Blow up Analysis for Anomalous Granular Gases
We investigate in this article the long-time behaviour of the solutions to the energy-dependant, spatially-homogeneous, inelastic Boltzmann equation for hard spheres. This model describes a diluted gas composed of hard spheres under statistical description, that dissipates energy during collisions. We assume that the gas is"anomalous", in the sense that energy dissipation increases when temperature decreases. This allows the gas to cool down in finite time. We study existence and uniqueness of blow up profiles for this model, together with the trend to equilibrium and the cooling law associated, generalizing the classical Haff's Law for granular gases. To this end, we investigate the asymptotic behaviour of the inelastic Boltzmann equation with and without drift term by introducing new strongly"nonlinear"self-similar variables.
This paper presents the application of Artificial Neural Network (ANN) modeling for the prediction of abrasion resistance of Persian handmade wool carpets. Four carpet constructional parameters, namely knot density, pile height, number of ply in pile yarn and pile yarn twist have been used as input parameters for ANN model. The prediction performance was judged in terms of statistical parameters like correlation coefficient (R) and Mean Absolute Percentage Error (MAPE). Though the training performance of ANN was very good, the generalization ability was not up to the mark. This implies that large number of training data should be used for the adequate training of ANN models.