Vol. 22 No. 2 (2025)

The SPOC (Small Private Online Course) model is a new teaching model derived from Massive Open Online Courses (MOOC) and is an important direction for the reform and innovation of future higher education. In the practical application of SPOC teaching, subjectivity issues may arise, namely the influence of individual opinions and judgment criteria. The evaluation may focus on certain aspects and neglect others, resulting in incomplete overall assessment results. This paper applies the SPOC teaching model in three time periods: before class, during class, and after class, with a focus on integrating biomechanical principles into the evaluation of student performance in basketball. The study employs various research methods such as literature review, Analytic Hierarchy Process (AHP) analysis, and fuzzy comprehensive evaluation to construct a comprehensive evaluation index system for student ability improvement in SPOC teaching in higher education. This system incorporates biomechanical factors such as movement efficiency, force production, and body mechanics, which are essential for enhancing basketball skills. Based on this, an evaluation method for student ability improvement in college SPOC elective basketball courses is proposed using the AHP-fuzzy evaluation model. This model not only assesses traditional skill metrics but also integrates biomechanical assessments, such as shooting mechanics, dribbling efficiency, and defensive posture. The application of the AHP-fuzzy evaluation model, enhanced with biomechanical insights, demonstrates that his method can effectively improve students basketball abilities. The incorporation of biomechanical principles allows for a more holistic evaluation of student performance, emphasizing the importance of physical mechanics in skill development. The integration of the SPOC teaching model with biomechanical principles in elective basketball courses provides a comprehensive framework for evaluating and improving student performance. By focusing on both skill acquisition and biomechanical efficiency, this approach not only enhances basketball abilities but also fosters a deeper understanding of the physical demands of the sport.

Epidermal growth factor-like domain 6 (EGFL6) plays a crucial role in angiogenesis in various malignant tumors. This study aimed to screen microRNAs (miRNAs) targeting EGFL6 and explore their mechanisms in regulating angiogenesis in esophageal squamous cell carcinoma (ESCC) cells. After analyzing the miRNA expression profiles of ESCC and using the target prediction algorithm, we screened three miRNAs that could potentially target EGFL6. By dual luciferase reporter gene assay and western blot, we found that miR-6747-5p could directly target EGFL6 and down-regulate EGFL6 expression in ESCC cells. The results of clone formation, CCK-8, Transwell, wound healing, and endothelial cell tube formation assay showed that miR-6747-5p exerted a significant inhibitory effect on the proliferation, migration, invasion, and angiogenesis of ESCC cells. At the same time, we observed that the phosphorylation levels of AKT and MAPK were decreased, the epithelial-mesenchymal transition (EMT) related E-cadherin expression was downregulated while N-cadherin was upregulated, and the protein expression of the pro-angiogenic factors, including platelet-derived growth factor subunit B (PDGFB), fibroblast growth factor 2 (FGF2), and angiogenin (ANG) were inhibited transfected with miR-6747-5p mimics. Further studies showed that the overexpression vectors of EGFL6 transfected into ESCC cells could reverse the inhibitory effects induced by miR-6747-5p. These findings reveal that miR-6747-5p could target EGFL6 and inhibit tumor angiogenesis and ESCC progression. miR-6747-5p may be a promising biomarker for the anti-angiogenic treatment of ESCC.

This paper discusses the application of sports biomechanics in optimizing the effect of sports training, finds out the existing problems through quantitative evaluation of athletes’ technical movements, and puts forward improvement suggestions to maximize the training effect. In this study, 12 professional long jumpers were taken as the object, and advanced equipment such as high-speed photography, computer analysis and photoelectric timer were used to record the long jump movements of athletes in all directions, and the biomechanical parameters were analyzed. The results show that after four weeks of personalized training, the key performance indexes of athletes such as take-off speed, horizontal displacement, vertical jump height, landing technique and muscle strength have been significantly improved, and the average long jump performance has been significantly improved. Principal component analysis (PCA) reveals the key biomechanical factors that affect the performance of long jump, including take-off speed, horizontal displacement and vertical jump height. In addition, hierarchical cluster analysis helps to identify the technical characteristics and training needs of different athletes, which provides a basis for making personalized training plans. The results show that the application of sports biomechanics not only improves athletes’ performance, but also helps to reduce sports injuries and prolong sports life, and provides scientific training guidance for coaches. This study has important theoretical and practical value for promoting the development of sports science and improving the level of competitive sports.

This study investigates the mechanical, biodegradation, and microstructural performance of cementitious composites reinforced with Corn Straw Ash (CSA) and Corn Straw Fiber (CSF) for applications in bio-inspired materials and sustainable engineering. CSA, a pozzolanic material, enhances matrix densification, while CSF provides crack-bridging and toughness improvement. Dynamic mechanical testing under cyclic loading demonstrated that CSA-CSF composites exhibit superior fatigue resistance, retaining 85% of their initial compressive strength after 1000 cycles. Biodegradation studies in simulated body fluid (SBF) and acidic environments revealed that the composites maintain 75% compressive strength in SBF over 28 days, highlighting their potential for bioactive scaffolds. Scanning electron microscopy (SEM) and quantitative porosity analysis showed that CSA-derived Calcium Silicate Hydrate (C-S-H) gel effectively filled voids, while CSF enhanced fiber-matrix bonding, mimicking the hierarchical structure of biological systems. The results emphasize the dual benefits of CSA-CSF composites in dynamic environments and their alignment with sustainable and bio-inspired design principles. This research provides insights into the development of materials for biomechanical applications, including tissue engineering scaffolds and earthquake-resistant structures.

Fatigue can significantly alter an athlete’s biomechanics and performance, which can increase their risk of injury. Important basketball moves like jump shots (JS) and countermovement jumps (CMJ) primarily use the muscles in the lower back and lower limbs. Basketball players’ jumping mechanics, performance, and risk of injury during CMJ, JS, and ankle sprains were all examined. A total of 415 male collegiate basketball players participated in the league, this season representing varying levels of jumping mechanics. Surface electromyography, a force plate, and a 3D motion analysis system were used to gather data. Field-goal percentage, the center of mass’s (CM) lowest point, joint angles during takeoff and landing, and Electromyography data from the lower leg muscles, erector spinae limbal, and rectus femoris were among the parameters that were recorded. Lower back muscle tiredness was created, and performance was evaluated after the fatigue. Data was analyzed using SPSS, with paired-sample t-tests, logistic, and Multiple Regression tests employed to examine the impact of fatigue on performance and injury risk. These tests assessed how fatigue affects shooting accuracy and joint angles, and increases the chance of injuries in basketball players. Following a period of tiredness, athletes’ field-goal percentages significantly decreased, whereas their CM lowest point increased on jump shots. In both CMJ and JS, fatigue leads to reduced knee flexion angles and increased ankle plantar flexion during landing, changing the contribution ratio of both legs. Due to impaired mechanics, these biomechanical changes suggest an increased probability of ankle sprains and an elevated risk of damage, especially during landing. The risk of lower back, knee, and ankle injuries increased due to major athletic impairments and altered landing mechanics caused by lower back muscular fatigue. To reduce basketball players’ risk of injury, these data highlight how crucial it is to address fatigue in training and recovery plans.

This study introduces a lightweight, multi-node IMU-based motion capture system optimized for biomechanical analysis, addressing limitations of traditional optical systems and challenges in sensor drift and noise. Multi-node IMU systems offer distinct advantages in biomechanical analysis, such as portability, affordability, and the ability to capture motion data in real-world environments, making them particularly suited for applications in gait analysis, sports performance, and rehabilitation. Enhanced calibration techniques correct biases in accelerometers, gyroscopes, and magnetometers, while an optimized Madgwick algorithm ensures accurate, real-time motion tracking. The system’s scalable design, supported by high-throughput USB 3.0 communication, enables precise capture of human motion. Experimental validation confirms the system’s affordability, robustness, and suitability for biomechanics, offering a practical and effective tool for advancing human movement research.

In recent years, advancements in technology have significantly transformed educational paradigms, particularly through the integration of biomechanics in teaching methodologies. The incorporation of biomechanical analysis in educational settings provides valuable insights into students' physical engagement and motor skills development. This study aims to leverage biomechanical data to enhance the effectiveness of physical education and sports training. Biomechanical sensors, such as motion capture systems and wearable devices, collect critical data on parameters like gait, balance, and muscle activity. By analyzing this data, educators can gain a deeper understanding of students' physical performance and identify areas for improvement. We propose a novel biomechanical optimization framework utilizing a multi-kernel support vector machine (MK-SVM) to assess students' physical strain levels during activities. In the preprocessing stage, a median filter is employed to eliminate noise from the motion data. Features are extracted using power spectral density (PSD) analysis to evaluate students' physical responses during instructional activities. The proposed method utilizes algorithms to create personalized training environments, identifying physical responses and facilitating real-time feedback for enhanced engagement in sports and physical education. The MK-SVM algorithm is applied for feature selection, effectively categorizing student strain levels to refine personalized learning strategies. Results indicate that our approach outperforms traditional methods, achieving high accuracy (92%), Recall (98%), precision (80%), and F1-Score (88%) in assessing students' physical strain.   This study demonstrates how biomechanics and technology can revolutionize physical education, fostering more adaptive and responsive learning environments.

This study explores the biomechanics of brushstroke dynamics in painting, focusing on the physical demands of different brushstroke types and their underlying kinetic elements. Through an experimental method combining motion capture, force sensors, and electromyography, we analyzed the joint angles, Muscle Activation (MA) patterns, and force application across four brushstroke types: broad strokes, fine detail, stippling, and circular motions. Key findings revealed that broad strokes required the most extensive range of motion, with shoulder and elbow joint angles averaging 45°–60° and 30°–40°, respectively, reflecting the involvement of larger muscle groups in creating expansive movements. Fine detail strokes, in contrast, relied predominantly on wrist flexion and extension (15°–20°), necessitating greater precision and stability from distal muscles. Force analysis showed that stippling generated the highest mean force (10.2 N) due to repetitive dabbing motions, whereas fine detail strokes exhibited minimal force variability, indicating controlled, delicate muscle engagement. Electromyography data indicated peak MA in the extensor carpi radialis and flexor carpi radialis during fine and circular strokes, highlighting the unique demands of rotational and fine motor control in painting. These findings underscore the complex interplay of movement, force, and MA required for different painting techniques, contributing valuable insights for optimizing technique and preventing repetitive strain in artists. This research provides a foundational biomechanical understanding of brushstroke execution, with implications for art education, rehabilitation, and ergonomic interventions in the arts.

Stroke continues to be a major public health concern involving high morbidity and mortality in most parts of the world with far reaching economic impact. About two thirds of these cases are caused by ischemic strokes that are linked to carotid artery disease. Identifying high-risk stroke patients is crucial for initiating appropriate management. At the cellular molecular biomechanics level, the carotid artery’s endothelial cells experience shear stress. Changes in blood flow patterns can disrupt the mechanotransduction pathways within these cells. Ideally, high risk stroke patients should first be identified for proper management to be begins. Carotid artery disease or carotid stenosis and plaque formation is one of the major and modifiable risk factors for ischemic stroke, thereby the importance of accurate diagnostic assessment cannot be overstressed. The current uses, accuracy, and future developments of carotid ultrasound as a diagnostic tool for stroke prevention are discussed in this paper. Carotid ultrasound that is noninvasive and relatively inexpensive is a central tool in evaluating the degree of stenosis and plaque features that are vital in risk of stroke. It can detect alterations in the intima-media thickness, which reflects changes in the cellular and extracellular matrix composition of the artery wall. The biomechanical properties of plaques, such as their stiffness and vulnerability, can also be inferred. In the review, difficulties in stroke risk detection and features of carotid ultrasound that were outlined include its effectiveness in identifying stroke risk, in comparison with other imaging techniques, the incorporation of carotid ultrasound into clinical practice for early strokes detection. New developments such as carotid elastography and imaging with the help of artificial intelligence algorithms are also presented to exemplify the increasing possibilities of enhancing diagnostic accuracy. The primary value of carotid ultrasound is in stroke prevention because it offers a preliminary and precise means of diagnosing carotid artery disease. However, further studies are needed to explore additional applications, enhance diagnostic precision, and develop more effective preventive healthcare strategies, taking into account the cellular molecular biomechanics of the carotid artery and its associated pathologies.

Physical Education Teaching concerns the process of leading students to perform different tasks, games, and workouts that improve physical fitness, body control, and health. In the realm of cell and molecular biomechanics, physical education teaching can be regarded as a means to induce specific physiological responses at the microscopic level. The various physical activities in which students partake, like diverse tasks and workouts, exert mechanical forces that permeate throughout the body and impinge upon cells and tissues. During physical exertion, cells within muscles, bones, and connective tissues are subject to biomechanical stress. This stress triggers a cascade of molecular events. Teachers focus on enhancing spatial and manual skills, promoting cooperation, and setting up priorities. In this research, it is proposed to learn about the teaching system of physical education in colleges and universities using artificial intelligence (AI) optimization algorithm. Thus, for predicting the achievements of college students in physical education, we propose the Blue Monkey optimization-driven Weight-Tuned AdaBoost (BM-WTAdaBoost) algorithm. The observations and variables were derived from typical physical education programs of college students during their training sessions. A data pre-processing technique known as min–max normalization is applied to the obtained raw data to enhance its quality. For nonlinear data, Kernel Principal Component Analysis (kernel-PCA) is employed as it helps in extracting the nonlinear information, which in turn helps in making accurate predictions. The following is our proposed model: BM opt with WTAdaBoost to improve selecting features and model accuracy in predicting college students’ physical education outcomes. Python program uses our suggested technique. The finding assessment phase assesses the suggested model’s prediction efficacy using several measures, including the accuracy ratio (99.8%), F1-score (95.56%), prediction ratio (98.24%), interaction ratio (97.2%), efficiency ratio (98.24%), performance ratio (97.2%), and error rate (5.62%). We also performed a comparative analysis with different traditional approaches to assess the efficacy of the suggested strategy. Comparative analysis with traditional methods shows the superiority of this approach in predicting physical education outcomes considering cell and molecular biomechanics, providing a novel perspective for understanding and optimizing physical education in relation to the microscopic biological world.

Rapid urbanization has resulted in decreased biodiversity, adversely impacting ecosystem functions and human health, especially in rural regions. Biomechanics-informed rural planning integrates principles of biological mechanics with biodiversity enhancement and public health objectives to establish sustainable communities. Purpose: This study aims to foster resilient ecosystems and healthier rural populations by introducing biomechanics-informed approaches to rural planning that synergize biodiversity enhancement with health promotion. Methods: This study bridges the knowledge gap by examining the relationship among biomechanically efficient behaviors, personal health, and ecosystem-based disaster risk reduction (EDRR) through the lens of the Health Belief Model (HBM). Structural equation modeling (SEM) was employed to investigate the correlations between the study's key variables. The research focused on a rural community impacted by disaster to test the hypotheses, exploring biomechanics-informed rural planning strategies that facilitate sustainable development and biodiversity enhancement. Results: The findings indicate that health perceptions and EDRR attributes indirectly influence biomechanically efficient behaviors. Specifically, participation in activities that support biodiversity is positively associated with perceptions of social integration benefits, EDRR awareness, and health promotion. Conclusion: This study underscores the potential to integrate biomechanics into Emergency Disaster Risk Reduction (EDRR) initiatives and community planning to encourage healthy lifestyles and enhance the environmental sustainability of resilient communities.

With the in-depth implementation of the Scientific Outlook on Development, in order to implement the national basic energy-saving policies and various emission reduction mechanisms, as well as the demonstration and promotion role of universities in energy conservation and emission reduction, the state has put forward the idea of further supporting the construction of energy-saving campuses in higher education and universities. This paper delves into the application of green and environmentally friendly materials in the construction of sports facilities, not only from the perspective of ecological sustainable development but also in close connection with the field of biomechanics. Biomechanics plays a crucial role in the design and use of sports facilities. When applying green materials, it is essential to consider how these materials interact with the human body's mechanics during sports activities. For example, different sports demand specific mechanical properties from the facilities, such as the right amount of elasticity, friction, and shock absorption. Green materials, when selected and designed with biomechanics in mind, can enhance the performance and safety of athletes while maintaining environmental friendliness. To assess the feasibility of green materials in this context, this paper presents an evaluation method that integrates the Analytic Hierarchy Process (AHP) and information entropy. This approach incorporates biomechanical factors into the index system. By doing so, the combined weight value of each component in the system can more accurately reflect the real-world situation. Compared to traditional evaluation methods, this integrated approach effectively mitigates the subjectivity in determining weight coefficients. It also ensures that the significance of each evaluation index, especially those related to biomechanical performance, is fully considered. The experimental results in this paper show that the use of AHP to evaluate the advantages of green environmental protection materials, the average weight reaches 0.8275. This finding strongly suggests that the application of green materials in sports facilities construction is highly viable. These materials not only contribute to environmental protection but also offer biomechanical advantages, ensuring the long - term sustainability and functionality of the facilities for athletes.

The development and improvement of a youth basketball training program founded on the fusion of education and sports is investigated in this study. Athlete performance and academic advancement must be balanced in light of the growing need for comprehensive youth development. Biomechanical factors play a significant role in both sports performance and injury prevention, making it essential to integrate them into the training program design. To increase the effectiveness and design of training programs, the suggested model makes use of the Tabu Search Optimized Intelligent Random Forest (TSO-IRF) algorithm. The TSO-IRF identifies important physical, technical, and cognitive elements affecting basketball play by combining search-based optimization with machine learning (ML) approaches from a biomechanical perspective. It focuses on elements such as joint forces, muscle activation patterns, and movement kinematics, which are fundamental in determining an athlete's performance and injury risk. The research gathers information on youth basketball training programs, with a specific emphasis on biomechanical aspects. This includes information on players' body mechanics during different basketball movements, like jumps, shots, and passes. By integrating this data, the study ensures that the goals of educational development and sports training are aligned, while also considering the biomechanical requirements of the athletes. TSO-IRF is used to evaluate these multidimensional features and provides individualized training suggestions in line with the performance and educational objectives of both sports. Experimental results indicate that the TSO-ERF model can perform better than traditional methods, providing higher prediction recall (94.26%), accuracy (97.81%) and precision (97.21%) in development metrics for players. Additionally, the model shows improved adaptability across various skill levels as it can adjust training recommendations based on an athlete's unique biomechanical characteristics. The proposed youth basketball training system optimizes loads in training, reduces risks of injury, and develops young athletes over the long term. It facilitates athletic success but fosters cognitive and emotional development so that the fields of sport and education may converge. Future work involves the application of this model in other sports disciplines and algorithm refinement to take care of larger datasets that would help deliver real-time performance feedback.

Alpine skiing turning technique requires high coordination of movements, but the existing training methods lack in-depth analysis of biomechanical characteristics. Athletes are prone to injuries during training. Technical optimization mainly relies on summarizing experience and lacks a precise quantitative basis. This paper aims to systematically analyze and optimize alpine skiing turning techniques from a biomechanical perspective, build a scientific action model and data analysis system, realize quantitative evaluation of technical movements, improve training safety and technical level, and provide scientific guidance for athletes. The study uses motion capture equipment to record three-dimensional motion trajectories and pressure sensors to collect mechanical data. By building an analysis model based on biomechanics, key features such as joint angles and torque changes are extracted, and an optimization scheme is designed in combination with a nonlinear multi-objective optimization algorithm. Based on these models and algorithms, a real-time feedback system is developed to provide personalized training suggestions to support athletes in adjusting movements and improving their technical level. The experimental results show that compared with the traditional training method, the coordination and balance of the optimized training model are improved by about 23.6% and 13.6% respectively, the action efficiency is improved by about 26.9%, and the risk of injury is reduced by more than 20%. In addition, the results of the model generalization ability test also show that the optimized training method has the characteristics of adapting to different groups. This shows that the optimized training method can significantly improve movement coordination and efficiency while reducing the risk of sports injuries, providing a new path for the scientific training of alpine skiing.

The design and development process of bio-inspired musculoskeletal robots in the artificial intelligence environment integrates mechanical design, control algorithms, real-time computing, variable sensing, and other fields of technology, which can provide effective mechanical support for the user’s actions in the process of use, and has a broader application prospect in a number of fields. In the process of exoskeleton robots moving from experimental research and development to practical applications, the comfort of the use process is an important evaluation criterion. Therefore, from the perspective of user comfort, this paper takes the waist exoskeleton robot as the research object, and conducts a design study on the structure and control of the exoskeleton robot.

Traditional e-commerce platforms have the problem that users cannot try on sports equipment in person, and it is difficult for consumers to perceive its size, comfort and dynamic performance before purchasing. This limitation leads to high return rates and difficult purchasing decisions. This paper introduces a virtual try-on solution with higher accuracy and more immersion. After using 3D scanning technology to obtain the user’s body data and combining it with SMPL (Skinned Multi-Person Linear Model) to generate the user’s body model, a posture optimization algorithm is used to adjust the dynamic posture of the user model and the PoseNet optimization model is used to adapt it to the user’s dynamic motion scenes. Next, Unity Physics is used to achieve the dynamic performance of sports equipment materials, high-definition texture mapping technology is used to reproduce the visual effects of equipment materials to ensure that the appearance is consistent with reality, and sports scenes are constructed to simulate the actual performance of equipment in different environments. Users can use motion capture devices to simulate running, jumping and other movements to feel the suitability of sports equipment. Then, based on the user’s body shape data and sports scene preferences, Deep Q-Learning is used to recommend sports equipment options suitable for the user. Finally, the system adjusts the virtual try-on experience in real time, showing a variety of combination effects, helping users quickly find their favorite products. Experiments show that the performance error between virtual equipment and real equipment is only 1.35%, the virtual try-on pass rate exceeds 90%, and the return rate is less than 10%, which verifies the feasibility of virtual reality technology in e-commerce and improves users’ online shopping experience.

Athletic injuries are a common problem in sports. Due to the insufficient processing of multimodal biomechanical data by traditional prevention strategies, personalized risk prediction cannot be achieved. To this end, this paper adopts an athletic injury prevention method based on sparse principal component analysis (SPCA) and spatio-temporal graph convolutional network (ST-GCN). The Vicon Vantage V5 3D motion capture system and the Noraxon Ultium EMG electromyography acquisition device are used to obtain the athlete’s joint angle change rate, ground reaction force (GRF) and electromyographic activity data, and the SPCA method is used to extract key biomechanical features, thereby reducing data redundancy and improving the representativeness of features. Subsequently, ST-GCN is used to construct a dynamic risk prediction model to capture the temporal changes and spatial dependencies in the motion sequence to achieve precise and efficient risk assessment. In the experimental verification, the prediction accuracy of the model reaches 95.3% when the number of features was 20, and the ability to provide risk feedback in real-time is realized to generate personalized injury prevention strategies. Studies have shown that the integration of statistics and sports biomechanics has effectively improved the efficiency of athletic injury prevention and provided new ideas for scientific and precise sports management.

This study addresses the problem of power battery health state assessment for electric vehicles, integrating biomechanical principles and machine learning algorithms to investigate the health state assessment accuracy of different types of power batteries under different working conditions. The study adopts a variety of data-driven methods to deeply analyse the performance degradation law of power batteries. The results show that the machine learning algorithm incorporating biomechanical principles can effectively improve the accuracy of power battery health state assessment, especially under complex working conditions, and exhibits better robustness. The current status of power battery health state assessment technology is reported, and it provides a useful reference for future power battery health management in electric vehicles.

Digital cultural tourism is an emerging form of tourism that presents cultural heritage and tourism resources digitally to users, providing immersive travel experiences. However, traditional methods of constructing virtual scenes often rely on manual modeling, leading to low efficiency and high costs. The existing digital cultural tourism platforms mostly provide static and pre-set content, lacking interaction with users, making it difficult to achieve personalized recommendations and interactive experiences. In response to these issues, this article is based on VR (Virtual Reality) scene intelligent generation and interactive algorithms, and aims to optimize the overall synergy between the presentation of cultural resources and user experience by constructing a digital cultural tourism ecological model. Drawing on biomechanical principles, the study emphasizes the importance of natural user interactions and physical engagement in enhancing the immersive experience. Firstly, the Lindenmayer system (L-system) and parameterized generation rules are used to generate complex natural landscapes and architectural structures. Natural and textured scene details are added using the Perlin noise algorithm. Using GANs (Generative Adversarial Networks) technology, generative and discriminative networks are trained to generate more realistic VR scenes, further enhancing the realism and detail representation of the scenes. At the same time, a gesture recognition technology combining CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) models, along with a speech recognition algorithm based on DNN (Deep Neural Networks), is adopted to enhance the natural interaction between users and virtual scenes. By combining collaborative filtering algorithms with user behavior data, personalized content recommendations are realized, enhancing user engagement and satisfaction. The efficiency test of scene modeling, the total time required to generate scenes using the model in this article is only 84 hours, which is much lower than manual modeling. In the interactive test, the highest success rate of the model in this article in gesture recognition reaches 94%. The experimental results have verified the advantages of the model in this article in improving scene modeling efficiency and enhancing immersive experiences through biomechanically informed interactions.

This paper explores the applications and challenges of artificial intelligence (AI)-driven 3D vision technology in biomedical engineering, with a specific focus on its integration with biomechanics. 3D vision technology offers richer spatial information compared to traditional 2D imaging and is increasingly applied in fields like medical image analysis, surgical navigation, lesion detection, and biomechanics. In biomechanics, AI-driven 3D vision is used for analyzing human movement, modeling musculoskeletal systems, and assessing joint biomechanics. However, challenges persist, including image quality, computational resource demands, data privacy, and algorithmic bias. This paper reviews the development of 3D vision technology and AI, discusses its applications in biomedicine and biomechanics, and addresses the key technical obstacles, offering insights into the future development of these technologies in the context of biomedical and biomechanical research.

This study investigates the biomechanical implications and ergonomic impacts of AI-generated content (AIGC) integration in visual communication design workflows. Through a comprehensive analysis of 23 professional designers in Chengdu, China, we examined the physical stress patterns, user interaction dynamics, and overall ergonomic outcomes when transitioning from traditional to AIGC-assisted design processes. The research employed a mixed-method approach combining quantitative biomechanical measurements with qualitative user experience assessments over 12 weeks. Results revealed significant reductions in muscle activity across key muscle groups, with the upper trapezius showing the most significant decrease (−3.6% MVC, p < 0.001) during AIGC-assisted tasks. This change in muscle activity can be further linked to alterations in the body's postural stability and load distribution, which are core considerations in biomechanics. Movement efficiency metrics, which are inherently related to biomechanical performance, demonstrated a 27.9% reduction in task completion time (p < 0.001) and a 33.3% decrease in design iterations. Quality assessment scores improved across all dimensions, with Creative Innovation showing the highest enhancement (+1.8 points, p < 0.001). User satisfaction metrics indicated significant improvements, with consistent gains of 1.1 points (on a 5-point scale) across all measured dimensions (p < 0.001). Notably, the study identified distinct adaptation patterns between novice and experienced users in terms of their biomechanical responses. Experienced users demonstrated significantly faster response times in AIGC prompt input (8.94 ± 1.87 s vs 18.62 ± 3.15 s, p < 0.001), which can be associated with differences in their neuromuscular coordination and motor learning abilities. While AIGC integration initially increased certain types of errors (+51.2% in input errors), it led to substantial reductions in tool misuse (−40.4%) and design revisions (−39.9%). These findings suggest that AIGC integration can significantly reduce physical stress while improving design efficiency and quality outcomes, all of which are intertwined with the biomechanical functioning of the body during the design process. The research provides evidence-based recommendations for optimizing AIGC implementation in professional design workflows, taking into account the biomechanical and ergonomic factors that contribute to the overall well-being and creative productivity. This study has important implications for software development, workplace health policies, and the future direction of AI-assisted creative work, as it highlights the significance of considering biomechanics in the integration of advanced technologies within creative domains.

With the acceleration of urbanization and rapid development of intelligent technologies, incorporating biomechanical elements into intelligent landscape design has become a crucial approach to enhancing urban landscape quality. This research conducts a systematic study on the innovative design and implementation approaches of biomechanical elements in intelligent landscapes, proposing a “Bio-Intelligence-Environment” trinity design principle system and developing new intelligent composite materials and biomimetic multi-level structural design methods. Experimental testing demonstrates that the developed intelligent composite materials achieve a tensile strength of 576 MPa, a 32.5% improvement over traditional materials, with intelligent response sensitivity increased by 45.3%. Through biomimetic multi-level structural design, component weight is reduced by 18.5% while bearing capacity increases by 22.3%, achieving a static load capacity of 2850 N/m². The intelligent control system reaches a recognition accuracy of 98.7%, an improvement of 15.4 percentage points over traditional systems, with control precision reaching ± 0.08 mm. Environmental adaptability tests show that the system maintains stable operation within a temperature range of −25 ℃ to 65 ℃, with performance degradation not exceeding 5.8%, and relative humidity adaptation ranging from 20% to 95%. Field application data indicates a system stability rate of 99.3%, with an average fault-free operation time of 8500 h and annual operation and maintenance costs accounting for 3.2% of initial investment, a 45% reduction compared to traditional systems. User experience evaluation shows an overall satisfaction score of 92.3, with intelligent interaction satisfaction reaching 95.2%. Economic benefit analysis reveals that mass production reduces single system cost to 325,000 yuan, with a 2.8-year investment recovery period and an internal rate of return of 24.5%.

With the comprehensive interface between “Made in China 2025” and Industry 4.0, the handling mode of the handling system is constantly updated and developed, and a new type of handling mode, which is assisted by exoskeleton and other equipments to complete the handling work of workers, has been gradually applied. However, most of the existing exoskeleton-assisted robots are expensive and complicated in structure, which are not applicable to the actual needs of ordinary workers. Therefore, it is of great significance to design an exoskeleton-assisted handling robot that is applicable to the needs of ordinary workers. Based on this, this paper designs a relatively simple structure and low cost exoskeleton-assisted handling robot, and introduces the BN-Q-learning algorithm to give the control strategy of the robot, and finally simulates and analyzes the reliability of the handling robot, and the results show that the exoskeleton-assisted handling robot designed in this paper has a high reliability, and the force situation and the human body’s joints are relatively well matched when handling heavy objects. The results show that the exoskeleton assisted handling robot designed in this paper is highly reliable, and the force situation when handling heavy objects matches the human body joints.

This study explores the application of Internet of Things (IoT) devices and biochemical sensors in sports performance monitoring, focusing on the biomechanical force characteristics of athletes to address limitations in traditional methods, such as limited data types, poor real-time accuracy, and insufficient visualization. Emphasizing mechanobiological principles, the analysis targets key force-producing regions of the body—such as the feet, legs, and torso—to optimize energy efficiency, motion precision, and overall athletic performance. Biochemical sensors were employed to monitor real-time biomechanical and physiological data, while IoT devices ensured accurate data transmission, visualization, and feedback. Data accuracy was enhanced through methods such as zero correction, timestamp synchronization, and Kalman filtering, while data transmission efficiency was optimized using a lossless compression algorithm, hierarchical structuring, the MQTT protocol, and encryption via the AES algorithm. Data organization utilized a star-structured MySQL database with composite indexing for swift access. Analytical tools such as the Apriori algorithm for data correlation, linear discriminant analysis for feature extraction, and multi-source data fusion enabled detailed visualization of performance metrics. Experimental applications in football and sprinting demonstrated the effectiveness of IoT-based monitoring. Football experiments captured multi-dimensional data on technical characteristics, while sprint tests recorded precise performance metrics, including real-time speed profiling and timing accuracy. For instance, in a 100-meter sprint test, an IoT system measured an athlete's performance at 12.54 seconds with 100% accuracy, surpassing manual timing methods. These findings highlight the transformative potential of IoT devices and biochemical sensors in sports analytics, offering enhanced accuracy, real-time tracking, and actionable insights to refine athletic performance and decision-making.

In order to explore the relationship between active participation and self-efficacy in the quality of sports facilities and exercise satisfaction, this study takes into account the perspective of biomechanics. A survey was conducted among 361 urban residents aged 16 and above in Yongzhou City. The “Sports Facilities Quality Scale”, “Active Participation Scale”, “Self-Efficacy Scale”, and “Exercise Satisfaction Scale” were utilized, with an added consideration of biomechanical factors. Biomechanics, which examines the mechanical aspects of human movement during exercise, provides a crucial framework for understanding how sports facilities interact with the human body. High - quality sports facilities, designed in accordance with biomechanical principles, can significantly influence the physical experience of exercise. For example, the surface material and structure of a running track can affect the impact forces on joints during running, and the ergonomic design of fitness equipment can enhance the efficiency and comfort of movements. These biomechanical factors directly impact a person’s active participation in exercise. When facilities are biomechanically optimized, individuals are more likely to engage actively in physical activities, as they experience less discomfort and reduced risk of injury. The results showed that: 1) The quality of sports facilities, active participation, self-efficacy and exercise satisfaction were significantly correlated with each other. 2) Active participation played a single intermediary role between the quality of sports facilities and exercise satisfaction, accounting for 26.81% of the total effect, and self-efficacy played a single intermediary role between the quality of sports facilities and exercise satisfaction; Active participation and self-efficacy play a chain intermediary role between the quality of sports facilities and exercise satisfaction, accounting for 26.34% of the total effect. Conclusion: Considering biomechanics, the quality of sports facilities indirectly affects the exercise satisfaction of urban residents in Yongzhou City through the chain mediation of active participation and self - efficacy. In the context of national fitness, high - quality sports facilities, designed with biomechanical principles in mind, play a positive and crucial role in promoting residents’ physical exercise, as they can enhance the overall exercise experience from a biomechanical perspective.

Drawing on the theory of biomechanics, this study explores the mechanism of promoting precision poverty alleviation through education informatization. Precision poverty alleviation is at the core of China’s poverty reduction strategy, addressing the root causes of poverty through targeted interventions. Under this framework, education informatization has become a key tool for bridging systemic inequalities by enhancing the quality and equity of education through information technology. From basic digital construction to the deep integration of big data, cloud computing and artificial intelligence, education informatization has made remarkable progress. Analogous to how biomechanical systems optimize force transmission and movement efficiency, it showcases great potential for the optimal distribution of educational resources, enhancing access to education in impoverished regions, and fueling long-term socio-economic progress. However, its role in rural and underdeveloped areas still faces many challenges. Viewing education informatization as a dynamic “biomechanical structure” and borrowing concepts like structural adaptation, load optimization, and connectivity from biomechanics, we posit that big data technology, acting as a “biomechanical signal”, can precisely pinpoint needy students and allocate educational resources adeptly, much like how forces are coordinated in a mechanical system. Based on the fuzzy breakpoint regression model and fixed-effects analysis of CFPS (China Family Panel Studies) data, the study finds that education informatization significantly improves the subjective well-being, health care, and employment outcomes of rural families. This study highlights the innovative role of education informatization in enhancing resilience, equity, and resource efficiency through the synergy of technological evolution and policy adaptation, providing a new perspective on precision poverty alleviation.

Objective: This study aims to evaluate the diagnostic accuracy of thyroid globulin (Tg) detection in elution fluid for intraoperative judgment of lymph node metastasis, and to explore the potential role of biomolecular mechanical behavior in influencing the detection results. By rapidly quantifying Tg levels, the optimal cutoff value was determined, and its potential value in clinical applications was further assessed. Methods: This is a prospective study that included 65 patients with papillary thyroid carcinoma who underwent surgery at Xiangyang Central Hospital's thyroid surgery department from November 2022 to May 2023. A total of 150 cervical lymph node samples were collected. Tg levels were detected intraoperatively using colloidal gold immunochromatographic assay (FNA-TG-GICA), and results were compared with routine paraffin pathology findings. Particular attention was given to the reactivity of Tg molecules in the elution fluid. The optimal cutoff value for Tg test to judge the benign or malignant nature of lymph nodes was determined by plotting the ROC curve and calculating the AUC, to evaluate the diagnostic performance of the intraoperative Tg detection in identifying lymph node metastasis. Results: A total of 150 lymph node samples were included in this study, of which paraffin pathology verification showed 50 metastatic and 100 non-metastatic lymph nodes. The optimal cutoff value for Tg test was 77 ng/mL, with sensitivity of 94.00%, specificity of 96%, and accuracy of 95%. The AUC from the ROC curve analysis was 0.97, indicating high diagnostic accuracy. Further analysis revealed that most of the positive samples were metastatic lymph nodes, and all negative samples were non-metastatic, suggesting that the Tg test performs excellently in determining lymph node metastasis. Conclusion: Elution fluid Tg test demonstrates high accuracy in intraoperatively determining lymph node metastasis. With an optimal cutoff value of 77 ng/mL, it shows excellent sensitivity and specificity. This detection method serves as a rapid and reliable diagnostic tool, providing effective decision support for clinical practice.

Traditional methods for predicting investment trends often rely on macroeconomic data, overlooking the influence of individual biomechanical characteristics on decision-making, particularly in the health and medical fields. This paper seeks to enhance the accuracy of healthcare investment trend predictions by integrating high-precision biomechanical data acquisition technology with advanced quantitative analysis methods. High-precision sensors and smart wearable devices are employed to collect individual biomechanical data, encompassing dynamic features such as sports performance, joint angles, and gait. To ensure data quality, a rigorous preprocessing procedure is implemented. Principal component analysis (PCA) is utilized for feature extraction, minimizing redundant information and isolating the most representative biomechanical features. During the data analysis phase, a hybrid model combining random forests and support vector machines (SVM) is employed to predict healthcare investment trends. Random forests are applied for feature selection and regression analysis, while SVMs address classification tasks for trend prediction. The results indicate that the proposed model achieves an accuracy and precision exceeding 0.9, with healthcare investment returns on investment (ROI) ranging from 20% to 50%. The findings underscore the potential of biomechanical data in providing valuable insights for healthcare investment trend predictions, ultimately driving innovation and progress in the industry.

Heatstroke is a thermal injury disease resulting from excessive water and electrolyte loss, as well as impaired heat dissipation, in hot and humid conditions. Modern medicine typically focuses on physical measures for early heatstroke intervention and prevention, with drug-related research being somewhat limited in scale and scope. In Chinese contexts, heatstroke is often referred to as “Zhongshu”, encompassing symptoms like nausea, vomiting, loss of appetite, emotional fluctuations and agitation, and headaches due to elevated body temperature. Traditional Chinese medicine boasts a long history and extensive literature on treating heatstroke. The Qing Palace Summer-avoiding Pearl, a treasured medicine used by ancient Chinese royalty for “Zhongshu” treatment and prevention, is of particular interest. This study aims to explore a new approach for early heatstroke prevention and intervention using the Qing Palace Summer-avoiding Pearl. We identified the disease types associated with this medicine through disease enrichment analysis and pinpointed the most likely therapeutic targets and effective substances via network pharmacology and molecular docking techniques. Furthermore, we conducted molecular thermodynamic analyses on six target Plant Extracts (PEs) using molecular dynamics simulations, examining parameters such as Root Mean Square Displacement (RMSD), Radius of gyration (Rg), and hydrogen bonds. The results indicated that the complexes exhibited favorable binding performance, which may facilitate further research on the Qing Palace Summer-avoiding Pearl.

The extensive use of antibiotics in the Taihu Lake Basin has led to a significant threat to human and environmental health. In this context, the QWASI model is developed to simulate the fate of typical antibiotics in Lake Taihu. Through this model, real-time tracking of the dynamic changes in antibiotic content is possible. The study primarily focuses on evaluating the fate and transfer of antibiotics in the water and sediment phases of the lake. The model results indicate that most of the simulated concentrations and mass fluxes are within the same order of magnitude as the measured values, demonstrating a good simulation effect. However, an underestimation of the simulated output value occurs in some cases. The sediment layer serves as the main accumulation site for antibiotics, and the mass balance equation is a crucial tool for simulating the environmental distribution of antibiotics. Sulfamethoxazole (SMX) exhibits a relatively high concentration in water due to its large model input. The sensitivity analysis reveals that for ATM, SMX, and OFX, the five input parameters with the most significant impact are the half-life in water, sediment-water partition coefficient, sediment solids concentration, sediment particle density, and sediment-water diffusion MTC. For OTC, the impact of lake water depth is more prominent than the sediment-water mass transfer rate. The uncertainty analysis effectively showcases the model’s stability, with the water phase concentration showing better stability. In this process, each factor is assigned a correlation coefficient to represent its influence on the original content. The sediment phase antibiotic concentration has a relatively high uncertainty. The source intensity assessment in this study utilizes direct monitoring data of the Taihu Lake water body, ensuring higher accuracy. The major factor contributing to prediction errors is the ambiguity of the sediment phase, which is affected by numerous environmental factors. Importantly, when considering the fate of antibiotics in the lake, the biomechanical characteristics of potential drug delivery systems play a vital role. The movement and dispersion of antibiotics within the water and sediment are influenced by biomechanical forces. In the sediment, the porosity and permeability, which are related to biomechanical properties, can determine the rate at which antibiotics penetrate and accumulate. Understanding these biomechanical aspects of drug delivery systems can help in devising more effective strategies for antibiotic remediation. It can also provide insights into how the physical environment interacts with the chemical behavior of antibiotics, ultimately contributing to a more comprehensive understanding of the environmental fate of antibiotics in Lake Taihu. This is the first study to explore the fate of antibiotics in Lake Taihu and offers valuable recommendations for the restoration of antibiotic-contaminated lakes. It enriches the research perspectives on water management, especially in relation to addressing water pollution caused by antibiotic abuse.

Postural mechanics and movement control play fundamental roles in artistic creation, particularly in painting, where precision and fluidity of motion directly influence artistic outcomes. This study investigated the biomechanical relationships between posture, movement, and artistic control in painting practice through a comprehensive analysis of 38 artists (22 Female, 16 Male) ranging from novice to expert-level practitioners in traditional Chinese and contemporary painting techniques. Using an integrated measurement approach combining Motion Capture System (MCS) (Vicon Motion System), electromyography (EMG), and force plate analysis, we examined postural dynamics, movement patterns, and their effects on artistic precision across varied painting conditions. Results revealed significant correlations between postural stability and painting precision (r = 0.82, p < 0.001), with experienced artists demonstrating superior postural control strategies compared to novices. Analysis of seated versus standing positions showed distinct advantages in stability metrics (88.5 ± 4.2 vs. 82.3 ± 5.6 stability index, p < 0.01), though standing positions offered a more excellent range of motion (58.7 cm ± 7.2 cm vs. 42.3 cm ± 5.6 cm brush reach, p < 0.001). Environmental factors, particularly easel configuration and lighting conditions, significantly impacted performance, with optimal easel height (90%–105% of eye level) correlating with enhanced precision scores (improvement of 18.4 ± 4.2%, p < 0.001). Tool selection analysis demonstrated that medium-length brushes (20 cm–30 cm) provided optimal comfort (8.7 ± 0.9 out of 10) and precision (88.6 ± 3.8 out of 100) scores. Extended painting sessions revealed progressive changes in muscle activation patterns, with expert artists maintaining more consistent movement patterns despite fatigue (8.4 ± 1.2% vs. 18.7 ± 3.2% movement variability, p < 0.001). These findings provide quantitative evidence for the importance of proper postural mechanics in artistic creation and offer practical insights for optimizing painting performance through improved biomechanical awareness and environmental setup.

For patients recovering from spinal diseases, ordinary seats often lack support and correction functions, which can easily lead to problems such as spinal curvature and lumbar disc herniation. The study uses MediaPipe to recognize postures and perform 3D graphics modeling, combined with finite element analysis to simulate the coordinated work of the patient’s muscles, bones, and joints, and optimize the design of ergonomic orthosis. First, MediaPipe Pose is used for posture recognition to capture the key point coordinates of the spine and joint positions and obtain dynamic data of the patient’s sitting posture. Next, a 3D model of the patient’s skeleton and spine is built, and the bone structure is generated based on the posture data and refined with ZBrush. Then, a finite element analysis is performed using ANSYS Workbench, and the stress distribution of the spine under the patient’s weight and different sitting pressures is simulated. The orthopedic seat’s geometry and support area distribution are optimized, and adjustment functions are added to improve adaptability. The data results show that the MSE (Mean Square Error) of the optimized spinal curve deviation is only 0.0009; the maximum stress of the intervertebral disc is reduced from 56.1 MPa to 42.4 MPa; the area of the high-stress area is reduced to 12.4cm²; the stress uniformity is improved by 28.1%; the local pressure is reduced by 24.6%. The designed method can significantly reduce the patient’s spinal deviation rate, relieve seat pressure, and have a good rehabilitation auxiliary effect.

Physical education plays an essential role in the growth of students’ overall health, fitness, and well-being. Wearable biosensors revolutionize physical performance monitoring by providing real-time data on physiological parameters, providing valuable insights into students’ fitness and flexibility during physical activities. The research aims to develop an approach for assessing students’ athletic adaptation and physical fitness using biosensors. Traditional monitoring systems have complexity in managing the huge volumes of data collected from several sensors because of noise and ambiguity. These research difficulties are addressed with the help of a deep learning (DL) based assessment model, which monitors students’ fitness using biosensor data. This research proposed a novel dynamic Bumblebee mating refined deep neural networks (DBBM-RDNN) to forecast student physical fitness and sports adaptability levels using biosensors. The biosensor dataset provides different data types that capture various aspects of physical activity and fitness. The data was preprocessed using low-pass filters to remove noise from the achieved data. Principal Component Analysis (PCA) is developed to extract the features from preprocessed data. DBBM is utilized to optimize the features in sensor data and RDNN to classify or predict fitness and adaptability levels in students based on data from sensors in real time. In a comparative analysis, the research assessed various performance metrics, such as accuracy (98.05%), precision (90.9%), recall (90.1%), F1-score (88.55%), MAE (1.915) and RMSE (2.505). Experimental results indicate the proposed model achieved superior performance in predicting student physical fitness compared to other conventional algorithms. The research highlights the integration of biosensor technology with DL, which provides an accurate and dependable system for tracking students’ physical performance.

In addition to helping athletes enhance their athletic abilities and spark their interest in sports, youth athletics amateur training can also set the groundwork for athletes’ future athletic growth. Physical training still has less than optimal results for Chinese youth, nevertheless. The physical training effect of athletes can be maximized with the aid of amateur training. We can direct the creation of the most sensible physical training plan and choose the best physical training technique in order to achieve the ideal physical training goal and realize the steady improvement of athletes’ physical fitness by focusing more on and analyzing the fundamental and unique physical training strategies. Therefore, in order to improve the scientific and state-of-the-art analysis of training effect and to provide more scientific guidance for youth physical training, this paper introduces scientific quantitative indexes and personalized customized indexes into physical training from the perspective of genetic algorithms.

As the only hinge connection between human body and the ground, ankle joint plays an important role in sports. Based on the theory of rehabilitation medicine, this paper puts forward an intelligent physical training system integrating mechanical evaluation and training functions, and applies it to the rehabilitation of athletes’ ankle injuries. The system adopts virtual reality (VR) technology, realizes active ankle rehabilitation training through high-precision sensors and advanced computer simulation technology, and provides accurate evaluation, monitoring and personalized training scheme for ankle rehabilitation. The system creates an immersive training environment for athletes by simulating real sports scenes and processes. The experimental results show that the system is excellent in training convergence speed and performance, and the error is small. Compared with the traditional algorithm, the accuracy is improved by 19.27%. The main contribution of this paper is that the intelligent physical training system integrating mechanical evaluation and training is applied to the rehabilitation of athletes’ ankle injuries, and good rehabilitation results have been achieved.

Object: To guide meridian external treatment based on the “Three Yin Theories” of Water Warmth, Earth Harmony, and Wood Reaching, and to observe from a cell molecular biomechanics perspective the clinical efficacy of electroacupuncture at the calf (Chuai) and inner thigh in the treatment of heart failure. This involves analyzing how the electroacupuncture might influence cellular and molecular events within cardiomyocytes and related cardiovascular tissues. Methods: Chronic heart failure patients hospitalized in general wards were selected, and the efficacy indices before and after treatment in patients, as well as the differences between groups, were comparatively observed and studied. To explore the mechanism, not only the correlation between the reduction of neuro-endocrine hormone levels and symptom improvement was analyzed through correlation studies, but also the impact on cell molecular biomechanics was investigated. This includes examining changes in the biomechanical properties of the extracellular matrix surrounding cardiomyocytes, as well as alterations in the molecular forces and interactions that govern cell adhesion and communication within the heart tissue. Results: There was a significant difference between the treatment and control groups before and after the treatment (Sig. (two-tailed) < 0.05), and the intergroup difference was not statistically significant (Sig. (two-tailed) > 0.05), failing the significance test. There was a significant positive correlation between the decrease in aldosterone and the decrease in NYHA classification scores and BNP levels; Conclusion: Electroacupuncture at the calf and inner thigh has a better therapeutic effect on heart failure and has certain clinical promotion and application value. From a cell molecular biomechanics standpoint, the treatment appears to modulate key cellular and molecular processes within the heart, potentially providing a new avenue for understanding and enhancing the treatment of heart failure.

Pronunciation is a complex physiological process. Traditional research usually uses static pronunciation tests and fails to observe the dynamic changes of tongue muscles during pronunciation. This paper aims to comprehensively analyze the structure and function of tongue muscles and their role in English vowel pronunciation from the perspective of tongue muscle biomechanics, and provide a systematic framework for understanding. This paper designs multiple pronunciation tasks to evaluate participants’ pronunciation accuracy and dynamic changes of tongue muscles. Through multi-modal technology, dynamic images and electromyographic signals of the tongue are synchronously acquired to analyze the precise relationship between tongue movement and muscle activity in the pronunciation of English vowels. A tongue biomechanical model is constructed based on finite element analysis and Hill model to precisely simulate the mechanical response of tongue muscle activity and tongue position changes during pronunciation. The experimental results show that there is a significant negative correlation between electromyographic activity and pronunciation quality. The closer the correlation coefficient is to −1, the higher the consistency. The tongue is positioned higher and forward during pronunciation, making it easier to control, so that the pronunciation can be more accurate with less deviation. The greater the movement and flexibility of the tongue, the better it is able to form clear vowel pronunciations. In short, the tongue muscles achieve precise control of tongue position through the coordinated action of internal and external muscles during vowel pronunciation, which is beneficial to improving pronunciation accuracy.

The relationship between physical activity and human well-being is based on biological mechanisms, and regular exercise positively affects physiological, psychological and social health dimensions. This study explores the impact of physical activity resource allocation on residents’ well-being in the Yellow River Basin from a biological basis. Using data from CGSS2015, CGSS2017, and SSY2017, the study combines OLS multiple linear regression modeling and spatial econometrics techniques to reveal how the accessibility of fitness infrastructure, sports services, and resource distribution can enhance well-being by facilitating increased sports participation. Findings reveal significant spatial agglomeration effects, suggesting that a balanced distribution of sport resources can optimize regional health outcomes. However, differences in resource distribution prevent physical activity from yielding physiological and psychological benefits across provinces.

Biosensing technologies, which monitor physiological responses such as Heart Rate Variability (HRL), Skin Conductance Level (SCL), and Electroencephalogram (EEG) activity, offer a novel approach to enhancing the dissemination of health information in ideological and political education (IPE). In this context, health information encompasses topics such as mental health, stress management, and healthy lifestyle practices, all crucial to students’ overall well-being. Traditional health education methods cannot often capture real-time physiological and emotional responses, which can improve engagement and learning outcomes. This research explores the effectiveness of biosensing technology in enhancing the dissemination of health information within IPE. It examines how physiological data can be utilized to assess student engagement, emotional responses, and learning outcomes related to health. A mixed-methods approach was adopted, combining quantitative data from wearable biosensors (heart rate monitors, Galvanic Skin Response (GSR) sensors, EEG headsets) with qualitative feedback from students. Physiological data were preprocessed using signal filtering techniques, such as the Savitzky-Golay Filter, and features such as heart rate variability, skin conductance, and EEG alpha waves were extracted using the Kalman Filter (KF). A Modified Runge-Kutta Optimizer Integrated with Deep Belief Networks (MRKO-DBN) classifier was employed to predict student engagement based on these features. The research revealed that physiological responses, particularly heart rate variability and skin conductance, were strongly correlated with student engagement. The MRKO-DBN model achieved accuracy in predicting engagement. Qualitative feedback further confirmed that Biosensing technology significantly improved students’ engagement. Integrating Biosensing technology into health education within ideological and political contexts offers significant potential for enhancing student engagement and learning outcomes. By providing real-time, personalized feedback, it fosters a more interactive and responsive learning environment.

The ideological and political education (IPE) ecosystem in universities is not only an essential component of the broader educational ecosystem but also a microcosm of it. In an era that emphasizes ecology and sustainability, integrating the concepts of the biomechanics field into the ecological environment of IPE in universities holds significant practical implications. Biomechanics, a discipline dedicated to studying the mechanical behavior of organisms, has a profound impact on aspects such as the ecological environment and human health. From the perspective of ecological philosophy, the ecological environment of IPE in universities should fully consider the sustainable development of biomechanical technologies and their influence on human health. This paper presents a dynamic early warning system for IPE in universities based on an improved Support Vector Machine (SVM) algorithm. This system aims to optimize the IPE model and enhance educational effectiveness while incorporating concepts related to biomechanics. We have constructed an evaluation index system for the quality of IPE in universities across five dimensions: basic quality, teaching attitude, teaching method, teaching ability, and teaching effect. These indicators are evaluated through expert scoring to generate a comprehensive assessment of the teaching quality of IPE. Taking into account the close connection between human mechanical responses and the ecological environment in the field of biomechanics, during the evaluation process, we incorporate the impact of the ecological environment on the human biomechanical state (such as bone development and muscle function) into consideration. This approach guides students to establish a correct ecological view and understanding of biomechanical technologies. Model simulations and performance verifications show that the fuzzy neural network undergoes 779 training iterations, with a training target set at 0.05 and a learning rate of 0.09. This model demonstrates a strong ability to provide comprehensive dynamic early warnings for IPE in universities. It has obvious advantages in adaptability, model fitting accuracy, and processing efficiency in the face of information overload, contributing to the continuous development of IPE in universities from the integrated perspective of ecological philosophy and biomechanics.

In the process of higher education reform, physical education plays a vital role in improving students’ comprehensive quality. The online hybrid teaching mode integrates the advantages of online and traditional teaching, which has been gradually applied to various teaching scenarios. However, establishing a comprehensive and effective evaluation model for hybrid teaching remains a challenge due to its complexity. This study introduces a teaching evaluation model based on the Genetic Algorithm Optimized Back Propagation (GA-BP) neural network, incorporating the principles of biomechanics to enhance the evaluation of motor skills, movement efficiency, and physical performance. By comparing the BP and GA-BP models using sample data, results demonstrate that the GA-BP model provides higher precision, offering a feasible framework for hybrid teaching quality evaluation. This integration of computational methods and biomechanical insights not only enriches the model’s applicability but also advances the evaluation of physical education quality and athletic performance.

The widespread use of smartphones, particularly among college students, has raised concerns about the negative impacts of prolonged device use on physical and cognitive health. While smartphones offer many conveniences, excessive usage can lead to a range of biomechanical and psychological issues, including posture-related strain, repetitive strain injuries (RSIs), eye strain, impaired cognitive function, and elevated levels of anxiety and stress. This study aims to examine the physical and cognitive impacts of prolonged smartphone use among college students, focusing on biomechanical strain, cognitive impairments, and psychological effects. It explores the relationship between smartphone addiction and its effects on posture, musculoskeletal health, eye fatigue, focus, memory retention, and mental health. The study was conducted with a sample of 37 college students in China. Data collection involved physical assessments, including posture analysis, musculoskeletal screening, and cognitive assessments, such as focus and memory tests. Mental health was evaluated using standardized surveys for anxiety, stress, and depression. Statistical analyses were used to interpret the data, including descriptive statistics, paired t-tests, correlation analysis, and multiple regression. The results indicated a significant increase in neck tilt angle, posture discomfort, and wrist strain over the study period, with higher smartphone usage correlating with worse physical outcomes. Cognitive performance, mainly focuses and memory retention, significantly declined with increased smartphone usage. In addition, elevated levels of anxiety and stress were observed among heavy smartphone users, with a strong correlation between high smartphone usage and negative psychological effects.

The conventional approach to elder care is no longer able to satisfy the rising need for medical attention for the elderly due to China’s aging population. The demographic trait of “getting old before getting rich” presents a challenge to the distribution of social healthcare resources, as this article first examines the current pattern of changes in the composition of the older population. The community-based “healthcare integration” paradigm of senior care services has emerged as a successful remedy in this regard. Drawing on biomechanical principles, we can envision the community healthcare system as a complex “biomechanical network”. In order to categorize and predict the health data of the elderly, this study constructs a mathematical model akin to analyzing biomechanical forces and movements. By employing methods similar to optimizing structural loads, such as the CART decision tree and support vector machine (SVM) optimization, we enhance the model’s precision. Just as biomechanical systems adapt to varying loads, our model adapts to handle complex health data. By building the optimal classification plane of the support vector machine and adding relaxation variables, the model application solves the classification problem of linearly indivisible data, further enhancing the model’s accuracy and effectiveness, much like how a biomechanical structure self-adjusts to external pressures. In this paper, a geriatric health service platform based on information technology, including big data and the Internet of Things (IoT), is formed. The service system is a tripartite linkage disease management service model that covers the synergistic cooperation of community hospitals, third-party enterprises, and the streets where they are located. A prediction model for common cases, such heart disease, was developed by preprocessing and cleaning the data of 2311 valid samples from the China Geriatrics Center. The dataset was then characterized. The findings demonstrate the model’s high operability and accuracy in predicting health and managing long-term care for older people who are mobility. In the context of an aging society, by integrating biomechanical insights into the design of this healthcare model, the research not only establishes a theoretical foundation for community health care integration but also provides valuable references for implementing digital senior care services and enhancing health management for the elderly in an aging society.

The membrane electrode assembly in a proton exchange membrane fuel cell (PEMFC) functions as the electrochemical reaction region, where the generated electric current relies on the diffusion of reactant gases and electron conduction. Drawing inspiration from biomechanics, this study embarked on constructing a database of PEMFC performance data. Similar to how biomechanical studies use advanced imaging and sensing techniques to map the internal workings of organisms, three-dimensional computational fluid dynamics (CFD) simulations were employed to capture the intricate fluid and gas behaviors within the fuel cell. The data was then used to train data-driven surrogate models based on artificial neural network (ANN) and improved differential evolution for rapid prediction and optimization. When considering the biomechanical aspects, we analyze the mechanical stresses and strains that occur within the membrane electrode assembly during operation. These biomechanical factors can affect the durability and performance of the fuel cell. The gas diffusion layer (GDL) is similar to the pore structure in biological tissues. The pore structure of biological organisms, such as bones, not only ensures the diffusion and transport of nutrients, but also provides space for the attachment of cells to maintain the growth and metabolism of bones. The optimization results revealed that the pores of the GDL, just like the pores of biological tissues, affect the diffusion efficiency of the reactant gases (similar to nutrients) to the catalytic layer, and an appropriate porosity ensures the supply of the reactants required for the electrochemical reactions inside the cell, and improves the PEMFC performance of the cell. By utilizing the random forest algorithm (RF) to conduct feature importance evaluation, we can gain further understanding and interpretation of the factors influencing coupling relationships. The researchers successfully identified the optimal values of GDL porosity and thickness, resulting in an 8.75% increase in power density and significant improvement in oxygen distribution uniformity. To validate the effectiveness and accuracy of the optimization, the optimized structural parameters were incorporated into CFD simulations. The validation results demonstrated close alignment between the optimized model's performance and actual values, confirming the efficacy and reliability of the optimization framework. Overall, this data-driven optimization approach provides an effective tool for multi-variable optimization of complex systems and holds significant importance in enhancing the performance and power density of PEMFC, while also taking into account the biomechanical factors that influence its long-term operation and stability.

Intelligent classrooms have demonstrated significant promise in enhancing learning efficiency as a result of the quick development of big data and artificial intelligence technologies. This study proposes a text semantic matching model (OM) that combines deep learning and K-means clustering algorithm, aiming to optimize vocabulary. Importantly, it delves into the biomechanical aspects of learning by considering how physical and physiological processes interact with language acquisition. By mimicking the learning mechanism of biological neural networks and further exploring the biomechanical correlates of neural activity during learning, such as the muscle tensions and postural changes associated with cognitive efforts, this model simulates how the brain processes and stores language information. These biomechanical factors can have an impact on concentration and fatigue levels, which in turn affect semantic understanding and memory performance during the learning process. The experimental results indicate that this method not only improves teaching effectiveness, but also provides a solid foundation for future research on intelligent language learning environments, taking into account the biomechanical underpinnings of learning.

The comprehensive evaluation of healthy community construction is an important means for managers and decision-makers to understand the development of community health within their jurisdiction, and also a scientific basis for formulating practical and feasible health policies and health development plans. Scientific evaluation of the overall level and differences in the construction of healthy communities has important theoretical significance and reference value. Based on the principal component clustering analysis method as well as the theoretical foundation, the multiple evaluation indicators in the evaluation index system of health community construction are standardized and dimensionally reduced. At the same time, the principal component factor load matrix clustering analysis is used, combined with the practical significance and evaluation direction of the indicator categories, to propose a design idea for the classification index system. The comprehensive principal component evaluation and clustering analysis are used to quantitatively examine the level of health community construction in specific regions and make horizontal comparisons, provide theoretical support and decision-making reference for the construction of healthy communities. The correctness and effectiveness of the proposed method system were verified by numerical examples.

This paper explores the three carvings in Huizhou from a novel perspective integrating cell molecular biomechanics. Taking the stone carving, wood carving, and pottery carving of the Ming Dynasty as examples, after gathering relevant literature via the network and classification, microscopic and scanning techniques were employed. The granite used in stone carving, at a cell molecular level, consists of silicate minerals with strong covalent bonds. These bonds endow the stone with hardness and resistance to deformation. The cells and molecules within the granite are arranged in a crystalline lattice, which dictates its mechanical properties. Mahogany in wood carving has cellulose and lignin molecules. The lignin provides rigidity and hydrophobicity, protecting the wood cells from moisture and external mechanical stresses. In pottery carving, the clay particles are sintered together during firing, creating a new molecular structure. The formation of a grease protective film and oxide on the brick carving is a result of molecular interactions at the surface. This layer can be seen as a self-assembled molecular barrier, similar to how cell membranes protect cells. The axisymmetric modeling and downward center of gravity influence the stress distribution at a molecular scale. A streamlined shape may reduce air or fluid resistance, minimizing mechanical forces acting on the carving's surface molecules. The delicate material with small particles implies a specific microstructure that affects its mechanical behavior.  Understanding these cell molecular biomechanical aspects not only reveals the hidden scientific secrets of Huizhou carvings but also aids in their conservation and restoration. It enriches our comprehension of their durability and aesthetic qualities from a microscopic and molecular vantage point, further enhancing their cultural and digital significance in the context of the museum city's development.

China is an important tea-growing country, and top quality tea, specialty tea and other high-quality tea is more and more favored by the tea industry, so that the production demand is rising year by year. The formation of high quality tea requires suitable soil factors, and soil is one of the two key factors affecting the production of high quality tea. The physicochemical properties and nutrient content of different soils can significantly impact the intracellular processes within tea plants. The absorption and transportation of trace elements such as selenium in tea roots are mediated by specific transporter proteins. These proteins operate based on molecular mechanisms and biomechanical forces that drive the movement of ions across cell membranes. In this paper, we utilize GPS technology to design the distribution area of the test tea production area, and adopt the S-type method to collect the soil of tea differential land in different ecological environments, to explore the physicochemical properties, nutrient content, pH value and water management of different soil types. The whole trace elements and effective trace elements in tea were determined through experiments. The antioxidant enzyme activity of tea was measured by TBA and other methods. The results showed that after planting in different soils, the selenium content of autumn tea was 0.08%, 0.04%, 0.04% in loamy soil > sandy soil = clay soil, respectively. The amount of selenium carried out was sandy soil > loamy soil > clay soil, and the amount of selenium carried out increased by 142.37 kg and 94.97 kg after comparing two by two. Compared with the tea leaves with added trace elements, the T-AOC of the tea leaves without added decreased by 18.75%, 14.20%, 28.06%, and 35.14%. These findings highlight the importance of understanding the cellular and molecular processes in tea plants influenced by soil conditions and trace elements for optimizing tea quality and production.

In the field of biology of Shaanxi universities, there are problems such as insufficient internationalization of course content, limited internationalization level of teachers, and difficulty in meeting the personalized needs of students’ foreign language ability. To this end, under the Belt and Road Initiative, this paper proposes an intelligent solution based on Transformer and GRU (Gated Recurrent Unit) models, aiming to improve the quality of international education in the field of biology of universities by optimizing course content and teaching methods. This study first uses the Transformer model to integrate and analyze a large number of international education resources, identify global cutting-edge knowledge and cross-cultural education elements in biology courses, including biomechanical principles that underpin biological functions and interactions. This provides scientific support for the optimization of course content. At the same time, the GRU model is used to dynamically analyze the progress of teachers’ international teaching and students’ learning feedback. Just as organisms can adjust their metabolic rate according to the changes in the environment, the model automatically adjusts the pace and difficulty of the subsequent teaching content according to the students’ speed of mastery and difficulties in understanding biomechanics knowledge, ensuring that each student can keep up with the pace of teaching. Additionally, the integration of biomechanical concepts into the curriculum helps students understand the mechanical properties and behaviors of biological systems, fostering interdisciplinary thinking and enhancing their global vision. Experimental results show that the students in Class A who adopt this research program are significantly better than the control Class B in terms of knowledge mastery, interdisciplinary thinking, and global vision (P < 0.05); after the experiment, the average foreign language ability score of the students in Class A is 7.04 points higher than that of Class B; the overall satisfaction of the students in Class A with the new teaching program is as high as 82.5%. This paper, based on the combination of Transformer and GRU models, can effectively promote the international talent training process of biology majors in Shaanxi universities, particularly by incorporating biomechanical insights, thereby enhancing the competitiveness of this major in global academic and scientific research cooperation.

At the current stage, the retail industry is undergoing unprecedented changes. From traditional physical stores to online shopping platforms, and then to the new retail model that integrates online and offline, customers’ demand for shopping experience is constantly changing. To meet these demands, retailers need to constantly explore and apply new technologies to optimize the retail environment and enhance customer experience. Biomechanics is the study of the internal and external mechanical behavior of living organisms, which is concerned with the structure, function and motion laws of living organisms. Applying the knowledge of biomechanics to retail environment design can effectively improve customers’ shopping experience. Based on this, this paper takes intelligent container as an example, gives a visual solution of detecting goods in intelligent container based on deep neural network, and proposes a twin-based pairwise image difference detection algorithm named DiffNet as the core algorithm of intelligent container solution, which aims to help enterprises deploy intelligent container flexibly, safely and at low cost. Enhance the customer’s offline self-service shopping experience.

With the widespread application of Global Navigation Satellite System (GNSS) in the fields of positioning and navigation, traditional single frequency and single system positioning methods are gradually unable to meet the requirements of high accuracy and high reliability. Especially in complex and dynamic environments, GNSS signals are affected by multipath effects, occlusion, and interference, resulting in a significant decrease in positioning accuracy. Therefore, it is particularly important to develop a multi-frequency and multi-system GNSS positioning data fusion algorithm. This article used Kalman filtering technology and combined the data characteristics of multi-frequency and multi-system GNSS signals to study a new positioning data fusion algorithm. By comprehensively processing different GNSS systems and frequency signals, the positioning accuracy and anti-interference ability were significantly improved. The experimental results showed that the algorithm studied improved the average positioning accuracy by more than 6.23% in complex environments compared to traditional methods, and also exhibited good adaptability and stability under dynamic conditions. Fully utilizing the advantages of multi-frequency signals and combining advanced data fusion technology is an effective way to improve GNSS positioning performance, providing new ideas and methods for future intelligent navigation applications.

From the perspective of inertia mechanics, sports flooring plays a critical role in controlling safety risks in athletic activities. Firstly, high-quality flooring materials and structural designs can effectively absorb impact forces generated during exercise, thereby reducing the physical burden on athletes and lowering the risk of injury. Secondly, flooring materials with good resilience can provide appropriate rebound properties, offering better support and reaction forces for athletes, thus improving both performance and safety. Additionally, high-quality flooring materials possess high durability and stability, maintaining their performance over prolonged use and minimizing safety hazards caused by material degradation. Lastly, flooring design should account for the characteristics of different sports, providing suitable coefficients of friction and elasticity to meet the needs of various athletes and reduce the likelihood of accidents during sports activities. Therefore, the design and material selection of sports flooring play an essential role in ensuring athlete safety and enhancing athletic performance. The findings of this study have significant implications for future research and practical applications, as they provide a scientific basis for the development of safer, more effective sports flooring solutions that can be tailored to meet the specific needs of different sports disciplines.

Red cultural venues are pivotal in preserving China’s revolutionary heritage and fostering national identity. However, these venues often encounter difficulties in engaging contemporary audiences, particularly younger visitors who seek more interactive and technologically enriched experiences. This study investigates the ergonomic and biomechanical challenges faced by young visitors, using students from the ideological and political education program and the cultural industry management program at Sichuan University of Arts and Science as a representative case study. Employing a mixed-methods approach, the research integrates quantitative surveys with 200 participants and qualitative interviews with 30 participants to assess physical discomfort and engagement levels. Statistical analysis reveals that prolonged standing significantly increases discomfort (β = 0.04, p < 0.001), while higher levels of interactive engagement (β = −0.30, p = 0.002) and overall satisfaction (β = −0.20, p = 0.013) are associated with reduced discomfort. Thematic analysis identifies key issues such as leg fatigue, back pain, limited interactivity, and restrictive venue layouts. Based on these findings, the study recommends enhancing interactive exhibits, optimizing spatial layouts, providing additional seating, and expanding venue spaces to improve visitor comfort and engagement. These evidence-based recommendations aim to inform the redesign of red cultural venues, making them more accessible and enjoyable for young visitors, thereby enhancing their educational and cultural impact. This research contributes to the limited literature on ergonomics in cultural venue settings and offers practical implications for improving the accessibility and user-friendliness of heritage sites.

The study explored the advantages of combining medium-range enhanced exercise biomechanics with high-intensity interval training (HIIT). Using new technologies such as virtual reality (VR) and augmented reality (AR), the research deepens biomechanical analysis techniques to enable instant feedback of athlete training data. The integration of biomechanical analysis is widely reflected in HIIT performance parameters, including speed change, endurance improvement, and overall exercise performance. In the process of technology intervention training, the virtual world environment shows a unique psychological auxiliary function, involving the enhancement of motivation, participation degree and psychological adjustment. The research data reveal that the meta-enhanced biomechanics technology has a good performance in improving sports performance and mental state, and this training mode is opening the innovative direction of sports training system.

Today, with the increasing popularity of football, more and more scholars will focus on using the digital and systematic management methods of football to further improve the safety and effectiveness of football. Similar to how cells within an organism operate in a highly coordinated and biomechanically regulated manner, football players' movements also involve complex biomechanical processes. Establishing a football data analysis system and guiding athletes is like understanding and modulating the molecular interactions and mechanical forces within cells to optimize their function. However, the current such systems are mostly based on video monitoring technology, and their actual operation process is limited by the deployment environment and expensive. In order to realize the widespread popularity of football movement analysis, this paper uses intelligent wearable devices, based on dual model convolutional neural network (DMCNN) for football players virtual step (behind), step (puskash), push progress (sliding), test (inside and outside cycling) and jump (Ronaldo), and by adjusting the convolutional core size and convolution step parameters optimize neural network performance. The resulting algorithm model, which outperforms the K nearest neighbor (KNN) and support vector machine (SVM) algorithms, provides a more accurate understanding of football players’ biomechanical patterns, just as advanced cell molecular biomechanics techniques offer deeper insights into cellular behavior and function, potentially leading to more refined training regimens and injury prevention strategies in football.

To improve the accuracy and practicality of sports training injury risk assessment (IRA), this paper constructs a model based on a complex network analysis algorithm and conducts performance comparison experiments across multiple dimensions. The research results demonstrate that the optimized model performs well in terms of risk assessment accuracy, real-time processing, robustness, adaptability, and user satisfaction. Specifically, the Area Under Curve of the Receiver Operating Characteristic Curve (AUC-ROC) of the optimized model reaches 0.928, indicating high accuracy in risk assessment. In addition to these metrics, this study includes a discussion on the biological mechanisms underlying sports injuries, emphasizing how biological signals can be integrated with the complex network analysis to enhance the model's predictive capabilities. This integration allows for a more comprehensive understanding of injury risk factors, such as muscle fatigue, joint stress, and tissue response, which are critical for effective injury prevention strategies. In the real-time experiment, the processing speed score is 4.9. In the robustness experiment, the fault recovery ability score is 4.3. In the adaptive experiment, the diversified data processing ability score is 4.5. In the user satisfaction experiment, the accuracy score of risk assessment is 4.9, and the convenience score is 5.0. These results indicate that the optimized model has significant advantages in handling complex data and adapting to changing environments. Therefore, this paper provides valuable insights for improving injury risk management and decision support in sports training by incorporating biological insights into the assessment model.

Parent-child triangulation is a pattern of negative parent-child relationships in which children are passively or actively involved in family conflict, which may lead to behavioral problems in adolescents. Latent profile analysis was used to explore the relationship between potential categories of parent-child triangulation and internalized and externalized problem behaviors in a sample of 1361 middle school students. The results showed that: (1) parent-child triangulation can be divided into four potential types according to the extent to which adolescents perceive it: low-profile equilibrium (26.89%), high parentification profile (28.07%), medium-profile difference (30.64%), and high-profile difference (14.40%); (2) adolescents with a low-profile equilibrium and high parentification profile have the lowest level of internalized problem behaviors, but the externalized problem behaviors of the high parentification profile were significantly higher than those of the low profile; the adolescents with a medium profile had a high level of both internalized and externalized problem behaviors, and the adolescents with a high profile had the highest level of both internalized and externalized problem behaviors; (3) the younger the age and the younger the adolescents living in towns, the less likely they were to perceive parent-child triangulation, and the highest level of parentification was found among boarding school students. Moreover, this research extends its scope by considering the biomechanical aspects. This holistic approach may provide new insights into the underlying mechanisms and potentially inform more effective intervention strategies.

This study delves into the integration of biomechanics in analyzing basketball performance and its profound impact on the physical and mental health of college students. By focusing on biomechanical factors influencing athletic performance, the research investigates how basketball, as a dynamic physical activity, contributes to improving physical fitness and mental well-being. Through advanced biomechanical analysis, the study examines key aspects such as joint kinematics, muscle activation patterns, and force distribution during basketball movements like jumping, running, and shooting. These insights provide a deeper understanding of how proper body mechanics—such as optimized alignment, enhanced movement efficiency, and effective load management—can elevate athletic performance and reduce the risk of injuries. The research further explores the relationship between students’ physical fitness and mental health, identifying prevalent psychological challenges among college students, including anxiety, depression, and interpersonal sensitivity. By integrating biomechanical evaluations with psychological assessments, the study highlights how the optimization of movement mechanics in basketball can promote physical endurance and agility while fostering psychological resilience and reducing stress. For example, improvements in core stability and proprioception not only enhance sports performance but also support mental clarity and emotional regulation. The findings advocate for incorporating biomechanics-informed physical education programs, particularly those centered on basketball, into university curricula. Such programs could utilize technologies like motion capture systems and wearable sensors to monitor and improve students’ biomechanical efficiency while simultaneously addressing their psychological needs through structured physical activities. This integrated approach underscores the dual benefits of sports for physical and mental health, providing actionable insights into how biomechanics can be leveraged to optimize athletic performance and promote holistic well-being among college students.

Soccer is recognized as one of the most widely played and commercially significant sports globally. An athlete’s performance is typically impaired during states of fatigue. Investigating the body mechanics of soccer players under states of fatigue can provide insights for coaches regarding the physical capabilities and movement deficiencies of their athletes. This understanding can facilitate the adjustment of game strategies and the development of tailored training regimens following competitive matches. Advancements in artificial intelligence have led to the maturation of image recognition technologies, which are increasingly applied across various industries, including promising applications within the realm of soccer. Consequently, this study analyzed a cohort of 5 amateur soccer players (mean age 19.8 years; mean height 1.82 m; mean weight 73.6 kg). The study involved participants who completed 10 shootings on goal as a control group before the implementation of a fatigue protocol, followed by an additional 10 shootings on goal as an experimental group after the completion of the fatigue protocol. Mediapipe image recognition tools and high-speed cameras were utilized to capture data on the various skeletal nodes of the athletes’ bodies and the velocity of the shooting ball, which were subsequently analyzed. The results of the study revealed that when in a state of fatigue, there were significant alterations in the angular displacement of the hip and knee joints in comparison to the ankle joint during shooting by soccer players. The decreased angular displacement of the hip and knee joints resulted in inferior contact between the foot and the ball, as well as a reduction in the speed applied to the ball, leading to a decline in shooting accuracy and ball speed. The findings substantiate the impact of fatigue on the shooting movements of athletes.

This study examines the impact of wearable biosensor technology on student engagement and academic performance in educational settings. The purpose was to investigate how experience with biosensors influences engagement levels and whether this engagement correlates with academic success. Key issues addressed include the unexpected negative correlation between biosensor experience and academic performance, indicating that reliance on technology may not always enhance learning outcomes. Innovative aspects of this research involve identifying the complex relationship between technology use and educational effectiveness, underscoring the need for strategic integration. Proposed solutions include enhanced educator training and active learning strategies that effectively utilize biosensors. The feasibility of these solutions is supported by existing literature on effective teaching practices. Ultimately, findings highlight the importance of thoughtful technology integration to foster meaningful learning experiences.

Objective: To systematically evaluate the influence of table tennis on the eyesight of primary and secondary school students. Methods: Literature research was conducted by computerized search of CNKI, Wanfang, and VIP databases, Web of Science, Embase, PubMed, and The Cochrane Library databases for randomized controlled trials on table tennis exercise on students’ visual health. Traditional Meta-analysis, subgroup analysis and sensitivity analysis were performed sequentially using Stata 17.0 and Review Manager 5.4 (RevMan5.4 for short). Results: A total of 11 papers with 670 study participants were included for analysis. The effect of table tennis exercise on students’ visual acuity improvement was statistically significant in the experimental group compared to the control group, whose total effect of the literature showed that the left eye [SMD = 1.41, 95% CI (0.95, 1.87), Z = 6.02, P < 0.001] and the right eye [SMD = 1.59, 95% CI (1.08, 2.11), Z = 6.07, P < 0.001] ; subgroup analysis also showed that table tennis sport intervened on left eye vision [SMD = 1.51, 95% CI (0.89, 2.13), P < 0.001] and right eye vision [SMD = 1.72, 95% CI (1.01, 2.42), P < 0.001] in primary school students versus table tennis sport in secondary school students’ left eye vision [SMD = 1.23, 95% CI (0.68, 1.78), P < 0.001], and right eye visual acuity [SMD =1.33, 95% CI (0.99, 1.67), P < 0.001] were statistically significant for prevention and protection of vision. Conclusion: Table tennis exercise interventions have been shown to have a positive effect on preventing and protecting the visual health of primary and secondary school students, and different table tennis exercise cycles, exercise duration, and frequency of exercise and other sports also have a significant effect on preventing and protecting the visual health of primary and secondary school students.

Background: Rock climbing is a comprehensive sport that integrates physical strength, coordination, and psychological resilience. Significant differences may exist in the psychological states and biomechanical performance of athletes at different levels. However, systematic studies on the psychological characteristics and biomechanical indicators of rock climbers at different levels remain limited. Objective: This study aimed to compare the psychological indicators (e.g., self-efficacy and sport motivation) and biomechanical characteristics (e.g., muscle activation levels, relative peak torque, and flexor-extensor peak torque ratios) of rock climbers to explore the differences and intrinsic relationships between athletes of different skill levels. The findings aimed to provide a theoretical basis for optimizing performance and designing training strategies. Methods: Twenty-two rock climbers participated in the study, including 11 elite athletes and 11 novice athletes. Psychological indicators were assessed using standardized questionnaires, including self-efficacy and five dimensions of sport motivation: Fun motivation, ability motivation, appearance motivation, health motivation, and social motivation. Biomechanical data were collected using the Noraxon DTS surface electromyography (sEMG) system and the Biodex System 4 isokinetic dynamometer, which measured muscle activation levels and the relative peak torque and flexor-extensor peak torque ratios of the shoulder, elbow, hip, knee, and ankle joints at speeds of 60°/s, 120°/s, and 180°/s. Muscle activation signals were normalized as %MVC, and peak torque values were extracted for analysis. The data were grouped by athlete level, and independent sample t-tests were conducted to compare group differences, with significance set at p < 0.05. Results: Elite athletes demonstrated significantly higher psychological indicators than novice athletes, particularly in self-efficacy (3.19 ± 0.671 vs. 2.77 ± 0.341) and fun motivation (3.28 ± 1.049 vs. 2.78 ± 0.47). Additionally, elite athletes exhibited higher muscle activation levels and relative peak torque in upper limb and core muscle groups compared to novice athletes (p < 0.05), indicating superior strength control and coordination. Conversely, novice athletes had relatively higher peak torque in lower limb muscle groups but showed deficiencies in strength balance and coordination. Conclusion: Significant differences were found in the psychological states and biomechanical characteristics of rock climbers at different levels. These differences likely contribute to variations in athletic performance. Elite athletes displayed stronger psychological advantages and superior strength in upper limb and core muscle groups. In contrast, novice athletes needed to enhance sport motivation and improve upper limb and core strength to develop comprehensive athletic abilities. This study provides a scientific basis for optimizing training strategies for rock climbers at different levels and lays a foundation for future research on the mechanisms underlying climbing performance.

Wushu movement full hair is a kind of fitness activity, and it is one of the ways for people to cultivate their self-cultivation and sentiment. It is of great significance to use motion capture system and data gloves to capture human movement posture and guide boxing practice in real time. The research of this topic uses neural network technology to construct a complete recognition framework of martial arts movements. Firstly, the collected martial arts movements are sorted out and a database is constructed. Because of the inherent defects of traditional BP algorithm, this is also because during the training of the modified algorithm, The network converges slowly, and easy to receive local minimum constraints, so this topic uses particle swarm optimization algorithm to optimize the initial weights and improved neural network algorithm to improve the learning rate and increase the reliability of the algorithm. Finally, through the martial arts action boxing recognition framework for testing, it is determined that the proposed algorithm is more effective.

This article explores how group leaders in community group buying influence consumer behavior regarding agricultural product purchases, analyzing their psychological motivations and relational mechanisms. The concept of biomechanics offers a novel and illuminating perspective to understand this phenomenon. First, the article defines the role and functions of community group leaders in promoting agricultural products. It examines the leaders’ promotional actions and behaviors as research subjects, selecting representative traits to analyze their specific effects on consumers’ decisions to purchase agricultural products endorsed by these leaders. Combining qualitative and quantitative research methods, the study conducts field interviews and surveys among community group buying users. The Theory of Planned Behavior (TPB) model is employed to test hypotheses, SPSS software is utilized for descriptive statistical analysis and reliability and validity testing, and AMOS software is used to construct a structural equation model to investigate consumer behavior in purchasing agricultural products promoted by group leaders. In summary, this study aims to unravel the mechanisms underlying consumers’ purchasing behaviors of products promoted by group leaders. From a biomechanical perspective, the linguistic and behavioral promotions of group leaders act as powerful stimuli. Their words and actions can be seen as biomechanical signals, much like the chemical signals insects use to communicate. For instance, a leader vividly describing the taste and texture of a freshly harvested fruit is equivalent to a bee releasing a pheromone trail to guide its fellows to a food source. By analyzing how these promotional actions shape consumers’ perceptions and expectations of agricultural products, we can draw parallels to how organisms respond to environmental cues. This study thus highlights the opportunities and challenges of group leader-led agricultural product promotion compared to traditional sales models, similar to comparing a newly evolved survival strategy in nature with an established one. Furthermore, in terms of energy efficiency and resource management, just as organisms have evolved to optimize their energy use, community group leaders must also streamline their operations. They need to balance the energy expended in promotion, coordination with suppliers, and logistics, similar to how a migrating animal conserves energy during its journey. By efficiently allocating resources, they can enhance the overall success of agricultural product promotion within the community, creating a sustainable model that benefits both consumers and the local agricultural economy, all while being inspired by the principles of biomechanics.

The protection of intangible cultural heritage (ICH) is not only respect and protection for traditional culture, but also plays a vital role in cultural inheritance, social identity, historical memory, economic development, and innovative vitality. With the rapid advancement of globalization and modernization, ICH is also facing unprecedented challenges. However, the traditional protection of ICH has problems such as focusing on static physical protection, insufficient information storage, limited transmission, insufficient modern transformation and innovation, excessive restoration of traditional elements and conservative protection. In response to the above problems, this paper designs an ICH resource construction system based on artificial intelligence (AI) biological perception. It can perceive ICH data through multimodal biology, store and reproduce it, perform feature analysis based on biological emotions and emotional interactions, capture the inheritance logic and emotional connotation of culture, and drive the digital modeling of ICH resources with intelligent design. Dynamic ICH content can be superimposed on real scenes to facilitate education and dissemination, and personalized ICH story content can be recommended based on user preferences to enhance the display and dissemination capabilities of ICH. The results show that the system uses multimodal perception and stores more than 100,000 ICH data items in four major categories and multiple subcategories, and designs a unique interactive tag cloud for users to choose from. When making recommendations for users, it recommends 200 ICH contents to users from the sorted list simultaneously, and the proportion of users clicking on the recommendations reaches 85%, while also achieving the widespread dissemination of ICH in Asia. Compared with traditional ICH protection, this study has achieved efficient digital storage of ICH content, strong modern conversion, and ease of acceptance by users. The scope of dissemination is also wider. This shows that the use of AI and biosensing technology in ICH protection is effective and can contribute to better preservation, publicity and promotion of ICH.

The complex and unpredictable path of the vertebral artery is closely related to symptoms such as headaches, dizziness, and cerebrovascular diseases. This study aims to explore the role of vertebral artery tortuosity and hemodynamics in the association with headaches and cerebrovascular lesion by quantitatively analyzing the morphological parameters and hemodynamics of the vertebral artery. A total of 85 patients with headache symptoms and vascular lesions identified through computed tomography (CT) scans were included. A comparative analysis was then conducted to assess how different levels of vascular tortuosity affect these hemodynamic parameters. These findings indicate that vertebral artery tortuosity is more prevalent among the elderly, women, and patients with headache and vascular disease. A multivariate stepwise Logistic regression analysis highlights the ratio of the distal diameter to the tortuosity index of the left vertebral artery (d1) as a significant risk factor for headache symptoms in patients with vascular lesions. Hemodynamic analysis reveals complex flow patterns within the highly tortuous left vertebral artery, including vortices at areas of significant vascular tortuosity. The left vertebral artery with a high degree of distortion presents with high time-averaged wall shear stress (TAWSS), a high oscillatory shear index (OSI) region, and a low relative residence time (RRT). This discovery not only provides essential reference information for the morphological and hemodynamic analysis of the vertebral artery but also offers critical predictive insights for future clinical evaluations and interventions targeting patients with vertebral artery tortuosity, aiding in predicting their risk of experiencing headaches or the onset of cerebrovascular diseases.

Aerobics is a kind of sports activity, which can not only exercise the body but also cultivate the sentiment and reduce the psychological burden. With the improvement of people’s living standards, aerobics is becoming more and more popular, but in fitness activities, there are often some sports injury accidents, which cause certain harm to human health. Therefore, people must take effective precautions against aerobics. Intelligent wearable sensor device is a new high-tech product developed based on Internet technology. It can not only realize real-time monitoring and diagnosis analysis of human body status information, but also combine with mobile terminals such as mobile phones to apply in the field of health management, and also provide personalized services according to user needs. The data collected by the sensor can be used to judge the human health status and exercise situation and make corresponding decisions, to help patients reduce or eliminate the disease burden. It collects the patient’s body data stores it in the database, and then generates corresponding action commands and corresponding motion tracks or speed control modules according to the results fed back by the sensors. At last, it sends these signals to the cloud server to complete the operation process required by the entire system, to achieve the purpose of real-time measurement, processing user health, and assisting athletes in learning training methods. According to human neuroscience, based on intelligent wearable sensing devices, this paper analyzed the prevention and rehabilitation of aerobics injuries and discussed the factors that lead to injuries in aerobics, injury treatment effects, rehabilitation time, and injury treatment satisfaction. The experimental results show that the patient’s satisfaction with the application of intelligent wearable sensing devices in the prevention of aerobics injury and rehabilitation training has increased by 6.84%. The intelligent wearable sensor device realizes the collection and processing of human health data. Applying big data analysis to user behavior analysis, can provide scientific and effective guidance and suggestions for athletes and improve their physical fitness and competitive level.

Marine environments are being investigated to identify microscopic forms of life which could produce botanic antibiotics as there is an increasing demand for newer antibiotics which could be used to treat all bacteria due to the ever-increasing resistance of various forms of bacteria. In this study, we enhance the techniques for recovering and characterizing antibiotic-producing bacteria from seawater samples. Seawater samples were obtained from different sea areas, microorganisms were concentrated, and potential antibiotic-producing microorganisms were sought on selective media and in enrichment cultures. Bacterial antibiotic activity screening was performed by agar diffusion assay, and the selected bacteria were characterized with morphological, biochemical and 16S rRNA sequencing methods. Incubation times, temperature, and nutrient media composition were modified, we incorporated biomechanical principles to assess the physical interactions between antibiotic-producing bacteria and target pathogens. Understanding how mechanical forces, such as shear stress in marine environments, influence bacterial growth and antibiotic production can provide insights into optimizing isolation techniques. Furthermore, advancements in bioimaging advance technologies allowed for real-time observation of bacterial behavior and interactions, revealing how physical characteristics, such as motility and biofilm formation, contribute to antibiotic efficacy. Our optimized methods significantly increased the efficiency of isolating antibiotic-producing bacteria, uncovering diverse antibiotic potentials and confirming several novel bacterial species. The integration of biomechanical analysis highlights the promising prospects of marine microorganisms as a source of new antibiotic substances and underscores the effectiveness of combined methods of isolation and identification in the fight against antibiotic resistance.

This study addresses the optimization and bioapplications of a deep learning algorithm for predicting the mechanical properties of metal matrix composites (MMCs), a critical task for efficient material design. And it is also beneficial for deploring more bioapplications of MMCs. Leveraging a comprehensive experimental dataset from multiple research institutions, we employ a Convolutional Neural Network (CNN) for feature extraction and the Recurrent Neural Network (RNN) for sequence analysis. The dataset encompasses mechanical properties such as tensile strength, elastic modulus, and yield strength for diverse MMCs with varying compositions and processing conditions. The research methodology involves rigorous data preprocessing, feature selection, model development, and performance evaluation using metrics like R² score, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), precision, and recall. Addressing the challenge of model robustness and generalizability, we utilize k-fold cross-validation for training and validation. Optimal hyperparameter settings are identified to enhance predictive accuracy. Our results reveal high predictive performance, with R² scores ranging from 0.89 to 0.92 for different mechanical properties, thereby demonstrating the model’s efficacy in facilitating material design and optimization processes for MMCs.

Aiming at the conflict of rights in the development of underground space of high-rise buildings, this paper puts forward a comprehensive analysis framework combining the influence of legal norms and biomechanics. Starting from biomechanics, this paper specifically discusses the rights disputes in three aspects of underground space land transfer fee, the ownership of underground civil air defense project and the ownership of community underground garage, and introduces a biomechanical evaluation model supported by GIS platform to ensure the safety and stability of the project. When considering the underground space land transfer fee, the stability and load-bearing capacity of the soil, which can be analyzed through biomechanical principles, directly affect the feasibility and cost of construction. Just as the skeletal structure of an organism provides the foundation for its movement and survival, the underground geological structure dictates the safety and economic viability of building projects. In the discussion of the ownership of underground civil air defense projects, biomechanical models can be employed to evaluate the potential impact of disasters. By simulating how the structure withstands external forces like earthquakes or explosions, similar to how a creature's body adapts to survive in harsh environments, we can better determine the responsibilities and rights of different parties. Regarding the community underground garage, the layout and design need to conform to human biomechanics. Adequate space for vehicle entry, exit, and pedestrian movement, considering factors such as comfortable walking angles and clear sightlines, ensures convenience and safety, much like how organisms' habitats are optimized for their daily activities. The study found that the municipal government has issued policies to clarify the specific content and scope of underground space use rights, established a unified approval process to strengthen supervision, and promoted the application of biomechanical evaluation models, thereby improving project quality and reducing potential risks. The experimental results show that this comprehensive management strategy effectively alleviates the conflict of rights, promotes the smooth progress of the project, and provides institutional guarantee and technical support for the sustainable development of the city. This paper not only solves the current right conflict problem, but also provides valuable experience for the rational use of urban underground space resources and the maximization of social benefits in the future.

Fuling Gancao Decoction (FGD) has been a typical formula for treating functional dyspepsia (FD) in China. Network pharmacology, molecular docking, and molecular dynamics simulation are applied to shed light on the comprehensive mechanisms of FGD against FD. The results showed that there were two core compounds (quercetin and kaempferol) and 16 crucial targets (KT1, SRC, EGFR, HRAS, and PIK3R1, etc.) of FGD against FD. Furthermore, 60 signaling pathways were modulated by the core targets, which contained estrogen signaling pathway, prolactin signaling pathway, cancer pathway, etc. The molecular docking analysis showed that quercetin and kaempferol had excellent binding affinity with the core targets. Molecular dynamic simulations indicated that quercetin-MAPK8 and kaempferol-AKT1 show favorable stability. The study successfully screened components, targets, and signaling pathways of FGD against FD, which provided a theoretical basis for further clinical application.

Objective: This study investigates the correlation between the methylenetetrahydrofolate reductase (MTHFR) gene C677T polymorphism, serum apolipoprotein E (ApoE) concentration, and the risk of epilepsy following traumatic brain injury (TBI), aiming to identify potential biomarkers for early diagnosis and intervention. Methods: A total of 60 patients with post-traumatic epilepsy (observation group) and 60 healthy controls were included. Serum ApoE concentrations were measured using enzyme-linked immunosorbent assay (ELISA), and MTHFR C677T polymorphisms were analyzed using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Statistical analysis included binary logistic regression to identify independent risk factors and Spearman rank correlation to evaluate relationships between variables. Results: Serum ApoE levels were significantly higher in the observation group (36.26 ± 6.16 mg/L) compared to controls (30.19 ± 5.27 mg/L, P < 0.001). The frequency of the TT genotype and C allele was also markedly elevated in the observation group (TT: 41.67% vs. 8.33%; C allele: 56.67% vs. 31.67%, both P < 0.05). Logistic regression analysis identified TT genotype (OR = 6.271, P < 0.001) and elevated ApoE levels (OR = 6.572, P < 0.001) as independent risk factors. A positive correlation between ApoE levels and the TT genotype was observed (r = 0.629, P < 0.001). Conclusion: The MTHFR C677T polymorphism and serum ApoE concentration are strongly associated with epilepsy after TBI. These findings suggest that they could serve as biomarkers for early identification of high-risk individuals, paving the way for targeted prevention and management strategies.

This paper aims to explore the effect of the expression of sports gene polymorphisms related to long-distance running on the physiological functions of athletes, especially on the physical adaptability of marathon runners. With the progress of genetic research, more and more scholars are paying attention to the effect of sports genes on the physical fitness and cardiopulmonary endurance of athletes, but there is still a lack of research on marathon runners. Therefore, this study used amplified fragment length polymorphism (AFLP) technology, combined with phenotypic data, to analyze the differences in sports gene polymorphisms between long-distance runners and the general population, and explored the relationship between gene expression and physiological function. In this paper, the AFLP technique was used for analysis. The results showed that under the same other conditions, the average value of foot force test in Group X and Group Y was 75.6 kg and 46.9 kg, respectively, with standard deviation of 18.5 and 10.4. The P value was 0.026, less than 0.05, indicating a significant difference between the two groups. This may indicate that there was a correlation between the expression of sports gene polymorphism and the physiological function of long-distance runners, and the relationship between the two was always positive.

Objective: This study aims to analyze trends, hot topics, and influential research on ballet-related injuries between 2000 and 2023, providing insights for prevention and management strategies. Methods: Using the Web of Science Core Collection, 787 articles were identified with subject terms related to ballet injuries. CiteSpace software (version 6.2.R2) was employed to conduct visual analyses of trends, keyword co-occurrences, and co-citations. The dataset was analyzed across dimensions such as time, geography, authorship, institutions, and hot topics. Results and Conclusions: The publication volume of ballet injury research has steadily increased, with the United States, England, and Australia leading in output. Central topics include risk factors, musculoskeletal disorders, and injury prevention strategies. Key clusters revealed five prominent research directions: risk factors, proprioception, physical activity, jump-related injuries, and bone mineral density. Authors such as Wanke Eileen M., Steinberg Nili, and Ambegaonkar Jatin P. emerged as influential contributors. Institutions like the University of Wolverhampton and Goethe University Frankfurt displayed significant academic impact. The research objects of ballet injury research are mainly female ballet dancers. Research on the causes of ballet injuries mainly focuses on physical activities and incorrect training plans. The most studied part of ballet injuries is the ankle joint.

In complex and volatile financial markets, investor behavior is an important factor driving market volatility. Traditional studies have mostly relied on methods such as financial market data, questionnaires and psychological scales, but these methods have limitations such as data lag, subjectivity and difficulty in quantification. In recent years, with the development of biomechanics and neuroscience, researchers have begun to explore the use of biomechanical data to predict the behavioral trends of financial investors, which provides a new perspective and methodology for financial market research. This study aims to predict financing behavior in the stock market by constructing an investor sentiment index and combining it with a planning recognition model. Based on the close relationship between biomechanics and emotions, the biomechanical representations of different emotions of investors and their effects on behavior are dissected. Meanwhile, the partial least squares method is used to construct the investor sentiment index, and a planning identification model consisting of Markov chain, planning identification graph and expected utility function is introduced to predict the stock market trend and financing behavior, which is more accurate than the Markov model based on objective data only. In order to enrich the theoretical system of financial market prediction through this study, it provides more accurate market prediction and investment advice for financial institutions and investors. By introducing biomechanical data and planning recognition model, it provides new ideas and methods for understanding the intrinsic connection between investor sentiment and market volatility, and promotes the development of financing business in the domestic market as well as maintains the health and stability of the domestic stock market.

Biomechanics, as an interdisciplinary field involving multiple fields, can help analyze individual differences, develop personalized training plans, and effectively prevent injuries to vulnerable areas of athletes. This article used a high-precision 3D motion capture system and various physiological monitoring devices to collect athletes’ motion and physiological data. Combined with biomechanical modeling and risk assessment methods, the impact of five key parameters, step frequency, stride, joint angle, muscle strength, and speed, on injury risk was analyzed. The experimental results showed that implementing the personalized biomechanical prevention strategy applied in this article reduced the incidence of sports injuries by 20%, and optimizing step frequency, stride length, and enhancing muscle strength can significantly reduce the risk of injury. This article provided a scientific basis for developing personalized prevention strategies, which can help improve athletes’ athletic performance and safety.

There is an interaction between behavior and health, and behavior monitoring information plays a role in reminding people of the need to maintain healthy behaviors or change unhealthy behaviors. Behavior monitoring information can explain the universality of behavior, analyze relevant influencing factors, determine specific actions to be followed, and carry out health education and publicity. It can also be used to trigger behavioral change trends, and evaluate and review health education and promotion programs. Behavioral monitoring information helps to explain changes in health status related to behavior. Because students are in a special period of physical and psychological development, their behavior is different from other periods. With the development of social culture, science and technology, the concept of modern health has changed from simple physical health to psychological and behavioral health. Based on this, this paper first investigated the definition and influencing factors of student behavior, focused on the definition of student behavior, expounded the classification and manifestation of student behavior, and analyzed the influencing factors and indicator system of student behavior. Then, from the construction of the student behavior monitoring system, it discussed the analysis and collection technology of student behavior data and the analysis and prediction model of student behavior, put forward the visualization model of student behavior big data, and discussed the design of the student behavior monitoring system based on data mining from two aspects, namely, the system functional requirements analysis and the overall system architecture design. Then the decision support algorithm was used to strengthen the detection of students’ health behavior. According to experiments and surveys, big data mining and decision support algorithms strengthen the construction of student behavior health monitoring system, and build a new student behavior health monitoring system, which is 33% more satisfied than the traditional student behavior health monitoring system.

Sports offer numerous benefits for physical and mental health, and current research focuses on a comprehensive evaluation of an athlete's well-being to enhance performance. Thermal imaging techniques are employed to observe athletes' activities in both psychological and physiological aspects. During competitive pursuits and exercise, thermal imaging records the athlete's temperature fluctuations. These temperature data, combined with other biometric information like heart rate, provide a more in-depth understanding of the athlete's state. To establish a connection with biomechanical performance, we consider muscle activation patterns and motion dynamics. Muscle activation during intense competition leads to increased metabolic heat, which is detectable via thermal imaging. For example, highly activated muscle groups may exhibit distinct temperature elevations. Motion dynamics, such as the speed and range of limb movements, also impact heat dissipation and distribution. Faster movements may cause more rapid heat convection, altering the thermal patterns captured. The collected thermal images undergo processing to reduce noise and enhance contrast. Specific regions are identified to extract relevant features, which are then analyzed for temperature patterns. Abnormal temperature distributions are detected using Gradient Optimized Recurrent Neural Networks (GO-RNN) to assess the athlete's physical and mental health. This analysis not only predicts potential injuries but also links thermal data with biomechanical performance. By correlating thermal imaging results with muscle activation and motion dynamics, we can develop more accurate performance evaluation models. This integration allows for a more precise understanding of an athlete's condition, enabling personalized training programs and better injury prevention strategies, ultimately leading to improved athletic performance and well-being.

The Internet of Things (IoT) paradigm is employed in different sports-related activities for health monitoring and performance assessments. Athlete training based on physical activities and observation is performed using IoT devices and computing systems. Clinical Decision Support (CDS) aims to improve health and health care by providing doctors, employees, patients, or other persons with knowledge and information tailored to each individual’s needs at the right moment. The state of being physically ready to do the actions required by a particular activity (usually a sport). Sports-specific skills that can only be mastered via repeated practice. The problem of consistent performance index management is limited due to large data validations. This article introduces a Reliable Index Assessment Technique (RIAT) for evaluating athlete performances. The physical attributes, such as oxygen level, stamina, tiredness, completion time, speed, etc., are observed using wearable sensors. The observed signals are processed for their appropriate declinations and stagnancy during the training sessions. The training index is constructed based on the declinations and stagnancy identified through an intense federated learning paradigm. This index assessment relies on multilevel training updates to prevent performance assessment inconsistencies. The index construction is made from the multilevel assessment using federated learning updates. This update is validated using the previous index and the currently observed inferences for preventing computation errors. Therefore, the distributed training data is accessed and updated for global indexing through the IoT elements. This technique achieves high precision, an assessment rate under measured computation time, and fewer errors.

The purpose of this study is to explore the influence of volleyball teaching in colleges and universities on students’ physical and mental health, and to systematically analyze it by multi-modal evaluation method. 300 college students were randomly divided into experimental group and control group. The experimental group received personalized intervention based on multimodal evaluation system during 16 weeks of volleyball teaching, covering multi-dimensional monitoring and feedback of physiological, psychological and technical performance. Through heart rate, body fat rate, heart rate variability (HRV), GHQ-12 scale, technical score and self-efficacy, the students’ physical quality, mental health, technical proficiency and self-efficacy were comprehensively evaluated. The results show that the multimodal intervention strategy is effective in improving physical health and mental health, and the experimental group is superior to the control group in heart rate, body fat rate, vital capacity, HRV, GHQ-12 score, technical proficiency and self-efficacy. The real-time monitoring and feedback of multimodal assessment in teaching optimizes the intervention effect and provides a scientific basis for the optimization of physical education courses in colleges and universities.

With the rapid development of modern communication technology and multimedia technology, learners can obtain various learning resources in various ways. But the problem that comes with it is to obtain the knowledge needed by learners from many resources efficiently, quickly and effectively, so as to complete the systematic study of college physical education personalized courses. Since 1980s, personalized learning and personalized service in the network learning environment have been studied accordingly. The related research involves many information science fields such as information retrieval, data mining, artificial intelligence, computer communication and network, but the research on the generation of personalized curriculum of college physical education is relatively few. This paper mainly focuses on the research and realization of personalized curriculum generation technology of college physical education, and explores the solution to realize the individualization of college physical education learning content in the process of large-scale online education. A hierarchical recommendation algorithm based on multi-dimensional feature vector is proposed, with a unique emphasis on integrating biomechanics. By analyzing biomechanical indicators like joint mobility, muscle strength, and body coordination of students, the algorithm can accurately assess their physical conditions. This integration can not only help physical education teachers make the overall teaching plan, but also meet the needs of college students’ individual knowledge and ability characteristics for curriculum learning. In addition, the hierarchical implementation of the recommendation algorithm distributes the recommendation of large-scale knowledge base and resource base at different levels, which effectively reduces the dimension, reduces the amount of calculation and improves the efficiency of the implementation of the personalized curriculum generation algorithm of college physical education. Biomechanics-driven optimization ensures that the recommended courses are not only knowledge - based but also safe and effective for students from a biomechanical perspective, enhancing the overall quality of personalized PE curricula.

In the field of biomechanics, the delicate structural layout of living organisms often brings many inspirations for engineering design. As in the case of aero-engine piping layout, the current single- and multi-tube layouts are ineffective and need to be optimised. Inspired by the efficient material transfer and space utilisation mechanisms of biological systems, we propose an automatic pipe layout method for aero-engine based on co-evolutionary algorithm and improved A^* algorithm. Taking inspiration from how biological networks adapt and optimize their connections, we first construct an improved A^* algorithm. Through optimizing node coordinate expression, enhancing the evaluation function, introducing a directional strategy, and improving the OPEN_LIST, it becomes a potent tool. When applied to single-pipe layout in aero-engines and compared with the original A^* algorithm, its advantages are evident. Subsequently, mimicking the collaborative evolution seen in ecological systems, we combine the co-evolutionary algorithm with a new evaluation function to develop a further improved A^* algorithm for multi-pipe layouts. Finally, simulations confirm the feasibility and effectiveness of our proposed method. For single pipes, similar to nature's way of streamlining structures, our method significantly reduces pipe length and the number of elbows while effectively avoiding key equipment. The improved A^* algorithm cuts pipe lengths by 12.8275% and 19.4843% respectively and boosts the computation speed by remarkable percentages. For multi-pipes, it enhances space utilization and reduces time cost. The improved algorithm reduces the number of traversing nodes from 3067 to 1968 and shortens the planning time from 20.34 s to 7.26 s, demonstrating its great efficacy.

FHOD1 is a crucial regulator of cellular actin dynamics, and growing evidence suggests its involvement in tumorigenesis. Nevertheless, the precise function of FHOD1 in colorectal cancer (CRC) is still not well-defined. FHOD1 expression was analyzed using TIMER 2.0, and its prognostic value was assessed using the Kaplan-Meier Plotter. Functional analysis was performed via LinkedOmics, and its role in immune infiltration was investigated using the TISCH2 database and GSVA package. Drug sensitivity related to FHOD1 was evaluated with R software. Additionally, the CCK-8 assay, colony formation assay, wound-healing assay, and Transwell migration assay were used to evaluate the impact of FHOD1 on the proliferation and migration of colorectal cancer cells. Our study proved that FHOD1 expression was substantially higher in CRC tissues than in normal tissues, correlating with poorer patient prognosis. Functional analysis indicated that FHOD1 was involved in immune-related processes and the tumor microenvironment, particularly affecting numerous types of immune cells, such as natural killer cells and T cells. FHOD1 expression was positively associated with sensitivity to multiple chemotherapeutic agents. Finally, knockdown of FHOD1 in HCT116 and RKO NL cell lines impaired cell proliferation and migration, highlighting its potential as a target for treatment in managing CRC. In conclusion, these findings underscore the importance of FHOD1 in CRC progression and treatment strategies.

Objective: This study aims to evaluate the clinical efficacy of Dan Shen Chuan xiong qin Injection in combination with conventional interventions for Acute Coronary Syndrome (ACS), emphasizing its biomechanical mechanisms and effects on serum levels of B-type natriuretic peptide (BNP) and cardiac troponin T (cTnT). Methods: This prospective cohort study involved 140 patients suffering from ACS, randomly divided into a control group and a treatment group. The control group received conventional Western pharmacotherapy, while the treatment group was additionally treated with Dan Shen Chuan xiong qin Injection. We measured serum BNP and cardiac troponin levels and analyzed their biomechanical relevance to cardiac function and hemodynamics on the 1st, 2nd, and 5th days post-treatment. Results: After treatment, particularly on the 5th day, the BNP levels in the treatment group receiving Dan Shen Chuan xiong qin Injection were significantly lower than those in the control group (p < 0.05). Both groups exhibited a downward trend in BNP levels on days 1, 2, and 5 (p < 0.001). Regarding troponin levels, the treatment group showed lower troponin levels on all days compared to the control group (p < 0.05), with statistically significant differences noted (p < 0.05). Biomechanical Analysis: The reduction in BNP and troponin levels suggests an improvement in cardiac function and hemodynamics, potentially indicating enhanced myocardial performance and reduced cardiac stress. The biomechanical implications of these biomarkers highlight their role in assessing cardiac workload and injury, reinforcing the cardioprotective effects of Dan Shen Chuanxiong Injection. Conclusion: The treatment group receiving Dan Shen Chuanxiong Injection demonstrated superior outcomes in clinical symptom relief, improvement in cardiac function, and reduction in biomarker levels compared to the control group. These findings indicate that Dan Shen Chuan xiong qin Injection possesses significant cardioprotective effects, reducing the risk of cardiac injury through biomechanical mechanisms. When used alongside conventional therapy, DSCXQ Injection shows potential for improving critical biomarkers related to ACS. This study provides valuable evidence for integrating traditional Chinese medicine with conventional Western approaches in managing ACS, emphasizing the importance of biomechanical analysis in understanding therapeutic effects.

With the development of high-precision sensors and data acquisition equipment, biomechanical data presents high-dimensional, strong time-series dependence and nonlinear characteristics, and it is difficult for traditional physical modeling and statistical methods to process such data efficiently and accurately. The purpose of this paper is to build a biomechanical data-driven prediction framework based on Transformer model, and realize high-precision prediction by deeply mining the time series characteristics of data, which provides theoretical support and practical application value for medical diagnosis, rehabilitation monitoring and sports science. In terms of methods, this paper preprocesses biomechanical data such as joint angle, electromyography (EMG) and joint stress, and designs a time series prediction framework based on the self-attention mechanism of Transformer model. Through the simulation experiment, five indexes, namely mean square error (MSE), mean absolute error (MAE), determination coefficient (R²), prediction time and Pearson correlation coefficient, are selected to evaluate and compare the performance of the model. The experimental results show that the Transformer model is superior to the traditional LSTM, GRU and ARIMA models in all kinds of biomechanical data prediction tasks: MSE is 0.0152, R² is as high as 0.982, and the prediction time is only 0.76 s. In addition, Pearson correlation coefficient is close to 1 in different data types, which verifies the high consistency between the predicted results of the model and the real values. The conclusion of this paper shows that the Transformer model can effectively capture the global spatio-temporal characteristics of biomechanical data, and has high precision, high efficiency and strong generalization ability, which provides new technical means and theoretical support for biomechanical data-driven analysis and application.

In response to the increasing demand for lean marketing management, enterprises are leveraging intelligent data technologies for risk management. This paper aims to construct a marketing business risk management model inspired by biomechanics, combining Internet of Things (IoT) technology and BP neural network to provide a proactive early warning mechanism. In constructing the risk management model, this paper draws on the adaptive mechanism in biomechanics, emphasizing the flexibility and responsiveness of the system when facing a complex environment. By simulating how organisms adjust their behavior in a dynamic environment, this paper proposes a project group resource conflict risk assessment model based on BP neural network. The model uses nonlinear fitting and self-learning capabilities to dynamically adjust prediction parameters to adapt to market changes and resource allocation issues. This biomechanically inspired design enables the model to better capture potential risks and improve the accuracy of predictions when processing complex data. In addition, this paper also introduces a Linux-based multi-task embedded management system to achieve seamless integration of the Internet of Things and neural network risk models. The design of this system is inspired by the multi-tasking ability of organisms, which can efficiently switch between multiple tasks, thereby improving the response speed and processing power of the overall system. In this way, enterprises can monitor and manage various risks in marketing operations in real time. The experimental results show that the proposed biomechanically inspired risk management model performs well in practical applications, can effectively model marketing business risks, and provides superior performance. The research in this paper provides new ideas and tools for enterprises to achieve more efficient active risk management in a complex and changing market environment, and emphasizes the importance of biomechanics in the application of intelligent data technology. This not only provides a scientific basis for corporate decision-making, but also points out the direction for future research. By combining the inspiration of biomechanics, companies can better cope with challenges in risk management and enhance their competitiveness.

This study provides insights into the application of biomechanics and digital twin technology in smart agriculture and its contribution to achieving the goal of carbon neutrality in the context of digital economy. The study analyses the application of biomechanics in the construction of crop growth models, the design and optimisation of agricultural machinery, and the improvement of agricultural soils, and reports on the role of digital twin technology in the monitoring of agricultural production processes, the optimal allocation of resources, and the early warning and prevention of disasters. The results of the study show that the integration and innovation of these two technologies play an important role in the carbon-neutral realisation of smart agriculture. By analysing the mechanical characteristics of crop growth through biomechanics and simulating the growing environment with digital twin technology, we are able to more accurately predict the response of crops to environmental changes, optimise planting strategies and reduce carbon emissions. Ultimately, the study proposes future directions for development, suggesting that technology integration, model optimisation and system integration and innovation will contribute to sustainable agricultural development.

With the intensification of study and work pressure, people's physical and mental health has been compromised. Sports, in general, can enhance human physiological systems like the cardiovascular and respiratory systems from a macroscopic perspective. However, from a cell molecular biomechanics viewpoint, during exercise, mechanical forces are exerted on cells.  This can lead to changes in cell shape and cytoskeletal organization. Appropriate mechanical stimulation can activate intracellular signaling pathways related to cell growth and repair. Yet, without proper guidance, issues arise. Inadequate exercise might not provide sufficient mechanical stress to trigger beneficial cell molecular responses. On the other hand, excessive exercise can cause excessive mechanical damage to cells, such as disrupting cell membranes and cytoskeletons, leading to sports injuries. To address this, this paper utilized Artificial Intelligence (AI) and Big Data (BD) to study sports. A sports auxiliary system integrating AI and BD was proposed. The research on students in Class A and Class B showed that many were in a sub-optimal state of health. After sports with the aid of the proposed system, significant improvements were observed. In Class A, the proportion of students with physiological sub-health declined by 53.33%, and in Class B by 40%. The number of students with psychological issues also decreased. This indicates that the sports auxiliary system has a clear application need. By analyzing data related to cell molecular responses during exercise using AI and BD, it can optimize exercise regimens. This helps maintain appropriate mechanical stimulation on cells, promoting beneficial cell molecular biomechanical adaptations and ultimately enhancing both physiological and mental health.

Background: Vitamin D is essential for numerous physiological functions. Earlier research has unraveled a significant correlation between vitamin D insufficiency and poor outcomes within intensive care unit (ICU) patients. Patients receiving renal replacement therapy (RRT) often experience vitamin D deficiency or insufficiency. Beyond metabolic regulation, vitamin D influences cellular biomechanics, enhancing resilience to mechanical stress and supporting tissue integrity, which are critical for ICU patients undergoing RRT. This research seeks to examine the influence of vitamin D intake on outcomes in ICU patients receiving RRT. Methods: This study examined data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. It included all adult patients undergoing RRT. The participants were grouped into two categories: administrated vitamin D throughout their ICU admission (vitamin D group) and did not administer (non-vitamin D group). In-hospital mortality (IHM) was the primary outcome measured. Kaplan-Meier (KM) method Cox regression models, and subgroup analyses were leveraged to evaluate the correlation between vitamin D intake and IHM. To strengthen the reliability of the conclusions, propensity score matching (PSM) was implemented. Results: A total of 1270 patients on RRT participated in this research, comprising 338 and 932 patients in the vitamin D and non-vitamin D groups, respectively. The KM survival curves indicated substantial differences in survival probabilities between the two categories. Following adjustments for possible confounding factors by Cox regression analysis, vitamin D intake was markedly related to a reduced likelihood of IHM (HR: 0.35; 95% [CI]: 0.19–0.63; p < 0.001). This association remained robust following propensity score matching (PSM). Further subgroup analysis exposed that vitamin D intake reduced the probability of IHM in liver disease patients. Conclusion: Vitamin D intake is independently correlated with a reduced likelihood of IHM in ICU patients undergoing RRT. Further interventional studies are warranted to validate the possible advantages of vitamin D intake in improving the health of RRT patients. This study provides robust evidence supporting the therapeutic potential of vitamin D supplementation. These findings highlight the need for personalized supplementation strategies to optimize outcomes in this vulnerable population.

Nanobiodyeing refers to an innovative process of dyeing by extracting pigments from various pigment-containing organisms in nature and processing them with nanotechnology. No or very few chemical additives are used in the dyeing process. The excellent color culture of natural biological dyeing should be given new meaning so as not to cause a break in traditional skills. Natural biological dyes are non-toxic and harmless, non-allergenic and non-carcinogenic to the skin. They have good biodegradability and environmental compatibility. Their colors are soft, natural and distinctive. At the molecular level, the application of nano-bio-pigments can involve the molecular structure and interaction of the dyes used in Yao patterns. Through biomolecular design, we can develop nano-pigments with specific colors and stability. These pigments can better integrate with the natural fibers in Yao patterns, providing more vivid and lasting colors.

In recent years, under the theme of vigorously promoting traditional Chinese culture in China, original picture books of traditional Chinese culture have ushered in development opportunities, and a variety of original children’s picture books of traditional culture have come onto the market in a variety of ways. Based on this, this paper explores the path of original picture book content creation from a cultural perspective. It introduces the origins and development of original picture books, as well as the methods and key points of content creation. It analyses the problems of creating original picture books based on a cultural perspective, including traditional story themes, the homogenisation of painting styles and the disconnect between creative content and modern life. Moreover, this paper extends its exploration by integrating biomechanics. Strategies for creating content for original picture books based on a cultural perspective are proposed, including combining tradition with fun, establishing the creator’s own unique style, and selecting stories that are drawn in line with modern education, but also integrating biomechanical knowledge. For example, accurately depicting the biomechanics of character movements to enhance authenticity, endowing cultural symbols with biomechanical - based meanings, and creating interactive experiences related to biomechanics. By incorporating biomechanics, picture books can offer readers a more immersive and educational experience. This paper aims to provide references and lessons for the creation of content for original picture books.

Access to mental health services remains a global challenge, particularly for marginalized groups. This research endeavors to enhance the accessibility of mental health services by integrating media communication technology with biomechanical biosensors, including electrodermal activity sensors and heart rate monitors. The proposed approach leverages mobile communication platforms and wearable biosensors for real-time biomechanical parameter monitoring (including heart rate, blood pressure, respiratory rate, body temperature, and galvanic skin response, etc.) and remote interventions. Judge the impact on the brain and neuroendocrine system through the changes in biomechanical indicators, and use this as a basis for judging mental health. The objective is to develop a telehealth model that merges bio-data-driven alerts with communication tools to deliver prompt psychological support. This study underscores the deficiencies of traditional health systems in ensuring comprehensive mental health monitoring and emphasizes the potential of media communication technologies as scalable and accessible tools for early interventions in underserved areas, and also emphasizes the relationship between the physiological indicators measured by biosensors and the biomechanical mechanisms of mental health. Despite the existence of online methods for detecting mental health issues, early detection remains problematic. This research presents a framework for integrating pre-processed biosignal data with user-generated content to facilitate proactive monitoring. To address the limitations of conventional classifiers, the study introduces a Fitness-Dependent Optimizer-tuned Upgraded Decision Tree (FDO-UDT) model, which enhances the early identification of at-risk individuals using personalized thresholds and real-time event detection based on biomechanical data, it is helpful to provide an early warning before the clinical symptoms of mental health problems occur. The results indicate that automated alerts triggered by biomechanical sensor thresholds improve responsiveness and engagement, ensuring timely interventions for those in need. The FDO-UDT model achieves performance metrics of 90.21% accuracy, 98.01% recall rate, and 86.38% precision, outperforming traditional methods. The study concludes that the integration of media communication technologies with biomechanical sensors offers scalable solutions to improve the delivery of mental health services, especially for rural and underserved populations.

Basketball is a physically demanding sport that involves high-intensity movement like jumping, cutting, and rapid direction changes. College basketball players are particularly susceptible to sports injuries due to the repetitive stress placed on their musculoskeletal systems. College basketball players frequently suffer from sports-related injuries due to improper movement techniques, excessive usage, and high physical demands. The study analyzes the biomechanical factors affecting sports injuries among college basketball players and develops optimized training programs that focus on injury prevention and performance enhancement. A mixed-methods approach combines biomechanical analysis, injury tracking, and performance assessments. Participants were separated into two groups: the intervention group, which engaged in an 8-week plyometric training program as a warm-up before their regular training, and the control group, which received a regular warm-up exercise program before training. The study outcomes from both groups were assessed in terms of (i) biomechanics during a leg drop-landing task, (ii) biodex balance system, and (iii) athletic performance. Key metrics, such as vertical ground reaction force, knee valgus, ankle dorsiflexion, knee flexion, and hip flexion were evaluated for the interventions before and after 8 weeks. The study identified several biomechanical factors, such as improper joint alignment, muscle imbalances, and incorrect landing techniques, contributing to common injuries like sprains and anterior cruciate ligament (ACL) tears. The intervention group showed important enhancements in athletic performance, biodex balance system, and biomechanics compared to the control group. The study highlights biomechanical analysis is crucial for injury risk identification and optimizing training programs for college basketball players, with plyometric training reducing injury incidence and improving functional performance.

The combination of physical activity with language acquisition has gained popularity as an effective approach for improving educational outcomes. In exploring the combination of physical activity and language acquisition, it's crucial to consider cell molecular biomechanics. When students engage in biomechanical training and exercise, at the cellular level, mechanical forces are exerted on various tissues. These events involve the activation of mechanosensitive channels, which then influence intracellular signaling pathways. Such pathways can modulate gene expression related to neuroplasticity, as neurons are also affected by these mechanical forces indirectly. This, in turn, impacts cognitive processing linked to language acquisition. This study highlighted the interconnection of physical and cognitive functioning, showing that biomechanical training and exercise might improve cognitive performance, including language acquisition. The third and fourth academic terms saw the collection of data, which comprised pre and post-test results for listening, vocabulary, reading, writing, and grammar, among the students. There were 360 students, with a range of academic backgrounds and English proficiency levels (73% female, 27% male). The data were investigated using descriptive statistics, independent t-tests, one-way ANOVA, paired-sample t-tests, and assessments of student improvement with level changes. The results show that students’ scores differ significantly in total and across skills. These findings have implications for curriculum and course design in terms of integrating biomechanical training and exercise, as well as formative assessment. These findings indicate that combining physical training with language learning programs can improve cognitive processing and result in higher educational performance. However, more studies are required to fully understand the long-term impacts and its suitability for other age groups and learning contexts, given the intricate cell molecular biomechanics at play.

This study investigates the integration of plant-cell morphological bionics into vertical greening systems to enhance thermal performance and environmental sustainability in urban architecture, with a focus on Suzhou, Jiangsu Province. Drawing inspiration from the honeycomb-like structure of plant cells, which are known for their exceptional strength-to-weight ratio and efficient material usage, we introduce a biomimetic façade system. This system uses hexagonal modules that mimic plant cell geometry, supporting locally adapted climbing vegetation to mitigate urban heat island effects. Advanced computational design tools were employed to optimize the system’s structural efficiency and cooling performance, tailoring it to the subtropical monsoon climate and rich architectural heritage of the region. Over a two-month experimental period during the peak summer season, the system demonstrated a significant reduction in surface temperatures, averaging a daily cooling effect of −5.4 ℃ and reaching a maximum reduction of −10.2 ℃ during peak solar radiation. Heat flux calculations and statistical analyses confirmed the system’s enhanced thermal regulation capabilities, leading to reduced heat transfer and energy consumption. The findings highlight the biomimetic system’s potential to harmonize contemporary building designs with traditional aesthetics, addressing urban sustainability challenges while preserving cultural continuity. Future research recommendations include year-round performance evaluations, material innovations, and scalability assessments in high-density urban areas to further validate and refine this promising approach.

With the advent of the era of big data, how to analyze the massive data of players’ passing, shooting and position in football matches more effectively has become a new topic for the development of football. Machine learning algorithm has been widely used in various fields in recent years, relying on its strong data processing ability. Football match data analysis based on machine learning can effectively mine the effective characteristics of football data and better assist coaches’ tactical arrangement, personnel arrangement and player evaluation. In this paper, machine learning algorithms such as clustering algorithm, classification algorithm, Markov chain model and kernel density estimation algorithm are used to analyze the spatio-temporal data of players’ passing, shooting and position in football match. Compared with the traditional data analysis methods based on simple statistics, the method in this paper has more intuitive visualizations and deeper data insights. This approach is instrumental in guiding tactical planning and personnel strategies in football. Additionally, by integrating biomechanics into the analysis, we enhance our understanding of player performance. Biomechanical factors such as movement patterns, force application, and body mechanics play a critical role in a player's effectiveness on the field. Incorporating physiological data, including players’ heart rate and movement intensity, allows for a more holistic perspective on performance, addressing potential fatigue and injury risks. By analyzing how biomechanics influence spatio-temporal data—such as optimal angles for passing, shooting mechanics, and body positioning during play—we can provide actionable insights into player training and development. This comprehensive approach not only improves tactical decision-making but also fosters player longevity and performance optimization.

In concrete constructions, carbon fiber-reinforced polymer (CFRP) bars had considered as an alternative to traditional steel reinforcement for enhancing the capability of loading in reinforced concrete (RC) components. Whereas the advantages of CFRP are well-known, its potential applications have been confined by concerns, such as lesser ductility, decreased strength of bonds under continuous loads, and elastic response. This research investigates the performance of 7 slender beams of concrete that are reinforced with CFRP beams with maximum loads to calculate the bearing capacity using biomechanical concepts to improve reinforcement design. CFRP reinforcement can improve the concrete constructions of load capacity, as demonstrated by findings of the applying biomechanical design concepts. Special emphasis is placed on the bond behavior at the CFRP bars’ anchorage lengths (600 mm), with biomechanical design concepts involving local confinement at these anchorage regions (90%) shown to impact cracking behavior and enhance overall bearing capacity. Results from experiments on CFRP-reinforced beams compared to other reinforced beams are examined. The research examines key performance indicators, such as load capacity (180 kN), and stiffness (25 kN/mm), as well as bond strength at anchorage (18 MPa), failure mechanisms with reducing failure, and crack propagation (0.2 mm/min). Experimental findings demonstrated that the CFRP beams are more efficient in the bearing capacity of concrete structure performance.

In the realm of modern applications, especially those related to human - involved scenarios such as vehicles and medical or wearable devices, the performance of RGB LED lights is of great significance. Beyond the traditional concerns of electronic - optical properties, the integration of biomechanics - related factors can bring new perspectives and optimizations. In vehicles, during prolonged illumination, the color shifts of various LED lights not only affect driving comfort but also have potential implications for driving safety from a biomechanical perspective. The human body’s visual and psychological responses to these color and luminance changes are complex. For example, sudden or inconsistent color changes can cause visual fatigue and distraction, which may impact a driver’s reaction time and decision - making ability, all of which are related to biomechanical aspects of human - machine interaction. Each LED shows deviations in wavelength and luminous intensity, and the light decay of RGB chips is inconsistent. To address these issues and ensure consistent color and luminance of all RGB ambient lights during use, this paper proposes a temperature prediction model (PT - model) based on constant current source output PWM. This model takes into account the impact of temperature changes on the color coordinates and luminous flux of LED lights. Moreover, considering the potential applications in medical and wearable devices, the model’s performance becomes even more crucial. In medical light - based treatment devices, the precise control of RGB LED temperature and light output is essential for ensuring the effectiveness of treatment while minimizing potential harm to human tissues. Biomechanics research can provide insights into how different tissues respond to light - induced heat and mechanical stress. Similarly, in wearable devices for health monitoring, the stability of RGB LED performance is related to the comfort and accuracy of the device’s operation on the human body. Experimental results show that compared to traditional models, the prediction accuracy of this model is significantly improved, with errors reduced to within 4.3%. The model’s effectiveness is verified under different ambient temperatures ranging from −40 ℃ to 120 ℃, which is crucial for ensuring its reliability in various real - world applications, especially those related to human - centered scenarios influenced by biomechanical factors.

With the rapid advancement of modern technology, multimedia has become a transformative force in various fields, including language education. This paper explores the integration of multimedia technology and task-based language teaching methods in Korean language instruction, examining its impact on learning efficiency and instructional effectiveness. Drawing inspiration from biomechanical principles, this study analyzes the cognitive and physical interactions involved in multimedia-assisted language learning, focusing on how dynamic, interactive content can enhance students’ comprehension and retention by considering the biomechanical aspects of motor skills used in speech production and language acquisition. By applying multimedia tools to Korean reading instruction, we aim to optimize learning processes in a manner that aligns with principles of cognitive biomechanics, such as task repetition, sensory engagement, and motor skill refinement. The proposed model leverages task-based approaches and interactive simulations to create a responsive and adaptive learning environment that considers the physical dimensions of language learning, such as articulation and phonetic accuracy. This integration of biomechanics-inspired educational design provides a framework for more effective, immersive language instruction, potentially setting a precedent for technology-enhanced learning across disciplines. By emphasizing the role of physicality in language learning, this research contributes to the understanding of how biomechanical factors can improve educational outcomes.

Sports activities induce significant changes in cell mechanics. Physical exercise prompts molecular adaptations in muscles, and analyzing the biomechanics of specific sports is crucial. Sports injuries, commonly occurring during exercise, often stem from overuse, crashes, or excessive forces. The physical and psychological rigors of sports and intense competitions heighten the risk of damage. For instance, hamstring strain injury is prevalent among football players. Understanding the biomechanics underlying such injuries is essential. This research focuses on gathering biomechanical data from physical education exercises, including joint angles, forces, velocities, and muscle activations. By preprocessing this data through cleaning and normalization, we aim to decipher the molecular and cellular level changes. Maximal hamstring flexibility and muscular tightness, identified as key factors, can provide insights into muscle cell mechanics and potential injury prevention. A novel Intelligent Flamingo Optimized Residual Network50 (IFO-ResNet50) is proposed. Through biomechanics analysis in physical education teaching, it targets the prevention of football muscle injuries. The method’s effectiveness is evaluated in terms of accuracy (98.1%), recall (98.4%), F1-Score (98.2%), AUC (98.5%), and precision (98.7%) in comparison to existing algorithms. This research not only aids in identifying the physiological and biomechanical changes at the cell or molecular level due to sports but also offers practical strategies for physical education teachers. By reducing injury risks, it can enhance student safety and performance in school sports programs, thereby contributing to a more comprehensive understanding of the relationship between sports and cell mechanics.

Under the guidance of the OBE educational concept of “student-centered, output-oriented and continuous improvement”, this article discusses the ideological and political teaching objectives, teaching contents, teaching models, teaching methods, assessment mechanisms, and promotion strategies of the “Engineering Ethics” course, particularly emphasizing its relevance to biomechanics. The overall teaching design was carried out by integrating biomechanical principles into the curriculum framework. Feedback from teaching evaluations indicates that this teaching framework has achieved a high degree of realization of curriculum objectives and produced positive teaching outcomes, providing a demonstration and reference for other curriculum reforms in related fields.

In order to improve the resolution of infrared images of single-molecule reconstruction of composite materials, this paper proposes an image super-resolution reconstruction method based on deep machine learning SRGAN, By replacing the residual blocks of the generated network in SRGAN with residual dense block, it can more effectively acquire and utilize the image features from various network layers, especially those containing high-frequency information, thereby ensuring that more details and textures are preserved during the magnification of infrared images. The SE attention mechanism is incorporated into the generative network by assigning a weight to each channel, which strengthens the focus on important features while reducing reliance on irrelevant information. Super-resolution reconstruction experiments conducted on CFRP composite material infrared images demonstrate that the improved algorithm achieves a 0.6 increase in Peak Signal-to-Noise Ratio (PSNR) and a 0.3% increase in Structural Similarity Index (SSIM) compared to SRGAN, providing valuable references for the super-resolution reconstruction of infrared images of composite materials.

Purpose: As mental health issues among the aging population become increasingly prevalent, effective interventions that incorporate physical movement are essential. To explore the influence of Qianlong health exercise on the mental health of the elderly people, emphasizing the biomechanical aspects of physical activity. Methods: A general situation questionnaire and the SCL-90 were used to measure the psychological status of 627 elderly people. Results: There were significant differences in the dimensions of somatization, obsessive symptoms, interpersonal sensitivity, depression, anxiety, hostility, terror, paranoia, psychosis, sleep diet and total score, especially in the dimensions of interpersonal sensitivity, depression, anxiety, paranoia and psychosis. Furthermore, the study identified significant variations in the length, frequency, and forms of Qianlong health exercise practiced by participants, indicating that these factors may play a critical role in influencing mental health outcomes. The biomechanical properties of Qianlong health exercise, characterized by controlled movements that enhance balance, coordination, and flexibility, may contribute to improved psychological well-being by reducing stress and promoting relaxation. Conclusion: Qianlong health exercise represents an effective intervention for enhancing mental health among the elderly, integrating physical activity with psychological benefits. Future research should focus on the specific biomechanical mechanisms that mediate these effects and explore how such exercises can be incorporated into comprehensive health promotion strategies for the aging population. This approach not only addresses physical fitness but also fosters mental resilience, making it a valuable addition to geriatric health programs.

The present study aimed to evaluate the effects of low-level laser therapy (LLLT) on exercise performance and skeletal muscle damage. A randomized, double-blind, placebo-controlled study involved 24 male college swimming athletes. LLLT was administered prior to exercise using a He-Ne laser at 632.8 nm, with a power output of 5 mW, a total irradiation duration of 300 s, and an energy density of 0.3 J/cm² per diode or placebo, applied to two points on the rectus femoris muscles bilaterally. The performance in a 200-m breaststroke swim, as well as thigh and leg girth, blood lactate levels, creatine kinase (CK), and lactate dehydrogenase (LDH) levels were assessed before and immediately after the swimming protocol. The LLLT group demonstrated a significant improvement in 200-m breaststroke performance (p < 0.05) and a significant reduction in thigh circumference, blood lactate, CK, and LDH levels (p < 0.05) when compared to the placebo group. Pre-exercise photobiomodulation by LLLT improved the 200-m breaststroke swimming performance, and reduced muscle fatigue and damage.

College students these days are under a great deal of psychological stress. This stress, whether originating from academic demands or personal life challenges, triggers a cascade of biological responses within the body. Physiologically, stress can disrupt the normal functioning of the endocrine system, leading to abnormal secretion of stress hormones like cortisol, affecting neurotransmitter levels and neural plasticity, thereby impacting mental health. If left unaddressed, this psychological stress can have severe and lasting negative effects on their well-being. In this context, it is crucial to quickly identify students experiencing mental health crises. However, the manual verification method has significant constraints and cannot effectively ascertain the mental state of the students. To address these challenges, this research proposes a Machine Learning-based Psychological Crisis Warning (ML-PCW) framework, integrating biomechanical indicators to provide unique insights into students’ psychological states. For instance, changes in gait patterns can be associated with different emotional states, where abnormalities in walking speed, stride length, or body sway may indicate increased stress or anxiety. In the digital age, college students are more inclined to express their emotions through online platforms. Big Data analytics has emerged as a powerful tool for analyzing this digital footprint, providing valuable insights into their psychological states. Additionally, statistical techniques are employed to establish an emotional assessment paradigm that considers not only traditional psychological factors but also biological and biomechanical cues. In this research, the honey badger search-joint adaptive kernelized support vector machine (HBS-AKSVM) technique is developed. This technique is designed to handle the labeling process of the initial data, which includes both psychological and biological data. By incorporating biomechanical indicators, the HBS-AKSVM can more accurately categorize and analyze the data while minimizing the computational load during the development of the PCW system. Research findings show that the suggested approach operates more effectively than current approaches in terms of supplying professionals with a psychological supplementary assessment that is dependable. By integrating biomechanical indicators, the ML-PCW framework offers a more comprehensive and accurate understanding of college students’ psychological states, enabling early detection and timely intervention in mental health crises.

This study focuses on the mechanical properties of biomolecules and their interactions within the extracellular matrix in the context of watershed water resource management. We utilized a modified WEAP-MODFLOW model to explore how these interactions influence the allocation and management of water resources. The WEAP model serves as a comprehensive tool for assessing the balance of surface water supply and demand, while the MODFLOW model is employed for simulating deep groundwater flow. By integrating these models, we can examine the mechanical behavior of biomolecules and cells in response to varying hydrological conditions, thus enhancing our understanding of their role in water resource dynamics. To improve model accuracy, parameters were calibrated using observed flow data from relevant biological systems. Key evaluation metrics, including the coefficient of determination, Nash efficiency coefficient, and deviation coefficient, were employed to assess simulation accuracy. On a monthly scale, the coefficient of determination reached 0.98, indicating a strong correlation between simulated and observed values. The Nash efficiency coefficient was 0.97, reflecting high accuracy in simulating flow dynamics. Furthermore, a deviation coefficient of −12% suggests minimal systematic bias in the simulation results. During the validation phase, these metrics maintained high accuracy, with a determination coefficient of 0.97, a Nash efficiency coefficient of 0.97, and a deviation coefficient of −3.30%. These findings highlight the reliability of the improved WEAP-MODFLOW model in simulating the mechanical properties of biomolecules and their interactions with water resources, ultimately contributing to optimized water resource management strategies.

The focus of the study is on the interrelation between hormonal secretion, cellular activity and skeletal growth with physiological advancement and depression in adolescents. To conduct a more in-depth analysis, it employed a longitudinal research strategy and recruited 436 adolescents who measured between the ages of 12 to 18 along with an 8–10 years Hispanic male. Over the course of 24 months, multiple sets of data were gathered, which included insulin-like growth factor-1 (IGF-1), cellular metabolism markers, GH, bone mineral density (BMD) and psychological indicators. The data gathered showcased multiple correlations between endocrine parameters and GH levels which were linked with depression r = 0.76, p < 0.001 and emotional regulation r = 0.82, p < 0.001. The clinical implications highlight the better understanding of the biomechanical-psychological interaction in development of adolescents and how to tackle such an issue with evidence-based intervention.

With the advancement of educational technology, biosensors are becoming valuable in enhancing classroom interactivity and adapting teaching strategies. In English language classrooms, maintaining student engagement and managing learning anxiety is essential for effective learning; traditional methods fail to offer real-time insights into student engagement and emotional states. The objective of the research was to enhance language instruction effectiveness by monitoring learners’ cognitive states using biosensor technology. Initially, biosensors were used to collect physiological data such as heart rate variability, eye movement, facial expression, posture, and seating data from students during English language lessons and also gathered over four weeks in a controlled classroom setting. The collected data underwent noise reduction using signal-to-noise ratio (SNR) to improve signal clarity and min-max normalization to scale the data within a consistent range for accurate analysis. Spiking neural networks (SNNs) are integrated with biosensors and areic brain neural processing, enabling dynamic adaptation of teaching content based on physiological signals and enhancing personalized learning by responding to student’s cognitive and emotional states. The findings offer that biosensor technology combined with SNNs significantly improves student engagement, reduces language anxiety, and increases learning efficiency. When compared to other weeks, student engagement (30%), cognitive load (10%), task completion efficiency (30%), attention focus (35%), and teacher-student interaction (35%), all showed better outcomes in Week 4. This suggests that biosensor-driven adaptive teaching, powered by SNNs, has the potential to transform interactive language learning.

The significance of outdoor games in the lives of children cannot be overstated, as outdoor play areas serve as vital laboratories for children to explore, imagine, and engage in social interactions. However, with the deepening of global urbanization, children’s freedom to play in urban environments is becoming increasingly restricted. Simultaneously, the increase of urban residential density and the proliferation of indoor activity spaces has led to a considerable reduction in outdoor play areas for children. This diminished exposure to and interaction with nature indirectly impacts the physical and mental health development of children, giving rise to a host of physical and psychological issues. Outdoor play spaces serve as dynamic environments where children can connect with nature, understand their surroundings, and cultivate curiosity. The design of these spaces must prioritize biomechanics to enhance children’s physical engagement and safety. High-quality outdoor play areas should be structured to promote movement that aligns with children’s natural motor skills, facilitating activities that encourage running, climbing, and jumping. Such designs not only stimulate physical development but also foster cognitive growth by providing opportunities for problem-solving and creativity. This review summarizes excellent design concepts and methods for outdoor play spaces for children from multiple countries and regions in recent years, including 77 papers. It delves into the design principles of children’s outdoor play spaces from a child-friendly perspective, taking into account children’s needs, safety, and sustainability. This study focuses on providing scientific support for the optimization of outdoor play spaces for children, with particular attention to aspects such as the diversity of outdoor play spaces, stimulation of intelligence, and seamless integration of natural elements. The aim of this research is to enhance the quality of outdoor play spaces for children, improve children’s outdoor play experiences, and promote children’s healthy development. By applying biomechanical principles, designers can create environments that enhance children’s interaction and exploration abilities, ultimately improving their overall quality of life. Emphasizing child-friendliness in the design of outdoor play spaces is crucial.

Music has long been considered an approach for improving physical performance and motivation in sports. This study looks into the impact of music on athletes’ performance in sports, employing biosensor monitoring to detect physiological and psychological changes. A total of 100 athletes were randomly assigned to two groups: Group A (n = 50) exercised with music, while Group B (n = 50) exercised without music. Participants participated in identical physical activities, such as endurance runs and strength exercises. Biosensors measured heart rate variability (HRV), oxygen saturation, muscle activity, and galvanic skin response (GSR). Participants also self-reported their motivation levels and perceived effort. The two groups’ performance measures were compared statistically using the independent t-test. Furthermore, within-group variations in recovery durations before and after exercise were assessed using paired t-tests. The association between motivation levels and performance results was evaluated using Pearson’s correlation. The impact of music on a number of physiological indicators was evaluated using multivariate analysis of variance (MANOVA). The findings showed that music (Group A) improved sports performance significantly by increasing endurance, lowering perceived exertion, and encouraging faster recovery. Biosensor data showed that the music condition resulted in higher HRV and less muscular tiredness, indicating enhanced physiological efficiency. Participants reported increased motivation and enjoyment when exercising to music. The findings suggest that incorporating personalized music playlists into training programs can improve athletic outcomes.

The economic consequences of ESG have been debated between the “stakeholder hypothesis” and the “management self-interest hypothesis”. This study not only analyzes the impact of ESG behavior on the cost of equity capital using panel data and regression models but also delves into the biophysical and ergonomic aspects within the corporate context. ESG initiatives can lead to changes in the work environment and operational processes. For example, improvements in environmental sustainability might involve the installation of ergonomic equipment to reduce employees’ physical strain during work, which in turn could affect their productivity and overall well-being. Socially responsible initiatives may lead to a more harmonious workplace atmosphere, reducing stress levels among employees and potentially influencing their physiological states. The study uses panel data and regression models to analyze the impact of ESG behavior on the cost of equity capital. The findings reveal that corporate ESG behavior significantly reduces the cost of equity capital, supporting the stakeholder hypothesis. Further analysis indicates that this effect is more pronounced in highly market-oriented regions and non-state-owned enterprises, highlighting the roles of market efficiency and organizational flexibility. Additionally, the consideration of biophysical and ergonomic impacts on stakeholders provides a more comprehensive understanding of how ESG strategies can have far-reaching effects within and outside the organization. This research provides empirical evidence for enterprises to actively implement ESG strategies and offers actionable insights for governments to formulate policies that foster sustainable development.

This paper aims to solve the problem of insufficient breath and lack of coordination between breathing and pronunciation in English learners’ coherent pronunciation. By analyzing the biomechanical characteristics of respiratory muscles, scientific breath control strategies are designed to optimize airflow management and improve speech fluency and expression efficiency. English learners of different language levels were recruited and divided into elementary, intermediate and advanced groups. Speech tasks including slow pronunciation, fast pronunciation and long sentence pronunciation were designed. Electromyography was used to record the activity characteristics of respiratory muscles, airflow meter was used to measure airflow output data, and high-sensitivity microphone combined with Praat software was used to extract speech features including speaking rate and pause frequency. This paper studies tasks such as slow pronunciation, fast pronunciation and long sentence pronunciation, clarifies the degree of involvement of key muscles such as the diaphragm, and designs targeted phased breath control strategies, including abdominal breathing training, long sentence reading and coherent reading simulation, and the introduction of speech tasks. The results show that the breath control strategy proposed in this paper significantly improves learners’ speech fluency, syllable and phonetic pronunciation accuracy under all task conditions. At a speaking speed of 180 words/minute, the correct pronunciation rate of phonetic symbols and syllables in this strategy intervention was 80%, and the correct pronunciation rate of syllables was 83%, and the breath maintenance rate and pause frequency were improved. The optimization strategy based on respiratory biomechanics can effectively improve language learners’ breath management ability and coherent pronunciation level, and provide an innovative theoretical basis and practical path for language learning and voice training.

Background: Recall of specific events, which is known as episodic memory, relies heavily on synaptic plasticity in the hippocampus. Molecular and cellular processes that mediate this phenomenon are intricate and include neuronal and glial functions, signaling pathways, and synaptic reorganization. Objective: This work will address the molecular and cellular aspects of synaptic plasticity in the hippocampus and its role in the formation of episodic memories through processes including dendritic spine remodelling, astrocytes and microglia, and epigenetics. Methodology: Literature review of recent findings and theoretical frameworks such as Morris’s neurobiological theory of the hippocampus was done to synthesize the molecular markers, signaling pathways, and neuromodulation. Experimental data regarding the involvement of calcium signaling, synaptic tagging, and protein synthesis dependent long-term potentiation (LTP) in memory formation were reviewed. Results: This paper discusses how calcium influx, CaMKII activation and CREB-mediated transcription contribute to the preservation of LTP. Dendritic spine remodeling is highlighted as a key structural process and astrocytes and microglia are identified to regulate synaptic plasticity and neural circuit function. Moreover, epigenetic mechanisms, such as histone acetylation and DNA methylation, relate synaptic activity to the expression of genes associated with memory. Conclusion: Results of this study explain the molecular and cellular mechanisms of hippocampal synaptic plasticity and the formation of episodic memory. These findings provide a basis for future research on potential treatment for memory related diseases and underscore the significance of molecular biology in cognitive neuroscience.

With the rapid development of bioinformation technology and its wide application in various fields, its combination with multi-sensory environmental art design provides new possibilities for creating more personalized, interactive and emotional user experience. With human experience as the core design concept, this paper discusses explore how to use bioinformatics to reveal the principles of molecular and cellular biomechanics to improve multi-sensory environmental art design, aiming to enhance users’ immersion and satisfaction in various environments by integrating advanced algorithms and technical means. This paper initially outlines the core elements of bioinformatics technology, encompassing fundamentals of bioinformatics, biological signal processing, and their capacity to detect and analyze biomechanical responses at the molecular and cellular levels. It delves into the potential interplay between multisensory environmental stimulation and molecular-cellular biomechanics, elucidating how, grounded in biomechanics principles, environmental cues elicit alterations at these microscopic scales. Furthermore, the paper presents research methodologies grounded in bioinformatics, leveraging VR/AR and other simulated multi-sensory art environments, in tandem with cellular experimental techniques, to investigate the biomechanical responses of molecules and cells, including alterations in cell morphology and molecular expression patterns. Machine learning algorithms are employed to analyze the data, aiming to uncover the relationships between multi-sensory environments, bioinformatics, and molecular-cellular biomechanics. Additionally, the paper explores the application of bioinformatics in enhancing user experience and social interaction through personalized adjustments based on physiological signals, emotional recognition algorithms, and the design of health-promoting environments tailored for specific populations. The pivotal roles of algorithms such as machine learning, adaptive optimization, and data mining are highlighted, demonstrating how they aid designers in comprehending and addressing user needs. Ultimately, bioinformatics offers insights into the biomechanical mechanisms of molecules and cells within multi-sensory environments, fostering innovative perspectives in art design. Finally, the paper summarizes the contribution of bioinformation technology to multi-sensory environment design and looks forward to future research, particularly the impact of emerging technologies like quantum computing and brain-computer interfaces. While these technologies show potential, the paper lacks an analysis of their application and technical feasibility in this context. Future research should focus on integrating these technologies with existing ones, addressing challenges such as compatibility, scalability, and cost, and outlining practical implementation steps. Overall, the paper presents current findings and points toward a more intelligent and humane future for the field.

Background: Ecological, cultural, and aesthetic values are considered vital in developing sustainable tourism within the rural tourism landscapes of Hunan, Guangxi, Guizhou, and Chongqing. Inadequacies in conducting biomechanical and visual analysis have led to a failure in creating a safe, comfortable, and aesthetically pleasing rural tourism landscape. The following study conducts an assessment of biomechanical and visual factors that shape the rural tourism landscape, indicating that an interdisciplinary approach is essential in addressing challenges from this perspective. Objective: The research will focus on integrating an analytic framework from biomechanical stability to aesthetic perspectives into molecular and cellular biomechanics to guide sustainable landscape design in rural tourism. Methodology: A mixed-methods approach combined literature review, expert input, public participation, and structured questionnaires. Factor analysis and reliability tests were conducted on 218 responses to assess key indicators such as terrain stability, vegetation resilience, spatial coherence, and cultural authenticity. This research also points out the possibility of developing methods of landscape evaluation even at the molecular level, considering cell wall composition and microbial community interactions. The results showed three factors that explain 63.26% of the variance: Natural Landscape, Rural Settlement Landscape, and Cultural Landscape. The Natural Landscape factor explained 52.34% of the variance and relates to resistance that vegetation has to resist wind and terrain usabilities, whereas in Rural Settlements, it shows coherence in space. The cultural landscape emphasized heritage conservation and aesthetic variation with the season. New dimensions that can be given for landscapes to enhance their stability and harmony further could be given to molecular biomechanics, vegetation behavior considering environmental stressors and interaction of soil-plant-microorganisms. Conclusion: This paper presents a biomechanical and visual framework for analyzing sustainable rural tourism landscapes. Integrating molecular and cellular biomechanics can help develop a deeper understanding of vegetation stability and its interaction with aesthetics, helping policymakers and designers plan and implement safer, functional, and visually appealing landscapes that support sustainable tourism and ecological preservation.

Atherosclerotic plaque rupture, a primary cause of acute cardiovascular events, is fundamentally influenced by biomechanical forces. While ferroptosis, an iron-dependent form of regulated cell death, has been implicated in atherosclerosis progression, its impact on plaque biomechanics and stability remains poorly understood. We developed a comprehensive biomechanical model integrating ferroptotic parameters with plaque structural mechanics. Human carotid endarterectomy specimens (n = 45) were analyzed using a multi-modal approach combining mechanical testing, molecular analysis, and computational modeling. Plaque samples were categorized into stable (n = 15), vulnerable (n = 15), and transitional (n = 15) groups. Changes in mechanical properties, ferroptotic markers, and stress distributions were assessed over 72 h under controlled conditions. Ferroptosis induction resulted in significant alterations of plaque biomechanics. Peak circumferential stress in the fibrous cap increased from 142.3 ± 12.4 kPa to 286.4 ± 22.7 kPa (p < 0.001), while cap thickness decreased from 165.4 ± 12.3 μm to 98.6 ± 18.4 μm (p < 0.001). The iron accumulation showed a strong negative correlation with plaque stability (r = −0.892, p < 0.001). Mechanical testing revealed a 56.5% reduction in tensile strength and a 52.3% decrease in strain at failure in vulnerable plaques. Sensitivity analysis identified fibrous cap thickness (NSC = 0.924) and iron concentration (NSC = 0.856) as critical determinants of plaque stability. Our findings establish ferroptosis as a significant mediator of plaque biomechanical deterioration. The strong correlations between ferroptotic markers and mechanical instability suggest that targeting ferroptotic pathways may provide novel approaches for maintaining plaque stability. This study provides a quantitative framework for understanding the mechanical consequences of ferroptosis in atherosclerotic disease progression and identifies potential therapeutic targets for plaque stabilization.

Vocabulary acquisition is a fundamental aspect of language learning, and mastery of vocabulary has a substantial impact on learners’ capacity to comprehend and communicate successfully in a foreign language. The research’s goal is to investigate how biological perception affects students’ engagement and the effectiveness of their learning of English vocabulary. A total of 286 various academic students participated in this research. The experimental group (EG) (biological perception-assisted learning) and the control group (CG) (standard vocabulary acquisition techniques) are selected at random from among these students. This research uses the SPSS software version 29.0. The data analyze the statistical methods, including descriptive statistics, paired t-test, and ANOVA, to identify any significant differences between the groups. The research assesses the influence of biological perception on vocabulary retention, learning speed, and general engagement of students in the language learning process through an empirical research including a diverse group of students. The results indicate that, when compared to traditional approaches, students exposed to the EG exhibit significantly greater understanding rates (87%), higher levels of engagement (92%), and enhanced learning efficiency (95%), demonstrating the favorable influence of adding biological sensing into language learning procedures. In contrast, the CG, which employed conventional VA techniques, demonstrated only modest improvements across the board, with a learning speed of 85%, understanding rates of 79%, and engagement levels of 81%. This research provides educators looking to improve vocabulary training with useful insights into the possibilities of biological perception-based tactics for language learning enhancement.

Biomechanics boost human health and performance. Sports not only promote the improvement of national physical fitness, but also promote the development of fitness equipment manufacturing industry. Sports injuries, such as fractures, require the consultation of orthopedic doctors, and fitness requires the guidance of coaches, which to a certain extent promotes the employment of biomechanics researchers. Therefore, the potential of biomechanics is for improving human health and Environmental, Social and Governance (ESG) performance is well established. Green finance can provide financial support for the sustainable development of the manufacturing industry. Whether green finance, as a financial tool that combines healthy, environmental and economic benefits, has a significant impact on the ESG performance of enterprises that are highly concerned by government departments and investors at present still requires in-depth research. This paper, from bio-mechanics perspectives, based on data from listed manufacturing companies in China from 2013 to 2022, examines the impact of green finance on the ESG performance of manufacturing enterprises (especially, sports facilities manufacturing enterprises) by constructing a fixed-effects model. The research findings are as follows: First, green finance prompts citizens choose low-carbon transport, such as cycling, running, or new energy vehicles. It is not only conducive to improving citizens’ physical fitness and the environment, but also conducive to the development of sports facility manufacturing companies and auto-mobile manufacturing companies. Second, green finance significantly enhances the ESG performance of manufacturing enterprises, and this conclusion has been robustly tested through methods such as replacing the explained variable, incorporating dummy variables, and employing instrumental variable techniques. Third, green finance improves the ESG performance of enterprises by alleviating financing constraints and promoting green technological innovation in the manufacturing sector. Finally, green finance has a more significant impact on enhancing the ESG performance of non-state-owned manufacturing enterprises and technology-intensive manufacturing enterprises. This study provides an in-depth exploration of the extent and mechanisms through which green finance influences the ESG performance of Chinese manufacturing enterprises, offering policy references for accelerating the green transformation and up-grading of China’s manufacturing sector.

Background: Congenital Muscular Torticollis (CMT) is defined as interstitial fibrosis and contracture of one side of the sternocleidomastoid muscle (SCM), typically resulting in the head and neck deviating to the affected side, the lower jaw turning to the opposite side, and limitation of the rotation of the head and neck. As China’s largest and highest region, the Qinghai-Tibetan Plateau (QTP) is recognized as one of the world’s critical biodiversity hotspots. Methods: The blood and SCM bio-samples from 20 patients and their parents at Qinghai Women and Children’s Hospital were collected. The clinical properties of these individuals, including gender, ethnic group, and age at initial diagnosis, were analyzed. Whole exon sequencing was then performed on the blood and SCM bio-samples to characterize the molecular properties of CMT patients living in the Qinghai-Tibetan Plateau (QTP) region. Results: The female to male ratio was 9:11 for these 20 patients, the age varied from 1 to 13 years old, 17 of them showed SCM fibrosis, and 18 of them were found CMT symptoms when they were born. The number of single nucleotide polymorphisms (SNP) varied a lot for across different chromosome and lots of them were located in chromosome 1 and 2. The variation of C→T and G→A were the most common alteration types, and patients have no significant differences in various ethnic groups. The comparison of molecular variations in family members suggested the genetic variations for CMT, which could provide targets in treatment of CMT. Conclusions: The findings indicated that glucose and lipid metabolism, as well as antigen processing, might be involved in the development of CMT. This could increase the efficacy of diagnosis and prognosis, ultimately leading to the development of optimal targeted therapeutics for CMP patients living in QPT.

In this paper, a control system of medical robot arm based on DRL algorithm is designed by combining deep reinforcement learning (DRL) with compliant control. The system uses a trial-and-error mechanism to collect data and gradually optimize control strategies through continuous interaction between the robotic arm and the environment. Considering the actual use cost, time cost and security problems, the model is trained by the simulator based on physics engine, and the trained model is transferred to the actual robot for verification. To ensure seamless communication between different software components, Robot Operating System (ROS) was chosen as a development platform to build modular and distributed systems that are easy to test and modify. The experimental results show that the maximum distance error and repeated positioning accuracy are obviously optimized after Modified Denavit-Hartenberg (MDH) parameter modification.

This review examines the impact of digital technology on child development, integrating economic and biomechanical perspectives. I reviewed existing literature, drawing upon human capital theory, skill formation theory, and parenting style theory, to establish a comprehensive framework for understanding this multifaceted issue. The current body of research suggests a concerning trend: Increased digital technology use often coincides with reduced physical activity and increased sedentary behavior. This shift potentially alters the mechanical environment experienced by children’s developing bodies, raising concerns about musculoskeletal development, motor skill acquisition, and long-term health outcomes. At present, there have been some studies in related fields, but there is a lack of overall review and integration. For better understanding and conducting studies, some suggestions were given: Investigations should focus on dose-response relationships between digital technology exposure and biomechanical outcomes, while also considering the influence of moderating factors such as age, sex, pre-existing conditions, and parenting styles. By clarifying the underlying mechanisms, we can inform the development of evidence-based interventions and guidelines to promote optimal physical development and ensure the well-being of children in the increasingly digital world.

China’s strategic objective of promoting national health is in line with the integration of the sports business with sports promotion programs. Biomechanics, which delves into the mechanical aspects of human movement and its interaction with the surrounding environment, is a linchpin in this integration. When it comes to the construction of sports venues and sports equipment development, biomechanical principles are fundamental. For example, the selection of surface materials for tracks, courts, and fields must consider factors such as ground reaction forces, coefficient of friction, and energy dissipation. These biomechanical parameters not only influence an athlete’s performance but also play a crucial role in injury prevention. Policies promoting the development of sports venues, increasing public access to recreational facilities, and investing in community health programs remain essential for expanding sports participation and promoting fitness. Through an examination of policy frameworks, economic benefits, and social impacts, this study identifies key factors facilitating this integration. Technological advancements, such as the use of inertial measurement units (IMUs) and force-sensitive sensors in sports equipment and training facilities, enable real-time monitoring of biomechanical variables like joint angles, muscle activation, and movement velocities. Public-private collaborations can then leverage these technologies to develop innovative biomechanics-based sports products and services, making them more accessible to the general public. The findings of this study emphasize the necessity of a comprehensive strategy for sports promotion and sector expansion. This strategy should not only focus on economic gains but also aim to achieve superior health outcomes. Biomechanics-informed sports promotion allows for a more in-depth understanding of how different physical activities impact the human body’s biomechanics. This knowledge can be used to customize exercise programs according to an individual’s anthropometric and biomechanical characteristics, ensuring maximum effectiveness and safety. From a social perspective, a health-conscious society with widespread access to sports resources can significantly enhance the quality of life. By minimizing the risk of injuries through biomechanics-optimized sports facilities and equipment, individuals can engage in a more active lifestyle, leading to a reduction in healthcare costs. Moreover, as people participate in sports events, fitness challenges, and wellness campaigns, the shift in public attitudes towards fitness can strengthen social cohesion and community engagement. In conclusion, this study offers recommendations for future legislation and programs to enhance the integration of the sports sector with public health promotion. By fully integrating biomechanics into the development of the sports industry, we can ensure a substantial contribution to the “Healthy China” goals, fostering a more robust sports economy and a healthier society.

With the aging of the population and changes in lifestyle, sustaining muscular function has become essential for enhancing quality of life. Muscle satellite cells, as the principal source of regeneration for skeletal muscles, are essential for muscle growth, maintenance, and repair. Our review explores how physical exercise (PE) impacts the remodeling of muscle structure and function by modulating the activity of Muscle satellite cells (MuSCs), and further identifies the underlying implications of this process for the prevention and treatment of degenerative muscle diseases. By exploring current evidences on the interaction between MuSCs and PE, our review investigating the effect of PE on the activity, proliferation, and differentiation capabilities of MuSCs, and how these changes improve the enhancement of muscle mass and function. Evidences confirmed that PE can enhance the contribution of MuSCs to muscle fibers, particularly by boosting muscle adaptability through changes in muscle fiber type and size. PE-induced activation of MuSCs is linked not only to an increase in the number of muscle fibers but also with promoted endurance and strength performance of muscles. Besides, the positive effects of PE on MuSCs may vary with the form, intensity, and duration of PE. Additionally, PE plays a crucial role in the remodeling of muscle structure and function through the activation and proliferation of MuSCs, stressing the potential value of developing appropriate PE interventions in the prevention and treatment of muscle-related diseases, particularly among the elderly. Future research should further explore the specific effects of various types and intensities of PE on MuSCs activities to maximize exercise prescriptions for strengthening muscle health and function.