Efeitos de ambiente e estimativas de parâmetros genéticos para características de carcaça em bovinos da raça Canchim criados em pastagem

Os objetivos neste trabalho foram estudar os efeitos de ambiente sobre a espessura de gordura subcutânea (EGS), a área de olho-de-lombo (AOL) e o peso aos 19 meses de idade e estimar parâmetros genéticos para essas características. Utilizaram-se informações obtidas de 987 bovinos da raça Canchim (5/8 Charolês + 3/8 Zebu) e do grupo genético animal MA (filhos de touros charoleses e vacas 1/2 Canchim + 1/2 Zebu) nascidos em 2003, 2004 e 2005. Os componentes de covariância foram estimados pelo método da máxima verossimilhança restrita utilizando-se um modelo animal com efeitos fixos (ano de nascimento, grupo genético, rebanho e sexo) e os efeitos aleatórios genético aditivo direto e residual. As médias de área de olho-de-lombo e peso foram mais altas nos machos que nas fêmeas. No grupo genético MA, as médias para todas as características foram mais altas que na raça Canchim e houve ainda efeitos de rebanho e de ano de nascimento. As estimativas de herdabilidade para AOL (0,33 ± 0,09), EGS (0,24 ± 0,09) e peso (0,23 ± 0,09) foram moderadas, enquanto que a estimativa de correlação genética (0,21 ± 0,24) entre EGS e AOL foi baixa, o que sugere que essas características são controladas por diferentes conjuntos de genes de ação aditiva. As correlações genéticas para peso estimadas com EGS (0,57 ± 0,23) e com AOL (0,62 ± 0,16) foram moderadas. Conclui-se que as características ao sobreano devem responder à seleção nos rebanhos estudados e que a seleção para aumento de peso também eleva EGS e AOL e vice-versa.

Breeding value accuracy estimates for growth traits using random regression and multi-trait models in Nelore cattle

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

Random regression analyses using B-spline functions to model growth of Nellore cattle

The objective of this study was to estimate (co)variance components using random regression on B-spline functions to weight records obtained from birth to adulthood. A total of 82 064 weight records of 8145 females obtained from the data bank of the Nellore Breeding Program (PMGRN/Nellore Brazil) which started in 1987, were used. The models included direct additive and maternal genetic effects and animal and maternal permanent environmental effects as random. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of age (cubic regression) were considered as random covariate. The random effects were modeled using B-spline functions considering linear, quadratic and cubic polynomials for each individual segment. Residual variances were grouped in five age classes. Direct additive genetic and animal permanent environmental effects were modeled using up to seven knots (six segments). A single segment with two knots at the end points of the curve was used for the estimation of maternal genetic and maternal permanent environmental effects. A total of 15 models were studied, with the number of parameters ranging from 17 to 81. The models that used B-splines were compared with multi-trait analyses with nine weight traits and to a random regression model that used orthogonal Legendre polynomials. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most appropriate and parsimonious model to describe the covariance structure of the data. Selection for higher weight, such as at young ages, should be performed taking into account an increase in mature cow weight. Particularly, this is important in most of Nellore beef cattle production systems, where the cow herd is maintained on range conditions. There is limited modification of the growth curve of Nellore cattle with respect to the aim of selecting them for rapid growth at young ages while maintaining constant adult weight.

Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows

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

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)

Milk yield persistency in Brazilian Gyr cattle based on a random regression model

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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)

Estimação de Fasores para Proteção de Sistemas Elétricos Baseada em Mínimos Quadrados e Morfologia Matemática

This work proposes a new technique for phasor estimation applied in microprocessor numerical relays for distance protection of transmission lines, based on the recursive least squares method and called least squares modified random walking. The phasor estimation methods have compromised their performance, mainly due to the DC exponential decaying component present in fault currents. In order to reduce the influence of the DC component, a Morphological Filter (FM) was added to the method of least squares and previously applied to the process of phasor estimation. The presented method is implemented in MATLABr and its performance is compared to one-cycle Fourier technique and conventional phasor estimation, which was also based on least squares algorithm. The methods based on least squares technique used for comparison with the proposed method were: forgetting factor recursive, covariance resetting and random walking. The techniques performance analysis were carried out by means of signals synthetic and signals provided of simulations on the Alternative Transient Program (ATP). When compared to other phasor estimation methods, the proposed method showed satisfactory results, when it comes to the estimation speed, the steady state oscillation and the overshoot. Then, the presented method performance was analyzed by means of variations in the fault parameters (resistance, distance, angle of incidence and type of fault). Through this study, the results did not showed significant variations in method performance. Besides, the apparent impedance trajectory and estimated distance of the fault were analysed, and the presented method showed better results in comparison to one-cycle Fourier algorithm

Mapping Macrophyte Species in the Amazon Floodplain Wetlands Using Fully Polarimetric ALOS/PALSAR Data

The purpose of this paper was to evaluate attributes derived from fully polarimetric PALSAR data to discriminate and map macrophyte species in the Amazon floodplain wetlands. Fieldwork was carried out almost simultaneously to the radar acquisition, and macrophyte biomass and morphological variables were measured in the field. Attributes were calculated from the covariance matrix [C] derived from the single-look complex data. Image attributes and macrophyte variables were compared and analyzed to investigate the sensitivity of the attributes for discriminating among species. Based on these analyses, a rule-based classification was applied to map macrophyte species. Other classification approaches were tested and compared to the rule-based method: a classification based on the Freeman-Durden and Cloude-Pottier decomposition models, a hybrid classification (Wishart classifier with the input classes based on the H/a plane), and a statistical-based classification (supervised classification using Wishart distance measures). The findings show that attributes derived from fully polarimetric L-band data have good potential for discriminating herbaceous plant species based on morphology and that estimation of plant biomass and productivity could be improved by using these polarimetric attributes.

Avaliação de estratégias de detecção e correção de perdas de ciclos na portadora GPS L1

To prevent large errors in the GPS positioning, cycle slips should be detected and corrected. Such procedure is not trivial, mainly for single frequency receivers, but normally it is not noticed by the users. Thus, it will be discussed some practical and more used methods for cycle slips detection and correction using just GPS single-frequency observations. In the detection, the triple (TD) and tetra differences were used. In relation to the correction, in general, each slip is corrected in the preprocessing. Otherwise, other strategies should be adopted during the processing. In this paper, the option was to the second option, and two strategies were tested. In one of them, the elements of the covariance matrix of the involved ambiguities are modified and new ambiguity estimation starts. In the one, a new ambiguity is introduced as additional unknown when a cycle slip is detected. These possibilities are discussed and compared in this paper, as well as the aspects related to the practicity, implementation and viability of each one. Some experiments were carried out using simulated data with cycle slips in different satellites and epochs of the data. This allowed assessing and comparing the results of different occurrence of cycle slip and correction in several conditions.

Unscented Kalman Filter Applied to the Spacecraft Attitude Estimation with Euler Angles

The aim of this work is to test an algorithm to estimate, in real time, the attitude of an artificial satellite using real data supplied by attitude sensors that are on board of the CBERS-2 satellite (China Brazil Earth Resources Satellite). The real-time estimator used in this work for attitude determination is the Unscented Kalman Filter. This filter is a new alternative to the extended Kalman filter usually applied to the estimation and control problems of attitude and orbit. This algorithm is capable of carrying out estimation of the states of nonlinear systems, without the necessity of linearization of the nonlinear functions present in the model. This estimation is possible due to a transformation that generates a set of vectors that, suffering a nonlinear transformation, preserves the same mean and covariance of the random variables before the transformation. The performance will be evaluated and analyzed through the comparison between the Unscented Kalman filter and the extended Kalman filter results, by using real onboard data.

The double sampling and the EWMA charts based on the sample variances

We propose a new statistic to control the covariance matrix of bivariate processes. This new statistic is based on the sample variances of the two quality characteristics, in short VMAX statistic. The points plotted on the chart correspond to the maximum of the values of these two variances. The reasons to consider the VMAX statistic instead of the generalized variance vertical bar S vertical bar is its faster detection of process changes and its better diagnostic feature; that is, with the VMAX statistic it is easier to identify the out-of-control variable. We study the double sampling (DS) and the exponentially weighted moving average (EWMA) charts based on the VMAX statistic. (C) 2008 Elsevier B.V. All rights reserved.

Identification of Mechanical Systems through Kautz Filter

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

Relevância do estado de hidratação na interpretação de parâmetros nutricionais em diálise peritoneal

OBJETIVO: Identificar determinantes do estado de hidratação de pacientes em diálise peritoneal crônica, bem como investigar os efeitos da sobrecarga líquida sobre o estado nutricional. MÉTODOS: Foi feito estudo transversal, realizado em 2006, avaliando 27 pacientes em diálise peritoneal crônica, acompanhados no Hospital das Clínicas da Faculdade de Medicina de Botucatu (SP), quanto a parâmetros clínicos, dialíticos, laboratoriais, antropométricos e de bioimpedância elétrica. Para avaliar a influência de parâmetros sobre o estado de hidratação empregou-se modelo de regressão linear múltipla. A amostra foi estratificada quanto ao estado de hidratação pela relação entre água extracelular e água corporal total (0,47 para homens e 0,52 para mulheres), parâmetros obtidos por meio de bioimpedância elétrica. Comparações foram realizadas por análise de covariância, Mann-Whitney, Qui-quadrado ou teste exato de Fisher. Considerou-se significância estatística quando p≤0,05. RESULTADOS: Pacientes com maior volume urinário e em modalidade dialítica automatizada apresentaram melhor estado de hidratação. Pacientes com maior sobrecarga líquida, comparados àqueles com menor sobrecarga, apresentaram menor ângulo de fase (M=4,2, DP=0,9 vs M=5,7, DP=0,7º; p=0,006), menor albumina (M=3,06, DP=0,46 vs M=3,55, DP=0,52g/dL; p=0,05) e maior % prega cutânea tricipital (M=75,3, DP=36,9 vs M=92,1, DP=56,9%; p=0,058), sem outras evidências antropométricas. CONCLUSÃO: Pode-se sugerir que os níveis reduzidos de albumina e ângulo de fase nos pacientes com maior sobrecarga líquida não estiveram relacionados a pior estado nutricional. Para o diagnóstico nutricional em vigência de sobrecarga líquida, deve-se considerar o conjunto de variáveis obtidas por diversos métodos, buscando relacioná-las e interpretá-las de maneira abrangente, possibilitando um diagnóstico nutricional fidedigno.

Detecção e diagnóstico de falhas não-supervisionados baseados em estimativa de densidade recursiva e classificador fuzzy auto-evolutivo

In this work, we propose a two-stage algorithm for real-time fault detection and identification of industrial plants. Our proposal is based on the analysis of selected features using recursive density estimation and a new evolving classifier algorithm. More specifically, the proposed approach for the detection stage is based on the concept of density in the data space, which is not the same as probability density function, but is a very useful measure for abnormality/outliers detection. This density can be expressed by a Cauchy function and can be calculated recursively, which makes it memory and computational power efficient and, therefore, suitable for on-line applications. The identification/diagnosis stage is based on a self-developing (evolving) fuzzy rule-based classifier system proposed in this work, called AutoClass. An important property of AutoClass is that it can start learning from scratch". Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for AutoClass (the number may grow, with new class labels being added by the on-line learning process), in a fully unsupervised manner. In the event that an initial rule base exists, AutoClass can evolve/develop it further based on the newly arrived faulty state data. In order to validate our proposal, we present experimental results from a level control didactic process, where control and error signals are used as features for the fault detection and identification systems, but the approach is generic and the number of features can be significant due to the computationally lean methodology, since covariance or more complex calculations, as well as storage of old data, are not required. The obtained results are significantly better than the traditional approaches used for comparison