Assessment of field fertility and several in vitro sperm characteristics following the use of different Angus sires in a timed-AI program with suckled Nelore cows

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Genetic parameter estimates for buffalo milk yield, milk quality and mozzarella production and Bayesian inference analysis of their relationships

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Bayesian inference in genetic parameter estimation of visual scores in Nellore beef-cattle

The aim of this study was to estimate the components of variance and genetic parameters for the visual scores which constitute the Morphological Evaluation System (MES), such as body structure (S), precocity (P) and musculature (M) in Nellore beef-cattle at the weaning and yearling stages, by using threshold Bayesian models. The information used for this was gleaned from visual scores of 5,407 animals evaluated at the weaning and 2,649 at the yearling stages. The genetic parameters for visual score traits were estimated through two-trait analysis, using the threshold animal model, with Bayesian statistics methodology and MTGSAM (Multiple Trait Gibbs Sampler for Animal Models) threshold software. Heritability estimates for S, P and M were 0.68, 0.65 and 0.62 (at weaning) and 0.44, 0.38 and 0.32 (at the yearling stage), respectively. Heritability estimates for S, P and M were found to be high, and so it is expected that these traits should respond favorably to direct selection. The visual scores evaluated at the weaning and yearling stages might be used in the composition of new selection indexes, as they presented sufficient genetic variability to promote genetic progress in such morphological traits.

Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil

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

Effect of lactation length adjustment procedures on genetic parameter estimates for buffalo milk yield

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

Milk fatty acid characterization and genetic parameter estimates for milk conjugated linoleic acid in buffaloes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Genetic parameter estimates for live weight and daily live weight gain obtained for Nellore bulls in a test station using different models

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

Composição florística da comunidade infestante em gramados de Paspalum notatum no município de Assis, SP

Os gramados de Paspalum notatum são os mais disseminados no Brasil, constituindo diferentes locais e propósitos. A infestação por plantas daninhas acarreta perda de qualidade estética quando a finalidade do gramado é ornamental. Com o objetivo de caracterizar a comunidade infestante em gramados de P. notatum no município de Assis/SP, foi realizado entre os meses de junho e julho de 2004 um levantamento florístico em áreas ensolaradas e sombreadas, sob copas de árvores. Cem amostras de 0,50 x 0,50 m foram coletadas nas duas condições de luminosidade (50 em áreas ensolaradas e 50 em áreas sombreadas), a partir das quais foram calculados os parâmetros freqüência, densidade, abundância, freqüência relativa, densidade relativa, abundância relativa e índice de valor de importância. Ao todo, foram identificadas 45 espécies de plantas daninhas, distribuídas em 15 famílias; Asteraceae apresentou o maior número de espécies nas condições ensolaradas e sombreadas. As espécies mais importantes nas áreas ensolaradas foram: Oxalis latifolia > Desmodium incanum > Cyperus flavus > Cyperus diffusus > Cyperus brevifolius; e nas áreas sombreadas: C. brevifolius > Alternanthera tenella > D. incanum > Elephantopus mollis > C. flavus. Para 80,0% das amostragens, a massa seca total da parte aérea das espécies de plantas daninhas não sofreu influência das condições ensolarada ou sombreada, constituindo uma produção máxima de aproximadamente 150 kg ha-1 para ambas as condições.

On developing efficient applications using conservative distributed simulation paradigm

This paper aims to present, using a set of guidelines, how to apply the conservative distributed simulation paradigm (CMB protocol) to develop efficient applications. Using these guidelines, even a user with little experience on distributed simulation and computer architecture can have good performance on distributed simulations using conservative synchronization protocols for parallel processes.The set of guidelines is focus on a specific application domain, the performance evaluation of computer systems, considering models with coarse granularity and few logical processes and running over two platforms: parallel (high performance communication environment) and distributed (low performance communication environment).

Deposition and characterization of BiVO4 thin films and evaluation as photoanodes for methylene blue degradation

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

On 3-Parameter Families of Piecewise Smooth Vector Fields in the Plane

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

A neural network approach for robust nonlinear parameter estimation in presence of unknown-but-bounded errors

Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. This paper presents a novel approach to solve robust parameter estimation problem for nonlinear model with unknown-but-bounded errors and uncertainties. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

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.

Variable parameter and double sampling (X)over-bar charts in the presence of correlation: The Markov chain approach

The general assumption under which the (X) over bar chart is designed is that the process mean has a constant in-control value. However, there are situations in which the process mean wanders. When it wanders according to a first-order autoregressive (AR (1)) model, a complex approach involving Markov chains and integral equation methods is used to evaluate the properties of the (X) over bar chart. In this paper, we propose the use of a pure Markov chain approach to study the performance of the (X) over bar chart. The performance of the chat (X) over bar with variable parameters and the (X) over bar with double sampling are compared. (C) 2011 Elsevier B.V. All rights reserved.

Multi-objective evolutionary particle swarm optimization in the assessment of the impact of distributed generation

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