In this paper we prove a common fixed point theorem for six compatible self mappings of type (A) in a complete non-Archimedean Menger PM-space.

In this paper, we show that some results of Sharma and Deshpande [19] are not valid, we also give supporting example. Finally, we consider the concept of weakly compatible mappings to improve the main result of Pathak [9].

NULL

NULL

The aim of this paper deals with the study of the Horn matrix function of two complex variables. The convergent properties, an integral representation of H2(A,A′,B,B′;C;z,w) is obtained and recurrence matrix relations are given. Some result when operating on Horn matrix function with the differential operator D and a solution of certain partial differential equations are established. The Hadamard product of two Horn’s matrix functions is studied, certain results as, the domain of regularity, contiguous functional relations and operating with the differential operator D and D2 are established.

NULL

DFT with B3LYP/6-311++G(d,p) level were used for all calculations in this work. In biological system, 5-FU- 3H2O has the highest stabilization energy compared to 5-FU- 3NH3, 5-FU dimer, and 5-FU monomer. The chemical interactions of 2′-deoxyribose radical with uracil and 5-FU radicals to form 2′-deoxyuridine and 2′-deoxy-5-fluorouridine show that the difference in stabilization energies, ΔE, for their formation are quite low which facilitates the exchange reactions in DNA structure. Size, shape density distributions, and chemical reactivity sites of 5-FU were obtained by mapping electron density isosurface with electronic surface. Additionally, the intermolecular hydrogen bonding in 5-FU (sugarphosphate) backbone system was simulated by NBO analysis to describe the role of intermolecular hydrogen bonding on the structure and chemical reactivity of 5-FU in biological systems. Molecular docking study of the interaction between 5- FU and human serum albumin (HSA) indicated that 5-FU binds to HSAwith low affinity and low specificity compared to other anticancer drugs.

Text detection from scene images is a challenging topics because of low resolution, complex background and font/font size variations. In this paper, we design a method to detect text based on Naïve Bayes classifier and connected component analysis. We used Naïve Bayes classifier to convert original gray level image into binary image, then connected component analysis is used to identify candidate text regions. In the last step we use empirical rules to determine threshold which used to discard non-text regions and keep the text regions. The proposed method compares between three classifiers outcome; the first is based on Otsu method, the second classifier outcome is derived using Naïve Bayes classifier based on mean feature and standard deviation feature, we named this method Bayes_Two_Features or shortly Bayes2. The last classifier outcome is derived using Naïve Bayes classifier based on just the mean feature, we named this method Bayes_Single_Feature or shortly Bayes1. Otsu’s method is used to convert grayscale image to binary image by assuming that image contains two classes; foreground and background. Experimental results show that Bayes2 classifier outperforms the other two methods, in the case of big letters especially when these letters are in non-horizontal and skewed form.

Text detection from scene images is a challenging topics because of low resolution, complex background and font/font size variations. In this paper, we design a method to detect text based on Naïve Bayes classifier and connected component analysis. We used Naïve Bayes classifier to convert original gray level image into binary image, then connected component analysis is used to identify candidate text regions. In the last step we use empirical rules to determine threshold which used to discard non-text regions and keep the text regions. The proposed method compares between three classifiers outcome; the first is based on Otsu method, the second classifier outcome is derived using Naïve Bayes classifier based on mean feature and standard deviation feature, we named this method Bayes_Two_Features or shortly Bayes2. The last classifier outcome is derived using Naïve Bayes classifier based on just the mean feature, we named this method Bayes_Single_Feature or shortly Bayes1. Otsu’s method is used to convert grayscale image to binary image by assuming that image contains two classes; foreground and background. Experimental results show that Bayes2 classifier outperforms the other two methods, in the case of big letters especially when these letters are in non-horizontal and skewed form.