The role of human resources on the economy: a study of the Balkan eu member states

In this paper we analyze the impact of the quality of human capital on the main economic indicators of South-Eastern Europe countries [SEE] at the NUTS 2 level. The subjects of this research are the human capital indicators of regional competitiveness. The quality of human capital depends largely on the age structure of the population and the quality of education. Those regions, which have the highest percentage of the working-age population and highly educated people, are able to achieve higher productivity and gain a competitive advantage over other regions. As main indicators of the quality of human capital we identified: population; persons aged 25-64 with tertiary education attainment; students in tertiary education and participation of adults aged 25-64 in education and training and human resources in science and technology. As main economic indicators, we identified: regional gross domestic product; employment and income of households. The aim of this paper is to determine whether there is a correlation between the indicators of the quality of human capital and economic indicators. As a main methodology we have used the correlation coefficient which shows interdependence of the analyzed indicators. As part of our analysis, we consider only EU member states that belong to the SEE countries: Slovenia, Croatia, Romania, Bulgaria and Greece. We conclude that in all countries there is a high multiple correlation coefficient between the indicators human resources in science and technology, number of students and employment.This paper is the result of the project No. 47007 III funded by the Ministry for Education, Science and Technological Development of Republic of Serbia

Multiscale Partition of Unity

We introduce a new Partition of Unity Method for the numerical homogenization of elliptic partial differential equations with arbitrarily rough coefficients. We do not restrict to a particular ansatz space or the existence of a finite element mesh. The method modifies a given partition of unity such that optimal convergence is achieved independent of oscillation or discontinuities of the diffusion coefficient. The modification is based on an orthogonal decomposition of the solution space while preserving the partition of unity property. This precomputation involves the solution of independent problems on local subdomains of selectable size. We deduce quantitative error estimates for the method that account for the chosen amount of localization. Numerical experiments illustrate the high approximation properties even for 'cheap' parameter choices.Comment: Proceedings for Seventh International Workshop on Meshfree Methods for Partial Differential Equations, 18 pages, 3 figure

slfm: An R Package to Evaluate Coherent Patterns in Microarray Data via Factor Analysis

The development of simulation-based methods, such as Markov chain Monte Carlo (MCMC), has contributed to an increased interest in the Bayesian framework as an alternative to deal with factor models. Many studies have used Bayesian factor analysis to explore gene expression data. We are particularly interested in the application of a sparse latent factor model (SLFM) based on sparsity priors (mixtures) to assess the significance of factors. The SLFM measures how strong the observed coherent expression pattern is in the data, which is an important source of information to evaluate gene activity. In the literature, this type of model has shown better results than other approaches intended for identification of patterns and metagene groups related to the underlying biology. However, a full Bayesian factor model relying on MCMC algorithms has an expensive computational cost, which makes it unattractive for general users. In this paper, we present the package slfm which uses C++ implementation via Rcpp to improve the computational performance of the SLFM within the widely used statistical tool R. We investigate real and simulated microarray data related to breast cancer

pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution

In this study, we present a new module built for users interested in a programming language similar to BUGS to fit a Bayesian model based on the piecewise exponential (PE) distribution. The module is an extension to the open-source program JAGS by which a Gibbs sampler can be applied without requiring the derivation of complete conditionals and the subsequent implementation of strategies to draw samples from unknown distributions. The PE distribution is widely used in the fields of survival analysis and reliability. Currently, it can only be implemented in JAGS through methods to indirectly specify the likelihood based on the Poisson or Bernoulli probabilities. Our module provides a more straightforward implementation and is thus more attractive to the researchers aiming to spend more time exploring the results from the Bayesian inference rather than implementing the Markov Chain Monte Carlo algorithm. For those interested in extending JAGS, this work can be seen as a tutorial including important information not well investigated or organized in other materials. Here, we describe how to use the module taking advantage of the interface between R and JAGS. A short simulation study is developed to ensure that the module behaves well and a real illustration, involving two PE models, exhibits a context where the module can be used in practice