MCBOpen Access

Music therapy is acknowledged as one of the effective non-pharmacological interventions to regulate neuronal function enhancing mental health. This study examines them with biosensing technology, including EEG, HRV, GSR, and fNIRS to record participants’ neurophysiological responses in real time, with a particular focus on biomechanical aspects. Hence, participants listened to controlled music intervention consisting of classical, ambient and binaural beats while biosensors recorded not only neural oscillations but also muscle tension and movement patterns. Since there are close links between music stimuli and neuronal regulation, data analysis through wavelet transformation and machine learning models enabled the discovery of significant patterns. The research revealed that alpha wave synchronization which occurred in the frequency range of 8–13 Hz and theta/delta binaural beats substantially facilitated the levels of relaxation, mood and cognition. HRV analysis revealed lowering of sympathetic values in the experimental group, thus proving the stress-relieving impact of music therapy, which may also lead to improved muscle relaxation and reduced physical tension.

Objective: To explore the effects of mechanical load on the behavior and related signaling pathways of pediatric hip cartilage cells, providing experimental evidence for optimizing cartilage regeneration and repair strategies. Methods: Human-derived pediatric hip cartilage cells were cultured in vitro and subjected to uniaxial tensile stress at a specific frequency (1 Hz) with varying strain amplitudes (0%, 2%, 5%, 10%). Cell proliferation curves were assessed using the CCK-8 assay, while qPCR was used to detect changes in the expression of COL2A1, ACAN, and genes such as ITGB1, TGF-β1, WNT3A, MAPK1, among others. Western Blot analysis was conducted to measure protein levels of Collagen II, Aggrecan, MMP-13, as well as p-ERK, p-p38, β-catenin, MAPK1, and MAPK14. The relationship between strain amplitude and cellular biological effects was also analyzed. Results: After mechanical strain application, cell proliferation significantly increased on days 5 and 7 (p < 0.05), and COL2A1 and ACAN gene expression levels were upregulated (p < 0.05). Protein levels of Collagen II and Aggrecan significantly increased, while MMP-13 levels decreased (p < 0.05). Upstream signaling molecules such as Integrin β1, TGF-β1, and WNT3A were upregulated (p < 0.05), while TGFBR2 showed no significant changes (p > 0.05). Downstream molecules p-ERK, MAPK1, and MAPK14 were significantly upregulated (p < 0.05), whereas p-p38 and β-catenin showed no significant differences (p > 0.05). ACAN and p-ERK expression levels exhibited a dose-response relationship with increasing strain amplitude (p < 0.05). Conclusion: Mechanical load promotes the proliferation and matrix synthesis of pediatric hip cartilage cells through specific regulation of upstream and downstream signaling pathway molecules, showing strain intensity-dependent molecular responses. This study lays the foundation for precision regulation strategies in cartilage development and repair.

With the growing popularity of tennis, improving players’ technical skills has become a key focus in tennis instruction. This study investigates the application of a multi-ball training method integrated with biomechanical principles in tennis teaching and its effect on technical optimization. A total of 40 university tennis enthusiasts participated in this research, divided into an experimental group (EG) and a control group (CG) using a double-blind randomized controlled trial. The experimental group used a data-driven intelligent training system that incorporated sports biomechanics analysis and provided real-time feedback, while the control group adhered to traditional teaching methods. The results showed that the experimental group significantly outperformed the control group in biomechanical parameters such as swing speed, shot accuracy, and body stability. Furthermore, the experimental group demonstrated improvements in cognitive neural response, sports anxiety, and achievement motivation. By combining biomechanics with the multi-ball training method, this approach optimizes technical performance, enhances motor control, and improves training efficiency. This study provides new scientific evidence and practical insights for tennis instruction.

The article explores the new applications of sports biomechanics in human health and athletic performance. Firstly, it introduces the fundamental principles of sports biomechanics, including its definition and mechanical principles, providing a theoretical foundation for subsequent research. In terms of health, sports biomechanics is applied to the prevention and assessment of sports injuries, enabling athletes and the public to gain a more comprehensive understanding of the potential risks in sports and to take effective measures to avoid injuries. Additionally, in rehabilitation therapy, sports biomechanics offers patients scientific training programs to enhance recovery outcomes. Furthermore, for adolescents with idiopathic scoliosis, sports interventions based on biomechanics have significantly improved athletic ability and quality of life. In the field of sports performance, sports biomechanics assists athletes in enhancing their competitive state and sustained performance by analyzing sports techniques and optimizing training methods. The article concludes by summarizing the importance and application prospects of sports biomechanics, demonstrating its potential in improving health levels and sports performance. By exploring the multiple functions of sports biomechanics, this paper provides new perspectives for improving sports training and health management, and advocates for the further application of this scientific tool in sports medicine and the fitness industry.

Effective stability and distribution of load are important in yoga practice. This is because it determines postural control, balance, and elimination of injuries. The current training and coaching strategies are based on visual observation and verbal cues without the means for quantifying biomechanics. This cross-sectional study utilized pressure platform system and 3D motion capture system to measure plantar pressure, COP aperture and mediolateral balance shift during different yoga postures. To investigate the effect of the skill level on stability, candidates with different levels of experience were also targeted for inclusion in the study. The findings show that balancing postures are more asymmetrical and associated with larger COP displacement, which demands better NMC. Comparing the results of COP variability (SD = 0.63 cm) and weight distribution, it can be stated that experienced practitioners exhibit less variability and accurate balance, proving the idea that expertise positively affects stability. The Symmetry Index results revealed significantly greater variability for single-leg stance postures, suggesting that balance unevenness is an important factor during these asanas. These findings are particularly relevant to teaching yoga sessions, physiotherapy, and rehabilitation that can implement evidenced-based balance training. The study presents biomechanical feedback techniques as valuable for enhancing the approaches used in teaching yoga and improving stability training.

A novel PD identification and location method based on cross-correlation is proposed in paper. The method aims to solve the problem of weak signal identification and interference suppression. Taking advantage of the characteristics that the time axis of PD pulses at both ends of the cable is symmetrical and the interference signal is asymmetric, the cross-correlation computation is carried out through the alignment of reference pulses, so as to suppress the interference effects such as corona. In the calculation of basis PD pulse time difference, the synchronous pulse actively injected is used to shorten the data time window and reduce the influence of noise, and the accurate time difference is extracted through generalized cross-correlation. In this paper, the above methods are verified by simulation and experiment. The analysis results show that the interference can be suppressed and the weak signal recognition ability can be improved through multiple correlation operations, which lays the foundation for accurate PD location.

Patients with low vision face significant fall and caregiving risks due to impaired visual function, posing challenges to daily life and safety management. The aim of this study was to explore the application of fall risk assessment in the care safety of low vision patients and its scientific significance. The study designed a nursing intervention programme based on a risk assessment sheet and a smart warning device to quantify the patient’s dynamic postural control through biomechanical techniques. The experimental grouping was based on the random number table method, and 75 low vision patients were divided into conventional and observation groups, and personalised interventions were implemented in the observation group through dynamic balance training, gait monitoring and environment optimisation. The results showed that the incidence of adverse events such as falls and nursing disputes in the observation group was significantly lower than that in the routine group. In terms of key kinematic parameters such as stride frequency, stride length and stride width, patients in the observation group showed significant advantages, indicating that the personalised nursing intervention effectively improved the dynamic stability and gait coordination of the patients. This study innovatively applies biomechanical technology to nursing practice, which provides a scientific basis for risk assessment and safety management of low vision patients, and at the same time promotes the development of precision and modernisation of the nursing model.

Neuromuscular control plays a critical role in athletic performance, influencing movement efficiency, coordination, and injury prevention. While strength training enhances neuromuscular efficiency, traditional electromyographic (EMG) analysis methods often fail to capture transient activations and complex neuromuscular adaptations. This study aims to evaluate neuromuscular adaptations in strength training using wavelet-based EMG analysis, nonlinear dynamics, and muscle synergy analysis via non-negative matrix factorization (NMF). The primary objective is to determine whether these advanced techniques provide a more comprehensive assessment of motor unit synchronization, stability, and movement coordination. A six-week strength training program was conducted with 47 competitive athletes (30 males, 17 females). EMG signals were analyzed to assess wavelet power variations, recurrence rate, fractal dimension, and synergy activation levels. Pearson correlation analysis identified relationships between neuromuscular parameters. Strength training significantly improved neuromuscular efficiency, reducing wavelet power (1.35 to 0.98), decreasing recurrence rate (0.74 to 0.50), and increasing coordination efficiency (71.4% to 92.4%). An unexpected plateau effect was observed after three weeks, suggesting a transition from early-phase neuromuscular adaptation to a stabilization phase. These findings highlight the importance of progressive overload variations in sustaining neuromuscular adaptation. The integration of wavelet-based EMG, nonlinear dynamics, and synergy analysis enhances training assessments, offering a data-driven framework for optimizing performance and injury prevention strategies.

Background: Non-specific back pain (NLBP) is one of the common musculoskeletal disorders, which can seriously affect the patient’s life. As one of the methods for treating low back pain, the kneading manipulation in TCM has shown unique advantages in relieving muscle tension and pain. Method: 70 NLBP patients were selected as the research subjects. The surface electromyography technology was used to test hardness value, pain value, surface electromyography during complete flexion, surface electromyography during dorsiflexion, and flexion extension ratio. Result: The hardness value before treatment was 47.84% ± 4.33%, which decreased to 44.56% ± 4.08% after treatment, with a P-value of 0.0017, indicating a significant effect of treatment on reducing hardness values. The average pain threshold before treatment was 25.45 ± 5.23 N. After treatment, the pain threshold increased to 26.78 ± 4.08 N, with a P-value of 0.2397, demonstrating that the treatment effect on pain values was not significant. Conclusion: The study reveals the therapeutic effect and mechanism of action of the kneading manipulation in TCM on NLBP patients. It is expected to provide scientific basis for the application of the kneading manipulation in TCM technique in the treatment of NLBP, and provide reference for optimizing treatment plans and improving efficacy.

Background: The effects of hyperoxic environments on red blood cell (RBC) deformability and mechanical properties in athletes during high-intensity exercise remain poorly understood. This study aimed to investigate these effects and their potential implications for athletic performance. Methods: Forty elite male endurance athletes participated in a randomized, double-blind, crossover study. Participants completed high-intensity interval training sessions under normoxic (21% O2) and hyperoxic (40% O2) conditions. RBC deformability, whole blood viscosity, and physiological parameters were measured pre-exercise, immediately post-exercise, 1-hour post-exercise, and 24 h post-exercise. Results: Hyperoxic exposure resulted in significantly enhanced RBC deformability, particularly at higher shear stresses (p < 0.001). Whole blood viscosity was reduced across all shear rates in the hyperoxic condition (p < 0.05). Oxygen saturation (SpO2) levels were consistently higher (p < 0.001), while blood lactate concentrations were lower (p < 0.001) in the hyperoxic condition. Individual responses to hyperoxia varied considerably, with some athletes showing markedly greater improvements in RBC deformability than others. Conclusions: Acute hyperoxic exposure during high-intensity exercise enhances RBC deformability and reduces blood viscosity in elite endurance athletes, potentially improving microcirculatory function and oxygen delivery to tissues. These findings suggest that hyperoxic training may offer performance benefits, but the observed individual variability highlights the need for personalized approaches in its application.

With the deepening of biomechanical research and the transformation of medical paradigms, finding safe and effective methods for neural modulation has become an urgent task. This study combines music therapy with biosensing technology to explore their regulatory effects on neuronal cell activity. Data were collected from individuals with diverse cultural backgrounds, musical preferences, and age groups using microelectrode array sensors and fluorescence sensors to monitor changes in neuronal electrical activity and ion concentration. The results show that different types of music have distinct characteristics in enhancing neuronal cell activity, with classical music being particularly effective, followed by pop and rock music; additionally, intervention duration is positively correlated with neuronal cell activity. Based on these findings, it is recommended to actively promote the clinical application of music therapy, expand research directions, optimize music selection, bring new breakthroughs to the field of neuroscience, and provide a basis for improving music therapy protocols. Future research will further explore the personalized application of music therapy and the optimization of biosensing technology.

This paper discusses the teaching application of human motion mechanics in dance education and its effect on improving students’ physical quality. Through literature review, this paper systematically combs the basic theory of human motion mechanics and its importance in dance education, and analyzes the positive influence of dance education on students’ physical quality. In this study, 120 students aged 12–15 in a dance school were randomly divided into experimental group and control group. The experimental group adopted the teaching method of integrating human kinesiology, while the control group adopted the traditional dance teaching method. The experimental period was 12 weeks. The results showed that the flexibility (increased by 40.1%), explosive power (increased by 16.5%), balance ability (increased by 81.2%) and coordination (increased by 48.4%) of the experimental group were significantly better than those of the control group (p < 0.01). The results show that the teaching application of human kinesiology can optimize the structure of dance movements, improve the efficiency of energy transfer, and significantly improve students’ physical fitness, which provides strong empirical support for the scientific and modern dance education.

The muscle fatigue is the inevitable phenomenon occurring in the process of athletes in sports training, usually after intense exercise or sustainability movement, characterized by muscle soreness, fatigue and so on. When the muscle fatigue to a certain extent can cause human body damage, to avoid this kind of circumstance, can choose reasonable physiological signals, determine the level of fatigue of human body, scientific and effective means of fatigue recovery can help athletes maintain good state of movement, reduce the risk of sports injury, and electromyographic signal because for observation and has high real-time performance, in terms of evaluation of muscle fatigue Attention. This paper in order to build the classification model of muscle fatigue, athletes improve the correct recognition rate of the model, first of all, analyzes the generation mechanism, features and electromyographic signal denoising method, the dimension of feature set, and reduce the redundancy between characteristics; Finally after dimension reduction and Fisher linear discriminant analysis, a new feature set K neighbor and three kinds of support vector machine classifier combination, nine fatigue classification model is set up, on muscle relaxed state, transition state of fatigue and fatigue state classification of three states, and the results show that the kernel principal component analysis and support vector machine classification model for the fatigue of the average recognition rate is highest, at 91.5%, higher than the other fatigue classification model, this method can obtain better athletes the classification results of muscle fatigue, for athletes muscle fatigue degree of judgment has important research significance.

This study aims to investigate the kinematic characteristics of the long jump in female college athletes. Video data from 10 Asian female college athletes in the Athletics Open Long Jump Competition were analyzed using high-speed digital cameras with a 240 Hz sampling rate. The captured footage was processed through motion analysis software, with joint markers manually digitized. The results showed that during the rhythmic movement phases, both the horizontal and resultant velocity of the center of gravity and the hip joint angle increase at the heel strike of the swing leg. However, at the toe-off of the swing leg, vertical velocity, angle, and height of the center of gravity decrease, leading to a reduction in flight distance. At the heel strike of the take-off leg, the center of gravity height decreases, and the ankle joint angle increases. At the toe-off of the take-off leg, both the resultant velocity of the center of gravity and the hip and ankle joint angles increase. This method allows coaches to use video analysis to guide athletes in refining their technique, ultimately improving performance and coaching efficiency.

This research aims to deeply explore the origin of the metaverse and its impact on the development of corporate strategy, especially in the field of non-fungible token (NFT) artwork design. Through the analysis of the current development status of NFT artworks, an artificial intelligence-based enterprise development strategy and a metaverse NFT artwork design method are proposed. In addition, the generation logic, technical attributes, and technical and legal risks of metaverse NFT artworks under different design methods are also studied. It is expected that this research will provide strong theoretical support and practical guidance for the legal protection of metaverse NFT artwork design.

To improve the training effectiveness of rehabilitation training for patients with lower limb injuries, the research optimized the long short-term memory network algorithm using convolutional neural network algorithm, and conducted big data analysis on the biomechanics of the human lower limb based on the optimized algorithm. Through the results of big data analysis, the mechanical response mechanism of the human lower limb during movement was studied, and a rehabilitation training intelligent assistive robot that aligns more closely with the biomechanical properties of the human body was designed. An analysis of the biomechanics of the lower limbs of the human body showed that under different exercise states, the muscle strength of the gastrocnemius and soleus muscles in the lower limbs showed similar trends, with the gluteus maximus muscle strength reaching its maximum value in the first 20% of the gait cycle. After optimizing the intelligent assistive robot based on this result, the weekly training efficiency of patients increased to 92.3%. From the above results, it can be concluded that the proposed intelligent assistive robot can significantly improve the rehabilitation training efficiency of patients with lower limb injuries.

In education, with the application of these information technologies, massive student data continue to produce, in order to realize the processing of data information, the traditional data mining technology is applied to the mass of education data processing process derived from a new technology, that is, education data mining technology. Among them, student performance prediction is an important application direction in education data mining, can help teachers to optimise their teaching decisions and help students to improve their learning plans. However, as of now, most of the models for student performance prediction suffer from weak generalization ability and poor feature correlation. Therefore, this paper proposes a student performance prediction method based on feature selection and Bagging integrated learning, which analyzes the model and a single prediction model, effectively solves the problem of low prediction accuracy of a single model, and improves the ability of the model to deal with the unseen examples to a certain extent, with a strong generalization ability.

The study seeks to examine the design, development, and assessment of an intelligent biosensor system for tracking the health status of university athletes with specific reference to motion, temperature, and blood pressure. The system incorporates state-of-the-art sensor technologies and data analytics for non-contact, continuous tracking of physiological signs during exercise. A series of tests under resting, moderate, and high activity levels provided high accuracy values with MAE below 5 mmHg for blood pressure and 0.3 °C for temperature. The motion data showed a significant correlation with the reference systems, indicating that the system was accurate in dynamic conditions. The findings show that the biosensor system can help manage sports health by detecting overexertion, illness, and stress early to protect the athletes and improve their performance.

The current research explores the benefits of the flipped classroom approach applied to college basketball classes with a focus on better skill acquisition and physiological improvement. The quasi-experimental design intervention was aimed at examining the effectiveness of integrating digital learning into conventional physical education methodology for 16 weeks among 88 undergraduate students. The experimental group went through a structured program of online theoretical teaching combined with practical exercises in class, whereas the control group received a traditional teaching approach. The results showed significant improvements in the experimental group in many aspects, including basketball-specific skills, which ranged from d = 0.78 to 0.91; physiological parameters, especially recovery duration, d = 0.91, and muscular endurance, d = 0.88; and academic performance, namely learning attitude, d = 1.12, and self-directed learning, d = 1.03. Notably, biomechanical adaptations were observed, such as improved upper and lower extremity coordination, reduced variability in shooting mechanics, and enhanced force generation efficiency during passing and shooting tasks. These findings emphasize the integration of biomechanical principles in flipped classroom settings, contributing to the optimization of movement patterns and kinetic chain efficiency. The flipped classroom approach greatly enhanced the ability of students to integrate theoretical knowledge into practice, especially in game strategy and tactical decision-making. The incorporation of biomechanical analysis further underscores its potential to align educational innovations with advanced physical performance outcomes. These findings should provide the evidence needed to ensure that technology-enhanced learning environments are valid methods through which traditional physical education paradigms can be transformed while pursuing dual objectives of sport-specific skill development and health promotion, and they provide valuable insight for educators and curriculum designers in higher education physical education.

This paper reviews biomechanical labor education and practice concepts with lasting health objectives for expectant mothers during childbirth. Some of the areas of concern highlighted are inadequacies of routine obstetric practice, lack of adequate training among healthcare givers, and lack of appropriate tools for assessment. The remedies recommended by the research study include developing a standard training package, financing biomechanical assessment technology, and encouraging professional interconnection among medical specialists. Such solutions are possible and realistic as they are based on current educational models and implemented technologies, focusing on applying researched methods. In incorporating biomechanics into maternity care, this study suggests better labor conditions and birth outcomes for mothers and babies.

This study investigates the integration of variational autoencoders (VAEs) and generative adversarial networks (GANs) to enhance the electromagnetic compatibility (EMC) of biomechanical data analysis platforms. Leveraging a comprehensive dataset from multiple wearable devices, we capture diverse biomechanical parameters, including muscle activity, joint angles, and kinematic data. The preprocessing phase involves normalization and feature extraction, followed by encoding the biomechanical data into a latent space using VAEs. The GAN component generates synthetic data that are indistinguishable from real data, which are then utilized to adjust the EMC parameters of the analysis platform. Our results reveal significant improvements in model performance, as indicated by reduced mean squared error (MSE) and enhanced structural similarity index (SSIM) across multiple training epochs. Furthermore, the EMC adjustment process effectively minimizes electromagnetic interference, as evidenced by a substantial decrease in electromagnetic interference error function values. The high similarity between real and synthetic data validates the quality of the generated data. This integrated VAE-GAN framework presents a promising methodology for augmenting the accuracy and reliability of biomechanical data analysis in various applications.

Biomechanics is referred to as the study of mechanical aspects of living organisms and plays a significant role in human movement analysis. In sports training and physical education (PE), biomechanical principles help to improve techniques, and performance, and prevent injury risks. Conventional PE programs often lack a scientific approach to movement analysis, leading to inefficient training strategies and an increased risk of injury. This research investigates the application of biomechanical theory in PE training, to improve skill acquisition, and athletic performance, reduce injury risk, and create personalized training programs. The data were collected based on motion analysis, performance metrics, and surveys on training methods, physiological assessments, and participant feedback during PE training sessions and classes. The collected data was split into two different categories an experimental group (EG) trained using plyometric training (PMT) and a control group (CG) who participated in standard PE activities. Biomechanical assessments were conducted on both groups using motion capture technology (MoCap) and force plates (FP) to analyze joint angles, movement patterns, and force generation. The training interventions based on biomechanical principles were implemented over 12 weeks. Statistical analysis was performed to evaluate the significance of pre- and post-intervention performance metrics. The findings demonstrated significant improvements in movement efficiency, technique precision, and performance outcomes among participants in the EG receiving PMT compared to the CG. The study highlights the importance of combining biomechanical theory into PE and sports training. It helps in enhancing athletic performance, prevents injuries, and creates personalized training strategies.

Background: Smoking remains a significant public health concern, particularly affecting cardiopulmonary function through various pathophysiological mechanisms. While exercise is known to improve cardiopulmonary health, the optimal exercise intensity for enhancing cardiorespiratory adaptations, especially in the context of smoking status, remains unclear. Aim: This study investigated the differential effects of exercise intensity on cardiopulmonary adaptations and lower limb biomechanical functions among university students, considering smoking status as a potential moderating factor in physiological and biomechanical responses. Methods: A randomized controlled trial was conducted with 120 university students, stratified by smoking status and randomly allocated to four groups: high-intensity interval training (HIIT, 85%–95% HRR), moderate-intensity continuous training (MICT, 65%–75% HRR), low-intensity continuous training (LICT, 45%–55% HRR), or control group. The 12-week intervention comprised three weekly sessions, with comprehensive assessment of cardiopulmonary parameters and biomechanical characteristics at baseline, week 6, and week 12. Results: The HIIT protocol elicited superior improvements in cardiopulmonary function, with a 29.6% increase in VO₂max (p < 0.05, η² = 0.78), compared to MICT (18.1%, p < 0.05) and LICT (9.7%, p < 0.05). These adaptations were consistent across smoking status categories, though smokers showed slightly attenuated responses. Heart rate variability parameters demonstrated enhanced autonomic regulation, particularly in the HIIT group (HF power increased 80.4%, p < 0.05), with smoking status moderating the magnitude of improvement. Conclusions: High-intensity interval training demonstrated superior efficacy in improving both cardiopulmonary and biomechanical parameters compared to moderate and low-intensity protocols, regardless of smoking status. These findings suggest that HIIT may be particularly beneficial for enhancing cardiorespiratory fitness in university students, though individual smoking status should be considered when prescribing exercise intensity. The implementation of structured HIIT programs in university physical education curricula may optimize physiological and biomechanical adaptations, potentially offering a time-efficient strategy for improving health outcomes in both smoking and non-smoking students.

This study examined the physiological adaptations in muscle glycogen storage and fat oxidation rates over a 16-week marathon training cycle. Forty recreational marathon runners (25 males, 15 females; age 32.6 ± 5.4 years) undertook periodized training with detailed physiological tests on four occasions. Muscle glycogen concentrations and fat oxidation rates were determined during incremental exercise tests using standardized methods. The results showed significant alterations in both variables throughout the training period. Muscle glycogen levels followed a typical pattern, decreasing by 42% at the most intense training periods and showing supercompensation of 15% above baseline values during subsequent recovery periods. The ability to oxidize fat increased substantially from 0.42 ± 0.08 g/min to 0.67 ± 0.11 g/min (p < 0.001) after 12 weeks, with peak fat oxidation occurring at higher exercise intensities (52% ± 6% VO2max). Three unique categories of responders were identified, with 35% exhibiting substantial adaptation responses (greater than 60% improvement in fat oxidation capacity). The research concludes that intentional modulation of muscle glycogen levels via periodized training can significantly improve fat oxidation capacity while maintaining performance levels. These results yield practical insights for refining marathon training regimens through tailored strategies that consider individual metabolic response patterns.

The Sino-U.S. trade conflict on 22 March 2018 the global system of trade and also the international stock market. This paper introduces the theories of biomechanics and bioinformatics to construct a dynamic analytical model based on Vector Auto-Regression (VAR) to investigate the impact of Sino-U.S. trade conflict on the stock markets of China, U.S. and other Asian economies. By employing concepts from biomechanics, the key variables in the co-movement of stock markets in China, the United States, and East Asia are analogized. The co-movement of stock is treated as a “state variable,” and the “stress and strain” experienced by each country’s stock market under the influence of other markets are analyzed. This approach reveals the patterns of variation within these stock markets and provides a quantitative basis for understanding their dynamics. The granger causality and co-integration analysis are conducted on the empirical daily stock price data from 21 September 2016 to 22 September 2019. Benchmarking on Sino-US trade conflict on 22 March 2018, the data is divided into two phases, including 21 September 2016 to 21 March 2018, and 22 March 2018 to 22 September 2019. The results of this study show that the Asian stock markets seems to be more independent with the U.S. stock market after the Sino-U.S. trade conflict. And the results of the dynamic analysis model based on VAR also suggest that the tariff and the trade barriers not only hurt the relationship between China’s stock market and U.S.’s stock market, but also hurt the relationships of stock markets among U.S.’s and other Asian economies. The results of this empirical study can provide information for both investors and policy-makers to have a sound understanding of the stock market.

In the clinical diagnosis and treatment of epilepsy, the biomechanical mechanisms, including the mechanical changes in brain tissue during seizures and the biomechanical effects of drugs on neural conduction, play a crucial role in influencing diagnostic and therapeutic outcomes. The study introduces a multidisciplinary collaborative teaching model that incorporates biomechanical principles into epilepsy education for the first time. The study introduces a multidisciplinary collaborative teaching model that incorporates biomechanical principles into epilepsy education for the first time. A total of 120 medical students participating in epilepsy education from September 2023 to June 2024 were randomly divided into experimental and control groups The control group received traditional teaching methods, while the experimental group adopted a problem-based learning (PBL) multidisciplinary collaborative teaching model. This study assessed key competencies, including diagnostic accuracy, treatment plan design, interdisciplinary collaboration, and clinical thinking. Results indicated that students in the experimental group developed a deeper understanding of the biomechanical mechanisms underlying epilepsy and demonstrated significant improvements in clinical diagnostic abilities and interdisciplinary collaboration skills. Notably, the experimental group outperformed the control group in treatment plan design and interdisciplinary collaboration skills. The PBL-based multidisciplinary collaborative teaching model significantly improved students’ clinical diagnostic abilities, treatment plan design, and interdisciplinary collaboration skills, contributing to enhanced clinical thinking and self-directed learning abilities. This innovative teaching model provides new insights for advancing epilepsy education.

The incorporation of smart technology in elderly care is improving the way health is managed and promoted for the elderly populations. This paper examines a new elderly care model that employs biosensing and exercise-based solutions for effective care and monitoring of the elderly. An effective system is proposed to integrate various biosensors that can track vital signs like pulse rate, blood oxygen level, muscle activity, and balance, thus providing constant updates on the user’s well-being. To accomplish this, information is collected by advanced data analytics and machine learning algorithms to identify signs of potential adverse health changes and further, prescribe suitable exercise regimes that will enhance mobility, balance, and cardiovascular strength for patients. Furthermore, a cloud-supported biosensing data feed into the predictive models to aid health care providers in decision-making in anticipatory manage. This type of approach is then compared in clinical trials to determine positive changes in physical mobility, ability to prevent falling, and quality of life. This accounts for the strong evidence supporting the use of a biosensing-driven skeleton to improve the physical and medical health of elderly citizens with notably less intervention from professional medical care.

The current research explores the predictive value of social fear and intimate fear for mental health and somatic responses among college student, while highlighting the interaction between the aforementioned variables with family factors. A sample of 856 college students aged 18–24 years (58% female) was surveyed at various universities in China during the fall semester of 2023. Participants completed questionnaires that dealt with social fear, intimate fear, symptoms of mental health, somatic responses, and family factors. Both types of fear showed significant associations with negative consequences. Social fear demonstrated stronger effects, with coefficients of β = 0.54 (p < 0.001) for mental health symptoms and β = 0.48 (p < 0.001) for somatic complaints. In comparison, intimate fear showed relatively weaker effects, with coefficients of β = 0.46 (p < 0.001) for mental health symptoms and β = 0.42 (p < 0.001) for somatic complaints. Further biomechanical analyses demonstrated that psychological fears were significantly associated with increased muscle tension (particularly in trapezius and cervical muscles) and reduced joint mobility, with social fear showing stronger effects on these physical parameters (β = 0.45, p < 0.001) compared to intimate fear (β = 0.38, p < 0.001).The findings further showed that family dynamics played an influential role in these relationships, with the correlations between psychological anxieties and negative life outcomes proving stronger in conditions where there was little support from family. The interaction effects were most marked with regard to mental health outcomes, where the positive family factors buffered the impact of social and intimate fears. These results strongly point out the importance of integrating family-oriented approaches into interventions for college students with psychological anxieties.

Background: The incidence of injuries caused by high-intensity repetitive hand movements is relatively high among pianists. Joint stiffness, muscle fatigue, and pain are often associated with the performance process, which, in severe cases, can impact their professional careers. Existing studies mainly focus on performance techniques or simple rehabilitation exercises, lacking systematic hand intervention programs based on biomechanical principles. Additionally, there is still no comprehensive method to quantify and evaluate the actual effects of specialized training. Objective: Based on biomechanical principles, this study investigates whether targeted hand training interventions can effectively enhance the flexibility of pianists’ finger joints and reduce fatigue and pain caused by overuse. Methods: A total of 50 professional pianists with more than five years of performance experience were enrolled and randomized into an intervention group and a control group, with 25 participants in each group. The intervention group received 8 weeks of targeted hand training in addition to routine practice, 5 days per week, 30 min per session. The control group continued their regular performance practice. Joint range of motion, electromyographic (EMG) parameters, grip strength, pinch strength, and fatigue and pain scores were collected at baseline, Week 4, and Week 8. Repeated measures analysis of variance and independent sample t-tests were used for comparisons. Results: By Week 8, the intervention group showed significantly greater maximum range of motion in the metacarpophalangeal joints, proximal interphalangeal joints, and distal interphalangeal joints compared to the control group (p < 0.05). Peak amplitude of the flexor digitorum superficialis, flexor digitorum profundus, extensor digitorum, interosseous muscles, and lumbrical muscles significantly increased (p < 0.01). Grip strength and pinch strength were markedly improved compared to the control group (p < 0.01), while fatigue and pain scores were significantly reduced (p < 0.01). The control group showed no significant improvement in these parameters (p > 0.05). Conclusion: Targeted hand interventions based on biomechanical principles can effectively improve finger flexibility and reduce fatigue and pain in pianists within a short period, offering substantial application value for preventing performance-related hand injuries.

5G and artificial intelligence (AI) technologies for the real-time processing and intelligent analysis of biomechanical data. We collected comprehensive biomechanical data from 500 participants, aged 18 to 65 years, through clinical trials encompassing gait analysis, muscle strength assessment, and joint mobility evaluation. High-resolution motion capture systems and wearable sensors transmitted this data in real-time via 5G networks to a centralized processing unit. The data underwent rigorous preprocessing, including normalization, smoothing, and feature extraction, followed by real-time analysis using deep learning models and support vector machines (SVM). The system’s performance was assessed based on throughput, latency, packet loss, and classification metrics such as accuracy, precision, recall, and F1-score. Our results demonstrate high throughput (5000 Mbps), low latency (1 ms), minimal packet loss (0.5%), and classification accuracies ranging from 91.8% to 94.0%. These outcomes validate the efficacy of our proposed framework in enhancing the accuracy and efficiency of biomechanical data analysis, highlighting the synergistic potential of 5G and AI in healthcare and sports science applications.

This research aims to create a novel framework that merges sports biomechanics and computer vision for automating the clothing suggesting process, with advancements in extracting biomechanical features, undertaking visual analysis, performing multimodal data fusion, and personalization modeling. The framework employs powerful computer vision techniques and deep neural networks alongside biomechanical sensors like the goniometer, pressure scanner, and other sensors capturing locomotor dynamics. In this study, for the first time, a profound fusion between multidimensional biomechanical variables and captured appealing semantic and visual components is made, with quantifiable relations between the functionality and aesthetic performance of the clothing design established. Judith, the core autonomous system, achieves high-accuracy personalized recommendations through analysis of joint movements, recognition of motion habits, and modeling of pressure distribution. In the framework, an entirely new paradigm for the clothing market is constructed by combining real and virtual models. The system solves the cold-start issue by utilizing cyclic domain transfer learning together with biomechanical features-driven analysis. The obtained results are impressive, with the system achieving a recall of 0.845, precision of 0.892, and NDCG of 0.901, as well as biomechanical-special metrics of body-fit score equal to 0.885, motion comfort 0.873, and pressure distribution uniformity 0.891. As different user groups were analyzed, the results were unchanged. This shows the framework’s practical usability and sustainability. Besides, it opened up a new avenue for intelligent recommendation systems that integrate biomechanical analysis.

With the continuous progress of sports science, the application of biomechanics in sports training has become an important tool to enhance sports performance and prevent sports injuries. Basketball, as a collective and confrontational sport, involves a large number of complex technical movements, such as shooting, dribbling, and jumping, which require precise mechanical regulation. The study of biomechanics can provide theoretical support for basketball teaching in colleges and universities, help optimize athletes’ technical movements, enhance training effects, and reduce sports injuries. Biomechanics is based on mechanical principles such as Newton’s laws of motion, kinematics, and dynamics, which can be effectively applied to basketball technical movements. For instance, in shooting, the motion can be divided into preparation, force application, release, and follow-through phases. Newton’s Second Law (F = ma) explains how the applied force influences the acceleration of the ball, while projectile motion principles determine the optimal angle and velocity for achieving maximum shooting accuracy. The Magnus effect also plays a role in guiding spin-based shooting techniques, affecting ball trajectory and stability. In dribbling, biomechanical analysis involves understanding how impulse (Impulse = Force × Time) affects ball control. By adjusting wrist force and contact time with the ball, players can improve dribbling efficiency and control under defensive pressure. Additionally, energy transfer and ground reaction forces are critical in jumping mechanics. Using the principles of conservation of momentum and the stretch-shortening cycle, athletes can maximize jump height and power through optimized force application and body positioning. This paper explores the application of biomechanics in college basketball teaching through experimental research. The experimental subjects are college basketball players, and biomechanical analysis of basketball technical movements (shooting, dribbling, jumping, etc.) is conducted using high-precision equipment such as motion capture systems and force platforms. The study collects physical data, mechanical characteristics, and sports performance data of the athletes during the execution of basketball technical movements, analyzing them in combination with biomechanical principles. This approach provides an in-depth understanding of movement efficiency and technique optimization. The results of the study show that training programs optimized through biomechanical analysis can significantly improve athletes’ technical performance. In shooting, dribbling speed, and jumping height, the experimental group demonstrated superior results compared to the control group, with statistically significant differences. Specifically, the shooting percentage of athletes in the experimental group increased by 6.3%, the dribbling speed improved by 9.6%, and the jumping height increased by 10.4%. These improvements confirm that the application of biomechanics in basketball teaching not only enhances performance but also reduces the risk of sports injuries by refining movement mechanics and optimizing force distribution. By integrating biomechanics into basketball training, educators and coaches can develop more scientifically grounded training methodologies, improving player efficiency while ensuring long-term physical well-being. This study highlights the necessity of incorporating mechanical principles in skill development, reinforcing the role of biomechanics in advancing sports education and training strategies.

This paper presents the concept and review of a virtual reality-based rehabilitation system that could be designed for tennis-related injuries and includes motion capture, real-time feedback mechanisms, and adaptive training protocols for an immersive rehabilitation environment. In a randomized controlled trial with 60 tennis players, significant improvements in rehabilitation were observed, with higher percentages in the VR group versus controls for dynamic balance, 90% versus 80%, p < 0.01, and functional recovery, 92% versus 84%, p < 0.001. System evaluation showed good user satisfaction, rated 4.4 of 5.0, and technical reliability, assessed as uptime, 99.7%, together with a 32% session time reduction. These results confirm that VR technology is useful in sports rehabilitation and is a promising solution to improve the recovery outcome in tennis injury rehabilitation.

Background: This study integrates biomechanical perspectives with clinicopathological data to develop a DNN model for survival prediction. By linking tumor size and lymph node status to biomechanical drivers such as solid stress and cell migration forces, we aim to uncover the mechanobiological mechanisms underlying prognosis heterogeneity. Methods: We analyzed data from 37,917 patients in the SEER database, encompassing clinical characteristics, pathological features, and treatment details. The DNN, featuring an attention mechanism, was evaluated using metrics such as accuracy, precision, recall, F1 score, and Area Under Curve (AUC). Interpretability techniques were applied to identify prognostic factors. Results: The DNN model achieved F1 scores of 0.928 and 0.935 for validation and test sets, respectively, with an AUC of 0.96, surpassing traditional models. Key factors identified included regional lymph node positivity, tumor size, and tumor grade, with a notable negative correlation between regional lymph node positivity and survival. Conclusions: DNN models with attention mechanisms demonstrate superior predictive performance and valuable interpretability in identifying critical prognostic factors.

In order to cope with biomechanical nonlinear optimization problems and explore the application of deep learning methods, the study focuses on the performance of neural network-based optimization models in complex biomechanical systems. By using a hybrid neural network structure, the optimization algorithm processes high-dimensional data to accurately model biomechanical nonlinear relationships. The experimental results show that the deep learning model shows significant improvement in multivariate biomechanics prediction compared to traditional methods, with the prediction error decreasing to less than 10% and the optimization efficiency increasing by more than 40%. Especially in the field of joint mechanics and skeletal implant design, deep learning is able to accurately capture complex nonlinear laws, which greatly improves the stability and reliability of the results.

Dance movements are a form of expressive physical activity that communicates emotions, stories, and cultural significance through the rhythmic motions of the body. Viewed through the lens of biomechanical theory, it offers a unique understanding of the body’s physical actions and interactions in space. Biomechanics, the science of movement explains the mechanical principles of human motion, including forces, motion, and body structure. It aims to analyze the biomechanical principles underlying various dance movements, including forefoot (FT) landing, entire foot (ET) landing, single-leg landing, bounce, rock step, and side chassé step. A total of 42 dancers performed these movements in the jive and cha-cha, synchronized with corresponding music. Data were collected using a Vicon motion capture system and pressure sensors, which were uploaded into the OpenSim simulation model to create musculoskeletal models. Statistical Parameter Mapping (SPM) analysis was used to assess biomechanical differences across various dance movements. Depending on the data distribution, ANOVA, multiple regression analysis, and paired t-tests were employed to examine muscle forces involved in the different dance movements. The biomechanical analysis revealed that FT landing increased ankle inversion and instability, while ET landing provided greater stability. Single-leg landing generated higher forces, while the bounce movement was energy-efficient with increased plantarflexion. It may also increase the risk of injury due to higher forces. With careful technique to avoid overloading and injury, these findings may be used in dance training by implementing controlled ET landings for stability and balance, as well as single-leg landings to increase force absorption and build lower limb muscles. The side chasse step and rock step required greater lateral stability, with higher muscle activation in the hip and ankle joints. In conclusion, the biomechanical analysis highlights significant differences in muscle activation, joint angles, and stability across the dance movements.

This study explores the sustainable development of fiber art by integrating biomechanical principles, aiming to delineate optimal strategies for enhancing both environmental and mechanical performance. Data were sourced from authoritative databases, including the Art and Design Database (ADD), Biomechanics Research Institute (BRI), International Textile Research Journal (ITRJ), and Craft Council Archive (CCA). The research employed a comprehensive methodology encompassing data collection, preprocessing, statistical analysis, biomechanical modeling, sustainability assessment, and optimization. Regression analysis revealed significant correlations between tensile strength and flexibility across various fiber materials. The sustainability of these materials was evaluated using a multi-criteria decision-making approach, accounting for environmental impact, resource efficiency, and social acceptability. Optimization results demonstrated improved sustainability scores for materials such as bamboo and linen when their biomechanical properties were optimized. Sensitivity analysis validated the robustness of the sustainability model under diverse weighting scenarios. This research offers a holistic framework for artists and designers to produce fiber art that is both environmentally sustainable and mechanically resilient, thereby bridging the gap between art and science.

This study investigates the influence of cultural intelligence on judo movement mechanics and cross-cultural adaptation among international judo athletes. Conducting a comprehensive biomechanical analysis and assessment of cross-cultural adaptation among 120 elite international judo athletes, this research reveals significant correlations between cultural intelligence levels and movement efficiency, technical adaptation, and cross-cultural integration. Higher cultural intelligence was associated with enhanced biomechanical performance metrics, including improved force application, movement timing, and technical efficiency. The research demonstrates that the cognitive, motivational, and behavioral aspects of cultural intelligence collaboratively enhance athletes’ performance and facilitate their adaptation across cultures. These results indicate that the cultivation of cultural intelligence ought to be incorporated into international judo training curricula to improve both technical skills and the ability to adapt to diverse cultural contexts in competitive settings.

Neuroglial cells, especially microglia and astrocytes, are crucial in the brain’s recovery process after ischemic injury. Recent studies have shown that Panax notoginseng saponins (PNS) have potential therapeutic effects on the mechanical responses of neuroglial cells and their related regulatory mechanisms after cerebral ischemia. This study investigated the regulatory effects of PNS on neuroglial cells after cerebral ischemia, with a focus on its impact on microglial activation and cellular mechanical responses. Experimental results demonstrated that PNS significantly enhanced the mechanical stiffness of microglial cells (Young’s modulus increased by 27.65%), a mechanism involving the scavenging of reactive oxygen species (ROS levels reduced, P < 0.01), stabilization of the cytoskeleton, and modulation of membrane tension, thereby suppressing the release of inflammatory factors and pathological activation. Additionally, LPNS pretreatment effectively protected the membrane integrity of astrocytes (LDH release decreased by 18.05%–29.54%), attributed to the synergistic effects of antioxidation, membrane stabilization, and anti-apoptosis. In the ischemia-reperfusion model, PNS markedly reduced leukocyte adhesion in cerebral blood vessels (72 h) by inhibiting endothelial adhesion molecule expression, improving nitric oxide (NO) production, and alleviating oxidative stress.

This study proposes a genetic algorithm-based optimization approach for resource allocation in vocational education skill development processes. The research addresses the critical challenge of efficiently distributing limited educational resources while maximizing learning outcomes and maintaining operational constraints. Through systematic implementation and rigorous evaluation, we developed a multi-objective optimization model incorporating educational effectiveness, resource utilization efficiency, and distribution equity considerations. The genetic algorithm demonstrated superior performance with a 27.3% improvement in resource utilization efficiency compared to traditional methods and achieved a 92.3% average goal satisfaction rate across defined targets. Experimental results across 12 vocational institutions show significant improvements in key performance indicators, including a 23.7% increase in equipment utilization rates and an 18.9% enhancement in instructor resource efficiency. Statistical analysis confirms the significance of these improvements (p < 0.001). The proposed approach consistently outperformed other contemporary optimization algorithms in terms of convergence speed, solution quality, and robustness across different problem scales. This research contributes to both theoretical understanding and practical implementation of resource optimization in vocational education, providing a robust framework for enhancing educational effectiveness through intelligent resource allocation.

In modern educational environments, biomechanics, as the study of the principles of human movement and mechanics, offers new perspectives and solutions, especially in online and long-term learning processes. Teachers need to focus not only on the optimization of teaching content and strategies but also consider students’ physical adaptations and cognitive load. The application of biomechanics can help to improve students’ learning experience by rationally designing the learning environment, adjusting the teaching objectives and methods, and reducing the negative impacts of improper body postures, exercise loads, and fatigue, thus enhancing learning efficiency. This study examines the multiple applications of biomechanics in education, including the impact on teaching goals, optimization of content selection, improvement of teaching methods, and protection of students’ physical health. The principles of biomechanics help teachers identify and analyze the physiological responses produced by students during the learning process, such as muscle fatigue and improper posture, and suggest appropriate adjustments. For example, reasonable study posture and regular breaks can significantly reduce students’ physical load and improve concentration and learning efficiency. Through the lens of biomechanics, teachers are able to design teaching activities that meet students’ physiological needs, prevent health problems caused by poor posture or fatigue, and ensure that students learn efficiently in a healthy state.

This study introduces an innovative approach to optimizing the scheduling of intelligent logistics and warehousing robots by integrating deep reinforcement learning (DRL) with biomechanical modeling. Leveraging a comprehensive dataset from a large-scale logistics company, the research formulates the scheduling problem as a Markov Decision Process (MDP) and incorporates biomechanical principles to accurately model robot energy consumption. A Deep Q-network (DQN) is employed to learn the optimal scheduling policy, which is further refined using policy gradient optimization. This integrated framework aims to maximize task completion efficiency while minimizing energy usage, addressing the complexity of balancing these competing objectives. Extensive simulations validate the proposed approach, demonstrating significant improvements in task completion rates, average travel distances, and energy consumption compared to baseline algorithms such as random scheduling and greedy algorithms. The methodology presents a robust and efficient solution for enhancing operational efficiency in intelligent logistics and warehousing systems.

In today’s society, as an important part of quality education, music education has been paid more and more attention by people. Music education can not only improve children’s music accomplishment and cultivate aesthetic taste, but also play a positive role in promoting children’s physical and mental health and all-round development. With the renewal of the concept of music education and the innovation of educational methods, how to use music education more effectively to promote children’s physical and mental development has become the focus of educators. Biomechanics is a science that uses physics and mechanics principles to study the movement laws of organisms. In recent years, the application of biomechanics in sports science has achieved remarkable results, while in the field of music education, the study of biomechanics is still in its infancy, and the study of the combination of music education and biomechanics can provide new theoretical support and methodological guidance for music education. It is helpful to improve the quality and efficiency of music education. Children’s joint flexibility is the degree of free movement of the knuckles within a certain range. This study aims to explore the application of biomechanics in music education, analyze the influence of music education on children’s joint flexibility, and find out effective ways to improve children’s joint flexibility, so as to provide theoretical support and guidance for the practice of music education

This study investigates the molecular mechanisms underlying alveolar epithelial cell responses to Qigong breathing patterns, focusing on mechanotransduction pathways and cellular adaptation. Using a combination of live-cell imaging, molecular biology techniques, and mechanical testing, we characterized the temporal dynamics of cellular responses across multiple scales. Our results demonstrate that specific breathing patterns trigger distinct mechanosensitive pathways, with Pattern 2 inducing the most robust cellular adaptation. Key findings include rapid PIEZO1 channel activation (τ < 18.5 ± 2.3 ms), sustained YAP/TAZ nuclear localization (3.8-fold increase), and significant epigenetic modifications (285% increase in H3K27ac marks). We identified 284 differentially expressed genes and characterized the temporal evolution of cellular mechanical properties, including a 23.5% increase in cell area and 2.8-fold enhancement in Young’s modulus. The study reveals three distinct phases of cellular adaptation: early response (0–6 h), intermediate adaptation (6–24 h), and long-term remodeling (24–72 h). These findings provide new insights into the cellular mechanisms of breathing-induced adaptation and suggest potential therapeutic applications through targeted mechanical stimulation.

Research on biological morphologies in microscience offers a rich source of inspiration for visual arts. By analyzing the mechanical properties of cells, molecules, and tissues, a deep integration of science and art can be achieved. Based on the cobweb lattice structure and combining the mechanical response of biological tissues in dynamic environments, a biomimetic design model is constructed. The experimental methodology centers on microscopic observation techniques, utilizing microscopes to collect three-dimensional morphological data of cells and tissues, and employing finite element analysis to simulate their stress behavior. On this foundation, morphological models with both biomechanical accuracy and artistic expressiveness are designed, and innovative applications in installation art are realized through technological means such as 3D printing. Research results indicate that the elastic modulus of cells plays a decisive role in morphological stability, with the optimized biomimetic morphological structure exhibiting a reduction of over 30% in deformation amplitude in dynamic environments. Analysis of intermolecular mechanical interactions provides a refined design basis for artistic creation. Research on biological morphologies in microscience not only enriches the expressive forms of visual arts but also opens up new technological pathways and creative spaces for biomimetic design.

Investigating the impact of psychological stress on the neural system from a biomechanical perspective has become a significant interdisciplinary research focus. Chronic psychological stress, through the interplay of endocrine and neural axes, profoundly affects cellular metabolism and neural plasticity. These biomechanical changes not only compromise the adaptive capacity of the neural system but also impair learning efficiency. In the post-pandemic era, heightened social pressures have exacerbated psychological burdens, further disrupting the development of students’ cognitive abilities. Research indicates that disruptions in circadian rhythms and a reduction in biomechanical stimulation within online learning environments contribute to the accumulation of chronic psychological stress, subsequently affecting the homeostatic regulation of neurotransmitters. Based on biomechanical principles, this study proposes that ideological and political education in higher education should prioritize students’ mental health by incorporating biomechanical intervention strategies—such as psychological stress monitoring, exercise interventions, and counseling—into online teaching environments. These measures aim to optimize neural adaptability, alleviate stress, and promote emotional regulation, thereby enhancing learning efficiency. This research offers a novel perspective on ideological and political education that integrates mental health considerations from a biomechanical standpoint and provides a theoretical foundation for the development of intelligent education support systems based on biomechanical principles.

Pars Plana Vitrectomy (PPV), combined with intravitreal tamponade Silicone Oil (SO), is one of the most popular and effective surgical interventions for Rhegmatogenous Retinal Detachment (RRD), achieving high rates of anatomic reattachment. However, long-term SO Tamponade (SOT) can induce structural and microcirculation alterations, affecting visual function, even after SO Removal (SOR). Therefore, for an appropriate SO Filling (SOF) duration, we investigated the dynamic changes of macular vasculature during SOF and after SOR. 51 eyes (51 patients) with macular-on RRD underwent single PPV and were randomly divided into 2 groups according to intravitreal SOT duration, either for 2 or 3 months. Optical Coherence Tomography (OCT) and angiography were used to evaluate the macular perfusion system, which was segmented into Superficial and Deep Capillary Plexus Flow Density (SCPFD, DCPFD) and Choriocapillaris Plexus Flow Density (CCPFD). The VA (VA) and the flow density were measured at 1 week, 1 month, 2 months, and 3 months SOF, and 1 week and 1 month post SOR. Both 2- and 3-month SOT strongly reduced VA, particularly in the first month. There was no significant difference in VA between the two groups during the opinion. Compared with that before the surgery, the VA had a 51% reduction after 2 months and a 57% reduction after 3 months of SOF, which was not recovered even after a 1-month SOR. 2-month SOT did not significantly affect macular microvascular. However, SCPED was starkly suppressed at 3-month SOF, following a significant increase after 1-month SOR. Moreover, 2-month SOT caused slight changes in macular microcirculation during the observation, together with a fast recovery of VA after 1-week SOR, about 90% of VA at 1-week SO. However, the flow densities in all three segmented layers upon 3-month SOT were correlated with each other, showing the same fluctuation trend, i.e., strong suppression at 3-month SO and slow recovery after SOR, which a low VA accompanied after 1-week SOR, about 50% of VA at 1-week SOT. Either 2- or 3-month SOT reduced VA of RRD eyes. However, unlike 2-month SOF, 3-month SOT could induce strong suppression of macular microcirculation, which might be detrimental to VA recovery of RRD eyes after PPV surgery. Therefore, a 2-month SO might be an appropriate time for SOR to achieve a better functional recovery of RRD.

In the era of molecular biology, the integration of life sciences and art design has raised new educational demands. This research introduces a teaching model for applied art talents inspired by biology, using analogies of macromolecular self-assembly dynamics and cell mechanotransduction pathways to reconstruct the interactive relationship between teaching subjects. By modeling the “fresh stress environment” as a dynamic molecular network (protein-protein interaction network), a fuzzy c-means clustering method combined with mechanobiological feature analysis was proposed to identify students’ interdisciplinary ability clusters. And a collaborative filtering recommendation system inspired by cell signal transduction cascades was designed to dynamically match students with personalized learning modules (molecular dynamics simulation of artistic modeling, spatial design inspired by mechanobiology). Simulation tests validated by a protein structure prediction dataset show that the model enhances students’ ability to translate abstract biological principles into visual/artistic expressions. Compared with traditional art education, it cultivates talents proficient in biomedical visualization, bio-inspired design, and molecular-scale aesthetic literacy—key capabilities that connect life sciences and creative industries.

The incidence of post hysterectomy pelvic floor dysfunction (PFD) is as high as 30%. Pelvic floor muscle training (PFMT) is a common treatment for PFD. However, its effectiveness is limited and the time required is long. In recent years, many studies show that combined interventions may improve the outcomes by addressing both the biomechanical and functional aspects of PFD. This study evaluated the efficacy of single PFMT, PFMT + ES (electrical stimulation), and PFMT + ES + psychological interventions in restoring pelvic floor biomechanics and function after hysterectomy. In this study, 40 patients aged 40–60 years who underwent hysterectomy were selected. The biomechanical outcomes, including pelvic muscle strength, force distribution, and bladder activity, were assessed using standardized biomechanical measurements. Their sexuality and bladder activity were compared after a 6-week period of treatment. The results showed that the patients who were treated with the combination of PFMT + ES + psychological intervention had the most significant improvement in all biomechanical and functional indicators. Specifically, this group exhibited enhanced pelvic muscle mechanics, improved force control, and greater bladder stability, highlighting the synergistic effects of the combined approach. This study concludes that the combined treatment of PFMT + ES + psychological intervention is a very effective conservative treatment, particularly in restoring pelvic biomechanics and function. The findings provide valuable insights into the biomechanical mechanisms underlying PFD recovery and underscore the importance of integrating mechanical, physiological, and psychological approaches for optimal outcomes. However, the small sample size of this single-center study limits the generalizability of the conclusions, which requires cautious promotion in clinical practice.

Introduction: Breast cancer continues to be one of the most common malignancies in females, with mortality rates among the highest worldwide. Despite the availability of numerous clinical tools for managing breast cancer, recurrence, metastasis, and drug resistance remain significant barriers for clinical experts. Scientists consider these tumorigenic processes to be closely associated with breast cancer stem cells (BCSCs). While extensive research focuses on breast cancer cell lines in vitro, replicating the intricate dynamics of the internal tumor microenvironment remains a challenge. This microenvironment is influenced not only by biochemical factors but also by biomechanical cues, such as ECM stiffness and shear stress, which regulate cancer cell behavior through mechanotransduction pathways. Recognizing these limitations, our team has drawn upon years of cancer stem cell research to establish a practicable method. This method aims to facilitate a series of experiments exploring drug resistance mechanisms and to provide deeper insights into the role of BCSCs in tumorigenesis and progression in vivo. Materials and methods: 3-dimensional (3D) mammosphere culture method was established to enrich the breast cancer stem-like cells in vitro. Mammospheres forming assay was performed and mammosphere forming efficiency was calculated. Flow cytometry Analysis was used to detect the subpopulation of CD44⁺CD24⁻, meanwhile, the biomarkers of BCSCs were detected by western blot. All these indicates the success establishment of 3D cell culture method and the breast cancer stem-like cells are enriched and collected. Furtherly, western blots were performed to detect the conjectures of the BCSCs that the Notch signaling pathway and MAPK-ERK signaling have the crosstalk in breast cancer microenvironment and the positive feedback loops could be activated by the enriching of BCSCs. All these data were analyzed with GraphPad^® Prism 9 software and Wilcoxon rank-sum test, nonlinear regression analysis, unpaired t tests were used. Results: MCF-7 breast cancer stem-like cells were observed as substantially distinct from native breast cell lines under 20x microscope and the mammosphere forming efficiency of MCF-7 breast cancer stem-like cells were higher than the native MCF-7 group. The subpopulation of CD44⁺CD24⁻ was significantly increased in BCSC-like group and the EMT (Epithelial-Mesenchymal Transition) markers of BCSC which includes Nanog; Vimentin; OCT3/4; Slug and Sox2 were significantly increased. Lastly, Cyclin D3 and Hes1 which play important roles in the Notch signaling pathway and ERK protein were all significantly increased. Conclusion: The three-dimensional (3D) mammosphere culture method is a highly effective approach for collecting breast cancer stem cells (BCSCs) in vitro. Unlike traditional 2D cultures, this method replicates key physiological conditions of the tumor microenvironment (TME) and captures phenotypic heterogeneity. By promoting cell-cell and cell-ECM interactions, the 3D system mimics essential biomechanical cues, such as ECM stiffness and spatial gradients, which regulate BCSC behavior. This method reliably supports investigations into the molecular mechanisms of tumorigenesis. BCSCs enriched through this approach drive processes such as epithelial-mesenchymal transition (EMT) and activate signaling pathways like Notch and MAPK-ERK, which are closely linked to the TME and play critical roles in tumor progression and resistance. The 3D mammosphere culture method thus provides a robust tool for advancing our understanding of cancer biology and therapeutic development.

The lipid-lowering properties of lotus leaf are primarily attributed to the active alkaloid, nuciferine. In this study, the role and underlying mechanisms of nuciferine in lipid metabolism regulation from a biomechanical perspective were investigated using molecular docking and in vitro and in vivo studies, employing HepG2 cells and Caenorhabditis elegans (C. elegans) as experimental models. The results indicated that nuciferine exhibited significant binding affinity to the targets Sterol Regulatory Element-Binding Protein-1c (SREBP-1c) and Peroxisome Proliferator-Activated Receptor alpha (PPARα), suggesting that it may modulate lipid metabolism via the SREBP-1c/PPARα pathway. In vitro experiments demonstrated that nuciferine significantly inhibited the oleic acid-induced accumulation of triglycerides (TG) and total cholesterol (TC) in HepG2 cells. Furthermore, atomic force microscopy (AFM) was employed to detect the changes in cell elasticity and adhesion force before and after nuciferine treatment. We hypothesized that the lipid-lowering effect of nuciferine might be related to the alterations in the mechanical properties of cells, which could further influence lipid metabolism pathways. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) analysis revealed that nuciferine could regulate the expression of SREBP-1c and PPARα, as well as their downstream target genes, which further affects the biomechanical properties of cells. In vivo experiments showed that nuciferine effectively attenuated hepatic steatosis and reduced TG levels in C. elegans, while modulating the expression of Sterol regulatory element-binding protein-1 (sbp-1), Nuclear hormone receptor 49 (nhr-49), and their downstream target genes. These changes may affect lipid metabolism through intercellular mechanical signaling. Furthermore, using mutant strains defective in sbp-1, nhr-49, fatty acid and triglyceride synthase 5 (fat-5); fat mass and obesity associated-like 6 (fat-6), and fat-6; fat mass and obesity associated-like 7 (fat-7) genes, the study provided further evidence that nuciferine’s lipid-lowering effects are mediated through the sbp-1/nhr-49 pathway. This study is the first to employ molecular docking to identify SREBP-1c and PPARα as the lipid-lowering targets of nuciferine and to confirm through in vitro and in vivo experiments that its efficacy depends on the SREBP-1c/sbp-1 and PPARα/nhr-49 pathways, thereby offering a new perspective for the treatment of lipid metabolic disorders from a biomechanical perspective.

The temporomandibular joint (TMJ) plays a critical role in speech articulation, yet its biomechanical adaptation during second-language pronunciation learning remains underexplored. Non-native English speakers often exhibit excessive jaw movements and inefficient neuromuscular activation, which can impede phonetic accuracy and speech fluency. Despite advancements in phonetic training, existing methodologies lack an integrated biomechanical approach that quantitatively assesses TMJ adaptation. This study investigates the biomechanical adaptation mechanisms of TMJ movement in English pronunciation learning, focusing on jaw kinematics, neuromuscular adaptation, and phonetic precision. The research aims to quantify TMJ adaptation and its influence on speech efficiency, providing an evidence-based framework for pronunciation training. A four-week structured pronunciation training program was conducted with 72 non-native English speakers. Three biomechanical techniques were employed: Motion Capture Analysis (MCA) for jaw kinematics, Electromyography (EMG) for neuromuscular activity, and Acoustic-Phonetic Analysis for pronunciation accuracy. Additionally, Structural Equation Modeling (SEM) was applied to evaluate causal relationships between TMJ biomechanics and phonetic precision. Findings demonstrated a 39.6% reduction in jaw displacement variability, a 33.3% decrease in masseter activation, and a 35.3% improvement in syllable timing variability. While kinematic and neuromuscular adaptations correlated with enhanced phonetic precision, SEM results suggested additional mediating factors in pronunciation learning. This study provides quantitative evidence that structured pronunciation training improves TMJ biomechanics, neuromuscular efficiency, and phonetic accuracy. The findings have implications for speech training, AI-assisted pronunciation tools, and clinical speech therapy. Future research should explore long-term TMJ adaptation, tongue biomechanics, and cross-linguistic differences in speech motor learning.

Sports fatigue represents a very important obstacle in athletic performance and it creates the movement inefficiencies, increased injury risk and longer recovery time. It puts forth an integrated fatigue monitoring framework using a biomechanical assessment, a physiological monitoring and a predictive modelling for optimizing fatigue management and training adaptations. The specific techniques utilized to quantify fatigue induced changes in movement efficiency, neuromuscular coordination, autonomic activity are 3D Motion Analysis Systems, Heart Rate Variability (HRV) monitoring, and Infrared Thermography (IRT). Using Bayesian inference, ARIMA time series forecasting and Dynamic Time Warping (DTW) analysis, fatigue thresholds are predicted to enable personalized fatigue management strategies. Throughout all experiments, fatigue led to a 10% decrease in stride length, a 15% increase in ground contact time and a reduction of 20% parasympathetic activity of the HRV, which coincides with a decreased biomechanical efficiency and autonomic system dysregulation. ARIMA predicts short term fatigue cycle with 91%, and Bayesian model estimates individual fatigue thresholds with 95% confidence (Table 1). IRT analysis also shows a fatigued muscle temperature increase of 1.15C, which corroborates on thermal regulation monitoring of fatigue. Moreover, the DTW analysis shows up to 9% deviations in the movement patterns during fatigued conditions, which calls for real time fatigued tracking. These results verify that the combination of real-time biomechanical tracking with predictive analytics offers a more effective, safer and more fatigue resistance way of endurance training. The proposed framework provides an effective data driven approach to real time fatigue monitoring and has practical utilizations in the sports training, injury prevention, and athletic performance optimization.

In today’s fast-paced work environments, accurately predicting stress levels is essential for effective healthcare workforce management, particularly among nurses in high-pressure settings. Despite the availability of various mental health initiatives, timely detection of stress remains challenging due to concerns over sensitive personal data privacy. To address this, we propose a federated learning (FL) framework that utilizes artificial intelligence (AI) to predict nurse stress levels by integrating distributed biomechanical data from wearable sensors, thereby preventing data leakage. Biometric features from datasets at each FL client are extracted and used to train local neural network (NN) models. After several aggregation rounds, the global model converges to predict nurse stress levels. Simulations demonstrate the effectiveness of our method, achieving over 90% prediction accuracy, which enhances the feasibility of privacy-preserving stress monitoring and offers scalable solutions for occupational health management.

Physical education instruction has several issues on injuries as a result of interruptions to the learning, participation, or physical activity of the students. Existing strategies concern the risk prevention and warm-up activities without addressing personal characteristics and anatomical and kinematic prerequisites that underlie the occurrence of injury. The biomechanical evaluation of human motion can determine factors such as joint stresses, muscle loads, and motion patterns. This proceeding strives at eradicating the aspects of biomechanics in expanding the protective measures of injury prevention in physical education teaching. Beside the motion capture system, the force plate measurement and the electromyography (EMG) data movement patterns and joint load are measured biomechanically as accurately as possible. The accumulative data have to pass through cleaning and standardization steps to provide a certain level of reliability. In this case, the use of Fast Fourier Transform (FFT) extracts features of the movements relating to the frequency domain to undergo further analysis. Subsequently, an efficient Earthworm Optimized Graph Neural network (EEO-GNN) is employed to identify injury risk elements through modeling complex biomechanical relationships and patterns. The EEO-GNN model efficiently predicted ability injury hotspots by analyzing joint stresses, muscle activation, and motion irregularities. It is surpassing previous approaches in terms of F1-score (96.2%), recall (95.2%), accuracy (96%), and precision (95%). It underscores the ability to integrate superior biomechanical analysis and deep learning procedures to enhance injury prevention, enhance motion mechanics, and foster safer and greater effective physical education environments.

Martial arts training involves high-impact motions, which can have both beneficial and negative consequences. Understanding these consequences via a biomechanical perspective is critical for improving performance and reducing injury risk. The purpose is to use a biomechanical model to predict impact effects and evaluate martial arts training outcomes. It involved two separate groups of martial artists to evaluate the impact and training effects at different skill levels. The professional martial artists group had at least 5 years of martial arts training experience, and the beginner group had less than 1 year of experience. The data-collecting procedure involved recording motion and force data during martial arts training sessions. A set of standard martial arts movements (punching, kicking, blocking, striking, grappling, and elbow strike) was chosen for this investigation. A biomechanical model was constructed by recording motion and force data utilizing motion capture systems and force sensors. The sensor data were linked to the biomechanical simulation program, such as OpenSim. To predict impact forces and training effects, variables such as joint angles, muscle forces, impact forces, and movement efficiency were examined using descriptive statistics, ANOVA, and regression analysis. A statistical analysis using SPSS 25 indicated significant variations in impact forces between professional and beginner martial artists. Professional practitioners displayed more efficient biomechanics, which reduced joint stress and injury risk. The combination of a biomechanical model and SPSS-based statistical analysis yielded an effective instrument for assessing impact effects and training outcomes in martial arts. The findings provide useful suggestions for improving training programs and injury prevention techniques. It contributed to a better knowledge of movement efficiency and injury prevention techniques.

Background: Knee osteoarthritis (KOA) is highly prevalent among the elderly population, with traditional treatments focusing primarily on medication or surgery, while precise rehabilitation and health economic evaluations remain insufficient. Biomechanics-oriented rehabilitation interventions may offer higher efficiency and safety. Objective: To explore the clinical efficacy, equipment performance, and cost-effectiveness of a novel rehabilitation training system based on biomechanical analysis for KOA patients and to verify the correlation between changes in joint torque and functional improvement. Methods: A total of 80 KOA patients were enrolled and randomly assigned in a 1:1 ratio into the intervention group and the control group, with 40 cases in each group. The intervention group utilized a novel rehabilitation training system incorporating biomechanical analysis, while the control group used conventional mechanical equipment. Three-dimensional gait parameters (e.g., peak joint angle, peak torque, loading rate) were measured at baseline, 6 weeks, and 12 weeks post-intervention. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores and equipment performance indicators were assessed, while total treatment costs and cost-benefit ratios were calculated. The intervention effects were evaluated using independent sample t-tests, chi-square tests, and Pearson correlation analysis. Results: The intervention group showed significant improvements in peak joint angles, peak torque, and loading rates compared to baseline (p < 0.05), while soft tissue pressure did not increase significantly (p > 0.05). The novel equipment demonstrated significantly better performance in terms of angle and torque detection errors compared to conventional equipment (p < 0.05). The intervention group had lower total treatment costs and a superior cost-benefit ratio (p < 0.01), with no statistically significant difference in adverse event incidence (p > 0.05). Gait trajectory improvements were significant at multiple time points (p < 0.05), and clinical function (WOMAC score, walking distance) and healthcare efficiency also improved (p < 0.05). Changes in joint torque were strongly correlated with WOMAC score improvement (r = 0.628, p < 0.001). Conclusion: The biomechanics-driven rehabilitation training system significantly enhances clinical efficacy, equipment performance, and economic burden management, achieving precise rehabilitation and resource optimization, with demonstrable application value in the health industry.

Biomechanics plays a crucial role in optimizing athletic performance while mitigating physiological and cognitive stress in competitive sports. Stress-induced biomechanical inefficiencies contribute to movement instability, increased injury risk, and reduced mental resilience. However, limited research has integrated biomechanical, physiological, and neurophysiological assessments to provide a comprehensive analysis of stress adaptation in elite athletes. This study aims to quantitatively assess the impact of biomechanics on stress management and mental toughness using an integrated multi-modal approach. Specifically, it examines the relationship between movement efficiency, physiological stress markers, and cognitive load in high-performance sports. Three advanced measurement techniques were employed: (i) 3D Motion Capture and Force Plate Analysis to evaluate movement precision and force asymmetry; (ii) Heart Rate Variability (HRV) and Cortisol Quantification to assess autonomic nervous system regulation under stress; and (iii) EEG and fNIRS-Based Mental Load Measurement to analyze cognitive workload and neural adaptations. Data were collected under baseline, moderate stress, and high-stress conditions. Findings revealed a 44.4% decline in biomechanical efficiency, a 56.3% reduction in HRV-based autonomic regulation, and a 52.9% increase in cognitive workload under high-stress conditions. Increased joint angle variability, force asymmetry, cortisol elevation, and EEG beta power shifts were key indicators of stress-induced performance deterioration. The results underscore the necessity of integrating biomechanical optimization, stress management protocols, and cognitive resilience training in athlete development. This study highlights the interconnected nature of movement biomechanics, physiological stress regulation, and neurocognitive resilience. Future research should explore predictive modeling and real-time monitoring to enhance individualized stress-adaptive training strategies.

This research adopts a scientific perspective based on biomechanics and molecular-level analysis to explore the synergistic mechanisms and implementation effects of mobile technology and intelligent management systems in rural tourism. The study constructs a three-dimensional “technology-human-environment” framework, employing mixed research methods and a multi-case research strategy to conduct experiments at eight different types of rural tourism destinations across three representative provinces in China, recruiting 458 effective sample participants who wore lightweight wearable motion capture systems and other multimodal biomechanical data collection devices, while simultaneously testing the “BioPark” mobile application and “SmartRural” intelligent management system in coordination. The findings reveal that tourists exhibit significantly different biomechanical characteristics and molecular physiological indicator patterns in various terrain environments, such as knee joint maximum torque being significantly higher on steep slopes than on flat ground (1.64 ± 0.27 vs. 0.87 ± 0.16 Nm/kg, p < 0.001), and cortisol/endorphin ratio showing a strong negative correlation with satisfaction (r = −0.72, p < 0.001). Mobile technology primarily influences experience through three pathways: information enhancement, experience optimization, and interaction enhancement, while the intelligent management system improves decision-making accuracy by 48.3% and resource allocation efficiency by 28.3%. The synergistic effect of the systems produces results exceeding the simple addition of individual systems through three mechanisms: data sharing (path coefficient 0.73), functional complementarity (0.68), and information feedback (0.61), resulting in increased overall tourist satisfaction by 28.1% (p < 0.001), extended stay duration by 36.4% (p < 0.001), and improved actual revisit rates by 77.8% (p < 0.01). Based on these results, the study proposes “physiologically friendly” rural tourism spatial design principles and technology-management collaborative optimization strategies, establishing a biological data-driven service closed-loop system that provides theoretical support and technical solutions for the scientific development of rural tourism, while also discussing research limitations and future development trends.

This study explores the potential impact mechanism of biomechanical factors in physical activities on the achievement emotions and academic performance of higher vocational students in Jiangsu Province. The research employed a multi-stage stratified random sampling method, selecting 842 higher vocational students from 13 prefecture-level cities in Jiangsu Province as research subjects. Advanced equipment, including three-dimensional motion capture systems, surface electromyography, and three-dimensional force platforms, was used to measure biomechanical parameters during physical activities, while the revised Achievement Emotions Questionnaire (AEQ) was applied to assess students’ achievement emotional experiences, alongside collected data on learning efficacy and academic performance. The research results indicate: (1) Significant biomechanical characteristic differences exist in physical activities among higher vocational students in Jiangsu Province, with professional background, physical activity type, and participation frequency influencing their biomechanical efficiency performance; (2) biomechanical efficiency shows a significant positive correlation with positive achievement emotions (r = 0.628, p < 0.001) and a significant negative correlation with negative achievement emotions (r = −0.608, p < 0.001), with 8-week biomechanical optimization intervention significantly increasing positive emotions by 36.2% and reducing negative emotions by 28.7%; (3) achievement emotions play a partial mediating role between biomechanical efficiency and academic performance, with the mediating effect accounting for 43.3% of the total effect; biomechanical efficiency still demonstrates a significant direct effect on academic performance (β = 0.263, p < 0.001); (4) professional background, physical activity type, and individual differences significantly moderate the impact pathway of biomechanical factors, with arts students showing the strongest emotional mediating effect (0.236), sports students demonstrating the strongest direct effect (0.325), and technical activities exhibiting more significant emotional regulation effects. These findings reveal the dual mechanism pattern of biomechanical factors influencing academic performance through emotional regulation and cognitive promotion, providing new perspectives and a scientific basis for higher vocational physical education reform. Future research should incorporate neuroscience methods to further explore the neurophysiological mechanisms, conduct longer-term tracking studies, and extend to broader vocational education student populations.

Purpose: To compare the therapeutic effects of unilateral biportal endoscopic lumbar interbody fusion (ULIF) using a 3D printed cage and posterior lumbar interbody fusion (PLIF) using a 3D printed cage in the treatment of lumbar spondylolisthesis. Method: To retrospectively analyze the clinical data of 105 patients with lumbar spondylolisthesis in Qianfoshan Hospital of Shandong Province from Among them, 50 cases were treated with ULIF (ULIF group) and 55 cases were treated with PLIF (PLIF group). The operation time, intraoperative blood loss, postoperative drainage volume, hospitalization days, postoperative complications, waist and leg pain vas scores, and OSTC pain scores, were all measured. The operation time, intraoperative blood loss, postoperative drainage volume, hospitalization days, postoperative complications, waist and leg pain vas scores, and Oswestry Disability Index (ODI) were compared between the two groups. Results: The operation time in the ULIF group was longer than that in the PLIF group, and the difference was statistically significant (p < 0.05). There were statistically significant differences in intraoperative blood loss, postoperative drainage volume, and hospital stay (p < 0.05). There were 1.1 cases of cerebrospinal fluid leakage in the ULIF group and the PLIF group after surgery, and 3 cases of infection in the PLIF group. No other complications occurred. No other complications occurred. The difference in the incidence of complications between the two groups was statistically significant (p < 0.05). Patients in both groups were followed for 6 to 12 months. In the ULIF group and the PLIF group using 3D printed intervertebral fusion cages, the intervertebral fusion rates were as high as 1 to 1.5 times the intervertebral fusion rate. In the ULIF group and the PLIF group using 3D printed intervertebral fusion cages, the intervertebral fusion rates at 6 months (80%/81.8%, p > 0.05) and 12 months (96%/96%, p > 0.05) after surgery were statistically undifferenced. Conclusion: For patients with lumbar spondylolisthesis, ULIF group can achieve similar efficacy to PLIF group using a 3D printed fusion cage. They are similar in terms of pain relief and improvement in functional disability. ULIF group has less intraoperative and postoperative bleeding and shorter hospitalization days, lower infection rate and less damage to muscle tissue. ULIF has less intraoperative and postoperative bleeding and shorter hospitalization days, lower infection rate and less damage to muscle tissue. However, ULIF takes a long time, has a long learning curve and requires high equipment.

The study proposes a diabetes detection scheme based on a novel flexible glucose electrochemical sensor for the current situation of diabetes management in China, especially for the elderly diabetic population. The sensor is fabricated using optimized conductive materials and diluents with set printing parameters. It mainly realizes non-invasive monitoring of human blood glucose by detecting human sweat, thus effectively detecting elderly diabetic patients. Simulation experiments showed that the sensor had a detection limit of 7.33 μM (S/N = 3) and a high sensitivity of 23.5 μA mm⁻² for simulated sweat, demonstrating good stability and durability. Moreover, in the actual in vitro detection experiments, the sensor detected elderly diabetic patients with an accuracy of more than 98.3%. In addition, the response time of the sensor was very short, only 10.5 s to complete a detection, which was suitable for elderly patients. The above illustrated that the flexible glucose electrochemical sensor prepared in the research had certain feasibility and accuracy in the blood glucose monitoring of elderly diabetic patients. The research results not only provide theoretical basis and technical support for the preparation of flexible glucose electrochemical sensors, but also provide new ideas and methods for blood glucose monitoring and treatment of elderly diabetic patients.

In order to explore the biomechanical interactions between postural adaptation in pregnant women and the Knowledge-Attitude-Practice (KAP) model of prenatal anxiety in Southwest China, this cross-sectional study integrated psychometric assessments with kinematic analysis and conducted a survey among pregnant women in Nanchong between September and November 2022. A web-based questionnaire was employed to collect data on demographic characteristics, biomechanical features, prenatal anxiety KAP scores, and anxiety status. A total of 515 valid questionnaires were recovered, and 120 women (23.30%) had anxiety. The mean knowledge, attitude, and practice scores were 5.59 ± 2.73 (possible range: 0–9), 33.59 ± 4.36 (possible range: 10–50), and 21.85 ± 3.77 (possible range: 6–30). College or above education (OR = 3.66, 95% CI: 1.88–7.13, P < 0.001) and planned pregnancy (OR = 0.60, 95% CI: 0.37–0.99, P = 0.046) were independently associated with better knowledge. Without a history of adverse pregnancy (OR = 0.64, 95% CI: 0.42–0.95, P = 0.029) and freelancer (OR = 0.46, 95% CI: 0.26–0.80, P = 0.007) were independently associated with a favorable attitude. The knowledge (OR = 1.71, 95% CI: 1.01–2.89, P = 0.047) and attitude (OR = 0.54, 95% CI: 0.33–0.89, P = 0.016) were independently associated with anxiety. Pregnant women had a moderate KAP toward prenatal anxiety. It is recommended to learn about it and applying biomechanical knowledge to positive practices may help prevent prenatal anxiety.

This study proposes a network performance optimization strategy based on cloud computing to address the stringent demands of biomechanical big data on the efficiency of distributed storage systems. Biomechanical data, including motion capture, force plate measurements, and tissue strain analysis, involve large-scale, high-frequency, and heterogeneous datasets that necessitate efficient storage and real-time processing. By optimizing data transmission paths, designing an efficient caching mechanism, dynamically allocating bandwidth resources, and implementing network congestion control, the system significantly enhances throughput, reduces latency, and improves bandwidth utilization and data transmission reliability.

As the biosensor technology rapidly develops, the application of flexible sensors in health monitoring is receiving increasing attention. To achieve high-precision, non-invasive, and continuous blood pressure monitoring, a flexible pulse biosensor based on modified multi-walled carbon nanotubes is studied, designed, and prepared. The sensor adopts a dual conductive layer resistive structure and combines crack structure design to enhance the sensitivity and response speed of the sensor. This design fully considers the biomechanical properties to ensure that the sensor can adapt to the strain changes caused by human movement, thereby improving the accuracy and reliability of monitoring. In addition, the study combines time-frequency analysis methods with fast Fourier transform to extract key feature points of pulse signals and uses a BPNN model to predict current blood pressure values. The results show that within a small strain range, the response time of the sensor is only 56.14 ms, and the strain coefficient is as high as 1572.4, effectively achieving real-time monitoring. This high response speed and sensitivity enable the sensor to accurately capture changes in pulse waveforms related to biomechanics, providing more reliable data support. The error of the average arterial pressure obtained by the prediction model is only −0.070 mmHg, which proves the accuracy of the current blood pressure value prediction. In summary, the intelligent flexible pulse monitoring system based on biosensors studied can achieve high-precision real-time blood pressure measurement and has good stability and anti-interference ability, providing effective technical support for home health management and early monitoring of hypertension. This research not only promotes the development of biosensor technology but also provides a new research direction in the field of biomechanics.

Thoracolumbar kyphosis in ankylosing spondylitis poses multiple hazards to patients. To optimize the surgical correction of internal fixation, the biomechanical characteristics and corrective efficacy of different strategies for surgical correction of kyphosis were analyzed based on finite element analysis. The outcome revealed that there were significant deformational displacement differences and stress differences between the different orthopedic internal fixation schemes. Cortical bone track screw fixation significantly reduced the deformation displacement in flexion-extension and lateral flexion conditions. Among the proximal stresses, the cortical bone track screws had the highest stresses in the rotational condition, which could reach 446.661 MPa and 398.16 MPa. They were more resistant to pullout as the traditional all-pedicle screws. Different internal fixation protocols produced different orthopedic clinical outcomes. Patients in the experimental group of conventional all-pedicle screws versus cortical trajectory screw fixation had better Cobb angle, Oswestry incapacity index, sagittal equilibrium distance, and Scoliosis Research Society-22 patient questionnaire scores. This study may provide optimized recommendations for the development of surgical corrective internal fixation protocols and guide the development of surgical strategies, which in turn may promote patient recovery and reduce complications.

Objective: The improvement of bone repair scaffolds to enhance their bioactivity and in vivo vascularization is a current research hotspot. Method: HUVECs are subjected to both fluid shear stress (FSS) and chemical stimuli simultaneously. The release of ATP, NO, and the expression of eNOS were examined. The adhesion spots and cytoskeleton formed by HUVEC on the material surface were also observed. Result: LFSS (low fluid shear stress, 5 dyn/cm²) did not trigger a response on Glass and -NH₂ HUVECs, but induced a strong response on -OH and -CH₃, while PFSS (physiological fluid shear stress, 15 dyn/cm²) and HFSS (high fluid shear stress, 20 dyn/cm²) generated responses of all groups of cells, among which the strongest response level was from the -NH₂ group, followed by Glass, and among which equal response levels of the -OH and -CH₃ groups existed at the lowest. Conclusion: The chemical functional groups changed the initial threshold of HUVECs response to FSS and the shear force stimulation threshold for optimal cellular response by influencing the quality of adhesion spots and cytoskeleton formed by HUVECs on the surface of the material, thereby altering the response state of endothelial cells to shear force stimulation.

Martial arts have their origins in a variety of cultural traditions that represent a wide range of combat practices and disciplines. Martial arts encompass a variety of practices, extending from ancient traditions such as kung fu and karate to more modern forms like Brazilian jiu-jitsu. The martial arts practitioners engage in the cultivation of self-defense techniques, the enhancement of physical fitness, and the demonstration of profound concepts that transcend the boundaries of the physical domain. Ensuring the safety and efficacy of practitioners is achieved by comprehensive training, increased awareness, devotion to proper techniques, and maintenance of physical fitness. In this study, we gathered primary data from 75 skilled football players with varied training provided for comprehensive football kicking analysis. The research employed the VICON MX40, a three-dimensional (3D) motion-capture system with nine cameras. The system recorded motion at a rate of 200 frames per second. The study aimed to examine the 3D movements observed in adolescent sports through the construction of biomechanical models. This analysis enabled the identification of many risk variables associated with repetitive stress injuries (RSIs). We propose preventative strategies for reducing injuries caused by RSI among young individuals. These strategies encompass specific stretching exercises, dynamic warm-up routines, the restriction of intense movements, and the promotion of sufficient recovery periods. The utilization of biomechanical modeling plays a significant part in the prediction and optimization of strategies designed for minimizing the various elements that contribute to muscular RSIs throughout the process of motor skill acquisition and training.

Objective: To explore the effects of different external treatment methods on biofeedback stimulation in patients with autism, analyze the impact of different biofeedback stimuli on the transmission of neural network signals in patients, and evaluate the therapeutic effects on autism patients. Method: 120 autistic patients under the age of 13 were selected and divided into a control group and an experimental group. The experimental group patients were intervened with a combination of painting therapy and traditional treatment. The control group patients were only intervened with traditional therapy. During the experiment, relevant instruments were used to collect changes in the patient’s eye electrical signals during the treatment process. The relationship between changes in eye electrical signals and patient efficacy was analyzed. Results: After four courses of treatment, the experimental group demonstrated a significant increase in the infant/junior high school social and biological competence scale and goals achievement and learning scale (p < 0.001), indicating that painting therapy had a good therapeutic effect on children with autism. During the treatment process of painting therapy, the total electrooculogram approximate entropy of the patient’s eye electrical activity significantly decreased (p < 0.001). During the painting process, the patient’s attention and state were more focused. Conclusion: The biofeedback signals of children with autism undergoing painting therapy show that the patient’s attention is more focused and their concentration is higher than usual. Painting therapy can promote the further development of neural networks in children with autism by intervening in their bioelectric signals.

Hydraulic gantry cranes (hereinafter referred to as “gantry cranes”) are highly susceptible to instability during dynamic operations due to high-speed unsteady airflow in mountainous and canyon areas, leading to safety risks such as derailment and overturning. Traditional single data-driven or model-driven methods fall short in ensuring real-time performance, accuracy, and comprehensiveness for the safety state perception of gantry cranes during dynamic operations. To overcome this, we draw inspiration from the way biomechanics integrates multiple data sources and models. A digital prototype of the gantry crane was established, mimicking the creation of a virtual model of a biological structure for in-depth analysis. A surrogate model for the dynamic response of the gantry crane under the coupled effects of wind load, lifting load, and self-driving force was constructed. In biomechanics, models are developed to simulate the combined actions of different forces on biological tissues and organs. Here, we approach the gantry crane’s force analysis in a similar fashion, considering the complex interactions of various loads. Based on this, a data model fusion driven method for safety state perception during dynamic operations of gantry cranes was proposed. This method is in line with the practice in biomechanics of integrating experimental data and theoretical models to gain a more complete understanding of biological processes. By fusing data and models, we aim to enhance the safety state perception of gantry cranes, just as biomechanics uses integrated approaches to improve our understanding of biological systems. Simulation results of a 150 t gantry crane at a hydropower station demonstrate the feasibility and practicality of the proposed method. This validation process is comparable to how biomechanical models are tested and verified through experiments on biological specimens or simulations of biological movements, providing evidence for the effectiveness of our approach inspired by biomechanics.

This study aims to investigate the functional electrical stimulation (FES) effects of different sizes of stimulation electrodes and different numbers of stimulation channels. Taking the biceps brachii as the target muscle, four FES electrode configurations were designed, varying in electrode size and stimulation channel number. This study recruited ten healthy subjects. Under each FES electrode configuration, a high (30 Hz)-frequency/low (1 Hz)-frequency alternating stimulation was conducted to collect effective muscle contraction strength data and surface electromyography (sEMG) signals. The effects of different FES electrode configurations on muscle contraction strength were analyzed using fatigue-related indicators, and those on myoelectric activity property involving motion unit (MU) recruitment and muscle fiber conduction velocity (MFCV) were explored by means of sEMG data analysis. Both enlarging stimulation electrode size and increasing the number of stimulation channels delayed muscle fatigue, enhanced motor unit recruitment, and generated stronger muscle contractions at the same current intensity. Enlarging the electrode size is more conducive to recruiting more MUs and enhancing muscle contraction output, while increasing the number of stimulation channels is more conducive to delaying muscle fatigue effects. The research results of this article can provide scientific guidance for clinical doctors to develop personalized FES plans, thereby improving treatment effect.

Objective: Left ventricular thrombosis is one of the complications of acute myocardial infarction, and the high-risk factors in first acute anterior myocardial infarction have not been fully elucidated. This study aimed to explore the risk factors for left ventricular thrombosis in patients with their first acute anterior myocardial infarction. Methods: We retrospectively analyzed the clinical data of 84 patients diagnosed with their first acute anterior myocardial infarction in the Department of Cardiovascular Medicine, Gulou Clinical College, Xuzhou Medical University, from March 2019 to June 2023 and divided them into a thrombosis group (left ventricular thrombosis, n = 35) and a control group (no left ventricular thrombosis, n = 49) according to the presence or absence of left ventricular thrombosis. The baseline data and imaging characteristics of the two groups of patients were retrospectively analyzed. Univariate and multivariate logistic regression analysis was used to evaluate the risk factors for left ventricular thrombosis. Results: In univariate analysis, female sex, diabetes, hypercholesterolemia, ALT, AST, CRP, NT-ProBNP, D-dimer, CKMB peak, cTnI, LVEF, and WMS (wall motion score 1–5) were significantly associated with left ventricular thrombosis (P < 0.05). Multivariate analysis showed that female sex, diabetes, hypercholesterolemia, ALT, AST, CRP, NT-ProBNP, D-dimer and WMS (wall motion score 1–5) were independent risk factors for left ventricular thrombosis (P < 0.05). Based on the analysis of hemodynamic characteristics, left ventricular segmental systolic dysfunction after anterior wall myocardial infarction leads to changes in the velocity gradient of intraventricular blood flow, local blood flow stasis, and abnormal shear stress. This is especially true in patients with significantly reduced LVEF (< 40%) and wall motion scores ≥ 3. The vortexes formed by changes in ventricular geometry, together with endothelial injury, jointly constitute the physical basis for thrombosis formation. Conclusion: This study found that gender, diabetes, hypercholesterolemia/lipidemia, inflammatory markers (such as CRP), myocardial injury markers (such as NT-ProBNP, CKMB, cTnI), thrombosis markers (such as D-dimer), and left ventricular dysfunction were closely associated with left ventricular thrombosis after the first acute anterior wall myocardial infarction. These results help to better understand the pathogenesis of left ventricular thrombosis after myocardial infarction and provide important clues for early prevention and intervention.