Effect of Fe-doping on the structural, microstructural, optical, and ferroeletric properties of Pb1/2Sr1/2Ti1-xFexO3 oxide prepared by spin coating technique

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

Social-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identification

Evolutionary algorithms have been widely used for Artificial Neural Networks (ANN) training, being the idea to update the neurons' weights using social dynamics of living organisms in order to decrease the classification error. In this paper, we have introduced Social-Spider Optimization to improve the training phase of ANN with Multilayer perceptrons, and we validated the proposed approach in the context of Parkinson's Disease recognition. The experimental section has been carried out against with five other well-known meta-heuristics techniques, and it has shown SSO can be a suitable approach for ANN-MLP training step.

Proinflammatory Cytokines Correlate with Depression and Anxiety in Colorectal Cancer Patients

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

Mimosa (Mimosa caesalpiniifolia) prevents oxidative DNA damage induced by cadmium exposure in Wistar rats

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

Mielografia lombar no diagnóstico de extrusão do disco intervertebral toracolombar em cães: estudo prospectivo

Thoracolumbar disk extrusion is the most common cause of extradural compression of the spinal cord in dogs. Myelography is one of the most commonly performed techniques for the diagnosis of this affection. This study aimed to evaluate the applicability and effectiveness of lumbar myelography in the diagnosis of thoracolumbar intervertebral disk extrusion in dogs, as well as its major complications. Twenty dogs were used in this study. Animals were included when neurological examination suggested thoracolumbar spine lesion, myelography was used as a complementary diagnostic method, and diagnosis of disk extrusion was surgically confirmed. The accuracy of the exam to predict location and lateralization of extruded disk material were evaluated, as well as complications associated to the procedure. Lumbar myelography exhibited 95% and 60% accuracy for location and lateralization of the lesion, respectively, with minimal complications.

Harmony Search applied for Support Vector Machines Training Optimization

Since the beginning, some pattern recognition techniques have faced the problem of high computational burden for dataset learning. Among the most widely used techniques, we may highlight Support Vector Machines (SVM), which have obtained very promising results for data classification. However, this classifier requires an expensive training phase, which is dominated by a parameter optimization that aims to make SVM less prone to errors over the training set. In this paper, we model the problem of finding such parameters as a metaheuristic-based optimization task, which is performed through Harmony Search (HS) and some of its variants. The experimental results have showen the robustness of HS-based approaches for such task in comparison against with an exhaustive (grid) search, and also a Particle Swarm Optimization-based implementation.

Riscos e desastres ambientais causados por inundações em áreas urbanas: um estudo da paisagem sob a visão sistêmica

For a long time, humankind has lived together with flooding, and lately, those events have grown enormously, in particular, in the urban areas from lots of Brazilian cities. The flooding has been causing uncountable disadvantages to population and to the cities. The present paper aimed to study, through a bibliographic review, the risks and natural disasters caused by those events. Trying to approach their causes and effects under a systemic view it emphasized the landscape point.

Introducing the Discriminative Paraconsistent Machine (DPM)

This paper introduces a new tool for pattern recognition. Called the Discriminative Paraconsistent Machine (DPM), it is based on a supervised discriminative model training that incorporates paraconsistency criteria and allows an intelligent treatment of contradictions and uncertainties. DPMs can be applied to solve problems in many fields of science, using the tests and discussions presented here, which demonstrate their efficacy and usefulness. Major difficulties and challenges that were overcome consisted basically in establishing the proper model with which to represent the concept of paraconsistency.

Método para reconhecimento de íris baseado na sua região interna

Recognition of individuals through the characteristics of the iris has in recent years become a well accepted technique due to both the high reliability of this procedure and the its non invasiveness. The methods used in such procedures seek information all over the iris, which depending on the algorithm used may result in high computational costs. Considering that most characteristics of the iris are in its inner region the goal of this work is to develop an algorithm for the recognition of individuals using only this region. To bring the outcome of our approach to the level of the best techniques described in the literature this technique has to be further elaborated, even so the results show a promising technique.

Resistência antimicrobiana e prevalência de sorovares de salmonella spp. isolados de fezes e linfonodos de suinos

Pós-graduação em Medicina Veterinária - FMVZ

Individual and contextual factors related to dental caries in underprivileged Brazilian adolescents

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

Quality of life and socio-dental impact among underprivileged brazilian adolescents

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

Presença de fatores de risco de doenças cardiovasculares e de lesões em praticantes de corrida de rua

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)