THE ELECTROMYOGRAPHY CHARACTERISTICS BETWEEN DIFFERENT LEVELS OF SOCCER PLAYER ON INSTEP KICKING

This study improves kicking performance by comparing muscle activity between different levels of players. Twelve soccer players in the college cup in division I and division II volunteered to participate in this study. A VlCON motion capture system (200 Hz) was used to capture the kicking motion including back-swing and forward-swing. The Noraxon electromyography system was used to collect and analyze the percentage of maximum voluntary contraction on rectus femoris, bicepsfemoris, tibialis anterior, and gastrocnemius. The Mann-Whilney U (a = -05) test was applied to assess significant differences in this study. The results indicated that division II players had a greater percentage maximum voluntary contraction in tibialis anterior in the back-swing. To avoid stiff movements in soccer kicks, division II players should decrease muscle contraction in the tiblalis anterior In the back-swing

The Influence of Customer’s Sharing Behavior in Social Commerce

All transaction behaviors between enterprises and customers directly take place on social media. Using social media for people to interact with their friends and family become a routine in the daily life. This study aims to figure out the critical factors and relations of brand community and social commerce. Meanwhile, this study is to investigate the influence on consumers’ engagement by considering the building of a brand community for social commerce. This study reviews many key literatures of social commerce and brand community. This study employs a survey base strategy to figure out the proposed research questions

Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problems

Quantum computing holds great potential for advancing the limitations of machine learning algorithms to handle higher dimensions of data and reduce overall training parameters in deep learning (DL) models. This study uses a trainable variational quantum circuit (VQC) on a gate-based quantum computing model to investigate the potential for quantum benefit in a model-free reinforcement learning problem. Through a comprehensive investigation and evaluation of the current model and capabilities of quantum computers, we designed and trained a novel hybrid quantum neural network based on the latest Qiskit and PyTorch framework. We compared its performance with a full-classical CNN with and without an incorporated VQC. Our research provides insights into the potential of deep quantum learning to solve a maze problem and, potentially, other reinforcement learning problems. We conclude that reinforcement learning problems can be practical with reasonable training epochs. Moreover, a comparative study of full-classical and hybrid quantum neural networks is discussed to understand these two approaches' performance, advantages, and disadvantages to deep-Q learning problems, especially on larger-scale maze problems larger than 4x4

Quantum Embedding with Transformer for High-dimensional Data

Quantum embedding with transformers is a novel and promising architecture for quantum machine learning to deliver exceptional capability on near-term devices or simulators. The research incorporated a vision transformer (ViT) to advance quantum significantly embedding ability and results for a single qubit classifier with around 3 percent in the median F1 score on the BirdCLEF-2021, a challenging high-dimensional dataset. The study showcases and analyzes empirical evidence that our transformer-based architecture is a highly versatile and practical approach to modern quantum machine learning problems