Vol. 22 No. 4 (2025)

Cellular mechanics behavior, encompassing properties such as elasticity, viscosity, and stress-strain responses, is fundamental to understanding disease mechanisms, tissue regeneration, and drug development. This study proposes a deep learning-based framework integrating Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and federated learning to model and analyze cellular mechanics while enabling secure data sharing. The proposed methods preserve critical biomechanical insights, such as force-displacement curves and cellular deformation patterns, while mitigating re-identification risks during multi-institutional collaborations. Experimental evaluations demonstrate the framework’s effectiveness in maintaining data utility and analytical accuracy, paving the way for advancing biomechanics research and fostering applications in regenerative medicine and tissue engineering.

In order to improve the reliability and efficiency of foundation design in complex geological environments, this paper proposes a computer-assisted mechanical characterization model based on biomechanical principles, which is combined with bionic design methods to optimize the construction strategy. By integrating the stress distribution and deformation mechanism of biomaterials, this paper designs a foundation structure that is more adaptable to the geological uncertainty, and uses optimization algorithms and dynamic feedback mechanisms to analyze the foundation bearing capacity, settlement control and structural response. The results show that the optimized model significantly improves the foundation safety, reduces the overall construction cost, and provides valuable guidance for engineering practice.

This study introduces three methodological innovations in enhancing children’s prefrontal cortex activity and executive functions using TOMATIS filtered audio therapy and PASS theory training. The interventions synergize to improve cognitive processing and neural plasticity. Divided into two stages, the initial focuses on physical and emotional adaptation, while the latter targets cognitive enhancement. After five weeks, significant improvements in attention and executive functions were observed in the treatment group compared to controls (P < 0.05). The study also explores AI exoskeletons and near-infrared technology to optimize therapy, offering new insights into ADHD treatment.

Background: Binge Eating Disorder (BED) has gained attention in recent years due to its complexity and the challenges it poses in diagnosis and treatment. Unlike other eating disorders such as anorexia nervosa and bulimia, BED has been less researched, particularly regarding the influence of genetic factors and biomechanical on eating behaviors. This study introduces a novel approach by individually analyzing how genetic predisposition and biomechanical factors impacts the diagnosis of BED. The primary objective of this research was to demonstrate the diagnostic variability and intervention possibilities in patients with BED, highlighting the importance of an interdisciplinary approach that integrates biomechanical principles. Additionally, it aimed to identify individual factors—clinical, psychosocial, biomechanical and genetic—that influence the presence of the disorder, and to evaluate the efficacy of personalized treatments that include psychological, psychiatric, and nutritional interventions tailored to each patient’s unique needs. Case presentation: The main concerns of the patients were how to manage their food-related anxiety, which was often exacerbated by biomechanical stressors. Many felt stigmatized by their weight and guilty for being unable to control their binge eating, which they initially attributed to a lack of self-control. However, upon learning about their genetic profile and the biomechanical underpinnings of their condition, patients began to better understand their eating behaviors, allowing them to reduce the associated guilt. Clinically, it was observed that after receiving interdisciplinary treatment, which included both psychological interventions and precision nutritional management, and biomechanical modulation, binge eating episodes significantly decreased. In four out of five cases, episodes disappeared. Conclusions: This reinforces the importance of tailoring treatments to the genetic and psychosocial, and biomechanical specifics of each patient. By incorporating biomechanical insights into therapeutic strategies, new research opportunities are opened, and the therapeutic approach for BED is significantly improved. This interdisciplinary framework not only addresses the psychological and nutritional aspects of BED but also leverages biomechanical principles to optimize treatment outcomes, offering a more holistic and effective approach to managing this complex disorder.

Purpose: To investigate how bidirectional encoder representations from transformers (BERT)-based models help extract treatment response information from free-text radiology reports. Materials and methods: This study involved 400 brain MRI reports from 115 participants with multiple sclerosis. New MRI lesion activity including new or enlarging T2 (newT2) and enhancing T1 (enhanceT1) lesions for assessing treatment responsiveness was identified using the named entity recognition technique along with BERT. Likewise, 2 other associated entities were also identified: the remaining brain MRI lesions (regT2), and lesion location. Report sentences containing any of the 4 entities were labeled for model development, totally 2568. Four recognized BERT models were investigated, each with conditional random field integrated for lesion versus location classification, trained using variable sample sizes (500–2000 sentences). Regularity was then applied for lesion subtyping. Model evaluation utilized a flexible F1 score, among others. Results: The Clinical-BERT performed the best. It achieved the best testing flexible F1 score of 0.721 in lesion and location classification, 0.741 in lesion only classification, and 0.771 in regT2 subtyping. With growing sample sizes, only Clinical-BERT performed increasingly better, which also had the best area under the curve of 0.741 in lesion classification at training using 2000 sentences. The PubMed-BERT achieved the best testing flexible F1 score of 0.857 in location only classification, and 0.846 and 0.657 in subtyping newT2 and enhanceT1, respectively. Conclusion: Based on a small sample size, our methods demonstrate the potential for extracting critical treatment-related information from free-text radiology reports, especially Clinical-BERT.

Bone marrow mesenchymal stem cells (BMSCs) tissue engineering has been an emerging field of research in recent years. Given the increasing global interest, we utilized a bibliometric analysis and visualization of studies on BMSCs in the field of tissue engineering published from 2004 to 2023 to explore research progress and identify future research directions. Data was collected from the Web of Science Core Collection (WoSCC), and in-depth analysis was conducted using various bibliometric tools, including CiteSpace, VOSviewer, and R-Bibliometrix. Our study revealed the historical development and evolution of active topics in BMSCs in terms of temporal dynamics, covering 2967 publications, 65 countries, 2454 academic institutions, and 605 journals, with significant growth observed over the last 20 years. China and the United States dominate the global research landscape. Shanghai Jiao Tong University is one of the most significant contributors to the field. In terms of co-citation analysis, Biomaterials was identified as a key journal. Our analysis also revealed current trends such as extracellular vesicles, exosomes, 3D printing, hydrogels, and nanomaterials. These findings provide a clear perspective for future research on the tissue engineering of BMSCs. This study fills a gap in the field of bibliometrics, enabling researchers to identify popular research areas and providing a comprehensive perspective and broad outlook on this emerging field of research.

This study aimed to evaluate the combined effects of music therapy and biomechanical interventions on chronic pain management, focusing on pain intensity, functional impairment, and quality of life. A mixed-methods approach was employed, integrating quantitative measures (pain intensity, functional impairment, and quality of life) with qualitative interviews to capture participants’ experiences. The study involved 120 participants with chronic pain conditions, including fibromyalgia, arthritis, and neuropathic pain. Moreover, participants were selected through purposive sampling. Descriptive and inferential statistics revealed significant improvements in pain intensity visual analogue scale (VAS: 7.8 to 4.6, p < 0.001), functional impairment pain disability index (PDI: 45.6 to 32.3, p < 0.001), and quality of life (SF)-36: 62.4 to 78.2, p < 0.001). Qualitative findings highlighted emotional and cognitive benefits from music therapy and physical improvements from biomechanical interventions, particularly enhanced mobility and reduced pain. The integration of both therapies demonstrated a synergistic effect, significantly improving overall pain management (β = −0.5, p < 0.001). The study concludes that a combined approach offers a comprehensive, effective treatment for chronic pain. Clinical implications include incorporating multimodal interventions into rehabilitation programs with a personalized approach based on pain type and severity. Future research should explore long-term effects and further refine individualized treatment strategies. In contrast, the limitation of this study is the relatively small and homogeneous sample, which may limit generalizability to broader chronic pain populations. Additionally, the short intervention period does not allow for assessing long-term effects.

Traumatic brain injury (TBI) caused by explosions is the most common injury suffered by front-line soldiers. However, research on protective gear has primarily been limited to different types of helmets or their internal padding systems. Aerogels, with their microporous structures and high acoustic impedance properties, can effectively buffer the impact of explosions and generate significant acoustic mismatches between adjacent layers, making them promising materials for reducing the damage of blast shock waves to the head. This study aims to enhance the performance of protective equipment in mitigating explosion-induced head injuries and proposes a novel helmet mask structure based on polycarbonate and aerogel laminated composites. The coupled Eulerian-Lagrangian (CEL) method in Abaqus is employed to analyze the mechanical responses of different helmet-mask protective structures under blast shock waves through numerical simulation. The study emphasizes the influence of the type and thickness of the protective structure on head injury. Our findings indicate that a helmet with a face shield can significantly slow down the propagation of the blast wave to the face, thereby reducing craniocerebral injury. Further analysis reveals that the combination of polycarbonate and aerogel layers is more effective than a fully polycarbonate face shield in mitigating intracranial pressure (ICP) in the frontal and parietal regions. Additionally, masks with 3-layer configurations (featuring a single 0.6 mm thick aerogel layer) and 5-layer configurations (with double 0.6 mm thick aerogel layers) performed best in preventing moderate and severe traumatic brain injury (TBI). These results provide a scientific basis and a new direction for the design and optimization of future protective helmets.

Background: Periodontitis is prevalent among large population, which may induce in bone destruction, attachment loss and finally tooth loss. Polychlorinated biphenyl (PCB) is one of the persistent organic pollutants (POPs), which are endocrine disruptors may destroy the integrity of tissue through possible mechanisms. Recent research has suggested that PCBs can accumulate in adipose tissue and increase the risk of periodontal disease by disturbing the immune system. This cross-sectional study investigated the relationship between PCBs and periodontitis in the general population. Methods: In general, cross-sectional associations of PCBs with the prevalence of periodontal disease were investigated in 263 patients in the National Health and Nutrition Examination Survey 1999–2002. Multivariate and stratified analysis was used to measure the association between PCBs and periodontitis. Results: From 1999 to 2002, the total number of patients in the National Health and Nutrition Examination Survey (NHANES) database was 21,004, and 3082 patients were finally enrolled after removing the patients who had not been tested for PCBs. Fully adjusted multivariable logistic regressions was performed on PCB lipid adjustments, and the results suggested a positive correlation between PCB180 and periodontitis. Subgroup analysis showed a negative correlation between PCB180 lipid adjustment and periodontitis in patients aged < 20 years (P for interaction = 0.002). Conclusion: PCB180 is positively correlated with periodontitis of the age over than 20s. However, further studies need to be investigated that whether PCBs affected biomechanical pathways to destroy tissue integrity. This study provides new insights for the prevention of periodontitis from the perspective of environmental exposure.

Objective: To investigate the effects of Pingchuanning prescription (PCN) and Ferrostatin 1 inhibitors on airway inflammation in asthmatic rats from the perspective of cell iron death. Methods: Seventy SD rats were randomly divided into 7 groups: normal group, model group, Pinbuening group, Ferrostatin 1 inhibitor group, Pinbuening + Ferrostatin 1 inhibitor group, dexamethasone group, and Guilong Kechuanning group. 10% chicken egg albumin (OVA) was sensitized by peritoneal and limb subcutaneous injection. The asthmatic rat model was stimulated by 2% OVA atomization combined with cold (2–4 ℃) air stimulation. Pingchuanning (6.43 g/kg), Ferrostatin-1 (10 mg/kg), Pingchuanning (6.43 g/kg) + Ferrostatin-1 (2.5 μmol/kg), dexamethasone (0.5 g/kg), Guilong Kecchuanning (10g/kg) by gavage and atomization, Continuous intervention for 3 weeks. After the last stimulation, the lung tissues of rats were stained with hematoxylin-eosin (H&E) to observe airway inflammation and cell proliferation. The contents of IL-10, IL-22, IL-33 and ALOX15 in serum and LF of asthma were detected by enzyme-linked immunosorbent assay (ELISA). Real-time fluorescence quantitative polymerase chain reaction (RT-PCR) was used to detect the mRAN expression levels of SLC7A11 and GPX4, and Western blot was used to detect the protein expression levels of SLC7A11 and GPX4. Results: Compared with blank group, the diet, body weight, emotional irritability, respiratory shortness, airway inflammatory cell infiltration, goblet cell hyperplasia, serum and serum LF IL-10, IL-22, IL-33, ALOX15 inflammatory factors increased significantly in model group. The mRNA and protein expression levels of SLC7A11 and GPX4 were decreased. Compared with the model group, the diet of the rats in the Pinbuening, Ferrostatin 1 inhibitor and Pinbuening +Ferrostatin 1 inhibitor groups was gradually improved, wheezing was relieved, and airway inflammatory cell infiltration was significantly reduced. IL-10, IL-22, IL-33 and ALOX15 inflammatory factors in serum and LF of asthma were decreased (P < 0.001), while the mRNA and protein expressions of SLC7A11 and GPX4 were promoted (P < 0.005). Conclusions: Pinbuterin and its Ferrostatin 1 inhibitors can significantly improve airway inflammation induced by OVA combined with cold stimulation in asthmatic rats, and are related to SLC7A11/GPX4 signaling pathway and cell iron death. The efficacy of Pinbuterin combined with Ferrostatin 1 inhibitors is more obvious. It is suggested that the effect of combined treatment is better than that of single compound or western medicine.

With the wide application of electric vehicles, smart robots and Internet of Things (IoT) devices, efficient scheduling of mobile charging systems has become an important research direction in smart energy management. However, the traditional cloud computing architecture is difficult to meet the requirements of low latency, high reliability and privacy protection, and the existing scheduling strategies still have challenges in terms of energy optimization, task balancing and dynamic adaptability. To this end, this paper proposes an intelligent mobile charging scheduling method that integrates edge computing and biomechanical modeling, constructs a biomechanical-based charging demand modeling and energy consumption analysis framework, and combines bionic optimization algorithms to achieve efficient path planning. Meanwhile, an edge computing architecture is adopted to optimize resource scheduling, and a federated learning mechanism is designed to enhance cross-domain data processing capability. To safeguard user privacy, a multi-level privacy protection mechanism is proposed, combining differential privacy, homomorphic encryption and zero-knowledge proof to ensure data security. Experimental results show that the method outperforms traditional methods in terms of task response time, energy consumption optimization, load balancing and privacy security, and can significantly improve the charging scheduling efficiency and provide effective technical support for large-scale distributed charging networks. The research results provide a theoretical basis and engineering practice reference for the application of smart charging networks, edge intelligent computing and privacy protection technology.

This paper proposes a biomechanics-based street environment optimization scheme for urban regeneration, integrating biomechanical principles into urban street designs to enhance pedestrian comfort, safety, and overall health. The approach optimizes sidewalks, barrier-free facilities, public seating, and traffic flow lines, focusing on the biomechanical needs of pedestrians, including gait stability, joint stress, and muscle load. To further validate the effectiveness of the proposed approach, additional empirical studies were conducted in diverse urban settings with varying pedestrian densities, surface types, and weather conditions. Simulations were also carried out to predict the scalability and robustness of the design strategies under real-world conditions, ensuring their applicability for future large-scale urban regeneration projects. This practical assessment provides a foundational framework for future urban regeneration projects, particularly in enhancing accessibility and safety for vulnerable groups such as the elderly and people with mobility impairments. Furthermore, these findings contribute to the development of smart cities by integrating biomechanics into urban planning, fostering more sustainable and health-conscious public spaces.

This study explores the biomechanical mechanisms of social media addiction, with a particular focus on its long-term effects on brain function and hand muscle control. By combining neurobiological and biomechanical models, this article analyzes how social media use enhances user dependency by activating the brain’s reward system, particularly the dopamine system, and leads to muscle fatigue and precision adaptation through repeated hand movements such as sliding and clicking. The dopamine release model we proposed reveals temporal changes in dopamine during social media interactions, further influencing users’ behavioral patterns and self-control abilities. Based on the muscle fatigue model, we demonstrate the adaptation process of hand muscles during continuous repetitive operations, resulting in improved hand accuracy but also accelerating the accumulation of fatigue. In addition, the prefrontal cortex activity model suggests that long-term social media use may weaken an individual’s impulse regulation function by reducing self-control. To verify these biomechanical effects, we have demonstrated through experiments that the SVD recommendation algorithm exhibits significant advantages over traditional recommendation algorithms in improving operational accuracy, reducing reaction time, and alleviating muscle fatigue. The experimental results show that the SVD model not only improves the accuracy of the recommendation system, but also optimizes the interaction experience between users and the platform, effectively reducing the biomechanical and cognitive burden.

In this article, it investigates the relationship between biomechanical properties and its contact interface between crops and agricultural machinery, thereafter the procession of how could biomechanical properties affect the mechanical behavior with relative crop damage mechanisms during harvest operation was discussed. This paper first gave an overall perspective of the mechanical features of pressure, friction, and shear forces about the interface between agricultural machinery and crops, thereafter came up a conclusion that these features shall be the essential factors causing crop damage during harvest. Through the analysis of how biomechanical force impacts corps during harvest operation, one step more, relevant mechanisms were revealed in the cellular structure and physiological and biochemical scales about how these mechanical properties worked on crops, in the result causing damage. Furthermore, this article gave a brief discussion about the mechanical behavior with crop damage mechanisms during harvesting, and came up with some potential strategies in optimizing agricultural machinery design and operating methods to reduce crop damage. This result indicates that improvements such as adjusting mechanical structures, standardizing harvest operation tactics, and adopting biomimetic principles can effectively reduce the mechanical stress of the contact interface thereafter minimize crop damage. In conclusion, this article summarizes the current research achievements and proposes future research directions, including in-depth study of crop damage mechanisms, and improvement of new agricultural machinery, in order to further promote the sustainable development of agricultural production.

With the continuous emergence and rapid development of modern advanced technologies, people’s average economic level and quality of life have been better improved. Meanwhile, various medical technologies have also begun to combine with traditional diagnosis and treatment models, which has led to new ideas or breakthroughs in diagnosing or treating various diseases. In the modern medical field, tumor is a relatively common disease, which can be divided into benign tumor and malignant tumor according to its various properties. Benign tumors have little impact on people’s health and can be cured through a series of operations, while malignant tumor has a great impact on people’s health, the development progress of which is relatively fast and the mortality of which is relatively high. Systemic defects in people’s immune systems can also lead to the occurrence of tumors and promote the rapid growth of cancerous cells, with a significant impact on the health of patients. The occurrence of a tumor can change the living environment around it, which is generally called the tumor microenvironment (TME), including all kinds of cells, matrices, and blood vessels around the tumor. TME can act as a “biomechanical culture dish”, where mechanical interactions between tumor cells and their microenvironment accelerate tumor growth and invasion. These mechanical forces can influence cell signaling pathways, gene expression, and cellular behavior, ultimately promoting tumorigenesis and metastasis. This paper uses the genome map to study the characteristics and prognosis differences of TME and finally analyzes the differences between different evaluation indicators of the results of the analysis of the characteristics and prognosis differences of TME using the conventional method and the genome map method through simulation experiments. The analysis results of the characteristics and prognosis differences of TME determined by the genome map improve the performance of multiple evaluation indicators by about 24.9% on average. From a biomechanical standpoint, the integration of genome mapping with mechanical analysis offers a novel approach to understanding the complex interactions within the TME. This interdisciplinary approach not only advances our understanding of tumor biology but also opens new avenues for the development of biomechanically informed treatments for cancer.

Introduction: Obesity not only affects the physical health of adolescents, but may also lead to psychological and social problems. Treatment strategies for adolescent obesity have become particularly important. Increasing evidence suggests that exercise training, especially aerobic exercise, can not only improve obesity, but may also affect obesity and metabolic diseases by regulating hormone levels in the blood. Objectives: The relationship between aerobic exercise combined with dietary intervention and irisin was analyzed by observing the effects of aerobic exercise combined with dietary intervention on body weight, body fat, skeletal muscle, and adipose tissue protein expression in obese rats. Methods: Eighty Sprague-Dawley rats were randomly divided into high-fat diet quiet, low-fat diet quiet control, low-fat diet aerobic exercise, high-fat diet aerobic exercise. During the intervention period, the high-fat group continued to be fed high-fat diet and the low-fat group was fed low-fat diet. Aerobic exercise was performed in the exercise group, and the relevant indexes were tested at the end of the intervention. Results: Rats in the aerobic exercise and low-fat eating intervention group had considerably lower body weights and body fat percentages than rats in the low-fat feeding calm group (P < 0.01). Serum irisin levels were higher in the aerobic exercise intervention group than in the quiet group (P < 0.05). Compared to the quiet group, the aerobic exercise intervention group’s soleus muscle showed a significantly greater expression level of associated proteins (P < 0.05). Moreover, rats in the aerobic exercise group had significantly higher levels of associated protein expression in their white fat at the perirenal area than rats in the quiet group (P < 0.05). Conclusion: Lower dietary fat content significantly reduced body weight and body fat percentage in rats, and the fat loss effect was more obvious when combined with aerobic exercise. Therefore, the combination of aerobic exercise and dietary intervention can be used as an effective fat loss modality for adolescent obese adolescents.

This research explored the physiological changes and fatigue tolerance acquisition as a result of a basketball-specific training program for elite athletes. 30 male basketball players (10 elite, 10 sub-elite, and 10 control) were recruited for a 12-week training program. During the intervention period, heart rate recovery, fatigue tolerance and blood lactate analysis were collected. Evidence from this study indicates that elite athletes are superior in heart rate recovery, who achieve a decrease of 40 ± 2.5 bpm in the first minute as post-exercise heart rate, while the sub-elite and control participants had 35 ± 3.2 bpm and 30 ± 3.8 bpm respectively. Fatigue tolerance testing results shows a statistical significance in performance maintenance time between elite (75 ± 8 min) and sub-elite athletes (45 ± 6 min). Training load and physiological parameters r = 0.78, p < 0.01; fatigue tolerance and performance maintenance r = 0.82, p < 0.01. Alterations of autonomic regulation were observed in athletes after completing the systematic basketball specific training. The results also suggest that basketball-specific physiological conditioning training develops fatigue resistance and allow for effective basketball performance.

This study explores the dynamic evolution of physical fitness data in college students from a biomechanics perspective, analyzing the impact of different exercise intensities on physical health. Using biomechanical modeling methods, combined with the Euler-Lagrange equation, joint torque calculation formula, and health index variation model, experiments were designed and data were collected. The exercise process under different intensities was simulated, measuring indicators such as health index change rate, peak joint torque, energy efficiency ratio, physical fitness distribution, and dynamic coordination score. The experimental results indicate that high-intensity exercise significantly improves the health index of college students, with the greatest changes observed in the moderate and high-intensity groups. High-intensity exercise results in larger joint torques and energy efficiency ratios, suggesting higher joint loads and energy consumption. The improvement in health index is closely related to an individual’s initial fitness level, while dynamic coordination scores are lowest in the high-intensity group, indicating that high-intensity exercise may affect coordination. Therefore, exercise intervention programs should be adjusted based on individual differences to optimize health improvement outcomes.

Product design has increasingly become the process of creating stronger relationships between people and products while improving utility and emotional involvement in today’s fast-paced technological environment. Biosensors that measure physiological and neurological responses have been revolutionary tools in this field. To establish the biosensor-driven design methodology to enhance interactivity and user experience in cultural and creative product design. The device employs electroencephalography (EEG), a sophisticated biosensor, to capture users’ emotional states and preferences as they interact with various cultural elements. The pleasure-arousal-dominance (PAD) model is used to evaluate EEG data. To extract consumers’ perceptual image semantics for product design, factor analysis is used concurrently. An Intelligent Sea Lion Optimization (ISLO), combined with a Resilient Long Short-Term Memory (RLSTM), evaluates user interaction, reducing fatigue from repeated interactions. Designers employ cultural factors to inform the first product prototypes, and the system iteratively refines ideas by matching them to the emotional demands of users. The results indicate the effectiveness of integrating user feedback into interactive design processes. As a result, the ISLO-RLSTM method performed better in RMSE at 1.58, MAE at 1.22, and MSE at 2.17. This approach demonstrates the way biosensors can revolutionize product creation and improve user experiences by bridging the gap between functional design and emotional engagement.

In recent years, the combination of emotional intelligence (EI) and biological perception has emerged as a significant strategy in mental health, notably in ideological and political education. EI, which involves understanding and managing emotions, fosters self-awareness, empathy, and interpersonal relationships. The purpose was to explore a novel approach integrating EI with biological perception to enhance mental health and ideological and political education. The dynamics of EI and its effects on mental health are examined by analyzing patterns in biological data and emotional reactions using a machine learning (ML) algorithm. The research presents a novel Intelligent Sailfish Optimized Driven Categorical Boosting (ISO-CatBoost) to predict mental health based on emotional outcomes and biological signals. It uses biological data, behavioral reactions, and EI to predict mental health outcomes. The data was preprocessed using data cleaning and normalization from the obtained data. Fast Fourier Transform (FFT) was used to extract the data collection. The results demonstrate that the ISO-CatBoost model effectively predicts mental health outcomes by performance metrics such as accuracy (88.8%), precision (87.5%), recall (98.5%), F1-score (93.2%), and specificity (85.7%). This method advances customized mental health education by providing ways for more effective emotional resilience training within ideological and political frameworks.

The purpose of this study is to explore the application of the integration of biomechanics and artificial intelligence (AI) technology in the field of music therapy and its impact on psychological rehabilitation. The results of the study show that the personalized music composition method based on biomechanics and AI technology can effectively improve the relevance and effectiveness of music therapy, and significantly promote the rehabilitation of patients with anxiety, depression and other psychological disorders. By analyzing the relationship between patients’ physiological and psychological data and music parameters, the superiority of personalized music therapy in terms of psychological recovery indicators is confirmed. The results of the study provide theoretical basis and practical guidance for the innovative development of the music therapy field, pointing out the future research direction of optimizing the AI model, expanding the scope of application and exploring the therapeutic mechanism in depth. The integration of biomechanics and AI in music therapy presents a novel approach to enhancing psychological rehabilitation. This addition will discuss the potential of this interdisciplinary approach to offer more precise, tailored treatments that can adapt to the individual needs of each patient in real-time. It will also address the ethical considerations and potential challenges associated with the use of advanced technologies in therapeutic settings, such as data privacy and algorithmic transparency.

Craniocerebral injury is one of the main causes of injury to soldiers in modern warfare, with explosive shock waves causing particularly severe damage to soldiers’ heads. The research aims to optimize the protective performance of existing combat helmets through numerical simulation techniques, providing safer and more effective head protection equipment for soldiers on the battlefield. The Lagrange multiplier method is used to establish the numerical simulation model of explosion shock wave, and the finite element model of the head wearing combat helmet is created to analyze the defects of existing helmets under the explosion impact, so as to complete the optimization of the shape, material distribution and cushion foam structure of the helmet. The results show that wearing the new helmet results in a 36% lower incidence of traumatic brain injury compared to wearing traditional combat helmets. When polyurea material is used as the inner and outer double-sided layer, the deformation degree of the helmet material is the highest, and the shock wave energy absorption value is 23.5 J per impact. The results indicate that the optimized combat helmet significantly improves the explosion shock wave protection performance and reduces the risk of traumatic brain injury. The research results provide new ideas for the design of military protective equipment, which can enhance the survival ability of soldiers in complex battlefield environments.

This study systematically investigated the identification methods of traditional Chinese medicinal materials through microscopic morphological analysis combined with biomechanical principles. Firstly, select medicinal samples and clean the surface, observing the samples with different background colors and angles to reveal their microscopic morphological characteristics. In the process of observation and shooting, the parameters of microscope and camera were adjusted, and multiple images were synthesized using the Extended Field (EDF) technology to obtain high-resolution images and clearly present the microstructure of the medicinal materials. Then, FCSnap image processing software is used to enhance, adjust contrast, and perform hierarchical synthesis on the collected images to highlight key structural features of the medicinal herbs, such as cell wall patterns, oil chamber distribution, and fiber arrangement. After image processing, key microscopic features such as oil chambers and fibers were quantified through precise measurement of microstructure dimensions, which are directly related to the biomechanical properties of medicinal materials. For example, the distribution and density of oil chambers may be closely related to the mechanical strength, compressive strength, and volatile oil content of medicinal herbs. By comparing and analyzing the microscopic morphological characteristics of different medicinal herbs, this study reveals the relationship between structural features and biomechanical properties, providing a scientific basis for quality control and biomechanical research of traditional Chinese medicine.

This study introduces novel Adaptive Machine-Learning-Based Smart Wearable Biosensors (AML-SWB) for real-time monitoring of athletes’ health. By integrating accelerometers, gyroscopes, and biometric sensors, AML-SWB can collect comprehensive physiological data. Machine learning algorithms, especially Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) units, are incorporated to analyze the data, enabling accurate assessment of athletes’ health status, injury risk prediction, and performance optimization. An evaluation of motion efficiency, identification of gait asymmetry, and measurement of joint stress are all parts of the biomechanical analysis that the proposed AML-SWB incorporates to improve conventional monitoring. These findings pave the way for individualized training modifications and early intervention to reduce the likelihood of injuries. Despite challenges such as data accuracy and user acceptance, continuous technological advancements and algorithm refinement are expected to overcome these obstacles.

Periodontal regeneration is the ultimate goal of periodontal therapy. In the study of constructing periodontal tissue in vitro, attempts are made to simulate the regeneration process of periodontal tissue. The research and clinical application of periodontal ligament stem cells have made many breakthroughs, but they still face many challenges. To achieve true periodontal tissue regeneration, in-depth research on its molecular mechanism and signaling pathway is still needed. Combined with the research progress in recent years, this paper discusses the challenges and possible solutions faced in the process of periodontal tissue regeneration.

The extraction method and bioactivity of tea polysaccharides from waste tea leaves and stems were investigated, with a particular focus on their biomechanical influence. Firstly, the extraction of tea polysaccharides was carried out using subcritical water, and the impact of various extraction conditions on the physicochemical properties of the polysaccharides was examined. Subsequently, this study evaluated the antioxidant activity of extracted tea polysaccharides using hydroxyl radical scavenging methods, and analyzed their effects on cell growth through cell viability experiments. In addition, the effects of tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6) levels on anti-inflammatory effects were measured. The immunomodulatory effects of tea polysaccharides were further explored through immune function assays. Moreover, the biomechanical properties of cells, such as their elasticity, membrane stiffness, and tissue flexibility, were assessed to understand the impact of tea polysaccharides on cellular and tissue mechanics. All data were subjected to statistical analysis to ensure the reliability of the experimental results. The findings indicate that tea polysaccharides possess significant antioxidant, anti-inflammatory, immunoregulatory, and biomechanical properties, providing a reference for the resource utilization of waste tea leaves and stems, as well as potential application value for the development of new health products with integrated biomechanical benefits.

This paper focuses on the optimization of operation efficiency of rape crawler mower header in Agricultural Machinery Cooperatives. In view of the important position of rape in agriculture and the problems existing in the cutting, conveying and laying of the existing windrower header, biomechanical methods were introduced. The working principle, structure and main parameters of the windrower are introduced in detail. The biomechanical analysis of the header operation process is carried out, and the key components such as reel, cutter and conveyor are designed and optimized. The header device frame topology is also optimized. The results showed that the first three natural frequencies of the header were increased after optimization, which effectively avoided the resonance risk, and the amplitudes of monitoring points in vibration test were significantly reduced, indicating that the optimization strategy was effective, which was of great significance to improve the efficiency and quality of rape harvest and promote the development of agricultural mechanization.

This paper explores the application of AI technology in the field of English essay grading, inspired by biomechanics. Biomechanics, which studies the mechanical aspects of biological systems, offers unique insights that can be analogously applied to the grading of English compositions. Just as biomechanics analyzes the complex structures and functions of biological entities by understanding the relationships between different components, we focus on natural language processing (NLP) and machine learning algorithms, with the primary objective is to analyze how these advanced technologies, inspired by biomechanical concepts, can enhance the accuracy, efficiency, and objectivity of grading English compositions. By employing various NLP techniques such as lexical analysis, syntactic parsing, and semantic understanding, combined with machine learning models for classification and regression, the study demonstrates significant improvements in grading performance. The findings indicate that AI-powered systems, inspired by biomechanics, can provide consistent and reliable evaluations, thus offering valuable support to educators and students alike.

Inspired by biomechanics, the behavior of employees within an enterprise bears resemblance to the mechanical properties of biological structures, which plays a crucial role in the achievement of an enterprise’s green development. Just as the proper coordination of biological components in biomechanics is essential for efficient functioning, effective human - resource management can stimulate employees’ enthusiasm to participate, thereby influencing the development of the organization.The questionnaire for this study was developed for data collection based on a questionnaire developed by a biotechnology Y company established in 1991 as the main respondent. The collected data were analyzed using tools such as Statistical Package for the Social Sciences (SPSS), Analysis of Moment Structures (AMOS), and computer-assisted decision-making system for descriptive statistical analysis, testing of reliability and validity, and regression analysis. The relationship between green human resource management and employees’ eco-friendly behaviors was examined, and the mediating role of psychological contract was tested. Drawing an analogy from biomechanics, where forces and structures interact in complex ways, the relationship between green human - resource management and employee behavior is also complex.  The results of the study showed that green human resource management explained 33.7% of the variance in employee eco-friendly behavior and that there was a significant positive effect of green human resource management on employee eco-friendly behavior (|β| = 0.615, p < 0.001). In the context of biomechanics - inspired thinking, this positive effect can be seen as a beneficial force promoting the "structural stability" of the enterprise’s green development.Meanwhile, the psychological contract played a partial mediating role in this process, accounting for 44.47% of the total effect. The results of this study can provide an important basis for enterprises to implement green human resource management practices and promote eco-friendly behaviors among employees. Similar to how biomechanical principles guide the design of efficient biological systems, these results can guide enterprises to build a more sustainable and "biomechanically-optimized" human-resource management model for green development.

Change in fitness levels as a result of exercise in professional athletes based in sport-oriented regions is an area worthy of exploration in terms of impact on sports medicine and even public health. This paper attempts to explore from a qualitative perspective the molecular and cellular dynamic adaptations during exercise which are associated with conducting structured exercises in areas with adequate sports facilities. As part of the study, a 12-week follow-up design was conducted where 120 respondents aged between 30–55 years were equally divided into two groups of 60 each, which were randomized into control and experimental. The exercise program not only helped augment the residents’ aerobic capacity but also the resistance strength training component. These exercises ensured the assessment of cellular mechanical properties, the analysis of molecular signaling pathways and their respective fitness. Data collection was conducted at four times intervals; Baseline, 4 weeks, 8 weeks, and 12 weeks. Management of cellular and physiological activities yielded encouraging results from the present studies. The cells were found to have a 45.3% increase in both elastic modulus and a higher level of cellular skeletal system organization. The ratio of total lipids and phosphorylation was estimated to have an increase in AMPK pathway activation by 28-fold as well as an increase in FAK activation of phosphorylation by 2.3-fold as revealed through Eastern immunoaffinity chromatography. They also observed a rise in the ratio of VO₂ max of twenty-four points two percent as well as an increase by twenty-three points five percent in muscular strength compared to eighteen points seven percent in the past. Our research was able to establish distinct time ‘windows’ that defined different phases of the adaptation process where we were able to reinterpret the interrelation between structural and molecular vector alteration embedding. This increases the scope of our knowledge as a community and our practice of exercise adaptations in terms of an environmental-decompartmentalized approach to the community, further validating the application of organized exercise program in sporting towns. The findings are useful in structuring exercise guidance and measures.

Since biological technology is developing so quickly, one of the most important responsibilities for scientific research and technological innovation in higher education institutions is evaluating innovation capability. In order to thoroughly monitor and document the creative advancements made by higher education institutions in the field of biotechnology, this paper suggests a novel assessment technique that blends cutting-edge communication technology with Internet of Things technology. We can create a multifaceted innovation capability evaluation system by using the Internet of Things (IoT) technology to gather a variety of data in real time during biological experiments, including experimental results, feedback information from technology applications, and environmental data of biological samples. This study offers an assessment framework for biological technology innovation that can successfully identify and analyze important technical factors and possible bottleneck issues in the innovation process by combining the real-world scenario of biological research with intelligent computing and machine learning algorithms. According to the experimental data, this approach may reliably assess how well higher education institutions do biological technology research and identify key indications of biological technology innovation. In addition to offering a fresh viewpoint on how to improve the capacity for innovation in biology at higher education institutions, this study offers scientific underpinnings and technical assistance for biotechnology research and implementation.

This study explores the application of artificial intelligence (AI) technology in preventing sports injuries in Chinese Southern Lion Dance teaching. As a traditional Chinese art, Southern Lion Dance requires athletes to demonstrate superb skills and coordination during performances. The high difficulty of the movements and continuous jumping particularly increase the risk of sports injuries. This paper first outlines the origins, development, and technical requirements of Southern Lion Dance and analyzes common types of training-related sports injuries. It then introduces the theoretical basis for injury prevention and existing prevention strategies. In this context, the paper discusses in detail the current applications of AI technology in sports medicine and its advantages in preventing sports injuries. Through empirical research, we used convolutional neural networks (CNN) from deep learning models to analyze and monitor the movements of Southern Lion Dance athletes during training in real-time, establishing an early warning system to prevent potential sports injuries. The study selected Southern Lion Dance athletes with different training experiences, recorded their training and performance movements using high-precision cameras, and input these data into the designed CNN model for analysis. The model identifies athletes’ movement postures and muscle load conditions, provides real-time feedback, and issues warnings to help athletes adjust promptly when there is movement deviation or overuse of certain muscle groups. Experimental results show that after applying AI technology, the incidence of sports injuries significantly decreased, and training efficiency markedly improved. Detailed data analysis indicates that AI technology has broad application prospects in Southern Lion Dance teaching and helps enhance the safety and effectiveness of athletes’ training.

This study proposes an innovative credit evaluation system for small-scale farmers by integrating biomechanical characteristics analysis with traditional credit assessment methods. Through the Analytic Hierarchy Process (AHP), we develop a comprehensive evaluation framework encompassing five dimensions: farmers’ personal characteristics, solvency, credit status, loan guarantee, and production operations. The research introduces a novel biomechanics-driven credit risk assessment model (BICAM) that establishes quantitative relationships between plant mechanical properties and agricultural management risks. The study particularly focuses on three key biomechanical indicators: root system extension force, stem supporting strength, and leaf-environment interaction, which provide objective measures of farmers’ technical capabilities and risk management potential. The integration of these biomechanical parameters has significantly improved credit risk prediction accuracy, with the Area Under the Curve (AUC) showing a 16% improvement compared to traditional evaluation methods. A multi-scale modeling approach combining fractal-mechanical coupling for root systems, beam theory for stem dynamics, and mechanical-physiological coupling for leaves provides a robust theoretical foundation. The findings suggest that farmers demonstrating superior understanding and management of crop biomechanical properties typically exhibit better credit reliability and operational stability, offering financial institutions new insights for agricultural lending risk assessment while promoting more scientific approaches to agricultural risk management.

Hepatocellular carcinoma (HCC) is a globally significant malignancy with high morbidity and mortality. Anti-tumor targeted drug therapy is a promising therapeutic strategy, but the strategy faces challenges related to delivery efficiency and mechanical interactions within the tumor microenvironment. In our previous study, we found that the transcription factors ONECUT2 (OC2) and CD44 receptor have important roles in HCC progression. We designed high molecular weight hyaluronic acid-modified cationic liposomes (HMW-CL) to take advantage of the binding affinity between hyaluronic acid and CD44 to deliver plasmid DNA (pshOC2) encoding a short hairpin RNA targeting OC2 to HCC cells. The results showed that the prepared HMW-CL had a uniform particle size of 179.5 nm, a moderate zeta potential of 15.8 mV, a high encapsulation efficiency of 86%, which not only protected pshOC2 from degradation but also ensured favorable mechanical stability under physiological shear stresses. Biomechanical characterization revealed that the liposomes maintained structural integrity under simulated blood flow conditions, with minimal deformation and optimal adhesion to CD44-expressing HCC cells. In vitro experiments, HMW-CL/pshOC2 liposomes were characterized by high transfection efficacy, lysosomal escape, and low cytotoxicity. They could efficiently deliver pshOC2 to cells, affecting HCC cell proliferation, migration, invasion, and triggering apoptosis. Biomechanical assays further confirmed that the liposomes altered the mechanical properties of HCC cells, reducing their stiffness and migratory capacity, which are critical factors in tumor progression. In vivo experiments, intravenous injection of HMW-CL/pshOC2 liposomes effectively reduced OC2 expression in HCC tumors and inhibited tumor growth at low toxicity with an inhibition rate of 81.77%. Our study demonstrated that OC2 may be a candidate gene suitable for HCC targeted-therapy, and our HMW-CL/pshOC2 liposomes were prepared based on the hyaluronic acid/CD44 binding strategy, with good stability, high transfection efficacy, and low cytotoxicity. Moreover, their favorable biophysical and biomechanical properties make them a promising delivery system for HCC therapy, with potential applications in modulating the mechanical microenvironment of tumors.

Based on the principle of cell molecular biomechanics, this study delves into the human movement energy consumption model for landscape planning. Human movement is underpinned by muscle cell activities. Muscle cells' actin and myosin filaments, regulated by calcium and ATP, cause contractions. Integrating diverse data, a precise prediction model is built. It factors in cell molecular aspects like ATP consumption efficiency related to mitochondria and energy transduction pathways. Also considered are biomechanical stresses on muscle and connective tissues during movement and cellular responses to environmental elements. Applied to landscape cases, the model uncovers optimization strategies. By understanding cell molecular biomechanics, landscape designs can be tweaked to ease muscle cell workload, cutting energy use. This lessens muscle fatigue and potential cell damage, enhancing environmental comfort. The results prove the model boosts landscape planning's scientific and practical value. It offers strong theoretical and practical support for sustainable urban growth and public health, spotlighting its vast potential and broad application scope in landscape planning.

Inspired by biomechanics, studying the relationship between industrial structure upgrading and carbon emissions and the specific impact paths is of great practical significance to the coordinated development of China’s environment and economy. Biomechanics, with its in-depth understanding of the interaction and energy-efficiency principles in natural systems, provides a novel perspective for this study. This paper selected the panel data of 30 provincial administrative regions from 2001 to 2020. Inspired by the concepts of biophysical economics, which are closely related to the energy-matter flow principles in biomechanics, a two-way fixed-effect model of carbon emissions was employed to empirically analyze the relationship between industrial structure upgrading and carbon emissions. Just as biomechanics analyzes the most efficient movement patterns in organisms to minimize energy consumption, this model aims to find the most efficient industrial structure patterns to reduce carbon emissions. The conclusions show that: (1) Industrial structure upgrading can effectively reduce carbon emissions; (2) due to the differences in the economic development levels of different regions, the intensity of industrial structure upgrading on carbon emissions is different. Among them, the effect on the eastern region is the most obvious, followed by the central region, while the effect on the western region and the northeast region is not obvious. (3) Through the mediation effect model, it is found that technological innovation and labor quality improvement are effective ways to upgrade the industrial structure and reduce carbon emissions. Finally, this paper analyzes carbon emission treatment technologies from the direction of biodegradation, which has attracted wide attention due to its environmental friendliness. In biomechanics, natural degradation processes in organisms provide inspiration for human-made biodegradation technologies. Based on biomechanics, six major disposal technologies are compared and analyzed from three aspects: Indirect carbon emissions from operation energy consumption, direct carbon emissions from plastic decomposition and carbon compensation for resource recovery. This paper provides a reference for the selection of waste biodegradation disposal technology from the perspective of helping “double carbon” goal, by drawing on the energy-efficient and sustainable principles from biomechanics.

The quality of teaching in private higher education institutions has become a significant concern in recent years. Traditional evaluation methods, such as student surveys and academic performance, are often insufficient in capturing the full complexity of teaching effectiveness, particularly in terms of teacher-student interaction. This study proposes a novel approach for assessing and optimizing teaching quality at Shanghai Lida University, a private institution in China, by integrating biomechanical models to analyze non-verbal communication between teachers and students. A mixed-methods approach was adopted, combining survey data from 150 students and 20 teachers with biomechanical modeling techniques to evaluate the impact of teacher behaviors—such as gestures, eye contact, posture, and body movements—on student engagement. The findings reveal that teacher non-verbal communication, especially consistent eye contact and frequent use of hand gestures, significantly enhances student attentiveness and participation. Additionally, classroom environmental factors, such as lighting and temperature. They are found to influence student engagement levels. A multiple linear regression model identified teacher non-verbal behaviors and student engagement as the strongest predictors of teaching effectiveness. The study highlights the potential of biomechanical models to offer real-time insights into teacher-student interactions and presents actionable strategies for improving teaching practices. This research offers valuable contributions to the understanding and optimization of teaching quality in private universities.

This paper reviews the application of biomechanics and deep learning models in water quality monitoring, highlighting their potential to enhance the accuracy and efficiency of environmental pollution detection and prediction. Traditional water quality monitoring methods are difficult to deal with nonlinear and dynamic pollution data. This article reviews the fusion application of biomechanical models and deep learning (such as convolutional neural network (CNN), long short-term memory (LSTM)), and proves that it significantly improves monitoring accuracy (an average of 20% in cases) by simulating pollutant diffusion mechanisms (biomechanics) and mining complex data patterns (deep learning). In the future, it is necessary to establish an interdisciplinary collaboration framework to promote the deployment of lightweight models in real-time systems.

This study investigates the application of biomechanics-inspired principles to optimize classroom interaction models in business English education, with a focus on the interplay between physiological dynamics and learning performance. By integrating biomechanical frameworks for analyzing human physiological responses, and cardiovascular adaptability, this research establishes a data-driven teaching model to enhance educational outcomes. Using experimental research methods, 120 business English majors from a university were studied over a 16-week teaching experiment to systematically analyze the biomechanical correlates of learning efficiency and classroom engagement. The research found that the biomechanics-informed teaching model significantly improved students’ physiological adaptability and cognitive performance. The experimental group showed improvements in attention levels (α-wave energy values) from 10.2 ± 2.3 μV to 12.6 ± 2.1 μV, stress indices decreased from 7.8 ± 1.2 to 5.2 ± 0.9, and heart rate variability (HRV) SDNN values increased from 42.3 ± 8.5 ms to 54.6 ± 7.8 ms. In terms of classroom interaction quality, the proportion of quality interactions increased from 35.6 ± 4.8% to 68.4 ± 5.2%. Regarding business English competency development, the experimental group’s business communication skills improved from 71.3 ± 7.8 to 87.6 ± 6.5 points (an improvement rate of 2.9%), while cross-cultural business competency increased from 72.1 ± 7.6 to 88.2 ± 6.3 points (an improvement rate of 22.3%). The results indicate that the biological data-driven teaching model can effectively optimize classroom interaction quality and enhance business English teaching effectiveness. By treating learning interactions as a biomechanical system governed by energy expenditure, stress-strain balance, and adaptive feedback loops, we provide a novel paradigm for understanding and improving pedagogical efficacy. The results highlight the potential of biomechanics to bridge educational technology and human performance science, offering actionable strategies for curriculum design and teacher training. This innovative model provides new insights and methods for business English teaching reform while offering practical references for educational technology innovation.

Biomechanical image recognition has important applications in clinical diagnosis and biomedical engineering, but traditional convolutional neural network (CNN) has limitations in capturing global features. In this paper, a biomechanical image recognition method based on Vision Transformer (ViT) is proposed to improve the classification performance of complex images. Biomechanical image dataset containing five types of data is constructed, and ViT input features are represented by standardization, data enhancement and Patch segmentation. Accuracy, precision, recall, F1 score and confusion matrix are used to evaluate the performance, and compared with ResNet-50 and DenseNet-121. The experimental results show that the accuracy of ViT model is 92.3%, and it performs best in the categories of “normal bones” and “soft tissue lesions”, and other indicators are better than the traditional CNN model. ViT realizes global feature modeling through self-attention mechanism, which significantly improves the recognition accuracy and robustness, provides efficient and accurate technical support for clinical diagnosis, disease screening and surgical planning, and shows its application potential in the field of biomechanical image recognition.

Objective: To investigate the median effective dose (ED50) of ropivacaine for unilateral spinal anesthesia in total knee arthroplasty (TKA) in elderly patients with different heights. Methods: Sixty ASA ⅱ-ⅲ patients, aged ≥ 60 yr, BMI 20.0–29.9 kg/m², undergoing total knee arthroplasty under unilateral spinal anesthesia, were enrolled in this study. The patients were divided into three groups according to their height. The height of the patients was 151–155 cm, which was recorded as S group. Group M (height 156–160 cm); Group H (height 161–165 cm), puncture was performed in the L3-4 space, and 0.25% ropivacaine was used (such as 1% ropivacaine hydrochloride 1 mL, plus sterile water for injection 3 mL). According to the results of the preliminary experiment and the principle of the sequential method, the first patient in group S was given a dose of 0.25% ropivacaine of 6.0 mg, the first patient in group M was given a dose of 7.0 mg, and the first patient in group H was given a dose of 8.5 mg. If the dose of local anesthetic used in the previous patient met the criteria for efficacy, the dose of local anesthetic was reduced by 0.5 mg for the next patient. Otherwise, it was upregulated by 0.5 mg. The study was completed when 7 inflection points were obtained. The median effective dose (ED50) of ropivacaine was estimated by means of the turning point method, and then Probit regression analysis was used to calculate the more precise ED50, ED95 and 95% confidence interval (CI) of ropivacaine. CI) was calculated. Vital signs, level of sensory block and motor block were recorded at each time point after administration. Results: ED50 of group S, group M and group H was 6.04 mg, 7.11 mg and 7.96 mg, respectively. Probit regression analysis showed that ED50 and ED95 in group S were 6.02 mg (95% CI: 5.29–6.74 mg) and 6.24 mg (95% CI: 5.52–6.97 mg), and ED50 and ED95 in group M were 7.05 mg (95%CI: 5.29–6.74 mg) and 7.05 mg (95% CI: 5.52–6.97 mg), respectively. The ED50 and ED95 of group H were 7.97 mg (95%CI: 7.26–8.68 mg) and 8.18 mg (95% CI: 7.47–8.90 mg), respectively (P < 0.05). No adverse reactions such as hypotension and bradycardia occurred in all patients during the operation. There was no significant difference in the level of sensory block on the affected side among the three groups (P > 0.05). Conclusions: The ED50 of hypogravity ropivacaine for unilateral spinal anesthesia in total knee arthroplasty increases with the increase of body height. The median effective dose of ropivacaine for unilateral spinal anesthesia in elderly patients with different body height groups is 6.02 mg, 7.05 mg and the 95% effective drug doses were 6.24 mg, 7.24 mg and 8.18 mg, respectively.

Background: Mendelian randomization (MR) is a powerful tool. This method has garnered attention for its potential to circumvent the limitations of observational studies, such as confounding factors and reverse causation. In this study, we aimed to explore the causal effect of physical exercise on cardiovascular health using MR analysis. Methods: We used genetic variants strongly linked to physical exercise as instrumental variables from large-scale Genome-Wide Association Study (GWAS), based on data from over 300,000 European individuals in the UK Biobank. Exercise levels were measured through self-reports and accelerometer data, while cardiovascular outcomes were assessed using medical records, biomarkers, and imaging. Results: Demonstrated a significant causal relationship between higher levels of physical exercise and improved cardiovascular health outcomes. Specifically, an increase of one standard deviation in genetically predicted physical exercise was associated with a substantial reduction in the risk of coronary artery disease (OR: 0.75, 95% CI: 0.65–0.86, p < 0.001), stroke (OR: 0.80, 95% CI: 0.69–0.93, p = 0.004), and hypertension (OR: 0.82, 95% CI: 0.74–0.91, p < 0.001). Conclusions: Our findings provide strong evidence for a causal relationship between physical exercise and improved cardiovascular health. This study underscores the potential of physical exercise as a modifiable risk factor for cardiovascular disease and highlights the importance of incorporating physical exercise into public health interventions aimed at reducing cardiovascular risk. Future research should focus on identifying the mechanisms underlying this relationship and developing targeted strategies to increase physical exercise levels across populations.

The use of artificial intelligence (AI) technology in English education has promised new possibilities in the explanation and interpretation of technical biomechanical research analysis. This article investigates how artificial intelligence is filling the gap between factual biomechanical data and relevant, meaningful information needed from an educational perspective, by making it possible for different groups to comprehend it. Instructors, educators, and learners are now able to accomplish much more by utilizing visualizations, language processing (NLP), and adaptive learning. This article investigates the technology-related barriers to interdisciplinary communication, analyzes the efficacy of AI-related tools deployed, and presents a case study on the use of AI instruction and guidance on fundamental biomechanical concepts. The results from this study suggest that AI may provide fresh avenues for making biomechanical research more engaging to English learners, thus improving scientific literacy and fostering interdisciplinary collaboration. The work ends with some comments intended for educators and researchers to look for smarter ways of manipulating AI technology in English teaching and learning in a science-focused world.

Purpose: To look into the influence of DNAJC12 knockdown on the progression and radio-sensitivity of esophageal squamous cell carcinoma (ESCC), with a focus on cellular mechanics and tumor microenvironment interactions. Methods: The TCGA database combined with immunohistochemical staining was used to validate the DNAJC12 expression in ESCC patients from the perspective of the clinic. DNAJC12 knockdown was performed in TE-1 and KYSE-150 cell lines to assess changes in proliferation, migration, invasion, apoptosis, and cellular mechanical properties (e.g., stiffness, adhesion, and contractility). The downstream molecule regulated by DNAJC12 was explored using Western blotting and biomechanical assays. The effect of DNAJC12 knockdown on tumor radiosensitivity was evaluated in vivo, with a focus on tumor stiffness and extracellular matrix (ECM) remodeling under irradiated conditions. Results: Upon analyzing the TCGA database and examining tumor tissue samples from patients, it was discovered that DNAJC12 exhibited high expression levels in tissues of ESCC. Vitro experiments showed that DNAJC12 knockdown significantly decreased cellular proliferation and migration (P < 0.05). Biomechanical assays revealed that DNAJC12 knockdown decreased cellular stiffness and contractility, suggesting a role in regulating cytoskeletal dynamics. Molecular analysis showed downregulation of P-ERK, MMP-2, N-Cadherin, P-P38, Snail, Vimentin, β-Catenin, Fibronectin, and Twist alongside upregulation of E-Cadherin (P < 0.05). Overexpression of SNAI1 could restore the proliferative and migratory capabilities of cells with downregulated DNAJC12. In vivo experiments, knockdown of DNAJC12 resulted in faster tumor growth under irradiated conditions (P < 0.05). Conclusion: DNAJC12 knockdown slows ESCC progression by modulating cellular biomechanical properties and molecular pathways. However, it enhances tumor growth post-radiotherapy, potentially due to altered mechanosensitive signaling and ECM remodeling. These findings highlight the interplay between molecular biology and biomechanics in ESCC progression and treatment response.

Objective: To investigate the effects and mechanisms of Protein Kinase B (AKT) activation on the biological behavior of malignant B-cells. Methods: This study used immunohistochemical staining to detect the differences between AKT tumor tissue and short and fat tissue. The RCC cell line Caki-2 was screened using real-time fluorescence quantitative PCR detection and Western blot assay. Analyze the effect of AKT activation on cell adhesion and migration through biomechanical experiments.in order to select the best siRNA experimental group. The effects of CCK8, the Transwell invasion experiment, and flow cytometry on Caki cell proliferation, invasion, migration, apoptosis, and cell cycle were detected. Results: According to the immunohistochemical staining results, it was found that compared to the surrounding normal tissues, AKT1 was expressed higher in tumor tissues (P < 0.05). After AKT activation, it can affect cell adhesion and regulate the function of integrins by phosphorylating various substrates. Integrins are cell adhesion molecules that can mediate cell adhesion to the extracellular matrix. AKT can enhance the adhesion between cells and matrix by phosphorylating integrins or their related signaling molecules. After AKT activation, it can promote the migration and invasion ability of cells. This is mainly achieved by activating multiple signaling pathways, such as Rac and Rho family proteins. These signaling pathways play important roles in cytoskeleton remodeling and cell movement. Phosphorylated AKT can activate these pathways, thereby promoting cell migration. AKT activation in Caki-2 cells via siRNA transfection demonstrated that AKT activation promoted the proliferation, invasion, After Caki-2 cell migration, AKT can transition Caki-2 cells from the G1 phase to the S phase. Conclusion: AKT may be involved in the malignant growth of RCC and holds potential as a therapeutic target.

Aerobics is a comprehensive sport, which requires participants to have good muscle strength and coordination ability in the process of training. With the continuous development of aerobics, the completion quality of difficulty element has become an important index to evaluate students’ Aerobics level. Among them, the strength training of core muscle group is particularly important for the completion quality of Aerobics difficulty element. The core muscle group includes abdominal muscle, back muscle, hip joint surrounding muscle, etc., which provide stability and strength for the trunk and support for the movement of upper and lower limbs. Core strength training can strengthen the body control ability of Aerobics Gymnastics athletes, maintain the posture of athletes, promote the coordination of athletes’ limbs, and help athletes achieve good results. Therefore, this study aims to explore the influence of Aerobics core strength training on the quality of students’ difficulty element.

This paper explores the principle of aerobics exercise and proposes the process of aerobic exercise on energy metabolism and lipid metabolism based on this principle, breaking down fat metabolism into several parts of digestion, absorption, decomposition and synthesis to simulate the process of fat metabolism. A two-way ANOVA was used to analyze the multivariate effects of aerobics exercise on the regulation of human adipocyte metabolic patterns. The results showed that after 6 weeks of intervention, the total cholesterol level of the aerobics exercise group returned to the normal range, and compared with the pre-intervention total cholesterol level decreased by 0.391 mmol/L and there was a significant difference, with a p value of less than 0.05. After 12 weeks of intervention, the total cholesterol level of the aerobics exercise group and the tai chi exercise group returned to the normal range, and the total cholesterol level of the aerobics exercise group compared with the pre-intervention total cholesterol level decreased by 0.676 mmol/L and the total cholesterol level decreased by 0.676 mmol/L. level decreased by 0.676 mmol/L, again with a significant difference. After aerobics exercise, the changes of adipocytokines of serum leptin and resistin were lower than those of the control group, and the changes of cytokines were 7.889 μg/dl and 6.948 μg/dl, respectively, which showed that the effect of aerobics exercise on the metabolism of adipocytes in the human body was more obvious. After the intervention of aerobic exercise lasting 12 weeks, it can effectively alleviate the cell morphological changes caused by high total cholesterol, and play a preventive role in the cell damage induced by high total cholesterol.

Lower limb rehabilitation exoskeletons are wearable assistive rehabilitation devices designed to protect and aid patients in rehabilitation training. However, traditional lower limb rehabilitation exoskeleton systems are limited by information acquisition technology, mostly adopting passive training with fixed trajectories and lacking real-time motion data interaction, resulting in deficiencies in the overall system’s safety and autonomy. Based on this, this study proposes a lower limb rehabilitation exoskeleton system based on digital twin technology. By leveraging digital twin technology, the system achieves a deep integration of virtual and physical spaces, improves human-machine information interaction technology, and enhances the effectiveness of rehabilitation training. Experimental results demonstrate that the system can achieve personalized gait trajectory planning and real-time motion data interaction, providing a new solution for lower limb rehabilitation.

The auxiliary teaching platform of physical education is becoming more and more important in college physical education, and it has become an ideal tool to promote the interaction between teachers and students and improve the teaching effect. Compared with the traditional physical education teaching mode, the platform has stronger interaction, efficient information sharing function and rich and varied multimedia display. However, there are still some shortcomings in the application of biomechanics in the online sports teaching platform. In this study, a computer-aided physical education teaching platform based on the dynamic model of human body-ground impact force is proposed. The study aims at analyzing the mechanical characteristics of human body in contact with the ground during exercise from the perspective of biomechanics, and helping teachers and students to choose sports equipment more accurately according to different course contents and biomechanical needs. By introducing the biomechanical model, the platform can simulate and analyze the key biomechanical data such as impact force, change of center of gravity, joint stress and so on, and provide quantitative feedback for students to optimize the effect of sports training. At the same time, the platform also integrates a variety of functional modules, such as sports resource information module and real-time sports evaluation module. It greatly enriches students’ learning resources, prolongs the time and space of self-directed learning, promotes the transformation of learning methods, and improves students’ autonomy, learning enthusiasm and sports performance. With the help of this platform, students can access rich learning resources including high-quality biomechanical analysis videos, interactive sports simulation, detailed theoretical explanation and real-time online evaluation system to comprehensively improve their sports literacy and sports performance.

Research purpose: To explore the influence of dance training and creation methods based on the combination of biomechanics and artificial intelligence (AI) technology on dance expression, biomechanics indicators and subjective satisfaction of dancers, to promote the modern development of dance art, and to optimize the dance education and training methods. Study method: A control experiment was designed, and then the subjects were divided into experimental group and control group. The experimental group adopted the dance training and creation method based on the combination of biomechanics and AI technology, while the control group adopted the traditional dance training and creation method. During the experiment, the biomechanical data of the dancers were collected using the 3D motion capture system and EMG measurement equipment, and the AI model was developed for data analysis and training guidance. Study content: The experiment involved the assessment of dance performance, the comparison of biomechanical indicators, and the subjective satisfaction survey of dancers. Rate the dance works, evaluate the innovation of dance movements, the depth of emotional expression, etc., use advanced equipment to measure the biomechanical indicators of peak joint strength and muscle fatigue, and understand the feelings and opinions of dancers on the experiment through questionnaire and interview. Results: The experimental group showed obvious advantages in dance performance, biomechanics, and subjective satisfaction of dancers. Dance works are equally expressive. All scores were significantly higher than the control group, peak joint stress was relatively low, muscle fatigue increased slowly, and dancer satisfaction scores also were significantly higher.

Heart rate variability (HRV) is a widely used biomarker for assessing physiological stress, recovery, and training load in sports science. During exercise, the mechanical changes in various parts of the body, such as muscle contraction and relaxation, joint movement, and the dynamic response of the cardiovascular system, are closely related to HRV. However, traditional analysis methods face significant challenges in handling HRV’s nonlinear dynamics and noise sensitivity. These limitations reduce their effectiveness in complex sport scenarios. To address these limitations, this study proposes an innovative HRV feature extraction framework that integrates reinforcement learning (RL) with an attention-based Long Short-Term Memory (LSTM) network. The framework dynamically optimizes feature selection and weighting through RL. The integration of an attention mechanism enables the model to prioritize critical temporal segments, improving its ability to capture and interpret key physiological patterns. Additionally, the model combines time-domain, frequency-domain, biomechanical factors, and nonlinear features, providing a comprehensive and robust representation of HRV signals. The framework was validated on four publicly available datasets covering resting, exercise, stress, and recovery states. It achieved an average accuracy of 95.0% and an F1-score of 90.8%, outperforming state-of-the-art baselines by 2.7% to 3.4%. These results demonstrate the proposed method’s superior performance in stress detection, training load prediction, and recovery assessment, establishing it as a scalable and adaptive tool for HRV-based sports training monitoring and health management. The framework’s innovative design offers significant advancements in the analysis of complex HRV data, paving the way for intelligent and personalized applications in sports science and healthcare.

This paper innovatively applies computational biomechanical models to the field of human capital flow research, establishing a novel analytical framework. By introducing the potential field concept from biomechanics to describe economic development dynamics, employing continuum mechanics methods to characterize talent flow patterns, and integrating numerical computation techniques, we achieved systematic simulation of the relationship between human capital flow and economic growth. The research reveals that human capital flow promotes economic growth through three primary mechanisms: knowledge accumulation effect, innovation-driven effect, and industrial upgrading effect. In the short term, human capital flow can contribute to a 1.35 percentage point increase in GDP growth within one year; in the long term, its total contribution to economic growth rises from 3.19% to 7.42% over a decade. The study identifies four flow patterns: agglomeration, gradient, network, and circular, with agglomeration-type flow showing the most significant economic effect, contributing 42.5% to economic growth. Policy simulation results indicate that innovation-driven strategies can drive GDP growth by 2.85 percentage points, industrial upgrading strategies contribute 2.42 percentage points, talent incentive strategies achieve 2.15 percentage points growth, while comprehensive optimization strategies can realize a 3.65 percentage point growth effect. Based on these findings, we propose policy recommendations including building a multi-level talent support system, implementing a “gradient cultivation, collaborative development” regional development strategy, and following the principle of “top-level design, phased implementation, key breakthrough.” This research not only achieves methodological innovation but also provides a theoretical foundation and practical guidance for formulating scientific talent policies.

With the rapid development of science and technology and the continuous progress of society, the traditional educational and teaching model of single subjects can hardly meet the needs of future talent cultivation. As an emerging research paradigm that breaks disciplinary barriers, integrates multidisciplinary knowledge, and solves complex problems, interdisciplinary research is gradually becoming an important direction of educational and teaching reform. This research integrates the biomolecular migration and diffusion principles into interdisciplinary education reform. By setting up an experimental group and a control group of students, a series of teaching activities were carried out. Finally, the teaching effectiveness was evaluated through statistical analysis of students’ test scores and other relevant data. The results show that under the interdisciplinary teaching model combined with biomolecular migration and diffusion principles, students’ innovative thinking, problem-solving abilities, and comprehensive qualities have all been improved. This study provides references for educational and teaching reform.

Wheelchair basketball is a sport that requires high levels of strength, endurance, and motor control, with an important metabolic and cardiovascular component. Athletes must develop strong propulsion to optimize mobility and improve performance. Circuit training has been widely used to improve aerobic and anaerobic capacity in able-bodied athletes, but its specific effects on wheelchair basketball athletes are still poorly explored. This study evaluated the effects of a circuit training program on propulsive force, metabolic efficiency, and athletic performance in wheelchair basketball players. 120 wheelchair basketball athletes were divided into an experimental group (n = 60) and a control group (n = 60). The experimental group followed a 12-week circuit training program, with three weekly sessions, while the control group continued their standard training. Before and after the intervention, all participants underwent specific tests to assess average propulsive force (Wingate Peak Power Test), metabolic efficiency, endurance, and maximum heart rate. The experimental group showed significant improvement over the control group in mean propulsive power (+80 W, p < 0.05), metabolic efficiency (+2.5, p < 0.05), and mean RSA time (−0.3 s, p < 0.05). Maximum heart rate decreased slightly in both groups, suggesting better cardiovascular adaptation over time. Circuit training has proven to be an effective method of improving performance in wheelchair basketball players, with benefits in terms of strength, endurance, and metabolic efficiency. These results confirm the effectiveness of a structured approach to training for athletes with disabilities and offer useful indications to optimize physical preparation in this discipline.

In recent years, the proportion of cross-border e-commerce in China’s foreign trade has been steadily increasing. Many enterprises have opted to engage in international trade by establishing a presence on overseas e-commerce platforms, making the integration of research findings from various fields with e-commerce product selection strategies a popular research direction. This study focuses on the Southeast Asian e-commerce platform Lazada and incorporates biomechanical theories to conduct cluster analysis. The objective is to uncover product classification patterns through biomechanical aspects and provide strategic recommendations for product selection to novice cross-border e-commerce merchants seeking entry into the Southeast Asian market. First, this study provides a comprehensive analysis of the current development trends in cross-border e-commerce and the application prospects of biomechanical theories in this field. Subsequently, key variables were selected based on biomechanical concepts such as the “stress-strain” relationship and dynamic equilibrium. These core concepts were utilized to draw analogies with the critical variables in cross-border e-commerce product selection. A set of evaluation indexes for product selection was constructed from two dimensions: product attributes and customer reviews. In this framework, product selection strategies were analogized as state variables, product price and brand strength as “external forces,” and product ratings and online reviews as “internal forces.” Data collection techniques were employed to acquire product data from the first 20 pages of listings across secondary categories on Lazada’s Vietnam platform. The data underwent rigorous cleaning, preprocessing, and descriptive statistical analysis. Finally, a clustering algorithm was used to classify the primary product categories into four distinct clusters. Based on comparative analysis of the features among these clusters, the study quantitatively elucidates the patterns of changes in product category under the combined influence of “internal” and “external” forces, and proposes actionable recommendations for product selection strategies. Based on the findings of this study, biomechanical theory provides new insights into e-commerce product selection strategies, demonstrating promising applicability in this field. From a biomechanical perspective, the study identifies women’s fashion and accessories, as well as home and lifestyle categories, as the most popular segments in the Vietnamese market. However, these categories are characterized by intense price competition, making them unsuitable for novice merchants to enter impulsively. In contrast, the electronics category, driven by robust market demand and promising growth potential, emerges as an ideal product selection direction for foreign trade enterprises with reliable quality assurance. Drawing on biomechanical theory and clustering analysis, this study offers product selection recommendations, emphasizing that merchants should carefully select target categories and products based on their resources and market positioning. By adopting appropriate market strategies, businesses can achieve steady development in the Southeast Asian market, gradually build and enhance their brand image, and drive the long-term growth of their cross-border e-commerce ventures.

This study integrates principles of biomechanics to develop a neural network-based behavior modeling approach for enhancing cost and efficiency management in financial business process outsourcing (BPO). Drawing inspiration from the adaptive and efficient characteristics of biological systems, we model the financial BPO landscape using neural networks on cloud computing platforms. This approach mirrors the interconnected and dynamic nature of biomechanical networks, enabling proactive adaptation and optimization in financial environments. By utilizing financial SMOTE algorithms and integrating network storage infrastructure, data resources, management platforms, and financial service applications, we construct a comprehensive decision-support architecture. This model achieves a significant reduction in financial costs by 60% and enhances the adaptability and operational efficiency of financial management systems. By conceptualizing financial systems as dynamic, interactive networks, our method provides innovative solutions for mitigating operational risks and enhancing enterprise resilience in competitive markets. The incorporation of biomechanical concepts into financial modeling offers novel insights into optimizing resource allocation and improving system adaptability within complex financial ecosystems.

This study focuses on the crucial role of English curriculum construction in biological sciences for molecular and cellular biomechanics research. Taking a school’s biotechnology major as an example, it analyzes the existing problems in current English courses and teaching methods. By applying theories like spiral curriculum and constructivism, teaching materials and classroom methods are updated. The results show that this reform significantly improves non-native English speakers’ professional English abilities in the field of molecular and cellular biomechanics. Specifically, it promotes the development of scientific thinking and expression skills, which is expected to enhance international communication and collaboration in molecular and cellular biomechanics research, thus directly contributing to the progress of this research field in China.

Acute Respiratory Distress Syndrome (ARDS) is a severe complication often seen in patients with extensive burns, driven by systemic inflammation mediated by interleukin-1β (IL-1β). Understanding the biomechanical and inflammatory pathways, as well as long-term complications, is critical for improving therapeutic interventions. However, existing approaches often fail to comprehensively address the interplay between IL-1β and the systemic inflammatory response, particularly in the context of biomechanical stress on lung tissue, leading to limited efficacy in mitigating ARDS-related morbidity and mortality. Furthermore, these methods lack precise strategies to predict and monitor disease progression in burn patients, especially in terms of biomechanical alterations in pulmonary function. The proposed framework emphasizes ARDS as the focal point for addressing systemic inflammation in burn patients by targeting IL-1β-mediated inflammatory pathways (IL-1β-MIP) and their biomechanical consequences. The method integrates advanced biomarker analysis and molecular-level therapeutic interventions focusing on IL-1β inhibition to assess the impact of inflammation on lung compliance and tissue stiffness. The proposed approach utilizes a combination of precision medicine, including cytokine-modulating therapies, alongside early diagnostic tools such as IL-1β serum level monitoring. This framework aims to alleviate acute symptoms and mitigate the risk of long-term complications such as pulmonary fibrosis and immune dysregulation, which are often associated with altered tissue mechanics. The findings demonstrate that targeted IL-1β modulation significantly reduces ARDS severity, improving survival rates and reducing long-term complications. IL-1β-MIP highlights the potential of personalized anti-inflammatory therapies in transforming ARDS management in patients with extensive burns, providing a foundation for future clinical advancements that integrate biomechanical insights into therapeutic strategies.

Currently, multidrug-resistant (MDR) pathogens are becoming a human and economic burden worldwide and have posed a grave threat to public health. In this case, mild-temperature photothermal therapy (PTT) has become a promising alternative to conventional antibiotics because it has the characteristics of minimally invasive property, targeted destructiveness, and low toxicity. To realize effective photothermal therapy, it is imperative to fabricate multifunctional photothermal formulations with better performance. In this study, novel Fe-doped polyoxometalate (Fe-POM) nanoclusters were successfully prepared by the one-pot method. Due to the strong absorption efficiency in the near-infrared (NIR) region, high photothermal conversion efficiency, and photothermal cycling stability, Fe-POM nanoclusters are sufficient to be used as an efficient photothermal agent for highly effective mild-temperature photothermal therapy. From a biomechanical perspective, the Fe-POM nanoclusters not only generate heat under NIR irradiation but also produce hydroxyl radicals (·OH) through the redox cycling of Fe²⁺/Fe³⁺ and Mo⁵⁺/Mo⁶⁺ pairs. The reactive oxygen species (ROS) generated disrupt bacterial cell membranes and alter their biomechanical properties, such as membrane stiffness and permeability, rendering the bacteria more susceptible to mild-temperature PTT. The synergistic effect of ROS-induced biomechanical stress and photothermal heating significantly enhances bacterial disinfection. Antibacterial experiments demonstrated that Fe-POM nanoclusters, under PTT conditions, effectively induce bacterial death while maintaining good biocompatibility. This study highlights the potential of Fe-POM nanoclusters as a multifunctional photothermal agent for combating bacterial infections. This work not only advances the field of photothermal therapy but also provides a novel strategy for treating bacteria-associated diseases through biomechanical modulation.

Background: Eating disorders are potentially persistent mental illnesses that can lead to death. Our study is determined to found out the casual relationship between immune cell phenotypes and eating disorders via Mendelian randomization (MR) method. Aim: To explore the causal relationship between 731 immune cell phenotypes and eating disorders. We conducted a two-sample Mendelian randomization (TSMR) analysis to find out the association between 731 immune cell phenotypes and eating disorders. Materials and methods: All the data we used in this study were obtained from GWAS. We conducted a TSMR analysis. We used 731 types of immune cells as exposure and eating disorders as outcome. Our analysis uses a variety of methods to ensure the robustness of the experiment. The inverse variance weighted (IVW) method was the main MR analysis method, we also proceeded sensitivity analyses to validate the robustness, heterogeneity and horizontal pleiotropy of the MR results. Results: Our study identified potential causal relationships between various immune cells and eating disorders. We identified 20 types of immune cells that are potentially causally linked to eating disorders linked to eating disorders. There are 7 types of immune cells that act as protective factors for eating disorders. Conclusions: There are 20 types of immune cells have possible relationship with eating disorders via MR method, which can provide more information for clinical practice.

The biomechanical behavior of college students, particularly their physical interactions with digital devices, significantly influences their intention to purchase online music, with the perceived ergonomic value of online music platforms playing a crucial role in this influence process. College students’ perception of online music is closely related to their biomechanical habits, such as posture, hand-eye coordination, and repetitive motion patterns during device usage. This study establishes a mediating model to investigate the relationship between college students’ biomechanical behavior and their intention to purchase online music. The findings indicate that factors such as ergonomic comfort, movement efficiency, and physical fatigue positively predict college students’ intention to purchase online music. Perceived ergonomic value acts as a mediator between biomechanical behavior and purchase intention, partially mediating the impact of physical comfort, movement efficiency, and fatigue reduction on online music purchase decisions. This study highlights the importance of integrating biomechanical principles into the design of online music platforms to enhance user experience and purchase intention.

Background: Most surgical patients experience moderate to severe pain, which makes postoperative pain management a challenge in healthcare. Traditional approaches to managing pain are often not successful since they do not take into account individual differences along with multifaceted pain mechanisms. Objective: The aim of this study is to develop and validate an artificial intelligence-based biomechanical model which aids in predicting postoperative pain patterns by utilising pre-anesthetic and post-anesthetic clinical features. Methods: In this retrospective cohort study, 324 elective orthopedic surgery patients were analysed between January 2020 and December 2023. This study made use of an integrative AI model catering to biomechanical parameters alongside anesthesia features and clinical parameters. Biomechanical modelling and model evaluation comprised deep learning architectures with cross-validation methods alongside conventional machine learning methods as well. Results: Traditional algorithms were significantly outperformed internally to an absolute value accuracy of 93.7% (p < 0.001). Age and socio-economic factors took the lead predictive model and together comprised 63.9% of the outcome variance, with the influence of the former being more than the latter. There was strong generalisation between the performance mean values of training and validation of delta margin of <0.05. Conclusion: AI-aided clinical features alongside a biomechanical model can clearly aid in predicting a patient’s postoperative pain pattern. Not only does this mindset centre around pain relief, it can also help and be effective in tailoring pain management techniques and have an impact on patient outcomes in a clinical environment.

The lesion area in magnetic resonance imaging (MRI) of anterior cruciate ligament (ACL) injury is small, the features are difficult to focus, and the multiangle imaging features are scattered, which presents great challenges to clinicians for ACL injury. The ACL plays a critical role in maintaining knee stability. An injury can result in increased laxity, making the knee more vulnerable to further damage. This paper proposes a new neural network model, FDMRNet, which automatically focuses on the area of ACL injury and improves the accuracy of intelligent discrimination of the degree of ACL injury. Understanding the biomechanical effects of ACL injuries is crucial for developing effective rehabilitation protocols aimed at restoring normal knee function and preventing re-injury. First, FDMRNet enhances the focus of lesion features and reduces noise interference through the feature selection module (FSM), thereby improving the lesion localization ability. Secondly, the dimensional feature fusion module (DFFM) is used to fuse multi-angle features, which enhances the accuracy of the fusion representation of multi-angle features. To evaluate the performance of FDMRNet, real datasets from the Guangdong Provincial Armed Police Corps Hospital were used for model training and verification. The experimental results show that compared with the mainstream methods, the AUC (Area Under Curve), accuracy, precision, recall, and f1-score of the proposed model are improved by 2.52%, 3.17%, 5.79%, 4.14% and 4.54% respectively, which fully proves the effectiveness and accuracy of the proposed model in MRI classification of anterior cruciate ligament injury. Recognizing the biomechanical consequences of ACL injuries highlights the importance of accurate diagnosis and effective treatment strategies, which can be significantly enhanced through advanced models like FDMRNet.

Extracellular vesicles (EVs), which are small vesicles secreted by various cells, have been proven to play a significant role in the progression of multiple diseases, including multiple sclerosis (MS). This study explores the potential regulatory mechanisms of EVs in MS progression and their role as carriers of cytokines such as TNF-α and IL-6. Through numerical simulations and experimental methods, we investigated the impact of EVs and their cytokine cargo concentrations on immune responses and neuroinflammation. The results from numerical simulations indicate that EVs not only serve as carriers for cytokines but also modulate inflammatory responses through interactions with immune cells, thereby influencing the pathological process of MS. Experimental data further validate the role of pro-inflammatory cytokines carried by EVs in enhancing immune activation and promoting neuroinjury. These findings suggest that EVs may be important mediators in regulating immune responses and could potentially become new targets for MS therapy.

This study focuses on the molecular biomechanical mechanisms of muscle contraction during musical instrument performance. Through systematic investigation of 120 musicians from different instrument groups (30 each in string, keyboard, wind, and percussion sections), using fluorescence resonance energy transfer, single-molecule tracking techniques, and high-precision electromyography systems, we analyzed molecular motor movement characteristics, calcium signal dynamics, and cross-bridge cycling mechanisms during performance. The research revealed significant differences in molecular mechanical parameters among different types of instrumentalists: the string group demonstrated the highest cross-bridge cycling rate (458 ± 35 s⁻¹) and fastest calcium signal response (τon = 1.2 ± 0.1 ms); the keyboard group showed the highest ATPase activity (42.3 ± 3.6 μmol/min/g) and the highest proportion of strongly bound cross-bridges (35.8 ± 3.2%). Performance proficiency significantly correlated with molecular mechanical parameters (r = 0.856, P < 0.01), indicating that molecular-level adaptive changes are fundamental to skill improvement. Based on these findings, we propose molecular biological strategies for optimizing practice methods, including intermittent training patterns based on ATP supply characteristics, progressive loading schemes considering calcium signal adaptation periods, and preventive measures targeting molecular motor fatigue patterns. The results provide new perspectives for understanding the biomechanical mechanisms of musical instrument performance while offering theoretical foundations for improving performance levels and preventing occupational injuries.

Combining neural network technologies and computational techniques, this research establishes a career development promotion system based on a multi-modal neural network. It reveals that computer simulation technology and multimedia have positive intervention effects on college students’ career decision-making behaviors, similar to how biomolecular interactions regulate biological processes. This technology ensures scientific rigor, objectivity, and authenticity. A knowledge fusion algorithm, built on attributes and rules within the Hadoop platform and MapReduce parallel computing framework, facilitates effective data integration. Additionally, inspired by the regulatory mechanisms in biomolecular systems, a neural network-based algorithm, utilizing gradient descent, is applied to cultural learning, augmented by feedback analysis to assess students’ psychological changes, posture, and response dynamics during the learning process. To further optimize the career development framework, an Evolutionary Algorithm (EA) is used to enhance the performance of neural networks. Numerical simulations demonstrate the robustness of the proposed algorithm, achieving high accuracy (0.981), recall rate (1.0), and F-measure (0.997) in similarity computations. These results are particularly notable when biomechanic metrics, such as gesture and posture tracking, are integrated with linguistic data, such as spelling and vocabulary. The findings underscore that incorporating neural network insights into multimedia teaching methodologies can significantly enhance psychological motivation, behavioral adaptability, and engagement in college students, fostering improved educational outcomes and advancing interdisciplinary innovation in neural networks. It effectively enhances the internal driving force of “technology empowering psychological development” in the career planning system and provides a cognitive computing and biomechanic perspective for the construction of the smart education ecosystem.

Ceramic art holds a significant position in China’s traditional arts and crafts, with its materials demonstrating unique value across multiple fields. As the tourism industry continues to develop, there is an increasing demand for innovative, practical, and artistic tourism product designs. Modern designers study the biomechanical properties of ceramic materials, such as their stress response, processing characteristics, and usability, and combine them with their unique surface gloss and durability to transform them into visually appealing and functional products. This study introduces an enhanced Convolutional Neural Network (CNN) model for ceramic craft image recognition and structural optimization design. The research focuses on: 1) the enhancement of mechanical properties of ceramics combined with bioactive substances; and 2) the simulation of force distribution during ceramic manufacturing and the impact of craftsmen’s hand-applied forces on shaping results. A parametric approach was employed to extract graphic elements from ceramic sketches, and simulation analysis was used to optimize material structural stability and stress distribution during use. Experiments show that the improved CNN model achieves over 95% accuracy in recognizing the visual features of ceramic products, effectively capturing product edge contours and identifying microscopic surface stress distribution. Furthermore, experiments on bio-ceramic materials confirmed their higher toughness and adaptability, with significant improvements in biomechanical performance, such as bending strength and contact force distribution. This study not only reveals the core role of biomechanical principles in the manufacturing and design of ceramic crafts but also provides an innovative framework for optimizing the artistic expression and mechanical performance of tourism products.

This study explores bio-inspired strategies for recycling cathode materials in lithium batteries by integrating biomechanical models with optimization algorithms to enhance recycling efficiency. We developed a biomechanical model to examine the recovery process of metal ions, analyzing their dynamic behavior and reaction rates to assess the potential of bio-inspired algorithms for model optimization. Based on this model, we designed an optimization algorithm to boost metal ion recovery by varying experimental conditions such as reaction temperature, solvent concentration, pH, and reaction time. Experimental results indicate that reaction temperature, solvent concentration, adsorption and desorption rates, and pH significantly influence recovery efficiency. The optimal conditions identified were 55 ℃, a solvent concentration of 0.7 mol/L, and a pH of 5.5, yielding a recovery efficiency of 80.3%. Additionally, extending the reaction time positively correlated with recovery rates, achieving a maximum of 86.4% at 50 min. By combining biomechanical analysis with algorithm optimization, this research enhances our understanding of material recycling mechanisms and provides a theoretical foundation and technical support for future industrial recycling processes. These findings offer valuable insights for optimizing lithium battery recycling technologies and improving resource utilization efficiency.

To evaluate the flavonoids, iridoid glycosides, phenolic acids, free amino acids and nucleosides contents in Zhi-zi-chi decoction (ZZCD), a sensitive and rapid method was developed using UFLC/QTRAP-MS for the simultaneous quantification. The results showed that all known ingredients were determined in ZZCD and the method was suitable for the simultaneous determination of 53 target components in ZZCD. Of which three isoflavones, nine free amino acids and eight iridoid glycosides were the main constituents. This study simultaneously determined 53 target components of 5 major categories in ZZCD for the first time and made up the blank on the target components contents of ZZCD, and promoted the construction of the ZZCD fingerprint at the same time, which have great significance for the comprehensive guarantee of the clinical therapeutic effect of ZZCD. The present study also offered an experimental foundation for more in-depth research on the pharmacochemistry analysis of ZZCD and effective fractions selection. The determined iridoid glycosides have been reported to possess antidepressant effects, which also provides a material basis for the subsequent research on the anti-depressant effects of ZZCD.

Objective: To explore the effect of a combination of biomechanical intervention and medication adjustment on the risk of falls and the incidence of cerebral hemorrhage in elderly people. Method: A total of 300–500 high-risk elderly individuals from October 2023 to June 2024 were included and randomly divided into an intervention group and a control group. The intervention group received a 12 week biomechanical intervention (including balance and gait training) and personalized medication adjustment; The control group continued with routine treatment. Evaluate the changes in balance ability, gait stability, medication compliance, fall incidence, and cerebral hemorrhage incidence between two groups of elderly people, and compare the evaluation data. Result: The intervention group improved balance ability and gait stability by 26.5% and 25.6%, respectively, with a fall incidence rate of 8.3%, significantly lower than the control group’s 18.5% (p < 0.05). The medication adherence score of the intervention group increased from 61.7 to 81.9 (p < 0.01), and the blood pressure control compliance rate increased from 74.1% to 88.3% (p < 0.05). The incidence of cerebral hemorrhage in the intervention group was 2.5%, which was lower than the 7.1% in the control group (p < 0.05). Conclusion: A comprehensive approach based on biomechanical intervention and medication adjustment can significantly improve the balance ability and medication compliance of the elderly, and effectively reduce the risk of falls and cerebral hemorrhage, providing a new intervention strategy for the health management of the elderly population.

Biomechanical analysis has gained prominence in optimizing movement physical education (PE) teaching. Understanding the mechanics of human movement techniques within can lead to enhanced performance, skill acquisition, and injury prevention among students. The potential benefits of biomechanical analysis and its integration into PE programs remain limited, and educators often lack the tools and knowledge to apply these insights effectively in their teaching practices. The study aims to investigate the application of biomechanical analysis to optimize movement techniques in PE, focusing on its impact on student performance and engagement. A mixed methods approach was employed, with qualitative surveys and interviews. Participants were divided into two groups. Group A (the experimental group, EG) engaged in strength and conditioning activities enhanced by biomechanical analysis interventions, including motion capture and force plate assessment, over a six-week period. Group B (the control group, CG) receives standard PE instruction without biomechanical feedback. The findings revealed significant improvement in movement techniques within Group A, with increased efficiency and reduced injury risk compared to Group B. Group A demonstrated enhanced performance in strength and conditioning activities. This study highlights the significance of integrating advanced biomechanical strategy into PE programs to promote effective teaching and learning practices.

Use the method of functional movement screening to find out their wrong movement patterns, investigate the root cause, combine the special technical characteristics, formulate a functional training plan, improve the body’s functionality, and finally improve the special performance. A total of 40 students from Class 1 and Class 2 of the 2021 grade sports dance major of Shandong Normal University were selected as the experimental subjects; Class 1 was the experimental group, and Class 2 was the control group. There were 20 people in each of the experimental group and the control group (10 males and 10 females). The training time is 19:00–20:00; the training period is 8 weeks; the experimental group carries out functional training, and the control group carries out the original quality training. After 8 weeks of traditional training, the overall FMS score of the control group did not increase significantly after the experiment (P < 0.05), indicating that traditional physical training cannot effectively improve the ability of athletes to prevent sports injuries. The FMS scores of the experimental group were significantly improved after the experiment (P > 0.05). Therefore, it is believed that physical functional training is more scientific, safe and efficient than traditional physical training, and improves the sports level of college sports dance students.

In order to explore the mechanism of peer support in students’ behavioral norms, this paper constructs a theoretical framework based on the biomechanical model, and verifies the relationship between variables through empirical data, uses structural equation modeling to analyze the direct and indirect effects of peer support on behavioral norms, and at the same time, explores the moderating effect of behavioral inertia and the mediating effect of rule awareness. The results show that peer support can significantly improve behavioral norms through social adaptation and task completion, and behavioral inertia has a significant moderating effect on path strength. The model has good fit and explanatory power, which provides a theoretical basis for optimizing behavioral regulation strategies in educational practice.

In the cultivation of medical ethics, sense of responsibility and humanistic care, the role of Ideological and political education is increasingly significant. This paper studies and analyzes the practical path of Ideological and political education in the concept of life education, and explores its shaping effect on medical students’ values of life, professional ethics and humanistic quality. Through a questionnaire survey of 300 medical students, combined with quantitative data analysis, the study found that ideological and political education can significantly improve students’ cognition level of outlook on life (p < 0.05), and has a positive effect in strengthening medical ethics and professional ethics. In addition, in terms of practice path, combined with case analysis and classroom teaching mode innovation, the study proposed a systematic implementation scheme of Ideological and political education, and verified that the scheme has high operability and effectiveness in cultivating the effect of students’ Outlook on life (the effect increased by about 22%). The results show that the value of Ideological and political education in the life outlook education of medical majors has become an important support point for the cultivation of medical talents, and has far-reaching significance for improving the comprehensive quality and humanistic care consciousness of medical students.

Optical fiber communication coding and modulation techniques play a key role in high-speed and high-capacity transmission but are still limited by problems such as signal attenuation, nonlinear effects and increasing bit error rate. In order to optimize the performance of optical communication systems, this study draws on the biomechanical signal conduction mechanism to construct an optical fiber modulation scheme that integrates pulse time coding, adaptive modulation and redundancy coding. The experimental results show that this method significantly reduces the Bit Error Rate (BER), improves the signal-to-noise ratio, and enhances the signal robustness at different transmission distances. Compared with the conventional Quadrature Phase Shift Keying (QPSK) and 16-QAM modulation, the proposed scheme reduces the BER by about 37.5% and improves the Signal-to-Noise Ratio (SNR) by 2.1 dB at 150 km transmission, which verifies its advantages in terms of interference immunity and energy utilization. The research results provide a novel optimization strategy for optical fiber communication systems and lay a theoretical foundation for the research of next-generation intelligent modulation techniques.

This study investigated the relationship between students’ level of competence in Ice and Snow sports and their quality of life at Heilongjiang Institute of Construction Technology. It examined the participants’ profile namely gender, grade level, and program of study, assessed their level of competence in knowledge, skills, and attitude towards Ice and Snow sports, and measured their quality of life across physical, psychological, environmental, and social domains. The study also explored significant differences in competence and quality of life across participant profiles, investigated the relationship between these two constructs, and identified problems and challenges faced by students in relation to Ice and Snow sports. Biomechanical analysis reveals that regular participation enhances postural stability (ROM improvement 15%–22%) and muscle coordination efficiency (EMG variance reduction 18% ± 3%), mediating 31% of QoL improvement through physiological adaptation pathways. It employed a mixed research approach and included students and teachers as participants. Participants were selected through a random sampling technique using the Lynch formula. Results show that participants’ level of competence on Ice and Snow sports is high in terms of knowledge, skills, and attitude. Additionally, they have a high level of quality of life in terms of physical, psychological, environmental, and social relationships. Based on the findings, the research proposed a plan of action to enhance both students’ competence in Ice and Snow sports and their overall quality of life, aiming to provide insights for improving educational and recreational programs within the context of the Heilongjiang Institute of Construction Technology.

The goal of the study was to reveal a common pathogenic link of immune-mediated skin conditions such as disorders of interaction of adrenocorticotropic hormone and pro-inflammatory cytokines directly in the skin. 94 patients aged 18 to 45 years, of both sexes, with immune-mediated conditions, including atopic dermatitis, limited scleroderma, chronic spontaneous urticaria, and plaque psoriasis, were studied. A majority of patients, except for scleroderma, had atopic constitution with or without manifestation of respiratory allergic disease and food allergies. All patients also had various concomitant chronic conditions, primarily of cardiovascular and gastrointestinal systems. A patented modification of “skin window,” when a chamber with saline is installed on the scarified skin area to accumulate exudate containing targeted molecules, corticotropin, and cytokines Interleukin-1β, Interleikin-18, Interleukin-6, and Tumor Necrosis Factor-α, is used. Determination of values of molecules is carried out using electro chemiluminescent immunoassay and other analyses. In all patients, the skin exudate adrenocorticotropic hormone value was significantly reduced compared to the control group, whereas the content of cytokines obtained from the “skin window” exceeded similar indicators in healthy individuals. A high degree of correlation between adrenocorticotropic hormone and IL-6 was registered. The forgotten “skin window” technology demonstrates a proper opportunity to acquire biological material from the skin for investigation of targeted molecules at the local level.

Introduction: Microplastics are plastic particles less than 5 mm in diameter, mainly from synthetic textiles, home decoration materials, cleaning supplies and plastic products wear. These microplastics can enter the body through respiratory inhalation, skin contact or dietary ingestion, posing a potential threat to human health. Studies have found that inhaling microplastics can trigger respiratory inflammation, allergic reactions, and even chronic respiratory diseases (such as bronchitis and asthma). Microplastic particles can accumulate in the lungs, and long-term exposure can exacerbate respiratory diseases. In addition, microplastics may also enter other organs through the blood circulation, affecting the immune system and nervous system function. In the indoor environment, the release of microplastics is closely related to daily activities, with higher concentrations of microplastics in high-frequency activity areas (such as living rooms) and greater exposure risks. Therefore, the health effects of indoor microplastic pollution on long-term residents should not be ignored, and further research on its long-term health effects and measures to reduce exposure risks are needed. Objectives: To precisely determine the concentration levels of microplastics in indoor air and comprehensively assess their potential risks to human health, with a focus on how these microplastics interact with the biomechanical aspects of the human body. Methods: This study explores the impact of indoor building air microplastics on human environmental health and analyzes the human exposure risk of microplastic distribution in different regions. Results: The results showed that the microplastic content in the living room area was 241 ± 21 n/m³, with the highest content, while the kitchen had the lowest. In the assessment of human exposure risk, subjects had the highest daily and annual exposure levels in the living room, with some experiencing symptoms such as allergies and coughing, indicating moderate exposure risk. The daily average exposure of subjects P1 and P5 could reach 1364MPS/day and 1142MPS/day, with an average annual exposure of 1,124,000 and 1,214,000 particles, respectively. Microplastics in indoor air are mainly small particles of 20–100 microns, mainly in the form of fragments, and synthetic rubber and packaging plastics are the most common types. Health risk assessment shows that individuals exposed to high concentrations of microplastics for a long time are prone to allergies, mild cough and other problems, and exposure time is negatively correlated with health scores. Daily and annual exposure levels varied significantly by region, with the living room highest and the kitchen lowest. Conclusion: The study provides quantitative data on indoor exposure levels of microplastics and provides a scientific basis for assessing their health risks. The potential harm mechanism of microplastics to respiratory tract was revealed from the perspective of biomechanics, which filled the research gap. The seriousness of microplastic pollution in indoor environment was emphasized, which provided reference for formulating indoor environmental quality standards and health protection measures. To remind the public to pay attention to the problem of indoor microplastic pollution, especially in high-frequency activity areas, such as living rooms, measures should be taken to reduce the release and accumulation of microplastics.

With the acceleration of urbanization and the impact of climate change, landscape gardening plays an increasingly important role in improving urban ecological environments, enhancing biodiversity and improving ecological service functions. Based on the principle of biomechanics, this paper discusses the strategy of enhancing the ecosystem service function of landscape gardening and proposes to optimize the sustainability of landscape gardening by means of multi-scale planning frameworks, ecological corridor construction, and microclimate regulation techniques. Through the implementation of biomechanical optimization practices in the study area, in this study, multiple technical methods were used for data collection to ensure the accuracy and reliability of the data. For monitoring plant growth, a regular sampling method was adopted to measure growth parameters (such as plant height, crown width, root depth, etc.) of plants in the study area on a monthly basis. In addition, high-precision meteorological monitoring equipment (such as temperature and humidity meters, anemometers, etc.) was used for continuous monitoring of microclimate conditions, with a data collection time span of one year, covering seasonal changes. Soil stability assessment quantifies soil stability and water management capacity through regular sampling of different soil types, using soil shear tests and water retention capacity tests. All data were processed using statistical analysis methods and combined with model simulations to validate the effectiveness of optimization strategies, ensuring the scientific and reproducible nature of research results. It was found that the optimization of vegetation configuration, soil structure and water flow management could significantly improve soil and water conservation capacity, wind speed regulation and microclimate regulation. The high density of vegetation not only enhanced the soil water retention capacity but also effectively reduced the wind speed and improved the local climate environment. In addition, the construction of ecological corridors and reasonable vegetation layout enhances the ecological connectivity and stability of the landscape. By evaluating the sustainability under different seasons, this paper further discusses how to dynamically optimize the landscape according to seasonal changes to ensure the stability of its long-term ecological service function.

Biomechanics is becoming increasingly important in the field of biomedical science, impacting areas such as molecular biology and nanotechnology. This study draws on the principles of biomechanics to analyze the rural e-commerce ecosystem, focusing on the analogy of intercellular interactions among new farmers, e-commerce platforms, logistics companies, and local governments. The purpose of this study is to construct a four-party evolutionary game model that encompasses the dynamic interactions and strategic behaviors within the rural e-commerce ecosystem, aiming to explore the cooperative dynamics and strategic evolution among these four main entities. From a biomechanical perspective, treating these entities as “cells” and market forces as “mechanical signals,” we apply game theory and numerical simulation to analyze their strategic choices and the stability of the ecosystem at different evolutionary stages. The study finds that: government subsidies have a double-edged sword effect, stimulating the adoption of e-commerce, but requiring careful adjustment to prevent market imbalances. Cooperation between e-commerce platforms and logistics companies is crucial for improving supply chain efficiency. The strategic shift of new farmers towards active use of e-commerce is vital for the upward movement of agricultural products. System stability is a dynamic balance influenced by subsidy policies, the degree of cooperation, and the willingness of new farmers to adopt e-commerce. This research provides a new perspective for understanding strategic interactions by introducing biomechanical concepts into the analysis of business ecosystems. In practice, it offers actionable insights for policymakers and industry players to optimize the rural e-commerce ecosystem, promoting agricultural modernization and rural revitalization.

This study examines the effects and intervention outcomes of Yi Jin Jing and Tai Chi on lower limb muscle strength, gait characteristics, and fall risk among elderly males from a biomechanical perspective. Methods: A total of 96 participants were randomly assigned to the Yi Jin Jing Group (YJG) and the Tai Chi Group (TCG), with 48 individuals in each group. The study employed lower limb muscle strength testing, gait analysis, fall index assessment, the Berg Balance Scale (BBS), and the Fugl-Meyer Assessment Scale (FMA) to evaluate the differences in lower limb muscle groups, gait characteristics, fall risk, dynamic balance ability, and sports function between two groups of elderly males. Results: The relative peak torque of the three major joint muscle groups in the lower limbs of the YJG and TCG during isokinetic concentric contractions at 60°/s and 120°/s exhibited a significant increase compared to pre-intervention levels, with the most pronounced changes observed in the ankle joint muscle group (60°/s: Cohen’s d = 1.68/0.62, 95% CI: [1.16 to 2.20, 0.16 to 1.08]; 120°/s: Cohen’s d = 1.22/1.66, 95% CI: [1.16 to 2.20, 0.16 to 1.08]; P < 0.05). Notable differences were also identified in the gait and fall index (30.32 ± 9.64 vs. 57.23 ± 6.67; 31.72 ± 7.42 vs. 46.67 ± 5.93; Cohen’s d = 1.67, 95% CI: [−2.27 to −1.21]; 51.19 ± 4.72 vs. 36.50 ± 3.94; 50.58 ± 3.12 vs. 43.78 ± 4.41, Cohen’s d = −1.74, 95% CI: [−2.27 to −1.21]; P < 0.05). Significant differences were also observed in the BBS and FMA scores (BBS: 51.27 ± 3.57 vs. 43.63 ± 4.09, Cohen’s d = 1.99, 95% CI: [1.43 to 2.54]; FMA: 65.76 ± 5.37 vs. 62.86 ± 3.27, Cohen’s d = 0.65, 95% CI: [1.43 to 2.54]; P < 0.05). Furthermore, YJG demonstrated a significant advantage over TCG. These changes in parameters suggest enhanced lower limb strength, improved gait stability, and better dynamic balance in elderly males, effectively reducing the risk of falls. Conclusion: Both the YJG and TCG interventions can significantly improve lower limb muscle strength, dynamic balance, gait ability, and overall motor function in elderly males. However, the efficacy of YJG is notably superior to that of TCG. This finding provides valuable insights and important clinical implications for fall prevention strategies in the elderly population.

To provide a more efficient and real-time blood routine monitoring method for elderly patients with diabetes, and to explore the correlation between blood routine indexes and Electrocardiogram (ECG) characteristics. A real-time blood routine detection device based on electrochemical sensor was designed, and a portable instrument based on optical sensor was developed to monitor trace biomarkers in blood using a labeled electrochemical biogold nanoparticle sensor. The results show that the sensor can monitor bilirubin level in real time, and the correlation coefficient with blood routine results is 0.95, so that the clinical monitoring time is shortened from 2 h to 30 min. The detection limit of white blood cell count was 0.85 × 10⁹/L, and the data collection rate was 50 times/s, which improved the detection accuracy by 15.8% compared with the traditional laboratory method. In addition, the study revealed the mechanism of potential influence of changes in blood routine indicators on ECG characteristics. The monitoring device in this study can reduce the cost, improve the efficiency, and provide a precise and convenient clinical application scheme for the health management of elderly diabetes patients.

Research on college women’s football in the field of sports mainly focuses on macroscopic performance, with too much emphasis on the analysis of macroscopic sports performance and energy consumption, ignoring how mechanical forces indirectly affect athletes’ metabolic processes through the behavior of cells and tissues. This paper takes college women’s football players as the research object, combines biomechanical analysis at the cell and tissue level, and explores how mechanical forces indirectly change athletes’ metabolic processes by affecting cell and tissue behavior. Through multi-scale analysis, this paper studies how to reveal the transmission and conversion mechanism of mechanical effects in organisms from the molecular, cellular to tissue levels, thereby affecting overall metabolism and energy consumption. Taking mechanical force as the starting point, combined with biomechanical analysis at the cell and tissue levels, this paper systematically explores how mechanical force indirectly changes the metabolic process of athletes by affecting cell and tissue behavior. The study adopted a multi-scale analysis framework, from the molecular, cellular to tissue levels, to reveal the transmission and conversion mechanism of mechanical action in the organism, affecting the overall metabolism and energy consumption. Cell mechanics experiments and metabolic modeling, a comprehensive metabolism and energy consumption analysis model was constructed by combining single-molecule mechanics experiments, verifying the key role of high-intensity mechanical force in improving energy consumption efficiency. The experimental results show that compared with normal training, training under mechanical force intervention can effectively improve the training effect of college women’s football. This conclusion also has certain reference significance for other high-intensity athlete groups. Although the training intensity and physical response of different sports may vary, the same mechanical force intervention may also produce significant energy consumption effects in other types of sports. Under the high-intensity training mode, mechanical force intervention training can increase the maximum oxygen uptake, blood lactate concentration and muscle thickness by 6.2 percentage points, 33.9 percentage points and 7.2 percentage points respectively compared with training without mechanical force intervention. The research results provide support for cell and tissue level analysis in sports biomechanics research, as well as theoretical support for optimizing athlete training plans and sports rehabilitation, and reliable technical support for the integrated development of biomechanics and sports science.

Background: With the increasing demand for equipment performance in the coal industry, traditional coal crushers face problems such as high vibration, high noise, insufficient wear resistance, and frequent mechanical failures during long-term operation. To this end, the study adopts a combination of biomimetic structural design and multi-layer composite material optimization to improve the performance of coal crushers. Method: Based on biomechanical characteristics, this method optimizes the design of the coal crusher from three aspects: overall results, crushing components, and shock absorption and noise reduction performance, in order to reduce its vibration and noise while maintaining or improving its mechanical strength and wear resistance. Result: The optimized support structure of the coal crusher improved its seismic performance by at least 20% when subjected to impact forces, effectively reducing the concentration of local stress and the occurrence of mechanical failures. The hammer crushing component made of multi-layer composite materials has an HRC hardness of about 67 and an impact toughness of about 15 J/cm³, significantly improving the crushing efficiency and service life of the coal crusher. Conclusion: The optimized coal crusher exhibits excellent performance in vibration control, noise reduction, and wear resistance, providing a more efficient and environmentally friendly coal crushing equipment for the coal mining industry.

Lower limb asymmetry associated with running can reveal relevant information about sports injuries. Although the biomechanical study of bionic shoes (BS) has developed well, the understanding of how BS affects lower limb asymmetry during running is limited. The objective of this study was to compare the asymmetry between the dominant and non-dominant limbs of the participants under NS and BS conditions. The research involved the enrollment of 26 male individuals who were actively involved in running (age: 27.30 ± 3.70 years old, height: 1.72 ± 0.03 m, body mass: 66.70 ± 8.20 kg, body mass index: 22.40 ± 2.30 kg/m²). Participants were required to run at a speed of 12 km/h wearing BS and neutral shoes (NS) respectively. Lower limb asymmetry during running was analyzed by investigating biomechanical parameters such as range of motion, peak angular velocity, peak moment, power, and work of the bilateral knee and ankle during the running stance phase. A two-way analysis of variance (ANOVA) was employed to determine the differences in joint biomechanics (p < 0.05) using a factorial design. Additionally, paired sample t-tests were conducted to determine the differences in symmetry angles (SA) for each of the analyzed biomechanical parameters. Compared to NS, BS optimized the asymmetry in knee (p = 0.015) and ankle (p < 0.001) angles between the dominant and non-dominant lower limbs during the push-off phase, and the BS optimized the asymmetry in knee extension work (p = 0.049) between the dominant and non-dominant lower limbs in the stance phase of running. However, it also resulted in increases in peak angular velocity (p = 0.049), power (p = 0.018), and work (p = 0.035) during dominant lower limb ankle dorsiflexion. Without considering the effects of the shoes, there would be differences in peak extension moment (p = 0.05) and flexion work (p = 0.005) of the bilateral knee during running, as well as differences in peak dorsiflexion angular velocities (p = 0.001) and plantarflexion work (p = 0.039) of the bilateral ankle. These differences can also affect the peak angular velocity in dorsiflexion and the work in plantarflexion. The findings suggest that BS improved asymmetry of the knee and ankle and demonstrated bilateral lower limb asymmetry during running. These findings provide insights into understanding sports injuries such as anterior cruciate ligament injuries of the knee and information relating to ankle sprains. The findings also offer beneficial information in the design of BS.

Skiing, as a popular winter sport, attracts a large number of tourists to ski resorts for an immersive experience. To improve tourists’ travel experience and ensure their safety, this study proposes a skiing biomechanical analysis method based on the Statistical Shape Model. The study conducts 3D reverse modeling of tourists’ ankle joints and constructs mechanical models for the talus and calcaneus. Finally, the model is used to analyze the impact of ankle joint morphology on the axis of rotation. Experimental results show that among the three indicators in the evaluation of the Statistical Shape Model, the compactness of the talus increases from 29,800 to 41,500, and the calcaneus increases from 39,800 to 64,700. The generalization value of the talus decreases from 97,800 to 93,400, and the calcaneus decreases from 116,900 to 111,500. After the statistical shape model of the improved skiing equipment, the tourist experience satisfaction is more than 85%. The results indicate that the morphology of the talus and calcaneus significantly affects the axis of rotation. By analyzing the biomechanics of tourists’ ankle joints during skiing, a deeper understanding of the mechanical characteristics in skiing is obtained, providing a theoretical basis for optimizing tourists’ travel experience. The morphological features of the ankle joint directly impact tourists’ balance and stability during skiing, thereby influencing both the safety and enjoyment of the sport.

Flow cytometry is a technology based on the detection of scattered light signals and fluorescent signals for multi-parameter, high-throughput, rapid analysis of individual cells or particles. It has broad application prospects in the biomedical field and is an indispensable technique, especially in immunological research. The preparation of a high-quality single-cell suspension is a key and often challenging step that directly affects the results of flow cytometry analysis. This study selected a primary tissue sample that is relatively difficult to prepare and commonly used in immunological research: The lamina propria of the mouse intestine. Three common enzymatic digestion methods were compared in terms of cell yield, viability, and immune cell markers. The results indicated that an enzymatic digestion method primarily using collagenase D is suitable for obtaining lymphocyte-derived immune cells from the mouse intestinal lamina propria, while enzymatic digestion methods primarily using Liberase^TM and DNase I are more suitable for obtaining myeloid-derived immune cells from the same tissue. Additionally, by combining a multi-parameter staining scheme, the analysis of T cells, macrophages, and dendritic cells within the intestinal immune cells was achieved. Furthermore, immunolabeling analysis was conducted on the commonly used colitis model, which was induced by dextran sulfate sodium, providing a reliable detection method for accurately analyzing immune cell populations in the mouse intestinal lamina propria.

This paper presents an advanced deep learning framework that integrates convolutional neural networks (CNNs) with temporal attention mechanisms for real-time swimming turn analysis. The proposed architecture features a hybrid spatial-temporal design with multi-scale feature fusion and adaptive normalization, achieving robust performance in challenging underwater environments. The system demonstrates 96.2% accuracy in standard conditions and 91.8% accuracy under low-light scenarios, with a 15% improvement over existing methods. By optimizing computational complexity, the framework achieves 32 frames per second with a 99.99% error recovery rate and a 23% improvement in resource utilization efficiency. Extensive validation shows robust performance across varying water qualities, lighting conditions, and motion scenarios. In addition to its technical robustness, the framework introduces a novel adaptive error handling mechanism, hierarchical state machines, and hybrid deep learning architecture, ensuring stable operation with a mean time between failures (MTBF) of 8760 h and mean time to recovery (MTTR) of 1.2 s. Tested in Olympic-standard facilities, the system reliably delivers precise biomechanical feedback for athletes and coaches. Future research will extend the system to multi-object detection, integrate advanced acoustic sensing for zero-visibility conditions, and explore federated learning for privacy-preserving model updates. This work sets new benchmarks for underwater motion analysis, advancing both athletic training and aquatic research.

In order to improve the robot’s motion stability and handling ability in complex terrain, a bionic handling robot imitating the crawling of ants is designed. The study is based on the ant’s locomotion mechanism, and by analyzing its gait and handling behavior, an optimized design of the hexapod robot’s mechanical structure and drive system is proposed. The control system adopts a layered architecture and integrates multi-sensor data fusion technology to achieve gait planning, balance control, and path planning functions. The prototype test shows that the robot can maintain stable operation in different terrain environments, with a gait error of less than 5%, and complete the handling task under a load of 800 g, with good terrain adaptability and load capacity. The performance evaluation shows that the energy consumption is well balanced with the task efficiency, and the design scheme is highly reliable in motion control and practical applications. The study provides a new technical path for intelligent handling tasks in complex environments and lays the foundation for subsequent optimization work.

Lumbar spondylolysis is related to weightlifting. The biomechanics of lumbar spondylolysis in weightlifting and the connection between lumbar spondylolysis and muscles are still unclear. Therefore, this study clarified the influence of decreased muscle strength on lumbar spondylolysis through finite element (FE) analysis. We used computed tomography to scan the L1-S1 segment of the patient and constructed a three-dimensional FE model. Apply a moment of 7.5 N·m and a weight of 280 N at the top of L1 after fixing the sacroiliac joint. The dumbbell weight was set to 15 kg. Apply muscle strength and follower loads representing the muscles of the back and abdomen in the FE model. The back muscle strength was reduced to 50%. The results showed that L4 with incomplete lumbar spondylolysis under decreased muscle strength and L5 with incomplete lumbar spondylolysis under normal muscle strength had the higher range of motion (ROM) in the flexion stage (45°). The ROM of L4 was affected by the decreased muscle strength, and the ROM of L5 was affected by the lumbar movement. L4 and L5 of incomplete lumbar spondylolysis showed the greatest stress range changes in the lifting and the final stage, respectively. Stresses at L4 and L5 are affected by the defect and decreased muscle strength. This study shows that the ROM of incomplete spondylolysis is vulnerable to flexion during weightlifting. Decreased muscle strength leads to increased stress on the defect and adjacent segments in the lifting and final stages, which might aggravate the fracture.

Background: Growing evidence suggests that mental disorders are associated with an increased risk of atopic dermatitis (AD). But until now, the causal association between them has been unclear. We conducted a Mendelian randomization study to determine the bidirectional causal association between atopic dermatitis and mental disorders. Simultaneously, the correlation between biomechanics and atopic dermatitis (AD) has been analyzed. Objective: This two-sample Mendelian randomization (MR) study aims to assess the causal relationship between mental disorders and the incidence of atopic dermatitis (AD). Methods: Single nucleotide polymorphisms (SNPs) associated with severe depression, generalized anxiety disorder, sleep disorders, schizophrenia, and AD were selected from the genome-wide association study (GWAS) databases. Causal effects between exposure and outcomes were analyzed using methods such as inverse-variance weighted (IVW), weighted median (WM), and MR-Egger, with results primarily assessed by the P-values, odds ratios (ORs), and 95% confidence intervals (CIs) from the IVW method. Sensitivity analyses were conducted using IVW, MR-Egger, and MR-PRESSO methods. Results: The findings indicate a positive causal effect of severe depression on the risk of developing AD, with the IVW method yielding an OR of 1.177 (95% CI 1.083–1.280, P < 0.001) and the WM method showing an OR of 1.182 (95% CI 1.061–1.317, P < 0.001). Heterogeneity tests using the IVW method’s Cochran Q test resulted in a P-value of 0.132 and an I² of 19.34%. Pleiotropy tests with MR-PRESSO showed a P-value of 0.193, and the MR-Egger regression intercept yielded a P-value of 0.009, indicating the presence of heterogeneity or pleiotropy. No causal relationships were found between generalized anxiety disorder (OR = 1.024, 95% CI 0.996–1.052, P > 0.05), sleep disorders (OR = 0.970, 95% CI 0.928–1.014, P > 0.05), or schizophrenia (OR = 1.006, 95% CI 0.983–1.029, P > 0.05) and the incidence of AD. Conclusion: Major depressive disorder exhibits a unidirectional causal influence on Alzheimer’s disease development risk. It is recommended that patients with severe depression undergo enhanced screening and prevention for AD to mitigate the risk of developing this condition. Biomechanics plays a significant role in the onset and progression of atopic dermatitis (AD).

Music serves as a vital medium for emotional expression and cultural heritage, evolving significantly through advancements in digital education. This study introduces an integrated framework to enhance online music education via innovative music generation and genre classification techniques. Central to this research is the MGED (Music Generation and Education Development) framework, which utilizes Convolutional Neural Networks (CNNs) for feature extraction and Highway Networks for deep learning. By incorporating spatial attention mechanisms with Bidirectional Gated Recurrent Units (Bi-GRU), the framework generates high-fidelity spectrograms, while the Griffin-Lim algorithm ensures temporal coherence in the outputs. For genre classification, the study employs the CMBRU (Classification Model for Bi-GRU and Residual Units) framework, leveraging Mel-Frequency Cepstral Coefficients (MFCC) and multi-channel CNNs to achieve robust representation learning. This model effectively captures temporal dependencies, resulting in over 70% accuracy across five genres. Additionally, this research explores the design of a multi-label classification model tailored for online teaching and learning environments aimed at music scholars, viewed through a biomechanical lens. As online education becomes increasingly prevalent, the need for effective classification models that can handle multiple labels simultaneously is critical, particularly in music education, where diverse skills and knowledge areas intersect. The study employs biomechanical principles to analyze the physical aspects of music performance and learning, integrating these insights into the classification model. This approach not only enhances the educational experience for music scholars but also contributes to the broader field of online music education, paving the way for future research and practical applications.

Artistic creation is a means of expressing human emotions. To intuitively capture the emotions conveyed by the artist in their works, we propose an improved CNN-based emotion detection method that incorporates biomechanical elements. Recognizing that emotions are accompanied by physiological and biomechanical responses such as heart rate variations, facial muscle activity, and speech tone fluctuations, we collect and integrate multi-sensor data, including heart rate, facial expression, and verbal expression. This information is processed through a multi-sensor signals fusion method based on an enhanced Convolutional Neural Networks (CNN), which allows for the extraction of rich and accurate emotional feature representations from the creator’s biomechanical signals. In particular, the facial muscle movements and subtle variations in speech tone, which are integral to understanding emotional states, are effectively captured and analyzed. Furthermore, we introduce a Conditioning Diffusion Model for Emotion Prediction, where emotional features, informed by biomechanical responses, serve as semantic conditions to boost the accuracy of emotion detection. This approach enables precise identification of the artist’s emotions by considering the intricate interplay of physiological and biomechanical signals. Experimental results demonstrate that our proposed method achieves an mAP score of 85.36%, an MSE score of 0.73%, and a runtime of 87 milliseconds, providing technical support for predicting the emotions of creators based on their biomechanical responses.

Biomechanics plays a crucial role in understanding the physiological and mechanical processes within the medical field. In the context of medical education, integrating ideological and political education with bioethical concepts has become an important aspect. This study explores the intersection of these elements and its impact on medical students. The cultivation of bioethical concepts in medical education plays a critical role in enhancing medical students’ professional ethics and overall quality. Bioethics education not only helps students understand the ethical challenges in medical practice, but also shapes their values and social responsibility, improving their ethical decision-making abilities in real-world situations. The results show that through a survey of 300 medical students and GNN model analysis, it was found that ideological and political education significantly enhanced students’ understanding of bioethics and decision-making abilities. Students’ ethical awareness scores increased by 57% (p < 0.05), and the accuracy of ethical decision-making tasks improved by 30%. This indicates a strong positive correlation between ideological and political education and bioethical concept formation, which may have implications for the biomechanical aspects of medical practice and education. The educational effect is evident not only in the improvement of knowledge but also in the development of students’ ethical judgment and social responsibility. This study validates the effectiveness of integrating ideological and political education with medical ethics education, providing a theoretical basis and practical guidance for reforms in medical education.

In the traditional education assessment landscape, the manual grading of subjective exam questions poses significant challenges. The labor-intensive nature of this process and the potential for human error can negatively impact teaching and learning outcomes. As society transitions towards a low-carbon future, there is a pressing need to reform educational evaluation methods, reduce paper-based exams, and leverage advanced intelligent technologies. Inspired by the principles of biomechanics, this research introduces a novel image-based handwritten text recognition algorithm powered by recurrent neural networks, specifically designed for the automated scoring of primary school mathematics subjective questions. Drawing insights from the human visual and cognitive systems, the proposed approach mimics the hierarchical and adaptive nature of biological information processing to tackle the complexities inherent in handwritten text detection, recognition, and understanding. The study first constructs a comprehensive dataset of real primary school math exam answer sheets, capturing the diverse range of handwriting styles and mathematical notations. This dataset serves as a robust training and evaluation platform, akin to the diverse sensory inputs that biological systems process. The recurrent neural network architecture employed in this work exhibits biomimetic properties, such as the ability to dynamically process sequential information and adaptively refine its internal representations, much like the human brain's neural networks. This allows the algorithm to effectively handle the contextual cues and structural patterns present in handwritten mathematical responses, enabling accurate recognition and interpretation. Rigorous comparative and ablation experiments were conducted to assess the performance of the proposed algorithm. The results demonstrate high accuracy in recognizing and interpreting handwritten subjective responses, showcasing the practical value of this biomechanics-inspired approach. These findings align with the study's overarching goal of developing resource-saving and environmentally-friendly education evaluation systems, paving the way for the widespread adoption of intelligent technologies in the assessment of subjective questions. By drawing inspiration from the elegant and efficient information processing mechanisms observed in biological systems, this research contributes to the advancement of intelligent handwritten text recognition, ultimately supporting the transition towards a more sustainable and equitable educational landscape.

The aim of this study was to optimise the jumping performance of basketball players through biomechanical analysis and to provide targeted training strategies for athletes of different skill levels. Using a motion capture system and a force measurement platform, the study analysed the jumping movements of 200 basketball players in detail, covering key metrics such as joint angle, ground reaction force (GRF), jump height and jumping speed. The results showed that high-level athletes were significantly better than middle- and low-level athletes in terms of joint angle control, GRF and jump height. Based on the results of the analysis, a personalised training plan including strength training, jumping exercises and technique optimisation was proposed, with an emphasis on adjusting the training content according to the athletes’ feedback and progress. The study concluded that scientific training methods can significantly improve jumping ability and reduce the risk of injury, and future research should further investigate the long-term effects and economic feasibility of training strategies.

In the field of modern biomedical science, biomechanics focuses on the mechanical properties and interactions of molecules, cells, tissues, and organs, which is crucial for a deeper understanding of how the human body perceives and adapts to changes in the external environment. As an important environmental factor, light’s impact on the human body not only involves psychological and physiological aspects, but is also closely related to biomechanical mechanisms. Therefore, this study aims to explore the regulatory effects of light on residents’ emotions and physiological rhythms from a biomechanical perspective, providing a new perspective for revealing the mysteries of the interaction between light and the human body. By measuring physiological parameters such as heart rate, respiration, and skin conductance response, investigate whether there is a certain resonance between exposure to light of different wavelengths (red, green, blue) or color temperatures (3000 K, 6000 K) and hearing. The results indicate that auditory or visual environmental stimuli can indeed cause changes in human physiological parameters and emotions; The dynamic lighting environment has a stronger impact on emotional perception; Revealed the feasibility of using physiological parameters as the basis for acousto-optic fusion perception and judgment. Understanding the relationship between color and psychology is crucial for creating a living environment that meets people’s psychological needs. Finally, summarize the principles of human living environment lighting design based on color psychology, providing guidance for future design practices. This study reveals the intrinsic relationship between light and human biomechanical response by measuring a series of biomechanical related indicators, providing scientific basis for optimizing human living environment design.

In this study, a Transformer-based video generation technique is proposed for accurately modelling biomechanical movement patterns, and its performance is systematically evaluated in walking, running, throwing and other movement tasks. The experimental results show that Transformer outperforms traditional methods (RNN, CNN, GAN) in terms of motion trajectory consistency, temporal synchronization, and video clarity, and is capable of generating high-quality motion videos that comply with biomechanical constraints. This study not only expands the application scope of Transformer in biomechanical analyses, but also provides high-precision solutions for tasks such as gait reconstruction, abnormality detection, rehabilitation training, and motion prediction.

This research explores the scientific foundations, practical pathways, and effects of integrating Chinese language education with Chinese language and literature education in complex environments, using the systems thinking of biomechanics and molecular mechanisms as a perspective. The study employed a randomized controlled experimental design, selecting 184 Chinese language and literature majors from six universities across Eastern China, Northern China, and Southwest regions. Participants were divided into an experimental group (receiving an integrated education model) and a control group (receiving a traditional separated teaching model). Through a 12-week teaching intervention, using diversified data collection tools and mixed research methods, the study systematically evaluated the effectiveness of integrated education. The research found that: (1) Integrated education significantly improved learners’ originality (+27.75%), understanding of complexity (+25.27%), functional application (+23.16%), and aesthetic perception (+28.92%); (2) it achieved a win-win situation in learning efficiency and quality, forming an “efficiency-quality virtuous cycle”; (3) learning engagement, as a key mediating variable, explained 62.8% of the positive impact of integrated education on learning outcomes; (4) interdisciplinary theme integration (r = 0.72), diverse forms of expression (r = 0.68), and problem-oriented task design (r = 0.64) were key teaching elements affecting the effectiveness of integrated education; (5) integrated education positively impacted learners with different characteristics, but integrative learners (d = 0.59) and intrinsically motivated learners (d = 0.65) benefited more; (6) teacher professional competence (relative importance = 0.86), curriculum integration (relative importance = 0.76), and learner autonomy (relative importance = 0.78) were key conditions for successful integrated education, while time pressure (impact intensity = 0.74) and evaluation system incompatibility (impact intensity = 0.68) were major limiting factors. Based on these findings, the study constructed a theoretical framework for the integration of language and literature education and proposed practical implications including reconstructing the curriculum system, innovating teaching methods, transforming evaluation mechanisms, and strengthening teacher development. These conclusions not only confirm the scientific rationality and practical feasibility of integrating Chinese language education with Chinese language and literature education, but also provide a theoretical basis and practical pathways for constructing innovative education models adapted to complex environments, which has important implications for promoting Chinese language education reform.

With the continuous progress of science and technology, dynamic environmental art installations have gradually become an important part of modern urban space design, especially in public art, architectural landscape and interactive experience design, which has an important expressive power and functionality. This paper discusses how biomechanical principles can promote the innovative design of dynamic environmental art installations, proposes a series of innovative design concepts based on biomechanical principles, and analyzes and practically verifies them through several specific cases. The study shows that the combination of bionics, sustainable design and interactive design principles can effectively enhance the interactivity, sustainability and functionality of dynamic art installations, providing new perspectives and technical references for the design of future environmental art installations.

This study is centered around optimizing the layout of new railroad energy systems, drawing inspiration from biomechanics and integrating deep-learning and big-data technologies. The overarching aim is to boost energy utilization efficiency and simultaneously minimize the ecological disruptions brought about by energy infrastructure, which contributes to the “dual carbon” goals (carbon peaking and carbon neutrality) by enhancing energy efficiency and reducing environmental impact. This approach not only promotes green transportation but also aligns with sustainable development objectives. Inspired by the complex and well-coordinated mechanisms in biomechanics, a comprehensive biological-effect-inspired evaluation index system is devised. This system takes into account the diverse impacts of energy systems on the surrounding environment, similar to how living organisms interact with their habitats. Just as a living body’s various parts work in harmony, this index system captures the multi-faceted relationships between the energy system and the environment. A hybrid neural network model, designed with inspiration from the neural-like processing in biological systems, combines advanced convolutional and long short-term memory networks. This combination is aimed at effectively extracting both spatial and temporal features, much like how biological neural systems process different types of information related to space and time. For instance, in the human body, the nervous system can quickly respond to changes in the surrounding space and also remember past experiences over time. Additionally, multi-task learning techniques are employed to enable simultaneous analysis of multiple environmental indicators, such as noise, temperature, and magnetic field strength. Experimental results reveal that the proposed biomechanics-inspired approach far surpasses traditional heuristic algorithms. It showcases remarkable prediction accuracy and computational efficiency. By harnessing the power of advanced machine-learning frameworks inspired by biological systems, this method offers precise evaluations and practical insights for optimizing energy layouts. This research not only facilitates the scientific planning of railroad energy systems but also aids in reducing their ecological footprint, in line with the principles of sustainable development. The findings establish a solid foundation for achieving a balance between energy requirements and environmental conservation. They underscore the transformative potential of intelligent technologies, inspired by the wonders of biomechanics, in modern infrastructure planning.

This study delves into the mechanical properties of polycrystalline tantalum nanomaterials with varying grain sizes and their influence on fibroblast behavior, utilizing molecular dynamics simulations. The cutting simulations revealed that tantalum nanomaterials with larger grain sizes exhibit superior mechanical performance. Detailed analyses of stress-strain curves, elastic modulus, and elongation at break further demonstrated that larger grain-sized tantalum nanomaterials possess enhanced toughness and compressive strength, positioning them as promising candidates for biomedical applications. Additionally, cellular experiments evaluated the biological effects of these nanomaterials on fibroblasts, showing that larger grain sizes significantly promote fibroblast proliferation. This highlights their potential in tissue engineering and regenerative medicine. By uncovering the intricate relationship between the mechanical properties of tantalum nanomaterials and cellular responses, this study underscores the critical role of material mechanics in biological applications and provides valuable insights for the future design of advanced biomedical materials.

Background: Childhood is an essential period for psychological and physical development since it establishes the basis for lifelong physical activity and positive behaviors. Early experiences shape the physical capacities of preschool-aged children, who are growing at a rapid pace. Therefore, it is crucial to provide preschool-aged children with sufficient physical exercise to improve their motor skills, balance, and coordination, general physical and mental health. Purpose: The research’s goal is to examine the impact of implementing a biomechanics-based physical training program for preschoolers. The program incorporates age-appropriate biomechanical concepts into planned exercises, emphasizing the development of motor skills, flexibility, strength, balance, and general physical development while encouraging social engagement and mental resilience. Methods: A total of 157 preschool children were randomly divided into two distinct categories for the analysis. The intervention group (IG) received a biomechanics-based physical training program, and the control group (CG) received standard physical training. Assessments conducted before and after the program included measures of physical fitness (motor proficiency, agility, and balance) and psychological well-being (emotional regulation, cognitive development, and self-esteem). Results: Furthermore, statistical analysis techniques (SPSS) were used to highlight how different variables affect the result within research. The data is analyzed using descriptive statistics, paired t-tests, correlation, and independent samples t-test to compare post-intervention data between groups. The IG showed significant improvements in motor proficiency, balance, and self-esteem, outperforming the CG. Independent samples t-test confirmed better outcomes in the IG, especially in Emotional Regulation (+6) and Self-Esteem (+5). Correlation analysis revealed strong associations between physical fitness and psychological well-being. Conclusion: The biomechanics-based physical training program for preschool children effectively enhances motor skills, physical fitness, and overall developmental outcomes, fostering a strong foundation for long-term health and well-being.

With the growing emphasis on health management and the widespread adoption of smart devices, the demand for motion recognition and health anomaly monitoring has become increasingly pressing. In particular, for students at the Zhejiang Police College, maintaining good physical fitness and health is critical because they often face high-intensity work pressure and unpredictable work environments. Fundamentally, health monitoring is a pattern recognition challenge, wherein the objective is to extract critical features from multimodal data to detect abnormal behaviors or health conditions, thereby enabling real-time surveillance of individual health statuses. Against this backdrop, this paper introduces a novel health monitoring framework (PSCLM), which integrates convolutional neural networks (CNN), long short-term memory networks (LSTM), and modal decomposition techniques, aiming to enhance the precision and resilience of action recognition and health anomaly detection. Initially, the proposed framework analyzes inertial sensor data and constructs a convolutional long short-term memory (CLM) model utilizing CNN and LSTM to facilitate movement type recognition. Subsequently, given the significance of heart rate as a vital indicator during physical activity, heart rate and electrocardiogram (ECG) signals, along with their variational mode decomposition (VMD) decomposition features, are incorporated with movement recognition features to achieve multidimensional and hierarchical fusion of physiological signals during exercise. Finally, leveraging these fused features, the framework achieves comprehensive monitoring of exercise-induced health states. Experimental evaluations reveal that the PSCLM framework attains an average accuracy exceeding 0.9 for action recognition on publicly available datasets, while its accuracy for abnormal state detection reaches 0.95, outperforming traditional approaches and single deep learning models significantly. This research not only provides an innovative technological approach for sports health management and anomaly detection but also establishes a critical foundation for advancing next-generation intelligent health monitoring systems.

In order to explore the change path of agricultural socialized service organization mode and operation mechanism to adapt to the needs of farmers, a fitness analysis framework is constructed based on biomechanics theory, and the operation characteristics of the organization mode are measured from the perspectives of mechanical equilibrium, resource flow and dynamic adjustment. Taking the North China Plain, the Middle Yangtze River Plain and the Northeast Plain as examples, we measured the density of socialized service supply, the resource flow rate and the fitness index, and the results show that the Middle Yangtze River Plain has the highest fitness index (0.83), the resource flow rate reaches 324.7 tons/day, the information transmission efficiency is 5.21 bit/s, and the response time to the farmer’s demand is the shortest (5.7 h), which indicates that the organizational structure is stable and the precision of resource allocation is high. Resource allocation precision is high. The biomechanics theory can effectively optimize the agricultural socialized service model and improve the adaptability and operational efficiency of the agricultural production system.

The advancement of endoscopic visual navigation is critical for accurate diagnosis and treatment of colonic disease. However, traditional techniques face challenges in adaptability to complex colonic conditions. This is exemplified by their limited ability to respond effectively to different situations, which results in less accurate navigation planning. This study proposes a navigation system to overcome this issue and improve navigation adaptability. An adaptive strategy has been designed based on a multidimensional discrimination method to guide endoscopes in traversing complex colonic environments. This strategy takes into account the biomechanical properties of the colon, such as tissue flexibility and kinematic characteristics, enhancing navigation accuracy. Furthermore, specific strategies for calculating navigation points have been developed for colonic collapses and tumors to ensure effective navigation under various biomechanical conditions. In simulated tests for the colon model, the system achieved an overall success rate of 92.5% (multiple scenarios), with average deviations of 3.15 mm horizontally and 2.51 mm vertically. Additionally, the system is characterized by its ease of operation, thereby reducing the reliance on operational experience and protracted training. By combining the principles of biomechanics, this study not only improves the accuracy of endoscopic navigation but also provides a new perspective for the treatment of colon diseases and emphasizes the importance of biomechanics in clinical applications.

From the perspective of fluid mechanics, combined with the knowledge of fluid mechanics, this paper analyzes the laws of several different rotation forms of basketball, and on this basis, uses the mechanical method and the vector synthesis method of speed to explore the influence of forward spin ball, back spin ball, and side spin ball on the shooting percentage. This paper points out the importance of ball rotation in the process of shooting and provides theoretical guidance for basketball teaching and training.

Traditional emotion prediction methods rely heavily on large amounts of labeled data and often struggle to capture subtle variations and individual differences in emotional expression. The goal of this paper is to enhance Generative Adversarial Networks (GANs) to improve emotion prediction accuracy, thereby providing college students with a more intelligent and personalized learning experience in interactive music interpretation. Firstly, a spatial channel attention mechanism is incorporated into the generator of the D2M-GAN multimodal generative adversarial network to improve the model’s ability to focus on important information. Additionally, the traditional large kernel convolutional layer is replaced by a convolutional layer with multi-scale convolution, enhancing the model’s ability to assess the authenticity of the generated data. To further optimize the model, the generative network is both rewarded and penalized using music theory rules, and the convergence speed is accelerated by optimizing the loss function. This improves the intelligence and personalization of interactive music interpretation. In this study, the accuracy and generalization ability of the proposed Deep Two-Modal Generative Adversarial Network with Spatial Channel Attention model (D2M-GAN-SCA) are evaluated using cross-validation and comparative validation. The experimental results demonstrate that the generator structure with the spatial channel attention mechanism, combined with the discriminator optimization strategy involving multi-scale convolutional layers, significantly enhances the accuracy of sentiment prediction. An accuracy of 97.03% is achieved after 1400 training iterations. Furthermore, the model shows notable improvements in loss function stability, convergence speed, and the quality of generated music. These advancements provide robust support for sentiment prediction and real-time interactive music generation, facilitating a more engaging and personalized online learning experience for college students in music interpretation.

The serve is a crucial technique in tennis, providing players the opportunity to master and organize their attacks during competitions. Current tennis serve training methods often lack sophisticated, data-driven tools, and existing recognition techniques rely on single-feature extraction methods focusing on isolated attributes like trajectory or speed. These methods do not fully utilize the comprehensive spatio-temporal information present in video data, resulting in limited accuracy and robustness in recognizing and analyzing different serving techniques. To address these limitations, this paper proposes a tennis serve recognition method using a bi-directional long short-term memory neural network (BiLSTM). Our approach first employs a modified convolutional neural network (CNN) to extract spatial features from images, enhanced by self-attentive weighting to improve feature extraction. It then uses BiLSTM to capture and represent important temporal features, thereby enhancing the model’s ability to recognize and evaluate serving actions. Experimental results demonstrate that our method outperforms existing neural network models in server recognition tasks, effectively addressing the limitations of previous approaches.

With the popularization of e-readers, electronic reading rooms, digital libraries, and other new ways of reading in libraries and society, libraries have also entered a new stage of development because of “low-carbon” construction. The low-carbon development of intelligent libraries reduces the application of traditional literature carriers, increases the popularity and application of modern equipment, makes the replacement of paper materials, and reduces its own energy consumption. To achieve personalized recommendations in the lending system, this paper, inspired by biomechanical concepts, constructs a tree intelligent recommendation system via a collaborative filtering algorithm. This system functions like a neural network in a biological system, processing and analyzing data to make informed decisions. By verifying the system with actual borrowing data of students, it proves effective, much like how a biomechanical adaptation is tested and validated in nature. This approach offers a valuable reference for intelligent book management in universities, aligning library operations with the principles of efficient resource utilization and adaptation seen in the biomechanical world. book management in universities. In addition to these advancements, integrating biomechanics into the design and operation of smart libraries can enhance user experience and engagement. Understanding the biomechanics of reading—such as posture, hand movements, and eye tracking—can inform the development of ergonomic reading spaces and devices. For instance, optimizing seating arrangements and reading environments based on biomechanical principles can reduce physical strain and improve comfort for users. Moreover, incorporating biomechanical feedback into the recommendation system could personalize user interactions further. By analyzing how different users engage with reading materials—considering factors like reading speed, preferred formats, and physical interactions with devices—libraries can refine their recommendation algorithms. This approach not only enhances the effectiveness of title recommendations but also promotes a healthier reading experience, aligning with the low-carbon goals of reducing physical strain and energy consumption associated with inefficient reading practices.

Forest parks constitute one of the crucial types of urban green spaces. Studying cultural ecosystem services (CESs) in forest parks is critical for ecological conservation and human well-being. Taking Xijiao Forest Park (XFP) in Dalian, Liaoning Province, as the study area, the Latent Dirichlet Allocation (LDA) model was applied to extract latent topic classifications from web text data. An evaluation system was established by integrating the public participation geographic information system (PPGIS) and the social values for ecosystem services (SolVES) model, which is a spatially explicit tool that quantifies perceived social values through participatory mapping and environmental variable integration. This approach was used to analyze the spatial distribution of the value of its CESs and to identify the environmental factors influencing these values. The results indicate the following: (1) Public perception encompasses 28 topic levels, which incorporate seven value dimensions: Aesthetics, recreation and ecotourism, social relationships, wellness, education, cultural heritage, and inspiration. (2) The values of diverse CESs within the park (XFP) exhibit considerable spatial heterogeneity, depicting a pattern where high values occur in the north and south and low values occur in the east and west. The aesthetic, recreation and ecotourism, social relationship, and wellness values are relatively highly favored by the public. (3) The three environmental factors, namely, scenic spots, water bodies, and roads, have a more pronounced effect on the CES value. This study uses the novel approach of constructing models for web text data to evaluate the CES value.

Purpose: To observe the short-term efficacy and safety of ranibizumab combined with dexamethasone intravitreal implant (DEX) in the treatment of macular edema (ME) secondary to retinal vein occlusion (RVO) (RVO-ME), with a focus on biomechanical changes in retinal vascular and tissue dynamics. Methods: A prospective clinical case study was conducted. A total of 88 patients (88 eyes) with RVO-ME were included in the study. According to the treatment strategy, they were divided into the DEX and ranibizumab combination group (combination group), the DEX group, and the ranibizumab group, with 33 eyes, 24 eyes, and 31 eyes, respectively. Patients underwent best-corrected visual acuity (BCVA), spectral domain optical coherence tomography (SD-OCT), optical coherence tomography angiography (OCTA), contrast sensitivity (CS), and visual field testing. The combination group received DEX one week after the first ranibizumab injection, while the other groups followed standard regimens. Follow-up assessments were conducted at 1 week, 2 weeks, and monthly for 6 months. Results: All groups showed significant improvements in BCVA, the retinal thickness (CRT), superficial and deep macular vascular density (SVC-MVD) and macular vascular density of deep macular vascular complex (DVC-MVD), mean defect of light sensitivity (MD), contrast sensitivity (CS), compared to baseline (P < 0.001). The combination group demonstrated superior vascular microstructure improvement, with better SVC-MVD and DVC-MVD at multiple time points (P < 0.05). Biomechanical analysis revealed enhanced vascular compliance and hemodynamic stability in the combination group, contributing to its efficacy. The average injection frequency was lower in the combination group (4.39 ± 0.55) compared to the ranibizumab group (4.65 ± 0.92, P < 0.05). Adverse events included subconjunctival hemorrhage and transient intraocular pressure elevation, with no serious complications. Conclusion: The combined group had a more significant effect in improving vascular microstructure, good short-term efficacy, could reduce the number of injections, and had better safety. The biomechanical benefits, including improved vascular compliance and hemodynamics, highlight its potential for optimizing retinal microcirculation.

In the realm of throwing sports, athlete stability and coordination are crucial for achieving optimal performance and minimizing the risk of injury. From a biomechanical perspective, single-leg stability training has been demonstrated to enhance an athlete’s dynamic balance, improve joint stability, and increase the coordination and reaction speed of muscle groups. These physiological and functional enhancements for throwing athletes translate to better body control during high-speed movements and explosive power bursts, thereby reducing the incidence of sports injuries caused by imbalance or instability. This review offers insights into the potential benefits of single-leg stability training for reducing sports injuries among throwing athletes. It highlights the crucial role of such training in enhancing sports efficiency, preventing injuries, and improving overall performance. Ultimately, this paper proposes future research directions, including the application of advanced biomechanical analysis tools to customize individualized training programs and the validation of the effectiveness and sustainability of these strategies through long-term follow-up studies. The findings of this review not only provide theoretical support for the training practices of throwing athletes but also establish a solid foundation for further research in the field of sports science.

With the rapid development of Internet of Things (IoT) technology, its integration into intelligent medical monitoring devices has significantly transformed the healthcare landscape. This integration not only enhances the functionality of medical monitoring equipment but also improves the real-time accuracy of data collection. This review comprehensively discusses the data acquisition and processing methods of intelligent medical monitoring devices based on IoT, with a particular focus on their applications in molecular and cellular biomechanics. In the context of biomechanics, IoT technology offers new perspectives and tools for biomechanics research. By accurately monitoring mechanical changes at the cellular and molecular levels, IoT technology enhances our understanding of biological systems, thereby providing a scientific foundation for the early diagnosis and treatment of diseases. For instance, by observing the mechanical responses of cells, we can gain insights into how cells sense and react to changes in their external environment. We summarize the current research progress related to IoT data acquisition and processing methods for these devices, analyze the advantages and limitations of existing technologies, and explore future development trends. The review seeks to foster technological innovation and practical applications within this field, ultimately enhancing the quality of medical care and improving the overall quality of life for patients.

With the development of information technology and digital media, graphic design and animation technology play an increasingly important role in modern communication. Through the combination and optimization of visual elements, graphic design realizes the efficient communication of information and the fast focusing of audience’s attention, while animation technology makes complex information visualized and vivid by virtue of its dynamic performance and narrative ability, which enhances the comprehensibility and infectiousness of the content. This paper starts from the point of view of biomechanics discusses the application of graphic design and animation technology in information 7dissemination, analyzes the key technologies such as skeletal animation, motion capture and GPU-accelerated rendering, and proves its advantages in the fields of advertisement, education, and cultural dissemination through the comparison of specific application examples. The results of the study show that graphic design and animation combined with modern technology can significantly improve the effect of information dissemination and provide technical support and development direction for future communication innovation.

This study aims to explore the application of various basic machine learning algorithms in the task of TCM (Traditional Chinese Medicine) disease classification, and select the best performing model for optimization through comparative analysis. After experimental verification, the random forest model has excellent performance in various evaluation indexes, and its accuracy and recall are 65.1%, 65.1%, respectively, showing its comprehensive performance advantage in the classification of TCM disease types. In order to further improve the performance of the model, the sparrow search algorithm was introduced to optimize the random forest model. The performance of the optimized model on the test set is significantly improved, with an accuracy of 74.4%, a recall rate of 70.2%, an accuracy rate of 76.3%, and an F1 score of 73.1%. Compared with the random forest model before optimization, the accuracy of the optimized model increased by 9.3%, the recall rate increased by 0.51, the accuracy rate increased by 9.1%, and the F1 score increased by 8%. These results show that the sparrow search algorithm has a significant effect in optimizing the random forest model, and can effectively improve the performance of the model in the task of TCM disease classification. This study not only verified the applicability of random forest model in TCM disease classification, but also improved the classification effect of the model through the introduction of sparrow search algorithm.

The rapid development of information technology has enhanced the accuracy and depth of intelligent algorithms. However, significant challenges remain in expanding the application scope of intelligent systems and achieving synergistic coupling between mechanical and digital technologies. Inspired by multiscale dynamic feedback mechanisms in cellular biomechanics, such as cytoskeletal remodeling and mechanotransduction in extracellular matrix fiber networks, this study proposes a bio-inspired hybrid modeling framework that analogizes the integration of mechanical and digital systems to the coordinated molecule-cell-tissue multiscale mechanical responses in biological systems. A high-precision mechanistic model is constructed using Recurdyn multi-body dynamics simulation software to capture the physical characteristics of the drafting section through a strategy analogous to multiscale mechanical modeling of ECM (extracellular matrix) fibrous networks. Simultaneously, a GRU (Gated Recurrent Unit) neural network-based data-driven model is developed to emulate the adaptability of biological neural systems, particularly the feedback regulation of neuronal networks under dynamic mechanical stimuli. By calculating residuals between the mechanistic model, data-driven model, and experimental measurements, a dual-channel SKNet (Selective Kernel Networks) architecture is introduced to mimic the multiscale signal extraction properties of mechanosensitive ion channels in cellular biomechanics. Convolutional kernels of different scales extract residual features, and an LSTM (Long Short-Term Memory) residual model is constructed for compensation. Experimental validation demonstrates that the hybrid model significantly improves prediction accuracy and robustness, with its residual compensation mechanism functionally resembling the dynamic repair processes of cells under mechanical stress. This study provides an efficient solution for drafting section modeling and offers methodological insights for interdisciplinary applications in biomaterial fabrication and tissue engineering bio-inspired design.

With the continuous refinement of sports law, the scientific safeguarding of athletes’ rights to fair competition and their physical and mental well-being has emerged as a critical issue. This study introduces an innovative approach that integrates biomechanical technologies with a multidimensional cloud model algorithm, applied within the realm of sports law. Its primary objective is to optimize refereeing decisions and prevent sports injuries through objective data analysis. The research employs track and field events as its experimental setting, where athlete data—including joint angular displacement, angular velocity, angular acceleration, and force measurements—are captured using the Mediapipe motion capture system in conjunction with force platform technology. A multidimensional cloud model is then employed to conduct both quantitative and qualitative analyses of infractions (such as false starts) and fatigue states. Results indicate that, compared to traditional refereeing, this method not only achieves higher accuracy in identifying false starts but also effectively detects fatigue through joint motion parameters, thereby providing a robust scientific basis for athlete health monitoring. Furthermore, the study examines legal risks associated with the application of biomechanical data, such as issues related to data privacy, ownership, and evidentiary validity, and proposes compliance measures including informed consent, data encryption, and standardized protocols. Empirical findings demonstrate that biomechanical technology can significantly enhance the objectivity of adjudication, reduce misjudgment rates, and support the management of athletes’ training loads—thus playing a pivotal role in maintaining fair competition and safeguarding athletes’ well-being. Future research should further integrate intelligent algorithms and expand sample diversity to promote a deeper convergence between biomechanics and sports law.

Hyperlipidemia is a major contributor to cardiovascular diseases, impacting vascular function by altering hemodynamic parameters such as blood flow velocity and vascular pressure. This study investigated these effects using Doppler ultrasound, vascular pressure sensors, and computational fluid dynamics (CFD) modeling in a cohort of 100 participants, divided into 50 hyperlipidemic individuals (total cholesterol: 250.4 ± 15.2 mg/dL, triglycerides: 180.3 ± 20.5 mg/dL, low-density lipoprotein (LDL) cholesterol: 170.1 ± 10.3 mg/dL) and 50 controls (total cholesterol: 190.7 ± 10.4 mg/dL, triglycerides: 130.2 ± 15.7 mg/dL, LDL cholesterol: 120.5 ± 9.8 mg/dL). Future research could explore the differential effects of LDL and HDL on blood flow velocity and vascular pressure, as LDL is known to increase blood viscosity and contribute to endothelial dysfunction, while HDL may have a protective effect by improving endothelial function and reducing vascular resistance. Results showed a significant reduction in blood flow velocity in the hyperlipidemic group (28.1 ± 3.1 cm/s) compared to controls (32.5 ± 2.3 cm/s, p < 0.001) and an increase in vascular pressure (98.6 ± 5.4 mmHg vs. 85.2 ± 4.1 mmHg, p < 0.001). CFD analysis revealed increased turbulence and shear stress variability, contributing to endothelial dysfunction and vascular resistance. Statistical correlations indicated a strong negative association between LDL cholesterol and blood flow velocity (r = −0.624, p < 0.001) and a positive association between total cholesterol and vascular pressure (r = 0.583, p < 0.001). These findings highlight the biomechanical consequences of hyperlipidemia, emphasizing the need for early intervention through lipid-lowering therapies and lifestyle modifications to improve vascular health. The integration of CFD modeling into clinical practice may enhance risk assessment and personalized treatment strategies for hyperlipidemic individuals.

This study examines the impact of biomechanical emotional intelligence (EI) on investment decisions in the context of external uncertainties such as trade conflicts. The study shows that market co-movement is increasing in the context of globalization, especially during trade conflicts, where market volatility increases in tandem and investor emotional volatility rises significantly. Biomechanical EI plays a key role in emotion management by modulating investors’ physiological responses (e.g., heart rate, muscle tension, etc.). Investors with higher emotional intelligence are able to reduce the negative impact of emotional volatility by regulating biomechanical states (e.g., lowering heart rate, relaxing muscles), thus improving rationality and stability in decision-making. Empirical analysis shows that investors with higher emotional intelligence realized excess returns of 2.5% during the 2018 US-China trade conflict, while those with lower emotional intelligence faced losses of 1.2%. This study reveals the important role of emotional intelligence in complex market environments, especially during periods of high volatility, where its biomechanical regulation mechanism helps to enhance the quality of investors’ decisions and optimize risk management.

This study discusses the biomechanical and cellular factors that affect the speed and accuracy of tennis service. By combining biomechanical characteristics (such as power chain, hitting point control, ground reaction, etc.) with cellular factors (such as muscle fiber type, energy metabolism efficiency, neuromuscular coordination ability, etc.), this paper analyzes the key mechanism of improving serve performance. The results show that the collaborative optimization of biomechanics and cellular factors has a significant effect on improving the service speed and accuracy. The research adopts measured data, literature review, and motion sensor data and draws the conclusion that high-level athletes are significantly better than ordinary athletes in various indexes, which provides scientific guidance for tennis training.

This study presents a new approach combining biomechanical models and clustering algorithms for the analysis of international political communication pathways. The effectiveness of simulation method in revealing information propagation paths and key nodes in political events. The study first reviews the application of biomechanical models and clustering algorithms in multiple fields, based on which we construct a propagation path model combining biomechanical and cluster analysis. Experimental results show that the proposed method outperforms traditional clustering algorithms in terms of clustering accuracy, propagation efficiency and stability. In particular, in specific political events, the transmission paths exhibit significant clustering effects, mainly focusing on several key nodes with high transmission potential, highlighting the non-linear characteristics of the propagation process. The transmission efficiency of the transmission pathway based on the biomechanical model reached 88.4%, which is much higher than the traditional transmission pathway of 73.2%. Although biomechanical clustering algorithms run longer when dealing with large-scale networks, their accuracy and stability are significantly better than conventional algorithms. This study provides a new analytical tool and theoretical framework for the field of international political communication, and reveals the influence of network structure on the transmission path.

This study discusses the strategy of combining biomechanics principle and data mining technology to optimize the user experience of e-commerce platform. Through biomechanical principles, the interface design and interaction mode are optimized, and the operation comfort and efficiency are improved. Data mining technology deeply analyzes user behavior data, reveals user needs and pain points, and provides decision support for personalized service and interface design. The study proposes specific optimization strategies, such as personalized recommendation, interaction process simplification, response speed improvement and equipment adaptation, and emphasizes the importance of user feedback and continuous optimization mechanism. These strategies effectively improve the user experience and enhance the user stickiness and market competitiveness of the platform.

Under the background of big data-driven education digitization, physical education in colleges and universities is facing the problems of data isolation (< 35% compatibility rate of multi-source devices) and training homogenization (only a 28% coverage rate of personalized programs). In this study, we integrated six existing data specifications, including the Student Physical Fitness Standard, and designed a standardized data collection scheme for physical fitness monitoring: (1) We developed a data meta-standard that included 12 types of wearable devices (heart rate/step rate/oxygen uptake) and three types of training scenarios (classroom/outside classroom/competition) and unified the collection protocols for the 23 core metrics; (2) We adopted a machine-learning preprocessing method K-Nearest Neighbors (KNN) filling of missing values + Isolation Forest detection of outliers) to make the data standardized. (3) Using machine learning preprocessing (missing value KNN filling + anomaly detection by Isolation Forest), the data completeness rate is increased from 61% to 89%; (4) Constructing a comprehensive physical fitness scoring model (XGBoost algorithm, with 8 basic physical fitness and 5 dynamic response indicators) and combining it with the reinforcement learning recommender engine, generating a hierarchical teaching plan (with 3 levels of difficulty adaptation and 5 types of sports intervention templates) for teachers. Empirical evidence shows that the program improves the efficiency of teachers’ lesson planning by 46.42% and increases the matching degree of students’ personalized training by 3.2 times. Compared with traditional empirical teaching, the standardized data-based intelligent model reduces the error rate of physical fitness assessment from 19.7% to 8.3% and provides teachers with a three-in-one tool: class heat map—individual warning list—lesson plan knowledge base, which pushes the physical education teaching from “experience-driven” to “data-driven”. This study provides a reusable methodological framework for the digital transformation of college sports.

Highway asphalt pavements are subject to mechanical stress, deformation, and environmental interactions that lead to damage such as ruts, cracks, water infiltration, and depressions. The role of biomass materials in diagnosing and repairing these damages is explored in this research, emphasizing the integration of advanced analytical methods and bio-based repair technologies. The research begins by analyzing the mechanical and environmental factors contributing to pavement degradation, with a focus on the potential of biomass additives to mitigate these effects. Using the Analytic Hierarchy Process (AHP), road condition indices and damage metrics were quantitatively assessed before and after repair on a section of the Shanghai-Suzhou Expressway. Post-repair results demonstrated a 30-point reduction in the road damage index, highlighting the effectiveness of biomass materials in enhancing pavement functionality and durability. This study underscores the value of sustainable material principles and diagnostic frameworks for optimizing repair strategies. The findings provide actionable insights into leveraging bio-based materials to improve pavement engineering practices and support sustainable infrastructure maintenance.

Objective: This study aims to investigate the epigenetic mechanisms by which KDM6B regulates epithelial-mesenchymal transition (EMT) in differentiated thyroid cancer cells in response to changes in extracellular matrix (ECM) stiffness, and to elucidate its role in tumor invasion and metastasis. Methods: The differentiated thyroid cancer cell line K1 was used to prepare soft matrices (1 kPa), moderately stiff matrices (10 kPa), and stiff matrices (30 kPa). KDM6B was knocked down using siRNA and overexpressed using pcDNA3.1-KDM6B. The expression and activity of EMT markers (E-cadherin, N-cadherin, vimentin) and KDM6B were detected using real-time quantitative polymerase chain reaction (qRT-PCR), Western blot, Chromatin Immunoprecipitation-quantitative Polymerase Chain Reaction (ChIP-qPCR), and RNA sequencing (RNA-seq). Additionally, Transwell migration and invasion assays were performed to assess the migratory and invasive capabilities of the cells. GO and KEGG enrichment analyses were conducted to explore the key genes and signaling pathways regulated by KDM6B. Results: Under different ECM stiffness conditions, the mRNA and protein expression levels and enzymatic activity of KDM6B significantly increased (p < 0.001). Increased matrix stiffness led to a significant decrease in E-cadherin and a significant increase in N-cadherin and vimentin (p < 0.001). Following KDM6B knockdown, E-cadherin significantly decreased, N-cadherin and vimentin significantly increased, and cell migration and invasion capabilities were enhanced (p < 0.01). Conversely, overexpression of KDM6B significantly upregulated E-cadherin, significantly downregulated N-cadherin and vimentin, and inhibited cell migration and invasion capabilities (p < 0.01). ChIP-qPCR analysis indicated that KDM6B binding to the promoters of EMT genes such as Snail and Twist was significantly enhanced in high-stiffness matrices and regulated their transcriptional activity through demethylation of H3K27me3 (p < 0.05). RNA-seq and enrichment analyses revealed that KDM6B regulates key genes and multiple signaling pathways, including PI3K/Akt and MAPK, which are involved in biological processes such as cell proliferation and apoptosis. Conclusion: KDM6B regulates the EMT process in differentiated thyroid cancer cells in response to changes in ECM stiffness, primarily by affecting the expression of EMT-related genes through epigenetic modifications, thereby regulating cell migration and invasion capabilities. KDM6B provides a new theoretical basis as a potential therapeutic target for differentiated thyroid cancer.

This research presents a comprehensive big data analysis and visualization platform specifically designed for cancer cell engineering, with a primary focus on understanding the mechanical properties and interactions of cancer cells, particularly in the context of ABC transporter-mediated drug resistance mechanisms in breast and lung cancer cells. The platform integrates advanced machine learning algorithms with real-time analysis capabilities of cellular mechanics and interactive visualization techniques, addressing critical challenges in cellular biomechanics visualization and multi-dimensional data integration. To strengthen its relevance, specific case studies are included that demonstrate the platform’s practical applications in biomechanics research, such as analyzing cell movement, mechanical properties, and cell-cell interactions. The integration of biomechanical models with the platform’s analysis tools is discussed to enhance the understanding of cellular behaviors. Comparative analysis with existing cellular visualization systems demonstrates significant improvements in data processing capabilities and analytical accuracy, particularly in correlating processed data with biomechanics-related metrics like cellular stress and deformation. The system’s performance has been extensively validated across multiple experimental scenarios. Algorithm accuracy achieved 98.5% in feature extraction and 97.9% in pattern recognition tasks, specifically in identifying patterns related to ABC transporter-mediated drug resistance. The platform’s distributed architecture demonstrated exceptional scalability, maintaining stable performance with up to 5000 concurrent research users while achieving 88.4% resource efficiency—a significant advancement over current cellular analysis platforms. Integration testing confirmed robust interoperability between analytical modules with a 99.8% success rate in analyzing cellular transport mechanisms. Stress testing revealed sustained system stability under loads up to 175% of designed capacity, with graceful degradation beyond this threshold. Validation experiments across diverse cancer cell analysis scenarios yielded a 98.8% success rate with statistical significance (p < 0.001). The platform introduces novel approaches to cellular transport visualization, particularly in analyzing ABC transporter activity, offering real-time interactive 3D visualization capabilities not available in existing systems. This platform significantly advances the field of cell engineering data analysis by providing a reliable, scalable, and efficient solution for complex biological data processing and visualization, specifically addressing the challenges in cancer cell research, mechanical properties analysis, and drug resistance.

This study investigated the mechanisms of metatarsal cellular mechanical stress responses to plantar pressure during basketball activities. A comprehensive analysis was conducted using integrated biomechanical and cellular approaches, involving 120 professional basketball players divided by playing positions. Plantar pressure distribution was measured during specific basketball movements using a high-precision pressure measurement system, while cellular responses were analyzed through morphological, biochemical, and genetic markers. Results demonstrated a non-linear relationship between applied pressure and cellular stress response, with a threshold effect at 300 kPa. Significant position-specific differences were observed in pressure distribution patterns, with centers exhibiting higher peak pressures (698.3 kPa ± 52.4 kPa) compared to forwards (642.5 kPa ± 48.6 kPa) and guards (584.2 kPa ± 42.3 kPa). Cellular adaptation mechanisms showed peak activity between 24–48 hours post-stimulation, characterized by increased aspect ratios and upregulation of mechanosensitive genes. Multiple regression analysis identified peak pressure, loading duration, and recovery time as primary factors influencing cellular responses, accounting for 85% of observed variance. These findings provide novel insights into the relationship between basketball-specific mechanical loading and cellular adaptation mechanisms, offering implications for injury prevention and training program optimization.

This study investigates the application of machine learning technology and biomechanical analysis in predicting sports injuries to enhance athlete life expectancy. The purpose is to explore the relationships between training practices, previous injuries, biomechanical factors, and athlete engagement with injury prevention technologies. Key issues addressed include the gap between awareness and practical application of these technologies, as well as the need for standardized data collection methods. A quantitative research design was employed, utilizing survey questionnaires distributed to 110 athletes to gather data on their training practices, previous injuries, and engagement with technological tools. Descriptive statistics and correlation analyses revealed significant relationships among the variables, highlighting the importance of effective training practices in reducing injury risks. Findings suggest that while athletes recognize the value of biomechanical assessments and machine learning, there is a need for improved engagement with injury prevention technologies. Recommendations include standardizing data collection protocols, enhancing educational initiatives for athletes and coaches, and addressing ethical concerns related to data privacy.

To study the correlation between organic matter enrichment and sedimentary environment in the Ordos Basin, hydrocarbon source rocks in the region of the eastern edge of the central plain of the basin are selected for analysis. With reference to the related theories of sedimentary rocks and organic geochemistry, the means including pyrolyzed rocks, organic carbon determination, and detection of main trace elements are adopted. The relationship between the regional sedimentary environment and organic matter enrichment is examined after the features of the paleo-sedimentary environment in the study area are examined. At the same time, from the perspective of biomechanics, the concepts of fluid mechanics and material transport are introduced to analyze the influence of hydrodynamic conditions in sedimentary environments on the migration and enrichment of organic matter. The following results were obtained through the study of organic matter enrichment and sedimentary environment in the Ordos Basin: (1) In the analysis of microscopic features, the study area is mainly composed of matrix vitrinite (accounting for more than 68%) and sericite, and also found woody fibers, microbial debris, and pyrite. (2) In organic matter analysis, it was found that the hydrocarbon generation potential (S1 + S2) of the B-S group was divided into multiple levels. According to the relationship, coal can be classified as a good source rock, while the carbonaceous mudstone in the S group belongs to a poor source rock. Group B-T is mainly composed of type III kerogen (humic type). (3) In the analysis of geochemical indicators, the concentrations of Si, Al, and Fe are very high. Further analysis shows that Si and Ti are the main enriched elements, while Re and Mo are abnormally enriched elements. (4) In the analysis of sample physical properties, sample characterization indicates that the source rock is mainly microporous, with few micropores in sandstone, mainly consisting of fractures and medium to large pores. Research has found that there is a correlation between the pore conditions of the sample and organic matter, and the pore volume increases relatively with the increase of organic matter. (5) Organic matter, abdominal muscles, and sediment environment analysis, according to the examination of geochemical indicators, the research area’s environment changed significantly from humid to arid and semi-arid conditions at various points during the early, middle, and end of the study period. The salinity of the water body in different regions differed significantly, which was related to the regional climate. The study of the carbon content of organic matter and the characteristics of the sedimentary environment displayed that the redox reaction in the early stages of the region and the local climate had a major influence on the enrichment of organic matter. These two factors became the primary controlling factors affecting the region’s organic matter enrichment. The investigation of geological structures and the extraction of local rock gas and minerals can both benefit from the technological assistance this study can offer. At the same time, by introducing the principles of fluid mechanics and material transport in biomechanics, a new perspective is provided for understanding the migration and enrichment mechanism of organic matter in sedimentary environments.

In the Olympic Games and other high-level events, the forehand forward loop technique has always played an important role. Therefore, the forward loop ball technology should be promoted and mastered. Based on biomechanics, this paper analyzes the change of the movement characteristics of the forehand forward loop shot in table tennis players. Specifically, the experimental method and statistical method were used to make an empirical study on the changes of the joint angular velocity of the upper and lower limbs of the study object. The results showed that the peak angular velocity of the shoulder joint was 14.75 ± 3.881 when hitting a backspin ball, which was higher than that of 4.2 ± 3.58 when hitting a topspin ball, p = 0.253 > 0.05. The peak angular velocity of the elbow joint was −10.97 ± 1.74, which was higher than that of the wrist joint, 5.10 ± 1.2, p = 0.537 > 0.05. The peak angular velocity of the hip joint was 6.52 ± 1.73, which was higher than that of the hip joint when hitting a topspin ball (4.84 ± 1.57, P = 0.011 < 0.05). The peak angular velocity of the knee joint was 2.20 ± 0.8, which was larger than that of the knee joint when hitting the topspin ball 2.03 ± 0.66, P = 0.231 > 0.05. The peak angular velocity of the naked joint was 1.75 ± 0.65, which was higher than that of 1.43 ± 0.54, P = 0.078 > 0.05. It can be seen that when hitting the backspin ball, the peak angular velocity and speed of the joint of the upper and lower limbs of the body are greater than that when playing the top spin ball; the peak angular velocity difference between elbow and hip joints is the largest. Therefore, when hitting the backspin ball, pay attention to the lower arm to achieve the purpose of accelerating elbow flexion. At the same time, the rotation speed of the body should be properly reduced, and the speed of the body translation should be increased so as to maximize the peak angular speed of the joint.

Injectable aesthetic procedures, such as dermal fillers, have become increasingly popular for facial rejuvenation. However, predicting the deformation of soft tissues during these procedures remains a challenge. This study presents a biomechanical model that combines finite element analysis (FEA) and Python programming to simulate soft tissue deformation during full-facial injectable treatments. The model integrates patient-specific anatomical data to predict the spread and effect of injectable materials, considering factors such as volume, material properties, and underlying facial musculature. Performance metrics, including Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), were calculated and compared against clinical data, demonstrating strong predictive accuracy. The results show that the model effectively simulates localized tissue expansion and provides insights into how different injection volumes and filler viscosities affect tissue deformation. The model’s ability to simulate muscle-filler interactions and predict long-term filler behavior offers significant potential for enhancing treatment planning and optimizing outcomes. Despite its strengths, the model has limitations, including simplified tissue properties and the exclusion of long-term filler behavior. Future research should focus on incorporating more dynamic muscle activity, patient-specific tissue data, and long-term effects to refine the model further. This work provides a valuable foundation for personalized, data-driven approaches to injectable aesthetic procedures.

Biomechanical principles have been widely applied in multiple industries in recent years, providing new perspectives for evaluating and analyzing complex systems. In this research, the feasibility of integrating biomechanical principles into government environmental performance audits to develop a new approach to environmental impact assessment is explored. By analyzing core principles in biomechanics such as mechanical equilibrium, energy conservation, and biological adaptability, it helps to propose a series of evaluation frameworks and indicators based on biomechanical principles to quantify the key factors in environmental performance audits. The research findings indicate that the application of biomechanical principles in government environmental performance audits can not only enhance the accuracy and scientific nature of the assessment but also offer strong support for environmental protection and sustainable development, highlighting the superiority of incorporating biomechanical knowledge into environmental auditing.

As a high-intensity competitive sport, martial arts has high explosive power and complex joint movement patterns. Ankle injury is one of the common sports injuries. This paper analyzes the mechanism of ankle injury and the influence of relevant mechanical factors on ankle injury in combination with the biomechanical characteristics of martial arts movements. At the same time, with the continuous development of biomedical engineering, the application of tissue engineering bone repair materials in bone injury repair has received widespread attention. This paper focuses on applying tissue engineering bone repair materials after ankle injury in martial arts sports and analyzes its biocompatibility, mechanical properties, and clinical effects in the bone repair process. Studies have shown that tissue engineering materials can effectively promote bone regeneration, accelerate healing, and show good prospects in reducing the recovery period of athletes. Finally, this article proposes the future application prospects and development direction of tissue engineering bone repair materials in martial arts sports injury repair.

This study is based on breast cancer data from the National Cancer Institute (NCI) database, focusing on triple-negative breast cancer (n = 200) and LumB subtype breast cancer (n = 400). A data generation and analysis process combining non-parametric statistics and machine learning was designed. In the initial stage, the wgain algorithm was developed by integrating Wasserstein Generative Adversarial Networks (WGAN) and Random Forest algorithms. The generated expanded dataset was consistent with the original data, with a Pearson correlation coefficient of approximately 0.9, and Principal Component Analysis (PCA) confirmed the high accuracy and consistency of the generated data. The optimal threshold for differential gene selection was determined using the High-Confidence (HC) high-order identification method, and significance analysis was performed using rank-sum tests, Kolmogorov-Smirnov (K-S) tests, and edgeR tests. The results indicated that the rank-sum test performed the best (False Discovery Rate (FDR) = 0.099). A comparison with GAN and Wasserstein GAN Gradient Penalty (WGAN-GP) algorithms showed that wgain had a significant advantage in data consistency and differential gene reproduction (accuracy 83%). This study demonstrates the advantages of combining non-parametric statistics with machine learning, providing a new method for biological data generation and precise analysis.

Background: With the increasing competition in the shipbuilding industry, enterprises are facing more stringent environmental protection requirements and challenges posed by the marine environment, particularly the corrosion of ship materials caused by microorganisms in seawater. This creates an urgent need for innovation management to introduce new technologies that can enhance the corrosion resistance of ships, extend their service life, and reduce maintenance costs. Objective: This paper aims to explore how molecular science and biomechanics can drive product research and development in the shipbuilding industry, especially in addressing the issue of seawater microbial corrosion, thereby promoting technological progress and market expansion for enterprises. Method: This study adopts a combination of case analysis and technological application research, focusing on how molecular technology can be used to develop antibacterial and anti-corrosive ship materials. It also investigates how biomechanics, through biomimetic principles, can enhance the adaptability and durability of ship designs. Additionally, the paper explores the application of molecular science and biomechanics in hull design, examining how nature’s biological defense mechanisms can be mimicked to design more corrosion-resistant, low-maintenance ship structures. Innovation: This paper systematically applies molecular technology and biomechanics to the shipbuilding sector, proposing new technical approaches for the prevention and control of seawater microbial corrosion. This not only effectively enhances the durability and environmental performance of ships but also reduces operational costs. Results: By developing novel anti-corrosion materials, molecular and biomechanical technologies significantly improved the adaptability of ships in marine environments and reduced microbial corrosion on hulls. The introduction of biomimetic design principles not only improved the structural stability of ships but also enhanced their performance in harsh marine conditions.

Background: Obstructive sleep apnea syndrome (OSAS) is closely related to multiple biological mechanisms, particularly its impact on the cardiovascular system. Long term OSAS may exacerbate issues such as hypertension, cardiovascular disease, and aortic disease. The aim of this study is to explore the effects of continuous positive airway pressure (CPAP) therapy on the biological mechanisms of OSAS patients, with a focus on analyzing the biological responses of blood pressure, respiration, and aortic changes. Method: This study retrospectively analyzed a case of a 75 year old male patient who was admitted with severe nighttime snoring and apnea symptoms, diagnosed with acute OSAS, aortic dissection, and hypertension. During the five-year follow-up after receiving CPAP treatment, the patient’s apnea index (AHI), blood pressure, changes in aortic diameter, and related biological indicators were monitored. Comprehensive evaluation of treatment efficacy using multiple biomarkers and electrocardiogram (ECG) data, combined with optimization analysis of respiratory rate and electrocardiogram signals using electrophysiological models. Result: After five years of treatment, the patient’s AHI significantly decreased to less than 6 beats per hour, blood pressure returned to normal, and the aortic diameter decreased from 4.5 cm to 4.1 cm. Electrophysiological analysis shows that CPAP treatment has a significant effect on adjusting respiratory patterns, restoring normal blood oxygen saturation, and optimizing the correlation between electrocardiogram and respiratory rate. In addition, using the TDNN model to estimate ECG signals shows a close biological correlation between respiratory rate and blood oxygen changes. Conclusion: CPAP treatment has a profound impact on the biological mechanisms of OSAS patients, effectively improving blood pressure control, reducing the progression of aortic disease, and optimizing changes in respiratory and electrocardiogram biomarkers. This study provides a new perspective for understanding the biological effects of OSAS treatment and provides a basis for optimizing future treatment strategies.

The present research work aimed to evaluate the integration of wearable biosensors for real-time monitoring of molecular and cellular biomechanics during physical training. The present work assessed their accuracy, applicability, and predictive capabilities in optimizing athlete performance and preventing injuries. This research used wearable biosensors to monitor biomarkers such as lactate, glucose, cortisol, and heart rate variability in athletes. Data was collected from 100 participants actively engaged in professional or semi-professional training regimens across endurance and high-intensity sports, including long-distance running, cycling, soccer, and basketball. Statistical, machine learning and physiological modelling techniques are employed to analyze the data. The findings revealed that wearable biosensors effectively track key biomarkers related to metabolic stress and hydration status, providing insights into performance optimization. However, challenges like sensor stability, motion artefacts, and individual variability in sweat composition were noted. We could use this extra enhancement with AI algorithms to do predictive analytics to predict injury or train and to use a much better-sliding scale on how to train and load them. However, we are not standardized in all the data from all the different sensor platforms. Wearable biosensors have great promise in revolutionizing medicine in sports, altering sports performance and injury prevention. However, accuracy, data standardization and motion artefact reduction are required to adopt these devices in the broader population.

Over the past decade, translation process research has undergone significant evolution, driven by advancements in cognitive science related to the neural mechanisms underlying language processing in biological systems, technology with applications in biological research translations, and data-driven methodologies considering the unique characteristics of biological data. Recently, there has been an emerging connection between this translation process research and biomechanics. For instance, the cognitive strategies in translation, such as chunking and bilingual control, which are influenced by the brain’s biological architecture involved in language processing, may also be related to the body’s mechanical responses. The neural pathways responsible for these translation strategies could potentially interact with the body’s mechanical systems. In terms of technological tools, neural machine translation (NMT) and computer-assisted translation (CAT) systems, which play a crucial role. in biological translating research papers, could be further optimized by considering biomechanical factors. For example, understanding how the body’s physical movements during long-term translation tasks (like sitting posture and muscle fatigue) affect cognitive performance and translation accuracy. Data resources, including parallel corpora and community-driven initiatives, have become foundational to supporting both high- and low-resource languages. However, integrating these with biomechanical considerations, such as how data collection and use impact the physical well-being of translators, adds a new layer of complexity. Challenges persist in integrating diverse tools, addressing cultural and contextual subtleties, and developing comprehensive evaluation metrics that capture creativity and cultural fidelity. Ethical considerations, such as data privacy and representation of marginalized languages, underscore the need for inclusive and responsible practices. Future research directions focus on context-aware technologies, cross-cultural studies, and hybrid human-machine workflows while also incorporating biomechanical insights to enhance translation quality and cultural sensitivity. By integrating interdisciplinary approaches, including biomechanics, translation process research aims to bridge linguistic divides and foster global understanding.

In order to explore the impact of digital skills enhancement on farmers’ income growth, the relationship between digital skills, productivity and income was analyzed in conjunction with biomechanical optimization of learning paths. By investigating data from a sample of farmers in four regions, Hebei Province, Jiangsu Province, Guizhou Province and Sichuan Province, it was found that the level of farmers’ digital skills was significantly and positively correlated with annual income growth. The data showed that farmers with higher levels of digital skills had annual income growth rates of 7% to 10%, while farmers with lower skill levels had income growth rates of only 2% to 3%. The biomechanically optimized learning model significantly enhanced farmers’ digital skills learning efficiency by improving postural alignment, finger dexterity, muscular endurance, and neurofeedback, with a 62.7% improvement in learning outcomes. In addition, the optimized interaction design not only reduced operational burden, but also enhanced skill transfer, enabling farmers to apply digital technologies more efficiently for agricultural production and market interface, thereby contributing to income growth. The findings highlight the key role of biomechanics in enhancing farmers’ digital skills and provide theoretical support for the digital development of agriculture.

With the comprehensive docking of “Made in China 2025” and Industry 4.0, the handling mode of the handling system is constantly updated and developed, and a new handling mode which is assisted by exoskeletons and other equipment to complete the handling work is gradually applied. However, most of the existing exoskeleton assisted robots are expensive and complex in structure, and are not suitable for the actual needs of ordinary workers, so it is of great significance to design an exoskeleton assisted handling robot suitable for the needs of ordinary workers. Based on this, this paper designs a relatively simple structure, relatively low cost of exoskeleton assisted transport robot. The simulation analysis of the reliability of the robot is carried out, and the results show that the exoskeleton assisted robot designed in this paper has high reliability, and the force condition of carrying heavy objects is more matching with each joint of the human body.

This paper focuses on the field of sports biomechanics, and deeply explores its key applications in improving the technical level of athletes and reducing the risk of sports injury. Using rugby and football, two highly representative sports, the underlying internal link between athletes’ skill level and sports injury. It has been found that gender differences in athletes and the range of exercise have different effects on the range of exercise in their muscles and joints. In studies of sport football, the data show distinct sex differences. In the male experimental group, the Angle and torque of the knee plane decreased, and those in the ankle decreased more significantly, while in the female experimental group, the knee angle increased slightly, and the ankle Angle and torque increased to varying degrees. This change shows that the adjustment of sports mechanics of the football players has a positive effect, especially male athletes, the movement significantly reduced, effectively reduce the risk of injury, fully proved the sports mechanics adjustment in reducing the key value of injury rate, help athletes to maintain good physical condition in training and competition, reduce injuries. In the study of rugby movement, in terms of hip frontal torque, the female experimental group was 8.73, which is lower than the female control group. This data intuitively shows that the female experimental group has lower damage in this dimension, reflecting that the adjustment of motor mechanics also plays an important role in rugby movement.

This study explores how to establish a quantitative balance mechanism between the lightweight demand of new energy vehicles and the collision safety of occupants/batteries through multidisciplinary collaborative optimization. Integration of topology optimization and biomechanical simulation to facilitate the design and injury prediction of new energy vehicle (NEV) crash safety systems. Using extensive data from the National Highway Traffic Safety Administration (NHTSA) and the Center for Automotive Research (ARC), first, topology optimization is applied to reduce vehicle weight while maintaining crashworthiness. Subsequently, biomechanical simulations were performed using finite element analysis to simulate the human response to impact. These models are then combined to predict injury risk. Our results show that the weight of key vehicle components is substantially reduced, while the effect on structural stiffness is negligible. Biomechanical simulations provide detailed injury severity scores (ISS) for different body parts under different impact scenarios. The comprehensive model shows that compared with the unoptimized vehicle structure, the optimized vehicle structure is expected to reduce the overall weight of the new energy vehicle and reduce the damage probability of the optimized structure in the collision process by 18.2%. This study highlights the great potential of combining topology optimization and biomechanical simulation to improve the crash safety and injury prediction of new energy vehicles.

In this paper, the problem of designing an asynchronous sampling observer and controller for the biomechanical nonlinear systems is investigated. The central nervous system controls biomechanical limb movements through complex neurophysiological mechanisms. The state sampling input observation obtained by the proprioceptor exhibits asynchronous phenomena when transmitted to the central nervous system. The observation information about parts of the body suffers unmodeled dynamics and nonlinear dynamics in transmissions to the central nervous system. Firstly, unmodeled dynamics are introduced to a class of biomechanical hybrid systems, which can be mitigated by increasing the gain of the observer. The high-gain observer designed can solve the problem of obtaining the state information of various parts of the human body and analyze the stability of the error system. Then, the asynchronous sampling controller is structured to realize the nervous system feedback control based on the high-gain observer. Sufficient conditions for the existence of controllers for observer-based sampled data in discrete time are obtained by using the Lyapunov functions and separation principle. Finally, the effectiveness of the method is illustrated by a numerical example.

With the continuous development of science and technology, flexible wearable electronic products are flourishing in many fields, especially in the areas of health monitoring and medical improvement. In the realm of biomechanics, the human body is a complex mechanical system, and monitoring physiological parameters like body temperature has a unique connection to biomechanical research. Body temperature, as one of the most important physiological parameters of the human body, is not only important for health monitoring but also has implications in understanding the body’s mechanical-thermal balance. Biomechanics studies how forces and mechanical stress affect the body’s functions, and temperature can influence the mechanical properties of biological tissues. Researchers have extensively studied the various properties of wearable flexible temperature sensors, such as high precision, good biocompatibility, flexibility, agility, light weight, and high sensitivity, continuously improving real-time and sensitive detection of temperature in various parts of the human body. This article reviews the research progress of high-sensitivity flexible temperature sensors for monitoring body temperature changes. Firstly, the commonly used active materials for flexible temperature sensors were summarized. Secondly, the imaging manufacturing method and process of flexible temperature sensors were introduced. Then, the performance of flexible temperature sensing was comprehensively discussed, including temperature measurement range, sensitivity, response time, and temperature resolution. Additionally, the article explores the potential of flexible sensors in biomechanical applications, such as monitoring joint angles, muscle activation patterns, and pressure distribution during movement. Finally, the challenges faced by flexible temperature sensors in the future were summarized and discussed. By combining temperature sensing with biomechanical data collection, this study highlights the potential of flexible wearable technologies to revolutionize health monitoring and motion analysis.

Sepsis is a life-threatening condition triggered by infection. According to the 45th Critical Care Medicine Sepsis 3.0 criteria, sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated immune response to infection. Renal injury is a common manifestation of organ dysfunction in this setting. Acute kidney injury (AKI) that develops within seven days of a sepsis diagnosis is classified as sepsis-associated acute kidney injury (SA-AKI). Earlier studies proposed that renal damage during sepsis was primarily attributed to insufficient renal blood flow. However, more recent experimental and clinical evidence suggests that renal blood flow often remains stable or even increases during sepsis. As a result, reduced renal blood flow is no longer considered the primary mechanism underlying AKI. Current research efforts are increasingly focused on elucidating the roles of immune dysregulation, inflammatory cascades, coagulation abnormalities, and metabolic reprogramming in the pathogenesis of sepsis. The identification of novel kidney stress and injury biomarkers has also advanced risk prediction and early diagnosis of acute kidney injury in the context of sepsis. This paper primarily reviews the pathophysiological mechanisms and early diagnostic biomarkers of sepsis-associated acute kidney injury from a cellular perspective, aiming to enhance clinicians’ understanding of this condition and improve patient outcomes.

In the contemporary era characterized by rapid advancements in science and technology, artificial intelligence (AI) has emerged as a transformative force across various domains, particularly in the realm of sports rehabilitation training. Its unique advantages position AI as a pivotal element in enhancing training efficacy and facilitating recovery from injuries. The objective of this study is to thoroughly investigate the intricate relationship between training load and recovery rate within the context of AI-driven sports rehabilitation training, thereby offering robust theoretical foundations and practical guidance for the optimization of rehabilitation training programs. This paper commences with an overview of the latest developments in the application of AI in sports rehabilitation training, highlighting critical components such as data-driven monitoring of training loads, the formulation of personalized rehabilitation programs, and the assessment of recovery outcomes. Subsequently, the paper delves into a detailed analysis of the interaction mechanisms between training load and recovery rate, examining the impact of varying training loads on the recovery rates of both athletes and patients, as well as strategies for optimizing the recovery process through precise control of training loads. Finally, the paper emphasizes innovative strategies employed by artificial intelligence technology to adjust training loads and enhance recovery rates. This includes the utilization of machine learning algorithms for predicting individual recovery potential and the application of deep learning techniques for real-time monitoring and adaptive modification of training programs. Through this comprehensive investigation, the paper aspires to offer novel insights and methodologies for the scientific and individualized advancement of sports rehabilitation training, thereby contributing valuable knowledge and support to the swift recovery of athletes and patients.

The global epidemic of adolescent obesity is becoming increasingly severe, posing significant threats to both the physical health of adolescents and their psychological well-being and social adaptability. Exercise biomechanics, as an interdisciplinary field that integrates biology, mechanics, physics, and medicine, focuses on examining the mechanical behavior of organisms during physical activity and its implications for physiological and pathological processes. This paper aims to provide a comprehensive review of recent advancements in the research on molecular mechanisms related to exercise biomechanics in the context of adolescent obesity intervention to offer scientific theoretical support for the prevention and treatment of obesity. In recent years, researchers have elucidated the impact of exercise interventions on the expression and signaling of obesity-related genes by modulating critical pathways, including fat metabolism, inflammatory responses, and insulin sensitivity. This has been achieved through the application of advanced technologies such as high-throughput sequencing, proteomics, and metabolomics, thereby providing molecular-level insights into the mechanisms by which exercise contributes to the prevention and treatment of obesity in adolescents. Building upon this foundation, the present paper aims to further investigate future research directions and the potential applications of exercise biomechanics to enhance the optimization and development of intervention strategies for adolescent obesity.

Whether sports make us healthier is one of the current hot topics in society at large. This study examines the connection between physical and mental health and sports using Thomson Reuters’ Web of Science^TM Core Collection, using bibliometric methods and CiteSpace visualization technology. This study concludes that sports play a beneficial function in improving our physical and mental well-being, which can effectively relieve stress, improve mood, and enhance the quality of existence, but the key lies in finding the right intensity and amount of exercise to avoid psychological problems caused by overtraining or over-exercise, such as exercise addiction and other similar psychological disorders. At the same time, physical activity helps to enhance our physical fitness by promoting cellular health, including positive effects on the immune system, increased metabolic rate, and increased muscle strength and endurance. In conclusion, sport is a “double-edged sword” for physical and emotional well-being, and it is necessary to precisely regulate the impact of physical activity on both mental and physical health through the use of rational exercise methods, for which this study provides practical suggestions for the promotion of people’s physical fitness and the refinement of sport research.

The maintenance of skeletal muscle function depends on the structure and mechanical properties of the extracellular matrix (ECM), which not only offers mechanical support but also regulates muscle cell behavior. Exercise, as a non-pharmacological intervention, can modulate ECM remodeling through various mechanical signals, hence facilitating muscle adaptation, regeneration, and repair. However, the molecular mechanical mechanisms underlying ECM remodeling during exercise remain systematically unexplored. Our review seeks to explore how exercise regulates skeletal muscle ECM remodeling through mechanical signals, elucidates the role of ECM in muscle regeneration and repair, and summarizes the molecular mechanisms by which various types of exercise modulate ECM. Besides, we assess the potential applications of exercise in muscle injury repair and fibrosis inhibition. Research reveals that exercise regulates ECM stiffness and elasticity through mechanical signals such as stretching, compression, and shear stress. These signals are transmitted via integrin receptors, which activate intracellular signaling pathways such as YAP/TAZ and PI3K/Akt, facilitating muscle cell proliferation, migration, and differentiation. Exercise plays a significant role in muscle injury repair by strengthening ECM synthesis and degradation, slowing down, or preventing excessive fibrosis. Especially in aging and muscle-related diseases, exercise intervention displays potential for promoting muscle function and slowing aging.