Buffalos milk yield analysis using random regression models

Data comprising 1,719 milk yield records from 357 females (predominantly Murrah breed), daughters of 110 sires, with births from 1974 to 2004, obtained from the Programa de Melhoramento Genetic de Bubalinos (PROMEBUL) and from records of EMBRAPA Amazonia Oriental - EAO herd, located in Belem, Para, Brazil, were used to compare random regression models for estimating variance components and predicting breeding values of the sires. The data were analyzed by different models using the Legendre's polynomial functions from second to fourth orders. The random regression models included the effects of herd-year, month of parity date of the control; regression coefficients for age of females (in order to describe the fixed part of the lactation curve) and random regression coefficients related to the direct genetic and permanent environment effects. The comparisons among the models were based on the Akaike Infromation Criterion. The random effects regression model using third order Legendre's polynomials with four classes of the environmental effect were the one that best described the additive genetic variation in milk yield. The heritability estimates varied from 0.08 to 0.40. The genetic correlation between milk yields in younger ages was close to the unit, but in older ages it was low.

Preliminary study of a methodology based on the degree-days concept for predicting the phenological phases of crops

This work develops a methodology (using the degree-days concept and linear regression), to forecast the duration of phenological phases in crops. An experiment was conducted in the greenhouse with three cultivars of cowpea (Vigna unguiculata (C.) Walp.), cv. California-781, Tvx 5058-09C and IT 81D-1032. The methodology was based on the relative thermal efficiency rate, determined for each species or cv. The results show that the proposed methodology may be a good alternative in works involving crops, especially because it does not require the repetition of the experiments.

Weather-based prediction of anthracnose severity using artificial neural network models

Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.

Kinetics of eutectoid decomposition in Cu-Al and Cu-Al-Ag alloys

The kinetics of eutectoid decomposition beta(1)' --> gamma(2) + (alpha + gamma(2)) in Cu-12.86 wt% Al and Cu-12.84 wt% Al-1.98 wt% Ag alloys was studied by hardness measurements, using the Johnson-Mehl-Avrami equation. The results indicate that the presence of silver seems to influence the nucleation rate and the activation energy of the reaction.

TG studies of a composite solid rocket propellant based on HTPB-binder

Thermal decomposition kinetics of solid rocket propellants based on hydroxyl-terminated polybutadiene-HTPB binder was studied by applying the Arrhenius and Flynn-Wall-Ozawa's methods. The thermal decomposition data of the propellant samples were analyzed by thermogravimetric analysis (TG/DTG) at different heating rates in the temperature range of 300-1200 K. TG curves showed that the thermal degradation occurred in three main stages regardless of the plasticizer (DOA) raw material, the partial HTPB/IPDI binder and the total ammonium perchlorate decompositions. The kinetic parameters E-a (activation energy) and A (pre-exponential factor) and the compensation parameter (S-p) were determined. The apparent activation energies obtained from different methods showed a very good agreement.

Kinetic parameters obtained for thermal decomposition of acrylic resins present in commercial paint emulsions

Samples of water based commercial acrylic resin paints were spread in a film form on slides, dried at room temperature and exposed to solar radiation for up to eight months.The characterization and quantification of resins and charges in the white paint emulsion were carried out for the thermal decomposition. Besides this, X-ray diffractometry was used to identify CaCO3 as charge and TiO2 (rutile phase) as pigment.It was observed through thermal techniques similar behavior to the samples even though with varied exposure time.Kinetic studies of the samples allowed to obtain the activation energy (Ea) and Arrhenius parameters (A) to the thermal decomposition of acrylic resin to three different commercial emulsion (called P-1, P-2, P-3) through non-isothermal procedures. The values of E. varied regarding the exposition time (eight months) and solar radiation from 173 to 197 U mol(-1) (P-1 sample), from 175 to 226 W mol(-1) (P-2 sample) and 206 to 197 kJ mol(-1) (P-3 sample).Kinetic Compensation Effect (KCE) observed for samples P-2 and P-3 indicate acrylic resin s present in these may be similar in nature. This aspect could be observed by a small difference in the thermal behavior of the TG curves from P I to P-2 and P-3 sample.The simulated kinetic model to all the samples was the autocatalytic estdk Berggreen.

Estimating the aboveground biomass and wood volume of savanna woodlands in Brazil's Pantanal wetlands based on allometric correlations

The quantity and distribution of vegetal biomass are important aspects to consider in ecosystem studies. However, little information is available about Brazil's Pantanal woodland savannas. This work involved the development of regression equations of the aerial biomass and wood volume of native tree species in a region of woodland savanna on Rio Negro farm in the Pantanal of Nhecolandia, Brazil. Samples were taken from 10 trees of each of five species: Protium heptaphyllum (Aub1.) Marchand, Magonia pubescens A. St.-Hil., Diptychandra aurantiaca Tul., Terminalia argentea Mart. and Zucc. and Licania minutiflora (Sagot) Fritsch and from a miscellaneous group of I I different species. Linear and nonlinear regression analyses were developed relating the diameter at breast height to the dry weight of wood, branches and leaves, wood volume and total aerial biomass. All the regressions showed a significance of P < 0.05 and an R-2 close to or above 0.8. The biomass curve predicted by linear regression analysis of the studied species was similar to the nonlinear regression, with the exception of L. minutiflora and the miscellaneous group. The breast height diameter proved a good choice for estimating biomass and wood volume. The estimated wood volume and biomass of the Pantanal woodland savanna is crucial information for understanding the carbon cycle and for ensuring the region's conservation and sustainable use. (c) 2006 Elsevier B.V. All rights reserved.

Predicting body and carcass characteristics of 2 broiler chicken strains using support vector regression and neural network models

As a new modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression and approximation. In this study, the SVR models had been introduced and developed to predict body and carcass-related characteristics of 2 strains of broiler chicken. To evaluate the prediction ability of SVR models, we compared their performance with that of neural network (NN) models. Evaluation of the prediction accuracy of models was based on the R-2, MS error, and bias. The variables of interest as model output were BW, empty BW, carcass, breast, drumstick, thigh, and wing weight in 2 strains of Ross and Cobb chickens based on intake dietary nutrients, including ME (kcal/bird per week), CP, TSAA, and Lys, all as grams per bird per week. A data set composed of 64 measurements taken from each strain were used for this analysis, where 44 data lines were used for model training, whereas the remaining 20 lines were used to test the created models. The results of this study revealed that it is possible to satisfactorily estimate the BW and carcass parts of the broiler chickens via their dietary nutrient intake. Through statistical criteria used to evaluate the performance of the SVR and NN models, the overall results demonstrate that the discussed models can be effective for accurate prediction of the body and carcass-related characteristics investigated here. However, the SVR method achieved better accuracy and generalization than the NN method. This indicates that the new data mining technique (SVR model) can be used as an alternative modeling tool for NN models. However, further reevaluation of this algorithm in the future is suggested.

Bayesian outlier analysis in binary regression

We propose alternative approaches to analyze residuals in binary regression models based on random effect components. Our preferred model does not depend upon any tuning parameter, being completely automatic. Although the focus is mainly on accommodation of outliers, the proposed methodology is also able to detect them. Our approach consists of evaluating the posterior distribution of random effects included in the linear predictor. The evaluation of the posterior distributions of interest involves cumbersome integration, which is easily dealt with through stochastic simulation methods. We also discuss different specifications of prior distributions for the random effects. The potential of these strategies is compared in a real data set. The main finding is that the inclusion of extra variability accommodates the outliers, improving the adjustment of the model substantially, besides correctly indicating the possible outliers.

A decentralized approach for optimal reactive power dispatch using a Lagrangian decomposition method

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Lanthanum-based perovskites obtained by the polymeric precursor method

Cobalt oxides, specially the ones with perovskite structure, are of a high technological interest, due to their interesting optical, electrical and magnetic properties. La(1 -x)Ca(x)CoO(3) powder samples were synthesized by the polymeric precursor method, with x varying from 0 to 0.4. The powder precursors were characterized by TG/DTA, XRD and IR. The TG curves showed several thermal decomposition steps; the first one is ascribed to the loss of water and the remaining steps are related to the combustion of the organic matter. The XRD patterns indicated only the presence of the perovskite phase. Moreover, the structure changes from rhombohedral to cubic, as calcium is added to the perovskite and the calcination temperature increases.

Urea-Based Synthesis of Zinc Oxide Nanostructures at Low Temperature

The preparation of nanometer-sized structures of zinc oxide (ZnO) from zinc acetate and urea as raw materials was performed using conventional water bath heating and a microwave hydrothermal (MH) method in an aqueous solution. The oxide formation is controlled by decomposition of the added urea in the sealed autoclave. The influence of urea and the synthesis method on the final product formation are discussed. Broadband photoluminescence (PL) behavior in visible-range spectra was observed with a maximum peak centered in the green region which was attributed to different defects and the structural changes involved with ZnO crystals which were produced during the nucleation process.

TWO DECOMPOSITION APPROACHES APPLIED TO THE MULTI-AREA OPTIMAL REACTIVE POWER FLOW PROBLEM

This paper applies two methods of mathematical decomposition to carry out an optimal reactive power flow (ORPF) in a coordinated decentralized way in the context of an interconnected multi-area power system. The first method is based on an augmented Lagrangian approach using the auxiliary problem principle (APP). The second method uses a decomposition technique based on the Karush-Kuhn-Tucker (KKT) first-order optimality conditions. The viability of each method to be used in the decomposition of multi-area ORPF is studied and the corresponding mathematical models are presented. The IEEE RTS-96, the IEEE 118-bus test systems and a 9-bus didactic system are used in order to show the operation and effectiveness of the decomposition methods.

Assessing relationships between genetic evaluations using robust regression with an application to holsteins in Uruguay

Globalization of dairy cattle breeding has created a need for international sire proofs. Some early methods for converting proofs from one population to another are based on simple linear regression. An alternative robust regression method based on the t-distribution is presented, and maximum likelihood and Bayesian techniques for analysis are described, including the situation in which some proofs are missing. Procedures were used to investigate the relationship between Holstein sire proofs obtained by two Uruguayan genetic evaluation programs. The results suggest that conversion equations developed from data including only sires having proofs in both populations can lead to distorted results, relative to estimates obtained using techniques for incomplete data. There was evidence of non-normality of regression residuals, which constitutes an additional source of bias. A robust estimator may not solve all problems, but can provide simple conversion equations that are less sensitive to outlying proofs and to departures from assumptions.

Autoregressive decomposition and pole tracking applied to vocal fold nodule signals

This letter describes a novel algorithm that is based on autoregressive decomposition and pole tracking used to recognize two patterns of speech data: normal voice and disphonic voice caused by nodules. The presented method relates the poles and the peaks of the signal spectrum which represent the periodic components of the voice. The results show that the perturbation contained in the signal is clearly depicted by pole's positions. Their variability is related to jitter and shimmer. The pole dispersion for pathological voices is about 20% higher than for normal voices, therefore, the proposed approach is a more trustworthy measure than the classical ones. © 2007.