscopic wear particles. The classification of the wear particles was performed according to their morphological attributes of size and aspect ratio, among others. (C) 2010 Journal of Mechanical Engineering. All rights reserved.

System identification and active vibration control of a flexible structure

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

Adaptive filter feature identification for structural health monitoring in an aeronautical panel

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Identification of dementia cases in the community: A Brazilian experience

OBJECTIVES: To describe the application in Brazil of a simple, low-cost procedure, developed in India by the 10/66 Dementia Research Group, for the identification of dementia cases in the community.DESIGN: Community-based dementia case-finding method.SETTING: Piraju, São Paulo, Brazil.PARTICIPANTS: Twenty-five community health workers were trained to identify dementia cases in 2,222 people aged 65 and older in Piraju, a Brazilian town with 27,871 inhabitants.MEAUREMENTS: After the training, the health workers prepared a list of possible cases that afterward an experienced psychiatrist clinically evaluated, according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), criteria and the Clinical Dementia Rating.RESULTS: of the 72 cases that were clinically assessed, 45 met the DSM-IV diagnostic criteria for dementia. Therefore, the positive predictive value of this case finding method was 62.5%; the estimated frequency of dementia was 2%. Most of the confirmed cases met clinical criteria for Alzheimer's disease and vascular dementia.CONCLUSION: This simple method was appropriate to identify cases of dementia in the general population and can possibly be extended to other developing countries with limited resources to be applied in health programs.

Correlation between API 50 CH and multiplex polymerase chain reaction for the identification of vaginal lactobacilli in isolates

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

In silico network topology-based prediction of gene essentiality

The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on the network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision-tree-based machine-learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes. Finally, the decision-tree classifier generated is applied to the set of genes of this organism to estimate essentiality for each gene. We applied the NTPGE approach for discovering the essential genes in Escherichia coli and then assessed its performance. (C) 2007 Elsevier B.V. All rights reserved.

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.

Boron transport in Eucalyptus. 2. Identification in silico of a putative boron transporter for xylem loading in eucalypt

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