Funções de covariância para produção de leite no dia do controle em bovinos Gir leiteiro

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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)

Dois critérios de seleção na pré- desmama em bovinos da raça Gir. 2. Efeito na classificação dos animais

A utilização de dois critérios de seleção na pré-desmama, ganho médio diário do nascimento à desmama (GMD) e dias para ganhar 160 kg do nascimento à desmama (D160), foi estudada, analisando-se informações de 16.592 animais, provenientes do controle de desenvolvimento ponderal da Associação Brasileira dos Criadores de Zebu, nascidos no período de 1978 a 1994. Foram incluídos no modelo o efeito fixo de grupo de contemporâneos e os efeitos aleatórios genético aditivo de animal e materno, de ambiente permanente materno e o erro. A covariância entre os efeitos direto e materno foi considerada igual a zero. As estimativas dos componentes de variância e herdabilidade foram obtidas pelo método da máxima verossimilhança restrita e os valores genéticos preditos (VGs), por modelos animais uni-característica. As estimativas de herdabilidade foram: 0,12; 0,05; 0,10 e 0,05 para GMD (efeito direto), GMD (efeito materno), D160 (efeito direto) e D160 (efeito materno), respectivamente. Foram estimadas a correlação genética entre GMD e D160 (efeito direto e materno) e a correlação de classificação (Spearman) entre os valores genéticos para as categorias de touros, vacas e bezerros. As estimativas de correlação genética entre GMD e D160 foram 0,86 e 0,88, para o efeito direto e materno, respectivamente. As estimativas de correlação de ;rank;, também foram altas, entretanto, nenhuma foi igual a um, resultando em alterações na classificação dos animais. A relação entre as médias aritmética (A) e harmônica (H) e o desvio-padrão (S) do GMD ajustado para efeitos ambientais e maternos (GMDc) foi verificada utilizando-se um modelo restrito, sem intercepto, mediante as regressões linear e quadrática do S do GMDc sobre a diferença entre a média aritmética e média harmônica (A-H). Os resultados evidenciaram que, semelhantemente a H, o critério D160 apresentou a propriedade de discriminar touros com progênie mais uniforme.

Modeling of average growth curve in Santa Ines sheep using random regression models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Genetic parameters for milk yield analyzed by test-day models in Murrah buffaloes in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Genetic parameters of total milk yield and factors describing the shape of lactation curve in dairy buffaloes

The objective of this study was to apply factor analysis to describe lactation curves in dairy buffaloes in order to estimate the phenotypic and genetic association between common latent factors and cumulative milk yield. A total of 31 257 monthly test-day milk yield records from buffaloes belonging to herds located in the state of São Paulo were used to estimate two common latent factors, which were then analysed in a multi-trait animal model for estimating genetic parameters. Estimates of (co)variance components for the two common latent factors and cumulated 270-d milk yield were obtained by Bayesian inference using a multiple trait animal model. Contemporary group, number of milkings per day (two levels) and age of buffalo cow at calving (linear and quadratic) as covariate were included in the model as fixed effects. The additive genetic, permanent environmental and residual effects were included as random effects. The first common latent factor (F1) was associated with persistency of lactation and the second common latent factor (F2) with the level of production in early lactation. Heritability estimates for Fl and F2 were 0.12 and 0.07, respectively. Genetic correlation estimates between El and F2 with cumulative milk yield were positive and moderate (0.63 and 0.52). Multivariate statistics employing factor analysis allowed the extraction of two variables (latent factors) that described the shape of the lactation curve. It is expected that the response to selection to increase lactation persistency is higher than the response obtained from selecting animals to increase lactation peak. Selection for higher total milk yield would result in a favourable correlated response to increase the level of production in early lactation and the lactation persistency.

Genetic parameters for test-day yield of milk, fat and protein in buffaloes estimated by random regression models

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

Stochastic modelling considering ionospheric scintillation effects on GNSS relative and point positioning

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Extraction of Building Roof Contours From LiDAR Data Using a Markov-Random-Field-Based Approach

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

Stochastic stability for Markovian jump linear systems associated with a finite number of jump times

This paper deals with a stochastic stability concept for discrete-time Markovian jump linear systems. The random jump parameter is associated to changes between the system operation modes due to failures or repairs, which can be well described by an underlying finite-state Markov chain. In the model studied, a fixed number of failures or repairs is allowed, after which, the system is brought to a halt for maintenance or for replacement. The usual concepts of stochastic stability are related to pure infinite horizon problems, and are not appropriate in this scenario. A new stability concept is introduced, named stochastic tau-stability that is tailored to the present setting. Necessary and sufficient conditions to ensure the stochastic tau-stability are provided, and the almost sure stability concept associated with this class of processes is also addressed. The paper also develops equivalences among second order concepts that parallels the results for infinite horizon problems. (C) 2003 Elsevier B.V. All rights reserved.

Excitations and finite-size properties of an integrable supersymmetric electronic model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A novel neural model to electrical load forecasting in transformers

The paper describes a novel neural model to electrical load forecasting in transformers. The network acts as identifier of structural features to forecast process. So that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through load data extracted from a Brazilian Electric Utility taking into account time, current, tension, active power in the three phases of the system. The results obtained in the simulations show that the developed technique can be used as an alternative tool to become more appropriate for planning of electric power systems.

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.

CERN LHC signals for a neutrino mass model with bilinear R-parity violating minimal anomaly mediated supersymmetry

We investigate a neutrino mass model in which the neutrino data is accounted for by bilinear R-parity violating supersymmetry with anomaly mediated supersymmetry breaking. We focus on the CERN Large Hadron Collider (LHC) phenomenology, studying the reach of generic supersymmetry search channels with leptons, missing energy and jets. A special feature of this model is the existence of long-lived neutralinos and charginos which decay inside the detector leading to detached vertices. We demonstrate that the largest reach is obtained in the displaced vertices channel and that practically all of the reasonable parameter space will be covered with an integrated luminosity of 10 fb(-1). We also compare the displaced vertex reaches of the LHC and Tevatron.