Uso de extratos vegetais, vitaminas e sua associação sobre o desempenho, temperamento e qualidade de carne de bovinos nelore confinados com dieta de alto grão

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

Estudo da resistência à tração de cápsulas e troqueis metálicos cimentados, em função de alívio e retenção para o cimento

Pós-graduação em Odontologia Restauradora - ICT

Epidemiologia e prevalência de infecções pelo Vírus da Diarreia Viral Bovina (BVDV) em suínos de criações não tecnificadas

Pós-graduação em Medicina Veterinária - FCAV

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.

EIGENVALUES of FIBONACCI STOCHASTIC ADDING MACHINE

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Seeder-fertilizer machine: energetic demand as a function of vegetal covering handling and manure deposition shank depth in no-tillage system

O trabalho teve por objetivo avaliar a demanda energética de uma semeadora-adubadora, em função do tipo e manejo da cultura de cobertura vegetal e da profundidade da haste de deposição de adubo. Foi utilizado um trator Valtra BM100, instrumentado, para tracionar uma semeadora-adubadora de precisão equipada com quatro fileiras de semeadura espaçadas de 0,9 m para cultura de milho. O experimento foi conduzido em parcelas subsubdivididas, na área experimental do Laboratório de Máquinas e Mecanização Agrícola (LAMMA) da UNESP-Jaboticabal, utilizando duas culturas de cobertura (mucuna-preta e crotalária), três manejos dessas coberturas, sendo dois mecânicos (triturador de palhas e rolo-faca) e um químico (pulverização com herbicida), realizados 120 dias após a semeadura das culturas de cobertura e três profundidades da haste de deposição do adubo (0,11; 0,14 e 0,17 m), perfazendo 18 tratamentos, com quatro repetições, totalizando 72 observações. Foram avaliados os parâmetros velocidade de deslocamento, patinagem, força na barra de tração, força de pico, potência na barra de tração, potência de pico e consumo de combustível. Pôde-se concluir que a força na barra de tração foi menor para as profundidades de 0,11 e 0,14 m da haste sulcadora de adubo, o mesmo ocorrendo para força de pico, potência na barra de tração e consumo volumétrico. O consumo específico foi menor na profundidade de 0,17 m da haste sulcadora de adubo. As culturas de cobertura e seus manejos não interferiram no desempenho das máquinas estudadas.

Analytical method for the design of a machine with aligned and skewed permanent magnets

This work aims to present the design and the evaluation of a standard multi-pole machine with permanent magnets inserted in the rotor by two different geometrical forms: aligned and skewed magnets. The design (new analytical method) was based on a standard 250 W three phase 12-pole induction motor (squirrel cage rotor type), beginning with the original stator constructive data to calculate the magnetic flux density to determine the permanent magnets. In the development of the work, a simple and modular rotor was built reusing the original 12-pole stator (concentrated windings). The machine was evaluated in a laboratory for the purpose of checking the quantity and quality of energy produced with the machine operating as a generator and its start, torque, and performance working as a motor. In conclusion, the modular skewed magnet is an option for electrical machines, for the generation of a reasonable quality, in the context of decentralized generation and a motor with high torque and low energetic consumption.

New approach of induction machine drive using an adaptive digital PID controller with gain planning based on artificial neural networks

The present work introduces a new strategy of induction machines speed adjustment using an adaptive PID (Proportional Integral Derivative) digital controller with gain planning based on the artificial neural networks. This digital controller uses an auxiliary variable to determine the ideal induction machine operating conditions and to establish the closed loop gain of the system. The auxiliary variable value can be estimated from the information stored in a general-purpose artificial neural network based on CMAC (Cerebellar Model Articulation Controller).

Identification of synchronous machine parameters using load rejection test data

This work shows a computational methodology for the determination of synchronous machines parameters using load rejection test data. By machine modeling one can obtain the quadrature parameters through a load rejection under an arbitrary reference, reducing the present difficulties. The proposed method is applied to a real machine.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

Estimation of electrical machine speed using sensorless technology and neural networks

The use of sensorless technologies is an increasing tendency on industrial drivers for electrical machines. The estimation of electrical and mechanical parameters involved with the electrical machine control is used very frequently in order to avoid measurement of all variables related to this process. The cost reduction may also be considered in industrial drivers, besides the increasing robustness of the system, as an advantage of the use of sensorless technologies. This work proposes the use of a recurrent artificial neural network to estimate the speed of induction motor for sensorless control schemes using one single current sensor. Simulation and experimental results are presented to validate the proposed approach. ©2008 IEEE.

Analysis of electric torque in single machine-infinite bus system using phase shifters

ResumoThe main idea of this work is based on the analysis of the electric torque through the acting of the PS in the power system, provided of a control for the compensation degree (PSC). A linear model of the single machine-infinite bus system is used with a PS installed (SMIB/PS system). The variable that represents the presence of PS in the net is associated to the phase displacement introduced in the terminal voltage of the synchronous machine by PS. For the input signals of the PSC are evaluated variations of the angular speed of the rotor, the current magnitude and the active power through the line where the PS is located. The simulations are accomplished to analyze the influence of the PS in the torque formation (synchronizing and damping), of the SMIB/PS system. The analysis are developed in the time and frequency domain.

Effect of toothbrushing with different manual toothbrushes on the shear bond strength of orthodontic brackets

The aim of this study was to evaluate the effect of brushing with a Colgate 360° or Oral B Indicator 35 toothbrush on the shear bond strength of orthodontic brackets bonded to extracted human teeth. The bristle wear and bristle tip morphology were also examined after simulated tooth-brushing. Orthodontic brackets (Roth-P/1 st and 2 nd pre-molar S/D- Slot 0.18) were bonded (Transbond XT ®) to the smoothest surface of each of 45 extracted human molar and premolar teeth. Test specimens were randomly divided into three groups: Group 1, control group with no brushing; Group 2, brushing with the Oral B Indicator 35; Group 3, brushing with the Colgate 360°. Samples were adapted to a machine that simulated tooth-brushing. The bond strength of each bracket to each tooth was assessed with a mechanical testing machine. The bristle wear and bristle tip morphology indices were also assessed. Statistically significant differences were defined for p ≤ 0.05. The average bond strengths (range: 90.18-90.89 kgf/cm 2) did not differ among the three groups. The Colgate 360° showed less bristle wear and a better bristle tip morphology than the Oral B Indicator 35 toothbrush. However, use of either toothbrush did not decrease the bond strength of the orthodontic brackets. Therefore, patients undergoing orthodontic therapy can safely use either toothbrush.

Characterization of texture in image of skin lesions by support vector machine

Due to the increased incidence of skin cancer, computational methods based on intelligent approaches have been developed to aid dermatologists in the diagnosis of skin lesions. This paper proposes a method to classify texture in images, since it is an important feature for the successfully identification of skin lesions. For this is defined a feature vector, with the fractal dimension of images through the box-counting method (BCM), which is used with a SVM to classify the texture of the lesions in to non-irregular or irregular. With the proposed solution, we could obtain an accuracy of 72.84%. © 2012 AISTI.

The assessment of the quality of sugar using electronic tongue and machine learning algorithms

The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE.