In this paper, CdS/Si hetrojunction solar cell has been made by
Chemical Bath Deposition (CBD) of CdS thin film on to
monocrystalline silicon substrate. XRD measurements approved that
CdS film is changing the structure of CdS films from mixed
hexagonal and cubic phase to the hexagonal phase with [101]
predominant orientation. I-V characterization of the hetrojunction
shows good rectification, with high spectral responsivity of 0.41
A/W, quantum efficiency 90%,and specific detectivity 2.9*1014
cmHz1/2W -1 .

 Powder of silver nanoparticles was prepared by Sol - Gel method successfully using silver nitrate , (AgNO3) gesture is added to sodium citrate (C6H5O7Na3) as a reducing agent and by using  Magnetic Stirrer to mix the solutions and heated then using centrifuge machine to separate the silver nanoparticles from solution .It is then dried in an oven at a temperature 40oC for 24 hours.     Structure characteristics was studied , the synthetic silver powder was prepared through the use of an (XRD). Results showed the composition of silver nanoparticles is a (fcc) and a constant lattice (4.086 ± 0.006 Ã…) by comparing it with standard tables (JCPDS) which is found perfectly matched to file with a number 04-0783, which

This research aims to demonstrate the impact of the going concern assumption in different accounting applications to provide a realistic look and more accurate result of activity and financial situation, as well as determining the responsibility of the Company's administration in compliance with the going concern assumption during the preparation for their financial statements, and to clarify the concept of integration between internal audit and external audit about going concern assumption, besides its importance and usefulness on the work of both of the internal auditor and the external auditor, as well as on the company under auditing process.This research purports preparing an internal audit program, including a set of auditing actio

Generally, radiologists analyse the Magnetic Resonance Imaging (MRI) by visual inspection to detect and identify the presence of tumour or abnormal tissue in brain MR images. The huge number of such MR images makes this visual interpretation process, not only laborious and expensive but often erroneous. Furthermore, the human eye and brain sensitivity to elucidate such images gets reduced with the increase of number of cases, especially when only some slices contain information of the affected area. Therefore, an automated system for the analysis and classification of MR images is mandatory. In this paper, we propose a new method for abnormality detection from T1-Weighted MRI of human head scans using three planes, including axial plane, co

This paper presents a hybrid approach called Modified Full Bayesian Classifier (M-FBC) and Artificial Bee Colony (MFBC-ABC) for using it to medical diagnosis support system. The datasets are taken from Iraqi hospitals, these are for the heart diseases and the nervous system diseases. The M-FBC is depended on common structure known as naïve Bayes. The structure for network is represented by D-separated for structure's variables. Each variable has Condition Probability Tables (CPTs) and each table for disease has Probability. The ABC is easy technique for implementation, has fewer control parameters and it could be easier than other swarm optimization algorithms, so that hybrid with other algorithms to reach the optimal structure. In the

     The hiding of information has become of great importance in recent times. With dissemination through the internet, and communication through satellites, information needs to be secure. Therefore, a new algorithm is proposed that enables secret messages to be embedded inside satellite images, wherein images of any size or format can be hidden, using a system’s image compression techniques. This operation is executed in three main steps: first phase – the original image is converted into a raster image; second phase– steganography, in which a binary secret message is hidden inside a raster image, using a 4×4 array as the secret key; and third phase– compre

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall

A new ligand complexes have been synthesis from reaction of metal ions of Mn(II), Co(II), Ni(II), Cu(II), Zn(II), Cd(II), Hg(II), Pd(II) and Pt(II) with schiff base LH. 5-[(2-Hydroxy-naphthalen-1-ylmethylene)-amino]-2-phenyl-2,4-dihydro-pyrazol-3-one, this ligand was characterized by Fourier transform infrared (FTIR), UV-vis, 1H, 13CNMR, and mass spectra. All complexes were characterized by techniques micro analysis C.H.N, UV-vis and FTIR spectral studies, atomic absorption, chloride content, molar conductivity measurements and magnetic susceptibility. The ligand acts as bidentate, coordination through nitrogen atom from azomethin group and deprotonated phenolic oxygen atom. The spectroscopic and analytical measurements showed that