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Post COVID-19 Effect on Medical Staff and Doctors' Productivity Analysed by Machine Learning
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The COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. These results reflect the overall impact observed following the entire course of the COVID-19 pandemic and are not specific to a particular wave. The analysis revealed that older participants experienced a more pronounced decline in productivity, with a mean decrease of 35% compared to younger participants. Female participants, on average, had a 28% decrease in productivity compared to their male counterparts. Moreover, individuals with lower socioeconomic status exhibited a substantial decline in productivity, experiencing an average decrease of 40% compared to those with higher socioeconomic status. Similarly, participants who slept for fewer hours per night had a significant decline in productivity, with an average decrease of 33% compared to those who had sufficient sleep. The machine learning analysis identified age, specialty, COVID-19 complications, socioeconomic status, and sleeping time as crucial predictors of productivity score. The study highlights the significant impact of post-COVID-19 on the productivity of medical staff and doctors in Iraq. The findings can aid healthcare organizations in devising strategies to mitigate the negative consequences of COVID-19 on medical staff and doctors' productivity.

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Publication Date
Fri Feb 08 2019
Journal Name
Journal Of The College Of Education For Women
نظرة المجتمع إلى التقنیات الحدیثة في تنظیم الأسرة (دراسة نظریة في علم الاجتماع الطبي)
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Human use means of birth control in multiple ways including (Isolate
women) and (breast-feeding) and (with drawl) and was used on a small scale for
religious social and economic reasons, but after the industrial Revolution and the
increase in migration from the country side to the city and the over cowing of
population and the limited income and a women going out to work out side the
home and poor relations of the kinship and the weakness of religious morals and
the rule of the individualism and the complexity of life and the Multiplicity of
needs and goals and openness to western culture and the application of love and
spread the principle of democracy and freedom and deviant use of the media, all
this has hel

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Publication Date
Sat Dec 01 2018
Journal Name
مجلة جامعة الانبار للعلوم البدنیة والریاضیة
أثر استخدام ثلاث أسالیب علاجیة للتعلم من أجل التمکن فی تعلم سلسلة حرکیة على جهاز العقلة فی الجمناستک الفنی
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The aim of this study tousethree remedial method to the learning for mastering which :(learning for mastery incooperative small groups)and style (Re teaching using presentations by PowerPoint ), and style (homeworklearning by Microsoft word ) to in learning Movement Concatenation On Horizontal Bar in artistic gymnastics for men, as the aim of the research to identify any better methods to learn the movement in question , use the the experimental method to design with pretest and posttest equal totals , the subject of the study included on students second class in physical education and sport sciences , Baghdad University (2014-2015) ,The species used the specific manner by lot for selection , the subject divided into three empirical groups

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Non-linear support vector machine classification models using kernel tricks with applications
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The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

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Publication Date
Tue Jan 02 2018
Journal Name
Journal Of Educational And Psychological Researches
Self-organized learning strategies and self-competence among talented students
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Investigating the strength and the relationship between the Self-organized learning strategies and self-competence among talented students was the aim of this study. To do this, the researcher employed the correlation descriptive approach, whereby a sample of (120) male and female student were selected from various Iraqi cities for the academic year 2015-2016.  the researcher setup two scales based on the previous studies: one to measure  the Self-organized learning strategies which consist of (47) item and the other to measure the self-competence that composed of (50) item. Both of these scales were applied on the targeted sample to collect the required data

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Publication Date
Mon Oct 02 2023
Journal Name
Journal Of Engineering
Microgrid Integration Based on Deep Learning NARMA-L2 Controller for Maximum Power Point Tracking
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This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i

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Publication Date
Sat Apr 01 2017
Journal Name
Journal Of Alloys And Compounds
Structural and optical characteristics of pre- and post-annealed sol-gel derived CoCu-oxide coatings
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Sol-gel derived CuCo-oxide coatings as solar selective surfaces, synthesized onto aluminium substrates at various annealing temperatures, are analysed by correlating their structural, chemical bonding states, and surface morphological topographies. As the annealing progressed, all the coatings displayed a Cu0.56Co2.44O4 (ICSD 78-2175) phase with preferential orientation along (400) reflection plane. Rietveld refinement of X-ray diffraction (XRD) data indicate that residual stress and microstrains developed around the coating surfaces are reduced resulting in mechanically stable thin films. Enhancement of the crystallite size and preferred orientation of the surface were confirmed via XRD, field emission scanning electron microscopy (FESEM),

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Publication Date
Sat Jun 25 2022
Journal Name
International Journal Of Drug Delivery Technology
Evaluation of IL17A, FGF21 and CXC12 in Post-menopause Iraqi Sample with Osteoporosis and Osteopenia
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Osteoporosis (OP) is a systemic skeletal disorder that is characterized by reduced bone mass and micro-architectural deterioration of bone tissue, with a consequent increase in bone fragility and susceptibility to fracture. The most frequent osteoporotic fractures are fractures of the hip, wrist, and spine. The exact causes of OP are still unknown; several factors contribute to the disorder. Subjects and Methods: This study consists of patient groups, this group (Group A) was composed of 80 postmenopausal women with OP and osteopenia and the patient group was subdivided into two group; First group (GroupA1) was composed of 50 postmenopausal women with OP and the second group (Group A2) composed of (30) Postmenopausal Women with oste

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Publication Date
Fri Jun 01 2018
Journal Name
J. Pharm. Sci. & Res
Periodontal health status for post-menopausal women with and without osteoporosis (Clinical and radiographical study)
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Publication Date
Tue Sep 04 2018
Journal Name
Al-khwarizmi Engineering Journal
Study the Effect of Cutting Parameters on Temperature Distribution and Tool Life During Turning Stainless Steel 316L
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This paper is focused on studying the effect of cutting parameters (spindle speed, feed and depth of cut) on the response (temperature and tool life) during turning process. The inserts used in this study are carbide inserts coated with TiAlN (Titanum, Aluminium and Nitride) for machining a shaft of stainless steel 316L. Finite difference method was used to find the temperature distribution. The experimental results were done using infrared camera while the simulation process was performed using Matlab software package. The results showed that the  maximum difference between the experimental and simulation results was equal to 19.3 , so, a good agreement between the experimental and simulation results  was achieved. Tool life w

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Publication Date
Wed Dec 18 2019
Journal Name
Baghdad Science Journal
Detecting Keratoconus by Using SVM and Decision Tree Classifiers with the Aid of Image Processing
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 Researchers used different methods such as image processing and machine learning techniques in addition to medical instruments such as Placido disc, Keratoscopy, Pentacam;to help diagnosing variety of diseases that affect the eye. Our paper aims to detect one of these diseases that affect the cornea, which is Keratoconus. This is done by using image processing techniques and pattern classification methods. Pentacam is the device that is used to detect the cornea’s health; it provides four maps that can distinguish the changes on the surface of the cornea which can be used for Keratoconus detection. In this study, sixteen features were extracted from the four refractive maps along with five readings from the Pentacam software. The

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