The integration of arti cial intelligence (AI), whether through devices or software, has become a critical tool in analyzing and evaluating technical performance. AI signi cantly contributes to enhancing athletic performance by enabling accurate data analysis and supporting educators in developing effective training programs and interactive curricula. This study addresses a noticeable gap in the literature regarding the attitudes and inclinations of educators toward AI in physical education and sport sciences—a gap often attributed to limited awareness and lack of access to moderntechnologies.Theprimaryaimofthestudyistoexaminethetendenciesandperceptionsoffemaleinstructorsin physical education and sport sciences toward the use of AI

Background: Postoperative nausea and vomiting (PONV) are one of the most common complaints following laparoscopic cholecystectomy.

Objective: This study was designed to compare the effects of dexamethasone, metoclopramide, and their combination on preventing PONV in patients undergoing laparoscopic cholecystectomy.

Methods: A total of 135 patients enrolled in the study. American Society of Anesthesiologists (ASA) physical status I and II patients were included in this randomized, double blind, placebo-controlled study. Patients were randomly assigned to group A administered 8mg iv dexamethasone, group B received metoclopramide 10 mg, group C received combination of 8mg de

   The study aims to test the relationship of work pressure to its dimensions (role conflict, ambiguity of role, workload and nature of work) as an independent variable and its effect on organizational alienation by its dimensions (disability, lack of power, indifference, animosity, social isolation and self-alienation) (Restraint and confidence in negation, initiative, adaptation and living conscience) as a mediator variable, in some faculties of Baghdad University of Science (Medicine and Engineering) and Humanity (Education and Literature). The data was collected on the practical side, which was applied randomly (306) of the teachers and teachers of the colleges (56) items, which included the main research variables

In this study a new antiseptic was formulated and tested to match the effectiveness against microorganisms. The formulation consisted of Povidone - Iodine (PVP-I) (10%), H₂O₂ (3%) and Aloe Vera gel (pure). Different ratios of these materials were prepared within the acceptable range of pH for an antiseptic (3-6). The prepared samples were tested. The In Vitro test was performed by using four bacteria, two were Gram-Positive (Staphylococcus aureus and Bacillus cereus) and two were Gram-Negative (Escherichia coli and Pseudomonas aeruginosa). The new antiseptic showed 100% killing rate for E. coli, Ps. aeruginosa and S. aureus and 96.4667% killing rate for B. cereus. When the new antiseptic was compared with two common

In this paper a thin films of selenium was prepare on substrates of n-Si by evaporation in a vacuum technique with thickness about 0.5μm. And then an annealing process was done on samples at two temperature (100 and 200) C ° in a vacuum furnace (10^-3 torr).
Some structural, optical and mechanical properties of prepared thin films were measured. Results showed that the prepared film was the crystallization, optical transmittance and micro hardness of the prepared thin films increased significantly after annealing.

In an earlier paper, the basic analytical formula for particle-hole nuclear state densities was derived for non-Equidistant Spacing Model (non-ESM) approach. In this paper, an extension of the former equation was made to include pairing. Also a suggestion was made to derive the exact formula for the particle-hole state densities that depends exactly on Fermi energy and nuclear binding energies. The results indicated that the effects of pairing reduce the state density values, with similar dependence in the ESM system but with less strength. The results of the suggested exact formula indicated some modification from earlier non-ESM approximate treatment, on the cost of more calculation time

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial