Estimation of a preference map of new consumers for spatial load forecasting simulation methods using a spatial analysis of points

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Reference evapotranspiration estimation inside greenhouses

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A simple and non-destructive model for individual leaf area estimation in citrus

Introduction. Leaf area is often related to plant growth, development, physiology and yield. Many non-destructive models have been proposed for leaf area estimation of several plant genotypes, demonstrating that leaf length, leaf width and leaf area are closely correlated. Thus, the objective of our study was to develop a reliable model for leaf area estimation from linear measurements of leaf dimensions for citrus genotypes. Materials and methods. Leaves of citrus genotypes were harvested, and their dimensions (length, width and area) were measured. Values of leaf area were regressed against length, width, the square of length, the square of width and the product (length x width). The most accurate equations, either linear or second-order polynomial, were regressed again with a new data set; then the most reliable equation was defined. Results and discussion. The first analysis showed that the variables length, width and the square of length gave better results in second-order polynomial equations, while the linear equations were more suitable and accurate when the width and the product (length x width) were used. When these equations were regressed with the new data set, the coefficient of determination (R(2)) and the agreement index 'd' were higher for the one that used the variable product (length x width), while the Mean Absolute Percentage Error was lower. Conclusion. The product of the simple leaf dimensions (length x width) can provide a reliable and simple non-destructive model for leaf area estimation across citrus genotypes.

Heritability estimation in early generations of two-way crosses in soybean

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

Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A comparison of statistical methods for genomic selection in a mice population

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

Step Counting and Energy Expenditure Estimation in Patients With Chronic Obstructive Pulmonary Disease and Healthy Elderly: Accuracy of 2 Motion Sensors

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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.

A novel approach to robust parameter estimation using neurofuzzy systems

A novel approach for solving robust parameter estimation problems is presented for processes with unknown-but-bounded errors and uncertainties. An artificial neural network is developed to calculate a membership set for model parameters. Techniques of fuzzy logic control lead the network to its equilibrium points. Simulated examples are presented as an illustration of the proposed technique. The result represent a significant improvement over previously proposed methods. (C) 1999 IMACS/Elsevier B.V. B.V. All rights reserved.

Estimation of the volumetric oxygen tranfer coefficient (KLa) from the gas balance and using a neural network technique

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

Load torque estimation in induction motors using artificial neural networks

The induction motors are largely used in several industry sectors. The dimensioning 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 paper is to use artificial neural networks as tool for dimensioning of induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach.

Use of gastric balloon manometry for estimation of intra-abdominal pressure in horses

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A Simple Method Based on ICP-MS for Estimation of Background Levels of Arsenic, Cadmium, Copper, Manganese, Nickel, Lead, and Selenium in Blood of the Brazilian Population

Throughout the world, biomonitoring has become the standard for assessing exposure of individuals to toxic elements as well as for responding to serious environmental public health problems. However, extensive biomonitoring surveys require rapid and simple analytical methods. Thus, a simple and high-throughput method is proposed for the determination of arsenic (As), cadmium (Cd), copper (Cu), manganese (Mn), nickel (Ni), lead (Pb), and selenium (Se) in blood samples by using inductively coupled plasma-mass spectrometry (ICP-MS). Prior to analysis, 200 l of blood samples was mixed with 500 l of 10% v/v tetramethylammonium hydroxide (TMAH) solution, incubated for 10 min, and subsequently diluted to 10 ml with a solution containing 0.05% w/v ethylenediamine tetraacetic acid (EDTA) + 0.005% v/v Triton X-100. After that, samples were directly analyzed by ICP-MS (ELAN DRC II). Rhodium was selected as an internal standard with matrix-matching calibration. Method detection limits were 0.08, 0.04, 0.5, 0.09, 0.12, 0.04, and 0.1 g//L for As, Cd, Cu, Mn, Ni, Pb, and Se, respectively. Validation data are provided based on the analysis of blood samples from the trace elements inter-\comparison program operated by the Institut National de Sante Publique du Quebec, Canada. Additional validation was provided by the analysis of human blood samples by the proposed method and by using electrothermal atomic absorption spectrometry (ETAAS). The method was subsequently applied for the estimation of background metal blood values in the Brazilian population. In general, the mean concentrations of As, Cd, Cu, Mn, Ni, Pb, and Se in blood were 1.1, 0.4, 890, 9.6, 2.1, 65.4, and 89.3 g/L, respectively, and are in agreement with other global populations. Influences of age, gender, smoking habits, alcohol consumption, and geographical variation on the values were also considered. Smoking habits influenced the levels of Cd in blood. The levels of Cu, Mn, and Pb were significantly correlated with gender, whereas Cu and Pb were significantly correlated with age. There were also interesting differences in Mn and Se levels in the population living in the north of Brazil compared to the south.

Bayesian estimation of generalized exponential distribution under noninformative priors

The generalized exponential distribution, proposed by Gupta and Kundu (1999), is a good alternative to standard lifetime distributions as exponential, Weibull or gamma. Several authors have considered the problem of Bayesian estimation of the parameters of generalized exponential distribution, assuming independent gamma priors and other informative priors. In this paper, we consider a Bayesian analysis of the generalized exponential distribution by assuming the conventional non-informative prior distributions, as Jeffreys and reference prior, to estimate the parameters. These priors are compared with independent gamma priors for both parameters. The comparison is carried out by examining the frequentist coverage probabilities of Bayesian credible intervals. We shown that maximal data information prior implies in an improper posterior distribution for the parameters of a generalized exponential distribution. It is also shown that the choice of a parameter of interest is very important for the reference prior. The different choices lead to different reference priors in this case. Numerical inference is illustrated for the parameters by considering data set of different sizes and using MCMC (Markov Chain Monte Carlo) methods.

Bayesian and multivariate methods applied to favorability quantification in recôncavo Basin and Ribeira Belt, Brazil

A methodology to define favorable areas in petroleum and mineral exploration is applied, which consists in weighting the exploratory variables, in order to characterize their importance as exploration guides. The exploration data are spatially integrated in the selected area to establish the association between variables and deposits, and the relationships among distribution, topology, and indicator pattern of all variables. Two methods of statistical analysis were compared. The first one is the Weights of Evidence Modeling, a conditional probability approach (Agterberg, 1989a), and the second one is the Principal Components Analysis (Pan, 1993). In the conditional method, the favorability estimation is based on the probability of deposit and variable joint occurrence, with the weights being defined as natural logarithms of likelihood ratios. In the multivariate analysis, the cells which contain deposits are selected as control cells and the weights are determined by eigendecomposition, being represented by the coefficients of the eigenvector related to the system's largest eigenvalue. The two techniques of weighting and complementary procedures were tested on two case studies: 1. Recôncavo Basin, Northeast Brazil (for Petroleum) and 2. Itaiacoca Formation of Ribeira Belt, Southeast Brazil (for Pb-Zn Mississippi Valley Type deposits). The applied methodology proved to be easy to use and of great assistance to predict the favorability in large areas, particularly in the initial phase of exploration programs. © 1998 International Association for Mathematical Geology.