Sentence examples for matrix is normally from inspiring English sources

The dolomite pervasively replaces the grains of all facies, whereas the matrix is normally only partially affected.

The mesangial matrix is normally composed of various macromolecules, including fibronectin, laminin, collagen, and thrombospondin, as well as various proteoglycans [ 4].

The ZOL-induced increase in bone volume may therefore be a result of elevated endochondral ossification, as this excess matrix is normally resorbed by osteoclasts.

The renal interstitial matrix is normally composed by collagen type I, III, V, VI, VII and XV, both sulphated and non-sulphated glycosaminoglycans, glycoproteins and polysaccharides.

The correlation coefficient matrix is normally used to measure the degree of correlation between logarithms of the elemental concentrations [ 70], and this matrix for the heavy metals of paddy showed highly significant positive correlations between the pairs of elements of soil samples Cd-Pb (0.65), Cr-Pb (0.95), Zn-Pb (0.96), Cd-Cr (0.83), Cd-Zn (0.69), and Cr-Zn (0.98) (Table 5(a)).

These test images are created by a convolutional mixing process using a set of mixing matrices generated randomly by MATLAB, and the criteria of this matrix are normally distributed random numbers.

The second order matrix differential equation containing mass, stiffness and damping matrices is normally transformed into a first-order state equation to deal with the general damping matrix.

It is shown that the original FD modifies to an essentially stochastic generalized eigenvalue problem with underlying matrices being normally distributed.

Such matrices are normally neither encountered in the sensor selection problem nor in many other engineering and scientific problems, which, to some extent, limits the applicability of the theory.

These matrices are normally used as surrogates, rather than deriving the maximum likelihood model from the dataset being examined.

Separability measures based on scatter matrices are normally preferable to probabilistic distances because they do not require an estimation of the probability density function of the classes or that those probability density functions are known a priori.