This study investigates the possibility of using waste plastic as one of the components of expired lead-acid batteries to produce lightweight concrete. Different percentages of lead-acid battery plastic were used in the production of lightweight concrete. The replacements were (70, 80 and 100%) by volume of the fine and coarse aggregate. Results demonstrated that a reduction of approximately 23.6% to 35% in the wet density was observed when replacement of 70% to 100% of the natural aggregate by lead-acid battery plastic. Also, the compressive strength decreased slightly with the increase in plastic content at different curing ages of 7, 28, 60, 90, 120 days. The lowest value of compressive strength was (20.7 MPa) for (wa

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The study aims to identify the level of health services provided in private suites to government hospitals from the perspective of the recipi

Represent a topic occupational safety and health management is one of the priorities of the loading organization in the business environment ,and one of the requirement for the success and its impact on the productivity of workers and their performance in an appropriate working environment .The international responses to the problems that accompany the technological and industrial development of the business environment are the issuance of ISO 45001:2018 aimed at providing an appropriate framework for controlling risks , reducing injuries and work accidents and improving work performance ,after realizing the environmental relationship between safe and sound work with competition .The search’s issue is represented with the exist

   This paper aims to prove an existence theorem for Voltera-type equation in a generalized G- metric space, called the -metric space, where the fixed-point theorem in - metric space is discussed and its application.  First, a new contraction of Hardy-Rogess type is presented and also then fixed point theorem is established for these contractions in the setup of -metric spaces. As application, an existence result for Voltera integral equation is obtained.  

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

Introduction: This study was carried out to assess the effect of natural wool fiber addition on the impact, tensile, and flexural strengths of the heat-cured acrylic denture base material. Methods: Short wool fibers with and without chemical surface treatment were added to polymethyl methacrylate (PMMA) at a percentage of 0.25% by weight. A total of 90 acrylic specimens were prepared and divided into three groups according to the tests performed. Impact strength, tensile strength, and flexural strength tests were evaluated. The statistical analysis of the results data was performed using the one-way analysis of variance (ANOVA) test and Tukey’s post hoc test

Background: One of the unique prosthesis for tooth or teeth replacement is the dental implant. Our attempt is using a biomaterial system that is easily obtained and applicable and has the ability to provoke osteoinductive growth factor to enhance bone formation at the site of application. One of these natural polymers is hyaluronic acid. Material and methods: Sixty machined surface implants from commercially pure titanium rod inserted in thirty NewZealand rabbits. Two implants placed in both tibia of each rabbit. The animals scarified at 1, 2 and 4 weeks after implantation (10 rabbits for each interval). For all of animals the right tibia’s implant was control (uncoated) and the left one was experimental (coated with 0.1ml Hyaluro

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