SPACE-TIME GRAPH-BASED CONVOLUTIONAL NEURAL NETWORKS OF STUDY ON MOVEMENT RECOGNITION OF FOOTBALL PLAYERS

Behaviour recognition technology is an interdisciplinary technology, integrating many research achievements in computer vision, deep learning, pattern recognition and other fields. The key information of bone data on human behavior can not only accurately describe the motion posture of the human body in three-dimensional space, but also its rigid connection structure is robust to various external interference factors. However, the behavioral recognition algorithm is influenced by different factors such as background, light and environment, which is easy to lead to unstable recognition accuracy and limited application scenarios. To address this problem, in this paper, we propose a noise filtering algorithm based on data correlation and skeleton energy model filtering, construct a set of football player data sets, using the ST-GCN algorithm to train the skeleton characteristics of football players, and construct a behavior recognition system applied to football players. Finally, by comparing the accuracy of Deep LSTM, 2s-AGCN and the algorithm in this paper, the accuracy of TOP1 and TOP5 is 39.97% and 66.34%, respectively, which are significantly higher than the other two algorithms. It can realize the statistics of athletes and analyze the technical and tactical movements of players on the football field

Heterojunction-composited architecture for Li–O2 batteries with low overpotential and long-term cyclability.

The crucial issue among lithium−oxygen batteries (LOBs) lies in the development of highly efficient catalysts to improve their large discharge−charge polarization, poor rate capability, and short cycle life. Herein, a composite of three-dimensional honeycomb graphene-supported a Mo/Mo2C heterojunction has been synthesized and can be utilized as a self-supported LOB cathode directly. The LOBs based on the Mo/Mo2C heterojunction composite cathode show a low overpotential of 0.52 V, a high discharge capacity of about 12016 mAh g−1 at 100 mA g−1, and a long-term cyclability (about 360 cycles) under a restricted capacity of 1000 mAh g−1 at 100 mA g−1, which exceeds the features of the majority of Mo-based catalysts for LOBs reported so far. Based on both experimental tests and density functional calculations, it is confirmed that the outstanding electrochemical performance is closely associated with a hierarchical porous structure for convenient oxygen/electrolyte diffusion, a large number of activity sites (interfaces/defects) for high capacity, and a high conductivity with metallic bonds for good rate capability. The method can be extended to prepare other metal based heterojunctions

Effect of isokinetic muscle strength training on knee muscle strength, proprioception, and balance ability in athletes with anterior cruciate ligament reconstruction: a randomised control trial

Objective: This study aimed to investigate the effects of regular isokinetic muscle strength training on knee muscle strength, proprioception, and balance ability in athletes after anterior cruciate ligament (ACL) reconstruction.Methods: Forty-one athletes who underwent ACL reconstruction were randomly divided into the experimental (n = 21) and control (n = 20) groups. The experimental group used an isokinetic muscle strength tester for 4 weeks (five times/ week) of knee flexion and extension isokinetic muscle strength training. The control group used the knee joint trainer (pneumatic resistance) for the same exercise regimen as the experimental group.Results: 1) Four weeks when compared with the baseline. Experimental group: the knee flexion and extension PT (60°/s and 240°/s) increased by 31.7%, 40.3%, 23.4%, and 42.9% (p < 0.01), and the flexion muscular endurance increased by 21.4% and 19.7% (p < 0.01). The flexion and extension kinaesthesia and the 30° and 60° position sense decreased by 36.2%, 32.3%, 40.0%, and 18.9% (p < 0.05). The anterior–posterior and medial–lateral displacement and speed decreased by 30.2%, 44.2%, 38.4%, and 24.0% (p < 0.05). Control group: the knee peak torque (60°/s) increased by 18.8% (p < 0.01). The anterior–posterior and medial–lateral displacement and speed decreased by 14.9%, 40.0%, 26.8%, and 19.5% (p < 0.01). 2) After 4 weeks, compared with the control group, the knee flexion and extension peak torque (60°/s), extension, peak torque (240°/s), and extension muscular endurance of the treatment group increased to varying degrees (p < 0.05). However, the kinaesthesia, 30° position sense, and anterior–posterior displacement decreased to varying degrees (p < 0.05).Conclusion: Adding regular isokinetic muscle strength training to rehabilitation training further improved the knee flexion and extensor strength and extensor endurance of athletes with ACL reconstruction, as well as enhanced the kinaesthesia and 30° position sense and the balance between the anterior and posterior directions. However, the treatment had limited effects on knee flexion kinaesthesia and muscle endurance

Exploring the Optimal Cycle for Quantum Heat Engine using Reinforcement Learning

Quantum thermodynamic relationships in emerging nanodevices are significant but often complex to deal with. The application of machine learning in quantum thermodynamics has provided a new perspective. This study employs reinforcement learning to output the optimal cycle of quantum heat engine. Specifically, the soft actor-critic algorithm is adopted to optimize the cycle of three-level coherent quantum heat engine with the aim of maximal average power. The results show that the optimal average output power of the coherent three-level heat engine is 1.28 times greater than the original cycle (steady limit). Meanwhile, the efficiency of the optimal cycle is greater than the Curzon-Ahlborn efficiency as well as reporting by other researchers. Notably, this optimal cycle can be fitted as an Otto-like cycle by applying the Boltzmann function during the compression and expansion processes, which illustrates the effectiveness of the method