The educational service industry is one of the most negatively affected industries by the spread of the COVID-19 pandemic. Government agencies have taken many measures to slow its spread, and then restrict movement and gatherings and stop recreational activities. Furthermore, the repercussions of the curfew had a significant impact due to the interruption in actual attendance for students and employees, and the severity of the Covid-19 crisis and its (economic, social, security, humanitarian and behavioral) effects on all societies and work sectors is no secret to anyone. Iraq, like other countries, was also affected by the negative impact of Covid-19 pandemic in all fields of institutional work, especially public fields, and specifically the field of education, given that It is based on the necessity for the administration to maintain the continuity of workers’ commitment to perform their duties, which raised the possibility of them being exposed to greater levels of pressure and workload due to the requirements to adhere to the new work procedures as specified by the crisis cell, such as full curfew and social distancing measures to preserve health, and since workers represent the lifeline of any an organization, managing and dealing with them was considered to be highly important because despite the lack of material and financial resources in business, employees remain one of the key assets that administrations of organizations must take care of and ensure the quality of their practical life and careers, not only during crises but at all times, by striving to satisfy them in order to maintain their organizational loyalty. These changes in work methods and procedures have likely had an impact on the performance and commitment of employees. Therefore, employee satisfaction has become one of the important topics that need examination and testing in light of crises in general and the COVID-19 crisis in particular. The global health crisis forced institutions to expedite the formulation of plans and a response strategy with little guidance as a result of the unprecedented nature of the epidemic, and then there was an impact on the predetermined factors that might have affected the satisfaction of employees in various institutions, especially service institutions whose work required them to continue providing services and complete work. In addition, the previously less relevant factors have become prevalent due to the nature of the pandemic; for example, the topics of job insecurity, unemployment and health risks have been identified as the most serious consequences of the epidemic globally. This research came to examine the level of job satisfaction of employees working in the educational institution (the University of Baghdad as a model) and its relationship to the degree of their organizational loyalty in light of the COVID 19 pandemic based on the knowledge generated by reviewing the literature that was used to identify the key factors that affect employee’s satisfaction and hence the degree of loyalty to his\her organization, and the foundational idea that employee’s satisfaction affects the overall performance level of the organization is based on several factors, including the degree of employee loyalty and devotion to work, thus, maintaining an adequate level of job satisfaction for employees is one of the key factors in maintaining effective organizational performance for any organization. In light of the changes that took place in the methods and procedures of institutional work in light of the COVID-19 pandemic, it became important to unveil the positive and negative factors that affected the job satisfaction of employees and then their organizational loyalty to achieve further organizational progress and improvement and then improve the overall performance of the organization. thus came this research to focus on analyzing the relationship between employee satisfaction and organizational loyalty during the COVID-19 pandemic, and for that reason, we developed questionnaires to identify job satisfaction and organizational loyalty at the job level for employees of the educational institution, the University of Baghdad (Colleges of Education for Girls and Science for Girls) in light of the COVID-19 pandemic. The study included (279) employees from the Colleges of Education for Girls and the College of Science for Girls, with a rate of (135) (144) employees, respectively. Employee satisfaction was considered an independent variable, and organizational loyalty was considered a dependent variable. Reliability tests, correlation analysis and regression were conducted to prove the research hypotheses, and the results of the research showed that satisfied employees tend to be more loyal and devoted to the organization and contribute positively to improving organizational performance. Furthermore, at the time of the outbreak of the COVID-19 pandemic, employees in the field of educational services at the University of Baghdad.
This study focused on extracting the outer membrane nanovesicles (OMVs) from Escherichia coli BE2 (EC- OMVs) by ultracentrifugation, and the yield was 2.3mg/ml. This was followed by purification with gel filtration chromatography using Sephadex G-150, which was 2mg/ml. The morphology and size of purified EC-OMVs were confirmed by transmission electron microscopy (TEM) at 40-200 nm. The nature of functional groups in the vesicle vesicle was determined by Fourier transforms infrared spectroscopy (FT-IR) analysis. The antitumor activity of EC-OMVs was conducted in vitro by MTT assay in human ovarian (OV33) cancer cell line at 24,48 and 96hrs. The cytotoxicity test showed high susceptibility to the vesicles in ovarian compared to normal
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This paper presents an application of a Higher Order Shear Deformation Theory (HOST 12) to problem
of free vibration of simply supported symmetric and antisymmetric angle-ply composite laminated plates.
The theoretical model HOST12 presented incorporates laminate deformations which account for the effects
of transverse shear deformation, transverse normal strain/stress and a nonlinear variation of in-plane
displacements with respect to the thickness coordinate – thus modeling the warping of transverse crosssections more accurately and eliminating the need for shear correction coefficients. Solutions are obtained in
closed-form using Navier’s technique by solving the eigenvalue equation. Plates with varying number of
Facial emotion recognition finds many real applications in the daily life like human robot interaction, eLearning, healthcare, customer services etc. The task of facial emotion recognition is not easy due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the facial expression accurately. Graph mining techniques are exploited in this paper to solve facial emotion recognition problem. After determining positions of facial landmarks in face region, twelve different graphs are constructed using four facial components to serve as a source for sub-graphs mining stage using gSpan algorithm. In each group, the discriminative set of sub-graphs are selected and fed to Deep Belief Network (DBN) f
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Well-dispersed Cu2FeSnSe4 (CFTSe) nanoparticles were first synthesized using the hot-injection method. The structure and phase purity of as-synthesized CFTSe nanoparticles were examined by X-ray diffraction (XRD) and Raman spectroscopy. Their morphological properties were characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The average particle sizes of the nanoparticles were about 7-10 nm. The band gap of the as-synthesized CFTS nanoparticles was determined to be about 1.15 eV by ultraviolet-visible (UV-Vis) spectrophotometry. Photoelectrochemical characteristics of CFTSe nanoparticles were also studied, which indicated their potential application in solar energy water splitting.
In recent years, the performance of Spatial Data Infrastructures for governments and companies is a task that has gained ample attention. Different categories of geospatial data such as digital maps, coordinates, web maps, aerial and satellite images, etc., are required to realize the geospatial data components of Spatial Data Infrastructures. In general, there are two distinct types of geospatial data sources exist over the Internet: formal and informal data sources. Despite the growth of informal geospatial data sources, the integration between different free sources is not being achieved effectively. The adoption of this task can be considered the main advantage of this research. This article addresses the research question of ho
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The aim of this paper is to present a method for solving third order ordinary differential equations with two point boundary condition , we propose two-point osculatory interpolation to construct polynomial solution. The original problem is concerned using two-points osculatory interpolation with the fit equal numbers of derivatives at the end points of an interval [0 , 1] . Also, many examples are presented to demonstrate the applicability, accuracy and efficiency of the method by compared with conventional method .
Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.
Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are
... Show MoreBiometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in
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