Background: Job satisfaction remains as an important concern for both employer and employee. A high-quality teaching staff is the cornerstone of a successful educational system. One step in developing a high quality faculty understands the factors associated with teaching quality and retention. One of these factors is job satisfaction, which has been studied widely by organizational researchers.Objectives: To evaluate the extent of job satisfaction of teaching staff working at AL-Kindy College of medicine and to identify some work-related factors associated with job satisfaction.Methods: A cross- sectional study was carried out. A convenient sample of 50 teaching staff, who had a teaching job at different scientific departments at AL-Kin

Objective(s): 1- Assess the effectiveness of health educational program on nurses' knowledge toward Hemodialysis at Pediatric Teaching Hospitals.

 2-To find out the relationship between nurses' knowledge about hemodialysis and their demographic Characteristics.

Methods: The study was designed in a pre-experimental pattern for the nurses' working in the Child Welfare Teaching Hospital and the Child's Central Teaching Hospital, and a targeted sample consisting of (30) nurses was selected. It is tested in three periods of pre-test, the first post-test, and the second post-test. Participants were tested before implementing the tutorial (tutorial lectures

     The scientific and technological developments and their practical applications in all fields of life in general and in the education field in specific have led to the emergence of variables in the educational structure, teaching methods and in education in their modern form which is consistent in its entirety with    the spirit of the age. We today live the age of knowledge increase full of wide ranging scientific and technological developments. Thus life demands human capabilities of a special kind able to develop and innovate. Here the increasing significance emerges for taking care of the human powers through educational systems much different from those current traditional systems.  System

ABSTRACTBackground: In Iraq smoking is a common habit in both the urban and rural areas. There are many respiratory diseases like chronic bronchitis, bronchial carcinoma and emphysema which are related to chronic tobacco smoking.Objectives: To study the effect of tobacco smoking on the respiratory system, to establish a correlation between chronic tobacco smoking and its effects on the parameters which were studied and to create awareness in tobacco smokers about the effects of tobacco on their healthMethod: In this study 100 male subjects, 50 chronic smokers and 50 non- smokers were assessed for their pulmonary function tests by using a computerized spirometer.Results: Almost all their respiratory parameters were significantly reduced.

Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co