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Detection of Depression among Nurses Providing Care for Patients with COVID-19 at Baqubah Teaching Hospital
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Objectives: The present study aims at detecting the depression among nurses who provide care for infected patients with corona virus phenomenon and to find out relationships between the depression and their demographic characteristics of age, gender, marital status, type of family, education, and years of experience of nurses in heath institutions, infection by corona virus, and their participation in training courses.
Methodology: A descriptive study is established for a period from October 10th, 2020 to April 15th, 2021. The study is conducted on a purposive (non-probability) sample of (100) nurse who are providing care for patients with COVID-19 and they are selected from the isolation wards. The instrument of the study is developed from Patients’ Health Questionnaire (PHQ) to achieve the study objectives. Content validity of the instrument is determined through panel of experts and internal consistency reliability is obtained through pilot study. Data are collected through the use of the questionnaire and analyzed through the application of descriptive and inferential statistical approaches which are applied by using SPSS version 22.
Results: The results of the present study showed that nurses who were providing care for patients with COVID-19 age group (30-39 years) )37%(, males constituent the higher percentage than female 87%, (77%) of them is married, (59%) Small family of Nurses, )64%( level of education among nurses have diploma in nursing, and they have (1-5) years of experience in heath institutions among nurses about (40%), also (61%) of nurses not sharing in epidemiological training courses, and (58%) of nurses had previous work in isolation wards, (39%) of nurses have source of information from network, duration of work in isolation wards is (83%) of nurses who are work for more than four weeks, (70%) of nurses are not infected with corona virus, (96%) of nurses are having no history of mental disorders, (54%) of nurses are not drinking alcohol and having no problem with drug abuse. By using PHQ-9, the study finds that depression among nurses is (43%).
Recommendations: Psychological care counseling and guidance are necessary to increase nurses’ vulnerability and strengthen their mental health which helps to encounter any psychological burden caused by COVID 19 pandemic.

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Publication Date
Mon Feb 14 2022
Journal Name
Journal Of Educational And Psychological Researches
Psychological alienation in relation to the motivation of achievement among incoming teachers in the schools of Dhofar Governorate
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The present study aimed at identifying the level of psychological alienation and motivation of achievement among the incoming teachers in the schools of Dhofar Governorate in the Sultanate of Oman a well as to explore the correlation relationship between psychological alienation and motivation of achievement. The sample of the study consisted of (238) expatriated teachers from Al- Dhofar Governorate in Oman who were selected randomly. The psychological alienation scale and the motivation of achievement scale for teachers were used by researchers and were conducted electronically by google forms. The data were processed statistically using the Statistical Package for Social Sciences (SPSS). The results showed that the level of psy

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Publication Date
Sat Jun 01 2024
Journal Name
مجلة الدراسات المستدامة
المسؤولية المدنية الناشئة عن الاخلال بالالتزام بضمان سلامة الاشخاص في عقد التحاليل الطبية
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Publication Date
Fri Dec 30 2011
Journal Name
Al-kindy College Medical Journal
Etiologies of chronic cough in adult patients: Is it hard to be diagnosed?
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Background: Chronic cough is often the key
symptom not only of chronic pulmonary diseases
but for other important extrapulmonary
pathologies, in particular upper airway and
gastrointestinal diseases.
Objective: This study was designed to
determine the etiology of chronic cough and the
usefulness of the available diagnostic tests in
reaching its causes.
Methods: One hundred patients presenting with
chronic cough at Baghdad Teaching Hospital
Outpatient Clinic were enrolled in this study. The
patients underwent a full clinical interview,
physical examination with indicated diagnostic
test(s) (such as chest x ray, bronchoscope, PFT,
GIT study, sinus X ray or CT).
Results: An etiology of chronic

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Publication Date
Sun Sep 01 2019
Journal Name
Indian Journal Of Public Health Research & Development
Effect of Thyroid Hormone Abnormalities on Hemoglobin A1c in Hemodialysis Patients Taking Erythropoietin
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Background: Hemoglobin A1c (HbA1c) is a widely used test for glycemic control. It is done for chronic kidney disease (CKD) patients. Renal disease is accompanied by thyroid abnormalities, which affect HbA1c, especially in those taking erythropoiesis-stimulating agents (ESAs). We aimed to find the effect of thyroid dysfunction on HbA1c in hemodialysis patients taking ESAs and those who do not. Materials and Method: Fifty six patients were included in this study, which was done between September 2017 and June 2018, in Baghdad Teaching Hospital. Thyroid stimulating hormone, free T3, free T4 and HbA1c measurements were done. The patients were divided into 2 groups; those who took ESAs and those who did not, then they were subdivided into those

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Publication Date
Tue Jan 31 2023
Journal Name
International Journal Of Nonlinear Analysis And Applications
Survey on intrusion detection system based on analysis concept drift: Status and future directions
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Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

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Publication Date
Sat Oct 04 2025
Journal Name
Mesopotamian Journal Of Computer Science
Enhanced IOT Cyber-Attack Detection Using Grey Wolf Optimized Feature Selection and Adaptive SMOTE
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The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat

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Publication Date
Tue Sep 01 2020
Journal Name
Baghdad Science Journal
Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection
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In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection

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Publication Date
Wed Apr 01 2020
Journal Name
Plant Archives
Land cover change detection using satellite images based on modified spectral angle mapper method
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This research depends on the relationship between the reflected spectrum, the nature of each target, area and the percentage of its presence with other targets in the unity of the target area. The changes occur in Land cover have been detected for different years using satellite images based on the Modified Spectral Angle Mapper (MSAM) processing, where Landsat satellite images are utilized using two software programming (MATLAB 7.11 and ERDAS imagine 2014). The proposed supervised classification method (MSAM) using a MATLAB program with supervised classification method (Maximum likelihood Classifier) by ERDAS imagine have been used to get farthest precise results and detect environmental changes for periods. Despite using two classificatio

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Publication Date
Tue Jun 24 2025
Journal Name
Baghdad Science Journal
Accelerating Face Mask Detection Training Model Based on Multi-GPUs and Multi-core CPU
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Modern machine-learning applications require GPUs, and modern platforms can leverage numerous GPUs on one or more machines to increase performance. Contemporary deep-learning models are too huge for CPU or GPU training. Training these models with many GPUs without performance degradation is necessary to train them rapidly and maximize GPU consumption. Thus, training deep convolutional neural networks (DCNN) with multiple GPUs has become necessary for improving training. Therefore, we presented a parallel design and development of an efficient model for enhancing face mask CNN performance and improving resource efficiency. This DCNN model is a parallel training system over multiple GPUs, a multi-core CPU, and a multi-process GPU platform wit

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Publication Date
Tue Nov 01 2022
Journal Name
Inorganic Chemistry Communications
Sarin chemical warfare agent detection by Sc-decorated XN nanotubes (X = Al or Ga)
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In order to scrutinize the impact of the decoration of Sc upon the sensing performance of an XN nanotube (X = Al or Ga, and XNNT) in detecting sarin (SN), the density functionals M06-2X, τ-HCTHhyb, and B3LYP were utilized. The interaction of the pristine XNNT with SN was a physical adsorption with the sensing response (SR) of approximately 5.4. Decoration of the Sc metal into the surface of the AlN and GaN led to an increase in the adsorption energy of SN from −3.4 to −18.9, and −3.8 to −20.1 kcal/mol, respectively. Also, there was a significant increase in the corresponding SR to 38.0 and 100.5, the sensitivity of metal decorated XNNT (metal@XNNT) is increased. So, we found that Sc-decorating more increases the sensitivity of GaNN

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