This research aims to study the relationship between sports anxiety and accuracy of aiming when jumping high in handball, considering that anxiety is one of the most important psychological factors affecting the skill performance of athletes, especially in precise and complex tasks. The study relied on the descriptive associative approach, where data were collected from a sample of fourth-stage students at the Faculty of physical education and sports sciences at the University of Baghdad for the academic year (2024-2025), numbering (16) students, and anxiety levels were measured using standardized scales, in addition to evaluating the accuracy of aiming when jumping high according to specific technical standards. The results showed that the average anxiety level of the players was (45.31 ± 5.86), while the average accuracy of aiming was (12.19 ± 2.93). The results also revealed a statistically significant negative correlation between sports anxiety and aiming accuracy (r = -0.58), a value that exceeds the tabular value (0.497), indicating that increased anxiety is associated with a decrease in performance accuracy. This result was explained by the fact that anxiety consumes attentional resources and distracts concentration, also increases muscle tension and affects visual–motor coordination, which impairs the ability to accurately execute the aiming skill when jumping. She recommended that psychological training programs such as relaxation exercises, mindfulness, and simulating stress situations should be integrated into the players' physical and skill preparation plans.
Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date. A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s
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Herein, a biocomposite of crosslinked chitosan polyethylene glycol diglycidyl ether (CS-PEDGE), montmorillonite (MMT), and foodgrade algae (FGA) was successfully prepared by a hydrothermal technique. The resulting absorbent (CS-PEDGE/FGA/MMT) was assessed for its adsorption property with methyl violet 2B (MV 2B) a toxic cationic dye. The physicochemical properties of CS-EDGE/ FGA/MMT were assessed via various analytical techniques, including BET, Elemental analysis, pHpzc, and spectroscopy (FTIR, XRD, SEM-EDX). The influence of three adsorption variables, namely adsorbent dose (A: 0.02–0.1 g/100 mL), solution pH (B: 4–10), and contact time (C: 10–420 min) on the rate of MV 2B dye removal was examined using the Box-Behnken design (RSM-
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One of the key challenges is reliable communication for Unmanned Underwater Vehicles (UUVs) since underwater environments are dynamic and challenging for signal transmission. The traditional acoustic communication systems allow long-distance communication but are limited by the low bandwidth, high latency, and doppler effect. Optical wireless, on the other hand, provides high data rates with water absorption and scattering occurring within the transmission distance, but has a short range of communication. To overcome these drawbacks, a smart hybrid optical-acoustic communication system is proposed that is a combination of Wavelength Division Multiplexing and On-Off Keying (WDM-OOK) to transmit a high-speed signal over optical and ac
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Manufacturing industry is constantly looking for ways to improve the efficiency of production processes so as to lower production costs, maintain product quality, and improve production planning. This study aims to provide an integrated optimization and forecasting framework that combines Cutting Optimization Pro5, linear programming, and an Enhanced N-BEATS deep learning model to minimize material waste and manufacturing cost and to predict the number of production items with a price-reduction strategy. The real industrial case study is based on production and pricing data from the Akad factory of the General Company for Electrical and Electronic Products in Iraq for the period from 2014 to 2025, concerning the manufacture of 80 L
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