The gravitational heat conduction and the hierarchical structure in solar interior

With the assumption of local Tsallis equilibrium, the newly defined gravitational temperature is calculated in the solar interior, whose distribution curve can be divided into three parts, the solar core region, radiation region and convection region, in excellent agreement with the solar hierarchical structure. By generalizing the Fourier law, one new mechanism of heat conduction, based on the gradient of the gravitational temperature, is introduced into the astrophysical system. This mechanism is related to the self-gravity of such self-gravitating system whose characteristic scale is large enough. It perhaps plays an important role in the astrophysical system which, in the solar interior, leads to the heat accumulation at the bottom of the convection layer and then motivates the convection motion.Comment: 8 pages, 2 figures, 1 table, 19 reference

The limit behavior of the evolution of Tsallis entropy in self-gravitating systems

In this letter, we study the limit behavior of the evolution of Tsallis entropy in self-gravitating systems. The study is carried out under two different situations, drawing the same conclusion. No matter in the energy transfer process or in the mass transfer process inside the system, when nonextensive parameter q is more than unity, the total entropy is bounded; on the contrary, when this parameter is less than unity, the total entropy is unbounded. There are proofs in both theory and observation that the q is always more than unity. So the Tsallis entropy in self-gravitating system generally exhibits a bounded property. This indicates the existence of global maximum of Tsallis entropy. It is possible for self-gravitating systems to evolve to thermodynamically stable states

Test of CPT symmetry in cascade decays

Cascade mixing provides an elegant place to study the $B^0-\bar{B^0}$ mixing. We use this idea to study the CPT violation caused by $B^0-\bar{B^0}$ mixing. An approximation method is adopted to treat the two complex $B^0-\bar{B^0}$ mixing parameters $\theta$ and $\phi$. A procedure to extract the parameters $\theta$ and $\phi$ is suggested. The feasibility of exploring the CPT violation and determining of $\theta$ and $\phi$ in the future B-factories and LHC-B is discussed.Comment: Latex, 17 pages, some errors modifie

Integration of Spatial and Spectral Information for Hyperspectral Image Classification

Hyperspectral imaging has become a powerful tool in biomedical and agriculture fields in the recent years and the interest amongst researchers has increased immensely. Hyperspectral imaging combines conventional imaging and spectroscopy to acquire both spatial and spectral information from an object. Consequently, a hyperspectral image data contains not only spectral information of objects, but also the spatial arrangement of objects. Information captured in neighboring locations may provide useful supplementary knowledge for analysis. Therefore, this dissertation investigates the integration of information from both the spectral and spatial domains to enhance hyperspectral image classification performance. The major impediment to the combined spatial and spectral approach is that most spatial methods were only developed for single image band. Based on the traditional singleimage based local Geary measure, this dissertation successfully proposes a Multidimensional Local Spatial Autocorrelation (MLSA) for hyperspectral image data. Based on the proposed spatial measure, this research work develops a collaborative band selection strategy that combines both the spectral separability measure (divergence) and spatial homogeneity measure (MLSA) for hyperspectral band selection task. In order to calculate the divergence more efficiently, a set of recursive equations for the calculation of divergence with an additional band is derived to overcome the computational restrictions. Moreover, this dissertation proposes a collaborative classification method which integrates the spectral distance and spatial autocorrelation during the decision-making process. Therefore, this method fully utilizes the spatial-spectral relationships inherent in the data, and thus improves the classification performance. In addition, the usefulness of the proposed band selection and classification method is evaluated with four case studies. The case studies include detection and identification of tumor on poultry carcasses, fecal on apple surface, cancer on mouse skin and crop in agricultural filed using hyperspectral imagery. Through the case studies, the performances of the proposed methods are assessed. It clearly shows the necessity and efficiency of integrating spatial information for hyperspectral image processing