Corona virus sickness has become a big public health issue in 2019. Because of its contact-transparent characteristics, it is rapidly spreading. The use of a face mask is among the most efficient methods for preventing the transmission of the Covid-19 virus. Wearing the face mask alone can cut the chance of catching the virus by over 70\%. Consequently, World Health Organization (WHO) advised wearing masks in crowded places as precautionary measures. Because of the incorrect use of facial masks, illnesses have spread rapidly in some locations. To solve this challenge, we needed a reliable mask monitoring system. Numerous government entities are attempting to make wearing a face mask mandatory; this process can be facilitated by using face mask detection software based on AI and image processing techniques. For face detection, helmet detection, and mask detection, the approaches mentioned in the article utilize Machine learning, Deep learning, and many other approaches. It will be simple to distinguish between persons having masks and those who are not having masks using all of these ways. The effectiveness of mask detectors must be improved immediately. In this article, we will explain the techniques for face mask detection with a literature review and drawbacks for each technique.
Pregnancy is a stressful condition in which many physiological and metabolic functions are altered to a considerable extent . Pregnancy is a physiological state accompanied by a high-energy demand and an increased oxygen requirement. The present study aim to study selenium ,zinc cupper in the first trimester of pregnancy. The study group comprised of Fourty five pregnant women and twenty six non pregnant women as control . The samples were taken from pregnant women who come to several heath center in Baghdad city to cheak up. Laboratory investigations including Cupper, Ceruloplasmin, Total Antioxidant (TAA), malondialdehyde (MDA), glutathione (GSH), Zinc, Uric acid, and Selenium had been measured in pregnant women and control . Th
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Abstract
The research aims to measure family negligence and its relationship with internet addiction among university students. The researcher has developed a scale of (20) items to measure the negligence of family, which was applied to (308) male and female university students in the first and fourth stages. The research concluded that University students suffer from family negligence. The research sample has an addiction to the Internet. There is a relationship between family neglect and addiction to the Internet among university students. The researcher came out with a number of suggestions and recommendations.
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Background: Axial spondyloarthritis (axSpA) is an inflammatory, systemic rheumatic condition that mostly affects the axial skeleton. Tenascin-C (TN-C) is a hexameric glycoprotein of considerable size, upregulated in many inflammatory conditions, while Interleukin-17 (IL-17) a cytokine that plays an important role in SpA symptoms. Objective: to investigate the upregulation between the serum levels of TN-C and IL-17 in Iraqi axSpA patients and the disease characteristics. Patients and Methods: Seventy-four axSpA patients and 28 matched controls were studied. Fifty-four patients received a tumor necrosis factor inhibitor (TNFi) and 20 did not. Serum TN-C and IL-17 concentrations were determined using the ELISA technique. The Bath Ankyl
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The purpose of this study is to explore whether the adoption of Beyond Budgeting (BB) as a management accounting practice (MAP) contributes to developing intellectual capital (IC) and creating value in Iraqi companies. This requires an understanding of the views of the Iraqi managers about the nature of the information provided by this practice, which may be used to determine whether this information is relevant in the management of IC in the context of Iraq. This research aims also to explore the challenges of the adoption of the BB in planning and controlling IC in Iraq. The study adopts a qualitative approach and an interpretive paradigm. It also adopts a semi-structured interview method of collecting data from executive managers
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Increased downscaling of CMOS circuits with respect to feature size and threshold voltage has a result of dramatically increasing in leakage current. So, leakage power reduction is an important design issue for active and standby modes as long as the technology scaling increased. In this paper, a simultaneous active and standby energy optimization methodology is proposed for 22 nm sub-threshold CMOS circuits. In the first phase, we investigate the dual threshold voltage design for active energy per cycle minimization. A slack based genetic algorithm is proposed to find the optimal reverse body bias assignment to set of noncritical paths gates to ensure low active energy per cycle with the maximum allowable frequency at the optimal supply vo
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