Molecular & Cellular Biomechanics

Machine learning-based prediction model for sports injury risk in biomechanics: A case study of joint injuries in basketball at a university in Xi’an

Liang Min, Nan Li, Peng Bi, Bo Gao — Fri, 06 Dec 2024 06:39:16 +0000

Basketball players are prone to joint injuries due to the sport’s high intensity and physical demands. Early prediction of injury risk is crucial for implementing effective prevention strategies. Incorporating biomechanics, this study focuses on basketball players at a university in Xi’an, China, aiming to develop a machine learning-based model to predict joint injury risk using easily collectable data such as training load, fatigue levels, and previous injury history. Considering regional differences, we observed that local and northern Chinese students are generally taller, while students from southern China are typically shorter. This anthropometric variation was included in our sampling and analysis. Utilizing data from 100 basketball players, the Random Forest algorithm achieved the best predictive performance with an accuracy of 85%. Key risk factors identified include high training load, elevated subjective fatigue scores, and a history of previous joint injuries. Additionally, biomechanical data were integrated to elucidate the underlying mechanisms of joint injuries, and the cellular responses to injury were explored. The results demonstrate that even with limited data types, machine learning methods can effectively predict joint injury risk among basketball players, providing a valuable tool for injury prevention.

Construction of tennis pose estimation and action recognition model based on improved ST-GCN

Yang Yu — Fri, 06 Dec 2024 07:30:00 +0000

With the rapid growth of computer vision and deep learning technologies, the application of pose estimation and action recognition in sports training has become increasingly widespread. Due to factors such as complex movements, fast speed, and limb occlusion, pose estimation and action recognition in tennis face significant challenges. Therefore, this study first introduces selective dropout and pyramid region of interest pooling layer strategies in fast region convolutional neural networks. Secondly, a pose estimation algorithm based on multi-scale fusion pose residual network 50 is designed, and finally a spatiotemporal graph convolutional network model is constructed by fusing channel attention module and multi-scale dilated convolution module. The data showed that the average detection accuracy of the improved attitude residual network 50 was 70.4%, and the accuracy of object detection for small, medium, and large objects was 57.4%, 69.3%, and 79.2%, respectively. The continuous action recognition accuracy and inter action fluency detection time of the improved spatiotemporal graph convolutional network were 93.8% and 19.2 ms, respectively. When the sample size was 1000, its memory usage was 1378 MB and the running time was 32.7 ms. Experiments have shown that the improved model achieves high accuracy and robustness in tennis action recognition tasks, especially in complex scenes and limb occlusion conditions, where the model shows significant advantages. This study aims to provide an efficient and accurate motion recognition technology for tennis posture analysis and intelligent training.

Comprehensive assessment of lower limb alignment and forces during dance landings under fatigue

Cheng Tian, Yeping Wang, Dingfang Zhang — Fri, 06 Dec 2024 08:10:10 +0000

This study investigates the impact of fatigue on Lower Limb Alignment (LLA) and Ground Reaction Forces (GRF) during dance landings, intending to understand how fatigue-induced changes affect joint mechanics and stabilization in trained dancers. Thirty dancers (mean age: 23.4 years) with a minimum of three years of training in high-impact dance forms, such as ballet and contemporary dance, participated in the study. A within-subject experimental design assessed each participant’s landing mechanics before and after a fatigue-inducing protocol. Kinematic data were captured using a 3D motion capture system, while kinetic data were recorded with force plates. Joint angles at the hip, knee, and ankle were measured during the landing’s initial contact, peak force, and stabilization phases. Vertical and medial-lateral GRF and time to stabilization (TTS) were also analyzed pre- and post-fatigue. The fatigue protocol consisted of plyometric exercises and repetitive dance-specific movements designed to mimic the physical demands of a dance performance. Measurements were taken immediately after the fatigue protocol and at intervals of 15 min, 1 h, 24 h, and 48 h post-fatigue to assess both immediate and delayed effects of fatigue. Significant changes in joint angles were observed across all phases of the landing. Post-fatigue, hip and knee flexion increased significantly at initial contact (hip: +2.7°, knee: +3.6°, p < 0.05), reflecting compensatory adjustments for impact absorption. Ankle dorsiflexion also increased significantly during stabilization (+2.7°, p = 0.028). Vertical GRF increased across all phases post-fatigue (initial contact: +4.4 N/kg, p = 0.009), indicating a reduced ability to absorb impact forces efficiently. TTS was significantly prolonged at all post-fatigue intervals, particularly within the first 15 min post-exertion (+34 ms, p = 0.008), suggesting impaired neuromuscular control and balance.

Biomechanical analysis and optimization of sports action training in virtual reality (VR) environment

Jianfeng Deng — Fri, 06 Dec 2024 00:00:00 +0000

Over the past years, virtual reality (VR) has become much more popular. VR combines several technologies to provide an immersive digital environment. This environment allows users to engage and react to their actions, creating a virtual world where users feel more present. In biomechanical analysis, researchers analyze the physical characteristics of biological tissues and model the relationship between tissue form and function. Utilizing VR headsets, motion-tracking apparatus, and realistic virtual worlds that simulate actual sports situations are all part of virtual sports training. VR lacks realism, which can be related to the absence of sensory input, making it unsuitable for training fine motor skills. The research aims to perform biomechanical analysis and optimize sports action training inside a VR setting. A mountain gazelle optimizer fine-tuned adjustable convolution neural network (MGO-ACNN) is proposed to examine the joint angle selections utilized by sports action. In this study, human motion image data are utilized to capture various angles of the training action. The data was preprocessed using a Wiener Filter (WF) for the obtained data. Analyzing spatial frequency and orientation in images for feature extraction is accomplished using the Gabor Filter (GF). This approach incorporates VR simulations to provide a more regulated and immersive setting for joint angle analysis during sports training. The proposed method is implemented using Python software. The result demonstrated by the proposed method significantly outperforms the existing algorithms. The performance parameters for accuracy (99.73%), precision (99.75%), recall (99.73%), and F1-score (99.72%) are assessed in this study. The VR experiments indicate that optimal sports preparation involves a sports action while maintaining a batting speed consistent with the joint to lower the center of gravity. This research highlights the more effective, personalized sports training system, leveraging VR to simulate real-world conditions while providing detailed biomechanical insights.

Evaluation of the effect of biofeedback system driven by optoelectronic conjugate materials in VR exercise rehabilitation

Ying Wang — Mon, 09 Dec 2024 03:28:42 +0000

Presently, Exercise Rehabilitation is crucial to restoring and improving a person’s well-being in physical fitness. The present research investigates the significance of Optoelectronic Conjugate Materials (OCM) in Biofeedback Systems to enhance the effectiveness of Virtual Reality (VR) Exercise Rehabilitation. This work presents the Design of Biofeedback System-driven Optoelectronic Conjugate Materials (DBS-OCM), which offers a novel methodology to enhance VR Exercise Rehabilitation. The DBS-OCM framework amalgamates synthesis and material characterization techniques for tissue healing during VR Exercise Rehabilitation. The present research aims to elucidate a resistance trainer’s design and preparation methodology during the Evaluation of the Effect of a Biofeedback System Driven by OCM. This approach seeks to optimize the flexibility of the system to cater to individual requirements during VR Exercise Rehabilitation. The experimental findings demonstrate notable advancements, including a 93.9% augmentation in tissue regeneration, a 95.5% enhancement in the efficacy of resistance training, and a 95.5% boost in involvement during virtual reality exercise. The results highlight the DBS-OCM’s capacity to enhance the Biofeedback System’s efficiency Driven by Optoelectronic Conjugate Materials in VR Exercise Rehabilitation. These findings provide essential insights that have the potential to shape future research endeavors and facilitate the development of practical applications in the domain of VR Exercise Rehabilitation.

Application of spatial metrology models in cell molecular localization and functional prediction

Hongtao Wang, Yulei Wang — Mon, 09 Dec 2024 05:37:55 +0000

Understanding a protein’s exact cellular location is often essential to understanding its function. Even with the advancements in computer approaches, protein localization prediction indeed faces major obstacles such as interpretability and handling numerous localization sites. In this research, a novel approach, Squirrel Search Optimized Dynamic Visual Geometry Group Network (SSO-DVGG), is proposed to improve protein sub-cellular localization predictions by utilizing spatial metrology models to tackle these problems. With its simplified architecture, SSO-DVGG can explain whether a protein is directed to particular cellular sites, as well as identify important sequence components like sorting motifs or localization signals. This model allows users to select acceptable error levels by providing a confidence estimate for each prediction and highlighting sequence properties that are responsible for localization. This makes the model interpretable. Furthermore, SSO-DVGG uses a probabilistic methodology and integrates a large amount of data from dual-targeted proteins, which enables it to predict multiple localization locations per protein accurately. SSO-DVGG outperforms the best predictors and shows superior capacity to predict multiple localizations when tested on several independent datasets. By providing a clear and accurate understanding of protein distribution and function, this method promotes the application of spatial metrology models in cell molecular localization and functional prediction.

Sports training posture recognition method based on Kinect body sensor and internet of things technology

Jie Rong, Huijuan Dong, Lei Wang, Hongxia Sun — Mon, 09 Dec 2024 06:01:30 +0000

Most of the routine sports training posture recognition uses the principle of image processing method, which has strong limitations, and there is a problem of data loss in the recognition process. The recognition error is large, which reduces the accuracy of the recognition results. Based on this, a new method of sports training posture recognition is proposed by introducing Kinect body sensor and Internet of Things technology. First, the mathematical description method is used to model the human body in three dimensions to represent the continuous posture changes of the trainer. Secondly, Kinect somatosensory sensor and the Internet of Things technology collect the sports training action information of trainers, track and capture the movement of limbs and the whole body, and extract the sports training behavior characteristics of the recognized people. On this basis, the recognition algorithm of sports training posture is designed to achieve the goal of sports training posture recognition. The experimental results show that after the application of the new method, the recognition error of sports training posture is small and the recognition accuracy is high.

Cardiac function monitoring during marathon training based on smart medical wearable sensor device

Hongjuan Cai, Miao Cai — Mon, 09 Dec 2024 07:23:41 +0000

In recent years, intelligent wearable sensor devices have developed rapidly and can be seen everywhere in daily life. With the rapid development of electronic components and the continuous improvement of their performance, intelligent wearable intelligent products have gradually become possible and have shown explosive growth. In addition, intelligent wearable electronic devices have many advantages that traditional devices do not have. With the popularity of fitness wearable devices, intelligent wearable devices can also be used for real-time heart rate and dynamic electrocardiogram (ECG) monitoring during marathon sports. It can effectively prevent sudden death. During marathon training and other health services, it is very important to use intelligent wearable sensor devices to monitor heart function. This paper puts forward a heart function monitoring system for marathon training based on intelligent wearable sensor, expounds the origin of marathon sports and the importance of heart function monitoring for marathon athletes during training. This paper discusses the technology and construction method of heart rate monitoring system based on intelligent wearable sensor device. At the same time, relevant experiments are carried out to verify the relevant performance of the intelligent wearable sensor device in the algorithm. The results show that the R wave detection accuracy of wearable devices based on traditional algorithms is usually between 92% and 93%. The R wave detection accuracy of the intelligent wearable sensor device improved by the algorithm in this paper has been improved to more than 97%, and the R wave detection accuracy of the algorithm in this paper is much higher than that of the traditional algorithm. This also reflects the effectiveness of the intelligent wearable sensor device of the algorithm during the training of marathon athletes.

Characteristic extraction of Tai Chi movement data—Based on self-powered wearable sensors

Ruijie Zhang, Chunlei Xue, Zijie Sun, Kim Junhee, Yunna Liu — Mon, 09 Dec 2024 09:12:16 +0000

Although visual recognition has good recognition accuracy, it brings great hidden danger of privacy leakage. Although signal recognition has the advantages of device-free and privacy protection, it is sensitive to environmental noise and is not suitable for crowded environment, so sensor-based human behavior recognition is a more feasible choice. Therefore, this paper proposes a multi-level decision behavior recognition method based on self-powered wearable sensor fusion. In this paper, we propose a CM-WOA-based automatic dynamic sensor deployment optimization method for the feature extraction of Tai Chi action data. In behavior recognition based on wearable sensors, different deployment schemes of self-powered wearable sensors, will lead to different recognition accuracy, However, the traditional empirical deployment scheme cannot guarantee the best sensor layout. In order to further improve the recognition accuracy. In this paper, we propose a CM-WOA-based autodynamic sensor deployment optimization method for the feature extraction of Tai Chi action data, so as to find a balance between recognition accuracy and sensor deployment cost, and deploy as few sensors as possible on the premise of maximizing recognition accuracy. Finally, by comparing the scheme proposed in this paper with the other seven schemes, The feature extraction and recognition rate of Taijiquan movement data based on self-powered wearable sensor can reach 94%, which proves that the proposed multi-sensor deployment optimization method based on CM-WOA is effective in improving the overall recognition rate of the recognition model.

Advancing an ecological framework for English language teaching in web-based environments

Fangyin Tong — Tue, 10 Dec 2024 01:09:49 +0000

As English language teaching (ELT) adapts to the digital age, web-based environments present new challenges for achieving an ecologically balanced instructional ecosystem. Applying an educational ecology framework, this study examines the structural and functional dynamics within web-based ELT, viewing the digital classroom as a biomechanical system where information flow, interaction forces, and adaptation mechanisms play crucial roles. By integrating Data Mining (DM) technology, the study evaluates ELT efficiency from an ecological standpoint, aiming to enhance both student engagement and learning outcomes through optimized instructional dynamics. Findings indicate that the applied algorithm achieves an accuracy of 95.27%, with system stability maintained above 90% under high parallel processing loads, demonstrating the robust performance of this approach. Ultimately, this paper contributes to the development of an ecologically balanced, web-based ELT model, supporting educators in creating adaptable, resilient digital learning environments.

Biomechanics of physical exercise: A data-driven approach to enhancing mental health in college students

Hai-zhen He — Tue, 10 Dec 2024 02:46:43 +0000

With growing awareness of the importance of mental health, the biomechanical mechanisms of physical exercise have gained attention as an effective intervention for improving mental well-being, particularly among college students. Physical activity not only enhances physical fitness and disease resistance but also contributes to cognitive and emotional health through specific biomechanical pathways. This study explores the interplay between exercise biomechanics and mental health by investigating the psychological challenges faced by college students. Utilizing advanced data analysis and correlation techniques, we refine the Apriori algorithm through a novel database partitioning strategy, achieving a 23.68% improvement in accuracy and a 10.17% reduction in runtime compared to the baseline. Additionally, this study examines how biomechanical factors, such as joint movement and muscle activity, influence brain function and mental health outcomes. The findings offer innovative perspectives for integrating biomechanical insights into mental health education and exercise-based interventions for college students.

Risk prediction of computer investment database information management system based on machine learning algorithms

Yi Guo — Tue, 10 Dec 2024 02:53:04 +0000

In recent years, with the continuous development of the financial market, the risk prediction of computer investment database information management systems (IMS) has high practical value. At present, there are risk issues in the information management system, which may cause drawbacks to investment data processing. To address these issues, this article used Machine Learning (ML) algorithms to analyze the risk prediction of computer investment database IMS. This article introduced and utilized typical Self-Organizing Map (SOM) and Artificial Neural Network (ANN) combination algorithms, regression algorithms, and Gradient Boosting Decision Tree (GBDT) algorithms to compare and analyze the prediction accuracy of these three algorithms. This article found that the GBDT algorithm has the highest prediction accuracy. Through a large amount of experimental data, it has been proven that the average testing accuracy using regression algorithms was 3.5% higher than that using neural network algorithms. It was found that the average test accuracy using the GBDT algorithm was 7.2% higher than the average test accuracy using the regression algorithm. The study also explores the combination of physiological and behavioral data collected by wearable devices to provide more comprehensive risk assessment and decision support, which provides an important reference for the optimization of enterprise risk management. Through this innovative data source integration, this paper provides a new perspective for the application and development of machine learning algorithms in computer investment database IMS.

High sensitivity detection of influenza virus using polymer-coated microcavity biosensor

Yuanyuan Wang, Shihao Cui, Yuchao Gu — Tue, 10 Dec 2024 03:28:21 +0000

Influenza viruses are a major global public health concern because they cause seasonal epidemics and sporadic pandemics. The sensitivity and specificity of viral detection can be improved through recent developments in biosensor technology. The capacity of microcavity biosensors to detect biomolecules, including viruses, in real-time without requiring labels caused interest among them. In this study, we present a novel polymer-coated microcavity biosensor for the high-sensitivity detection of the H1N1 influenza virus. The main microcavity structure of this label-free biosensor is intended to improve sensitivity by optimizing performance characteristics specific to viral samples. The simulation indicates the microcavity resonator’s outstanding sensitivity in H1N1. Our biosensor efficiently detects H1N1 at lower concentrations than conventional diagnostic techniques by utilizing a polymer coating that improves binding affinity and encourages the immobilization of certain antibodies. Using the polymer layer enhances the sensor’s functionality and confers biocompatibility, opening up possible uses in point-of-care environments. The polymer-coated microcavity biosensor, according to our research, is a potential platform for early, sensitive, and quick influenza virus detection, greatly assisting with public health response and monitoring activities.

Ice and snow sports behavior recognition based on multi-scale features and improved CBAM

Chunping Liu — Tue, 10 Dec 2024 08:07:24 +0000

Accurately identifying and correcting erroneous sports behaviors of athletes or beginners in ice and snow sports can improve the training quality. However, ice and snow sports scenes often have complex motion backgrounds, and the behavioral features during motion are difficult to extract, which affects the recognition accuracy. In order to solve the feature extraction in ice and snow sports behavior recognition, a behavior recognition model based on multi-scale features and improved convolutional block attention module is proposed. The model first utilizes multi-scale features to obtain multi-level features from the collected ice and snow motion images, ensuring that features of different scales in the images can be effectively captured. Then, one-dimensional convolution and spatial random pooling layers are introduced to improve the convolutional attention module, thereby constructing a behavior recognition model. The accuracy of the proposed model in the Ski-Pose dataset was 98.3%, which was 8.2% and 13.7% higher than other recognition models, indicating an obvious gap. The accuracy and F1 value were 89.5% and 91.2%, respectively, and the recognition rate for small targets reached 80%, which verified the effectiveness of the model. The research provides new technological support for intelligent monitoring and analysis systems for ice and snow sports.

Disturbance decoupling for biological fermentation systems with single input single output

Hongjie Li, Dandan Wang, Mingliang Zheng — Tue, 10 Dec 2024 09:02:02 +0000

The biological fermentation process has the characteristics of nonlinearity and multivariable coupling. To improve the performance of decoupling control in the fermentation process, a disturbance decoupling control based on the Lie symmetry method is proposed to obtain the analytical feedback control for a class of biological fermentation systems with single input single output (SISO). Firstly, the state-space equations and the disturbance decoupling model for a class of SISO biological fermentation systems are defined; Secondly, the key technologies and algorithm approaches of Lie symmetry theory for differential equations are introduced, and the conditions and the properties of Lie symmetry for nonlinear control systems under group action are given in detail; Finally, the derived distribution of Lie symmetric infinitesimal generators is used to prove the sufficient conditions for local disturbance decoupling in the system, and the closed-loop state feedback analytical law of the system is constructed. The proposed control method is applied to the disturbance decoupling control of mycelium concentration and substrate concentration in the biological fermentation process. Numerical simulation results show that the proposed control method can effectively improve the system decoupling control performance. Meanwhile, using Lie symmetry, the cascade decoupling standard form and the static state feedback law of biological fermentation systems can be constructed.

Biomechanical insights into goalkeeper preparatory movements during defensive dives for football shots

Yuefeng Jin — Wed, 11 Dec 2024 00:43:04 +0000

The kinetic demands of a football goalkeeper (GK) involve preventing opposing shots from entering the goal, which requires rapid and well-coordinated defensive actions. However, the biomechanics of these preparatory movements, particularly in response to diverse shot scenarios, remain insufficiently understood. Herein, the biomechanical characteristics intrinsic to the preparatory movements executed by GKs during defensive diving are elucidated. Three-dimensional coordinate data of the 10 GKs and 5 strikers were captured using two synchronized motion capture systems comprising cameras with an analog synchronization signal. A total of 172 trials were analyzed, during which GKs dived toward shots. GKs leaned forward, flexed their lower limbs, externally rotated and abducted the hips, and positioned their feet at 70–75% of leg length to respond quickly to shots. Preparatory takeoff occurred concurrently with the striker support leg contact, and GKs adjusted their movements after the striker-ground contact. These findings underscore the importance of effective preparatory movements for enhancing shot-stopping abilities, while also providing insights for optimizing training protocols to improve GKs adaptability and precision during matches.

Research on real-time collection and analysis of student health and physical fitness data using biosensors

Yuliang Zhou — Wed, 11 Dec 2024 01:04:44 +0000

Biosensors have emerged as efficient devices for monitoring personal fitness levels and health profiles as an important part of this technological development. With growing concern about students’ health and bodily fitness, educational and health experts as well as lawmakers have increasingly emphasized their importance. The goal of the study is to explore a real-time system for collecting and analyzing data on students’ physical fitness and health utilizing biosensors and advanced algorithms. The study proposed a novel Efficient Osprey Optimized Adjustable Random Forest (EOO-ARF) to predict the student health and physical fitness level. The student health and physical fitness data was gathered from a Kaggle source. To gather information using wearable biosensors to constantly monitor crucial health parameters such as blood oxygen levels, body temperature, heart rate, and physical activity. The data was pre-processed using the Z-score normalization to enhance the quality of the data. The Principal Component Analysis (PCA) was used to extract the features from pre-processed data. This model takes the indices of students’ physical health as the input parameters and produces an overall health score. EOO is used for optimization, and the process aims at selecting the most appropriate features to identify the health metrics most relevant to influencing students’ general fitness levels. ARF is applied to predict the health and fitness levels of students. The performance of the suggested approach is evaluated in terms of F1-score (98.13%), recall (98.2%), and accuracy (98.44%). The integration of biosensors with innovative analytic methods could transform the monitoring and improvement of the physical fitness and health of students take place in real-time.

Research on optimization and design of sports teaching actions based on biomechanics

Xiaojing Yang, Sunkai Qi — Wed, 11 Dec 2024 01:15:43 +0000

The design of sports teaching approaches is an important part of improving athletic performance and skill development among players. As sports grow more competitive, the demand for new and effective teaching methods has never been higher. The limitations of conventional coaching techniques sometimes depend on anecdotal evidence and subjective assessments, which provide inconsistent outcomes for training. The purpose of this study is to enhance athletic performance and reduce injury risks by developing sports teaching actions based on biomechanics. The proposed novel chaos sparrow search fine-tuned efficient random forest (CSS-ERF) employed the relationship between biomechanical parameters and performance outcomes. Biomechanical data was gathered from athletes utilizing wearable sensors and motion capture technologies as they performed several sports-related activities. The data preprocessing will be cleaned to remove noise and outliers from the dataset. Ground Reaction Forces (GRF) are used to extract key features relevant to performance and injury risk from the preprocessed data. Findings show that training strategies and athlete performance have significantly improved, and the CSS-ERF model has demonstrated a high degree of accuracy in forecasting the best biomechanical configurations with an F1 value of 0.984, accuracy of 0.989, recall of 0.985, and precision of 0.986. By offering an innovative approach to enhancing sports actions through biomechanical insights and promoting a greater comprehension of movement mechanics and their impact on athletic performance, this research advances the area of sports science.

A Chinese sign language recognition system combining attention mechanism and acoustic sensing

Yuepeng Shi, Yansheng Wu, Qian Li, Junyi Zhang — Wed, 11 Dec 2024 05:42:53 +0000

In recent years, with the widespread popularity of smart devices and the rapid development of communication and artificial intelligence technologies, sign language gestures that can break the communication barriers between ordinary people and those with speech and hearing impairments have received much attention. However, existing human gesture recognition methods include wearable device-based, computer vision-based and Radio Frequency (RF) signal-based. These methods have problems of being difficult to deploy, violating user privacy, and being susceptible to ambient light. Compared with the above methods, using ultrasonic signals to sense sign language gestures has the advantages of not violating user privacy and not being affected by ambient light. For that purpose, we use the built-in speaker and microphone of a smartphone to send and receive ultrasonic signals to recognize sign language gestures. In order to recognize fine-grained sign language gestures, we calculate the Channel Impulse Response (CIR) induced by the sign language action as a sign language gesture special. After that, we compute first-order differences along the time dimension of the Channel Impulse Response matrix to eliminate static path interference. Finally, a convolutional neural network containing convolutional layers, spatial attention, and channel attention is passed in order to recognize sign language gestures. The experimental results show that the scheme has a recognition accuracy of 95.2% for 12 sign language interaction gestures.

Before and after core strength training: A comparison of smash technique between badminton players

Hongkai Zhou — Wed, 11 Dec 2024 07:27:37 +0000

Objective: This paper aims to investigate the impact of core strength training on the smash technique in badminton. Methods: Twenty male badminton players were randomly assigned to Group A (core strength training) and Group B (conventional waist and abdominal strength training). They underwent training from 15:00 p.m. to 17:00 p.m. on Monday, Wednesday and Friday. Group A did core strength training exercises such as four-point support and cross-body crunch. Core strength, balance, and related indicators of the smash technique were measured before and after a 12-week experiment for comparative analysis. Results: Before the experiment, there were no significant differences in the indicators between the two groups (p > 0.05). However, after the experiment, Group A achieved a badminton throw performance of 8.61 ± 0.75 m, a 30s double-rocking rope skipping performance of 48.67 ± 3.77, an one-minute sit-up performance of 58.24 ± 3.71, an eight-level abdominal bridge performance of 3.38 ± 8.15 points, a closed-eye single-foot standing time of 56.12 ± 12.35s, a closed-eye straight-line walking displacement of 17.78 ± 10.12 cm, a batting speed of 145.12 ± 5.97 km/h, and a landing point stability of 34.22 ± 2.31 points, which showed significant differences compared to the performance before the experiment. It also performed better than Group B except for the eight-level abdominal bridge. Conclusion: The results indicate that athletes who undergo core strength training show significant improvements in physical fitness and smash ability, proving the reliability of core strength training and its practical application in training.

AET-net: A framework for subtype classification based on the multi-omics data of breast cancer

Thu, 12 Dec 2024 01:26:42 +0000

Breast cancer (BC) is one of the most prevalent cancers worldwide and remains a significant global public health challenge. The biomechanical characteristics of tumor microenvironments provide critical insights into cellular interactions and mechanical stress responses that potentially influence cancer progression. The integration and analysis of multi-omics data for BC subtype classification present substantial challenges, including high-dimensional data complexity and difficulties in integrating heterogeneous omics data characteristics. To address these challenges, we propose an Autoencoder and Transformer integrated neural network (AET-net) classification framework. The experimental results demonstrate that our model achieves significant performance improvements in predicting BC subtypes based on integrated multi-omics datasets, with an Accuracy of 0.912 and an AUC of 0.9862. These results not only validate the high accuracy of our model in BC subtype classification, providing a valuable tool for diagnostic decision support, but also demonstrate the potential of integrated multi-omics data analysis in enhancing the precision and efficiency of BC subtype identification.

Digital technology integration innovation and visual symbol design of Huizhou ink painting skills under bioengineering

Yuqing Xia, Kun Xing — Thu, 12 Dec 2024 02:42:21 +0000

In response to the problems of insufficient interactive experience and unstable inheritance caused by information loss and deformation in the teaching process of Huizhou ink painting skills, this article explores a new method to protect and inherit Huizhou ink painting skills through the integration of biotechnology and digital technology. At the same time, a modern visual symbol design is developed to revitalize this ancient art form in contemporary society and promote its dissemination and exchange worldwide. Firstly, traditional Huizhou ink painting materials were analyzed using biotechnology to understand their composition, structure, and aging mechanism. Scientific and effective protective measures were proposed, and a database was constructed by collecting and matching images of each step in the process. Then use 3D modeling to model the manufacturing tools. By extracting visual elements from micro ink artwork images, drawing basic graphics, and completing visual symbol design. Finally, integrate Unity and Vuforia Engine to design an interactive experience module for Weizhou ink painting production skills, creating an immersive digital interactive environment. In the improved micro ink production process experience display experiment, compared with the existing graphic and textual display methods, the audience's satisfaction and innovation scores for the digital integration method were 19.1% and 21.6% higher, respectively. The conclusion indicates that bioengineering analysis can accurately identify and delay the aging process of ink painting materials, while digital technology has successfully achieved high-quality digital reconstruction of classic works.

Digital infrastructure and cognitive ability of children

Haojian Dui — Thu, 12 Dec 2024 06:44:03 +0000

Digital technologies have become an integral part of most children’s lives, significantly influencing their development. This study takes the Broadband China Pilot Policy as a quasi-experiment, using the data from China Family Panel Studies (CFPS) to analyze the effects of digital infrastructure on children’s cognitive ability. The extended two-way fixed effects estimator was employed to conduct the Staggered Difference-in-Differences estimation. The findings indicate that the average treatment effect of treatment group of “Broadband China” is significantly positive on children’s cognitive ability. Additionally, heterogeneities of gender and urban-rural were found: (1) The Broadband China policy had a significant positive impact on boys only; (2) The policy had a greater and more significant impact on the word test scores results of urban children; (3) The policy had a negative impact on the math test scores of urban children, while showing a positive impact on the math test scores of rural children. Finally, the paper makes the following recommendations: (1) Digital infrastructure development should be emphasized; (2) More emphasis should be placed on rural areas when building digital infrastructure; (3) Gender differences should be considered when formulating policies to help girls benefit from digital technologies.

Teaching strategies for resolving gastrointestinal function from the perspective of cell biomechanics

Bo Qian, Zihao Qin, Shuo Liu, Fuzhen Wan, Rui Yu — Mon, 16 Dec 2024 01:24:38 +0000

Traditional gastrointestinal function teaching focuses on biochemical and physiological regulation, but fails to explore the effects of mechanical forces on gastrointestinal cell behavior and function, resulting in students’ one-sided understanding of gastrointestinal function. This paper proposes a new teaching strategy by combining cell biomechanics with teaching to help students master the gastrointestinal function mechanism more comprehensively. First, the membrane elasticity of gastrointestinal smooth muscle cells is measured. The elastic modulus is calculated by combining the Hertz model and the applied force is controlled to avoid cell membrane damage. Elasticity change data is obtained. Then, a flexible substrate is used to apply stretching and low frequency to simulate the mechanical force of cells during peristalsis and monitor the fluctuation of calcium ion concentration. Then, the distribution of intercellular cadherin is analyzed, and mechanical force is used to accelerate the permeability of gap junctions and the expression of Connexin43 to promote signal transmission. Finally, a teaching experiment based on cell biomechanics is designed, covering cell culture, mechanical stimulation, quantitative analysis and molecular biology verification, to help students understand how mechanical forces affect gastrointestinal cell behavior and function. The results show that before and after the application of the cell biomechanics teaching strategy, the students’ test scores increase by 16%, and the experimental results are good. Applying biomechanical factors into teaching by combining teaching strategies with cell biomechanics has a positive effect on medical education.

Cognitive and biomechanical interactions in language acquisition: A comparative case study of English and Japanese teaching for nonnative speakers

Dazhi Wu, Juan Lei, Yanzhe Wang — Mon, 16 Dec 2024 07:25:59 +0000

This study investigates the interplay between cognitive processes and biomechanical methods in language education, focusing on English and Japanese instruction for Chinese university students. A mixed-methods approach, combining quantitative experiments and qualitative feedback, was employed to evaluate the effectiveness of biomechanical teaching strategies compared to traditional methods. Over a 12-week intervention, students in the experimental group, who experienced biomechanical techniques involving gestures, physical activities, and multi-sensory inputs, consistently outperformed their counterparts in the control group. Notably, the experimental group achieved higher mean scores in vocabulary (English: 85 vs. 75; Japanese: 78 vs. 68), grammar (English: 82 vs. 73; Japanese: 85 vs. 70), and listening comprehension (English: 88 vs. 77; Japanese: 80 vs. 72), with statistically significant differences (p < 0.01 for most metrics). Additionally, effect sizes (Cohen’s d) were calculated to determine the practical significance of these findings. The effect sizes ranged from d = 1.60 to d = 2.08, indicating large and practically significant differences between the experimental and control groups. Qualitative data revealed enhanced engagement, memory retention, and motivation among students exposed to biomechanical methods. The cross-linguistic comparison highlighted that English learner benefited most from multi-sensory vocabulary acquisition techniques, while Japanese learners exhibited substantial improvements in grammar and listening comprehension through interactive activities. These findings not only demonstrate the adaptability of biomechanical approaches but also underline their potential for broader application across other languages and cultural settings. For instance, languages with tonal systems, such as Mandarin or Thai, could leverage gesture-based methods to reinforce pitch and tone distinctions, whereas languages with complex morphology, like Arabic or Finnish, might benefit from kinesthetic exercises targeting morphological structures. Furthermore, educational settings with resource constraints can integrate low-cost, physical movement-based interventions, making biomechanics a scalable solution. By tailoring these methods to align with specific linguistic features and cultural learning preferences, educators can enhance their global applicability, paving the way for interdisciplinary innovation in language teaching. The study provides strong empirical evidence supporting the integration of biomechanics into language education, emphasizing its potential to enhance learner performance and satisfaction in a globalized learning environment.

Application analysis of sports biomechanics in sprint physical training

Mengmeng Wang — Mon, 16 Dec 2024 07:44:09 +0000

Sports biomechanics is a multidisciplinary applied discipline for studying the human motion mechanics, which plays a key role in the scientific research and scientific guarantee of competitive sports. Integrating biomechanics into physical training can better help students master sports skills, complete sports exercises more easily, and minimize the possibility of sports injuries. The application of sports biomechanics is combined with every movement in students’ sports activities, which plays an important role in physical exercise, sports training and teaching. This paper mainly studies the application of biomechanics in sprinting through literature analysis, summarizes the training experience and skills summarized by predecessors using biomechanics in sprinting, and provides athletes and coaches with the best training methods to improve athletes’ understanding and practice of the essentials of movement, thereby improving performance.

Identifying sequential differences between protein structural classes using network and statistical approaches

Xiaogeng Wan, Xinying Tan — Mon, 16 Dec 2024 08:45:52 +0000

Protein sequence information is believed to embed the hint of their structures. To uncover the nature between protein sequence and their structures, this study motivates to inspect the dynamic interactions between various protein sequence features, and identify the sequential differences between the different protein structures. Protein sequence data from all structural classes in CATH and SCOP, and the structural disordered proteins from DisProt, as well as the structural motifs in PROSITE, are analyzed in this study. Betweenness and closeness centrality measures are employed to capture the topology of the networks constructed from amino acid feature interactions, while statistical tests are further implemented to compare the feature series distributions. Key findings suggest that in all structural classes, the features for Ala and α-helix and bend preference property, Ala and side-chain size, Ala and Gly, as well as Met and Leu attain significant interactions between each other, and the feature for Leu, Val, and Asn are acted as the critical sources of feature interactions, whereas Cys, His, Trp, and Met exhibit weak intra-type interactions with other features. These implicate that these feature interactions may have little impact in coding the structural differences. For the α structures, Glu, Pro and side-chain size, hydrophobicity properties exhibit high importance in feature interactions, whereas Gly, Thr and physical properties such as α-helix and bend preference, extended structural preference, pK-C value and surrounding hydrophobicity for β structures, show special high importance in β structures. Both α and β types of structures show Ser as the common sources of feature interactions, while the mixed α and β structures not only show common characters with the α and β types of structures, but also preferred interactions between Met, Lys and double-bend preference property, and between the sequence arrangements of Cys, His, Met, Tyr and amino acid composition features. The intrinsically disordered proteins (IDPs) present high frequency for the repetition patterns of certain amino acids, while the different structural motifs also show special characters. More sequential differences between the structures can also be identified from K-mers statistics and feature series distributions. The new discoveries reveal the nature of amino acid feature interaction mechanics, and show great importance of these interactions in coding the different types of protein structures. The results can not only contribute to future molecular design for protein-based vaccine or drug, but also enlighten the development for new protein structural classifiers.

COL5A3: A prognostic biomarker and potential therapeutic target in pancreatic cancer

Yongjie Li, Min Zeng, Ting Wang, Feng Jiang, Chengjian Li — Tue, 17 Dec 2024 02:56:20 +0000

Pancreatic cancer is a malignant tumor of the digestive system with a high mortality rate and a poor prognosis. While type V collagen 3 (COL5A3) is extensively expressed in many tumor tissues, its prognostic significance and immune infiltration in pancreatic cancer remain unknown. As a result, we investigated COL5A3’s predictive function in pancreatic cancer and its relationship to immune infiltration. COL5A3 is significantly expressed in pancreatic cancer tissues compared to normal tissues. High COL5A3 expression is associated with poor clinicopathological characteristics and a worse prognosis of pancreatic cancer. The Kaplan-Meier survival analysis revealed that pancreatic cancer patients with high COL5A3 expression had a poorer prognosis than those with low COL5A3 expression. According to the ROC curve, COL5A3 has high sensitivity and specificity in the detection of pancreatic cancer. Correlation studies revealed that COL5A3 mRNA expression is associated with immune cell infiltration. This work indicates for the first time that COL5A3 may be a novel predictive biomarker linked to immune infiltration, providing a new target for pancreatic cancer detection and therapy.

Construction of college student’s volleyball functional physical training system based on 5G embedded analysis from the perspective of biomechanics

Xiaoxia Shang, Jinliang Zhao, Cuifeng Gu — Tue, 17 Dec 2024 05:46:49 +0000

In volleyball, the movement and performance of athletes are closely related to biomechanical principles. The process of getting the ball beyond the opponent’s net and landing on the court involves complex biomechanical actions such as jumping, spiking, and blocking. These actions require appropriate muscle activation, joint movement, and force generation. Through a 5G embedded physical training system, college students’ biomechanical performance in volleyball can be effectively monitored and analyzed. For example, the Wireless Sensor Network (WSN) can be used to measure the forces exerted on the body during movements, such as the impact force on the fingers during spiking and blocking, and the stress on the lower back. By analyzing these biomechanical data, problems such as finger injuries and lower back pain can be better understood and addressed. Moreover, the 5G embedded system allows for a detailed analysis of the biomechanical characteristics of different teams and players, enabling coaches and athletes to optimize training strategies. The system can also provide visual feedback on biomechanical parameters, which helps students improve their overall coordination and muscle strength. By incorporating biomechanical analysis into the volleyball physical training system, students can better understand the scientific basis of their movements and improve their performance. Statistical analysis has been carried out effectively based on the study analysis with the existing open-source datasets, providing a quantitative basis for biomechanical research and training optimization.

Analysis of cellular and molecular biomechanical correlates in emotional response monitoring via biosensors and their impact on teaching strategy modification in Japanese language instruction

Yongping Chen — Tue, 17 Dec 2024 06:13:15 +0000

Emotion states have a significant impact on language acquisition and learning outcomes. In Japanese language teaching, traditional strategies often overlook students' emotional responses, which can lead to stress and disengagement, affecting performance. To address this, the study focuses on incorporating bio-sensor-based emotional reaction monitoring. At the cellular and molecular biomechanical level, emotions can trigger a cascade of physiological changes. For example, stress can activate the hypothalamic-pituitary-adrenal (HPA) axis, leading to the release of stress hormones like cortisol. These hormonal changes can affect neurotransmitter systems and cellular signaling pathways in the brain, influencing cognitive functions related to language learning. During Japanese language sessions, teachers' emotional states are recorded using surveys and EEG monitoring. The EEG signals can provide insights into the neural activity and related cellular and molecular events. Participants are divided into experimental and control groups. In the experimental group, teaching strategies are adjusted based on emotional monitoring data. The Extreme Gradient Boosting (XGBoost) model classifier is used for EEG signal feature selection to create a stress level identification model. This model can help in understanding the cellular and molecular correlates of stress during teaching. Statistical analysis evaluates the relationship between EEG features and stress levels, as well as the effectiveness of adjusted teaching strategies. Tailored teaching strategies based on these insights can enhance teacher resilience and improve the classroom environment. By considering the cellular and molecular biomechanical aspects of emotions, the study aims to improve teacher well-being and student learning experiences, leading to more effective Japanese language instruction.

Comparative analysis of biomechanical characteristics of knee flexion and extension muscles in volleyball physical training

Guishen Yu — Tue, 17 Dec 2024 06:43:25 +0000

This study aimed to analyze the biomechanical characteristics of knee flexion and extension muscles in volleyball physical training from the cellular and molecular biomechanics aspect. Multiple training modes like strength, elasticity, and comprehensive training were chosen to systematically evaluate relevant characteristics of these muscles among volleyball players. Advanced devices such as 3D motion capture systems, ground reaction force platforms, and electromyography equipment were used to gather precise biomechanical data. At the cellular and molecular level, different trainings impact muscle cells differently. For example, strength training might enhance the synthesis of contractile proteins within cells, while elasticity training could influence the elasticity-related molecular structures. With multidimensional data analysis, the effects of various training modes were compared. The comprehensive training group had a kinematic flexion extension angle of 528.27º ± 11.49º, an angular velocity of 135.52º ± 5.97º, and an angular acceleration of 3177.02º ± 116.88º, performing best. This could be due to its comprehensive influence on cellular and molecular processes in muscles, promoting better coordination and force generation. This article offers a theoretical basis for volleyball players to create scientific training plans and gives practical tips for coaches and athletes to optimize programs and prevent injuries. By focusing on cellular and molecular biomechanics, it fills research gaps and helps boost the development of biomechanics in volleyball physical training.

Analyzing the influence of physical posture on audience perception in mass media presentations

Yihan Wu — Tue, 17 Dec 2024 07:36:02 +0000

Non-verbal communication, especially physical posture, affects audience perception. From a cellular and molecular biomechanics angle, different postures may trigger unique intracellular responses. Upright or leaning forward postures might activate neural pathways that enhance neurotransmitter release related to positive perception. In contrast, a slouched posture could disrupt normal cellular signaling, potentially leading to a less favorable audience perception. This study explores the impact of four postures on audience views in a media setting, aiming to offer data on how posture shapes key perceptions and provide valuable insights for Mass-media Presentations (MMP), despite limited prior research on this aspect. A within-subject experimental design was employed, with 34 participants observing media presentations under four posture conditions. Posture was the independent variable, while credibility, trustworthiness, engagement, and authority were the dependent variables. Data were collected using surveys, posture monitoring devices, and eye-tracking data. Statistical analyses, including Analysis of Variance (ANOVA) and paired t-tests, were conducted to determine significant differences between posture conditions. Upright and leaning forward postures were associated with the highest audience ratings for credibility, trustworthiness, engagement, and authority. Slouched posture consistently led to the lowest ratings across all measures. The ANOVA results revealed significant differences in perceptions of engagement (F = 10.21, p = 0.0008) and credibility (F = 8.67, p = 0.0013). Paired t-tests and post-hoc analyses confirmed that upright posture significantly outperformed slouched posture across all metrics, with large effect sizes (Cohen’s d > 1.0). Posture significantly influences audience perceptions in mass media presentations. Upright and leaning forward postures enhance credibility, trustworthiness, engagement, and authority, while slouched posture diminishes these perceptions. These findings provide practical insights for media professionals, suggesting that careful attention to posture can improve the effectiveness of media presentations. Future research could investigate how gestures and facial expressions interact with these cellular and molecular mechanisms to shape audience engagement.

Biomechanical analysis of kinematics in the single whip movement of Tai Chi using video imaging

Xianghe Shan — Wed, 18 Dec 2024 03:06:24 +0000

“Single whip” is a typical movement in Tai Chi, and the study of its kinematics has important practical value for better understanding the laws and characteristics of Tai Chi movements. This study divided 30 Tai Chi practitioners into two groups based on their skill level: an excellent group and a beginner group. The Vicon motion capture system and a three-dimensional force platform were used to obtain kinematic data. Differences in movements between the two groups at various stages were compared. The results showed that the excellent group took longer in stages one and three, but shorter in stage two. In stage one, the left knee joint angle of the excellent group was 95.45° ± 16.02°, while the right knee and left hip joint angles were larger (148.62° ± 12.84° and 133.55° ± 18.61°). In stage two, the angles of the right knee, left ankle, and right ankle joints of the excellent group were significantly smaller than those of the beginner group. In stage three, the angle of the right elbow joint of the excellent group was larger (154.26° ± 12.06°), while the angles of the right wrist angle and the left and right ankle joints were significantly smaller than those of the beginner group (p < 0.05). In terms of vertical displacement of the center of gravity, the excellent group exhibited less fluctuation and significantly lower dynamic stability in both anterior-posterior and medial-lateral directions compared to the beginner group (p < 0.05).

Applications of physical exercise in frailty: Progress, mechanisms, and prospects

Xiongsi Tan, Ziyang Lin, Junzheng Yang — Wed, 18 Dec 2024 06:22:31 +0000

Frailty is a kind of clinical state or a kind of complex pathological syndromes characterized by impaired physiological functions, weakened physical and mental resistance to stress, and reduced ability to maintain homeostasis in multiple systems. Several types of risk factors can affect the occurrence and development of frailty either independently or in conjunction with one another, including age, gender, and exercise habits; and the multidimensional systemic treatment are often required to alleviate or improve frailty. Physical exercise is the various kinds of systematic and conscious activities, it has been demonstrated that regular exercise can promote the normal metabolism processes within the body, thereby preventing or alleviating the symptoms of various diseases. Physical exercise has the potential to regulate oxidative stress, immune response, and endocrine balance in the body of frail people by activating multiple signaling pathways including mitochondrial function, cytokine secretion, and regulation of inflammatory factors, its applications in frailty has been made the significant progress and the underlying mechanisms has been further elucidated. In this review, we have summarized the recent progress on the applications of physical exercise in frailty and the potential mechanisms, hoping that our reviews may provide some helpful guidance for the further research.

Effect of air pollution with Aspergillus spp.in occurrence of Aflatoxin M1 in milk and its products

Wed, 18 Dec 2024 06:42:07 +0000

Aspergillus fungi produce a family of mutagenicity and carcinogenicity mycotoxins called aflatoxins, which has been shown to cause hepatocellular carcinoma (HCC) in human tissues and can form derivatives with several cellular macromolecules. The study was conducted in Hawija, Kirkuk City, from January to June 2021. The feedlot storages are used for a stored ration that is used for cow nutrition, which is the source of milk. In the current study, we used 80 cow raw milk samples and their products (40 soft white cheeses and 40 yogurts). The results showed fungal growth was recorded in 71 feedlot storages out of 80 feedlot storages at a rate of 88.75%, and Aspergillus spp detection in 59 out of 71 feedlot storages at a rate of 83.1%. Also, the current study showed that 54% of milk samples, 62% of yogurt samples, and 86% of cheese samples were contaminated with Aflatoxin M1. Pollution of feedlot storage environments by Aspergillus spp. led to contamination of rations with Aspergillus spp. and its toxin. Animals fed with contaminated rations containing toxin will have produced this toxin in the milk.

Biomechanical analysis of seated posture and ergonomics in workspace interior design for improved user comfort

Yuhai He — Wed, 18 Dec 2024 06:56:34 +0000

Prolonged sitting in office environments poses significant occupational health risks, necessitating effective ergonomic interventions. This study investigated the biomechanical aspects of seated posture and the effectiveness of ergonomic interventions in a technology park setting. A 12-week randomized controlled study was conducted with 39 office workers divided into three groups: Control Group (n = 13), Intervention Group A (ergonomic setup, n = 13), and Intervention Group B (ergonomic setup with feedback, n = 13). Measurements included spinal angles, muscle activity (%MVC), seat pressure distribution, and postural compliance. Spinal alignment improved significantly in intervention groups, with Intervention Group B showing superior improvement (+32.6 ± 3.8°) compared to Intervention Group A (+24.8 ± 3.5°) and control group (−2.5 ± 1.2°, p < 0.001). Muscle activity in the trapezius reduced significantly in Intervention Group B (from 22.4% ± 3.2%MVC to 13.1% ± 2.1%MVC, p < 0.001). Peak pressure at ischial tuberosities decreased by 29.5% in Intervention Group B compared to control. By week 12, postural compliance reached 85.4% ± 6.8% in Intervention Group B versus 47.2% ± 5.0% in the control group, with user adaptation rates achieving 86.1% ± 6.9% compared to 45.6% ± 4.8% in the control (p < 0.001). The combination of ergonomic setup and real-time feedback demonstrated superior outcomes in improving seated posture, reducing muscle fatigue, and optimizing pressure distribution. Intervention Group B showed significantly better results across all parameters, with sustained improvements over the 12 weeks. These findings suggest that integrated ergonomic interventions with feedback mechanisms are more effective than traditional approaches in promoting healthy sitting behavior in office environments.

Dynamics analysis of rotor in disc centrifuge for separation of bioengineering

Yurong Wang, Shuxin Wang, Hong Li, Mingliang Zheng — Wed, 18 Dec 2024 09:08:32 +0000

In order to reveal the influence of gyroscope effect and structure parameters on the modal frequency and critical speed of the rotor system in disc centrifuge for separation of bioengineering. Based on the rotor rigid body dynamics, the fixed-point gyroscope motion of the rotating spindle of the disc centrifuge was analyzed. Establishing a finite element dynamic model for the rotor system of a disc centrifuge, taking into account the gyroscopic moment caused by the rotational inertia of the rotor disk and the elastic support of the bearings. We calculated the gyroscopic moment and the analytical expression of critical speed. Analyzing the quantitative relationship between the gyroscope effect, bearing support stiffness, drum material density and the critical speed of the rotor system. The results show that the calculated value of finite element is close to the measured value. The influence of gyroscopic force on the vibration characteristics of rotor system for dish centrifuge cannot be ignored. The critical speeds of the rotor system increase with the increase of elastic support stiffness, while the first critical speed decreases with the increase of drum density, but the second critical speed increases with the increase of drum density. These studies provide a theoretical reference for the dynamic response, structural design and dynamic balance of disc centrifuge.

Temporal analysis of cellular and molecular response-driven ground reaction forces in predicting volleyball spike jump height: Insight for optimizing spike jump performance

Thu, 19 Dec 2024 06:18:18 +0000

Ground reaction force (GRF) during jumping, which is an outcome of the complex cellular and molecular biomechanical processes within the lower limb, reflects the interaction of the lower limb with the ground. Previous studies, however, have been restricted to analyzing only the peak kinetics, overlooking the moment when the peak occurs and other essential details beyond the peak. Thus, the objective of our study was to explore the relationship between the full time series of GRF and jump height during volleyball spike jumps, considering the underlying cellular and molecular biomechanical mechanisms. Data on the kinematics and kinetics of 22 elite male (mean age: 21.56 years) collegiate volleyball players’ spike jumps were gathered via a motion capture system comprising 13 high-speed cameras and 2 force plates. Then, we analyzed the association between the full ground reaction force time series and jump height using statistical parameter mapping (SPM) regression. The results of the study demonstrated that the horizontal GRF of the dominant foot was significantly related to jump height in the 23%–80% interval of dominant foot contact (DFC) with the force plate to take-off (TO). This association is likely due to the coordinated activation and contraction of specific muscle cells and molecular signaling pathways within the lower limb muscles that govern force generation and transmission. The vertical GRF of the dominant foot was significantly associated with jump height in the 29%–35% and 80%–94% intervals of DFC to TO, which could be attributed to the differential recruitment and activity of muscle fibers at the cellular and molecular levels. Similarly, the non-dominant foot was significantly associated with jump height in the 48%–63% interval of non-dominant foot contact (NFC) with the force plate to TO. These data emphasize the significance of enhancing lower limb muscle capacity through interventions that target the cellular and molecular biomechanical aspects, in order to improve jumping technique and overall performance.

Self-made Chinese herbal compound decoction on the frequency of attacks and symptom improvement in patients with external contraction limb joint spasms

Junlai Xue, Teng Gao, Xuezhu Jin, Xiaolin Zhang, Yufeng Wang — Thu, 19 Dec 2024 06:40:40 +0000

Background: After suffering a stroke, external contraction limb joint spasms frequently occur as a sequela, leading to increased muscle tension in patients and affecting the normal control of contraction and relaxation of limbs and joints, significantly impacting their daily activities and productivity. Current clinical treatments often rely on surgery or anti-spasm medications, which may be accompanied by prolonged treatment durations and significant side effects. These methods only provide short-term relief from muscle tension and spasms, falling short of satisfactory results. Therefore, there is an urgent need to explore effective biomechanical therapies to alleviate limb joint spasms. Traditional Chinese medicine, with its unique theoretical framework and treatment modalities, has garnered global attention. This study aims to investigate the impact of a self-made traditional Chinese medicine compound decoction on the frequency of attacks and symptom improvement in patients suffering from external contraction limb joint spasms, hoping to offer additional avenues for the development of traditional Chinese medicine in treating limb joint spasms. Objective: To evaluate the biomechanical effects of the self-formulated Chinese medicine compound decoction on the frequency of attacks and symptom improvement in patients with external contraction limb joint spasms. Methods: A total of 94 patients with external contraction limb joint spasms following stroke were randomly allocated into two groups: the traditional Chinese medicine (TCM) group and the rehabilitation group, each consisting of 47 patients. The rehabilitation group received routine symptomatic treatment for stroke and biomechanical rehabilitation training, while the TCM group received the self-formulated Chinese medicine compound decoction in addition to routine treatment and rehabilitation training. Both groups underwent continuous treatment for one month. The number of limb joint spasm attacks before and after treatment was recorded for both cohorts. Comparisons were made regarding the degree of joint spasm at the elbow, wrist, knee, and ankle joints, clinic spasticity index (CSI) scores, Fugl-Meyer assessment (FMA) scale scores, and activities of daily living (ADL) scores between the two cohorts before and after treatment. Clinical efficacy was also compared between the two groups. Results: No significant difference was detected in the number of limb joint spasm attacks between the two groups before treatment (P > 0.05). After treatment, the number of attacks in both groups significantly decreased compared to before treatment (P < 0.05), with a lower number of attacks in the TCM group (P < 0.05). Prior to treatment, there were no statistically significant disparities in the degree of spasm at the elbow, wrist, knee, and ankle joints between the two cohorts (P > 0.05). Following treatment, notable improvement was observed in the spasm of each joint in both groups, with better improvement in the TCM group than in the rehabilitation cohort (P < 0.05). Before treatment, we found no substantial disparities in tendon reflex, muscle tension, and spasm scores between the two groups (P > 0.05). After treatment, scores in each parameter were reduced in both cohorts, with scores lowered in the TCM group compared to the rehabilitation group (P < 0.05). Prior to treatment, no substantial differences existed in FMA and ADL scores between the two groups (P > 0.05). After treatment, both FMA and ADL scores were elevated in both groups compared to before treatment, with higher scores observed in the TCM cohort than in the rehabilitation cohort (P < 0.05). The total effective rate in the TCM group reached 95.74%, while in the rehabilitation group, it was 80.85%, with significantly higher clinical efficacy in the TCM group compared to the rehabilitation cohort (P < 0.05). Conclusion: The use of self-formulated Chinese medicine compound decoction can reduce the number of attacks and achieve a more significant improvement in symptoms for the treatment of external contraction limb joint spasms, demonstrating good biomechanical therapeutic value and deserving clinical promotion.

Dynamic relationship between oral English pronunciation standard and mental health monitored by biosensor

Xuan Zhou, Wei Jia, Cuiping Shi — Thu, 19 Dec 2024 06:52:47 +0000

Oral English pronunciation is an important feature of language ability, especially among non-native speakers, as good pronunciation has a direct impact on communication efficacy and social integration. However, the difficulties connected with attaining a high standard of oral English pronunciation can lead to severe stress, anxiety, and other mental health disorders. The purpose of the research is to establish a dynamic correlation between oral English pronunciation standards and mental health, as monitored through biosensor data. The research aims to explore how variations in speech accuracy and fluency during English pronunciation tasks can reflect underlying psychological states, such as stress, anxiety, and overall emotional well-being. The study proposed a novel Improved Flower Pollination-tuned Resilient Deep Neural Network (IFP-RDNN) in this article, to predict the oral English pronunciation rating using biosensors. Electroencephalography (EEG)records patterns of cerebral waves using electrodes applied to the head to assess the electrical impulses in the cerebellum called EEG signals was acquired during the listening state and with the audio signals utilized in stimuli, as well as the spoken audio obtained from the subject. The data processing used a median filter to remove noise from the audio data. Fast Fourier transform (FFT) is used to extract the features from the preprocessed data. It is measured by biomedical data, can be predicted with the help of an optimization technique which draws inspiration called IFP helps to optimize the parameters effectively by mimicking natural pollination processes; RDNN is employed with the optimized parameters; it can predict oral English pronunciation ratings. Experimental results reveal that the spoken audio confirms the improvement in pronunciation throughout the trials. In a comparative analysis, the suggested method is assessed with various evaluation measures, such as F1-score (88.9%), recall (91.60%), precision (89.80%), and accuracy (90.3%). The result demonstrated the IFP-RDNN method to predict the oral English pronunciation rating using biosensors. The findings indicate a significant connection between the quality of oral English pronunciation and mental health, with deviations from standard pronunciation being associated with increased stress and emotional suffering.

Biomechanics of conjugated materials in tennis racket swing action

Denghui Tian — Thu, 19 Dec 2024 06:56:49 +0000

In response to the problem of insufficient elasticity and high swing load in tennis rackets, this article adopted a new π conjugated material to optimize the tennis racket and conducted research on the biomechanical analysis of swing actions. Firstly, the Hummer method was used to oxidize and dry graphite to prepare graphene materials with low dispersion. Poly1, 5-diaminoanthraquinone (PDAA) nanocomposites were introduced, and they were fused with graphene materials through chemical oxidation polymerization to produce a new π conjugated material. Then, they were applied to the string surface and handshake improvement of tennis rackets through impregnation and vacuum drying methods, improving the elasticity of the strings while reducing the weight of the tennis racket. Finally, on-site material validation was conducted on the self built survey athlete dataset. The experimental results showed that the accuracy of the tennis racket made of the new π conjugated material reached 99.41%, which was 6.73% higher than that of carbon fiber material. The bending strength reached 97.53 MPa, and the weight of the racket was only 255 g. The application of conjugated materials has enhanced the elasticity of tennis rackets, reduced the weight of the racket, and promoted the fatigue resistance and accuracy of tennis players’ swing actions.

Biosensor assisted measurement of cognitive participation in English reading: A psychological perspective

Yang Li, Yajie Chen — Thu, 19 Dec 2024 07:33:11 +0000

Cognitive involvement in English reading is important for understanding and engagement. Traditional techniques of measuring cognitive participation frequently rely on individual evaluations that do not capture real-time physiological reactions. Recent developments in artificial intelligence (AI) and biosensor technology provide intriguing options to close this gap by offering a goal, real-time data. This work seeks to improve the evaluation of cognitive participation in reading in English by combining biosensor data analysis with modern AI algorithms. Participants completed English reading activities, and their electroencephalogram (EEG) and Galvanic Skin Response (GSR) signals were recorded. A median filter was used as a pre-processing to reduce noise. Discrete wavelet transform (DWT) was utilized to extract features to extract specific patterns from the biosensor signals. The new Dynamic White Shark Infused Residual Neural Network (DWS-IResNet) approach was used to model and forecast the level of cognitive participation. The proposed method is implemented using the Python platform. The algorithm used was trained and evaluated based on performance indicators such as accuracy. Using the features of simple, technical, analytical, and emotional, the proposed DWS-IResNet approach is compared with metrics between males and females. In simple features, the accuracy was 90% higher for females; in emotional features, the precision was 90% better for females; and in males and females, the percentage of emotion features was greater at 90% of recall and 90% of F1-score. The suggested technique demonstrates the efficacy of the AI-enhanced biosensor method for assessing cognitive engagement. This study demonstrates the possibility of AI-powered biosensor readings for real-time, accurate evaluation of cognitive involvement during reading.

Prevention of sports injuries in college basketball players: An intervention study based on biomechanics

Yunbang Zhang, Gongxia Tan, Hao Zou — Thu, 19 Dec 2024 08:50:50 +0000

College basketball is becoming more and more popular, and as a result, more students are participating in the sport. This has led to a progressive rise in sports injuries, which has an impact on both academic performance and quality of life. Basketball players are mostly vulnerable to Anterior Cruciate Ligament (ACL) injuries due to certain movement patterns that raise their risk of damage. This study examines how biomechanical risk factors for ACL injuries in pre-adolescent basketball players are impacted by the JumpStart injury-prevention warm-up routine. The exercise that would lower the Peak Knee Valgus Moment (PKVM), a major risk factor for ACL injuries, was hypothesized. Further kinematic and biomechanical parameters were investigated. A total of sixty-two male and female basketball players were randomized and recruited into two divisions: the experimental group (EG) (n = 31) then the control group (CG) (n = 31). While the CG continued with their standard warm-up protocol, the experimental group took part in fifteen in-season JumpStart sessions. Motion capture data from pre- and post-season basketball-specific activities, such as jump landings, single-leg jumpers, and cutting movements, were gathered. SCONE simulator biomechanical modeling system was used to determine joint angles and moments in the lower extremities. Pre- and post-season data within groups were contrasted using paired t-tests, and the control and experimental groups were contrasted using independent t-tests. To assess the interaction effects among the EG and CG, an analysis of variance (ANOVA) was also achieved. The EG’s PKVM during jump landings was considerably lower (p < 0.01) than that of the CG, according to the results. These results show that the JumpStart program is beneficial in lowering the probability of ACL injuries in pre-adolescent basketball players and recommend future improvements to address other risk factors in single-leg and cutting activities.

Factors influencing success: Secondary cytoreductive surgery in the management of recurrent ovarian cancer

Huixing Yi, Rongdong Zeng, Xiaogang Lv — Fri, 20 Dec 2024 01:44:34 +0000

Objective: Secondary cytoreductive surgery (SCRS) plays a key role in the treatment of ROC. The aim of this study was to provide an in-depth analysis of the practical application of SCRS in the management of recurrent ovarian cancer (ROC) and to assess the specific impact of its therapeutic effect on the long-term prognosis of patients. Methods: We collected clinicopathologic data on 83 ROC patients who received SCRS from January 2010 to January 2020, including patient age, histological type, and SCRS results. Kaplan-Meier survival curve, Logistic regression test and Cox proportional risk model were used for univariate regression analysis. Results: We ended up with a detailed analysis of 80 patients. During the observation period, up to the prescribed end of follow-up, 26 patients were observed to be alive, while 57 patients had died. The mean survival of all patients was 56 months. The clinical factors affecting progression-free survival were neoadjuvant chemotherapy [HR (95% CI) = 1.40 (1.13–1.74)], The recurrence interval [HR (95% CI) = 0.51 (0.36–0.70)], previously used chemotherapy line number [HR (95% CI) = 1.46 (1.17–1.82)], recurrence period to the total number of cycles of chemotherapy [HR (95% CI) = 3.48 (2.65–4.57)]. Factors affecting the degree of SCRS completion include tumor stage [HR (95% CI) = 2.723(1.281–5.786)], tumor size [HR (95% CI) = 0.386 (0.153–0.896)], The number of tumors [HR (95% CI) = 2.893 (1.056–7.925)]. Conclusion: Tumor stage, time interval of recurrence, size and number of lesions are closely related to the success rate of SCRS. Recurrent ovarian cancer patients achieve complete elimination of tumor cells through SCRS to optimize treatment outcomes and prognosis.

Biomechanical analysis and application of an anti-fuzzy decomposition method for sports dance movement images based on multi-attention

Tang Tang, Junjian Zheng, Lei Wang, Fei Yang — Fri, 20 Dec 2024 01:46:03 +0000

In the realm of biomechanics, accurate analysis of sports dance movements plays a crucial role in understanding human motion patterns and optimizing athletic performance. To address the challenge of analyzing sports dance error movement images that often suffer from high segmentation difficulty due to blurriness, we propose an anti-fuzzy decomposition method based on multi-head attention. Firstly, the Multi-scale Retinex automatic color enhancement algorithm is employed to correct the color of sports dance error action images, as color correction can enhance the visual clarity which is essential for subsequent biomechanical feature extraction. Subsequently, an anti-fuzzy decomposition model of sports dance error action images based on Diffusion Model-U-shaped network (DM-Unet) is constructed. The corrected image is divided into image blocks by a block embedding layer and then input into the encoder which is constructed by the confrontation generation network. The encoder selects the residual network not only to extract image features but also to capture biomechanically relevant details such as joint positions, limb orientations, and body postures. The multi-head attention module is utilized to suppress the dynamic blur of the image, which helps in precisely identifying the key movement elements during sports dance. Moreover, through down-sampling operations, the dimension of image features is reduced while retaining the essential biomechanical information. The decoder then uses the up-sampling module to restore the encoder output results to the original size. Through the global residual connection module, the features of each layer of the encoder and decoder are connected, enabling the retention of shallow features of the image that are significant for analyzing the fine-grained biomechanical aspects of sports dance movements. The comprehensive loss function is used to train the model, and the anti-fuzzy decomposition results of sports dance error action images are outputted. The experimental results show that this method can effectively decompose the wrong action image of sports dance into structure and texture parts. Importantly, the peak signal-to-noise ratio of the decomposed image being higher than 26dB indicates enhanced clarity for further biomechanical analysis. For example, these decomposed images can be used to study the impact of different movement errors on joint torques, muscle activations, and overall body balance in sports dance, providing valuable insights for coaches and biomechanics researchers to improve training programs and understand injury mechanisms.

Effects of low-level laser therapy and functional exercises in the treatment of chronic ankle sprain: A randomized controlled trial

Yuanzheng Chen, Yicai Fan, Quan Zhou, Haonan Qian — Fri, 20 Dec 2024 01:59:14 +0000

Objective: Chronic ankle sprains can be described as a failure of the lateral ankle joint complex following an acute or recurrent ankle injury. It is estimated that 80% to 85% of ankle sprains and lateral ankle complex failures can be successfully treated with a functional ankle rehabilitation program. However, most previous functional training has focused on enhancing local ankle function, with less attention paid to the damaged tissue cells and to the systematic consideration of the human lower limb kinetic chain. The more comprehensive approach to combined therapy requires further research. The purpose of this study is to investigate whether low-level laser therapy (LLLT) combined with functional exercise, is more effective than functional exercise alone in treating chronic ankle sprain. At the same time, to observe the dose-response relationship of LLLT in the treatment of chronic ankle sprains. Methods: Thirty-three patients with chronic ankle sprains were randomly divided into three groups: exercise alone, low-dose laser plus exercise, and high-dose laser plus exercise, with eleven patients in each group. Two “laser groups” received laser irradiation at an intensity of 398 mW/cm² and 796 mW/cm² (doses of 119 J/cm² and 239 J/cm²), respectively, which were applied to two acupoints of Ki 3 (Taixi) and Bl 60 (Kunlun), and two pain points of the ankle. The functional exercise program consisted of ankle resistance exercises, resistance kinematic chain exercises, heel raise exercises, and BOSU ball exercises. All patients received five treatments per week for four weeks. At the end of this treatment and at baseline, observing the changes in ankle pain, range of motion (ROM), muscle strength, and balance postural control, and evaluating the curative effects. Results: After the 4-week intervention, all groups showed significant improvement in outcomes (P < 0.05). The high-dose laser irradiation combined with functional exercise resulted in more significant improvements in the primary outcomes than the other two regimens (P < 0.05). However, there were no significant differences (P > 0.05) between the low-dose laser irradiation combined with functional exercise and functional exercise alone. Conclusion: 1) Systematic functional exercise can significantly reduce the pain of patients with chronic ankle sprains, meanwhile improves the ROM, muscle strength, and balance control of the ankle joint. 2) LLLT enhances the efficacy of functional exercise in the treatment of chronic ankle sprains. 3) The therapeutic effect of LLLT on chronic ankle sprains is related to the irradiation intensity, and the effects of 796 mW/cm² irradiation being more effective than 398 mW/cm² irradiation.

Research on mechanism and prevention path of athletes’ lower limb injury in badminton teaching and training

Shaoyin Li, Zunyi Ma, Shuangmei Xie — Fri, 20 Dec 2024 02:10:00 +0000

Lower limb injuries are common among athletes, particularly during badminton training and competition, and are often caused by biomechanical imbalances, repeated strain, and ineffective training techniques. The basic mechanisms of lower limb injuries are examined in this study, along with preventive measures to improve athlete safety in sports instruction and practice. In addition, 275 badminton players participated in the study, and information was gathered via surveys and injury reports. Key injury patterns by gender, weight category, and injury type were identified by frequency analysis and cross-tabulation; Statistical significance was assessed using chi-square testing. Based on the results, the most frequent injuries are to the thighs and ankles, with those injuries increasing mostly throughout non-contact exercises similar to warm-ups and footwork drills. A thigh injury was dominant in lightweight competitions, as knee injuries were more frequent in men’s heavyweight competitions. Based on statistical analysis, ankle injuries were much more common in female athletes across all weight classes, and they were regularly caused by tiredness from repetitive motions and insufficient warm-ups. The distribution of the injuries among badminton players was as follows: warm-ups caused 80 injuries (29.1%), footwork drills caused 90 injuries (32.7%), strength training caused 40 injuries (14.5%), and miscellaneous activities caused 65 injuries (23.6%). Ankle and thigh injuries during warm-ups, ankle and knee injuries during footwork drills, and knee and thigh injuries during strength training were the most frequent injuries. According to our study, a lack of protective workouts, developed fatigue, and poor movement execution are all main causes of lower limb injuries. In light of these findings, we suggest particular preventive measures, such as modified warm-up routines, biomechanical evaluations, and recuperation management strategies. Athlete performance can be enhanced and injury risks reduced by integrating these preventive pathways into sports teaching and training.

The biomechanical characteristics of fencing lunge movements and their implications for physical training

Shen Wei, Dongyuan Wei — Fri, 20 Dec 2024 02:43:33 +0000

Fencing is a unique combat sport characterized by its dynamic movements, strategic interactions, and the need for rapid decision-making. Central to a fencer’s performance is the lunge, a foundational movement that enables athletes to engage their opponent effectively while maintaining balance and control. It executes precise and explosive movements, particularly during lunges, which are critical for both offensive and defensive strategies. Understanding the biomechanical characteristics of this action is critical for maximizing performance, improving training regimens, and lowering injury risk. The objective of this study is to investigate the biomechanical characteristics of fencing lunge movements and their implications for physical training. A total of 126 fencers participated in this study. They are randomly divided into two groups: Group A, who received physical training based on biomechanical intervention, and Group B, who received traditional physical training. Using motion capture data through cameras, sensors and force plates, the lunge technique of competitive fencers is analyzed, examining variables such as joint angles, force application, and ground reaction forces. The data analysis and statistical methods include descriptive statistics, t-tests, and ANOVA to identify significant differences between the groups. The findings demonstrate Group A significantly improved the optimal lunge execution, which is characterized by specific patterns of joint movement, particularly at the ankle, knee, and hip, which correlate with successful reach and stability. Also, Group A identified the importance of muscular strength, flexibility, and reaction time in enhancing lunge performance to Group B. The study describes that integrating these biomechanical concepts into training programs, coaches, and players can enhance performance, reduce the risk of injury, and encourage long-term fencing success.

Biosensor-based strategies for promoting innovation and assessing occupational health risks among college students

Fang Li — Fri, 20 Dec 2024 00:00:00 +0000

Higher education was increasingly emphasizing innovation and entrepreneurship strategy, encouraging college students to develop creative talents. The intense needs of both academic and entrepreneurial tasks provide serious occupational health hazards that affect students’ physical and emotional health. This paper provides a novel approach to risk assessment and management that combines biosensor technology and artificial intelligence (AI). The emphasis is on applying a novel Flower Pollination Optimizer Tuned Gate refined Long Short Term Memory (FPO-GLSTM) method to monitor and forecast health issues, such as stress, physical strain, and other risk factors in education and entrepreneurship situations. Data were gathered using biosensors that monitored physiological characteristics, such as heart rate, blood pressure, and stress levels in real-time. To capture crucial health information, the data was pre-processed with min-max normalization, and features were extracted using the Discrete Wavelet Transform (DWT). The FPO-GLSTM model to forecast potential health hazards and make individualized recommendations. The outcomes demonstrate that the FPO-GLSTM-based model accurately and precisely forecasts health risks, including stress-induced conditions. AI and biosensor data integration offer a viable way to monitor and manage health risks for students, improving their health in educational and entrepreneurial environments.

Investigating the biomechanical impact of lighting placement on visual and physical comfort in living room interior design

Yabin Chen, Qiong Wu, Shijia Wang — Fri, 20 Dec 2024 03:09:00 +0000

Lighting is a key factor in shaping comfort, ambiance, and functionality within residential spaces, influencing not only visibility but also a room’s overall experience and usability. In living rooms, where activities range from socializing and relaxation to reading and television viewing, lighting design must balance visual clarity, warmth, and adaptability to meet diverse needs. This study investigates the effects of specific lighting placements—overhead, wall-mounted, and floor/table lamp setups—across warm (2700 K), neutral (4000 K), and cool (6500 K) color temperatures on visual and physical comfort in a simulated residential living room environment. Using a mixed-methods approach, quantitative metrics, such as brightness consistency and luminance contrast, were combined with qualitative assessments of perceived comfort and activity suitability. Findings reveal that warm-toned floor and table lamps (2700 K) provide the highest levels of perceived warmth and relaxation, with average ratings of 4.9 and 4.8, making them particularly suitable for social and leisure activities. Overhead lighting in cool tones (6500 K) enhanced visual clarity, achieving an average clarity rating of 4.5, making it more suited to tasks requiring focused attention, such as reading. Wall-mounted lighting in neutral tones (4000 K) offered a balanced solution, with comfort and activity suitability ratings of 4.5, supporting a range of activities without compromising ambiance or clarity. These results underscore the importance of selecting lighting configurations that align with the intended use of residential spaces. Warm lighting, especially at lower levels, creates a cozy, inviting atmosphere most effectively, while cooler overhead lighting offers enhanced brightness and clarity for more visually demanding tasks. By highlighting the impact of lighting placement and color temperature on residential comfort, this study provides practical insights for interior designers and homeowners, contributing to developing adaptable, user-centered lighting solutions that optimize functionality and ambiance in home environments.

Optimization of adolescent and young adult mental health assessment and ideological-political intervention strategies based on biological analysis

Dawei Meng — Fri, 20 Dec 2024 03:44:23 +0000

The present work is a cross-sectional study seeking to improve the assessment of student mental health by including. It not only utilizes the standard rating scale but also incorporates cortisol levels and genetic factors. The study delves into the role of various factors. In terms of biological aspects, it focuses on the molecular mechanisms underlying cortisol and genetic markers, and details the methodologies for their measurements. Regarding sociopolitical factors, it analyzes the impacts of stigma, societal pressures, and academic policies on students’ mental wellbeing. Moreover, it emphasizes the intricate interactions between biological, psychological, and sociopolitical dimensions. The research adopts a quantitative research design complemented by qualitative research data collection and analysis tools. In the quantitative phase, 400 high school and university students are recruited. They fill in psychometric questionnaires such as the Beck Depression Inventory, State-Trait Anxiety Inventory, and Perceived Stress Scale, along with providing biological measures like cortisol levels Participants are matched by age, gender, and socio-economic status. As part of the qualitative analysis, 50 students are invited for focus group interviews to get their understanding of mental health issues, social norms, and the college rules and regulations. The data collection was done through stratified random sampling to ensure a comprehensive representation. The results indicate that high cortisol levels are positively related to depression and anxiety scores, which validates the significance of biological factors in mental disorders. Additionally, the quantitative findings highlight crucial sociopolitical factors like stigma and academic pressure that exacerbate mental health problems. To enhance the efficiency of mental health evaluation and treatment, a holistic approach considering biological, psychological, and Socio-political factors is essential. However, the study has limitations such as the lack of transferability of findings to other populations and the potential influence of self-report measures in psychometric results.

Research on the design of biomimetic cultural and creative products driven by mechanical force

Ping Dong, Xuefeng Li — Fri, 20 Dec 2024 05:54:58 +0000

The incorporation of biomimetic ideas into product design has emerged as a viable strategy for increasing creativity and utility in cultural and creative products. This study focuses on the design of a household appliance, especially a cultural one, by substituting standard materials with novel alternatives powered by mechanical forces. The objective of this research is to provide a thorough framework for assessing the quality of the newly constructed home appliance by utilizing a unique technique called Adaptable Pelican optimization fine-tuned Gradient boosting machine (APO-GBM). We apply powerful machine learning techniques to predict product quality by identifying essential features like design quality, durability, and user satisfaction. The results show that the application of mechanical forces increases the vessels’ functional efficiency and durability in addition to improving their appearance. The hybrid model is highly accurate in forecasting product quality, opening the path for future advances in biomimetic design. The study’s findings highlight the possibility of combining mechanical forces with biomimetic concepts to produce unique, cultural, and creative products. This information may be very helpful to manufacturers and designers who want to improve the sustainability and quality of their products.

Optimizing English pronunciation teaching through motion analysis and intelligent speech feedback systems

Jieru Wang — Fri, 20 Dec 2024 06:21:26 +0000

This study investigates the effectiveness of integrating Motion Analysis (MA) and Intelligent Speech Feedback Systems (ISFS) to enhance English Pronunciation (EP) accuracy among Chinese learners. Leveraging the OptiTrack Prime 13 Motion Capture System (MCS) and SpeechAce Pronunciation API, the study aims to address challenges non-native English speakers face, particularly in producing accurate articulatory movements and reducing Pronunciation Errors. Forty-three participants were divided into Experimental Groups (EG) and Control Groups (CG), with the EG receiving real-time feedback on articulation and phoneme accuracy. Key metrics, including Pronunciation Accuracy Score (PAS), Articulatory Movement Score (AMS), and Pronunciation Error Rate (PER), were measured alongside engagement indicators, such as session duration and self-corrections. The results show that the EG experienced a significant improvement in pronunciation accuracy, with a 31.2% increase in PAS and a 57.1% reduction in PER. Enhanced AMS scores also indicated refined articulatory precision across various articulatory points, including lip rounding and tongue positioning. Engagement metrics demonstrated higher consistency and task completion rates in the EG, suggesting increased motivation and sustained participation due to the real-time feedback provided. These findings suggest that combining MA with ISFS can provide targeted, adaptive support, enabling learners to make precise corrections and accelerate their progress in achieving native-like pronunciation. This study contributes valuable insights into the potential of advanced feedback-driven approaches in language learning and pronunciation training.

Biomechanical analysis of musicians’ posture and movement patterns for optimizing performance and reducing injury risk

Xuan Yao — Fri, 20 Dec 2024 06:31:34 +0000

Musicians often face unique physical demands that can lead to musculoskeletal disorders (MSDs) and performance-related injuries due to repetitive movements and poor postural alignment. This study examines the biomechanical factors contributing to these issues and explores the relationship between posture, movement efficiency, and performance quality across various instrument types. Using advanced motion capture technology, force plates, and electromyography (EMG), this research analyzes joint angles, ground reaction forces (GRF), muscle activation levels, and kinematic patterns in 84 musicians. Key findings include significant differences in joint angles across career stages, with mid-career musicians exhibiting the highest deviations in shoulder and elbow alignment (p < 0.05), suggesting that posture improves with experience but still presents a risk. GRF analysis revealed that standing musicians experience a significantly higher load (mean GRF = 489.6 N, p = 0.012), leading to greater postural instability and reduced performance quality. The study also found a positive correlation between movement efficiency and auditory performance (r = 0.61, p = 0.004), emphasizing the importance of efficient, fluid movements in producing high-quality musical output. Multivariate regression analysis indicated that violinists and cellists experience the highest muscle activation and fatigue rates, with violinists showing a fatigue rate of 0.29 %MVC/min (p < 0.05), highlighting the physical strain on string players. Pressure distribution analysis for seated pianists identified asymmetries in posture, with a significant imbalance in left and right side pressure (p = 0.023), contributing to discomfort and potential long-term injury risks.

Investigating the biomechanical reactions at the microscopic level of consumer behavior in e-commerce by means of motion tracking and physical interaction patterns

Wenming Wu, Yuxin Wang — Fri, 20 Dec 2024 06:46:33 +0000

The rapid expansion of e-commerce has reshaped consumer behavior. From the perspective of cellular and molecular biomechanics, understanding how users interact with online platforms becomes more crucial. This study analyzes consumer behavior via motion tracking and physical interaction patterns, focusing on variables like browsing time. When observing 126 Chinese participants on a simulated platform, we considered the influence of age, gender, etc. At the cellular level, gender differences in dwell time might relate to different neural cell activities and molecular signaling pathways in the brain. Male participants’ longer hover durations (p = 0.03) could imply varied cognitive processing at the molecular level compared to females. Cluster analysis showed three user groups, and Cluster 2’s higher engagement might be due to better cellular energy utilization and more efficient neuromuscular coordination for operating the platform. The results stress the importance of grasping these underlying biomechanical aspects of user behavior. Motion-tracking data can offer insights to optimize platform design, enhance user experience, and improve conversion rates, contributing to the literature on human-computer interaction in e-commerce.

Prolonged survival in epithelial ovarian cancer patients: Efficacy and safety of cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy

Xiaogang Lv, Wenjuan Wu — Fri, 20 Dec 2024 06:58:19 +0000

Objective: To investigate the effects of Cytoreductive surgery (CRS) combined with Hyperthermic intraperitoneal chemotherapy (HIPEC) on the clinical efficacy and safety of Epithelial ovarian cancer (EOC) patients. Methods: We selected 120 EOC patients treated in the Affiliated Cancer Hospital of Guangzhou Medical University during July 2010 to July 2020 as retrospective case-control study subjects. They were divided into 60 cases in the observation group (CRS and HIPEC) and 60 cases in the comparison group (CRS) according to the principle of balanced clinicopathological characteristics. Adverse effects and prognosis-related factors, Overall survival (OS) and safety were analyzed in the two groups. Results: The results of multifactorial Cox regression analysis showed that CC score [P = 0.013, HR (95%CI) = 2.153 (1.014−7.638)], postoperative chemotherapy cycle [P = 0.045, HR (95%CI) = 2.056 (2.004−6.730)], and treatment method [P = 0.025, HR (95%CI) = 2.409 (1.000−5.814)], lymph node status P = 0.019, [HR (95%CI) = 1.221 (1.032−10.136)], and ascites volume P = 0.034, [HR (95%CI) = 2.459 (1.072−5.643)] were independent influences on overall survival. Conclusions: CRS and HIPEC prolonged overall survival in patients with recurrent EOC with a high safety profile.

Biomechanical convergence of virtual reality and brand design for enhancing physical consumer engagement

Fujuan Zhang, Qiyi Liang — Fri, 20 Dec 2024 07:38:10 +0000

Virtual Reality (VR) has become a transformative tool in brand marketing, allowing for immersive, interactive consumer experiences that transcend traditional media. While emotional and cognitive engagement in VR environments has been extensively studied, the impact of biomechanically-informed interactions on consumer behavior remains underexplored. This study investigates how physical engagement in VR, through metrics such as joint angles, muscle activation, and interaction frequency, influences cognitive outcomes like brand recall and emotional responses such as immersion and satisfaction. Thirty-six participants engaged with branded content in three distinct VR environments—a clothing store, a luxury car showroom, and a home decor studio—while their physical movements were tracked using motion capture and electromyography (EMG) sensors. The results showed that environments demanding more significant physical interaction, such as the car showroom, led to higher muscle activation, longer interaction times, and more excellent brand recall. Repeated measures Analysis of variance (ANOVA) and correlation analysis further revealed significant relationships between physical engagement and cognitive-emotional metrics, suggesting that VR environments designed with biomechanics in mind can enhance the user experience and the effectiveness of brand interactions. This research provides valuable insights into the convergence of biomechanics and VR brand design, with implications for the future of consumer engagement in virtual.

Physical activity and mental health: Exploring the role of movement and posture in reducing stress and enhancing well-being

Tiantian Li, Fang Liu, Lei Zhang, Chao Ma — Fri, 20 Dec 2024 08:20:47 +0000

In recent years, mental health issues such as stress, anxiety, and depression have become increasingly prevalent worldwide, necessitating the exploration of practical and accessible interventions. This paper investigates the role of physical activity and posture in reducing stress and enhancing mental well-being, focusing on everyday movements and body alignment. Drawing on existing research, we explore the neurochemical and psychological mechanisms by which physical activity influences mental health, emphasizing the impact of both moderate and high-intensity exercises. The role of proper posture in managing stress and promoting relaxation is examined, focusing on postural interventions such as ergonomic adjustments and mindfulness practices. A case study conducted in Shanghai, China, assesses the effectiveness of a workplace-based intervention involving physical activity and posture correction among employees with high stress levels. In the 12-week intervention, participants in the intervention group experienced a 25% reduction in perceived stress, a 30% decrease in anxiety, and a 20% reduction in depressive symptoms. Physiological markers also improved, with a 15% increase in heart rate variability (HRV) and an 18% reduction in cortisol levels. In contrast, the control group showed no significant changes. These findings underscore the value of integrating regular physical activity and posture correction into daily routines to enhance psychological and physiological well-being. The study concludes with practical recommendations for incorporating these interventions into workplace and personal environments to promote long-term mental health.