Many objective optimizations (MaOO) algorithms that intends to solve problems with many objectives (MaOP) (i.e., the problem with more than three objectives) are widely used in various areas such as industrial manufacturing, transportation, sustainability, and even in the medical sector.  Various approaches of MaOO algorithms are available and employed to handle different MaOP cases. In contrast, the performance of the MaOO algorithms assesses based on the balance between the convergence and diversity of the non-dominated solutions measured using different evaluation criteria of the quality performance indicators. Although many evaluation criteria are available, yet most of the evaluation and benchmarking of the MaOO with state-of-art a

As we live in the era of the fourth technological revolution, it has become necessary to use artificial intelligence to generate electric power through sustainable solar energy, especially in Iraq and what it has gone through in terms of crises and what it suffers from a severe shortage of electric power because of the wars and calamities it went through. During that period of time, its impact is still evident in all aspects of daily life experienced by Iraqis because of the remnants of wars, siege, terrorism, wrong policies ruling before and later, regional interventions and their consequences, such as the destruction of electric power stations and the population increase, which must be followed by an increase in electric power stations,

CdS and CdS:Sn thin films were successfully deposited on glass
substrates by spray pyrolysis method. The films were grown at
substrate temperatures 300 C°. The effects of Sn concentration on the
structural and optical properties were studied.
The XRD profiles showed that the films are polycrystalline with
hexagonal structure grown preferentially along the (002) axis. The
optical studies exhibit direct allowed transition. Energy band gap
vary from 3.2 to 2.7 eV.

ZnO nanostructures were synthesized by hydrothermal method at different temperatures and growth times. The effect of increasing the temperature on structural and optical properties of ZnO were analyzed and discussed. The prepared ZnO nanostructures were characterized by X-ray diffraction (XRD), UV–Vis. absorption spectroscopy (UV–Vis.), Photoluminescence (PL), and scanning electron microscopy (SEM). In this work, hexagonal crystal structure prepared ZnO nanostructures was observed using X-ray diffraction (XRD) and the average crystallite size equal 14.7 and 23.8 nm for samples synthesized at growth time 7 and 8 hours respectively. A nanotubes-shaped surface morphology was found using scanning electron microscopy (SEM). The optic

The extraction of Cupressus sempervirens L. or cypress essential oil was studied in this paper. This cypress oil was extracted by using the hydro-distillation method, using a clevenger apparatus. Cupressus sempervirens L. leaves were collected from Hit city in Al-Anbar province – Iraq. The influences of three important parameters on the process of oil extraction; water which used as a solvent to the solid ratio (5:1 and 14:1 (ml solvent/g plant), temperature (30 to 100 °C) and processing time, were examined to obtain the best processing conditions to achieve the maximum yield of the essential oil. Also, the mathematical model was described to calculate the mass transfer coefficient. Therefore, the best conditions, that were obtained in

Zinc oxide (ZnO) nanoparticles were synthesized using a modified hydrothermal approach at different reaction temperatures and growth times. Moreover, a thorough morphological, structural and optical investigation was demonstrated using scanning electron microscopy (SEM), x-ray diffraction (XRD), ultra-violate visible light spectroscopy (UV-Vis.), and photoluminescence (PL) techniques. Notably, SEM analysis revealed the occurrence of nanorods-shaped surface morphology with a wide range of length and diameter. Meanwhile, a hexagonal crystal structure of the ZnO nanoparticles was perceived using XRD analysis and crystallite size ranging from 14.7 to 23.8 nm at 7 and 8 ℎ𝑟𝑠., respectively. The prepared ZnO samples showed good abso

Green synthesis methods have emerged as favorable techniques for the synthesis of nano-oxides due to their simplicity, cost-effectiveness, eco-friendliness, and non-toxicity. In this study, Nickel oxide nanoparticles (NiO-NPs) were synthesized using the aqueous extract of Laurus nobilis leaves as a natural capping agent. The synthesized NiO-NPs were employed as an adsorbent for the removal of Biebrich Scarlet (BS) dye from aqueous solution using adsorption technique. Comprehensive characterization of NiO-NPs was performed using various techniques such as atomic force microscopy (AFM), Fourier transform infrared (FTIR), X-ray diffraction (XRD), Brunauer-Emmett and Teller (BET) analysis, and scanning electron microscopy (SEM). Additionally, o

     In the present investigation two different types of fiber reinforced polymer composites were prepared by hand lay-up method using three different parameters (curing temperature, pressing load and fiber volume fraction). These composites were prepared from the polyester resin as the matrix material reinforced with glass fibers as first group of samples and mat Kevlar fibers as the second group, both with different volume fractions (4%, 8%, and 12%) of fibers. They were then tested by tensile strength and impact strength. The main objective in this study is to use Taguchi method for predicting the better parameters that give the better tensile and impact strength to the composites, and then preparing composites at

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet