The ZnO nanoparticles were synthesized at various precursor concentrations i.e. 0.05, 0.1, and 0.5 M by biosynthesis method based on Pometia pinnata Leaf Extracts. Initial nanoparticle concentration influenced the optical bandgap, shape, and structure of nanoparticles. The photodegradation process was carried out under UV illumination. The efficiency of MB degradation was determined by measuring the decrease in MB concentration and by analyzing the optical absorption at 663 nm recorded by UV-Vis spectroscopy. Results showed that the biosynthesized ZnO nanoparticles exhibited efficient photodegradation of MB, with a maximum degradation rate of 80% after 90 minutes of exposure to UV-C light. The study highlights the potential of Pometia pi

The research aimed at designing a rehabilitation program using electric stimulation for rehabilitating knee joint working muscles as a result of ACL tear using an apparatus developed by the researchers that stimulate the muscle vibration and work as well as the ability to rehabilitate the join in shorter periods. In addition to that, it aimed at identifying the effect of this program on rehabilitating the knee joint working muscles. The researchers used the experimental method on Baghdad clubs’ players who suffer from complete knee joint ACL tear aged (19 – 24) years old. The results showed that the training program developed the working muscles significantly achieving normal levels of activity.

As they are the smallest functional parts of the muscle, motor units (MUs) are considered as the basic building blocks of the neuromuscular system. Monitoring MU recruitment, de-recruitment, and firing rate (by either invasive or surface techniques) leads to the understanding of motor control strategies and of their pathological alterations. EMG signal decomposition is the process of identification and classification of individual motor unit action potentials (MUAPs) in the interference pattern detected with either intramuscular or surface electrodes. Signal processing techniques were used in EMG signal decomposition to understand fundamental and physiological issues. Many techniques have been developed to decompose intramuscularly detec

The aim of this study was to Identifying The Effect of using Linear programming and Branching programming by computer in Learning and Retention of movement concatenation(Linkwork) in parallel bars in Artistic Gymnastics. The searchers have used the experimental method. The search subject of this article has been taken (30) male - students in the second class from the College of Physical Education/University of Baghdad divided into three groups; the first group applied linear programming by computer, and the second group has been applicated branching programming by computer, while precision group used traditional method in the college. The researchers concluded the results by using the statistical bag for social sciences (spss) such as both

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

       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

As a result of the increase in wireless applications, this led to a spectrum problem, which was often a significant restriction. However, a wide bandwidth (more than two-thirds of the available) remains wasted due to inappropriate usage. As a consequence, the quality of the service of the system was impacted. This problem was resolved by using cognitive radio that provides opportunistic sharing or utilization of the spectrum. This paper analyzes the performance of the cognitive radio spectrum sensing algorithm for the energy detector, which implemented by using a MATLAB Mfile version (2018b). The signal to noise ratio SNR vs. Pd probability of detection for OFDM and SNR vs. BER with CP cyclic prefix with energy dete