Modelo simple para la permeabilidad dinámica de fluidos viscoelásticos

In the present work a simple form to obtain analytical expression for the dynamic permeability of Maxwellian fluids is presented. This expression gives the frequency dependent form of this dynamic permeability. In particular case, the analytic expression for the sinusoidal pressure pump fluid is illustrated in the configuration space. As an example of the feasibility of this expression the flow of human blood in a tube is presented finding that the human heart frequency has the same order that the frequencies where the dynamic permeability shows resonances. In order to make clear the above aspect of the dynamic permeability a model of pulsing pressure drops (gaussian like) are analyzed.

Classification using log Gaussian Cox processes

McCullagh and Yang (2006) suggest a family of classification algorithms based on Cox processes. We further investigate the log Gaussian variant which has a number of appealing properties. Conditioned on the covariates, the distribution over labels is given by a type of conditional Markov random field. In the supervised case, computation of the predictive probability of a single test point scales linearly with the number of training points and the multiclass generalization is straightforward. We show new links between the supervised method and classical nonparametric methods. We give a detailed analysis of the pairwise graph representable Markov random field, which we use to extend the model to semi-supervised learning problems, and propose an inference method based on graph min-cuts. We give the first experimental analysis on supervised and semi-supervised datasets and show good empirical performance.