The research aims to know the effectiveness of a training program based on multiple intelligence theory in developing literary thinking among students of the Arabic Language Department at Ibn Rushd School of Humanities and to achieve the goal of research, the Safaris Research Institute, and the research community of Arabic language students in the Faculty of Education the third section of Arabic Language: The research sample consists of (71) students. Divided into (35) students in the experimental group and (36) students in the control group, the researcher balanced between the two groups with variables (intelligence, testing of tribal literary thinking, and time age in months), and after using the T-test for two independent samples, the

Corona Virus Disease-2019 (COVID-19) is a novel virus belongs to the corona virus's family. It spreads very quickly and causes many deaths around the world. The early diagnosis of the disease can help in providing the proper therapy and saving the humans' life. However, it founded that the diagnosis of chest radiography can give an indicator of coronavirus. Thus, a Corner-based Weber Local Descriptor (CWLD) for COVID-19 diagnostics based on chest X-Ray image analysis is presented in this article. The histogram of Weber differential excitation and gradient orientation of the local regions surrounding points of interest are proposed to represent the patterns of the chest X-Ray image. Support Vector Machine (SVM) and Deep Belief Network (DBN)

During the last two decades at least, increased interest in Social attraction  variable by scientists  in psychology and sociology, to multi-dimensional and its association with psychological  and emotional variables , and what are of importance in the process of human communication , leadership, and social relations.

Social psychologists interested   in social attraction also  so its relation with  social influence , political and occupational  .
"Social attraction defined as "individual ability  functioned his skills in at

Background: Neonatal septicemia is a major health problem in developing countries furthermore data on bacteriological profile in early onst sepses (EOS) and late neonatal sepsis (LOS) are lacking in context of  continuous change in bacteriological profile and increasing resistant strains. Objectives: The study done to determine the pattern of organisms implicated in neonatal septicemia in a neonatal care unit and to measure the degree of bacterial resistance to some antibiotics.

Type of the study : cross –sectional study.

Methods: Confirmed cases of neonatal septicemia admitted at Al-Alwyia pediatric teaching hospital for the period from January 20

 There are obstacles to high levels of hypertension awareness that are embedded in gender, income and lifestyle habits which need to be addressed leading to high levels of undiagnosed and uncontrolled hypertension. This study aimed to explore the various factors which affect hypertension awareness among a hypertensive population in a tertiary care hospital.

 A quantitative study was conducted among hypertensive patients at a tertiary care hospital in Selangor, Malaysia. A validated and translated questionnaire was utilised as a data collection tool. Descriptive and inferential statistical analysis was done using SPSS version 25.

A thousand participants (female n=621, male n= 379) were recruited, and their

<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

Objectives: To study the prevalence of iron deficiency anemia in pregnant women and to find out the degree of
iron deficiency anemia and to find out any association this has been found between the socio- demographic
characteristics and the degree of iron deficiency anemia.
Methodology: A cross sectional study has been conducted in Ibn-Albaldy hospital in Baghdad among 258
pregnant women attending outpatient clinics during the period from the 1st of March 2011 to fifteenth of
October 2011. Data collection was by using a previously designed questionnaire including socio-demographic
characteristics, obstetrical history. A blood sample has been obtained to estimate the hemoglobin, mean
corpuscular volume, serum iron and

The number of infections caused by microorganisms is increasing significantly over the last few years. A total of 140 patients admitted to the central teaching hospital of pediatrics from the 1st of Jun 2017 to 31 October 2017. The Clinical samples was processed from culture and sensitivity testing. Antibiotic discs used for gram negative isolates. The most prevalent gram negative isolates included Escherichia coli 63 (45.0 %), Pseudomonas spp. 21 (15.0 %), Klebsiella spp. 19 (13.6 %) predominantly. Escherichia coli were the most prevalent isolates from urine 45 (71.4 %), Klebsiella spp. 11 (57.9 %) and Enterobacter spp. 11 (68.8 %) followed by Escherichia coli 10 (15.9 %) predominant from blood. 68 (48.6 %) of specimens were urine, 47 (33.