Phylogeny of SARS-CoV based on high-dimensional information geometry

We proposed a novel methodology, which firstly, extracting features from species' complete genome data, using k-tuple, followed by studying the evolutionary relationship between SARS-CoV and other coronavirus species using the method, called "High-dimensional information geometry". We also used the mothod, namely "caculating of Minimum Spanning Tree", to construct the Phyligenetic tree of the coronavirus. From construction of the unrooted phylogenetic tree, we found out that the evolution distance between SARS-CoV and other coronavirus species is comparatively far. The tree accurately rebuilt the three groups of other coronavirus. We also validated the assertion from other literatures that SARS-CoV is similar to the coronavirus species in Group I.

Multiplicity factor and diffraction geometry factor for single crystal X-ray diffraction analysis and measurement of phase content in cubic GaN/GaAs(001) epilayers

On the basis of integrated intensity of rocking curves, the multiplicity factor and the diffraction geometry factor for single crystal X-ray diffraction (XRD) analysis were proposed and a general formula for calculating the content of mixed phases was obtained. With a multifunction four-circle X-ray double-crystal diffractometer, pole figures of cubic (002), {111} and hexagonal {1010} and reciprocal space mapping were measured to investigate the distributive character of mixed phases and to obtain their multiplicity factors and diffraction geometry factors. The contents of cubic twins and hexagonal inclusions were calculated by the integrated intensities of rocking curves of cubic (002), cubic twin {111}, hexagonal {1010} and {1011}.

A novel image restoration approach based on point location in high-dimensional space geometry

The goal of image restoration is to restore the original clear image from the existing blurred image without distortion as possible. A novel approach based on point location in high-dimensional space geometry method is proposed, which is quite different from the thought ways of existing traditional image restoration approaches. It is based on the high-dimensional space geometry method, which derives from the fact of the Principle of Homology-Continuity (PHC). Begin with the original blurred image, we get two further blurred images. Through the regressive deducing curve fitted by these three images, the first iterative deblured image could be obtained. This iterative "blurring-debluring-blurring" process is performed till reach the deblured image. Experiments have proved the availability of the proposed approach and achieved not only common image restoration but also blind image restoration which represents the majority of real problems.

A face detection algorithm based on high dimensional space geometry

In this paper, a face detection algorithm which is based on high dimensional space geometry has been proposed. Then after the simulation experiment of Euclidean Distance and the introduced algorithm, it was theoretically analyzed and discussed that the proposed algorithm has apparently advantage over the Euclidean Distance. Furthermore, in our experiments in color images, the proposed algorithm even gives more surprises.

A multi-moment finite volume formulation for shallow water equations on unstructured mesh

A novel and accurate finite volume method has been presented to solve the shallow water equations on unstructured grid in plane geometry. In addition to the volume integrated average (VIA moment) for each mesh cell, the point values (PV moment) defined on cell boundary are also treated as the model variables. The volume integrated average is updated via a finite volume formulation, and thus is numerically conserved, while the point value is computed by a point-wise Riemann solver. The cell-wise local interpolation reconstruction is built based on both the VIA and the PV moments, which results in a scheme of almost third order accuracy. Efforts have also been made to formulate the source term of the bottom topography in a way to balance the numerical flux function to satisfy the so-called C-property. The proposed numerical model is validated by numerical tests in comparison with other methods reported in the literature. (C) 2010 Elsevier Inc. All rights reserved.

A global shallow water model using high order multi-moment constrained finite volume method and icosahedral grid

A novel accurate numerical model for shallow water equations on sphere have been developed by implementing the high order multi-moment constrained finite volume (MCV) method on the icosahedral geodesic grid. High order reconstructions are conducted cell-wisely by making use of the point values as the unknowns distributed within each triangular cell element. The time evolution equations to update the unknowns are derived from a set of constrained conditions for two types of moments, i.e. the point values on the cell boundary edges and the cell-integrated average. The numerical conservation is rigorously guaranteed. in the present model, all unknowns or computational variables are point values and no numerical quadrature is involved, which particularly benefits the computational accuracy and efficiency in handling the spherical geometry, such as coordinate transformation and curved surface. Numerical formulations of third and fourth order accuracy are presented in detail. The proposed numerical model has been validated by widely used benchmark tests and competitive results are obtained. The present numerical framework provides a promising and practical base for further development of atmospheric and oceanic general circulation models. (C) 2009 Elsevier Inc. All rights reserved.