A thin film of AgInSe2 and Ag1-xCuxInSe2 as well as n-Ag1-xCuxInSe2 /p-Si heterojunction with different Cu ratios (0, 0.1, 0.2) has been successfully fabricated by thermal evaporation method as absorbent layer with thickness about 700 nm and ZnTe as window layer with thickness about 100 nm. We made a multi-layer of p-ZnTe/n-AgCuInSe2/p-Si structures, In the present work, the conversion efficiency (η) increased when added the Cu and when used p-ZnTe as a window layer (WL) the bandgap energy of the direct transition decreases from 1.75 eV (Cu=0.0) to 1.48 eV (Cu=0.2 nm) and the bandgap energy for ZnTe=2.35 eV. The measurements of the electrical properties for prepared films showed that the D.C electrical conductivity (σd.c) increase

The idea of the design of combination Between split – plot and split block means that an experiment conducted with a design

formed by combination Between split – plot and split block, and it presents a precise manner to analytic who aimed to make appropriate statistical analysis for the experiment because such design contains four random errors , it make a high precision rather than another designs. The plan and the theoretical analysis were presented with application   to show its idia and the ability to use it in many fields  especially in agricultural experiments field .

We propose a novel strategy to optimize the test suite required for testing both hardware and software in a production line. Here, the strategy is based on two processes: Quality Signing Process and Quality Verification Process, respectively. Unlike earlier work, the proposed strategy is based on integration of black box and white box techniques in order to derive an optimum test suite during the Quality Signing Process. In this case, the generated optimal test suite significantly improves the Quality Verification Process. Considering both processes, the novelty of the proposed strategy is the fact that the optimization and reduction of test suite is performed by selecting only mutant killing test cases from cumulating t-way test ca

Periodontitis is one of the most prevalent bacterial diseases affecting man with up to 90% of the global population affected. Its severe form can lead to the tooth loss in 10-15% of the population worldwide. The disease is caused by a dysbiosis of the local microbiota and one organism that contributes to this alteration in the bacterial population is Prophyromonas gingivalis. This organism possesses a range of virulence factors that appear to contribute to its growth and survival at a periodontal site amongst which is its ability to invade oral epithelial cells. Such an invasion strategy provides a means of evasion of host defence mechanisms, persistence at a site and the opportunity for dissemination to other sites in the mouth. However, p

   A comparison of double informative and non- informative priors assumed for the parameter of Rayleigh distribution is considered. Three different sets of double priors are included, for a single unknown parameter of Rayleigh distribution. We have assumed three double priors: the square root inverted gamma (SRIG) - the natural conjugate family of priors distribution, the square root inverted gamma – the non-informative distribution, and the natural conjugate family of priors - the non-informative distribution as double priors .The data is generating form three cases from Rayleigh distribution for different samples sizes (small, medium, and large). And Bayes estimators for the parameter is derived under a squared erro

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet