15 resultados para Sample data

em Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho"


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Traditional methods of submerged aquatic vegetation (SAV) survey last long and then, they are high cost. Optical remote sensing is an alternative, but it has some limitations in the aquatic environment. The use of echosounder techniques is efficient to detect submerged targets. Therefore, the aim of this study is to evaluate different kinds of interpolation approach applied on SAV sample data collected by echosounder. This study case was performed in a region of Uberaba River - Brazil. The interpolation methods evaluated in this work follow: Nearest Neighbor, Weighted Average, Triangular Irregular Network (TIN) and ordinary kriging. Better results were carried out with kriging interpolation. Thus, it is recommend the use of geostatistics for spatial inference of SAV from sample data surveyed with echosounder techniques. © 2012 IEEE.

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We present the results of the combination of searches for the standard model Higgs boson produced in association with a W or Z boson and decaying into bb̄ using the data sample collected with the D0 detector in pp̄ collisions at √s=1.96TeV at the Fermilab Tevatron Collider. We derive 95% C.L. upper limits on the Higgs boson cross section relative to the standard model prediction in the mass range 100GeV≤M H≤150GeV, and we exclude Higgs bosons with masses smaller than 102 GeV at the 95% C.L. In the mass range 120GeV≤M H≤145GeV, the data exhibit an excess above the background prediction with a global significance of 1.5 standard deviations, consistent with the expectation in the presence of a standard model Higgs boson. © 2012 American Physical Society.

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Buccal mucosa (BM) cells have been used in human biomonitoring studies for detecting DNA adducts and chromosomal damage in an epithelial cell population. In the present study, we have investigated if human BM cells are suitable for use in the single-cell gel electrophoresis (SCGE)/Comet assay as an approach for estimating the exposure of epithelial cells to DNA-damaging agents. Our results indicate that only a few cells from BM cell samples yield comets that can be analyzed by current methods, and that the yield of cells with comets is independent of the percentage of viable BM cells in the sample. Data generated after enzymatic enrichment of viable cells and immunomagnetic separation of epithelial cells suggest that most of the BM cells that do form comets are probably leukocytes. Moreover, by reevaluating specific cells after running the Comet assay, we found that viable epithelial BM cells give rise to atypical comets that are not included in the analysis. Comparing DNA migration patterns between small groups of smokers and nonsmokers indicated that long-term smoking had no effect on the subpopulation of cells that yield typical comets. Our results indicate that the SCGE assay, as it is commonly performed, may not be useful for genotoxicity monitoring in human epithelial BM cells.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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A body of research has developed within the context of nonlinear signal and image processing that deals with the automatic, statistical design of digital window-based filters. Based on pairs of ideal and observed signals, a filter is designed in an effort to minimize the error between the ideal and filtered signals. The goodness of an optimal filter depends on the relation between the ideal and observed signals, but the goodness of a designed filter also depends on the amount of sample data from which it is designed. In order to lessen the design cost, a filter is often chosen from a given class of filters, thereby constraining the optimization and increasing the error of the optimal filter. To a great extent, the problem of filter design concerns striking the correct balance between the degree of constraint and the design cost. From a different perspective and in a different context, the problem of constraint versus sample size has been a major focus of study within the theory of pattern recognition. This paper discusses the design problem for nonlinear signal processing, shows how the issue naturally transitions into pattern recognition, and then provides a review of salient related pattern-recognition theory. In particular, it discusses classification rules, constrained classification, the Vapnik-Chervonenkis theory, and implications of that theory for morphological classifiers and neural networks. The paper closes by discussing some design approaches developed for nonlinear signal processing, and how the nature of these naturally lead to a decomposition of the error of a designed filter into a sum of the following components: the Bayes error of the unconstrained optimal filter, the cost of constraint, the cost of reducing complexity by compressing the original signal distribution, the design cost, and the contribution of prior knowledge to a decrease in the error. The main purpose of the paper is to present fundamental principles of pattern recognition theory within the framework of active research in nonlinear signal processing.

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In Bayesian Inference it is often desirable to have a posterior density reflecting mainly the information from sample data. To achieve this purpose it is important to employ prior densities which add little information to the sample. We have in the literature many such prior densities, for example, Jeffreys (1967), Lindley (1956); (1961), Hartigan (1964), Bernardo (1979), Zellner (1984), Tibshirani (1989), etc. In the present article, we compare the posterior densities of the reliability function by using Jeffreys, the maximal data information (Zellner, 1984), Tibshirani's, and reference priors for the reliability function R(t) in a Weibull distribution.

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The objective of this work was to evaluate the magnetic susceptibility efficiency for estimating the support capacity of areas for vinasse application. Two hundred forty-one soil samples were collected from a 380-ha area, on which soil chemical properties, clay content, and magnetic susceptibility were determined. Vinasse requirement was calculated for each sample. Data were subjected to descriptive statistical analysis, and regression models were developed between magnetic susceptibility and the other evaluated attributes. The analysis of data spatial dependence was performed using geostatistics. Kriging maps and cross variograms were built in order to investigate the spatial correlation between soil magnetic susceptibility and studied attributes. Based on the map of vinasse requirement, on the soil classes, and on the kriging map, calculations were done for average vinasse dose and average soil support capacity, weighted by the area. Magnetic susceptibility has significant linear spatial correlation with recommended vinasse doses and soil support capacity for the application of this effluent, and it can be used as a pedotransfer function for indirect quantification of soil support capacity.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Agronomia (Ciência do Solo) - FCAV

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Phosphorus is one of the limiting nutrients for sugarcane development in Brazilian soils. The spatial variability of this nutrient is great, defined by the properties that control its adsorption and desorption reactions. Spatial estimates to characterize this variability are based on geostatistical interpolation. However, inherent uncertainties in the procedure of these estimates are related to the variability structure of the property under study and the sample configuration of the area. Thus, the assessment of the uncertainty of estimates associated with the spatial distribution of available P (Plabile) is decisive to optimize the use of phosphate fertilizers. The purpose of this study was to evaluate the performance of sequential Gaussian simulation (sGs) and ordinary kriging (OK) in the modeling of uncertainty in available P estimates. A sampling grid with 626 points was established in a 200-ha experimental sugarcane field in Tabapuã, São Paulo State. The sGs algorithm generated 200 realizations. The sGs realizations reproduced the statistics and the distribution of the sample data. The G statistic (0.81) indicated good agreement between the values of simulated and observed fractions. The sGs realizations preserved the spatial variability of Plabile without the smoothing effect of the OK map. The accuracy in the reproduction of the variogram of the sample data obtained by the sGs realizations was on average 240 times higher than that obtained by OK. The uncertainty map, obtained by OK, showed less variation in the study area than that obtained by sGs. Thus, the evaluation of uncertainties by sGs was more informative and can be used to define and delimit specific management areas more precisely.

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