After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

Since its discovery in December 2019, corona virus was outbreak worldwide with very rapid rate, so it described by WHO as pandemic. It associated with severe acute respiratory distress syndrome, and can enter to cells through Angiotensin Converting Enzyme 2 (ACE 2) receptor which play an important role as regulator for blood pressure. Hypertension is a potential risk factor for sever acute respiratory syndrome COVID-19, and associated with high mortality rate as shown in many epidemiological studies. Moreover, specific antihypertensive medications that infected patients were receiving are not known; only data about renin-angiotensin-aldosterone system (RAAS) are available.  

Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt

The rabid development in life and technology arouse the anxiety, stress, and fear of people. Accordingly the maladjustment and deceived conceptions appear in their behaviors which influence their selves and others.
These changes and development in society influence the university students' life, personality, thoughts and behavior. An exceeding in anxiety and fear has negative effect on everyday life activities and. negative effect on both mind and body.
The problem of the study is centered around cognitive conception anxiety of the university students.
The university students have their special importance in society and nation development, therefore many studies search in their personality and the effect of anxiety on them to fi

The two parameters of Exponential-Rayleigh distribution were estimated using the maximum likelihood estimation method (MLE) for progressively censoring data. To find estimated values for these two scale parameters using real data for COVID-19 which was taken from the Iraqi Ministry of Health and Environment, AL-Karkh General Hospital. Then the Chi-square test was utilized to determine if the sample (data) corresponded with the Exponential-Rayleigh distribution (ER). Employing the nonlinear membership function (s-function) to find fuzzy numbers for these parameters estimators. Then utilizing the ranking function transforms the fuzzy numbers into crisp numbers. Finally, using mean square error (MSE) to compare the outcomes of the survival

The aim of the study was to find out the correlations and impact between the variable of ethical leadership behavior and university performance at Sumer University. Use the descriptive analytical method by adopting the questionnaire tool to collect data. The questionnaire was distributed electronically to 113 teachers at Sumer University and the response was from 105 teachers. The research results showed that there is a correlation and effect relationship between the search variables. In addition, the responding university does not have ethically defined standards in terms of performance of the work of the cadres working there. Finally, the research presented a set of recommendations aimed at tackling problems in the ethical lead

Coronavirus disease (Covid-19) has threatened human life, so it has become necessary to study this disease from many aspects. This study aims to identify the nature of the effect of interdependence between these countries and the impact of each other on each other by designating these countries as heads for the proposed graph and measuring the distance between them using the ultrametric spanning tree. In this paper, a network of countries in the Middle East is described using the tools of graph theory.

Abstract

Personality is one of the most important elements that should be developed in university student. Thus, we have to develop the positive traits and neutralize the negative traits of students as well as we have to pay attention to the level of student achievement at the same time, the researcher designed a scale to measure the traits of study sample. The research come out with a number of recommendations and proposals, the most important of which Enhancing the positive characteristics of university students in order to reach them to the level of mature personality with mental health. Enhancing the positive role of the high level of achievement by motivating the stude

Objective: To assess role of obesity in Covid-19 patients on antibodies production, diabetes development, and treatment of this disease. Methodology: This observational study included 200 Covid-19 patients in privet centers from January 1, 2021 to January 1, 2022. All patients had fasting blood sugars and anti-Covid-19 antibodies. Anthropometric parameters were measured in all participants. Results: The patients were divided into two groups according to body weight; normal body weight (50) and excess body weight (150). There was a significant difference between them regarding age. Diabetes mellitus developed in 20% of normal weight patients while 80% of excess weight patients had diabetes (p=0.0001). Antibodies production (IgM and