Higher education is important because it creates and develops human capital and provides qualified human cadres, which requires restructuring government spending so that an increase in funding allocated to education is brought about. During the period 1990-2020, government spending was weak on educational institutions in Iraq, which led to a decline in The role of these institutions in the economic development of the country. The highest percentage of spending on higher education of GDP was 0.47% in 2007 and the lowest was 0.01% in 2005. The number of public universities reached 35, and the number of private universities and colleges reached 64 universities and private colleges in 2020. This was accompanied by an increase in the number of students and a weakening of the absorptive capacity of public universities. Iraq is considered one of the countries most affected by the Corona virus pandemic in the Middle East, in terms of the rate of injuries and deaths, and as a result of the pandemic, education has been transformed to lectronic learning platforms. Granger causality also showed the existence of a significant causal relationship between government spending on higher education, gross domestic product, the number of graduate students, and unemployment.
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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Double-layer micro-perforated panels (MPPs) have been studied extensively as sound absorption systems to increase the absorption performance of single-layer MPPs. However, existing proposed models indicate that there is still room for improvement regarding the frequency bands of absorption for the double-layer MPP. This study presents a double-layer MPP formed with two single MPPs with inhomogeneous perforation backed by multiple cavities of varying depths. The theoretical formulation is developed using the electrical equivalent circuit method to calculate the absorption coefficient under a normal incident sound. The simulation results show that the proposed model can produce absorption coefficient with wider absorption bandwidth compared w
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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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Improving" Jackknife Instrumental Variable Estimation method" using A class of immun algorithm with practical application
Background: Lateral sinus augmentation and simultaneous insertion of dental implants is a highlypredictable procedure and associated with high rate of implants success.Aims: To evaluate implant stability changes following maxillary sinus augmentation utilizing deproteinizedbovine bone alone or mixed with platelet-rich fibrin.Materials and Methods: A total of 34 lateral sinus augmentation procedures were performed and 50 dentalimplants simultaneously installed. The lateral sinus augmentation cases were allocated randomly into 3groups: Group A comprised 13 procedures and 21 dental implants utilizing solely deproteinized bovine bone.Group B involved 10 cases and 16 dental implants using deproteinized bovine bone mixed with leukocyteand
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Adsorption of lead ions from wastewater by native agricultural waste, precisely tea waste. After the activation and carbonization of tea waste, there was a substantial improvement in surface area and other physical characteristics which include density, bulk density, and porosity. FTIR analysis indicates that the functional groups in tea waste adsorbent are aromatic and carboxylic. It can be concluded that the tea waste could be a good sorbent for the removal of Lead ions from wastewater. Different dosages of the adsorbents were used in the batch studies. A random series of experiments indicated a removal degree efficiency of lead reaching (95 %) at 5 ppm optimum concentration, with adsorbents R2 =97.75% for tea. Three mo
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A total of 96 stool samples were collected from children with bloody diarrhea from two hospitals in Baghdad. All samples were surveyed and examined for the presence of the Escherichia coli O157:H7 and differentiate it from other Non -Sorbitol Fermenting Escherichia coli (NSF E. coli). The Bacterial isolates were identifed by using morphological diagnostic methods, Samples were cultured on liquid enrichment medium, incubated at 37C? for 24 hrs, and then cultured on Cefixime Tellurite -Sorbitol MacConkey Agar (CT- SMAC). 32 non-sorbitol fermenting bacterial isolates were obtained of which 11 were identified as Escherichia coli by using traditional biochemical tests and API20E diagnostic system without differentiation between
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