In the current research, the work concentrated on studying the effect of curvature of solar parabolic trough solar collector on wind loading coefficients and dynamic response of solar collector. The response of collector to the aerodynamic loading was estimated numerically and experimentally. The curvature of most public parabolic trough solar collectors was investigated and compared. The dynamic response of solar collector due to wind loading was investigated by using numerical solution of fluid-structure interaction concept. The experimental work was done to verify the numerical results and shows good agreement with numerical results. The numerical results were obtained by using finite element software package (ANSYS 14). It was found

In this work, a deep computational study has been conducted to assign several qualities for the graph ⁠. Furthermore, determine the amount of the dihedral subgroups in the Held simple group He through utilizing the attributes of gamma.

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

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

Stable new derivative (L) Bis[O,O-2,3;O,O-5,6(carboxylic methyliden)]L-ascorbic acid was synthesized in good yield by the reaction of L-ascorbic acid with dichloroacetic acid with ratio (1:2) in presence of potassium hydroxide. The new (L) was characterized by 1H,13C-NMR, elemental analysis (C,H) and Fourier Transform Infrared (FTIR). The complexes of the ligand (L) with metal ion, M+2= (Cu, Co, Ni, Cd and Hg) were synthesized and characterized by FTIR, UV-Visible, Molar conductance, Atomic absorption and the Molar ratio. The analysis evidence showed the binding of the metal ions with (L) through bicarboxylato group manner resulting in six-coordinated metal ion.

Complexes of Au(III) ,Pd (II) , Pt (IV ) and Rh(III) with S – propynyle -2- thiobenzimidazole (BENZA) have been prepared and characterized by IR and UV- Visible spectral methods in addition to magnetic and conductivity measurements and micro – elemental analysis (CHN).The probable structures of the new complexes have been suggested.

In this research various of 2,5-disubstituted 1,3,4-oxadiazole (Schiff base, oxo-thiazolidine , and other compounds) were synthesized from 2,5-di(4,4?- amino-1,3,4-oxadiazole ) which use quently synthesized from mixture of 4-amino benzoic acid and hydrazine in the presence of polyphosphorus acid. The synthesized compounds were characterized by using some Spectral data (UV, FT-IR, and 1H-NMR).