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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
Wed Mar 26 2025
Journal Name
قضايا سياسية
The role of strategic planning in American decision-making
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The purpose of this research is to study the organic planning in the United Industry Alliance, focusing on an applied model. It takes the concept of good planning, and its importance in the overall picture, well into political, economic, and military policy. It also analyzes how the United States has used this year to address the challenges that nationalism targets. The research draws on typical examples to illustrate the differences between researcher and decision effectiveness. It also discusses the factors that lead to the success or failure of dynamic planning, and draws lessons from it in other countries. Finally, the researcher begins to help in planning the goal as a basic tool in enhancing effectiveness.

Publication Date
Mon Jun 19 2023
Journal Name
Journal Of Engineering
Data Classification using Quantum Neural Network
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In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that

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Publication Date
Thu Dec 01 2022
Journal Name
Baghdad Science Journal
Molecular Characterization of Potential Crop Pathogens Associated with Weeds as Endophytes in Uniilorin Plantations, Nigeria
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Crop diseases are usually caused by inoculum of pathogens which might exist on alternate hosts or weeds as endophytes. These endophytes, cum pathogens, usually confer some beneficial attributes to these weeds or alternate hosts from protection against herbivores, disease resistance, stress tolerance to secondary metabolites production. This study was therefore carried out to isolate potential crop pathogens which exist as endophytes on weed species in the University of Ilorin plantations. Green asymptomatic leaves were collected from 10 weed species across the plantations, and processed for their endophytic fungi isolation. Isolates were purified into pure cultures and used for molecular identification using the internal transcribed spac

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Publication Date
Mon Apr 28 2025
Journal Name
Chemical Papers
New chemiluminometric method for the determination of azithromycin in a continuous flow injection analysis system
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A novel analytical method is developed for the determination of azithromycin. The method utilizes continuous flow injection analysis to enhance the chemiluminescence system of luminol, H2O2, and Cr(III). The method demonstrated a linear dynamic range of 0.001–100 mmol L-1 with a high correlation coefficient (r) of 0.9978, and 0.001–150 mmol L-1 with a correlation coefficient (r) of 0.9769 for the chemiluminescence emission versus azithromycin concentration. The limit of detection (L.O.D.) of the method was found to be 18.725 ng.50 µL−1 based on the stepwise dilution method for the lowest concentration within the linear dynamic range of the calibration graph. The relative standard deviation (R.S.D. %) for n = 6 was less than 1.2%

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Publication Date
Sun Jun 20 2021
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
MULTIVARIATE ANALYSIS OF THE STEM ANATOMICAL CHARACTERS OF TERMINALIA L. (COMBRETACEAE) IN EGYPT
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A comparative investigation of the anatomical characters through a microscopical examination of the prepared transverse sections of the stem was carried out. Six plates with 32 photomicrographs were provided to convincingly show the considerable variations of anatomical characters within the nine examined species. The matrix of 18 anatomical characters which included nine quantitative and nine qualitative was applied for the clustering analysis (CA) followed by the principal component analysis (PCA) using the Multivariate Analysis of Ecological Data, PC-ORD.
The results exhibited significant variations among the species resulting in the construction of an artificial key; this key accurately represents a sufficient tool to display the

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Publication Date
Wed Dec 01 2021
Journal Name
Baghdad Science Journal
QTAIM study of the bonding in triosmium trihydride cluster [Os3(µ-H)3(µ3-ɳ2-CC7H3(2-CH3)NS)(CO)8]
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    The Atoms in Molecules (AIM) analysis for triosmium cluster, which contains trihydridede, carbon, carbonyl and 2-methylbenzothiazolide ligands, [Os3(µ-H)33-ɳ2-CC7H3(2-CH3)NS)(CO)8] is reported. Bonding features in this cluster has been analyzed based on QTAIM ("Quantum Theory of Atoms in Molecules") in this work. The topological indices derived from electron density of relevant interactions in triosmium compound have been studied. The major interesting point of the AIM analyses is that the core of part (Os3H3) reveals the absence of any critical points and bond paths connecting any pairs of O

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Publication Date
Thu Oct 31 2024
Journal Name
Intelligent Automation And Soft Computing
Fusion of Type-2 Neutrosophic Similarity Measure in Signatures Verification Systems: A New Forensic Document Analysis Paradigm
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Signature verification involves vague situations in which a signature could resemble many reference samples or might differ because of handwriting variances. By presenting the features and similarity score of signatures from the matching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy, a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertainties and ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values, which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1 neutrosophic representation is also unable to adjust to various

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Publication Date
Wed Sep 01 2010
Journal Name
Journal Of Economics And Administrative Sciences
اثر التعلم المنظمي في التمكين الإداري دراسة استطلاعية لآراء عينة في الشركة العامة للصناعات الكهربائية في بغداد
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This research aims at studying a contemporary and modern phenomenon in the Science of management in general and in the field of organizational behavior in private, The organizational learning and managerial empowerment in a governmental organization :"The General Company of Electric Industries" .The dimensions of organizational learning have been defined (Learning Dynamics، organization transformation, individuals empowerment, knowledge management and Technology application) as wells as the dimensions of managerial empowerment (possessing the information and its availability– Independency and the freedom of conduct and knowledge possession) Information has been gathered by a questionnaire distributed on a sample of professiona

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Publication Date
Tue Dec 01 2020
Journal Name
Journal Of Engineering
A Case Study of Bus Line Passenger Volumes of Bakrajo Bus Lines in Sulaimani City
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Transit agencies constantly need information about system operations and passengers to support their regular scheduling and operation planning processes. The lack of these processes and cultural motivations to use public transportations contributes enormously to the reliance on the private cars rather than public transportation, resulting in traffic congestions. The traffic congestions occur mainly during peak hours and the accidents happening as a result of road accidents and construction works.  This study investigates the effects of weekday and weekend travel variability on peak hours of the passenger flow distribution on bus lines, which can effectively reflect the degree of traffic congestion. A study of passen

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Publication Date
Mon Aug 01 2016
Journal Name
Journal Of Engineering
Prediction of Monthly Fluoride Content in Tigris River using SARIMA Model in R Software
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The need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2,0,0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlation coefficien

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