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Muscle fatigue is a physiological phenomenon that occurs when muscles are overused or continuously loaded during exercise or labor. Currently, analyzing the fatigue mechanism is still a complex and multi-layered research problem. In recent years, research methods focusing on surface electromyographic (sEMG) signals have garnered significant attention. The application of advanced signal processing techniques and machine learning algorithms has enhanced the precision of interpreting surface electromyographic data, deepening our understanding of the mechanisms underlying muscle fatigue. This, in turn, provides crucial scientific support for improving athletic performance, preventing sports injuries, and enhancing rehabilitation treatments.This comprehensive review of muscle fatigue research based on surface electromyographic signals covers various aspects. First, the definition of muscle fatigue and currently commonly used detection methods are explained, and the characteristics and application scope of various methods are pointed out; Secondly, the EMG characteristics that characterize muscle fatigue are introduced in detail from linear characteristics such as time domain, frequency domain, time-frequency domain and the use of nonlinear parameters, and the advantages and limitations of these characteristics are also discussed; Thirdly, combining fatigue characteristics as input data, the classification algorithms commonly used for muscle fatigue are explored, and the applicable conditions, advantages and disadvantages of each algorithm are accurately summarized from the aspects of machine learning and deep learning algorithms; Finally, the challenges faced by muscle fatigue research at this stage are pointed out, and on the basis of proposing feasible solutions, the future research directions are prospected.

Basketball teams rely heavily on the effectiveness of their practice regimens and game plans to succeed. The intricacy of the game and the range of skills players must possess make it extremely difficult to develop effective training plans and tactical techniques. The use of genetic algorithms (GAs) as a cutting-edge technique to enhance basketball players' practice regimens and game strategies is investigated in this work. Inspired by biology and natural selection, genetic algorithms provide a potent optimization method that, via the repeated steps of evolution, can find close to ideal solutions to challenging issues. As chromosomes made up of genes relating to different parameters and tactics, prospective training plans and game tactics are represented by GAs, which enable them to efficiently search across the large solution space and find combinations that optimize desired results. As part of the research approach, the goals and limitations of the optimization issue are defined. Fitness functions are intended to assess each potential solution's efficacy, directing development toward better solutions across a series of iterations. This study shows how effective genetic algorithms are in optimizing basketball players' training regimens and game strategy through simulators and real-world tests. In order to continually enhance player growth and team competitiveness, coaches can use GAs to refine and modify tactics based on feedback as well as performance data in an iterative manner. The results of this study have significance for team sports other than basketball, where results are heavily influenced by the interaction of players' individual abilities, teamwork, and decisions about strategy. In the end, incorporating algorithms based on genetics into sports analytics presents a viable way to improve coaching techniques and reach the highest levels of efficiency in sporting settings. [ABSTRACT FROM AUTHOR]

Crime type identification is crucial for improving public safety through more accurate prevention and efficient responses. However, practical applications often suffer from a significant lack of effective samples features, making it difficult to focus on the most informative aspects during identification. This study addresses these challenges by proposing a novel crime type identification method that leverages a deep neural network enhanced with multiple attention mechanisms. The approach includes a tailored data processing method involving target encoding to convert categorical data into numerical form, L2 normalizer to standardize data and ensure balanced feature contribution, and variance threshold feature selection to remove low-variance features. Additionally, a High-Order Deep Residual Network with Multiple Attention (HO-ResNet-MA) is developed, featuring an optimized Huta68 block (Huta-6(8)-MA ResBlock) with an enhanced Contextual Transformer (CoT) unit for local attention and a queue-and-exclusion layer for global attention. To validate the effectiveness of the proposed method, homicide reports data and Chicago crimes data are processed and fed into the crime type identification model, resulting in accuracies of over 84.1% and 99.5%, respectively. This study makes contributions to the field of crime analysis by validating the practical applicability of these approaches, and enhancing the efficiency of public safety workers. [ABSTRACT FROM AUTHOR]

Recently, the coexistence of topology and superconductivity has garnered considerable attention. Specifically, the dimensionality of these materials is crucial for the realization of topological quantum computation. However, the naturally grown materials, especially with one-dimensional feature that exhibits the coexistence of topology and superconductivity, still face challenges in terms of experimental realization and scalability, which hinders the fundamental research development and the potential to revolutionize quantum computing. Here, we report the first experimental synthesis of quasi-one-dimensional InNbS2 nanoribbons that exhibit the coexistence of topological order and superconductivity via a chemical vapor transport method. Especially, the in-plane upper critical field of InNbS2 nanoribbons exceeds the Pauli paramagnetic limit by more than 2.2 times, which can be attributed to the enhanced spin-orbit coupling and the weakened interlayer interaction between the NbS2 layers induced by the insertion of In atoms, making InNbS2 exhibit spin-momentum locking similar to that of monolayer NbS2. Moreover, for the first time, we report the superconducting diode effect in a quasi-one-dimensional superconductor system without any inherent geometric imperfections. The measured maximum efficiency is manifested as 14%, observed at μ0H ≈ ±60 mT, and we propose that the superconducting diode effect can potentially be attributed to the presence of the nontrivial topological band. Our work provides a platform for studying exotic phenomena in condensed matter physics and potential applications in quantum computing and quantum information processing. [ABSTRACT FROM AUTHOR]

To explore a methodology for tailoring the d-band center of metal oxide catalysts, a distinctive composite is synthesized by incorporating polyaniline (PANI) particles with specific atoms of Co3O4 nanorods. The introduction of the requisite amount of PANI significantly enhances the electrocatalytic activity, yielding an excellent functionality with a minimum detection limit of 0.676 μM and a high sensitivity of 3.024 μA μM–1 cm–2 for glucose. The opening potential for the oxygen evolution reaction registers at 1.41 V, while the overpotential is 290 mV. However, when the PANI layer completely covers the Co3O4 surface, a substantial decline in electrochemical properties is observed, indicating that active atoms on the Co3O4 plane should be preferentially exposed. Additionally, density functional theory calculations reveal that bonding of an appropriate quantity of polyaniline particles can modify bonding states and antibonding states of Co–O within the Co3O4 complex, leading to a shift of the d-band center closer to the Fermi level, thereby enhancing its electrocatalytic activity. These findings underscore the practicality of this manipulation strategy for glucose sensing and oxygen evolution reaction. [ABSTRACT FROM AUTHOR]