Rare earth elements (Cerium, Lanthanum and Neodymium) doped CdS thin films are prepared using the chemical Spray Pyrolysis Method with temperature 200 oC. The X-ray diffraction (XRD) analysis refers that pure CdS and CdS:Ce, CdS:La and CdS:Nd thin films showed the hexagonal crystalline phase. The crystallite size determined by the Debye-Scherrer equation and the range was (35.8– 23.76 nm), and it was confirmed by field emission scanning electron microscopy (FE-SEM). The pure and doped CdS shows a direct band gap (2.57 to 2.72 eV), which was obtained by transmittance. The room-temperature photoluminescence of pure and doped CdS shows large peak at 431 nm, and two small peaks at (530 and 610 nm). The Current – voltage measurement in da

In this paper, the speed control of the real DC motor is experimentally investigated using nonlinear PID neural network controller. As a simple and fast tuning algorithm, two optimization techniques are used; trial and error method and particle swarm optimization PSO algorithm in order to tune the nonlinear PID neural controller's parameters and to find best speed response of the DC motor. To save time in the real system, a Matlab simulation package is used to carry out these algorithms to tune and find the best values of the nonlinear PID parameters. Then these parameters are used in the designed real time nonlinear PID controller system based on LabVIEW package. Simulation and experimental results are compared with each other and showe

CdS and CdTe thin films were thermally deposited onto glass substrate. The CdCl2 layer was deposited onto CdS surface. These followed by annealing for different duration times to modify the surface and interface of the junction. The diffraction patterns showed that the intensity of the peaks increased with the CdCl2/annealed treatment, and the grain sizes are increased after CdCl2/annealed treatment

In this paper the effect of thermal annealing on the structural and optical properties of Antimony Selenide (Sb2Se3) is investigated. Sb2Se3 powder is evaporated on clean amorphous glass substrates at room temperature under high vacuum pressure (4.5×10-6 mbar) to form thin films. The structural investigation was done with the aid of X-ray diffraction (XRD) and atomic force microscopy (AFM). The amorphous to polycrystalline transformation of these thin films was shown by X-ray diffraction analysis after thermal annealing. These films' morphology is explained. (UV-Vis ) spectra in ranges from 300 to 1100 nm was used to examine the optical properties of the films .The absorption coefficient and optical energy gap of the investigated films are

Low- and medium-carbon structural steel components face random vibration and dynamic loads (like earthquakes) in many applications. Thus a modification to improve their mechanical properties, essentially damping properties, is required. The present study focuses on improving and developing these properties, significantly dampening properties, without losing the other mechanical properties. The specimens used in the present study are structural steel ribbed bar ISO 6935 subjected to heating temperatures of (850, 950, and 1050) ˚C, and cooling schemes of annealing, normalizing, sand, and quenching was selected. The damping properties of the specimens were measured experimentally with the area under the curve for the loadi

The a.c conductivity and dielectric properties of the prepared composites NiO:ZnO composites samples with various mixing ratios (100:0,80:20,60:40,40:60,20:80 and 0:100) wt. % were carried out . The structure phase, the crystallite size and purity of the fabricated NiO:ZnO composites were confirmed using x-ray diffraction pattern. The results declared that the diffraction spectrum of composites samples were compatible with cubic and hexagonal structures phases for 100%NiOand ZnO respectively while the spectrum were compatible with both phases for the residual composites samples. The crystallite size of the most pronounced plane (101) for crystal growth was changed from 23 nm to 49 .4 nm by

In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.