Compelling evidence proved that coronavirus disease (COVID-19) disproportionately affects minorities. The goal of the present study was to explore the effects of intersected discrimination and discrimination types on COVID-19, mental health, and cognition. A sample of 542 Iraqis, 55.7% females, age ranged from 18 to 73, with (M = 31.16, SD = 9.77). 48.7% were Muslims, and 51.3% were Christians (N = 278). We used measures for COVID-19 stressors, executive functions, intersected discrimination (gender discrimination, social groups-based discrimination, sexual orientation discrimination, and genocidal discrimination), posttraumatic stress disorder (PTSD), depression, anxiety, status and death, existential anxieties, and health. We conducted in

This paper examines the most important historical knowledge and knowledge of the Arabs in the pre-Islamic era based on the Holy Quran, which is the oldest and most reliable Arab Islamic sources recorded at all, as a major source of this study, to ensure a great interest in history, especially the news of the prophets, and the positions of their nations Some of them, as well as some of the news of the old Arab people such as Aad and Thamud and the owners of the elephant and others, and some of which are repeated in several Quranic fence and various methods of presentation, cited as a lesson and exhortation to the Arab opponents of Islam and a reminder of what befell the peoples of the world B relict of doom to discredit them the apostles

The problem of the study and its significance:

           Due to the increasing pressures of life continually, and constant quest behind materialism necessary and frustrations that confront us daily in general, the greater the emergence of a number of cases of disease organic roots psychological causing them because of severity of a lack of response to conventional treatments (drugs), and this is creating in patients a number of emotional disorders resulting from concern the risk of disease

     That is interested psychologists and doctors searchin

Sheikh Hanafi was born in one of the popular shops of Baghdad with interlocking social relations, and had a profound impact this camp where his talents in the first Venco loving to Mahalah and Baghdad and was born with this development since his days looked forward to the folklore and folk. In the middle of his youth, including authoring loves the heritage of folk legacies began in motion, from Baghdad, books, articles, research has brought him wide acclaim were not possible without the seriousness and diligence, independence and self-Asamath that mushroom on them
The trip was one of his tools in the scientific fame has gone to many Eastern and Asian countries and visited religious and literary institutes and delivered the lectures an

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 Objective: To identify correlation of elevated LDH & CRP levels with the outcomes of COVID-19. Methodology: The cross-sectional retrospective study consisted of 200 COVID-19 patients who presented at a private clinical in Baghdad, Iraq. It was carried out from February 2021 to February 2022. Data included age, gender and clinical presentation. Blood samples were taken for high sensitivity CRP and LDH in the serum. Results: Out of 200 patients, 50 were critical and 150 severe according to clinical features. LDH and CRP showed a significant increase (p=0.000) in critical patients. This group involved admission to the respiratory intensive care unit requiring mechanical ventilation than in patients with severe COVID-19 (760.5±6.3 vs.

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

Introduction: Breast cancer is a significant global health concern, affecting millions of women worldwide. While advancements in diagnosis and treatment have improved survival rates, the impact of this disease extends beyond physical health. It also significantly influences a woman's lifestyle and overall well-being. Objectives: The current study intends to analyze the lifestyle of breast cancer patients who are receiving therapy or are being followed up at the Oncology Teaching Hospital in Medical City, Baghdad, Iraq. Method: The present study uses a descriptive design with an application of an evaluation approach. A convenience sample of 100 women with breast cancer was selected from the Teaching Oncology Hospital at the Medical C

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol