The aim of our current study was to identify the effect of particulate matter of both types (PM2.5 and PM10) resulting from hookah smoking on the hemopoietic system of workers (smokers) in closed cafes. This study included six stations (cafes) on the Rusafa side of Baghdad city and conducted a blood test that included a complete blood count (CBC). A multifunctional air quality detector measured both types of particulate matter in the morning and evening. The study included 30 men (workers and smokers) and 30 men (non-smokers), whose ages ranged from 20 to 40 years. The study found that smokers had an increase in white blood cells and red blood cells, as well as an increase in the percentage of hemoglobin (HGB), hematocrit (HCT), the mean corpuscular volume (MCV), and the number of platelets (PLT), while also showing a decrease in the mean corpuscular hemoglobin concentration (MCHC). There was a significant difference between PM2.5 in the morning and hemoglobin, and platelets, in smokers. Additionally, smokers showed a significant difference between PM2.5 in the evening, PLT, and MCV. In the case of PM10, a significant difference was found between it and the blood platelets of smokers in the morning, while PM10 in the evening was found to have a significant difference with PLT and MCV among the smokers group.
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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
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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
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Case Report.
To present a case of a previous complicated mandibular orthognathic surgery that aimed to setback the mandible in a female cleft lip and palate (CLP) patient, which led to bone necrosis on one side with subsequent severe mandibular deviation and facial asymmetry. We additionally reviewed the previous reports of similar complications, the pathophysiology and the factors that could lead to this dreadful result.
A 27-year-old female patient presented with a severe dentofacial deformity secondary to a complicated bilateral sagittal spli
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This article presents the results of an experimental investigation of using carbon fiber–reinforced polymer sheets to enhance the behavior of reinforced concrete deep beams with large web openings in shear spans. A set of 18 specimens were fabricated and tested up to a failure to evaluate the structural performance in terms of cracking, deformation, and load-carrying capacity. All tested specimens were with 1500-mm length, 500-mm cross-sectional deep, and 150-mm wide. Parameters that studied were opening size, opening location, and the strengthening factor. Two deep beams were implemented as control specimens without opening and without strengthening. Eight deep beams were fabricated with openings but without strengthening, while
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Protection of the oil pipelineswhich extracted from the wells was found to shut the well and prevent the leakage of oil when broken using safety valve. This valve is automatically activated by loss of pressure between the well and pipelines, which take the pressure, signal from hydraulic pressure sensor through pressure control valve which has constant or variable value but it is regulated manually. The manual regulatory process requires the presence of monitoring workers continuously near the wells which are always found in remote areas. In this paper, a smart system has been proposed that work with proportional pressure control valve and also electronic pressure sensor through Arduino controller, which is programmed in a way that satisfie
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