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

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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.

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.

Environmental and genetic factors affecting the weaning-estrus interval in sows

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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)

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)

Uma proposta de um controlador adaptativo por posicionamento de pólos e estrutura variável

There are two main approaches for using in adaptive controllers. One is the so-called model reference adaptive control (MRAC), and the other is the so-called adaptive pole placement control (APPC). In MRAC, a reference model is chosen to generate the desired trajectory that the plant output has to follow, and it can require cancellation of the plant zeros. Due to its flexibility in choosing the controller design methodology (state feedback, compensator design, linear quadratic, etc.) and the adaptive law (least squares, gradient, etc.), the APPC is the most general type of adaptive control. Traditionally, it has been developed in an indirect approach and, as an advantage, it may be applied to non-minimum phase plants, because do not involve plant zero-pole cancellations. The integration to variable structure systems allows to aggregate fast transient and robustness to parametric uncertainties and disturbances, as well. In this work, a variable structure adaptive pole placement control (VS-APPC) is proposed. Therefore, new switching laws are proposed, instead of using the traditional integral adaptive laws. Additionally, simulation results for an unstable first order system and simulation and practical results for a three-phase induction motor are shown

Translocação orgânica, produtividade e rendimento de óleo essencial de Mentha piperita L. cultivada em solução nutritiva com variação dos níveis de N, P, K e Mg

O estudo objetivou avaliar translocação orgânica, índices fisiológicos da análise de crescimento e rendimento do óleo essencial de Mentha piperita L. cultivada em solução nutritiva com variação dos níveis de N, P, K e Mg. Assim, foram avaliados os quatro tratamentos contendo 50% N, P, K, 25% Mg; 50% N, P, K, Mg; 65%N, 50%P, 25%K, 100% Mg e 100% N, P, K, Mg. A translocação orgânica foi avaliada por meio da determinação da razão de massa foliar (RMF) e da distribuição de massa seca para os diferentes órgãos. Os índices fisiológicos razão de área foliar (RAF), área foliar específica (AFE), taxa assimilatória líquida (TAL) e taxa de crescimento relativo (TCR) derivadas que compõem a análise de crescimento foram estimados pelo programa ANACRES, após ajuste exponencial quadrático da área foliar e massa seca de lâminas foliares e total da planta em relação ao tempo. O rendimento do óleo essencial, em porcentagem, foi calculado após extração da parte aérea por hidrodestilação. As plantas submetidas ao tratamento com nível completo de nutrientes (100%N/P/K/Mg) exportaram com menor eficiência o material orgânico a partir da folha e a RMF mostrou queda mais lenta, devido à retenção desse material por mais tempo no local de sua produção. Além disso, não apresentaram melhor produtividade e as curvas da TAL e TCR mostraram quedas mais lentas. As plantas submetidas ao tratamento com 65%N/50%P/25%K/100%Mg revelaram adequada exportação de matéria orgânica da folha para caule e pecíolos, conforme demonstra a RMF e a distribuição de massa seca para esses órgãos. Revelaram ainda a RAF mais elevada no inicio do desenvolvimento e mais baixa aos 94 DAT, indicando sombreamento das folhas como resultado de seu crescimento. A AFE dessas plantas mostrou menor variação durante o ciclo, refletindo espessura mais constante de suas folhas. A TAL e a TCR apresentaram curvas decrescentes, com quedas bem acentuadas em relação às demais plantas. Todos esses índices indicam melhor produtividade das plantas submetidas ao tratamento 65%N/50%P/25%K/100%Mg, que também apresentaram rendimento de óleo essencial, em média, maior durante o ciclo. Conclui-se que a redução de 35% de N, 50% de P, 75% de K (65%N/50%P/25%K/100%Mg) é indicada para o cultivo e extração de óleo essencial de M. piperita.

Algoritmos genéticos aplicados a um comitê de LS-SVM em problemas de classificação

The pattern classification is one of the machine learning subareas that has the most outstanding. Among the various approaches to solve pattern classification problems, the Support Vector Machines (SVM) receive great emphasis, due to its ease of use and good generalization performance. The Least Squares formulation of SVM (LS-SVM) finds the solution by solving a set of linear equations instead of quadratic programming implemented in SVM. The LS-SVMs provide some free parameters that have to be correctly chosen to achieve satisfactory results in a given task. Despite the LS-SVMs having high performance, lots of tools have been developed to improve them, mainly the development of new classifying methods and the employment of ensembles, in other words, a combination of several classifiers. In this work, our proposal is to use an ensemble and a Genetic Algorithm (GA), search algorithm based on the evolution of species, to enhance the LSSVM classification. In the construction of this ensemble, we use a random selection of attributes of the original problem, which it splits the original problem into smaller ones where each classifier will act. So, we apply a genetic algorithm to find effective values of the LS-SVM parameters and also to find a weight vector, measuring the importance of each machine in the final classification. Finally, the final classification is obtained by a linear combination of the decision values of the LS-SVMs with the weight vector. We used several classification problems, taken as benchmarks to evaluate the performance of the algorithm and compared the results with other classifiers

Controlador preditivo não linear aplicado ao controle de golfadas em processos de produção de petróleo

Slugging is a well-known slugging phenomenon in multiphase flow, which may cause problems such as vibration in pipeline and high liquid level in the separator. It can be classified according to the place of its occurrence. The most severe, known as slugging in the riser, occurs in the vertical pipe which feeds the platform. Also known as severe slugging, it is capable of causing severe pressure fluctuations in the flow of the process, excessive vibration, flooding in separator tanks, limited production, nonscheduled stop of production, among other negative aspects that motivated the production of this work . A feasible solution to deal with this problem would be to design an effective method for the removal or reduction of the system, a controller. According to the literature, a conventional PID controller did not produce good results due to the high degree of nonlinearity of the process, fueling the development of advanced control techniques. Among these, the model predictive controller (MPC), where the control action results from the solution of an optimization problem, it is robust, can incorporate physical and /or security constraints. The objective of this work is to apply a non-conventional non-linear model predictive control technique to severe slugging, where the amount of liquid mass in the riser is controlled by the production valve and, indirectly, the oscillation of flow and pressure is suppressed, while looking for environmental and economic benefits. The proposed strategy is based on the use of the model linear approximations and repeatedly solving of a quadratic optimization problem, providing solutions that improve at each iteration. In the event where the convergence of this algorithm is satisfied, the predicted values of the process variables are the same as to those obtained by the original nonlinear model, ensuring that the constraints are satisfied for them along the prediction horizon. A mathematical model recently published in the literature, capable of representing characteristics of severe slugging in a real oil well, is used both for simulation and for the project of the proposed controller, whose performance is compared to a linear MPC

Esverdeamento em cultivares de batata avaliado pela escala visual e índice spad

Foi avaliado o esverdeamento em tubérculos de cultivares de batata utilizando dois métodos de determinação (escala visual de notas e índice spad). Utilizou-se o delineamento experimental inteiramente casualizado, com quatro repetições, em esquema fatorial 18 x 5, constituído por cultivares (Agata, Apuã (IAC-5977), Aracy (IAC-2), IAC Aracy Ruiva, Asterix, Bintje, Dali, Clone IAC-6090, Itararé (IAC-5986), Laguna, Remarka, Liseta, Mondial, Novita, Oscar, Picasso, Santana e Solide) e tempo de armazenamento (5; 10; 15; 20 e 25 dias). Todas as cultivares apresentaram desempenho linear e crescente pelo índice spad, com excecão da Bintje, com spad médio de 1,23. Pela escala visual, as cultivares apresentaram desempenho quadrático e linear. A cultivar Bintje é resistente ao esverdeamento determinado pelos dois métodos. O índice spad pode ser utilizado para determinação do esverdeamento do 10º ao 25º dia de armazenamento.

Crescimento aéreo e radicular de arroz de terras altas em função da adubação fosfatada e bioestimulante

The use of plant regulators that stimulate root growth can increase phosphorus uptake by upland rice. The objective of this study was to evaluate shoot and root growth of upland rice fertilized with different phosphorus doses with and without biostimulant. The experiment was carried out in greenhouse in the Faculdade de Ciencias Agronomicas-UNESP, in Botucatu-SP. The treatments consisted of six phosphorus doses applied in sowing (0, 12,5, 25, 50, 100 and 200 mg dm(-3)), with and without Stimulate (R) applied in the seeds (cv. Primavera). The plants were grown for 78 days and then cut at soil level to evaluate leaf area and leaves and collar dry matter. Root samples that were harvested on the same day had their root diameter and dry matter evaluated. The experimental design was the completely randomized, with three replications, arranged as a factorial 2x6. Variance analysis and regression were used to data evaluation. Linear and quadratic equations were adjusted at a probability level of 5%, using those with higher determination coefficient (R(2)). The increase on the phosphorus dose contributed to the lower matter production and leaf area of the plants when the biostimulant was applied. For shoot phosphorus accumulation and root evaluations, the same behavior was observed. It was concluded that the use of Stimulate (R) in seeds, for fitomass production or root system evaluation, was only efficient in low phosphorus doses.

Extração do óleo e caracterização dos resíduos da borra de petróleo para fins de reuso

The petroleum industry, in consequence of an intense activity of exploration and production, is responsible by great part of the generation of residues, which are considered toxic and pollutants to the environment. Among these, the oil sludge is found produced during the production, transportation and refine phases. This work had the purpose to develop a process to recovery the oil present in oil sludge, in order to use the recovered oil as fuel or return it to the refining plant. From the preliminary tests, were identified the most important independent variables, like: temperature, contact time, solvents and acid volumes. Initially, a series of parameters to characterize the oil sludge was determined to characterize its. A special extractor was projected to work with oily waste. Two experimental designs were applied: fractional factorial and Doehlert. The tests were carried out in batch process to the conditions of the experimental designs applied. The efficiency obtained in the oil extraction process was 70%, in average. Oil sludge is composed of 36,2% of oil, 16,8% of ash, 40% of water and 7% of volatile constituents. However, the statistical analysis showed that the quadratic model was not well fitted to the process with a relative low determination coefficient (60,6%). This occurred due to the complexity of the oil sludge. To obtain a model able to represent the experiments, the mathematical model was used, the so called artificial neural networks (RNA), which was generated, initially, with 2, 4, 5, 6, 7 and 8 neurons in the hidden layer, 64 experimental results and 10000 presentations (interactions). Lesser dispersions were verified between the experimental and calculated values using 4 neurons, regarding the proportion of experimental points and estimated parameters. The analysis of the average deviations of the test divided by the respective training showed up that 2150 presentations resulted in the best value parameters. For the new model, the determination coefficient was 87,5%, which is quite satisfactory for the studied system