TiO2 thin films were deposited by reactive d.c magnetron sputtering method on a glass substrate with various ratio of gas flow (Oxygen /Argon) (50/50, 100/50 and 150/50) at substrate temperature 573K. It can be observe that the optical energy gap of TiO2 thin films dependent on the ratio of gas flow (oxygen/argon), it varies between (3.45eV-3.57eV) also it is seen that the optical constants (α, n, K, εr and εi ) has been varied with the change of the ratio of gas flow (Oxygen /Argon).

in this paper, the current work was devoted to the manufacture of TiO2 nanoparticles doped with manganese,  synthesis by the sol-gel technique using a dip-conting device, for their hydrophilic properties and photocatalytic activity, and the products were characterized by X-ray diffraction, scanning electron microscopy, and Uv-Visible absorption, and the results  XRD showed an  phase Anatase  ,  and the results of the SEM Explained the shape of the morphology of the samples after the doping process compared with pure TiO2, and the results of a shift in light absorption from ultraviolet rays to visible light were evident. The results showed that the thin films have a high wettability   under visible rays

Zinc Oxide (ZnO) is considered as one of the best materials already used as a window layer in solar cells due to its antireflective capability. The ZnO/MgF2 bilayer thin film is more efficient as antireflective coating. In this work, ZnO and ZnO/MgF2 thin films were deposited on glass substrate using pulsed laser deposition and thermal evaporation deposition methods. The optical measurements indicated that ZnO thin layer has an energy gap of (3.02 eV) while ZnO/MgF2 bilayer gives rise to an increase in the energy gap. ZnO/MgF2 bilayer shows a high energy gap (3.77 eV) with low reflectance (1.1-10 %) and refractive index (1.9) leading to high transmittance, this bilayer could be a good candidate optical material to improve the performance

Background: This study aimed to evaluate the effect addition of polyester fibers on the some mechanical properties of heat cured acrylic resin (implant strength, flexural strength and hardness) Materials and methods: Ninety specimens were used in the study. Thirty specimens were used for impact strength measurements (80mm X 10mm X 4mm) length, width and thickness respectively. The specimens divided into three test groups (n=10), first group formed from heat cure acrylic resin without fiber reinforcement. Second group was formed from heat cure acrylic resin was reinforced with 2 mm length polyester fiber and third group was formed from heat cure acrylic resin reinforced with 4mm length polyester fiber, impact strength measured by impact test

In this experimental study, the use of stone powder as a stabilizer to the clayey soil studied. Tests of Atterberg limits, compaction, fall cone (FCT), Laboratory vane shear (LVT), and expansion index (EI) were carried out on soil-stone powder mixtures with fixed ratios of stone powder (0%, 5%, 10%, 15%, and 20%) by the dry weight. Results indicated that the undrained shear strength obtained from FCT and LVT increased at all the admixture ratios, and the expansion index reduced with the increase of the stone powder.

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

This work dealt with separation of naphthenic hydrocarbons from non-naphthenic hydrocarbons and in particular concerns an improved process for increasing the naphthenes concentration in naphtha, The separation was examined using adsorption by Y and  B zeolite in a fixed bed process. The concentration of naphthenes in the influent and effluent streams was determined using PONA classification. The effect of different operating variables such as feed flow rate (2- 4 L/hr); bed length (50 - 80 cm) on the adsorption capacity of Y and  zeolite was studied. Increasing the bed length lead to increase the naphthenes concentration, and increasing the flow rate lead to decrease in the concentration of naphthenes, It was found that the decrease