a numerical model for simulating residence time distribution (rtd) of turbulent flows in helical static mixers is proposed and developed to improve the understanding of static mixers. the results of this model is presented in terms of different volumetric flow rate to illustrate the complicated flow patterns that drive the mixing process in helical static mixers. the computed results are also u...

Nonlinear signal separation and underdetermined signal separation have received much attention in blind signal separation literature. However, neither of them can solve the situation where both nonlinear and underdetermined characteristics exist at the same time. In this paper, a new learning algorithm based on Bayesian statistics is proposed to solve the separation problem of the blind nonline...

One of the main problems in credit risk management is the correlated default. In large portfolios, computing the default dependencies among issuers is an essential part in quantifying the portfolio's credit. The most important problems related to credit risk management are understanding the complex dependence structure of the associated variables and lacking the data. This paper aims at introdu...

Human and animal diet reconstruction studies that rely on tissue chemical signatures aim at providing estimates on the relative intake of potential food groups. However, several sources of uncertainty need to be considered when handling data. Bayesian mixing models provide a natural platform to handle diverse sources of uncertainty while allowing the user to contribute with prior expert informa...

A numerical study of 3D electrokinetic flows through micromixers was performed. The micromixers considered here consisted of heterogeneous rectangular microchannels with prescribed patterns of zeta-potential at their walls. Numerical simulation of electroosmotic flows within heterogeneous channels requires solution of the Navier-Stokes, Ernest-Plank and species concentration equations. It is kn...

Our first focus is prediction of a categorical response variable using features that lie on a general manifold. For example, the manifold may correspond to the surface of a hypersphere. We propose a general kernel mixture model for the joint distribution of the response and predictors, with the kernel expressed in product form and dependence induced through the unknown mixing measure. We provid...

A nonparametric regression estimator is introduced which adapts to the smoothness of the unknown function being estimated. This property allows the new estimator to automatically achieve minimal bias over a large class of locally smooth functions without changing the rate at which the variance converges. Optimal convergence rates are shown to hold for both i.i.d. data and autoregressive process...

Park and Casella (2008) provided the Bayesian lasso for linear models by assigning scale mixture of normal (SMN) priors on the parameters and independent exponential priors on their variances. In this paper, we propose an alternative Bayesian analysis of the lasso problem. A different hierarchical formulation of Bayesian lasso is introduced by utilizing the scale mixture of uniform (SMU) repres...