This article shows evidence regarding the educational attainment and inequality of educational opportunities in Brazil based on the grade progression probability method (GPP) between the years 1981 and 2008. We describe some stylised facts about the educational trajectory in Brazil, then we test two hypothesis suggested by Mare (1979, 1980). The first hypothesis states that the effect of social origins decreases along the educational trajectory. The second states that the educational expansion between two periods would reduce the inequality of educational opportunities in a given grade. Results show an increase in grade probability in nearly all grades, but this trend is most striking in the earlier stages. Educational stratification results show that Mare's first hypothesis could not be corroborated. The second hypothesis was partly confirmed. We found a decline during the period analysed on the effect of household head's education on grade progression at the earlier transitions. Furthermore, the selectivity pattern seemed to be transferred to later grade transitions.

ABSTRACT:The influence of nonbonded interactions in the kinetics of formation of chalcogenol (thiol and selenol) esters from chalcogenoacetylenes was studied by molecular modeling. Using semiempirical and density functional theory methods it was possible to explain the differences between the reaction rates for the analogous sulfur and selenium chalcogenoacetylenes as well as evaluate the structural and electronic effects (nonbonded interactions) on the formation of the esters. The differences in the reaction rates can be explained in terms of the carbocation stabilization by the chalcogen atom. It is proposed that these differences are due to the differences in the intensity of the dominant interaction * CO /n Y between the nonbonding orbitals of sulfur and selenium with the vacant orbital of carbon in the cationic transition state.

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This paper presents a solution for the problem related to Partial Discharges (PD) source localization in oilisolated transformers, using Genetic Algorithms. As known in literature, the simple PD detection is not enough to take a decision about intervening, so the localization is necessary to assess the risk and to plan corrective actions. The parameters extracted from the acoustic signals collected by piezoelectric sensors installed outside of the transformer, provide the correct position of PD. From parameters extracted of acoustic signals collected from piezoelectric sensors installed outside the transformer, the proposed algorithm provides the position where the PD is occurring. This work compares the performance of the method using Genetic Algorithms and those using iterative method, which is normally used to solve this problem. The conclusion describes the advantages of the proposed solution.