Some applications of excited-state-excited-state transition densities

We derive an approximation for transition moments between excited states consistent with the approximations and assumptions normally used to obtain transition moments betwen the ground and excited states in the random-phase approximation and its higher-order approximations. We apply the result to the calculation of the photoionization cross sections of the 23S and 21S metastable states of helium by a numerical analytical continuation of the frequency-dependent polarizability. The procedure completely avoids the need for continuum basis functions. The cross sections agree well with the results of other calculations. We also predict an accurate two-photon decay rate for the 21S metastable state of helium. The entire procedure is immediately applicable to several problems involving photoionization of metastable states of molecules

Fortalecimento da ovinocultura de corte no Município de Dormentes, Alto Sertão de Pernambuco.

Publicado também na Revista Brasileira de Agroecologia, v. 4, n. 2, 2009

Regularized quantile regression applied to genome-enabled prediction of quantitative traits.

Genomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively

Reference Models for Production Planning and Control Systems: A Bibliometric Analysis and Future Perspectives

The activity of modeling business processes is still not a common practice among organizations which contributes to increase the cost and time of systems deployment, improvement projects and educational software, due to the need to develop new models related to Business Processes. In this context, one of the Business Processes essential for organizations, especially those located in countries such as Brazil, where production activities are more pronounced than product development, is Production Planning and Control (PCP). In this scenario, in order to present a picture of scientific production, contribute to the literature review and identify gaps in the scientific literature within the framework of the Reference Models and PCP approach. This work aims to perform a bibliometric research in these areas of study. In this study, we used the bibliometric revision method composed of four phases: definition of database, definition of research keywords, selection of papers and analysis of papers. As a result, it was found that most scientific studies are focused on very specific situations in industrial planning or addressing particular business sectors