MCBOpen Access

Aiming at the problems existing in the water supply and drainage design of high-rise buildings, this paper starts with the application advantages and design process of BIM technology in the water supply and drainage design of high-rise buildings. Suggestions for promoting the sustainable development of science and technology are put forward for the reference of relevant persons in charge. In the architectural design industry, architectural design is not only a literary and artistic creation, but also a comprehensive engineering project involving multiple industries. It mainly contains a large amount of information, which must be collected, classified, analyzed, searched and transmitted by powerful technical means. Building information entity model BIM technology is a new concept, new concept and new technology existing in data building technology, which brings a strong technical support point for the development trend of building customization. This paper discusses the energy -saving design application of this small high-rise residence based on BIM technology. This paper also integrates the principles of biomechanics and biomimicry to further enhance the application of BIM technology in green energy-saving design. By simulating biological structures and ecosystems in nature, it optimizes the building’s energy management and structural performance, thereby designing more efficient and sustainable architectural solutions.

Integrating artificial intelligence and advanced biosensor technologies represents a transformative paradigm in athletic performance optimization. This research explores the revolutionary potential of AI-driven fitness solutions to redesign training methodologies across professional and amateur sports disciplines fundamentally. These technologies offer unprecedented capabilities for personalized, data-driven athletic development by addressing critical limitations in traditional performance tracking. The study examines comprehensive approaches to physiological monitoring, performance prediction, and individualized training interventions enabled by advanced machine learning algorithms and sophisticated biosensor technologies. Key innovations include real-time physiological data collection, predictive performance analytics, and adaptive training strategies that maximize individual athletic potential while minimizing injury risks.

The rapid development of modern society and heightened competitiveness have led to increased expectations from parents, educators, and society regarding college students. This environment, coupled with academic pressures and employment challenges, significantly affects students’ physical and mental health. This paper employs big data analysis to explore the intrinsic connections between physical activity and mental health, incorporating biomechanical insights into the discussion. Biomechanics examines the mechanical aspects of human movement, providing a deeper understanding of how physical activity influences mental well-being. Engaging in regular physical activity enhances physiological responses, such as improved circulation, increased endorphin release, and reduced stress hormones, all of which contribute to better mental health outcomes. In our study, we utilized the SCL-90 for mental health assessment and conducted a survey on cognitive characteristics related to sports participation. A sample of 500 university students was analyzed to establish a behavioral cognitive model of sports activity participation. Correlation coefficients revealed that the intensity (0.1354) and duration (0.2455) of physical activity correlate positively with mental health scores. Furthermore, factors such as frequency and total volume of physical activity demonstrated varying degrees of correlation across five mental health dimensions. Regression analysis yielded a standardized coefficient of 0.6154, indicating that physical activity participation significantly positively influences mental health scores. By integrating biomechanical principles, this research highlights the importance of movement efficiency and physical engagement in promoting mental health, suggesting that enhancing physical activity can serve as a vital strategy for improving overall well-being among college students.

Such a study in the biomechanical characterization of the movements involved in directional dribbling can be undertaken with adolescent basketball players to provide evidence in training recommendations. It demonstrates complex interactions between different biomechanical components during directional dribbling by using large-scale analysis of joint kinematics, dynamic parameters, and movement control strategies. The movement execution and performance outcome are mostly influenced by individual, technical, and environmental factors. A structured training program combining physical conditioning, technical skill development, and injury prevention strategies was implemented and evaluated over a 12-week period. The outcomes of this program demonstrated significant improvements in performance factors, including movement efficiency, accuracy, and decision-making. These findings contribute to the theoretical understanding and applied implications in basketball training and offer key information for coaches and practitioners working with adolescent athletes.

Purpose: This study aimed to scientifically develop the Four-Dimensional Job Performance Evaluation Questionnaire for Clinical Nurses from a biological aspect and to assess its reliability and validity. Methods: Guided by Koopmans’ four-dimensional work performance theory, the questionnaire was structured around task performance, relational performance, adaptive performance, and counterproductive performance. The initial version was developed through a comprehensive literature review, analysis of hospital performance evaluation indices, and Delphi method consultations with experts. A survey was conducted among 549 clinical nurses in a tertiary hospital in Xinjiang Province, and the questionnaire’s reliability and validity were evaluated using the critical ratio method, factor analysis, and reliability and validity tests. Results: The content validity index (I-CVI) for each item ranged from 0.820 to 1.000, with an average level content validity index (S-CVI/Ave) of 0.970. The overall Cronbach’s alpha coefficient for the questionnaire was 0.962, with dimension-specific coefficients of 0.967, 0.901, 0.953, and 0.909. Exploratory factor analysis indicated a cumulative variance contribution rate of 63.235% for the four principal factors, and confirmatory factor analysis confirmed a good model fit, leading to the finalization of a 45-item questionnaire covering four dimensions of clinical nurses’ job performance. Conclusion: The Four-Dimensional Job Performance Evaluation Questionnaire for Clinical Nurses developed in this study demonstrates good reliability and validity, offering a comprehensive measurement tool for assessing nurses’ job performance levels in the biological context.

This study focuses on leveraging transfer learning technology to revolutionize fitness training from a cell and molecular biomechanics perspective. In the era of advanced biotechnology, understanding the minute biomechanical events within cells during exercise is crucial. We aim to apply computer-intelligent control concepts to fitness training, especially aerobics, by delving into the cell and molecular biomechanics. Via in-depth analysis of aerobics training's impact on cells and molecules and smart use of computer tech, a B/S mode simulation model integrating NET and SQL Server is crafted. This model offers a scientific framework for fitness training centered around cell and molecular biomechanics. The ID3 algorithm is then employed to dissect student sports test data related to cell and molecular changes, enabling personalized training plans based on individual cell and molecular traits. To enhance the model, the association rule algorithm is introduced. By scrutinizing extensive cell and molecular biomechanics training data, such as how mechanical forces influence gene expression and protein interactions, hidden patterns and correlative factors are unearthed. This refines the model's accuracy and practicality. During experimentation, comprehensive testing of the association rule algorithm in the context of cell and molecular biomechanics is carried out. Results confirm the viability of the computer-intelligent control-based aerobics training strategy, which effectively boosts fitness training effectiveness at the cell and molecular level. This research pioneers novel approaches for aerobics and other sports, providing valuable insights for optimizing training with respect to cell and molecular biomechanics.

This study investigates the dynamic mechanisms underlying creativity development through martial arts movement drawing training and combined with relevant knowledge of biomechanics, emphasizing cognitive adaptability and neurophysiological engagement. Using experimental methods and independent sample t-tests, we assessed differences across five creativity sub-dimensions—fluency, originality, flexibility, sensitivity, and insight—between an experimental group and a control group. The results showed significant improvements in the experimental group following the training, whereas no substantial changes were observed in the control group. These findings indicate that martial arts movement drawing training not only involves observing, analyzing, and expressing dynamic actions, but also requires participants to coordinate various parts of the body during movement. This biomechanical interaction promotes the body’s movement efficiency and coordination, promotes perception, imagination, and cognitive flexibility, thereby enhancing creativity. By integrating creative expression with motor and sensory coordination, this study underscores the potential of dynamic, art-based interventions to enhance cognitive adaptability and functional plasticity. Principles of biomechanics, such as kinematics and kinetics, can further explain how participants’ training can promote the development of brain function by optimizing movement patterns and improving motor control. This research offers an innovative approach to creativity development, with implications for interdisciplinary studies in cognition, biomechanics, and artistic training.

The integration of bioinformatics, biological adaptations and biomechanics into public physical education offers a promising direction for enhancing sport science education. This article comprehensively explores the evolution of historical research in sport, not only through the lens of biological concepts but also incorporating biomechanical principles. It focuses on how adaptations, bioinformatics, and biomechanical analysis work in tandem to deepen our understanding of sport. By analyzing 514 articles published between 2000 and 2019, the study identifies trends in the study of sport history, particularly in the context of athletes’ biological adaptations and their application in monitoring and enhancing physical performance techniques. Using CiteSpace visualization software, this paper constructs a knowledge map of key research themes and influential journals, demonstrating the dynamic development of the field. The research highlights how the biomechanical principles of adaptability, biological resilience and performance optimization are increasingly integrated into the study of sport history and physical education. Key findings include a staged trend in sport history research. The significant impact of biological adaptations, informed by biomechanical analysis, on training methods is evident. Bioinformatics further aids in processing and making sense of the vast amounts of biomechanical data collected. These insights suggest that integrating biological approaches into physical education teaching can significantly improve the theoretical and practical outcomes of physical education teaching in higher education. In addition, in an international comparative perspective, in the United States and Germany, college physical education teaching focuses more on optimizing athletes’ performance through wearable technology and biomechanical modeling, whereas Japan and China pay more attention to the integration of traditional training methods with bioinformatics and biomechanical technology. This cross-national comparison not only reveals the characteristics of different countries in sport science education, but also provides a more comprehensive global perspective for future research.

With the rapid evolution of educational technology, teaching methods have transcended traditional face-to-face instruction. Beyond physical classrooms, learners now benefit from the flexibility and accessibility of online learning environments. This research examines the innovative dynamics within online collaborative learning classrooms for football training courses, analyzed through a biomechanical lens. Utilizing an action research methodology, the study investigates strategies to cultivate creativity and innovation in virtual football training courses, with a focus on biomechanical principles such as motion analysis, kinetic chain efficiency, and neuromuscular coordination. Key approaches are highlighted: fostering an engaging and innovative classroom atmosphere as a cornerstone for enhancing students’ creativity and biomechanical understanding; ensuring learners have access to appropriate personal equipment, such as motion capture devices or wearable sensors, to facilitate accurate biomechanical data collection and analysis during online participation; leveraging course platforms to document and facilitate interactions between learners and instructors, particularly in the context of movement optimization and injury prevention; and addressing the specific requirements of biomechanics-oriented online teaching. Furthermore, the integration of “collaborative learning” and “problem-oriented learning” emerges as the most impactful approach to nurturing creativity and biomechanical proficiency among learners in this context. This study highlights the potential of combining biomechanical principles with online collaborative learning to enhance the quality and innovation of football training education.

In the realm of physical education in higher education institutions, dance courses have emerged as a vital component due to their holistic nature and technical demands. However, traditional teaching methods often face challenges, including limitations in teaching resources, learning interaction, and motion correction. To address these shortcomings and enhance teaching effectiveness, this study introduces a biometric and motion analysis system tailored for sports dance instruction. Grounded in biomechanical principles and leveraging wearable devices alongside intelligent mobile terminal technology, the system collects kinematic and dynamic data from students’ dance movements. By employing biomechanical models, it quantitatively evaluates movement standardization and provides real-time feedback to students. The research findings demonstrate that this innovative system significantly improves teaching interactivity and student movement accuracy, achieving a 14% increase in teaching efficiency. Furthermore, 93% of students expressed high satisfaction with the system. This study advocates for the integration of mobile intelligent terminals and biometric technology, the optimization of course design, the development of teaching resources guided by biomechanics, and the strengthening of the synergy between practice and theory. By doing so, it aims to establish a more scientific and effective sports dance teaching model.

With the continuous advancement of technology, intelligent sports technology has gradually become an important tool in sports training. This study aims to explore the application of intelligent sports technology in optimizing the movement training of kayaking athletes. By introducing advanced technologies such as motion capture, data analysis, and virtual reality, the research aims to improve athletes’ coordination and stability in their movements. Using kayaking athletes as research subjects, this study provides a detailed description of the application methods and experimental design of intelligent sports technology and systematically analyzes the collected data. The research results show that intelligent sports technology has a significant effect on improving the precision and efficiency of athletes’ movements. Specifically, through real-time feedback and data accumulation, coaches and athletes can develop more scientific and reasonable training plans, thereby significantly enhancing training effectiveness. However, the study also points out the shortcomings of intelligent sports technology in terms of portability and real-time data processing, which need further improvement and optimization in future research. Overall, this study provides evidence for the application of intelligent sports technology in kayaking training, having important practical significance and application value. It offers valuable references for the future development of sports training and intelligent technology.

Art has been a medium of self-expression, evolving with technological advancements. Using physiological signals, biometric painting directly affects the artistic process. By bridging the gap between the artist’s internal emotional state and the visual depiction of the painting, this fusion provides an innovative approach to examining and expressing human emotions. The objective is to investigate biometric painting, integrating biosensor data into the creative process. To expand the creative process of biometric painting by utilizing biosensor data to establish emotion recognition in biometric painting. A biometric painting system was created that used users’ real-time biosensor data to gather visual components that represented their emotional and physical states. The data is preprocessed using a median filter to remove noise from the sensor data. Then, the features are extracted using wavelet transform (WT). The research introduces an Intelligent Remora Optimized Flexible Deep Belief Network (IRO-FDBN) to recognize emotion in biometric painting using biosensor data. The results indicate that the established model outperforms an emotion recognition model. The approach emphasizes the smooth combination of visual and affective feedback, allowing audiences to engage with the artwork on an advanced level. This provides a foundation for incorporating biosensor data into the creative process, advancing artistic exploration and effective content development.

Background: The transformation of physical education in Chinese higher education institutions necessitates empirical evaluation of reformed teaching models’ effectiveness in promoting student health outcomes. This study investigates the longitudinal impact of innovative physical education reforms on students’ physical fitness parameters, emphasizing biomechanical adaptations alongside physiological changes. Methods: A controlled longitudinal study was conducted across three universities in Eastern China, involving 426 undergraduate students (213 intervention, 213 control) over 18 months. Comprehensive physical fitness assessments were performed using standardized protocols, measuring cardiovascular endurance, muscular strength, and body composition. Statistical analyses included repeated measures ANOVA, multivariate regression, and time series analysis. Results: The intervention group demonstrated significantly superior improvements in cardiovascular fitness (ΔVO₂max: +4.8 ± 1.2 vs. +2.1 ± 1.1 mL/kg/min, p < 0.001) and muscular strength parameters. Strong correlations between program participation and fitness outcomes (r = 0.68, p < 0.001) were observed. Longitudinal analysis revealed three distinct adaptation phases: initial rapid improvement, plateau phase, and sustained enhancement. Conclusions: The reformed physical education model effectively enhanced student physical fitness across multiple parameters, with sustained improvements throughout the intervention period. These findings provide empirical support for the implementation of innovative teaching methodologies in higher education physical education programs and highlight the critical role of biomechanical adaptations in understanding the effectiveness of these reforms.

Aerobic exercise is recognized for its multiple health advantages, which include increased cardiovascular endurance, metabolic efficiency, and mental well-being. Aerobic exercise is important for college students because it promotes general physical health and stress management during a pivotal period in their lives. The objective of this research is to analyze and compare the impact of different types of aerobics training on sports injury risk among college students. A total of 235 college students participated in this analysis; they were randomized and separated into four distinct groups: three experimental groups (EG), such as traditional aerobics training, high-intensity interval (HII) aerobics training, and dance-based aerobics training, and a control group (CG) received no aerobics training. The research consumed 8 weeks, with each group completing their allocated training mode two times per week. Self-reports, physical examinations (muscle tiredness, joint strain), and fitness tests were utilized to evaluate the risk of injuries. The data was analyzed using statistical methods and SPPS software. The findings suggest that high-intensity interval aerobics significantly increased fitness; they also increased the risk of injury, especially to the lower limbs. While traditional aerobics training showed modest improvements and a decreased injury rate, dance-based aerobics offered balanced fitness and injury prevention advantages, as well as increased joint mobility and flexibility. The CG demonstrated no significant changes in injury rates or fitness improvements. This analysis emphasizes how crucial it is to customize aerobics programs to each participant’s level of fitness reduce the risk of injury and maximize health benefits.

This research delves into the intricate relationship between cellular and molecular adaptations and their implications in educational advancements among adolescents engaged in sports dance routines within the framework of physical education settings. Utilizing a randomized controlled trial involving 120 participants (with 60 in the experimental group and 60 in the control group), the study spanned a 16-week intervention period. The assessment protocols were comprehensive, covering cellular and molecular indicators (biomechanical properties of muscle fibers, cardiac health markers), as well as educational outcomes (deliberate skill acquisition, academic engagement, and innovation in movement expression). Notably, the experimental group showed marked advancements across various parameters: an increase in cardiac health indicators (peak oxygen consumption, VO2max improvement: 21.3%, p < 0.001), enhanced biomechanical properties (muscle elasticity improved by 50%, muscle strength increased by 45%, p < 0.001), and an improvement in motor skills (technical proficiency rose by 41.5%, coordination by 48.3%, p < 0.001). There was a striking link between these cellular and molecular adaptations and the educational outcomes (r = 0.721–0.845, p < 0.01), which was further confirmed by regression analysis indicating cardiac metabolic fitness as a pivotal predictor of technical dexterity (β = 0.384, p < 0.001). The adolescents in the experimental group also exhibited considerable gains in mastering complex movement sequences (56.7% improvement, integration of performance: 55.3%, p < 0.001) and in academic engagement (motivation increased by 46.2%, collaborative interaction: 47.7%, p < 0.001). A pivotal “adaptive window” identified between the 8th and 12th week of training, suggested the most fruitful times for intervention to yield optimal outcomes. These results provide solid evidence that structured sports dance training is beneficial for both biomechanical development and educational success in adolescents, providing valuable guidance for physical education curriculum design and implementation.

This study aims to explore the application of sports sensors in motion correction within competitive sports, providing both theoretical and practical guidance. With the advancement of technology, sensor technology is increasingly being applied in sports training and competition, aiding athletes and coaches in more accurately analyzing movements, enhancing performance, and reducing injury risks. The research employs both qualitative and quantitative methods to collect and analyze data from athletes during training and competition, examining the roles of sensors in stride length, stride frequency, posture analysis, and real-time feedback. Additionally, through the formulation of personalized training plans and injury prevention strategies, the study showcases the practical effects of sensors in various sports disciplines. The findings indicate that sensor technology significantly improves movement optimization, injury prevention, and performance enhancement. However, the study also highlights challenges in data collection and analysis, suggesting future research to expand data samples and improve analysis models. This research not only offers new perspectives for theoretical exploration but also provides concrete guidance for practical applications, thus holding substantial practical significance.

This study aims to explore the development of interactive motion graphics themed around the “24 solar terms” from a biomechanical perspective, integrating user experience design with biorhythm concepts. Biorhythms refer to the biomechanical changes in humans, animals, and plants in response to different solar terms. The 24 solar terms, a significant part of traditional Chinese culture, not only reflect the ecological behaviors of animals and the biomechanical adaptations of plants to environmental changes but are also closely linked to the biomechanical variations in human physiological states. By incorporating biomechanical principles and bionic technology into motion graphic design, the study seeks to create interactive experiences that are more aligned with biological adaptability, catering to users’ physiological needs and cultural identity. The study begins by analyzing the cultural significance of the 24 solar terms and their impact on human life, revealing their critical connection to the biomechanics of human physiological states, such as dietary habits, sleep patterns, and emotional conditions. Based on these findings, a novel motion graphic design method is proposed, leveraging deep learning techniques grounded in bionic principles (e.g., the VGG-19 model) to monitor and analyze users’ biorhythms in real-time. This enables dynamic adjustments to graphic content and interaction modes, enhancing user immersion and engagement. Furthermore, the study explores how information modeling and reverse engineering techniques can digitize traditional cultural elements and biomechanical characteristics associated with different solar terms, creating interactive graphics that combine cultural depth with biological adaptability. This design not only improves user experience but also provides new perspectives and methods for the preservation of cultural heritage. The effectiveness of the proposed method is validated through analysis in terms of user satisfaction, interactivity, and information transmission efficiency.

With the development of economy, the public’s demand for cultural life is increasing. Sculpture brings a dynamic experience to the urban landscape, enabling people to actively participate and integrate into the landscape space, thus adding to the urban cultural construction and enhancing the variability and depth of space. Based on the principle of bionic design, this paper compares the degree of similarity of the original organisms, and divides the morphology bionic into figurative bionic, abstract bionic, and metaphorical bionic. Combined with the structural stress characteristics, the spatial finite element model is established, and according to D’Alembert’s principle, the vibration equilibrium equation is associated, and the vibration mode superposition method power analysis method is applied to analyze the modal state of a sculpture. The results show that: The horizontal direction of the vibration mode coefficient has reached more than 0.9, the period and frequency of the sixth and seventh order vibration modes are the same, both are 0.3948 s and 2.5699 Hz. Calculating the ultimate bearing capacity of the structure under all the conditions, the maximum stress ratio of the strength of each member of the sculpture is 0.96987, which indicates that the strength of all the structural members meets the requirements of the design of the sculpture. The overall stability of the sculpture is analyzed, when the order is 5, the buckling factor under three kinds of loading conditions are 208.5974, 114.1648, 124.9748, which indicates that the sculpture structure designed according to bionics has good overall stability.

In 2019, a sudden outbreak of novel coronavirus disease swept the world, seriously affecting education and teaching. Most universities around the world have adopted a “learning without classes” model, which is dominated by online teaching. Up to now, this teaching model under the normalization of the epidemic has been popularized all over the world. However, the evaluation of online teaching quality and the weight of influencing factors have become difficult points in measuring teaching quality, that is, the factors and weights affecting online teaching are problems that need to be studied and solved urgently. This study takes online physical education (PE) teaching in Chinese universities as the research object. While traditional research often centers on teaching evaluation methods, this research innovatively integrates biomechanics into the study. By analyzing the relevant research literature, it proposes a weight evaluation method for online PE teaching index in universities based on analytic hierarchy process (AHP)-entropy method-fuzzy comprehensive evaluation method. This method is actually a new teaching evaluation model that combines the subjective and objective weighting method with the fuzzy evaluation method. Integrating biomechanics offers a novel perspective on online PE. It aids in assessing students' exercise effectiveness and optimizing online educational resources. Advanced video analysis and motion capture can precisely measure students' joint angles, limb movement trajectories, and muscle activation patterns during exercises. This enables accurate evaluation of movement standardization. By comparing with optimal biomechanical models, teachers can provide targeted guidance, enhancing exercise effectiveness and reducing injury risks. In teaching video creation, incorporating biomechanical principles helps students understand the scientific basis of movements. For instance, in a basketball shooting tutorial, explaining the arm, wrist, and finger biomechanics can improve students' understanding and performance. Moreover, biomechanical simulation technology can create virtual sports scenes, enriching the online learning experience by allowing students to explore environmental impacts on body mechanics. We applied the method to the weight analysis of online PE teaching index in Chinese universities, and demonstrated that the method has good applicability. More importantly, we have condensed the conclusions of this research into practical countermeasures, and put forward strategies to improve the quality of online PE teaching from the macro level and the subjective research level. This research achieves innovation at the application level, improvement at the theoretical level and focus at the practical level. The research results can help Chinese universities to improve the quality of current online PE teaching, and even provide experience and reference for formulating relevant measures for international online teaching.

Martial arts include various fighting techniques, ideologies, and training programs from different civilizations worldwide. Combining mental and physical training, martial arts can be utilized for sport or self-defense. The research aims to investigate how martial arts training affects joint stability and muscular strength. A total of 146 respondents participated in this study. They are arbitrarily separated into two groups. Group A (n = 76) received martial arts (karate) training, and Group B (n = 70) received standard sports training. All participants underwent pretesting and post-testing focused on physical attributes, upper extremity flexibility, muscle strength, motivational level, balance, and joint stability. The data is analyzed using statistical methods such as correlation analysis, t-tests, and one-way ANOVA to compare pre-and post-training results. The post-training evaluations revealed that the karate group demonstrated significant improvements in joint flexibility and balance. These enhancements in flexibility, motivational level, balance, and strength are critical as they contribute to muscle strength and joint stability, essential for preventing sports-related injuries during growth. This study underscores the importance of martial arts training in developing physical fitness attributes that promote overall musculoskeletal health in children.

In recent years, the digitalization of ancient books has revitalized traditional works and highlighted the “humanistic” dimension of digitalization. This article employs the SikuBERT pre-training model, which is tailored for natural language processing in classical Chinese texts, and forges an innovative connection with the realm of biomechanics, to conduct a comprehensive analysis of spatial verbs in the pre-Qin dynasty text Zuo Zhuan. The analysis encompasses detailed annotation, quantitative analysis, automatic recognition, and evaluation of spatial verbs, culminating in the creation of a digital knowledge base for spatial verbs in Zuo Zhuan. The study identifies four main types of high-frequency spatial verbs in Zuo Zhuan: Motion, state, existence, and direction. To enhance theoretical depth, this classification is grounded in cognitive linguistic theory, which explains the semantic connotations and cognitive basis of each verb type. Motion verbs, are closely linked to the representation of dynamic spatiotemporal contexts, particularly in describing human movement patterns and behaviors, invoking principles from biomechanics such as kinematics and dynamics. These verbs can be integrated with biomechanical concepts like trajectory analysis and mechanical models to understand how ancient humans engaged in dynamic activities within specific spaces. State and existence verbs emphasize static relationships, while directional verbs highlight the guiding nature of movement trajectories, which can be further explored through concepts of displacement and velocity in biomechanics. These verb types interact to construct the spatiotemporal framework of the text, demonstrating how language encodes complex spatial and temporal relationships. Moreover, the study investigates the interactive mechanisms of the four verb types in language use, analyzing how they collectively construct the spatiotemporal context of ancient texts, thereby enhancing our understanding of narrative techniques in pre-Qin literature. For instance, motion verbs often act as the primary drivers of narrative progression, while state verbs provide contextual stability, existence verbs denote presence, and directional verbs guide interpretative focus. Quantitatively, the study examines the characteristics of these verbs from three dimensions: temporal-quantitative relations, behavioral-quantitative relations, and scene-component relations. The findings reveal distinct quantitative features among the four types, with motion verbs exhibiting the highest diversity and quantity. This nuanced exploration not only contributes to the re-interpretation of pre-Qin dynasty texts but also strengthens the ability to deconstruct and analyze digitalized texts from this period, thereby advancing the field of classical Chinese digital humanities. By integrating a biomechanical perspective, this study further explores the application of spatial verbs in describing human movement behaviors, utilizing biomechanical models to analyze the efficiency and postural changes of ancient humans in specific environments, revealing the deep connections between language expression and human activity. This interdisciplinary perspective enriches our understanding of ancient culture and provides a new methodological framework for research in modern humanities.

The goal of this study is to explore the biomechanical mechanism of digital music teaching resources in improving students’ musical expression, and to study how to optimize music teaching effect by means of technical means. By introducing collaborative filtering (CF) algorithm into the field of music education, a individualized teaching resource recommendation system is constructed. The system deeply analyzes students’ learning behavior, interest preference and learning effect, so as to achieve accurate matching of resources. In order to verify the effectiveness of digital teaching resources and recommendation system, a semester-long empirical study was designed and implemented. Select 100 music majors and divide them into traditional teaching resources group and digital teaching resources group. The study focuses on the differences between the two groups in mastering music theory, improving practical skills (especially musical expression in biomechanics) and stimulating learning interest. The results show that the students’ musical expressive power (especially the skills related to biomechanical mechanism) and learning interest in the digital teaching resource group are significantly improved, and the effect is far better than that in the traditional teaching resource group, which proves the great potential of digital teaching resources and individualized recommendation system in music education.

This paper discusses the impact of enterprise digital transformation on employee health management from the perspective of biomechanics, especially the change law of employee physiological response and the underlying mechanism under the new work mode and technology application. By introducing a theoretical framework of biomechanics, this study evaluates the specific impact of changes such as office automation, remote work and the use of smart devices on the physical load of employees, and uses computer modeling technology to predict the potential health risks under different working conditions. In this study, a hybrid model combining Probabilistic neural Network (PNN) and Gated Recurrent Unit (GRU) was used to deal with complex time-series data analysis tasks to improve the prediction accuracy of employee health status. Experimental results show that the proposed PNN-GRU model performs well in the task of health state recognition, especially in fatigue and pain detection, with the accuracy of 94.7% and 97.1% respectively, which is significantly better than other algorithms.

This study explores how the development of digital finance indirectly promotes the biomechanical adaptability and innovation ability of residents’ behavior by influencing consumption structure and payment environment. Data analysis shows that digital finance not only improves the utilization rate of personal financial services, but also generates significant heterogeneity effects among different regions and income groups. These effects can be understood from the perspective of biomechanics, which is the process by which individuals adapt to a rapidly changing technological environment. In biomechanical principles, adaptability is the key to survival. Digital finance encourages residents to optimize resource allocation, enhance consumer flexibility, and improve decision-making efficiency. This adaptation process is similar to the process by which organisms adjust their mechanisms to maintain survival and development in the face of changes in the external environment. The popularity of digital finance enables individuals to respond to market changes more quickly, thereby improving their adaptability in consumption. In addition, entrepreneurial activities and innovation levels serve as mediating variables, further stimulating residents’ adaptability in the digital ecosystem, similar to the mechanism in biomechanical systems that improves survival rates through diversity and innovation. This study reveals the important role that digital finance plays in modern consumer behavior and proposes that it may become a key driving force for the evolution of socio-economic systems and human behavioral biology. By introducing the principles of biomechanics, we can have a deeper understanding of how digital finance shapes changes in individual behavior and social structure, and thereby provide theoretical support for policy making and practice.

The new quality productive forces provide scientific guidance for countries to promote high-quality development and represent advanced productive forces in line with the new development concept. It is an inevitable requirement for adapting to the transformation of China’s economic development stage and a strategic measure to cope with the increasingly fierce international competition. Existing research primarily focuses on the theoretical connotations, characteristics, formation logic, and industrial practices of new-quality productive forces, but lacks comparative analysis and literature reviews of domestic and international studies. Drawing inspiration from the principles of biomechanics, this study delves into the intricate mechanisms underlying the rise of these advanced productive forces, aiming to unravel their potential to empower sustainable economic development. Akin to the dynamic interplay of form, function, and adaptation observed in biological systems, the new-quality productive forces embody the harmonious integration of scientific guidance, technological innovation, and market-driven optimization. Just as the human body's musculoskeletal system leverages the principles of force transmission and load distribution to achieve efficient movement, these productive forces harness the synergistic power of knowledge, technology, and market forces to drive economic progress. This paper employs the CiteSpace knowledge map tool to analyze the publication volume, author collaboration networks, keyword clustering, timelines, and emergent words from relevant literature in the core databases of CNKI and Web of Science. Drawing inspiration from biomechanics, the study highlights the importance of balancing the top-down and bottom-up forces that govern the formation and transformation of new-quality productive forces. Much like the human body's ability to adapt to changing environmental conditions, the successful integration of these productive forces into the economic landscape requires a delicate interplay of strategic planning, technological innovation, and market-driven optimization. By aligning the insights from this research with the principles of biomechanics, the study offers a unique perspective on the sustainable development of the economy. Just as biological systems exhibit elegant and efficient mechanisms to harness energy and resources, the new-quality productive forces hold the potential to empower countries to navigate the increasingly fierce international competition and achieve long-term, high-quality economic growth.

The computational complexity of graphic processing algorithms is increasing under the continuous development of information technology. At the same time, the medical product design field puts forward higher and higher requirements on the visual communication effect of related images. In this study, the up-sampling optimization algorithm and the threshold filtering algorithm are proposed to optimize the Laplacian graphics processing algorithm. Notably, in the threshold filtering algorithm, the Triangle algorithm is employed to address the grayscale of images pertinent to medical product design. The perception of the grayscale and binarized images by the human visual system triggers neural signals that propagate and can influence intracellular processes. When observing medical product images, neurons fire, leading to the release of neurotransmitters like glutamate. These neurotransmitters bind to receptors on cells, initiating signaling cascades such as the MAPK pathway. This pathway can affect gene expression and protein synthesis, potentially modulating cellular functions related to perception and response to the medical product design. The results show that the optimized graphical processing algorithms in this paper outperform the comparison algorithms in the CLBLAS library, and the floating-point computational values of the Laplacian algorithm are much higher than the comparison algorithms in the face of the large-scale input parameters. The Laplacian algorithm is able to accurately stitch and process the captured 2D images of cellular microtubules related to the design of the medical products in a guaranteed high efficiency (31 min), and The Laplacian algorithm was able to achieve an average subjective score of 0.856 for the visual communication of medical product design images. This graphic processing algorithm can generate images of superior perceptual quality, which holds substantial significance for augmenting the visual communication effect of medical product design images and considering the underlying cellular molecular biomechanical responses.

Traditional sports information processing methods often rely on manual observation and recording. This method is not only inefficient, but also susceptible to subjective bias, which affects the accuracy and reliability of the data. In this paper, a sports information processing and physiological condition monitoring system based on multimedia computer is constructed, which deeply integrates multimedia technology, computer technology and biomedical sensing technology. Through the integration of advanced multimedia processors and a variety of biosensors, the system can collect, process and analyze the physiological data and sports trajectory information of athletes in sports events in real time, so as to achieve comprehensive and accurate monitoring of the status of athletes. Using data compression algorithms, each byte can store two bits of data, reducing the space occupied by system operation. In terms of functional implementation, this system not only provides a user management module to ensure the security authentication of user identity, but also is equipped with a sports information data analysis module, which can provide users with scientific training guidance and optimize training plans. Experiments show that the system constructed in this article is functionally tested and all functions meet the design expectations; within 500 m, the packet loss rate of the system is 0; when the number of users reaches 1200, the response time of this system is 3.62 s; under low-intensity exercise and high-intensity exercise, the average accuracy of monitoring and early warning of users in this system is 90.44% and 95.11% respectively. The sports information processing and monitoring system can not only accurately and quickly collect and process various sports information, but also monitor and analyze the physiological data of athletes with high precision.

Intelligent decision-making in dynamic recommender systems is crucial for capturing temporal user preferences and optimizing long-term user satisfaction. Traditional recommender systems often rely on static modeling, neglecting the temporal dynamics of user-item interactions. To address this limitation, we propose a novel framework, Temporal Interpretability Graph Neural Network with Reinforcement Learning (TIGNN-RL), which integrates dynamic graph neural networks (DGNNs) and Proximal Policy Optimization (PPO) to optimize personalized recommendations. Specifically, our method models user-item interactions as dynamic graphs and utilizes temporal interpretability modules to encode both temporal features and node-specific static features. The temporal interpretability module assigns time-aware and interactions weights to user-item, enabling more time-sensitive and explainable dynamic embeddings. This TIGNN dynamic graph sequential embedding is processed by some LSTM modules to be used as the state of the deep reinforcement learning agent and states. We take a joint approach to training, earn graph embeddings that enable better PPO policy. To evaluate the proposed framework, we conduct experiments on three benchmark datasets: Last.fm 1K, MovieLens 1M, and Amazon Product Review. Results show that TIGNN-RL outperforms state-of-the-art baselines, which use GNNs for augmenting DRL-based RS, in terms of accuracy (NDCG@K) and diversity (ILD@K@K), demonstrating its effectiveness in dynamic and interpretable recommendation scenarios. In this research, some biomechanics knowledge is integrated to further enhance the understanding and application of the proposed framework in scenarios where user behavior is influenced by physical factors.

Effective sports teaching entails a deep consideration of biomechanics, which helps instructors and coaches improve athletes’ performance and reduce damage hazards. This study examines the integration of biomechanical philosophy in sports education to optimize teaching tactics for youth athletes. The primary aim is to evaluate how the biomechanical approach in sports teaching impacts the performance and skill acquisition of athletes, particularly persons exhibiting suboptimal force profiles. A randomized sample of 89 students participated in the intervention, separated into an experimental group receiving biomechanical training and a control group undergoing traditional physical education. Biomechanical analysis performance is employed to evaluate modification in performance variables, focusing on anaerobic power and sprinting mechanics. This study aims to address a specific deficiency in athletes’ force-velocity profiles, thereby enhancing their mechanical output during sprints. Paired t-tests are used in statistical analysis to assess the outcomes before and after the intervention, grouping comparisons, and performance outcomes of ANOVA. The conclusion discovered significant improvements in the experimental group, particularly in maximal horizontal force and sprint performance, with p < 0.01, indicating a strong impact of biomechanical training on athletic capabilities. The results suggest that incorporating biomechanical insights into sports teaching can significantly enhance the performance of youth athletes, making an important strategy for educators and coaches aiming to improve physical education outcomes.

The evaluation of corporate financial performance plays a critical role in driving enterprise transformation and fostering industrial development. To enhance the accuracy of financial performance evaluation, this study integrates knowledge from biomechanics and bioinformatics, exploring the application of a bio-inspired immune algorithm-optimized convolutional neural network (CNN) in financial performance evaluation. A biomechanics-based model is constructed using CNN to simulate the “mechanical response” of financial performance evaluation. By simulating the structure of biological visual systems, CNNs can effectively extract local features from input data, enabling efficient classification and recognition. During the optimization process, the biological immune algorithm adjusted hyperparameters such as the learning rate and kernel size through mechanisms of selection, reproduction, and mutation. The application of biologically inspired algorithms in deep learning effectively enhanced the model’s adaptability and robustness, providing new ideas and methods for financial performance evaluation and validating the effectiveness of bionic algorithms in complex tasks. In the experiments, a GRA-Entropy-SOM-CNN model was constructed, with initial test results showing an accuracy of 97.18% in the task. However, by introducing the biological immune algorithm to optimize the CNN, the final model achieved an accuracy of 98.5% on the test set, demonstrating significant performance improvement.

The necessity for an effective and sustainable curriculum reform has grown in importance since ice and snow sports have advanced so quickly. In order to maximize the development and delivery of ice and snow sports curriculum, this study investigates the integration of innovation and entrepreneurship education within the biomechanics framework. The study suggests using intelligent systems, like fuzzy c-means (FCM) and optimal clustering algorithms, to improve the assessment and advancement of ice and snow sports instruction from the perspective of biomechanics. This study promotes a comprehensive, data-driven approach to curriculum design that places a high priority on sustainable growth by assessing the state of information technology in ice and snow sports schools today and looking at the influence of biomechanical factors on athletic performance and injury prevention. For example, through advanced sensor technologies, coaches can precisely measure parameters like the force exerted on skis or skates, the angular velocities of joints during turns, and the balance dynamics of athletes. This data, when integrated with biomechanical principles, enables the customization of training programs. By implementing the suggested intelligent transformation model in Shenyang’s ice and snow physical education programs, the study further assesses its effectiveness and shows notable gains in both athlete performance and the instructional framework. In order to promote long-term growth and sustainability in the sector, this research combines cutting-edge technologies, biological principles, and creative teaching methodologies related to ice and snow sports education. It focuses on how biomechanical insights can drive curriculum innovation, ensuring that athletes not only master the skills but also minimize the risk of overuse injuries and enhance overall athletic efficiency in the challenging environment of ice and snow sports.

The application of neural network methods, especially convolutional neural networks (CNNs), has led to significant advances in machine translation technology. CNNs, inspired by the hierarchical organization and functional principles of biological systems, akin to how biomechanical structures adapt and respond, are able to effectively solve problems such as remote dependency and contextual nuances in language tasks, thus improving translation quality. In this study, multilayer CNN is introduced into neural machine translation (NMT), which significantly improves the BLEU score on the Chinese-English translation dataset. The optimal structure is a 6-layer CNN with 3 × 1 convolutional kernel, which performs well in context understanding. In terms of theoretical background, theories related to biological neural networks provide important insights. For example, biological neurons process information in a hierarchical structure to achieve decomposition and comprehension of complex tasks through feature extraction at different levels. CNNs mimic this biomechanically-inspired mechanism in language processing, employing convolutional layers to distill local traits and amalgamate them into comprehensive global knowledge. By exploring the successful mechanism of CNNs in language processing, this paper further reveals the transformative potential of neural hierarchical structures in computational linguistics, and opens up new paths for realizing more natural and accurate translation.

With the rapid advancements in sports science and athletic training, the integration of biomechanics and information technology has driven the development of innovative theories and practices in sports training. Traditional training methods, which lack a scientific foundation, are increasingly seen as ineffective. In contrast, the biomechanical-based sports training model proposed in this study offers a theoretical framework for precisely enhancing athletes’ performance. This model addresses several critical issues, including limited equipment adaptability, the lack of universal principles across various sports, and the challenge of tailoring training models to individual needs. To overcome these challenges, the study introduces a novel, biomechanical-based sports training model, validated through empirical research. The model is supported by a biomechanical data collection system built using multi-source sensor fusion technology, which ensures adaptability to complex training environments. This system gathers kinematic, kinetic, and electromyographic data from athletes during key activities such as double-legged downward longitudinal jumps and all-out acceleration runs. Devices like the VICON infrared camera system, a three-dimensional force measuring table, and a surface electromyography tester provide high-quality data essential for model development. Furthermore, the deep learning algorithm used in the model enhances the understanding of common principles across different sports. The model incorporates optimal designs for customized parameters to address various training needs. The empirical research employs a randomized controlled trial, dividing participants into experimental and control groups. After eight weeks of training, the model’s stability and applicability across different sports are confirmed. The experimental group’s training program is designed with a multi-phase approach, which includes injury prevention, targeted training, and recovery stretching, providing comprehensive support to athletes. The study’s findings show that the biomechanics-based sports training model significantly improves training effectiveness and fosters the integration of theoretical and practical aspects of competitive sports and sports science. This research serves as a crucial reference point for the future development of sports training models, highlighting the importance of scientific foundations in optimizing athletic performance.

Yunnan’s wetland ecosystems are essential for ecological services like water conservation and biodiversity sustenance. Analogously to biological systems in biomechanics, they are subject to diverse forces. Here, natural and anthropogenic factors act as external stimuli. Utilizing multi-source data, an evaluation index system for ecological service functions was established, similar to characterizing the biomechanical properties of an organism. Analyzing wetland dynamics and traditional plant resources is comparable to studying the structural and functional alterations of a biomechanical entity. The growth in wetland area and vegetation coverage can be regarded as a response to favorable biomechanical conditions, with the water conservation function as a crucial biomechanical attribute maintaining the system’s stability, much like a key structural element in a biological tissue. However, agricultural pollution and climate change pose challenges, acting as adverse biomechanical stressors. Agricultural pollution is like a harmful agent disrupting the normal biomechanical processes, and climate change resembles a fluctuating external force. To address these, strategies are proposed. Enhancing ecological compensation is similar to providing supplementary biomechanical energy to repair and strengthen the system. Optimizing land use structures is akin to adjusting the spatial organization of biomechanical components for enhanced efficiency. Improving management policy execution is like strengthening the regulatory biomechanical mechanisms. Through these, sustainable management of wetland resources and the enhancement of ecological service functions can be achieved, similar to restoring and optimizing the biomechanical health and functionality of a living system, ensuring the long-term viability and performance of Yunnan's wetland ecosystems in the face of complex environmental pressures.

Teachers’ mental health and general well-being have been negatively impacted in recent years by the increasing stress they experienced as a result of several difficulties in both their personal and professional lives. Teachers’ psychological stress is a crucial area for intervention since it results in burnout, decreased teaching effectiveness, and other health problems. However, there is still an abundance of research on the application of innovative technologies to track and manage teachers’ mental health. This research suggests using deep learning (DL) techniques like the Intelligent Bottlenose Dolphin-Inspired Feed Forward Neural Networks based Teacher Psychological Monitoring and Intervention Model (IDBI-FFNN-TPMIM) combined with biosensor technologies. This model offers a novel method for determining mental stress levels, identifying early indicators of burnout, and classifying emotional states as neutral, negative, or positive using biosensors like EEG and biomechanical data. Using feature extraction approaches, the model properly depicts the physical and emotional states of teachers, allowing for automatic classification and feedback for prompt interventions. According to experimental data, Biosensor-based IDBI-FFNN-TPMIM results are F1-score at 91.1%, accuracy at 93.7%, recall at 91.5%, and precision at 92.3%. While performing well in psychological monitoring and emotion recognition while achieving high prediction accuracy. These findings demonstrate how biosensor technology is employ to improve overall well-being and strengthen programs for teachers’ mental health care.

Gut microorganisms have become a hot spot of scientific research at home and abroad in recent years, in which the study of correlation between microbial community structure and intestinal diseases can provide theoretical basis for biopharmaceuticals for intestinal diseases. In this paper, we constructed in vitro simulated gastric and small intestinal digestion models, as well as large intestinal microbial fermentation models, to study the relative molecular weight and spatial structure changes of β-glucan in simulated gastric and small intestinal regions, and investigated the degradation of β-glucan in simulated large intestinal regions as well as its effects on intestinal microorganisms. In addition to the biochemical and metabolic aspects, integrating biomechanical principles into this research can enhance our understanding of how gut microbes interact with the host’s physiological environment. For instance, the biomechanical properties of the gut—such as motility, peristalsis, and the mechanical forces exerted on microbial populations—can influence the distribution and activity of gut microorganisms. Understanding these biomechanical factors may reveal how they affect the degradation of β-glucan and the overall microbial community structure. Secondly, fecal microorganisms from different batches of mice and different individuals of human volunteers were collected as inoculum for fermentation of β-glucan, to analyze the main microorganisms that stably responded to β-glucan in different batches of fermentation experiments as well as in gut microorganisms from different individuals and to further investigate the metabolic changes, metabolic pathways as well as the biomarkers of β-glucan in the simulated large intestine. L. murinus Mic06, L. murinus Mic07, L. murinus Mic08, and L. murinus Mic094 were validated to be able to utilize β-glucan and produce a small amount of reducing sugars in all four species of Lactobacillus intestinalis in mice, and there was no significant difference in the ability to utilize them; All nine species of human enterobacteriophages were able to utilize β-glucan and produce reducing sugars, with B. xylanisolvens Bac02 and B. koreensis Bac08 having a significantly greater ability to utilize β-glucan. This study contributes to a deeper understanding of gut disease-associated flora and provides strong support for the use of the gut microbiome for multidisease classification. Additionally, considering biomechanical aspects may lead to novel insights into the interactions between gut microbes and host physiology, enhancing the development of targeted biopharmaceuticals.

The application of games in sports not only brings new development to sports, but also brings new requirements to sports. To maintain and enhance students’ learning motivation and interest, more effective individualized teaching is needed. This study focuses on students’ individualized knowledge structure, tracking the differences in sports skills among different types of students, and designing a sports game model based on sports games. A Bayesian network model was used to model learners’ knowledge and establish an adaptive badminton competition mode. Then, a new feature intersection method is studied, and a method for deep knowledge tracking is established using feature embedding and attention mechanisms. Finally, on this basis, this method is combined with adaptive learning methods to establish a badminton game model based on adaptive learning and improved deep knowledge tracking. Additionally, this study explores the biological principles underlying sports skill learning. When students learn badminton skills. When students learn badminton skills, the body’s proprioceptive system constantly provides feedback. This biological feedback is vital as it helps students adjust their movements unconsciously. Motion capture technology can capture the kinematic data of students’ badminton movements, such as joint angles and limb velocities. By integrating this data with the biological feedback, we can optimize learning outcomes by enabling more precise identification of skill deficiencies and more effective remediation. The experiment shows that the acceleration Z is the maximum, approximately 100. The acceleration of X is the smallest, approximately between −200 and 200. The most unstable is the acceleration Y. After positive compensation, the value of the adaptive quantization parameter cascade algorithm increases. And the quantized adaptive quantization parameter cascade algorithm value does not have a significant impact on the evaluation of the reconstructed image. The average values of each scale and sub dimension are above 0.70, and the constituent reliability values are above 0.90, indicating that the internal quality of each scale is good. And the internal consistency between questions is also good, all of which have passed the validity test. The survey method used in this experiment has strong practicality and can effectively achieve game design, which has great practical value in teaching practice.

Injury detection plays a critical role in minimizing athlete downtime, ensuring safety, optimizing performance, and preventing long-term physical or mental consequences. In college sports, effective injury prevention and detection strategies enhance athlete safety, support peak performance, reduce healthcare costs, and contribute to sustainable athletic development programs. This research evaluates the application of biosensor technology in identifying injury risks, monitoring physiological metrics, and enhancing preventive strategies in college sports training to improve athlete performance and safety. A novel model, Egret Swarm Search-driven Scalable Deep Convolutional Neural Network (ESS-SDCNN), addresses the limitations of traditional approaches by combining SDCNNs with ESS algorithm for optimized feature selection, hyper parameter tuning, and real-time adaptability. Suitable data for injury detection and prevention include real-time physiological readings, motion sensor data, activity patterns, and injury records, with a focus on wearable technology. The Z-score normalization ensures consistent feature scaling. Independent Component Analysis (ICA) is used to extract hidden components from sensor data for improved feature representation. The SDCNN efficiently processes high-dimensional biosensor data, extracting spatial-temporal patterns related to injuries. The ESS algorithm further optimizes feature selection and hyper parameters, enhancing model accuracy, robustness, and adaptability for real-time applications. Results demonstrate that the hybrid ESS-SDCNN model significantly improves injury detection accuracy, enables faster convergence, and provides real-time monitoring and prevention insights. This approach enhances athlete safety, supports injury prevention, and fosters better performance outcomes in college sports training programs.

This work explores the biomechanical characteristics of key actions in fencing techniques using motion capture and biomechanical analysis technology, aiming to provide scientific evidence for athlete training and performance. The work combines eight infrared high-speed cameras with the Delsys surface Electromyography system for synchronized analysis, making an innovative contribution to the biomechanical research of fencing techniques. This technological combination allows for more precise tracking of an athlete’s three-dimensional movement trajectories and muscle activation, and offers new perspectives and more accurate guidance for training. The results are as follows. (1) During the forward lunge step, the integrated electromyographic activity of the deltoid muscle significantly increases (152.55 µV·s, p = 0.045), indicating a higher demand for arm stability in this movement. There are no significant differences in the activation levels of the biceps brachii and triceps brachii. The activation of the forearm muscles, specifically the extensor carpi radialis longus and extensor carpi radialis brevis, is significantly enhanced, at 81.61 µV·s (p = 0.047) and 98.72 µV·s (p = 0.049), respectively. For the lower limbs, the activation of the tibialis anterior muscle significantly increases (110.34 µV·s, p = 0.000). The activation of the gastrocnemius medialis and gastrocnemius lateralis also significantly enhances, with values of 53.22 µV·s (p = 0.001) and 35.75 µV·s (p = 0.000), respectively. The contribution of the deltoid muscle significantly increases to 31.2%, while the tibialis anterior muscle contribution increases to 26.5%. (2) The work also compares muscle activity, movement characteristics, and biomechanical parameters across athletes of different skill levels (beginner, intermediate, and advanced). The results show that the beginner group has the highest electromyography activity intensity (45.2 ± 5.1 µV), while the advanced group has the lowest (32.5 ± 3.8 µV). The movement trajectory stability is 12.3 ± 2.1 mm/s for the beginner group and 6.5 ± 1.2 mm/s for the advanced group. These results suggest that advanced athletes exhibit higher training effects in muscle activation efficiency and energy economy. These findings provide important theoretical support for optimizing fencing training methods and improving athletic performance.

Purpose: This study explored the clinical effect of acupuncture and moxibustion therapy, cupping therapy, and Zhuang medicine thread moxibustion therapy on cell and molecular biomechanics within the context of sports pains and injuries. Method: Divided athletes who experienced pain and injury symptoms during training into two groups and received traditional Chinese medicine treatment and conventional therapy respectively, and compared the therapeutic effects; The visual analogue scale was used to evaluate pain level, diagnose pain and injury severity, and evaluate efficacy. Result: Comprehensive therapy had excellent efficacy for mild patients, and moderate-to-low intensity training could be performed during recovery. The traditional Chinese medicine treatment group’s efficacy in delayed onset muscle soreness, mild injury, and severe injury was significantly better than that of the conventional treatment group (P ≤ 0.05). Conclusion: The amalgamation of these therapies engenders an integrated effect of treatment effect. It may potentially modulate cell and molecular biomechanics by, for instance, influencing the mechanical properties of cells and extracellular matrices. This could rectify aberrant biomechanical signaling associated with injury, enhance cell adhesion and migration for tissue repair, and optimize the mechanical milieu conducive to tissue regeneration. It clears blockages, unblocks meridians, alleviates pain, and promotes the proper circulation of Qi, blood, and body fluids. The comprehensive therapy is characterized by simplicity, manageability, and effectiveness. The integration of traditional Chinese and Western medicine can more efficaciously address campus sports injuries, foster the healthy progression of school sports, and capitalize on the unique prerogatives and functions of traditional Chinese medicine in the domain of cell and molecular biomechanics.

This study explores the integration of artistic expression of microbial forms with modern painting techniques, focusing on the application of digital art tools in the reproduction of microscopic structures. Through the use of computer-aided design, 3D modeling, and virtual reality technology, we accurately present the details, dynamics, and textures of microscopic organisms. Incorporating insights from biomechanics, the study explores how the physical characteristics and movement patterns of microorganisms can inform and enhance artistic representation. Biomechanics, the study of the mechanical aspects of living organisms, provides a unique lens through which artists can understand the fluidity and complexity of microbial life. By observing the locomotion and interaction of these organisms within their environments, artists can create more dynamic and realistic portrayals that capture the essence of microbial behavior. Drawing on knowledge of how microbes move, interact, and adapt in their minuscule habitats, as gleaned from biomechanics, we are able to animate our digital models in a more biologically plausible manner. For example, mimicking the undulating motion of cilia or the elastic deformation of cell membranes under stress imparts a new level of authenticity to our artistic reconstructions. Experimental results show that an increase in drawing resolution significantly enhances the fidelity of details and artistic effects. When texture details reach 100 ppi, the artwork’s expressiveness scores 9 points. Additionally, as the style fusion coefficient increases to 0.7, both the accuracy of the artistic style and the visual impact are notably improved. These findings suggest that the application of digital tools not only provides innovative expressive possibilities for the artistic creation of microbial forms but also allows for the exploration of their biomechanical characteristics, such as elasticity, motility, and response to external forces. By incorporating biomechanical principles into the artistic representation of microorganisms, this research opens new avenues for understanding how these organisms function and interact within their ecosystems. The fusion of art and biomechanics can lead to a deeper appreciation of the complexity of life at the microscopic level and inspire future studies in both scientific and artistic domains.

Individuals’ psychological states greatly influence how they participate and react to educational processes, especially when it comes to ideological and political education. Ideological and political education (IPE) is an essential component of educational systems that aims to develop a sense of national identity, social duty, and confidence in students. This research explores the application of biosensor technologies to analyze students’ psychological states within the context of ideological and political education. Students’ lifestyles and stress levels often lead to psychological issues, but conventional IPE techniques lack real-time, individualized data for effective mental health treatment. This research introduces a model, Efficient Osprey Optimized Dynamic Long Short-Term Memory (EOO-DLSTM), to identify the psychological state for Ideological and Political Education utilizing biosensing technologies to assess students’ stress and emotional states in real-time. The model uses biosensors to collect real-time physiological data that reflects the psychological state of students. The data was preprocessed using a Gaussian filter to remove noise from biosensor data. Power spectral density (PSD) is used to extract the features from preprocessed data. EOO is used to optimize and select the feature from the biosensor, and DLSTM can be employed to identify the psychological state. Based on experimental findings, the model can accurately identify the psychological states of students, including information about their stress levels and emotional involvement. The proposed EOO-DLSTM outperforms the existing systems such as Accuracy (95.32%), Precision (93.97%), Recall (96.18%), and F1 score (97.62%). The EOO-DLSTM model surpasses traditional models through the utilization of advanced optimization techniques for enhanced accuracy in recognizing psychological states from biosensor data. It is effective in handling overlapping features and complex temporal dependencies, thus being very suitable for real-time monitoring of mental health. The approach emphasizes how biosensing technologies can be used in educational frameworks to support students’ overall development.

This study explores the biomechanical mechanisms and long-term effects of sports intervention on improving the intrinsic capacity of elderly people. Through experiments and model simulations, the effects of sports intervention on muscle strength, gait stability, joint mobility, fall risk, physical function, and mental health of elderly people are evaluated. The experimental results showed that the experimental group showed significant improvement in various indicators after intervention, especially in muscle strength (38.4 ± 5.2 Nm increased to 45.6 ± 4.8 Nm, p < 0.05), gait stability (decreased from 45.3 mm to 35.1 mm, p < 0.05), joint range of motion (increased from 102.1 ± 8.3° to 114.3 ± 7.5°, p < 0.05), and fall risk (decreased from 27.3% to 12.3%, p < 0.05). In contrast, the control group did not show significant changes. The SF-36 physical function score also significantly improved (from 47.2 ± 6.3 points to 63.5 ± 5.6 points, p < 0.05), and the mental health score increased from 56.4 ± 7.1 points to 70.1 ± 6.3 points (p < 0.05). The simulation analysis results are highly consistent with the experimental data, and the model simulation has small errors in predicting muscle strength, gait stability, and other aspects compared to the actual experimental data, further verifying the effectiveness of the exercise intervention. In summary, this study indicates that sports intervention can effectively improve the physical function and mental health of elderly people by improving biomechanical indicators such as muscle strength, gait stability, and joint mobility, thereby reducing the risk of falls and enhancing their quality of life. The high consistency between model simulation and experimental data provides scientific basis and technical support for future health interventions for the elderly.

This study explores the intersection of biomechanics and sustainable animal husbandry, with a focus on optimizing animal health and productivity to promote ecological breeding practices. By integrating biomechanical principles with ecological breeding strategies, we aim to enhance both farm efficiency and environmental sustainability. Through an in-depth analysis of the mechanical forces involved in animal movement, posture, and interactions with their environment, we seek to design systems that improve animal welfare and reduce stress, which in turn enhances productivity. We emphasize the role of biomechanics in creating more efficient feeding systems, ergonomic housing, and transportation methods, all of which contribute to reducing injuries and improving overall livestock management. Moreover, we propose that biomechanical models can be applied to farm operations to optimize both animal health and ecological balance. This interdisciplinary approach not only improves animal welfare but also promotes sustainable farming practices that align with environmental conservation goals. By integrating animal biomechanics with ecological breeding techniques, this research highlights the potential for more efficient, sustainable breeding practices that support both economic growth and ecological preservation, thus advancing the long-term goals of sustainable development in animal husbandry.

Spinal cord injury (SCI) is a severe functional impairment of the limbs caused by direct or indirect external factors. The consequences of SCI can be extensive, encompassing the partial loss of sensation, movement, and sphincter function. This is attributable to the loss of neurons, an inflammatory response, and immune cell infiltration. SCI can result in long-term physical and psychological harm to patients, as well as imposing a significant economic burden on the entire society. Mesenchymal stem cells (MSCs) are a type of pluripotent stem cell that exhibits both self-renewal and multipotent differentiation abilities. They possess the anti-inflammatory properties, immunoregulatory abilities, and the capacity for neural and vascular regeneration ability, collectively rendering them a promising candidate for the treatment of SCI. In this review, we provide a comprehensive overview of the preclinical and clinical applications of MSCs in SCI and the underlying mechanisms, discuss the limitations of MSC application in SCI and the possible solutions, hope to provide some useful insights for the scientists.

Emotions have an important role in English language learners (ELL), impacting motivation, engagement, and performance. Understanding how emotions influence ELL performance is critical for creating effective instructional programs. Previous research has explored emotional reactions utilizing self-reports or behavioral observations, but only a small amount of research has used biosensing technology to offer objective, real-time data on emotional states. The purpose of this research is to examine English learners’ emotional responses using biosensing measurements to investigate the psychological mechanisms supporting these responses and offer knowledge about how emotions influence language learning. The investigation involved 150 English language learners. Biosensing devices were used to evaluate physiological indicators such as heart rate variability, skin conductivity, and facial expressions while students completed English language tasks. Furthermore, psychological mechanisms were investigated using questionnaires and interviews, yielding qualitative insights into how learners experience and handle their emotions during the learning process. Statistical analysis, such as regression, descriptive, correlation analysis, ANOVA, and chi-square statistics, was used to investigate the association between emotional responses and language learning efficacy. The findings show a strong association between good emotional reactions and better language learning outcomes, whereas negative emotions, such as anxiety, are associated with lower engagement and performance. Finally, this approach emphasizes the relevance of understanding emotional dynamics in language learning and the necessity of developing methods to enhance positive emotional involvement.

The continuous decline in China's birth rate has profound implications that intersect with biophysical and mechanistic aspects relevant to biomechanics. This article commences by scrutinizing the alterations in China's birth rate over the past decade. The diminishing birth rate, underpinned by biological factors, has a direct bearing on the quantity of children enrolling in kindergartens. This, in turn, instigates modifications in the requisition for preschool education institutions and educators. From a biomechanical vantage point, the reduction in the number of children alters the physical and mechanical environment within preschool settings. For instance, the collective body mass and force distributions during play and physical activities change. With fewer children, the forces exerted on play equipment and the floor surfaces vary, potentially affecting the wear and tear patterns and the biomechanical feedback that the environment provides to the children. The diversity in body sizes and physical capabilities among a smaller cohort of children also demands a reconsideration of the biomechanical suitability of furniture and teaching aids. Based on the biological metamorphoses in the birth population, data analysis prognoses an impending oversupply of preschool education resources in China in the forthcoming years, especially in the eastern region. Conversely, the central and western regions, along with the urban-rural divide, are anticipated to face a dearth of resources. These disparities not only pertain to the quantity but also to the biomechanical adequacy of the resources. Different regions may have children with varying genetic predispositions and environmental exposures that influence their biomechanical development, thereby necessitating region-specific resource optimization. The biological shifts in birth rates thus levy more exacting requisites on education policies, resource optimization, and the equilibration of regional education. To surmount this obstacle, this article proffers bespoke policy recommendations factoring in biophysical and biomechanical considerations. This includes calibrating resource allocation to harmonize with the biomechanical idiosyncrasies of children in diverse regions, augmenting the biomechanical relevance and quality of education in rural areas, and fortifying policy guidance to actualize the judicious utilization and sustainable progression of education resources that are conducive to the healthy biomechanical development of children.

Standardized English language and efficient translation techniques are required to guarantee global usability and cultural relevance because of the growing dependence on biosensors in communication technologies. In particular, the research aims to understand the role of biosensors, particularly wearable sensors, in enabling communication in other cultures, like using a sign language (SL) translator system. The information would be recorded through motion sensors designed to determine hand movements and gestures based on any recorded movement. Raw data would need bandpass filtering of both extraneous artifacts and the surrounding noise. Visual Geometry Group 16 (VGG16) is applied for feature extraction from biosensor data. A novel Adjustable Moth Flame-Tuned Efficient Recurrent Neural Network (AMF-ERNN) transformer model that is used to achieve translations from sensor signals to text sentences is introduced. AMF is used to select and optimize the features of an ERNN model to enhance its efficiency and performance while translating sensor signals to text. Results show that the suggested model outperformed traditional algorithms by achieving accuracy (98.5%), recall (98.3%), precision (98.2%), F1-score (98.4%), and WER (35.5%). These results demonstrate the way biosensors can promote accurate and culturally aware translations. The study concludes by emphasizing the importance of English terminology standardization to enhance the accessibility and effectiveness of biosensor-based translation systems across diverse cultural contexts.

To promote and strengthen the scientific nature of sprint training, this study investigates the interplay between biochemical indicators and biomechanical responses during athlete training using a Zigbee-based health monitoring system. This system conducts real-time health monitoring of sprinters. The system uses sensors to collect human physiological signals and transmits them to the monitoring center through wired or wireless methods. The human motion recognition algorithm is used to establish a state space model of human body acceleration, and dynamic modeling methods and Kalman filtering for precise estimation and analysis of the athlete's biomechanical state. significant differences in the biochemical indicators of sprinters at different training stages, highlighting how these changes correlate with biomechanical responses such as force production, joint angles, and movement patterns. This research underscores the importance of integrating biomechanical assessments with biochemical monitoring to provide a comprehensive understanding of athletes’ physiological status and optimize training regimens. The insights gained from this study contribute to the fields of biomechanics and mechanobiology, offering valuable implications for improving athletic performance and health monitoring.

Wushu, as a type of exercise nurtured in traditional Chinese culture, not only has profound cultural significance, but also possesses the physiological role of exercise. In order to investigate the effects of wushu exercise on human muscle fiber types and athletic ability, the study was based on the effects of exercise on skeletal muscle, and the cell biological mechanisms of skeletal muscle contraction during wushu exercise were investigated. The experimental subjects were modeled and grouped by designing experiments. The muscle fiber ratio, the ratio of fast and slow muscle fibers of gastrocnemius muscle and the cross-sectional area were collected by polyacrylamide gel electrophoresis, immunofluorescence staining and protein blotting in order to analyze the changes in the muscle fiber types of the subjects before and after the wushu exercise. Then, the effectiveness of martial arts exercise in enhancing athletic ability was investigated by comparing the intergroup and intragroup athletic ability before and after the experiment between the experimental group and the control group. Wushu exercise had an interaction effect on the ratio of fast and slow muscle fibers, the expression of PGC1α4 and PGC1α2/3, the expression of PPARδ, PDK4, and the protein expression of mitochondrial complex, and it did not have an interaction effect on the protein expression of P38MAPK/P38MAPK and P-AMPKα/AMPKα. Before the experiment, the motor ability of the experimental group and the control group was at the same level (P > 0.05), and after the experiment, the motor ability of the experimental group was much higher than that of the control group, and the P value of each dimension was less than 0.05. After the wushu experiment, the motor ability of the experimental group was greatly improved, and the P value of each dimension was less than 0.05, while the control group stayed at the same place, and the P value of each dimension was greater than 0.05. Wushu exercise could effectively improve the subject’s motor ability.

This study investigates the evolution of research at leading agricultural universities, with a particular focusing on genetics of agriculture and biomolecules as central themes. The objective is to identify trends, knowledge evolution pathways, and the relationship between scientific innovation and technological application. Utilizing the BERTopic model, a word-embedding-based topic extraction approach, the study analyzed data from cited articles and citing patents sourced from Web of Science and Lens databases. Key methodologies included advanced text preprocessing, topic clustering using Uniform Manifold Approximation and Projection (UMAP) and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), and knowledge evolution analysis based on topic heat and cosine similarity metrics. The findings indicate that research on the genetics of agriculture and biomolecules play a critical role in driving both fundamental science and application-oriented innovation. A strong correlation between cited articles and citing patents was observed, particularly at institutions such as the University of Tokyo and Kyoto University. Notably, genetics-related scientific outputs were associated with denser knowledge networks, while biomolecule-focused patents demonstrated more pronounced application trends, highlighting the translational potential of these innovations. Over time, research in genetics of agriculture and biomolecules intensified, underpinning their critical role in addressing global challenges like food security and sustainable development. This analysis offers insights into interdisciplinary convergence and the dynamic interplay between science and technology, contributing to strategic planning and policy development for agricultural innovation.

Main: Football players need to release power in a short time when they dribble or shoot quickly, so they need explosive power. Explosive power comes from muscle strength, and muscle strength comes from muscle mass. Result: Football players can increase leg muscle mass to increase the upper limit of muscle strength, the upper limit of explosive power, and the upper limit of jumping ability. Discussion: Agility ladder training (rope ladder) is mainly used to practice coordination, balance and agility. It can enhance the speed of lateral displacement, and also enable the feet to move faster and increase the frequency of steps. Football players need to do this training. During training, the toes should be stepped on the right rhythm, and the hips can be swung while stepping, allowing the body to twist to any angle. Combined with cross steps and single-foot in and out, it can greatly increase the flexibility of both offense and defense. Rope ladder training. It provides a great multi-plane dynamic warm-up, which can develop the connection between the brain and muscles, and is also good for eccentric contraction and stability. In addition, it can also enhance the reaction time to the ball and exercise the muscles of the feet. Methods: Short-distance sprinting can increase the activity of fast-twitch muscle fibers, which contribute most to explosive power. At the same time, dynamic weight training such as squats and box jumps can significantly increase the strength of lower limb muscles for football players. The increase in weight and number of repetitions in strength training should be gradual according to individual ability to avoid muscle damage. Focusing on strengthening the stability of the core muscles can effectively support the development of explosive power in the leg muscles. Conclusion: Football explosive power training can train the ability of muscle cycle lengthening and shortening, effectively utilize the elastic potential energy stored in the soft tissue during the lengthening process, reduce the energy consumption of direct muscle contraction, and improve explosive power energy.

To enhance the effectiveness of physical education and improve students’ learning experiences, this work explores the design of a physical education teaching platform system based on Virtual Reality (VR) technology. First, a VR-supported physical education system is constructed and project-based learning and Science, Technology, Engineering, Arts, and Mathematics (STEAM) educational concepts are innovatively integrated into the design of the system’s objectives and teaching content. The system’s functionality and performance are tested across various VR devices. The results indicate that the devices consistently and effectively support the operation of the VR system, providing reliable technical support for physical education activities. Additionally, taking tennis teaching as an example, a comparative experiment was conducted on college students in the experimental group and the control group to assess the impact of the VR system on students' tennis skills and learning attitudes and to explore how VR affects biomechanical performance in physical activities such as tennis. The visual cues of the ball's trajectory and speed can influence a player's anticipatory muscle activation and movement initiation. Through repeated exposure to different ball flight patterns in VR, players can develop more accurate muscle recruitment strategies. The kinematic chain of movements in tennis, from the feet to the torso, arm, and racquet, can be analyzed and optimized with the help of VR. It allows players to visualize and correct any inefficiencies or incorrect biomechanical sequencing. The results show that after the intervention, the experimental group achieves an average score of 91.8 in tennis skills, compared to 83.8 in the control group, with a p-value of < 0.001. In terms of overall learning attitude, the experimental group has a mean score of 4.04, while the control group scores 3.22, with a p-value < 0.001. These findings demonstrate that, compared to traditional teaching methods, VR-based teaching more effectively improves students’ tennis skills and learning attitudes. The results of this work provide theoretical support and practical guidance for the application of VR technology in physical education, contributing to the improvement of teaching quality and effectiveness.

With the increasing demand for enterprise economic management in complex and dynamic environments, the interdisciplinary application of biomechanics has shown significant potential. This article explores the innovative practices of genetic algorithms and deep learning in optimizing enterprise economic management. Genetic algorithm simulates biological evolution mechanisms such as natural selection and genetic variation to achieve multi-dimensional and multi-level optimization of enterprise economic models, improving decision-making efficiency and adaptability. Deep learning draws on the structural characteristics of biological neural networks to solve problems such as insufficient data and model overfitting, optimizing the intelligence level of resource allocation, performance evaluation, and strategic planning in enterprise management. On this basis, this paper introduces the concept of biomechanics to further improve the adaptability and efficiency of the model. Biomechanics emphasizes the movement and adaptability of organisms in complex environments, which provides a new perspective for corporate economic management. By simulating the dynamic adjustment mechanism of organisms in the face of external pressure, enterprises can respond to market changes more flexibly and optimize resource allocation and decision-making processes. In addition, this article proposes a comprehensive framework that combines multi-level genetic algorithms and deep learning, and verifies its effectiveness in dynamic market environments through case studies. Research has shown that biomechanics not only provides theoretical support for enterprise economic management, but also offers efficient and sustainable pathways for solving complex economic problems. By incorporating the inspiration of biomechanics into the optimization practice of corporate economic management, enterprises can better adapt to market changes, improve the flexibility and efficiency of decision-making, and point the way for future economic management innovation.

Anomaly detection is critical in identifying abnormal patterns in big data environments, where traditional techniques often struggle with scalability and efficiency. This paper explores machine learning-based anomaly detection techniques, focusing on their effectiveness in large-scale biomechanical data contexts. The study investigates three prominent methods: K-means clustering, autoencoders, and One-Class Support Vector Machine (SVM), each known for distinct strengths in handling biomechanical data. Through comprehensive simulations and experiments, precision, recall, F1-score, Area Under Curve (AUC), and time efficiency metrics are analyzed. The results highlight the trade-offs between accuracy and computational efficiency, offering insights into model performance in various biomechanical big data scenarios. The discussion emphasizes the suitability of autoencoders for detecting anomalies in complex biomechanical signals (e.g., gait analysis or joint kinematics) and the application of One-Class SVM in high-dimensional biomechanical datasets (e.g., muscle activation patterns or force plate data). The study concludes with recommendations for future research directions, including the integration of domain-specific biomechanical knowledge into machine learning models and the development of hybrid approaches for improved anomaly detection in biomechanics.

Aerobic exercise is an effective way to improve cardiorespiratory function and enhance physical fitness, which has a positive impact on the physiological health level of college students, and this study aims to investigate the impact of aerobic exercise with different combinations of intensity on the enhancement of physiological health level of college students. Twenty college students, aged 18–24, in robust health and devoid of long-term exercise backgrounds, were recruited. They engaged in three 30-minute aerobic exercise regimens: low-to-moderate, low-to-high, and moderate-to-high intensity. Biomechanically, each intensity level triggered distinct muscular activation patterns and energy demands. The physiological health indexes of the subjects were measured before and after the exercise, including the heart rate, the degree of self-consciousness of fatigue, the oxygen saturation, the perfusion index, and the emotional state. All indicators can be measured by professional equipment, and different intensity groups are determined according to the intensity of aerobic respiration. The results of the study showed that before the exercise intervention, there was no difference between the physiological health indicators of all subjects, which was homogeneous. In contrast, after the exercise intervention, physiological health indicators showed changes due to different intensities of aerobic exercise. In the two indicators of heart rate and conscious fatigue, there was a significant difference in the enhancement of the two indicators by aerobic exercise (P < 0.05), and the greater the intensity of aerobic exercise, the greater the effect on heart rate and conscious fatigue. Higher intensity aerobic exercise spurred the heart to work harder, pumping blood more vigorously to fuel active muscles, as dictated by biomechanical principles. In the two indicators of oxygen saturation and perfusion index, the effect of aerobic exercise did not have a significant difference (P > 0.05), and did not show a specific pattern of change. In terms of emotional state indexes, the effects of aerobic exercise of different intensities on the subjects were reflected in different emotional indexes, but in terms of the overall emotional disturbance index, aerobic exercise of different intensities did not have a significant effect on the emotional disturbance index. In sum, aerobic exercise’s biomechanical benefits for students’ health are clear. Colleges should boost its promotion, offer varied programs, and guide participation to harness these advantages for enhanced physical and mental health.

This study aims to propose a machine learning-based approach to assess the impact of physical education instruction, integrating biomechanical principles to addressing the challenge of difficult and inconsistent result evaluation. The evaluation index system is constructed based on key factors influencing teaching quality, including teaching attitude, content delivery, instructional methods, and teaching effects, with an added emphasis on biomechanical metrics such as movement efficiency, joint kinematics, and muscle activation patterns. This paper suggests a Biomechanically Enhanced Physical Education based Dwarf Mongoose Optimization Algorithm (PE-DMOA) for the advancement of PE, leveraging ML technology and biomechanical analysis to optimize PE teaching strategies. We pre-process the physical education dataset, incorporating biomechanical data from motion capture systems, force plates, and electromyography (EMG), alongside traditional teaching metrics gathered information from 2150 students and 72 teachers across various schools, to predict learning efficiency more accurately than previous methods. With a student satisfaction rate of 95.6%, the experimental results confirm the efficiency of the evaluation model developed in this article. The study’s results show that the suggested model (PE-DMOA) is 98.5% accurate. This means that it helps to look into the effects of machine learning and biomechanics on physical education teaching and gives good recall, accuracy, and precision results. Educators and learners can utilize the PE-DMOA evaluation model to enhance the quality and efficiency of instruction, streamline administrative tasks, and advance the scientific, standardized, and specialized delivery of physical education in classrooms.

Parkinson’s disease (PD) is character rised by numerous motor symptoms, including impaired coordination of the lower limbs, which reduces stability. The heel strike phase is crucial in gait, as it follows the single step phase and precedes the transfer of weight between limbs, requiring precise control to maintain balance. Impaired coordination during this phase can therefore compromise gait and stability. Therefore, this study aimed to investigate physiological coordination patterns of the lower limbs during the heel strike phase and identify potential alterations in patients with PD. Twenty-three patients with PD and 23 healthy controls participated in this study. Gait of each participant was recorded with a stereophotogrammetric system with 55 reflective markers and data were trimmed to ±15 milliseconds and analysed to assess joint coordination of ankles, knees and hips. Lower limb joint coordination was assessed through pairwise correlations of their acceleration time series. The results showed that PD patients exhibited reduced coordination during the left heel strike, involving all three joints of the left lower limb and ankle and hip of the right lower limb. However, increased coordination between knees was observed, possibly indicating compensatory mechanisms. Furthermore, the difference in coordination between each PD patient and the average coordination of the healthy control group significantly correlated with both disease duration and UPDRS-III scores, highlighting the association between coordination impairments, PD severity, and disease progression. In conclusion, individuals with PD show significant alterations in lower limb coordination during the heel strike phase. These alterations are associated with disease severity and progression, emphasising the need for targeted interventions to address gait dysfunction in PD.

Multimodal medical image synthesis plays a crucial supportive role in research within the field of biomechanics, providing high-precision data and analytical methods for studies on anatomical structures, tissue characteristics, and mechanical modeling. However, due to practical constraints, certain modalities of medical images may be difficult to obtain, posing challenges for model training and high-accuracy biomechanical research. Existing methods employ convolutional neural network (CNN)-based generative adversarial models to synthesize missing modality information across modalities. However, CNNs are limited in their ability to model long-range dependencies. Transformers offer a new paradigm to address these limitations, yet their high computational and memory demands remain a significant drawback. To tackle these challenges, we propose a novel generative adversarial model, termed the Convolutional Attention Latent Feature GAN (CALF-GAN), which leverages multi-scale convolutional attention for cross-modal medical image synthesis. A dedicated latent attribute separation module is employed to disentangle modality-specific features between source and target modality images, enhancing the synthesis of medical semantics, such as pixel intensity values. Furthermore, to improve the model’s capacity for long-range dependency modeling while reducing computational overhead, we design a generation module based on multi-scale convolutional attention, capturing long-range dependencies using only convolutional operations. Extensive experiments conducted on various medical image datasets demonstrate that CALF-GAN achieves remarkable generalizability and outstanding overall performance under low memory requirements, making it well-suited for application in high-precision biomechanics research.

Objective: To explore the regularity of medication in the treatment of arthralgia syndrome in three “Famous Medical Records”. Systematically explore the rules of drug use in the treatment of BI syndrome, to provide new ideas and theoretical support for modern TCM clinical treatment of Bi syndrome. Method: Using three “Famous Medical Records” as the source of literature, medical records for treating arthralgia syndrome were collected, and a database was established using Excel, IBM SPSS Modeler, and IBM SPSS Statistics software. Drug use, association rules, and drug clustering were analyzed. Result: 323 medical records and 650 prescriptions involving 429 traditional Chinese medicines were collected. The drugs with higher frequency of use include Angelica sinensis, Poria cocos, licorice, cinnamon twig, etc., among which the drugs are mostly drugs for tonifying deficiency and eliminating wind and dampness; The main drug properties are temperature; The medicinal taste is mostly sweet, bitter, and pungent; Drugs are mostly attributed to the liver, spleen, and kidney channels. Common drug compatibility combinations include: Angelica-Chuanxiong, Angelica-Lycium barbarum, etc. A total of 5 drug combinations were obtained through cluster analysis. Conclusion: Through data mining, it is found that the treatment of arthralgia syndrome in the three “Famous Medical Cases” focuses on reinforcing deficiency and balancing deficiency with reality. Multi use blood tonic drugs, Qi tonic drugs, and warming drugs that can eliminate rheumatic arthralgia, to tonify, unblock collaterals, and eliminate arthralgia, achieving the effect of Qi and blood homeostasis; Reuse wind medicine to treat rheumatism together; Be good at using animal drugs, especially insect drugs, to strengthen the health and eliminate pathogenic factors.

Background: Biosensing technology has developed as a capable tool for the rapid and perfect recognition of pathogenic microorganisms, determining real-time recognition capabilities that are crucial for early disease diagnosis and management. Purpose: This research investigates the integration of biosensors with Machine Learning (ML) techniques for the efficient detection of pathogens. Approaches: Data collection involved using various biosensors, including electrochemical, optical, and mass-based sensors, to capture the microbial signature of different pathogens. The collected data was pre-processed using adaptive filtering (AF) to remove noise and ensure signal clarity. Z-score normalization is utilized to standardize the dataset. Feature extraction was performed using the discrete wavelet transform (DWT) technique to reduce the dimensionality of the data while retaining crucial information. Results: This research proposes an Enhanced Snow Ablation Optimized Adaptive Support Vector Machine (ESAO-ASVM) model designed to enhance the accuracy and effectiveness of classifying complex biological data, such as microbial signatures. The proposed ESAO-ASVM model demonstrated optimal performance with an execution time of 0.58 s, memory usage of 42%, Root mean squared error (RMSE) of 0.01, Mean squared error (MSE) of 0.019, accuracy loss of 0.06, Structural similarity index measure (SSIM) of 80.4, accuracy of 98.5%, precision of 95%, recall of 94%, and an F1-score of 96%. This approach suggests a robust solution for rapidly identifying pathogenic microorganisms, making it an effective clinical diagnostic tool for food safety, and environmental monitoring. Conclusion: The incorporation of Biosensing technology with ML methods contains potential significant improvement in pathogen recognition, enabling faster and more consistent health involvements.

Background: PE is crucial for developing lifelong fitness habits among students. Traditional methods lack personalization and real-time feedback, limiting effectiveness. Biosensor technology, which can monitor various biomechanical parameters, offers a revolutionary approach to transform PE. It enables the provision of personalized training regimens based on each student's unique biomechanical characteristics, such as muscle force exertion patterns, joint kinematics, and body movement biomechanics. Purpose: The research aims to enhance and assess a new model of PE teaching that integrates personalized training with biosensor technology with a specific focus on how it impacts and interacts with the biomechanical and physiological aspects of students' physical performance. Methods: Data collection involves capturing HR, movement patterns, key biomechanical data and exertion levels during physical activities. The collected data are preprocessed using data cleaning and normalization techniques, ensuring the accuracy and reliability of this analysis. Feature extraction uses FFT to analyze the frequency domain characteristics of the physiological signals. The study proposes an ITSO-GRNN strategy aimed at developing PE teaching and personalized training. Results: The application of individual training together with biosensor technology contributes significantly and positively in terms of students’ performance and involvement, resulting in better physical results and good evaluations. The ISTO-GRNN model outperforms all existing methods in terms of physical training (97.80%), assessing students' biomechanical and physiological states (99.62%), and efficiency of the PE teaching process (98.74%). In terms of performance metrics, it performs effectively with accuracy (98.70%), precision (96.50%), recall (90.42%), and F1-score (92.50%) showing teaching effectiveness and evaluation that are highly superior to those of the former models. Conclusion: The study highlights the potential for such innovations to not only improve physical outcomes but also promote lifelong fitness habits among students.

With the rapid advancement of biomechanical research and educational big data, there is a growing need to integrate sophisticated analytical tools to enhance the understanding of human movement, learning behaviors, and their interactions. Traditional machine learning models often fall short in capturing the complex, multi-dimensional relationships inherent in biomechanical and educational datasets, leading to limited precision, inadequate personalization, and poor generalization capabilities, which restrict their applicability in dynamic teaching environments. To address these challenges, this paper proposes a machine learning model based on Factor Space Mathematical Theory integrated with the extreme gradient boosting (XGBoost) algorithm. By leveraging Factor Space Mathematical Theory, the model effectively captures the multi-dimensional characteristics of biomechanical and educational data, addressing the oversimplification and unidimensional nature of traditional models. Moreover, with the robust classification and prediction performance of XGBoost, the proposed model enhances the ability to generalize and process complex educational data. Experimental results demonstrate that the proposed model achieves an accuracy of 0.92 and an F1 score of 0.90 in predicting students’ biomechanical performance metrics, such as gait analysis and posture stability, which are critical for understanding learning behaviors in physical education and vocational training. The model outperforms the standalone XGBoost model by a significant margin of 0.05 in accuracy. Additionally, MSE analysis across diverse datasets reveals no evidence of overfitting, further validating the model’s strong generalization capabilities. This study highlights the effectiveness of combining Factor Space Theory with XGBoost, offering improved accuracy, operational efficiency, and adaptability in biomechanical data analysis and educational behavior prediction. The findings provide a novel perspective and practical approach to advancing biomechanical research and its application in educational reform, particularly in higher vocational education.

Although biofeedback has achieved remarkable results in many areas, it also has some limitations. For example, the measurement results of a biofeedback meter can be affected by a variety of factors, including physiological differences between individuals, environmental disturbances, etc. In addition, biofeedback training requires a certain amount of time and patience to learn and master the skills, which can be a challenge for some individuals who are impatient. In summary, biofeedback is an effective mental skill training method, which plays an important role in competitive sports and is gradually being borrowed and applied by other fields. As technology continues to evolve and improve, biofeedback is expected to play a greater role in more fields.

As the economy grows and living standards improve, there is an increasing awareness of the importance of health. Traditional sports monitoring tools often fall short in terms of functionality, accuracy, and efficiency, especially when it comes to biomechanical analysis. This study investigates the integration of blockchain and Internet of Things (IoT) technologies in health monitoring within the biomechanics field, involving 300 students, 100 teachers, and 300 other participants. The participants were divided into an experimental group that utilized blockchain-IoT monitoring and a control group that relied on traditional methods. By employing the Byzantine consensus mechanism, data were analyzed in terms of processing efficiency, accuracy, effectiveness, and achievement of biomechanical fitness standards. The results indicated that the experimental group achieved higher rates of meeting weight standards (with an average of 82%) compared to the control group (76%). These findings underscore the potential of blockchain-IoT integration in enhancing the accuracy and effectiveness of biomechanical monitoring. This technology can promote a deeper understanding of biomechanical principles and improve nationwide fitness and exercise engagement by providing more reliable and precise data on physical performance and movement patterns.

In this work, the glucose nanocarbon spheres (GC) were in situ decorated on the surface of lamellar peanut shell biochar (PSAC) to construct the self-modified conductive function unit of biomass (PSAC@GC). The effects of PSAC@GC at different carbonization temperatures on the conductivity, the antistatic properties, and the thermal stability of epoxy composites were investigated. This unique structure not only improves the dispersion of GC nanospheres in the epoxy resin, but the larger carbon framework of PSAC also contributed to forming a continuous conductive network. Moreover, the well-dispersed GC nanospheres on PSAC facilitate the transfer of surface free electrons, effectively improving the electrical conductivity and ultimately strengthening the antistatic properties of the composites. The results show that when the carbonization temperature was 900 ℃, the conductivity of PSAC@GC with a mass ratio of 1:1 was 41.19 S/m, which represents an increase of 135% and 43% compared to PSAC and GC, respectively. When 5 wt.% PSAC@GC was added, it showed good dispersion in the epoxy resin, and the surface resistivity of the composites was reduced to 1.89 × 108 Ω. The work provides a new approach for the development of environmentally friendly and cost-effective novel antistatic carbon material.

The current study delved into how aesthetic context intertwined with cell molecular biomechanics to influence pupils’ explicit and implicit altruistic behaviour. In Experiment 1, when looking at the effects of different aesthetic contexts on pupils’ explicit altruistic behaviour, it was found that explicit aesthetic context had a notable priming effect on implicit altruistic behaviour. From a cell molecular biomechanics perspective, perhaps in an explicit aesthetic context, specific cell surface receptors respond to external stimuli related to beauty, triggering intracellular molecular signaling pathways that eventually influence implicit altruistic responses more than in implicit or non-aesthetic contexts where such coordinated signaling is less pronounced. Experiment 2 on implicit altruistic behaviour again detected the implicit association test (IAT) effect. Implicit aesthetic contexts showed a significant priming effect. Here, at the cell molecular level, the microenvironment within cells might be altered by the implicit aesthetic perception, like changes in cytoplasmic viscosity or the movement of organelles affected by aesthetic feelings, which then play a key role in shaping implicit altruistic behaviour compared to explicit and non-aesthetic contexts. Overall, it’s clear that explicit and implicit altruistic behaviours rely on distinct processing mechanisms involving both aesthetic context and cell molecular biomechanics. The two aesthetic contexts have selective impacts via different path mechanisms related to these cellular processes.

Based on the theoretical knowledge of biomechanics, this paper parameterizes the biological structure of the human body and constructs a human biomechanical model in three dimensions: horizontal dimension, vertical dimension, and torsion dimension, and analyzes in detail the relationship between the stresses in the process of intelligent assistance for nursing robots. Based on the human biomechanical model, the overall design scheme of the home-based intelligent elderly care robot is determined, and corresponding software and hardware are used to realize the design of the home-based intelligent elderly care robot. Select experimental tools and test environments to verify and analyze the biomechanics-based elderly care robot. The error between the theoretical value (software simulation results) and the actual value (equipment test results) of the force of the intelligent assistive process of the nursing robot is kept below 5%. The correct rate of urinary and fecal flushing detection of the home smart elderly care robot is more than 0.95, and the response speed of the control system is controlled within 5 s, while the Central Processing Unit (CPU) occupancy rate is not more than 30.00%, which indicates that the home smart elderly care robot can be effectively used in the elderly care work. The combination of biometrics and information technology is a prominent contribution of this article.

Injuries among college athletes in physical education (PE) classes are a significant concern, impacting both their academic performance and athletic progression. Understanding the risk factors and mechanisms behind these injuries is crucial for developing effective prevention strategies. This research aims to identify and analyze the key risk factors contributing to injuries among college athletes participating in PE classes. Data were collected through questionnaires with PE instructors. The PE instructors were trained with wearable biomechanics sensors, which were used to record joint angles, ground reaction forces, acceleration, and muscle activation during various physical activities such as running, high jumping, and basketball. Risk factors like inadequate warmup, improper techniques, and overuse. Statistical tests were conducted using SPSS to analyze the relationship between biomechanical factors and injury occurrence among college athletes, including Chi-square, Descriptive statistics, Regression, and ANOVA showed that they were strong predictors of future injuries. Descriptive statistics revealed that a significant portion of athletes reported injuries, with sprains being the most common injury type. Chi-square analysis indicated a significant relationship between sport type and injury occurrence. Results indicate that PA lessons can help prevent injuries. Regression analysis offers insights into the relationships between variables and their impact on injury occurrence. The results highlight the need for preventive strategies, such as tailored warm-up routines and monitoring injury history, to minimize injury rates.

By collecting athletes’ basic information, exercise habits, historical injury records and sports performance data, this study constructs a random forest (RF) model to assess the risk of sports injuries. The model can effectively deal with high-dimensional data and capture nonlinear relationships, and has strong generalization ability. The study also defines a risk assessment index (RAI) to visually represent the risk level of athletes’ sports injuries. In addition, this study identified the specific rehabilitation needs of patients with different injury types and degrees through association rule mining technology and cluster analysis, and made a personalized rehabilitation plan. In particular, biomechanical data, such as joint stability and muscle strength balance, are also included in this study to more accurately assess the risk of sports injury and guide rehabilitation training. Through comparative experiments, the results show that personalized rehabilitation plan based on big data analysis can significantly shorten the rehabilitation cycle and improve the quality of rehabilitation and patient satisfaction. The results of this study not only provide scientific sports guidance and rehabilitation suggestions for athletes and fitness enthusiasts, but also provide decision support for sports coaches, rehabilitation teachers and other professionals, which promotes the development of theory and practice in the field of sports injury prevention and rehabilitation.

Background: Metabolomics can offer vital information into a cancer’s condition. Despite its potential, research on the metabolites linked to colorectal cancer (CRC) remains limited. From a cell molecular biomechanics perspective, understanding these metabolite associations can offer a deeper understanding of the disease’s underlying mechanisms. We performed Mendelian randomisation (MR) analyses to investigate causal associations between 486 blood metabolites and CRC. Methods: Data on blood metabolites were derived from a Genome-wide association study (GWAS) involving 7824 Europeans. Additionally, summary statistics for CRC were sourced from the FinnGen consortium database. To explore the causal relationship between CRC and blood metabolites, we primarily utilized the inverse variance weighted (IVW) analysis. Supplementary analyses incorporated MR-Egger and weighted median methods to ensure the robustness of our findings. The potential for pleiotropic effects was evaluated using the Cochran’s Q test and the MR-Egger intercept test. Furthermore, colocalization analyses were performed to ascertain whether the observed associations were influenced by specific genetic loci within the genomic region. Results: The results of this study indicated significant associations between eight metabolites: Indolelactate (OR = 2.62, 95% confidence interval (CI): 0.26–1.66, p = 0.007), 1-heptadecanoylglycerophosphocholine (OR = 1.37, 95% CI: 0.10–0.54, p = 0.005), 1-stearoylglycerophosphocholine (OR = 3.47, 95% CI: 0.65–1.84, p = 0.00005) , X-11792 (OR = 0.57, 95% CI: −0.94–−0.17, p = 0.005), X-12038 (OR = 0.44, 95% CI: −1.50–−0. 12, p = 0.021), X-12212 (OR = 1.96, 95% CI: 0.10–1.25, p = 0. 022), X-14056 (OR = 0.50, 95% CI: −1.28–−0.12, p = 0.018) , X-14745 (OR 0.41, 95% CI: −1.48–−0.31, p = 0.003) and CRC. These metabolites might play roles in altering the mechanical properties of cells in the colon. They could potentially affect the cytoskeletal structure, cell membrane fluidity, or the way cells interact with the extracellular matrix. Conclusion: The eight identified blood metabolites with causative influence on CRC provide valuable clues for understanding CRC from a cell molecular biomechanics angle, which can further aid in its screening, prevention, and treatment strategies.

The engagement of athletes in competitive Padel is becoming progressively prevalent, and this heightened participation elicits apprehensions regarding the prevalence of overtraining and sports-related injuries. In this framework, the objective of the present investigation was to examine the potential role of Heart Rate Variability (HRV) monitoring in identifying overtraining injuries among female professional Padel athletes. A cohort of 66 elite female Padel players, with ages ranging from 17 to 32 years, was recruited for the study. The duration of the investigation extended over a 13-week period, during which the participants were observed concerning HRV indices and musculoskeletal injuries while adhering to their customary Padel training protocols. The assessment of HRV indices was executed to ascertain the autonomic nervous system’s response to a state of overload injury, while the Oslo Sports Trauma Research Center (OSTRC) Overuse Injury Questionnaire was administered to assess the prevalence of musculoskeletal overuse injuries, both at baseline and upon completion of the observational period. Utilizing a Paired Samples t-test analysis, the results indicated a statistically significant difference between pre- and post-test assessments, which illustrated that the observational cohort experienced a notable decline in HRV concomitant with an escalation in the incidence of musculoskeletal overuse injuries. Consequently, the findings imply that the monitoring of HRV responses may facilitate the early detection of overuse injuries, inform rehabilitation strategies, and advance return-to-sport protocols, thereby safeguarding and promoting the recovery of injured tissues.

The activation of hepatic stellate cells (aHSCs) play a role for the occurrence and progression of liver fibrosis. However, effective drugs that can prevent or reverse this pathological process remain unavailable. Zanthoxylum bungeanum Maxim. (Rutaceae) is an edible and medicinal plant with diverse bioactivities, including antiparasitic, antimicrobial, and anti-inflammatory effects. This study investigates the therapeutic potential and underlying mechanisms of Zanthoxylum bungeanum-derived extracellular vesicles (ZEVs) in liver fibrosis, using the human HSCs LX-2 cells and alcohol-induced mice model of liver fibrosis. The results show that ZEVs significantly inhibit the proliferation and migration of LX-2 cells, while downregulating the fibrosis-related proteins and genes expression. Furthermore, oral administration of ZEVs significantly decreased serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels in mice with liver fibrosis, reducing liver inflammation, collagen deposition, and lipid droplet accumulation. Additionally, miR-9 and miR-17 in ZEVs were found to significantly reduce the synthesis of fibrosis-related proteins in activated LX-2 cells. Mechanistic studies further revealed that ZEVs suppressed the gene levels of TGF-β1, Smad2 and Smad3 in activated LX-2 cells. In conclusion, ZEVs are a possible treatment option for liver fibrosis, potentially through modulation of the TGF-β1/Smad signaling pathway.

This article proposes a biomechanical cost control strategy using fuzzy logic and neural networks. A cost model for the biomechanical system is established, and a fuzzy logic strategy addresses its uncertainty and complexity. By integrating neural networks with fuzzy logic, the accuracy and adaptability of budget control are enhanced. Experimental results show the proposed strategy outperforms traditional methods (GNN-GA, DP-PSO, A-DRL) in cost savings, system stability, and response time. The deviation between target and actual costs is minimal, confirming the strategy’s efficiency and accuracy. This integrated approach offers significant cost savings, strong adaptability, and real-time performance, providing new solutions for biomechanics budget control with practical applications and theoretical value.

The use of technology to enhance educational experiences has gained significant attention, particularly in the field of emotional engagement monitoring. Active student participation can promote a greater knowledge of values, ethics, and social duties, which is particularly crucial in university ideological and political education. The research objective is to establish a biosensor-based emotional monitoring and feedback system for university ideological and political education. This research proposed a novel Battle Royale fine-tuned Deep Bidirectional Long Short-Term Memory (BR-DBiLSTM) to detect both cognitive and emotional engagement in students. The system uses a combination of biosensors to monitor physiological and behavioral indicators and collect emotional data. The feedback system uses an instructor dashboard to display emotional states and engagement levels and alerts to trigger responses if students show disengagement or stress. The data was preprocessed using Z-score normalization to reduce noise from the obtained data. Feature extraction was implemented using the Fast Fourier Transform (FFT), BR is to optimize and select the features and DBiLSTM model to improve its classification accuracy. The experimental findings show that the suggested model has a high degree of reliability in identifying cognitive and emotional involvement, with a Micro-F1 of 90.62%, Micro-P of 89.95%, and Micro-R of 88.34%. This system demonstrates the potential for enhancing engagement in ideological and political education through adaptive feedback mechanisms based on biosensor data.

To increase the use of wearable biosensors in language learning environments, approaches for accurately extracting small signs of cognitive load are necessary. However, assessing subjective cognitive states, such as the load experienced during language acquisition, provides significant obstacles. This research uses data from physiological sensors worn on the wrist, such as skin conductance, skin temperature, heart rate, and R-R intervals, to organize a machine learning (ML) challenge to develop techniques for quantifying cognitive load in English learners. Participants used data from respondents who completed English language tasks of various difficulty levels. A robust evaluation of preprocessing approaches such as Z-score normalization, signal detrending, and moving average filtering, as well as feature extraction methods such as time-domain and frequency-domain analysis, demonstrated that robust models efficiently used biosensor data. Classical classifiers, such as Adaptive Random Forest (ARF), performed better when optimized with Barnacle Mating Optimization (BMO) for hyperparameter tuning. The proposed method of BMO-ARF has attained accuracy at 95.89%, F1-score in the cognitive load of low at 0.95, medium at 0.90 and high at 0.97, sensitivity in the cognitive load of low at 80.3%, medium at 88.5% and high at 93.0% and specificity in the cognitive load of low at 87.5%, medium at 91.8% and high at 95.1%. The results show that cognitive load classifications were more accurate for higher-difficulty tasks and particular learners, potentially impacted by model overfitting and the subjective nature of physiological responses. The research highlights the need for more sophisticated annotation techniques to improve cognitive load monitoring in language learning environments and handle student response variability.

In response to the problems of insufficient accuracy, slow speed, and poor stability in the current parameter identification process of photovoltaic cells, this study designs a parameter identification method based on Improved Red-tailed Hawk (IRTH) algorithm optimization. Firstly, four photovoltaic cell models and one photovoltaic module model are constructed, and corresponding objective functions are established. Secondly, combining Gaussian mutation and cuckoo search ideas, a Gaussian cuckoo mutation mechanism is proposed to reprocess positional information, thereby optimizing the algorithm population and improving solving efficiency. And further analogize photovoltaic cell units as biomaterial units with specific mechanical response characteristics. By studying its current voltage characteristics, the dynamic response of its photoelectric conversion unit under different lighting and load conditions is revealed, similar to the nonlinear and time-dependent characteristics exhibited by biomaterials under external forces. Again, based on the idea of individual extinction in the white whale algorithm, a red-tailed hawk descent mechanism is proposed to improve the convergence speed. The results of the effectiveness test on the proposed IRTH algorithm showed that it converged the fastest and obtained significantly smaller root mean square errors than other optimization algorithms. Finally, the IRTH was further utilized to parameter identification in RTC France photovoltaic cells and photovoltaic modules Photowatt-PWP 201, with an average improvement rate of 79.94%. Therefore, the improved algorithm has better parameter identification effect and higher reliability.

This study explores the integration of Virtual Reality (VR) technology within physical education at higher education institutions, with a focus on its biomechanical implications. VR not only addresses issues like outdated content and monotonous teaching methods but also provides an immersive environment for students to understand the principles of biomechanics through interactive simulations. By improving the understanding of movement patterns and physical interactions at the molecular and cellular levels, VR significantly enhances students’ skill mastery and physical fitness. Challenges such as high equipment costs and technical maintenance remain pertinent. Future research should aim to optimize VR content from a biomechanical perspective and integrate it with traditional teaching methods to further advance the field.

This study aims to analyze the current status and challenges of intellectual property protection in the networked environment from a biomechanical perspective. The research objectives are achieved through literature review, case studies, surveys, the application of risk factor algorithms, and biomechanical analysis. By examining real-world cases and conducting surveys, the study provides an in-depth understanding of the current state and challenges of intellectual property protection. Additionally, a gated recurrent unit (GRU) model is employed to compute the weights of risk factors. Finally, biomechanical analysis is used to evaluate intellectual property risks. Initially, historical intellectual property risk data is collected using the GRU model, relevant risk factors are extracted, and their weights are calculated. Drawing on biomechanical concepts, key variables in risk are analyzed by analogy, where risk is treated as the state variable, risk factors are represented as external forces, and weights denote the magnitude of these forces. A biomechanical model is constructed to analyze the “stress-strain” relationship of risks, thereby uncovering the dynamic interactions between factors and risks from a biomechanical perspective. Ultimately, the study evaluates and predicts future intellectual property risks. manuscript. The results show that within a year, the number of patent infringement, invention infringement, utility model infringement and reprint infringement cases encountered by website creators fluctuates between different quarters. For example, the number of patent infringement cases increased from 52 in the first quarter to 61 in the third quarter, and then decreased to 54 in the fourth quarter; while the number of reprint infringement cases continued to rise, from 78 in the first quarter to 91 in the third quarter, and then decreased slightly to 62 in the fourth quarter. In addition, after strengthening the supervision of intellectual property protection, creators in different occupations have shown an increase in their satisfaction with intellectual property protection, with creators in occupations D and F having the highest satisfaction.

Objective: More attention should be paid to glucose metabolism in children with Williams-Beuren syndrome (WBS). Methods: The clinical data of a child diagnosed with WBS due to diabetic ketoacidosis (DKA) were retrospectively analyzed, and the related literature was reviewed. Results: An 8-year-old boy presented with thickened upper lip, low palatal arch, strong heart sound, rumbling murmur in the apex area, a little pigmentation in the webbed margin of fingers and toes, and atypical elfin features. Blood gas analysis showed severe ketoacidosis with significantly elevated amylase, significantly increased amylase, elevated blood lipids, abnormal thyroid function, negative C-peptide, diabetic. Echocardiography showed supravalvular aortic stenosis and abnormal continental valve. The large copy number variation of the nuclear genome revealed a heterozygous variation in the 7q11.23 region, with a 1.4 Mb deletion in the 7q11.23 region, and the related gene in the region was elastin gene. Conclusion: DKA was reported for the first time as the first symptom of WBS diabetes. The mechanism of concurrent DKA in WBS is not well understood.

Background: This study aims to model the kinematics of human joints using the Denavit-Hartenberg matrix method (hereinafter referred to as D-H matrix method) and combine biomechanical analysis for posture evaluation, thereby providing a more accurate and efficient detection solution. It ensures the implementation of complex calculations under low-power conditions and has broad application prospects in fields such as rehabilitation medicine, sports analysis, and virtual reality. Objective: The aim of this study is to design a sensor fusion-based embedded electronic system by integrating nine-axis sensors such as accelerometers, gyroscopes, and magnetometers. This system combines the D-H matrix method and forward kinematics for human posture detection and biomechanical analysis, to improve the system’s detection accuracy and response speed. Methods: Traditional forward kinematics and the D-H matrix method are used for kinematic modeling to enhance the accuracy and efficiency of posture calculation. Innovation: The D-H matrix method, a classical analysis technique in robotics typically used for kinematic analysis of robotic arms, is successfully applied in this study to human posture detection, breaking through traditional posture analysis methods. By utilizing the D-H matrix method to model the movement relationships between human joints, this study provides a more precise mathematical model for posture detection. By combining embedded electronic systems with biomechanical analysis to evaluate human posture, and introducing real-time monitoring of biomechanical loads from a biomechanical perspective, this study ensures that real-time human posture detection is not only efficient but also capable of performing complex calculations under low power conditions. Results: To further improve the accuracy of the sensors, this study analyzed the error characteristics of the inertial sensors and applied preprocessing algorithms to correct the errors in the signals from the magnetometer, accelerometer, and gyroscope. Combined with a high-pass and low-pass complementary filter fusion algorithm, the experiment showed that this algorithm successfully resolved the random drift and cumulative errors in the attitude angles calculated. The posture calculation system using the D-H matrix method outperforms the traditional forward kinematics method in terms of response time and root mean square error (hereinafter referred to as MSE). For instance, the response time for the right upper arm is reduced by 74.67% compared to traditional methods, while the MSE remains within a reasonable range.

Advancements in mobile technology (MT) are revolutionizing biomechanics education by enhancing the understanding of motion, deformation, and forces within biological systems. This study investigates the integration of MT tools—specifically augmented reality (AR), virtual reality (VR), and gamified learning platforms—in biomechanics curricula, focusing on mechanobiology encompassing genes, proteins, cells, tissues, and organs. Utilizing a combination of literature synthesis and empirical data from two pilot studies involving 205 students, the research evaluates the effectiveness of these technologies in improving comprehension, engagement, and retention. The first pilot study with 120 undergraduate students demonstrated a 35% increase in comprehension scores through AR/VR tools compared to traditional textbook methods, while the second pilot study with 85 postgraduate students revealed a 20% improvement in knowledge retention and a 42% enhancement in spatial understanding of protein deformation processes via VR-based simulations and gamified platforms. These results highlight MT’s significant potential to transform biomechanics education by providing immersive, interactive, and personalized learning experiences. Additionally, the study addresses key challenges such as the high cost of AR/VR devices, the need for comprehensive educator training, and ensuring equitable access across diverse educational institutions, proposing strategies like developing cost-effective solutions and establishing standardized content frameworks. Overall, the findings affirm that integrating MT into biomechanics education advances pedagogical practices and aligns with the evolving demands of modern biomedical sciences, fostering a more engaging, effective, and accessible learning environment for both educational institutions and learners.

Biomechanics is an investigation of the mechanical and physiological aspects of human movement. By relying more on this field, educators can give better teaching to their students. Applying biomechanical concepts can help athletes enhance their performance by increasing their knowledge of efficient movement, approach optimization, and injury prevention. As a result, athletes can potentially improve their overall performance. The significant problems highlighted in the study that identify these impediments include inadequate resources, resistance to implementing new teaching methods, and a shortage of physical education instructors with specific experience. The Conceptual Pedagogical Physical Education Strategic Frameworks (CPPESF) method has been developed and is now being shared to address these issues. This approach provides a holistic strategy for incorporating biomechanical principles into the curriculum through organized modules, activities encouraging active involvement, and ongoing professional development for teachers. The CPPESF aims to give physical education instructors the tools they need to teach their students biomechanics advantageously. Numerous simulation evaluations show that CPPESF helps enhance students’ athletic performance, reduce injuries, and increase their engagement in physically active games and activities. Using simulations, students can see and analyze the practical application of biomechanical concepts. This engaging and hands-on learning experience is truly distinctive. This approach additionally encourages lifelong physical activity and wellness by providing a greater understanding of bodily mechanisms. It helps athletes maximize their training routines, which boosts their power overall. The research presented here shows that biomechanics can revolutionize future athletes’ self-awareness and competence and support the expansion of CPPESF in physical education programs. The proposed method increases the Athletic Performance Improvement Analysis ratio of99.8%, Injury Reduction Analysis ratio of 91.2%, Simulation Efficacy Analysis ratio of 92.4%, Resource Accessibility Analysis ratio of 97.9%, Curriculum Integration and Flexibility Analysis ratio of 98.3% compared to existing methods.

This study aims to explore the dynamic relationship between biomechanical modeling and manufacturing productivity in the context of the digital economy, with a particular focus on the interaction between worker movement patterns and production efficiency. By combining biomechanical analysis with VAR (Vector Autoregressive) models, this study reveals the dynamic effects of worker movement patterns on productivity. The VAR model, as a multivariate time series analysis tool, can effectively examine the causal relationship between factors such as production efficiency, labor input, technological level and worker movement patterns. In the study, kinematic and dynamic analysis were combined, with a focus on analyzing biomechanical variables such as the movement trajectory, velocity, acceleration, and applied forces of workers’ arms and joints during the execution of production tasks, in order to evaluate the impact of these factors on production efficiency. Biomechanics modeling helps quantify the effects of long-term repetitive movements on joint load and muscle fatigue in workers, providing potential pathways for optimizing worker movement patterns and work postures to reduce energy consumption and improve productivity. In addition, this study also ranked the development level of the digital economy in various provinces of China through principal component analysis (PCA) and found that the high development of the digital economy is closely related to the construction of information infrastructure. Based on the lag analysis of the VAR model, this study further explores the feedback effects of technological progress and automation level on workers’ movement patterns and production efficiency. The results indicate that with the advancement of automation technology, the interaction mode between workers and automation equipment has changed, and the optimization of worker actions is closely related to productivity improvement. Through this multidimensional analytical framework, this study provides theoretical support for the combination of the digital economy and biomechanics and offers new perspectives and methods for industrial optimization and labor productivity improvement in the manufacturing industry.

This observational study investigates the stress distribution in knee joint structures, specifically focusing on the meniscus and primary ligaments of the supporting leg during the support phase of a maximal-effort soccer instep kick executed by an elite-level player. Understanding these stress patterns is crucial for injury prevention and enhancing performance in high-impact soccer actions. A three-dimensional (3D) knee model was developed utilizing CT and MRI data. Finite element (FE) analysis was conducted to evaluate stress distribution patterns across the lateral and medial menisci, medial collateral ligament (MCL), and anterior cruciate ligament (ACL). The analysis revealed peak von Mises stresses of 16.127 MPa in the lateral meniscus, 10.845 MPa in the medial meniscus, 36.613 MPa in the MCL, and 22.863 MPa in the ACL. These findings indicate significant stress concentrations in the lateral meniscus, proximal MCL, and femoral insertion of the ACL. The identified stress distribution patterns specifically related to the knee joint during the instep kicking phase provide critical insights into the internal mechanical demands placed on the joint structures. This study enhances the understanding of stress concentrations in the meniscus and ligaments during soccer kicks, emphasizing the potential for targeted analysis of these stress patterns to inform injury prevention strategies. It suggests that a deeper comprehension of the stress distribution mechanics could contribute to more effective training protocols and rehabilitation approaches for athletes, ultimately improving performance and reducing the likelihood of knee injuries during soccer activities.

Background: The latest observational studies revealed a possible association between Alzheimer ‘s disease (AD) and obstructive sleep apnoea (OSA). In AD, the accumulation of amyloid-beta plaques and tau tangles disrupts the normal structure and function of neurons in the brain. These pathological changes can affect the mechanical properties of neural tissues, altering their elasticity and stiffness. Such alterations might extend to the neural circuits responsible for regulating sleep and respiration, potentially influencing the occurrence of OSA. We used bidirectional Mendelian randomization approach to analyze causalities between OSA, its typical symptoms (snoring and daytime sleepiness) and AD in European populations. Methods: Single nucleotide polymorphisms of AD and OSA, snoring, daytime dozing were selected as instrumental variables. Preliminary MR analysis was inverse-variance weighted (IVW) method. The Cochran Q test, MR-Egger analysis, ‘leave-one-out’ test were used to verify the data robustness. Results were adjusted for Bonferroni correction thresholds. Results: Results from IVW demonstrated a suggestive correlation between AD and higher risks of sleep apnoea (OR 1.0008, 95%CI 1.0000–1.0016, p = 0.044) after Bonferroni correction. However, reverse MR analysis showed no association of genetically primed sleep apnoea towards AD (p = 0.294). No causal effect was detected between genetic AD and snoring, daytime dozing. From a biomechanical perspective, the positive correlation between AD and increased risk of sleep apnoea could be due to the fact that the structural changes in the brain caused by AD might lead to changes in the biomechanical forces exerted on the brainstem regions that control breathing during sleep. Conclusions: The findings of the MR study support that AD might increase the risk of OSA. Therefore, individuals with AD should strengthen sleep monitoring, sleep hygiene and develop a regular sleep-wake pattern. Understanding these underlying biomechanical mechanisms could provide valuable insights for developing targeted interventions to mitigate the risk of OSA in AD patients and potentially improve their overall sleep quality and disease management.

In today’s complex and volatile world economic situation, extending the life cycle of sports enterprises and maintaining sustainable growth is a top priority for enterprise development. Under the background of advocating high-quality development, sports enterprises practicing ESG development concept can help realize sustainable growth. This paper empirically examines the impact of ESG performance on the sustainable growth rate of sports enterprises through the panel data of A-share sports listed companies in 2018–2022, and finds that good ESG performance can significantly enhance the sustainable growth rate of sports enterprises, and the enterprise’s R&D investment in biomechanical technology has a certain promotion effect on the relationship between the impact of ESG performance and the sustainable growth rate of sports enterprises by grouping the enterprise group regression according to industry and property rights to further derive the results of heterogeneity analysis. The results found that ESG performance has a significant positive impact on the sustainable growth rate of both sporting goods enterprises and non-state-owned enterprises. Therefore, this paper concludes that sports enterprises should actively practice the ESG development concept to ensure sustainable growth, extend the enterprise life cycle, promote the high-quality development of the sports industry, and form a virtuous cycle of economic development.

This study investigated the impact of a reformed physical education (PE) curriculum on middle school students’ physical health and exercise habits in Beijing, China. Using a quasi-experimental design, 1187 students were assessed at baseline, post-intervention, and follow-up. The reformed curriculum, featuring student-centered learning and technology-enhanced instruction, led to significant improvements in physical health indicators such as BMI, cardiovascular fitness, and muscular strength. Additionally, the proportion of students meeting WHO physical activity guidelines increased significantly in the intervention group compared to the control group, alongside notable improvements in exercise frequency, duration, and intrinsic motivation. By addressing both physical health outcomes and behavioral changes, this study highlights the effectiveness of a comprehensive and contextually tailored PE curriculum. These findings contribute new evidence to the limited body of research on PE reforms in China and offer actionable insights for developing strategies to promote active lifestyles and overall well-being among adolescents.

AI public sports teaching technology is applied to daily indoor or outdoor fixed sports tests, ability assessments, and sports classroom teaching scenarios. It provides automated voice broadcast guidance, sports video AI intelligent engine automatic analysis, real-time detection of illegal actions, and intelligent real-time output of sports test results; Through AI intelligent guidance, the whole process of basketball test sports analysis and training suggestions are realized, and students are encouraged to conduct self-evaluation, promote training through testing, and practice more frequently. It also promotes the normalization of sports testing/teaching in public physical education in colleges and universities through teaching demonstration video explanations and a combination of various classroom forms, accumulates campus physical test data, and assists public physical education teaching and testing in colleges and universities. AI technology has intelligent joint detection and marking, multi-subject tracking and recognition. It does not require any markers to be installed on the captured object, and can capture accurate motion through video. During the basketball test, if an abnormal situation occurs, the student needs to stop the test. AI technology uses motion capture and analysis systems to help students and teachers better understand the sports skills and techniques tested in basketball, thereby developing more effective sports teaching and training plans. Motion capture and analysis systems can be used to evaluate athletes’ gait and posture to help them improve their techniques and reduce the risk of injury.

With the rapid development of the logistics industry, traditional supply chain management faces challenges such as inefficiency and resource wastage. Research on logistics optimization based on bionic algorithms, which is inspired by behavioral patterns and biomechanical principles, enabling these algorithms to effectively address complex issues like logistics path optimization, resource scheduling, and inventory management. In intelligent supply chain management, the application of bionic algorithms not only enhances the intelligence level of decision-making but also improves the adaptability and flexibility of the system. By integrating Internet of Things (IoT) technology, real-time monitoring of logistics resources is achieved, allowing bionic algorithms to comprehensively optimize the logistics process. A cloud computing-based platform architecture enables efficient information sharing among different participants, thereby improving the overall efficiency of the supply chain. This study explores practical applications of bionic algorithms in logistics optimization, demonstrating their effectiveness in enhancing logistics management accuracy, reducing costs, and improving customer satisfaction.

Sports injuries can significantly impact athletes’ performance and career longevity, making their early prediction and prevention a critical area of research. Traditional methods often fall short of capturing the complex, nonlinear interactions between various risk factors that contribute to injuries. The early prediction of sports injuries is vital for the well-being and performance optimization of athletes. This paper introduces Intrinsic Permutation Entropy Deep Learning (IPE-DL), a novel framework that synergizes permutation entropy with deep learning architectures to enhance the prediction of sports injuries. The IPE-DL method leverages the concept of permutation entropy to quantify the complexity and regularity of time-series data derived from athletes’ physiological and biomechanical signals. These entropy measures serve as critical features, capturing the inherent nonlinear dynamics within the data. The experiments demonstrate that the IPE-DL model outperforms traditional machine learning approaches and state-of-the-art deep learning models in predicting sports injuries. The proposed deep learning model is trained on a comprehensive dataset encompassing various risk factors, including athlete-specific metrics, training load parameters, and environmental conditions. Our dataset includes data from over 1,000 athletes, with a total of 100,000 training sessions recorded. The experiments demonstrate that the IPE-DL model outperforms traditional machine learning approaches and state-of-the-art deep learning models, achieving an accuracy of 92%, a sensitivity of 89%, and a specificity of 94% in predicting sports injuries. The results highlight the model’s capability to provide early warnings by identifying subtle changes in athletes’ physiological and biomechanical states that precede injuries.

The live-streaming e-commerce industry has thrived with the progress of mobile Internet, becoming an integral part of consumers’ daily lives. Driven by the green consumption and low-carbon living concepts, online consumption has gained popularity, making e-commerce live marketing a significant platform for consumer spending. Resolving how to heighten consumers’ purchase intention and augment sales is a pressing concern for e-commerce live marketing teams and platforms. This research, grounded in grounded theory, dissects the determinants of consumers’ purchase intention in e-commerce live streaming. Four main aspects are identified: people, cargo, market, and consumers’ internal state. Human aspects encompass personal charm and professionalism; cargo elements comprise product features, price incentives, and transaction volume; venue-related factors involve live streaming interaction and comprehensive service; and consumers’ internal state involves a sense of value and trust. Drawing an analogy from biomechanics, the interactions among these factors can be likened to mechanical forces. The influences of people, cargo, and market on consumers’ internal state are analogous to external forces exerted on a biological structure, which then triggers a response in the form of consumers’ internal state change. This internal state, in turn, acts as a driving force for purchase intention, similar to how a biological entity responds and adapts. The mediating role of value and trust is also vital. The findings of this study furnish a solid foundation for enhancing the sophistication and quality of e-commerce live streaming marketing, strengthening consumers’ purchase intention, enriching their purchasing experience, and facilitating the stable and sustainable growth of the e-commerce live streaming sector.

To assess and forecast the mental health conditions of university students utilizing machine learning methodologies, focusing particularly on the influence of the nine psychological symptom dimensions encompassed by the Symptom Checklist-90 (SCL-90). The prevalence of mental health issues among college students is a significant concern. Traditional methods for assessing mental health may lack the precision required for early detection and intervention. Machine learning offers advanced tools to analyze complex data and predict outcomes based on multiple variables. The primary objective is to construct and evaluate predictive models for the mental health status of college students using various machine learning algorithms, optimize their performance, and identify the most impactful psychological symptom dimensions. Psychological health data from 11,943 college students were gathered via an online questionnaire platform. Multiple machine learning algorithms were utilized to develop predictive models. Hyperparameter optimization was achieved through K-fold cross-validation and the northern goshawk optimization algorithm. To tackle class imbalance, the synthetic minority over-sampling technique was employed to create synthetic samples for underrepresented classes. Model performance was assessed using metrics such as accuracy, recall, and f1 score. The light gradient boosting algorithm demonstrated superior performance, with only 6 misclassifications out of 2,388 test samples. Tree-based ensemble methods like random forest and extreme gradient boosting consistently outperformed non-ensemble methods such as k-nearest neighbors, multi-layer perceptron, and kernel discriminant analysis. A detailed analysis using Shapley additive explanations values indicated that features such as obsessive-compulsive symptoms and anxiety were the most influential in the model’s predictions. This study underscores the efficacy and potential of machine learning in mental health assessment. The results provide a robust scientific foundation for the development of early warning systems and targeted intervention strategies to enhance the mental well-being of college students.

Objective: To construct a classified evaluation model of middle school students’ physical health based on biomechanical parameters and to achieve accurate intervention of adolescents’ physical health through biomechanically informed strategies. Methods: Cluster sampling was used to select 1261 male students’ physical health samples from eight junior middle schools in Jinhua City, Zhejiang Province from 2022 to 2023 as a data set. Second-order clustering and BP neural network algorithm were used to establish a physical health classification evaluation model for middle school students, and the accuracy of the model was verified. Results: The classification model of physical health evaluation of middle school students has an accuracy of about 90%. The integration of biomechanical data improved the model's ability to identify at-risk students and tailor interventions. Conclusion: The classification model has high prediction accuracy and good generalization performance, and provides scientific data support for subsequent accurate intervention of students’ physical health in different categories. By incorporating biomechanical insights, the model not only identifies health risks but also informs targeted, biomechanically sound interventions to improve physical health outcomes. This study bridges the gap between physical health assessment and biomechanics, offering a novel approach to promoting adolescent well-being.

Gene editing technology, particularly CRISPR has revolutionized the field of biology, agriculture and biomechanics, significantly enhancing the global agricultural economic efficiency and enabling advancements in biomechanical research. CRISPR technology has emerged since around 2012, and has rapidly become a hotspot for research on gene editing technology. Utilizing the precise and efficient genome editing capability of CRISPR, the technology has made breakthroughs in enhancing crop resistance to insect pests, improving drought resistance, and increasing the nutritional value, and enhanced agricultural economic benefits. In parallel, CRISPR has also enabled innovative applications in biomechanics, such as the development of genetically modified organisms (GMOs) with optimized musculoskeletal structures for improved mechanical performance and the study of gene functions in biomechanical systems. In this study, we integrated CRISPR patent data from 13 countries and regions with different regions and development levels from 2015–2022, constructed the Agricultural Economic Index (AEI) through principal component analysis, and used regression models to quantitatively assess the economic benefits of CRISPR technology, while also exploring its potential impact on biomechanical research and applications. The results of the study show that there is a significant positive correlation between the number of CRISPR patents and agricultural economic efficiency, validating its key role in promoting agricultural transformation and sustainable development. This study highlights the dual impact of gene editing technology in promoting economic growth and fostering bio-innovation, while also exploring its potential impact on biomechanical research and applications. This research provides empirical evidence of CRISPR's strategic value in agriculture and biomechanics, emphasizing the importance of technological innovation f enhancing economic efficiency and advancing scientific frontiers.

In order to change the current situation of discontinuous services, fragmented resources, and non-sharing of information in the postpartum market, the related maternity and health services are integrated and developed to form a diversified and integrated postpartum health management service centered on postpartum women. From the perspective of caring ethics, this paper analyzes the logical correlation between the caring practice process and the double-diamond model, innovates the double-diamond model, builds the double-diamond model-EC double-diamond model from the perspective of caring ethics, and guides the output and practice of postpartum women’s health management service design strategy by insight into the four stages of caring, undertaking care, enhancing care and responding care. Notably, we incorporate biomechanical principles into this framework. By assessing postpartum women's physical conditions through biomechanical methods, such as measuring muscle strength, joint flexibility, and postural alignment, we can provide more targeted and effective recovery plans. For example, based on biomechanical data, we can design customized exercise programs for muscle rehabilitation and posture correction. Finally, under the guidance of service design thinking, with the help of “Internet +” technology integration to build postpartum health management service system. Driven by the wave of Innovation 2.0 (Internet +), we use the advantages of Internet information technology to integrate and optimize postpartum service resources, build a postpartum health management service platform, provide personalized postpartum health management solutions for postpartum women, and finally produce the “MAMALAND” app. Multiple service scenarios are connected to form a combination of online and offline services for postpartum health management services, giving postpartum women a full range of care. This paper, from the dual perspectives of caring ethics and biomechanics, explores postpartum women’s health management service design and provides continuous, scientific and personalized services for postpartum women by integrating postpartum market service resources and innovating postpartum women’s health management service model.

Acute lung injury (ALI) is a pulmonary condition caused by various factors, characterized by high mortality rates and significant clinical challenges. The onset of lung injury is closely associated with excessive inflammatory responses, making the modulation of these responses a critical target for ALI therapy. Despite the growing interest in phytochemicals, the mechanisms through which they modulate inflammatory pathways in lung injury remain poorly understood. This knowledge gap underscores the need for further investigation into the biological targets of phytochemicals in regulating lung injury-induced inflammation. The purpose of this study is to identify specific phytochemical compounds that can mitigate inflammation by targeting key signaling pathways, particularly by inhibiting Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-κB); p38 Mitogen-Activated Protein Kinase (p38 MAPK); and c-Jun N-terminal Kinase (JNK) pathways, thereby reducing pro-inflammatory cytokines such as TNF-α, IL-6, and IL-1β. A mouse model of acute lung injury was established, and different concentrations of phytochemicals were administered. Combined with biochemical analyses, immunohistochemical staining, and Western blot experiments, the inhibitory effects of phytochemicals on lung injury and related inflammatory cytokines were evaluated. The results indicate that phytochemicals can significantly suppress the inflammatory response caused by lung injury, reducing the levels of inflammatory cytokines such as TNF-α, IL-6, and IL-1β (p < 0.01). Pathological analyses revealed substantial improvement in lung tissue, particularly in the high-concentration intervention group. Western blot results demonstrated that phytochemicals exert anti-inflammatory effects by inhibiting the activation of NF-κB, p38 MAPK, and JNK signaling pathways. This study provides novel insights into the potential molecular mechanisms underlying the anti-inflammatory effects of phytochemicals in ALI, and identifies specific biological targets for their clinical application, highlighting their promising therapeutic potential.

In the current information age, knowledge sharing, as one of the core features, provides a new perspective for innovation and entrepreneurship education (IEE) innovation. This study aims to explore the process of cultivating university students’ entrepreneurial ability, optimize the knowledge sharing environment by establishing a specialized biomechanics knowledge sharing platform, strengthen the proportion of practical teaching in biomechanics-related majors, integrate the latest research results and innovation cases in biomechanics into the innovation and entrepreneurship education, stimulate the students’ innovation interest and entrepreneurial awareness, and further promote the college students in biomechanics field to further development of IEE for college students in the field of biomechanics.. By using the research methods of literature review, questionnaire survey, multiple linear regression and structural equation model, this article analyzes the influence of knowledge sharing environment on students’ entrepreneurial will, biomechanics application ability and overall educational effect after incorporating biomechanics concept. It is found that the various complex problems faced in biomechanics research provide opportunities for college students to cultivate innovative thinking and problem-solving abilities; for example, by introducing the application of biomechanics in medical devices, sports science, biomedical engineering and other fields, students are guided to think about how to develop new products or services using biomechanical principles, thus cultivating their innovative thinking and entrepreneurial spirit. the frequency of dissemination and application of biomechanical knowledge in the knowledge sharing mechanism is positively related to students’ entrepreneurial intention, which plays a significant role in improving the biomechanical knowledge and utilization of the students in the process of entrepreneurship. Carrying out biomechanics-related entrepreneurship programs can cultivate the teamwork ability of college students. In the entrepreneurial process, students from different professional backgrounds, such as biomechanics majors, engineering majors, marketing majors, etc., need to work together to transform biomechanical research results into actual products and market them. This kind of inter-disciplinary teamwork can exercise the communication and coordination ability, resource integration ability and team management ability of college students. The results of this study not only confirm the positive effect of biomechanics-based knowledge sharing mechanism based on biomechanics on IEE, but also provide theoretical support and practical guidance for tertiary education institutions on how to optimize the knowledge sharing environment and improve the quality of entrepreneurship education.

Objective: To investigate the biomechanical characteristics of ileal microbiota in patients with metabolic associated fatty liver disease (MAFLD). Methods: 72 patients with MAFLD were recruited in our hospital between January 2024 and November 2024. They were divided into mild fatty liver (MF group, n = 36) and moderate to severe fatty liver (SF group, n = 36) based on CAP values, and a healthy control group (H, n = 36) was included. The ileal microbiota was sampled using endoscopy with newly designed accessories, followed by 16S rDNA sequencing to analyze the biomechanical properties of the microbial community. Results: Compared with the control group, Alpha diversity of ileal flora in MAFLD group elevated, a gradual decrease in the percentage of Turicibacter, a decrease in the abundance of Lactobacillus and Veillonella, but an increase in the abundance of Prevotella, Leptotrichia, Porphyromonas. Conclusion: The biomechanical diversity and abundance of ileal microbiota in MAFLD patients change, which is related to the severity of the disease. The reduction of Turicibacter and the migration of oral colonizing bacteria are characteristic features of the ileal microbiota in MAFLD patients, potentially influencing the biomechanical interactions within the gut environment.

The post-fifth-generation communication (B5G) and Internet of Things (IoT) environments have not only revolutionized the technological aspects of live English instruction in educational technology but also hold potential implications for the physical and physiological experiences of students during learning. In this research, a mathematical modeling approach based on B5G-IoT is employed to assess the impact on teaching efficacy. Through a set of constructed mathematical formulas, the influence of live broadcasting is statistically examined in a number of areas, including network flexibility, interactive delay, audio and visual clarity, and transmission quality, by building a set of mathematical formulas. The experimental design compares live English teaching effects under 4G, 5G, and B5G network conditions, collecting key indicators such as video clarity, audio signal-to-noise ratio, interactive delay, content delivery completeness, and learning concentration. Results demonstrate that in the B5G environment, video quality achieves 4 K resolution using H.266/VVC codecs, the audio signal-to-noise ratio improves to 30 dB through real-time noise cancellation algorithms, and interaction delay is reduced to less than 30 ms by leveraging ultra-reliable low-latency communication (URLLC) not only foster efficient teacher-student exchanges and a seamless teaching process but also have consequences for students’ physical states. For instance, the improved visual and auditory quality may reduce the strain on students’ eyes and ears, which in turn could affect their overall physical comfort and ability to maintain focus. Additionally, the reduction in interaction delay might lead to more natural and less fatiguing postural adjustments and muscle movements as students interact with the teaching materials. The enhanced reliability and scalability of the B5G network, ensuring 98% knowledge point delivery completeness, can potentially minimize the physical and mental stress associated with disrupted or incomplete learning experiences. This, in turn, may have a positive impact on students’ concentration and active participation in the classroom. By comprehensively considering these aspects, this detailed exploration of B5G and IoT technology applications and implementations offers a more profound understanding of their role in enhancing live English teaching and the associated physical and physiological aspects of student learning.

Energy metabolism is a central topic in physical activity and sports sciences. However, some concepts still require biological contextualization and more precise terminology in scientific literature. In this regard, the purpose of this review was to highlight certain concepts that deserve to be reconsidered and possibly excluded from the vocabulary of exercise and sports sciences. It is argued that the terms “anaerobic” and “aerobic”, used to classify exercises or sports activities, are incorrect and imprecise. Similarly, the persistent use of the term “lactic acid” (i.e., the interchangeable use of lactate and lactic acid, often incorrectly considered the same) consequently leads to the misrepresentation of the phenomenon of “lactic acidosis”, which lacks rigorous biochemical support. Therefore, a conceptual reframing is needed to align with recent findings in exercise biochemistry and molecular biology. The following issues are addressed: i) The estimation of energy system contributions during physical exercise, with emphasis on the most commonly used methods in humans; ii) the classification of energy metabolism—and by extension, exercises—into “anaerobic” and “aerobic”, challenging this dichotomy and proposing a more precise classification into oxygen-independent energy systems (phosphagen and glycolytic) and oxygen-dependent energy systems (mitochondrial oxidative system); iii) the concepts of lactic acid production and lactic acidosis, refuting the idea that lactate accumulation results from oxygen deprivation and highlighting its role as an important metabolic intermediate; and iv) the interaction and contribution of energy systems during physical exertion, stating that energy systems are not activated sequentially but simultaneously, with their predominance depending on metabolic demands. By aligning terminology with contemporary findings in biochemistry and molecular biology, this perspective enhances the understanding and critical analysis of metabolic concepts in sports science education and professional practice, encouraging their adoption based on scientific evidence.

Introduction: Yoga, combining physical postures, meditation, and controlled breathing, may restore balance between mind and body, potentially alleviating KOA symptoms and enhancing quality of life. This systematic review aims to evaluate the efficacy of yoga in treating KOA, focusing on pain relief, mobility, and quality of life. Materials and Methods: A comprehensive literature search was conducted in Medline/PubMed, Google Scholar, and the Cochrane Library databases to identify studies up to 2022. Using a combination of MeSH and free-text terms, the search targeted randomised controlled trials (RCTs) investigating yoga interventions for KOA. Data extraction was performed independently by three authors, focusing on study characteristics, intervention details, control group interventions, outcome measures, and results. Results: Nine studies involving 372 participants were included. Sample sizes varied from 11 to 125 participants, with ages ranging from 51 to 71 years. Yoga interventions varied in style, duration, and intensity, with sessions ranging from 40 to 90 min, three to six times per week. Yoga significantly reduced pain compared to control groups. Mobility improvements were noted in walking tests and chair-stand exercises. Conclusion: The evidence suggests yoga is a beneficial adjunct therapy for managing KOA, enhancing pain relief, mobility, and quality of life. However, heterogeneity in study designs, participant characteristics, and yoga protocols limits definitive conclusions.

This systematic review explores that exercise can increase the levels of neurobiological factors such as BDNF and dopamine, thereby improving mental health such as depression and anxiety, and enhancing the effect of information communication. Utilizing data from PubMed, Scopus, Web of Science, and PsycINFO, the study encompasses research published in English between 2000 and 2022. Inclusion criteria necessitated empirical studies focusing on the impact of exercise on neurobiological mechanisms and mental health outcomes, published in peer-reviewed journals. Two independent reviewers extracted data on study design, sample size, participant characteristics, exercise intervention specifics, neurobiological mechanisms, and mental health outcomes. Both qualitative thematic synthesis and quantitative meta-analysis were employed for data analysis. Mathematical formulas, including effect size calculations, odds ratios, and confidence intervals, quantified the results. The review identified 45 studies, consistently demonstrating positive effects of exercise on neurobiological factors such as BDNF, dopamine, GABA, serotonin, and norepinephrine levels, which are correlated with improvements in mental health conditions like depression, anxiety, and stress. Despite some heterogeneity, these findings highlight the potential of exercise as an effective intervention for enhancing mental health through neurobiological modulation.