In this work, pure and Ag-doped nickel oxide (NiO) thin films were deposited on glass substrates with different dopant concentrations (0.1, 0.2, 0.3 and 0.4 wt.%) by pulsed-laser deposition (PLD) technique at room temperature. These films were annealed at temperature of 450 °C. The structural and optical properties of the prepared thin films were studied. It was found that annealing process has lead to increase the transmittance of the deposited films. Also, the transmittance was found to increase with doping concentration of silver in the deposited NiO films. The optical energy gap was decreased from 3.5 to 3.2 eV as the doping concentration was increased to 0.4 %.

Background: There are several diseases in the body following recovery from COVID-19 infection because this virus operates on human genes in various types of peripheral tissue in the human body. It penetrates host cells via Angiotensin-converting enzyme-2 receptors and may have effects on bone remodeling, leading to osteopenia or osteoporosis, which are characterized by low bone mineral density, resulting in diminished bone strength. Bone Alkaline Phpsphatase is an enzyme released into the bloodstream as a soluble homodimer after being cleaved by a phospholipase and can be utilized as a biomarker of bone development. Objective: This research was designed to investigate the alteration of bone homeostasis balance in Iraqi post-COVID-19

Eco-friendly concrete is produced using the waste of many industries. It reduces the fears concerning energy utilization, raw materials, and mass-produced cost of common concrete. Several stress-strain models documented in the literature can be utilized to estimate the ultimate strength of concrete components reinforced with fibers. Unfortunately, there is a lack of data on how non-metallic fibers, such as polypropylene (PP), affect the properties of concrete, especially eco-friendly concrete. This study presents a novel approach to modeling the stress-strain behavior of eco-friendly polypropylene fiber-reinforced concrete (PFRC) using meta-heuristic particle swarm optimization (PSO) employing 26 PFRC various mixtures. The cement was partia

The taxonomy of Ficus L., 1753 species is confusing because of the intense morphological variability and the ambiguity of the taxa. This study handled 36 macro-morphological characteristics to clarify the taxonomic identity of the taxa. The study revealed that Ficus is represented in the Egyptian gardens with forty-one taxa; 33 species, 4 subspecies and 4 varieties, and classified into five subgenera: Ficus Corner, 1960; Terega Raf., 1838; Sycomorus Raf., 1838; Synoecia (Miq.) Miq., 1867, and Spherosuke Raf.,1838; out of them seven were misidentified. Amongst, four new Ficus taxa were recently introduced to Egypt namely: F. lingua subsp. lingua Warb. ex De Wild. & T. Durand, 1901; F. pumila L., 1753; F. rumphii Blume, 1825, and F. su

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