24,017 research outputs found

    A SOCIO-COGNITIVE BASIS FOR STRATEGIC GROUPS: COGNITIVE DISSONANCE IN SWINE GENETICS

    Get PDF
    Institutional and Behavioral Economics, Livestock Production/Industries,

    Complete characterization of sink-strengths for 1D to 3D mobilities of defect clusters.II. Bridging between limiting cases with effective sink-strengths calculations

    Full text link
    In a companion paper, we proposed new analytical expressions of cluster sink-strengths (CSS) indispensable to any complete parameterization of rate equations cluster dynamics accounting for reaction between defect clusters populations having a 1D-mobility. In this second paper, we first establish simulation setup rules for truly converged estimates of effective CSS by Kinetic Monte-Carlo, and then we grid on a wide set of radii, rotation energies, diffusion coefficients and concentrations of both reaction partners. Symmetric roles of some parameters are used to infer a generic form for a semi-analytical expression of CSS depending on all these interaction parameters: it is composed of the various analytical limiting cases established and fitted transition functions that allow a gradual switching between them. The analysis of the residuals shows that the overall agreement is reasonably good: it is only in the transition zones that discrepancies are located and this is due to the asymmetry of the actual transition functions. The expression can be easily extended to temperatures at least a few hundred degrees around the reference. But further extending the CSS evaluations to much smaller diffusion coefficients ratios, we see that the domain for 1D-1D mobility is very extended: for a 10310^{-3} ratio the computed CSS is still not correctly described by the 1D-CSS with respect to a fixed sink (1D-0), but rather by the established 1D-1D expression. For our typical sets of conditions, it is only when approaching a ratio of 10610^{-6} that the 1D-0 CSS starts to become more relevant. This highlights the counter-intuitive fact that the growth kinetics of moderately trapped 1D mobile loops, whose effective mobility is greatly reduced, may not be described by 1D-0 kinetics but rather by appropriately corrected 1D-1D CSS which have completely different order of magnitude and kinetic orders.Comment: 21 pages, 12 figure

    Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics

    Get PDF
    First, this paper investigates the effect of good and bad news on volatility in the BUX return time series using asymmetric ARCH models. Then, the accuracy of forecasting models based on statistical (stochastic), machine learning methods, and soft/granular RBF network is investigated. To forecast the high-frequency financial data, we apply statistical ARMA and asymmetric GARCH-class models. A novel RBF network architecture is proposed based on incorporation of an error-correction mechanism, which improves forecasting ability of feed-forward neural networks. These proposed modelling approaches and SVM models are applied to predict the high-frequency time series of the BUX stock index. We found that it is possible to enhance forecast accuracy and achieve significant risk reduction in managerial decision making by applying intelligent forecasting models based on latest information technologies. On the other hand, we showed that statistical GARCH-class models can identify the presence of leverage effects, and react to the good and bad news.Web of Science421049

    Categorization of interestingness measures for knowledge extraction

    Full text link
    Finding interesting association rules is an important and active research field in data mining. The algorithms of the Apriori family are based on two rule extraction measures, support and confidence. Although these two measures have the virtue of being algorithmically fast, they generate a prohibitive number of rules most of which are redundant and irrelevant. It is therefore necessary to use further measures which filter uninteresting rules. Many synthesis studies were then realized on the interestingness measures according to several points of view. Different reported studies have been carried out to identify "good" properties of rule extraction measures and these properties have been assessed on 61 measures. The purpose of this paper is twofold. First to extend the number of the measures and properties to be studied, in addition to the formalization of the properties proposed in the literature. Second, in the light of this formal study, to categorize the studied measures. This paper leads then to identify categories of measures in order to help the users to efficiently select an appropriate measure by choosing one or more measure(s) during the knowledge extraction process. The properties evaluation on the 61 measures has enabled us to identify 7 classes of measures, classes that we obtained using two different clustering techniques.Comment: 34 pages, 4 figure
    corecore