the purpose of this review article is to characterize all of the parameters regarding the types, polymers used, and release kinetics of matrix tablets. matrix system was the earliest oral extended release platform for medicinal use. matrix tablets are most commonly used methods to modulate the release profile of drugs. they are much desirable and preferred for such therapy because they offer be...

Abstract In this article, we show that the rank of 2-Steiner distance matrix a caterpillar graph having N N vertices and p p pendant veritices is 2 − 1 2N-p-1 .

In this paper we develop mixed proportional hazard models in discrete time when there is cross sectional duration dependence because of the social interactions. To capture the cross sectional dependence, we use the lagged binary indicators of neighbors, which are weighted by the spatial weight matrix. We investigate the nonparametric specification for the baseline hazard and the unobserved hete...

BACKGROUND When analysing spatial data, it is important to account for spatial autocorrelation. In Bayesian statistics, spatial autocorrelation is commonly modelled by the intrinsic conditional autoregressive prior distribution. At the heart of this model is a spatial weights matrix which controls the behaviour and degree of spatial smoothing. The purpose of this study is to review the main spe...

this research presents a numerical tool to estimate the green's function operator matrix of intraplate faults. having this matrix and its inverse, spatial distribution of fault slippage could be investigated through the inverse analysis of geodetic data. this information could be employed to predict the location of future powerful earthquakes. to implement fault sliding in fe calculations,...

Our goal is to learn a Mahalanobis distance by minimizing a loss defined on the weighted sum of the precision at different ranks. Our core motivation is that minimizing a weighted rank loss is a natural criterion for many problems in computer vision such as person re-identification. We propose a novel metric learning formulation called Weighted Approximate Rank Component Analysis (WARCA). We th...