Thin films whose compositions can be expressed by (GeS₂)_100-xGa_x (x=0, 6,12,18) formula were obtained by thermal evaporation technique  of bulk material at a base pressure of ~10^-5 torr. Optical transmission spectra of the films were taken in the range of 300-1100 nm then the optical band gap, tail width of localized states,  refractive index, extinction coefficient were calculated. The optical constants were found to increase at low concentration of Ga (0 to12%) while they decreases with further addition of Ga. The optical band gap was found to change in opposite manner to that of optical constants. The variation in the optical parameters are explained in terms of average bond energy

Zinc sulfide(ZnS) thin films of different thickness were deposited on corning glass with the substrate kept at room temperature and high vacuum using thermal evaporation technique.the film properties investigated include their absorbance/transmittance/reflectance spectra,band gap,refractive index,extinction coefficient,complex dielectric constant and thickness.The films were found to exhibt high transmittance(59-98%) ,low absorbance and low reflectance in the visible/near infrared region up to 900 nm..However, the absorbance of the films were found to be high in the ultra violet region with peak around 360 nm.The thickness(using optical interference fringes method) of various films thichness(100,200,300,and 400) nm.The band gap meas

Abstract: This paper presents the results of the structural and optical analysis of CdS thin films prepared by Spray of Pyrolysis (SP) technique. The deposited CdS films were characterized using spectrophotometer and the effect of Sulfide on the structural properties of the films was investigated through the analysis of X-ray diffraction pattern (XRD). The growth of crystal became stronger and more oriented as seen in the X-ray diffraction pattern. The studying of X-ray diffraction showed that; all the films have the hexagonal structure with lattice constants a=b=4.1358 and c=6.7156A°, the crystallite size of the CdS thin films increases and strain (ε) as well as the dislocation density (δ) decreases. Also, the optical properties of the

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

Thin films of CdS:Cu were deposited onto glass substrate temperature 400 °c. The optieal properties have been studied for Cds doped with (1,3, 8) wt% of Cu before and after Gamma irradiation. It was found that the irradiation caused an ( Frenkel defects) where the atom is displaced from its original site leaving vacancy and forming on interstitial atom. It was found the irradiation caused an absorption edge shifting towards long wavelength as a result of the increasing of Cu concentration.

The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be