a matrix $pintextmd{c}^{ntimes n}$ is called a generalized reflection matrix if $p^{h}=p$ and $p^{2}=i$‎. ‎an $ntimes n$‎ ‎complex matrix $a$ is said to be a reflexive (anti-reflexive) matrix with respect to the generalized reflection matrix $p$ if $a=pap$ ($a=-pap$)‎. ‎in this paper‎, ‎we introduce two iterative methods for solving the pair of matrix equations $axb=c$ and $dxe=f$ over reflexiv...

matrix metalloproteinases (mmps) are a family of neutral proteinases that are important in normal development, cellular differentiation or migration, angiogenesis, neurogenesis, wound repair, and a wide range of pathological processes such as oxidative stress and neuroinflammation. mmps have been demonstrated to increase the permeability of the blood–brain barrier (bbb) by degrading the compone...

Hyperspectral image unmixing is an important task for remote sensing processing. It aims at decomposing the mixed pixel of to identify a set constituent materials called endmembers and obtain their proportions named abundances. Recently, number algorithms based on sparse nonnegative matrix factorization (NMF) have been widely used in hyperspectral with good performance. However, these NMF only ...

he main purpose of this research is to answer the question that "How neighborhood of Iran's trading partners will have an effect on Iran intra-industry trade? For this purpose, the impact of spatial neighborhood effects of 23 major trade partners on Iran’s intra-industry trade for the period of 1995-2014, has been investigated through Spatial Panel Econometric and Maximum Likelihood Estimator (...

The paper evaluates by means of Monte Carlo simulations the estimator of the regression coefficient obtained by the classical W-based spatial autoregressive model and the structural equations model with latent variables (SEM) on the basis of data sets that contain two types of spatial dependence: spillover from (i) a hotspot and (iia) first order queen contiguity neighbors or (iib) inverse dist...

In the analysis of spatial data, the inverse of the covariance matrix needs to be calculated. For example, the inverse is needed for best linear unbiased prediction or kriging, and is repeatedly calculated in the maximum likelihood estimation or the Bayesian inferences. Since the spatial sample size can be quite large, operations on the large covariance matrix can be a numerical challenge if no...

We consider the steady-state error covariance for a discrete-time system with banded dynamics. Such systems frequently arise from the spatial and temporal discretization of partial differential equations. In such systems, the magnitudes of the entries of the steady-state covariance matrix typically decrease as the distance from the diagonal increases. We obtain a bound on the entries of the cov...

For 1990–2019, this study presents two-step GMM estimates of EU members’ demands for defense spending based on alternative spatial-weight matrices. In particular, spatial connectivity is tied to membership status, contiguity, contiguity and power projection, inverse distance, arms trade. At a Nash equilibrium, our demand equations are derived explicitly from spatially game-theoretical model all...