Solar energy usage in Iraq is facing many issues; one of those is the accumulation “of the dust on the surface of the solar module which” would highly lower its efficiency. The present work study the effect of dust accumulation” on installing fixed solar modules with different inclined angles 15^o, 33^o, 45^o, 60^o. Evaluation of the solar modules performance under different circumstance conditions such as rain, wind and humidity are considered in study of dust effect on solar module performance. The results show that the lowest output average efficiencies of solar modules occurs at 15^o horizontally inclined angle are 7.4% , 6.7% , 8.0% , 8.1%, and 8.4% for the cor

Objective: This study aims to examine how implementing Extensible Business Reporting Language (XBRL) enhances the efficiency and quality of environmental audits and sustainability reporting in eco-friendly universities. Aligned with Sustainable Development Goal 12 (Responsible Consumption and Production), the study emphasizes promoting transparency and precision in sustainability reporting to encourage responsible management of resources within academic institutions.   Theoretical Framework: The importance of our study is evident in the importance of accurate and transparent reports in the development of environmental performance with theories of sustainable reporting and environmental auditing. One of the most important digital

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an

The study included adding antimony oxide to mixtures of coating metal surfaces (Enameling), after it was selected ceramic materials used in the coating metal pieces of the type of steel and cast iron in two layers. The first is called a ground coat and the second is a cover coat.
Ceramic materials layer for ground coat have been melted down in
platinum crucible at a temperature of 1200oC to prepare the glass
mixture (Frit). It was coated on metals at a temperature of 780oC for
two minutes, while the second layer was prepared glass mixture
(Frit) at a temperature of 1200oC, but was coated at a temperature of
760oC for two minutes.
Underwent tests crystalline state of powders (Frits) and enameled samples using X-ray di

This paper experimentally investigated the dynamic buckling behavior of AISI 303 stainless steel aluminized and as received intermediate columns.  Twenty seven specimens without aluminizing (type 1) and 75 specimens with hot-dip aluminizing at different aluminizing conditions of dipping temperature and dipping time (type 2), were tested under dynamic compression loading (compression and torsion), dynamic bending loading (bending and torsion), and under dynamic combined loading (compression, bending, and torsion) by using a rotating buckling test machine. The experimental results werecompared with tangent modulus theory, reduced modulus theory, and Perry Robertson interaction formula. Reduced modulus was formulated to circular cross-

Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 m

This research deals with the effects of welding variables using MIG/MAG spot by using Argon (Ar) gas and CO₂ to show their effect on the mechanical characteristics and microstructure of low alloy steel type DIN15Mo3 and determine the optimum condition for the process of welding ; current & time. The results show the possibility of using CO₂ and also Ar in low alloy steel welding with a little decrease in the shear force of not more than 13% for 4mm thickness and time 2sec. The shear force increased when using Ar instead of CO₂ to be , The shear force reach 36KN when using Ar at 2mm thickness  time of 8 sec and current of 220 Amp. , when used CO₂ instead of Ar d

Abstract

     Machining residual stresses correlate very closely with the cutting parameters and the tool geometries. This research work aims to investigate the effect of cutting speed, feed rate and depth of cut on the surface residual stress of steel AISI 1045 after face milling operation. After each milling test, the residual stress on the surface of the workpiece was measured by using X-ray diffraction technique. Design of Experiment (DOE) software was employed using the response surface methodology (RSM) technique with a central composite rotatable design to build a mathematical model to determine the relationship between the input variables and the response. The results showed that both