Short-term multinodal load forecasting in distribution systems using general regression neural networks

Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first individually forecast the local loads and the second forecast the global load and individually forecast the load participation factors to estimate the local loads. To design the forecasters it wasn't necessary the previous study of the local loads. Tests were made using a New Zealand distribution subsystem and the results obtained are compatible with the ones founded in the specialized literature. © 2011 IEEE.

BBA: A binary bat algorithm for feature selection

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be in-viable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the bats behaviour, which has never been applied to this context so far. The wrapper approach combines the power of exploration of the bats together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in five public datasets have demonstrated that the proposed approach can outperform some well-known swarm-based techniques. © 2012 IEEE.

Random regression models using Legendre polynomials or linear splines for test-day milk yield of dairy Gyr (Bos indicus) cattle

Studies investigating the use of random regression models for genetic evaluation of milk production in Zebu cattle are scarce. In this study, 59,744 test-day milk yield records from 7,810 first lactations of purebred dairy Gyr (Bos indicus) and crossbred (dairy Gyr × Holstein) cows were used to compare random regression models in which additive genetic and permanent environmental effects were modeled using orthogonal Legendre polynomials or linear spline functions. Residual variances were modeled considering 1, 5, or 10 classes of days in milk. Five classes fitted the changes in residual variances over the lactation adequately and were used for model comparison. The model that fitted linear spline functions with 6 knots provided the lowest sum of residual variances across lactation. On the other hand, according to the deviance information criterion (DIC) and Bayesian information criterion (BIC), a model using third-order and fourth-order Legendre polynomials for additive genetic and permanent environmental effects, respectively, provided the best fit. However, the high rank correlation (0.998) between this model and that applying third-order Legendre polynomials for additive genetic and permanent environmental effects, indicates that, in practice, the same bulls would be selected by both models. The last model, which is less parameterized, is a parsimonious option for fitting dairy Gyr breed test-day milk yield records. © 2013 American Dairy Science Association.

Comparison of random regression models to estimate genetic parameters for milk production in Guzerat (Bos indicus) cows

Random regression models have been widely used to estimate genetic parameters that influence milk production in Bos taurus breeds, and more recently in B. indicus breeds. With the aim of finding appropriate random regression model to analyze milk yield, different parametric functions were compared, applied to 20,524 test-day milk yield records of 2816 first-lactation Guzerat (B. indicus) cows in Brazilian herds. The records were analyzed by random regression models whose random effects were additive genetic, permanent environmental and residual, and whose fixed effects were contemporary group, the covariable cow age at calving (linear and quadratic effects), and the herd lactation curve. The additive genetic and permanent environmental effects were modeled by the Wilmink function, a modified Wilmink function (with the second term divided by 100), a function that combined third-order Legendre polynomials with the last term of the Wilmink function, and the Ali and Schaeffer function. The residual variances were modeled by means of 1, 4, 6, or 10 heterogeneous classes, with the exception of the last term of the Wilmink function, for which there were 1, from 0.20 to 0.33. Genetic correlations between adjacent records were high values (0.83-0.99), but they declined when the interval between the test-day records increased, and were negative between the first and last records. The model employing the Ali and Schaeffer function with six residual variance classes was the most suitable for fitting the data. © FUNPEC-RP.

Simultaneity of the anthropometric markers of adiposity and chronic diseases in women

Aim. The aim of this paper was to investigate the relationship between the accumulation of different anthropometric indicators and the prevalence of some chronic diseases in women over 40 years of age. Methods. The sample was comprised of 562 women between 40 and 95 years of age (64.5 ± 11.4) attended by the research projects were carried out in two cities in southeastern Brazil. Anthropometric measurements were taken: weight, height, waist circumference, hip circumference, and the values of BMI and WHR were later calculated. The referenced morbidity questionnaire was also applied, based on the Standard Health Questionnaire (SHQ), which analyzes the presence of degenerative chronic diseases in the adult population. For the statistical treatment, the chi-square and binary logistic regression tests were performed, with significance set at 5%. Results. The relationship between three changes in the anthropometric indicators and the greater incidence of diseases continued significant for hypertension (OR=3.77 [95% CI: 2.14-6.65], =P=0.001), and for endocrine and metabolic diseases (OR=2.59 [95%: 1:47 to 4:32], =P=0.001), regardless of the effects of age and physical activities. Conclusion. The simultaneity of body fat indicators is more strongly associated with the prevalence of some chronic diseases (hypertension, endocrine, and metabolic), relative to the individualized use of anthropometric indicators.

Rheological behavior of binary aqueous solutions of poly(ethylene glycol) of 1500 g·mol-1 as affected by temperature and polymer concentration

The rheological behavior of poly(ethylene glycol) of 1500 g·mol -1(PEG1500) aqueous solutions with various polymer concentrations (w = 0.05, 0.10, 0.15, 0.20 and 0.25) was studied at different temperatures (T = 283.15, 288.15, 293.15, 298.15 and 303.15) K. The analyses were carried out considering shear rates ranging from (20 to 350) s-1, using a cone-and-plate rheometer under controlled stress and temperature. Classical rheological models (Newton, Bingham, Power Law, Casson, and Herschel-Bulkley) were tested. The Power Law model was shown suitable to mathematically represent the rheological behavior of these solutions. Well-adjusted empirical models were derived for consistency index variations in function of temperature (Arrhenius-type model; R2 > 0.96), polymer concentration (exponential model; R2 > 0.99) or the combination of both (R 2 > 0.99). Additionally, linear models were used to represent the variations of behavior index in the functions of temperature (R2 > 0.83) and concentration (R2 > 0.87). © 2013 American Chemical Society.

Influence of risk behavior aggregation in different categories of physical activity on the occurrence of cardiovascular risk factors

Background: We aimed to verify the association of risk behavior aggregation in different categories of physical activity (PA) with the presence of cardiovascular risk factors (RF) employees at a public university. Method. We analyzed data of 376 employees, which were visited in their workplace for measurement of weight, height and questionnaires to identify the risk behaviors and risk factors. Chi-square test was used to analyze the association between the dependent and independent variables and binary logistic regression was used to construct a multivariate model for the observed associations. Results: Associations were found between the aggregation of following risk behaviors: smoking, alcohol consumption and physical inactivity, considered in different categories of PA, and the increase in RF, except for the presence of hypertriglyceridemia. Individuals with two or more risk behaviors in occupational PA category are more likely to be hypertensive (3.04 times) and diabetes (3.44 times). For the free time PA category, these individuals were 3.18 times more likely to have hypercholesterolemia and for locomotion PA, more likely to be hypertensive (2.42 times) and obese (2.51 times). Conclusion: There are association between the aggregation of two or more risk behaviors and the presence of cardiovascular RF. © 2013 Bernardo et al.; licensee BioMed Central Ltd.

Irregular satellites of Jupiter: Three-dimensional study of binary-asteroid captures

Among the hidden pieces of the giant puzzle, which is our Solar system, the origins of irregularsatellites of the giant planets stand to be explained, while the origins of regular satellites arewell explained by the in situ formation model through matter accretion. Once they are notlocally formed, the most acceptable theory predicts that they had been formed elsewhere andbecame captured later, most likely during the last stage of planet formation. However, underthe restricted three-body problem theory, captures are temporary and there is still no assistedcapture mechanism which is well established. In a previous work, we showed that the capturemechanism of a binary asteroid under the co-planar four-body scenario yielded permanentcaptured objects with an orbital shape which is very similar to those of the actual progradeirregular Jovian satellites. By extending our previous study to a 3D case, here we demonstratethat the capture mechanism of a binary asteroid can produce permanent captures of objects byitself which have very similar orbits to irregular Jovian satellites. Some of the captured objectswithout aid of gas drag or other mechanisms present a triplet: semi-major axis, eccentricityand inclination, which is comparable to the already known irregular Jovian objects. © 2013 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society.

Genetic evaluation using multi-trait and random regression models in Simmental beef cattle

The Brazilian Association of Simmental and Simbrasil Cattle Farmers provided 29,510 records from 10,659 Simmental beef cattle; these were used to estimate (co)variance components and genetic parameters for weights in the growth trajectory, based on multi-trait (MTM) and random regression models (RRM). The (co)variance components and genetic parameters were estimated by restricted maximum likelihood. In the MTM analysis, the likelihood ratio test was used to determine the significance of random effects included in the model and to define the most appropriate model. All random effects were significant and included in the final model. In the RRM analysis, different adjustments of polynomial orders were compared for 5 different criteria to choose the best fit model. An RRM of third order for the direct additive genetic, direct permanent environmental, maternal additive genetic, and maternal permanent environment effects was sufficient to model variance structures in the growth trajectory of the animals. The (co)variance components were generally similar in MTM and RRM. Direct heritabilities of MTM were slightly lower than RRM and varied from 0.04 to 0.42 and 0.16 to 0.45, respectively. Additive direct correlations were mostly positive and of high magnitude, being highest at closest ages. Considering the results and that pre-adjustment of the weights to standard ages is not required, RRM is recommended for genetic evaluation of Simmental beef cattle in Brazil. ©FUNPEC-RP.

Intra-abdominal fat is related to metabolic syndrome and non-alcoholic fat liver disease in obese youth

Background: Previous studies have shown an association between adiposity, especially intra-abdominal adipose tissue, and hemodynamic/metabolic comorbidities in adults, however it is not clear in pediatric population. The aim of the study was to analyze the relationship between non-alcoholic fatty liver disease (NAFLD) and components of metabolic syndrome (MS) with values of intra-abdominal (IAAT) and subcutaneous (SCAT) adipose tissue in obese children and adolescents.Methods: Cross-sectional study. Subjects: 182 obese sedentary children and adolescents (aged 6 to 16 y), identified by the body mass index (BMI). Measurements: Body composition and trunk fat by dual-energy X-ray absorptiometry- DXA; lipid profile, blood pressure and pubertal stage were also assessed. NAFLD was classified as absent (0), mild (1), moderate (2) and severe (3), and intra-abdominal and subcutaneous abdominal fat thickness were identified by ultrasound. The MS was identified according to the cut offs proposed by World Health Organization adapted for children and adolescents. The chi-square test was used to compare categorical variables, and the binary logistic regression indicated the magnitude of the associations adjusted by potential cofounders (sex, age, maturation, NAFLD and HOMA-IR).Results: Higher quartile of SCAT was associated with elevated blood pressure (p = 0.015), but not associated with NAFLD (p = 0.665). Higher IAAT was positively associated with increased dyslipidemia (p = 0.001), MS (p = 0.013) and NAFLD (p = 0.005). Intermediate (p = 0.007) and highest (p = 0.001) quartile of IAAT were also associated with dyslipidemia, independently of age, sex, maturation, NAFLD and HOMA-IR (homeostatic model assessment-insulin resistance).Conclusion: Obese children and adolescents, with higher IAAT are more prone to develop MS and NAFLD than those with higher values of SCAT, independent of possible confounding variables. © 2013 Silveira et al.; licensee BioMed Central Ltd.

Binary Bat Algorithm for Feature Selection

Feature selection aims to find the most important information to save computational efforts and data storage. We formulated this task as a combinatorial optimization problem since the exponential growth of possible solutions makes an exhaustive search infeasible. In this work, we propose a new nature-inspired feature selection technique based on bats behavior, namely, binary bat algorithm The wrapper approach combines the power of exploration of the bats together with the speed of the optimum-path forest classifier to find a better data representation. Experiments in public datasets have shown that the proposed technique can indeed improve the effectiveness of the optimum-path forest and outperform some well-known swarm-based techniques. © 2013 Copyright © 2013 Elsevier Inc. All rights reserved.

A decoding method of an n length binary BCH code through (n + 1)n length binary cyclic code

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

Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes

In this study, genetic parameters for test-day milk, fat, and protein yield were estimated for the first lactation. The data analyzed consisted of 1,433 first lactations of Murrah buffaloes, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, with calvings from 1985 to 2007. Ten-month classes of lactation days were considered for the test-day yields. The (co)variance components for the 3 traits were estimated using the regression analyses by Bayesian inference applying an animal model by Gibbs sampling. The contemporary groups were defined as herd-year-month of the test day. In the model, the random effects were additive genetic, permanent environment, and residual. The fixed effects were contemporary group and number of milkings (1 or 2), the linear and quadratic effects of the covariable age of the buffalo at calving, as well as the mean lactation curve of the population, which was modeled by orthogonal Legendre polynomials of fourth order. The random effects for the traits studied were modeled by Legendre polynomials of third and fourth order for additive genetic and permanent environment, respectively, the residual variances were modeled considering 4 residual classes. The heritability estimates for the traits were moderate (from 0.21-0.38), with higher estimates in the intermediate lactation phase. The genetic correlation estimates within and among the traits varied from 0.05 to 0.99. The results indicate that the selection for any trait test day will result in an indirect genetic gain for milk, fat, and protein yield in all periods of the lactation curve. The accuracy associated with estimated breeding values obtained using multi-trait random regression was slightly higher (around 8%) compared with single-trait random regression. This difference may be because to the greater amount of information available per animal. © 2013 American Dairy Science Association.

BCS: A Binary Cuckoo Search algorithm for feature selection

Feature selection has been actively pursued in the last years, since to find the most discriminative set of features can enhance the recognition rates and also to make feature extraction faster. In this paper, the propose a new feature selection called Binary Cuckoo Search, which is based on the behavior of cuckoo birds. The experiments were carried out in the context of theft detection in power distribution systems in two datasets obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques. © 2013 IEEE.

Optimizing feature selection through binary charged system search

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be inviable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the Charged System Search (CSS), which has never been applied to this context so far. The wrapper approach combines the power of exploration of CSS together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in four public datasets have demonstrated the validity of the proposed approach can outperform some well-known swarm-based techniques. © 2013 Springer-Verlag.