The equilibrium properties of a multi‐component plasma of charged particles that interact through the coulomb and short‐range potentials are investigated. In particular, this article presents density expansions of the reduced distribution functions for more particles, our calculations are based on the Bogoliubov‐Born‐Green‐Kirkwood‐Yvon (BBGKY) hierarchy, we used the results to calculate the binary and triplet distribution functions. We obtained the analytical form of the classical triplet distribution functions for two component plasma; one of them is based on the Kirkwood superposition approximation (KSA) which is consisting of the assumption that the potential in a set of three particles is the sum of the three pair potentials, this is equivalent to assuming that the triplet distribution function is the product of the three radial distribution functions, and the other form is calculated by integration the triplet distribution function (© 2012 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Text categorization (TC) is the task of automatically organizing a set of documents into a set of pre-defined categories. Over the last few years, increased attention has been paid to the use of documents in digital form and this makes text categorization becomes a challenging issue. The most significant problem of text categorization is its huge number of features. Most of these features are redundant, noisy and irrelevant that cause over fitting with most of the classifiers. Hence, feature extraction is an important step to improve the overall accuracy and the performance of the text classifiers. In this paper, we will provide an overview of using principle component analysis (PCA) as a feature extraction with various classifiers. It was observed that the performance rate of the classifiers after using PCA to reduce the dimension of data improved. Experiments are conducted on three UCI data sets, Classic03, CNAE-9 and DBWorld e-mails. We compare the classification performance results of using PCA with popular and well-known text classifiers. Results show that using PCA encouragingly enhances classification performance on most of the classifiers.

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The aim of this paper is to calculate the magnetic quantum equations of state of plasma, the calculation is based on the magnetic binary Slater sum in the case of low density. We consider only the thermal equilibrium plasma in the case of nλab3≪1, where λab2=ℏ2mabKT is the thermal De Broglie wave length between two particles. The formulas contain the contributions of the magnetic field effects. Using these results we compute the magnetization and the magnetic susceptibility. Our equation of state is compared with others.

This paper aims to obtain the thermodynamic variables (temperature, thermodynamic volume, angular velocity, electrostatic potential, and heat capacity) corresponding to the Schwarzschild black hole, Reissner-Nordstrom black hole, Kerr black hole and Kerr-Newman-Anti-de Sitter black hole. We also obtained the free energy for black holes by using three different methods. We obtained the equation of state for rotating Banados, Teitelboim and Zanelli black holes. Finally, we used the quantum correction of the partition function to obtain the heat capacity and entropy in the quantum sense.