The structure, optical, and electrical properties of SnSe and its application as photovoltaic device has been reported widely. The reasons for interest in SnSe due to the magnificent optoelectronic properties with other encouraging properties. The most applications that in this area are PV devices and batteries. In this study tin selenide structure, optical properties and surface morphology were investigated and studies. Thin-film of SnSe were deposit on p-Si substrates to establish a junction as solar cells. Different annealing temperatures (as prepared, 125,200, 275) °C effects on SnSe thin films were investigated. The structure properties of SnSe was studied through X-ray diffraction, and the results appears the increasing of the peaks

Copper electrodeposition by electrorefining process in acidic sulfate media contains 40 g/l of cupric ions and 160 g/l of sulfuric acid was achieved to study the influence of the operating parameters on cathode purity, surface morphology, deposition rate, current efficiency and power consumption. These operating parameters and there ranges are: current density 200, 300 and 400 A/m², electrolyte temperature 35, 50 and 65 ^oC, electrodes spacing 15, 30 and 45 mm and electrolyte residence time 6, 4 and 2 h were utilized. XRF, SEM and EDX analyses were attained to clarify the properties of the produced cathode.

Accountancy unit is looked is upon as unit that established for the purpose achieve it goals and programmers for unlimited time. Unless otherwise take place such as liquation whether voluntary or mandatory.  Thus going concern logic is considered to be the logical foundation witch the familiar accounting principles are based upon.  The future of a Company real its financial statues and position and the extent of it ability to face events in future.  Hence the success and continuity its activities depend on the extent of the company activity to generate profits. And its ability to retain appropriate liquidity to serve its debts.

Therefore financial statements of the company consider to be on

This paper includes a comparison between denoising techniques by using statistical approach, principal component analysis with local pixel grouping (PCA-LPG), this procedure is iterated second time to further improve the denoising performance, and other enhancement filters were used. Like adaptive Wiener low pass-filter to a grayscale image that has been degraded by constant power additive noise, based on statistics estimated from a local neighborhood of each pixel. Performs Median filter of the input noisy image, each output pixel contains the Median value in the M-by-N neighborhood around the corresponding pixel in the input image, Gaussian low pass-filter and Order-statistic filter also be used. Experimental results shows LPG-PCA method

In this study, we investigate the run length properties for the EWMA charts with time - varying control limits, and fast initial Response (FIR), for monitoring the mean of a normal process with a known standard deviation , by using non - homogeneous markov chain approach.

Free Space Optical (FSO) technology offers highly directional, high bandwidth communication channels. This technology can provide fiber-like data rate over short distances. In order to improve security associated with data transmission in FSO networks, a secure communication method based on chaotic technique is presented. In this paper, we have turned our focus on a specific class of piece wise linear one-dimensional chaotic maps. Simulation results indicate that this approach has the advantage of possessing excellent correlation property. In this paper we examine the security vulnerabilities of single FSO links and propose a solution to this problem by implementing the chaotic signal generator “reconfigurable tent map”. As synchronizat

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes