Estudio de PMN cerámico dopado con KNbO3 y LiNbO 3 sintetizado por Pechini

PMN belongs to a special class of materials named relaxor ferroelectrics. It has high volumetric efficiency due to its high dielectric constant, which makes it in a perfect material for application in multilayer capacitors. When prepared the columbite route its preparation has many advantages. In this work, the preparations of columbite and PMN were done by Pechini and Partial Oxalate methods, respectively. The effects of the KNbO3 and LiNbO3 dopants added in various concentrations. The idea is founded on the correlations that they have with BaTiO3 y PbTiO3, respectively. The whole process was supervised by TG/DTA, XRD, SEM and determination of the specific surface area of the powders. LiNbO3 carries out the pre-sinterization of the particles, observed by a reduction in the surface area. There are not particle grow, but occur its lengthening. However, for KNbO3 these particle growth, but the agglomerates are softer. The effect produced by the doping during the synthesis of the PMN powder is different from the one produced in the columbite precursor. Pure precursor shows an average particle size of 0,2μm, but the addition of 5,0mol% of dopants carries out the formation of agglomerates close to 4μm. LiNbO 3 addition carries out spherical particles and pre-sinterization, while KNbO3 addition does not change the particles shape.

An environment for knowledge discovery in biology

This paper describes a data mining environment for knowledge discovery in bioinformatics applications. The system has a generic kernel that implements the mining functions to be applied to input primary databases, with a warehouse architecture, of biomedical information. Both supervised and unsupervised classification can be implemented within the kernel and applied to data extracted from the primary database, with the results being suitably stored in a complex object database for knowledge discovery. The kernel also includes a specific high-performance library that allows designing and applying the mining functions in parallel machines. The experimental results obtained by the application of the kernel functions are reported. © 2003 Elsevier Ltd. All rights reserved.

Indicadores de floresta urbana a partir de imagens aéreas multiespectrais de alta resolução

The present work evaluated urban forest indicators, acquired through airborne high-resolution multiespectral images, on the quality of the urban design and its vegetative fraction, in special its trees, in nine neighborhoods of Piracicaba, SP. There were made supervised classifications for characterization of intra-urban elements and the proportions obtained, as exposed soil, tree cover, lawns, asphalt, concrete pavements and roofs. They were studied for the measurement of the urban forest in each place. These variables were related to each other, as well as with the independent variables: population density, people with more than fifteen years of study and family heads with income above twenty minimum wages, obtained through population census. Through the analysis of linear regression variables were identified for intra-urban areas evaluation. Correlations were made and linear regressions among the data obtained from the image and among the proposed indicators. Negative correlations were obtained among population density and arboreal covering and the evaluated indices, in accordance with the predicted in the literature. Composite indicators are proposed, as: the proportion between arboreous space on waterproof space (PAW) and the proportion between arboreous space on building space (PAB). It is concluded by the possibility of the use of those indicators for evaluation of the urban forest and definition of priorities in the execution of ordinances to the improvement of the urban forestry, being prioritized the application of resources in the most lacking neighborhoods.

Assessment of the diet of 0- to 6-year-old children in municipal schools in a Brazilian city

Diet control is one of the important factors in the prevention of dental caries because food functions as substratum for fermentation and, consequently, for the formation of the organic acids that demineralize the tooth surface. This study aims to descriptively assess school diet and the associated caries-preventive methods applied to children in all municipal nursery schools of a Brazilian city (Aragatuba/SP). For this, a questionnaire with open and closed questions was used. The results showed that all schools serve school meal, which is composed mainly of sugar, carbohydrates, and proteins. The students enjoy the meal very much because for most of them, the meal served at school is the only source of food. It was observed that 90% of the schools offer other kinds of food besides the main school meal. The snacks served such as cakes, white hominy, and milk fudge are composed of sweet and highly cariogenic foods. It was also verified that in 13.30% of the schools, the daily supervised dental hygiene, an important procedure that should not be neglected, is not carried out. This procedure introduces the children to healthy habits that are added to those acquired in the family environment. It was concluded that the school diet is potentially cariogenic and, in association with the lack of daily dental hygiene, this potential may become even higher.

Fluoridation of the public water supply and prevalence of dental fluorosis in a peripheral district of the municipality of Bauru, SP

Objectives: The objectives of this study were to assess the fluoride concentration in the public water supply and the prevalence of dental fluorosis in schoolchildren between 7 and 15 years old, living in a peripheral district of the municipality of Bauru. Material and Methods: For this, fifty two water samples were collected on three different days of one week. These samples were analyzed for fluoride by means of the ion-sensitive electrode method (Orion 9609) coupled to a potentiometer (Procyon, model 720). In this method, 1.0 mL of TISAB II (Orion) was added to 1.0 mL of the sample. For the epidemiological survey of fluorosis, 52 schoolchildren of both genders, aged between 7 and 15 were assessed, with prior authorization from their caretakers. Only one person examined the children, after supervised toothbrushing and drying with cotton wool rolls. The TF index was used. Results: The fluoride concentrations in the water samples ranged from 0.62 to 1.20 mg/L, with a mean of 0.9 mg/L. The prevalence of dental fluorosis was 33%, with severity ranging from TF1 to TF4 (Kappa of 0.73 and concordance of 83.33%). Conclusions: The results from the analysis of water samples indicated a fluoride concentration greater than recommended for Bauru. The fluorosis levels found were higher than expected for a peripheral district, in which water is one of the few sources of fluoride.

Prevalence of dental fluorosis in Bauru, São Paulo, Brazil

Objective: To evaluate the prevalence of dental fluorosis in scholars aging 12 to 15 years old, residents in the city of Bauru, State of São Paulo, Brazil. Methods: 1318 volunteers were enrolled in this study and examined in 18 public schools of the State of São Paulo. The examinations were performed in the schools' court by three dentists (with a Master's degree in Public Health), after toothbrushing supervised by another dentist. The teeth were dried with cotton pellets and examined under natural light by visual inspection, using an explorer as recommended by the WHO, a plane mirror and a tongue depressor. The Thylstrup-Fejerskov (TF) index was used for rating fluorosis. Intra and inter-examiner reproducibility was calculated and data were submitted to descriptive analysis. Results: Approximately 36% of the children presented dental fluorosis, of which 28% was diagnosed as TF1 while the remaining received scores between TF2 and TF4. Conclusion: The prevalence of dental fluorosis in Bauru is within the expected range, based on previous studies. Although fluoride is an important resource for caries control, its use must be adequate to the needs of each specific population.

Efetividade de um programa de exercícios no condicionamento físico, perfil metabólico e pressão arterial de pacientes hipertensos

Background and objective: It has been shown that aerobic exercise is useful to reduce arterial pressure, however, the effectiveness of an exercise program is still controversial and not very well analyzed among populations with low-income. The objective of the present study was to set up an individualized physical fitness program - Projeto Hipertensão - focused on hypertensive people, patients from a Health Basic Unit (HBU) and, after that, to investigate the effects of this program on physical fitness, metabolic profile and pressure levels. Methods: Sixteen hypertensive women (56 ± 3yrs) under regular pharmacological treatment underwent 4 months of a supervised aerobic and stretching exercise program (3 sessions/wk, 90 min/session, 60% of V̇O 2 max). Several physical and metabolic variables were compared before and after 4 months of training. Results: Training significantly reduced systolic arterial pressure (SAP, -6%), improved cardio-respiratory fitness (+42% of V̇O2max), flexibility (+11%) and plasma glucose content (-4%). BMI and % fat did not change. Besides modifying metabolic profile, it was found that training presented significant correlations between individual initial values of cholesterol total level (CT), high density lipoprotein (HDL-C) and low density lipoprotein (LDL-C) and its responses after exercise. Conclusions: The study shows that exercise programs can be personalized for hypertensive patients from a HBU and confirms the effectiveness of exercise on AP, physical fitness, flexibility and lipid profile on hypertensive patients. The expressive reduction of AP in hypertensive subjects suggests that this exercise intervention should be emphasized on other health centers which assist low-income population.

Avaliação do uso da terra em micro̧bacia utilizando uma matriz de partição fuzzy

Informatics evolution presently offers the possibility of new technique and methodology development for studies in all human knowledge areas. In addition, the present personal computer capacity of handling a large volume of data makes the creation and application of new analysis tools easy. This paper aimed the application of a fuzzy partition matrix to analyze data obtained from the Landsat 5 TMN sensor, in order to elaborate the supervised classification of land use in Arroio das Pombas microbasin in Botucatu, SP, Brazil. It was possible that one single training area present input in more than one covering class due to weight attribution at the signature creation moment. A change in the classification result was also observed when compared to maximum likelihood classification, mainly when related to bigger uniformity and better class edges classification.

Efeito de um mês de destreinamento sobre a aptidão física relacionada à saúde em programa de mudança de estilo de vida

Physical exercise induces hemodynamic/ventilatory and neuromuscular adaptations that can be reverted with interruption of the training program. The aim of the present study was to evaluate the effect of detraining on physical fitness related to health. Forty-four healthy subjects, both male and female, aged 57.6±8.9 years performed the 'Mexa-se Pró-Saúde' protocol with nutritional orientation and supervised physical exercises for nine months. The program consisted of aerobic, localized muscular endurance and flexibility exercises, with duration 80 minutes/session, five days/week. Only subjects who participated in the program for more than three days/week have been selected. The detraining period was one month. Weight (kg) and height (m) were measured and the body mass index (BMI) calculated. Additionally, motors tests to evaluate the flexibility (FLEX), strength of lower limbs (SLL) and upper limbs (SUL), and maximal oxygen uptake (VO2máx) were conducted in the beginning of the study (MI), after nine months of practicing (MT) and after detraining period (MD). ANOVA (p<0.05) and Tukey test to show the difference between groups when it evidence were used for statistical treatment. The results showed that the gains of 22% and 7% on SLL and VO2máx respectively, obtained with the training, have not changed during the detraining period; however, the flexibility gain of 8% returned back to baseline after the detraining period. BMI and SUL did not change during the study. Although the lower limbs strength gains and maximal oxygen uptake obtained have been kept, one month of detraining was enough for losing the flexibility acquired.

Infrared face recogniton by optimum-path forest

This paper presents a novel, fast and accurate appearance-based method for infrared face recognition. By introducing the Optimum-Path Forest classifier, our objective is to get good recognition rates and effectively reduce the computational effort. The feature extraction procedure is carried out by PCA, and the results are compared to two other well known supervised learning classifiers; Artificial Neural Networks and Support Vector Machines. The achieved performance asserts the promise of the proposed framework. ©2009 IEEE.

Fast non-technical losses identification through Optimum-Path Forest

Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as Artificial Neural Networks and Support Vector Machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the Optimum-Path Forest classifier for a fast non-technical losses recognition, which has been demonstrated to be superior than neural networks and similar to Support Vector Machines, but much faster. Comparisons among these classifiers are also presented. © 2009 IEEE.

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.

Robust and fast vowel recognition using optimum-path forest

The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

Optimizing optimum-path forest classification for huge datasets

Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for the automatic recognition of white matter and gray matter in magnetic resonance images of the human brain. © 2010 IEEE.

Parkinson's disease identification through Optimum-Path Forest

Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification. © 2010 IEEE.