This research focuses on the epidemic of Job burnout and seeks to achieve sustainable preeminence among the employees of the University of Baghdad. It also identifies the meaning of the differences between the two genders of males and females according to the variables, and it also does the correlation between the two variables. First, the two researchers developed a test of job burnout that incorporates 18 items, which has been proven to be statistically fit. Moreover, they developed a competitive superiority measure consisting of 22 items in it, and its psychometric properties confirm their fit. The two tools were applied to a sample of 120 employees who were randomly selected from six colleges at the University of Baghdad, alongside university staff. As for API 2021–2022, the research concluded that it is found that the research sample of the employees of Baghdad University did not have any job burnout. Research reveals that females and males differ in job burnout, and it is more normal for females to be depressed, which leads to burnout in the workplace. The within-group result of the research has shown the fair competitive advantage of the University of Baghdad students. No differences in male and female brains reach significance, as institutional forces push for achieving dominant positions in competition. The results of the statistical analysis confirmed that the competitive sustainability of work belonging to the staff of the University of Baghdad does not correlate with job burnout.
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The purpose of this study was to measure serum levels of insulin-like growth factor-binding protein (IGFBP7), Insulin-like Growth Factor 1 (IGF-1), Growth Hormone (GH), Interleukin 6 (IL-6) and insulin in acromegaly patients and healthy controls. The acromegaly group had 60 patients, while the population group had 30 people who had never had acromegaly before. The concentration of IGFBP7, IGF-1, GH, IL-6, and insulin were determined. The results of the present study indicate that IGFBP7 level in the acromegaly group was significantly lower (1.690.07 ng/mL vs. 2.740.12 ng/mL, respectively, p = 0.001). IGF-1, GH, IL-6, and insulin concentrations were also significantly higher in acromegaly patients. The diagnostic accuracy (2.194) was exce
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Release of industrial effluents comprising dyes in water bodies is one of the foremost causes of water pollution. Therefore, the proper and proficient treatment of these dyes contaminated left-over material before their release is crucial. Herein, an eco-friendly biological macromolecule Gum-Acacia (GA) integrated Fe3O4 nanoparticles composite hydrogel was manufactured via co-precipitation technique for effective adsorption of Congo red (CR) dye existing in water bodies. The as-prepared magnetic GA/Fe3O4 composite hydrogel was characterized by FTIR, XRD, EDX, VSM, SEM, and BET techniques. These studies discovered the fruitful fabrication of biodegradable magnetic GA/Fe3O4 composite hydrogel possessing porous structure with large surface are
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This paper proposes a hybrid artificial intelligence (AI) model that combines an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) to predict the Quality of Service (QoS) in 5G networks. The model utilizes radio channel indicators such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength Indicator (RSSI), and Channel Quality Indicator (CQI) to forecast throughput and latency levels. These indicators are critical factors affecting network performance; however, the nonlinear relationship among them makes traditional analytical models inadequate for accurate QoS prediction. The importance of the current study lies in estimating QoS in 5G networks by combining
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Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to
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This study was undertaken to introduce a fast, accurate, selective, simple and environment-friendly colorimetric method to determine iron (II) concentration in different lipstick brands imported or manufactured locally in Baghdad, Iraq. The samples were collected from 500-Iraqi dinars stores to establish routine tests using the spectrophotometric method and compared with a new microfluidic paper-based analytical device (µPAD) platform as an alternative to cost-effective conventional instrumentation such as Atomic Absorption Spectroscopy (AAS). This method depends on the reaction between iron (II) with iron(II) selective chelator 1, 10-phenanthroline(phen) in the presence of reducing agent hydroxylamine (HOA) and sodium acetate (NaOAc) b
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The reaction of methyldopa with o-vanillin in refluxing ethanol afforded Schiff base and characterized through physical analysis with a number of spectra also the study of biological activity. The geometry of the Schiff base was identified through using (C.H.N) analysis, Mass, 1H-NMR, FT-IR, UV-Vis spectroscopy. Metal complexes of Cr3+, Mn2+, Co2+, Ni2+, Cu2+, Zn2+, Cd2+ and Hg2+ with Schiff base have been prepared in the molar ratio 2:1 (Metal:L), (L = Schiff base ligand) except Hg2+ at molar ratio 1:1 (Hg:L). The prepared complexes were characterized by using Mass, FT-IR and UV-Vis spectral studies, on other than magnetic properties and flame atomic absorption, conductivity measurements. According to the results a dinuclear octahe
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