bluuebunny/crossref_metadata_2025 huggingface dataset:
A biomechanics-based study of stroke technique in student tennis players
Authors: Kang Wanchun , Liu Yang
Published: June 2024

Studying the hitting technique of student tennis players is of great significance in improving the technical and tactical level of student tennis and promoting the popularization and scientificization of tennis. Since the performance of tennis hitting action recognition depends on the representation of the action, this study utilizes the Gaussian mixture distribution as a statistical distribution of human action data to provide a more accurate description of tennis hitting action. This paper presents the LM skeletal point coordinate fitting method to enhance the structure of the Gaussian mixture distribution-based Hidden Markov Model to capture the act of hitting the ball. In the end, the biomechanical properties of the tennis hitting technique were examined by analyzing the motion images of forehand and backhand hitting techniques of college tennis players. The mean values of the angle between the racket surface and the ground during the forehand stroke and the topspin stroke were 82.96° and 35.95°, respectively, and the speed of the grip point and wrist joint was faster than that of the flat stroke during the topspin stroke. The angles and velocities of the knee, hip, and ankle joints of the lower limbs were faster than those of the open backhand stroke in the closed backhand stroke. The angle and speed are smaller than that of the open backhand stroke, while the angle of the shoulder and elbow joints is larger than that of the open backhand stroke. The tennis action recognition model and algorithm designed in this paper can effectively acquire and analyze the technical characteristics of tennis hitting.
Application of Deep Learning Techniques in UAV Image Recognition and Tracking
Authors: Li Hong , Hussin Norriza
Published: June 2024

The advancement of computer vision technology, coupled with developments in deep learning theory, has facilitated the widespread application of deep learning across various domains. This paper leverages the YOLOv7 deep learning algorithm to incorporate the MobileVITv3 lightweight Transformer architecture and a positional attention mechanism, culminating in the creation of the MobileVIT-YOLO-Tiny algorithm for uncrewed aerial vehicle (UAV) image recognition and detection. Furthermore, utilizing the principles of the Kalman filter algorithm, this study designs an optimized Kalman filter tracking algorithm specifically for UAVs. Subsequent experimental evaluations are conducted to validate the efficacy of the proposed algorithms in UAV image detection and tracking. The results indicate that the overall average precision (AP) of the UAV image detection algorithm is 0.8931, with leading performance in simple and complex background scenarios among participating algorithms. Moreover, the UAV tracking optimization algorithm achieves success rates of 0.827 and 0.629, respectively, fulfilling the real-time operational demands of UAV image tracking. The findings underscore the significant potential for practical applications of the UAV image detection and tracking algorithm developed in this study, as evidenced by the experimental outcomes.
A meta-analysis-based study of the effects of rural tourism activities on tourists’ physical and mental health
Authors: Li Yan , Xu Baoli
Published: June 2024

As a convenient and easily accessible green space place in the environment, the countryside provides people with the opportunity to get in touch with nature intimately, so in the process of this tourism experience, people’s health benefits have been more and more widely concerned by academia. Based on this, this study utilizes the questionnaire survey method and scientific experiments to conduct an in-depth exploration of the mechanism of the impact of rural tourism activities on the physical and mental health of tourists by taking tourists who have had rural tourism experiences as the research object. This paper derives the physical and mental health indicators of tourists before tourism based on the evaluation method of physiological indicators and the test method in psychology, proposes a perceptual measurement tool to study the obtained data of tourists’ state of mind, and concludes that after tourism, the overall mean values of tourists’ average heart rate, minimum heart rate, and maximum heart rate were reduced by 0.65 bpm, 3.28 bpm, and 7.5 bpm, respectively. The mean value of ventilation per minute was reduced by 1.11 L- min-1, indicating that there is a significant effect of different ages on the physical and mental health benefits of tourists. Tourists with tour time under half an hour had a poor state of mind, and tour time of 4 hours or more had the highest POMS index of 298, with a more favorable emotional state, indicating that there is also a significant effect of tour time length on tourists’ state of mind.
A study on the application of optimization algorithms in vocal music teaching strategies
Authors: Sun Ke
Published: June 2024

Subjective interference is a common difficulty in vocal music teaching, and human ear audition cannot fully objectively analyze the students’ problems in vocal practice due to the influence of environment and other factors. This paper takes the convolutional neural network as the vocal music recognition algorithm and the Mel spectrum as the vocal music feature extraction algorithm and constructs the vocal music analysis model based on the optimization and improvement of the two algorithms. Then select the support vector machine, the nearest neighbor node, Wavenet, LSTM, GAN, SAGAN, CLDNN_BILSTM, and other models, as well as this paper’s model, for comparison experiments. Finally, the model was utilized in the vocal education classroom to evaluate the singing practice of four students. It is found that the MSE value of Arousal’s algorithm in this paper is the lowest, and the R2 values of 0.51197 and 0.71058 are the highest in the test of the MFCC vocal music feature dataset. Valence’s model in this paper has the MSE value of 0.51996, which is still the lowest, and the R² value of 0.76946, which is still the highest. This paper’s model has the best performance and results. The average rate of professional singers is 61 beats, and the model calculates the average singing rate of the four students as 77, 66, 63, and 61 beats. The first three still have a large gap compared to the standard level, and the student D level is higher. The problem of student practice analysis and vocal feature extraction and recognition in vocal teaching can be solved using new ideas and methods provided in this study.
Applications and Challenges of Statistics in Large-Scale Data Mining
Authors: Yang Siwen , Xie Wanqiu
Published: June 2024

As mathematical statistics evolve, their incorporation across diverse fields has markedly increased. This study examines specific challenges within statistical applications to data mining. By synthesizing theoretical frameworks and practical applications, this research delves into the utilization of statistical methods in data mining, enriched with practical examples. Notably, enhancements to the K-Means clustering algorithm are introduced through the optimization of initial clustering centers and the integration of a Gini index-based weighting system. This refined algorithm is subsequently applied to segment student behavioral groups, utilizing behavioral data from university students as the sample. Additionally, multiple linear regression models are employed to scrutinize variables related to student performance and to formulate a predictive model for their academic achievements. The analysis results in the identification of eight consumer behavior groups and nine academic effort groups, facilitating the classification of students. The variables exhibit varying levels of correlation with student performance, which are statistically significant (p < 0.05). Specifically, the total time spent on the Internet shows a negative correlation (-0.074), whereas grades from the previous semester display a positive correlation (0.593), both of which are particularly pronounced. The predictive model demonstrates a high accuracy, exceeding 80%, in forecasting student grades. Although the convergence of data mining and mathematical statistics presents challenges, it simultaneously offers substantial opportunities for the advancement of the field.
Product development strategy of non-heritage cultural and creative products under the fusion of traditional crafts and modern technology
Authors: Deng Haibei
Published: June 2024

In the contemporary digital era, the transformation of intangible cultural heritage (ICH) products represents a critical trend in their evolutionary trajectory. This study advocates for the synergistic integration of traditional craftsmanship with advanced technologies within the ICH sector. A standardized electronic image repository was established by scanning these items under uniform lighting conditions, forming the basis for generating non-heritage experimental datasets. Subsequently, a clustering algorithm was applied to extract color features from the images. Owing to its resilience against rotation, scale, and noise variations, the Scale-Invariant Feature Transform (SIFT) algorithm was utilized to delineate pattern features of non-heritage items. The analysis primarily focused on the predominant colors in contemporary ICH products, paving the way for innovative developments that incorporate augmented reality (AR) technology with non-heritage characteristics. Quantitative analyses indicated that the average hue of these products is 118.15°, predominantly within the warmer green spectrum of the color wheel. Moreover, the evaluation of pattern style similarity of non-heritage items, as determined by the SIFT algorithm, yielded an average of 88.36%. Further, regression analysis demonstrated a significant influence of hue, brightness, pattern, and texture on the effectiveness of product development, with regression coefficients of 0.276, 0.236, 0.237, and 0.159, respectively, at a significance level of P<0.001. This research highlights the potential of contemporary science and technology to invigorate traditional arts, thereby contributing significantly to the preservation and revitalization of valued cultural legacies.
Pattern Recognition and Automatic Rendering of Silkscreen Printmaking Technology in Animation and Game Production
Authors: Du Chuanyong
Published: June 2024

With the continuous progress of image processing technology and deep neural networks, image style conversion has a wide range of applications in game production. This paper focuses on the application of silk screen printmaking technology in animation game production, using the DiracNet algorithm to express the features of image data, to realize the feature extraction of silk screen printmaking, and add attention mechanism and auxiliary classifier on the AnimeGANv2 model for improvement, to complete the style migration process of silk screen printmaking in animation game production, and to complete the silk screen printmaking technology’s pattern recognition and automatic rendering. Through the key point matching test of the model and the subjective and objective quantitative evaluation of the style migration of screen prints, the model is mined for its effect in pattern recognition and automatic rendering of screen prints technology. The model in this paper has better performance in feature extraction in multi-scenes, and the matching scores and mAP values are improved by 7.14%~25.82% and 11.51%~35.14% over the comparison algorithms, respectively. Most of the SSIM values of the sample screen print style migration images are greater than 0.3, and the overall aesthetic and migration effect evaluations are greater than 3.5. The model in this paper is capable of recognizing screen print patterns and auto-rendering effects, and it has a wide range of potential applications in the production of animation games.
Evaluation Model Construction of School-Enterprise Cooperation Innovation Ability Based on Big Data Analysis
Authors: Zhou Mingyi
Published: June 2024

This study analyzes SCIE papers published by 39 universities as a substantial big-data sample, employing latent class analysis to explore the characteristics of university-enterprise cooperation in Chinese institutions. It identifies five categorical attributes to construct a latent class model. This paper integrates hierarchical analysis with fuzzy mathematics to develop a comprehensive fuzzy evaluation model. By organizing and layering the factors, a rational set of indicators is established, and a hierarchical analysis structure is constructed. Utilizing a judgment matrix, the study calculates the importance rankings of all indicators, thereby highlighting the significant influence of the factors. Empirical results indicate that the model achieves optimal fit with five latent categories. Within the five primary indicators for evaluating the innovation capability of university-enterprise cooperation, the weight of innovation outcomes is the highest at 0.24, followed by industrial innovation at 0.22. The analysis reveals a regional imbalance in university-enterprise collaboration across the eastern coastal area, with Guangdong achieving the highest rating of 84.03, closely followed by Jiangsu. In assessing the innovation capability of university-enterprise cooperation in Guangdong's applied universities, the overall scores for universities categorized as B, C, and D are 3.641, 3.522, and 3.409, respectively. Notably, the scores for R&D investment intensity and Chinese patent applications are relatively low, suggesting these areas require enhancement to bolster support and development, thereby improving university-enterprise cooperation and innovation capabilities.
A Mechanism Study of Corporate Culture and Organizational Innovation Enhancement for Structural Equation Modeling
Authors: Guo Yali , Zhang Miao
Published: June 2024

The role of innovation capability is becoming more and more important for the survival and development of an enterprise, and how to improve organizational innovation capability is an urgent problem for contemporary enterprises. This study adds the mediating variable of corporate learning to the model of corporate culture's influence on organizational innovation capability, puts forward hypotheses, and uses structural equation modeling to study the mechanism of corporate culture-corporate learning-organizational innovation capability. First, the sample data is tested for reliability and validity. The initial structural equation model is then established and tested. Finally, the initial model has been corrected. The correction results show that the effects of innovation values on all three dimensions of corporate learning are significant, with standardized estimates of 0.396, 0.842, and 0.489, respectively. The effects of the innovation incentive system on all three dimensions of corporate learning are confirmed. The hypothesis that innovative behavioral patterns affect vision sharing is rejected. The assumption that all three dimensions of corporate learning have an impact on organizational innovation capability is adopted. This paper concludes with managerial insights on innovation management in firms, makes recommendations, and suggests ways to enhance organizational innovation capability.
Construction and validation of a new media marketing influence assessment model
Authors: Hu Xia
Published: June 2024

In the digital era, the proliferation of new media, facilitated by digital platforms, has positioned new media marketing as a predominant marketing strategy. This study introduces a novel node ranking algorithm, leveraging the principles of PageRank, and proposes the UIEM-CMR influence assessment model derived from it. Utilizing data extracted from microblogging platforms and Facebook, this model evaluates the influence of various new media marketing nodes. Subsequently, the ten most influential nodes within the microblogging dataset were analyzed to examine the correlation between node influence in new media marketing and both the thematic distribution of new media marketing and the community distribution of followers. The findings reveal that the MR value of node influence, as calculated by the model, is impacted by the combined effects of AR and BR values, with these values alone identified as pivotal. The influence ranking, when solely based on AR and BR values, tends to diverge significantly from real-world scenarios. Nodes ranked highly under popular new media marketing topics exhibit a substantially greater probability of distribution compared to lower-ranked or randomly chosen nodes. Conversely, the distribution probabilities among influential nodes under less popular (cold) new media marketing topics show no significant disparity. Nodes with high influence ratings tend to attract followers from at least two distinct communities. The UIEM-CMR model, as developed in this study, proves to be an effective tool for assessing the influence of new media marketing strategies.
Optimization Exploration of Digital Identity Authentication Algorithm Based on Blockchain
Authors: Lan Fuji , Jiang Yihuo
Published: June 2024

In order to solve the problems of the cumbersome user authentication process, insecure identity information, and vulnerable authentication system in traditional identity authentication, a distributed identity authentication method based on blockchain technology is proposed, which utilizes decentralization and is not easy to tamper with characteristics of blockchain and asymmetric encryption to improve the security and stability of user identity authentication system. On this basis, a digital identity authentication optimization scheme is designed, including the use of a hash chain to improve the data structure and the consistency of node data by optimizing the consensus mechanism to enhance authentication security and efficiency. Finally, the automation of authentication is also achieved by introducing smart contracts. The experimental results show that the computational spend of blockchain technology is 55.47% and 97.36% less than the two traditional algorithms, and the overall security score is 22, while the conventional scheme is only 12.5. The blockchain-based authentication mechanism improves the reliability and efficiency of the authentication system.
Evaluating the Effectiveness of the ACT Model in Enhancing Students’ Coping Skills in Higher Education Counseling
Authors: Li Yongbo
Published: June 2024

The outbreak of a new coronavirus pneumonia epidemic in early 2020 has had a great impact on the mental health status of college students, especially elevating the prevalence of PTSD among students. Based on the cognitive theory of ACT, this study develops a mobile networked intervention program to enhance students’ psychological state and students’ coping ability under the intervention of the ACT model and realizes the function of ACT intervention in the form of networked self-help. Through Latent Gold analysis software, the potential profiles of multiple characteristics of the subjects were categorized in order to scientifically examine the effect of the ACT model on enhancing students’ coping abilities. The results indicated that the ACT intervention group was able to significantly alleviate students’ depression levels (d = 0.71), and enhance students’ positive mental health (d = 0.42), compared to the waiting group. The results verified the effectiveness of the ACT network intervention application, which can provide a reference for the networked reform of psychological counseling in colleges and universities.
Research on the Innovation of Teaching Mode of Accounting Computerization Based on Internet
Authors: Lin Lin
Published: June 2024

The development of information technology such as big data, intelligence, mobile Internet, and cloud computing and their application in the field of accounting have expanded the function of accounting and promoted the development of accounting informatization. This paper innovates and applies the teaching mode of accounting computerization under the Internet mode by constructing a complete teaching system of accounting computerization, introducing the Bayesian knowledge tracking model, and improving the K-means algorithm of the teaching mode of the Internet. The application test validation shows that the average score after the implementation of the teaching mode is higher than the average score before the implementation of the teaching mode, indicating that there is a significant difference between the scores before and after the experiment.... All the learning behaviors in this teaching mode have a strong correlation with the learning effect, and the correlation degree is more than 0.6. Through the knowledge tracking model, this teaching mode successfully achieves timely mastery of students’ learning levels and strengthens their cognition.
Collaborative Filtering Algorithms in Financial Performance Evaluation Indicators for Intelligent Innovation Dynamic Research
Authors: Liu Hong
Published: June 2024

This study develops a financial performance evaluation system for S Group, employing a collaborative filtering algorithm to address the limitations inherent in traditional financial performance evaluation methodologies. Improvements have been made to the similarity measure method, data imputation technique, and rating prediction approach within the model. Subsequently, the financial status of S Group is scrutinized, with a thorough exploration of the input and output indicators, leading to the calculation of the efficiency values within the financial indicator system. Through sensitivity analysis, this research investigates the impact of input and output indicators on the outcomes derived from collaborative filtering. Based on the study, recommendations are formulated in alignment with the evaluation outcomes. The findings reveal that the average comprehensive efficiency of S Group over the past decade is 0.772, with a median comprehensive efficiency of 0.788, indicative of a robust financial condition. This research provides a valuable reference for the formulation of financial performance evaluation systems and the analysis of financial performance across various industries.
Exploration of Quantum Cryptography Security Applications for Industrial Control Systems
Authors: Li Hengyu , Dong Yun , Zhang Yusheng , Wang Haiping
Published: June 2024

The exploration of security applications of quantum cryptography for industrial control systems is a key research effort aimed at enhancing the security of industrial control systems through quantum cryptography. In this paper, we study the security threats faced by industrial control systems, including network attacks, data leakage, and system tampering, and propose to utilize quantum key distribution and quantum invisible state transfer algorithms to ensure the secure transmission of industrial control system data. The simulation test environment of the upper and lower computers of the industrial control system is built. The quantum encryption and decryption algorithms are deployed in the embedded environment and PCs to test the effectiveness of quantum cryptography to enhance the advanced encryption standard key scheme. The experimental results show that the quantum cryptography technology successfully realizes the encryption and decryption of data, and the total time consumed in the whole process is less than 61.8 seconds, which meets the requirements of a real-time industrial control system. Therefore, quantum cryptography is suitable for protecting field-level data in industrial control systems.
Constructing and Experimental Research on the Feedback Mechanism of Mathematics Teaching Based on Error Analysis
Authors: Liu Ji , Zuo Lijuan
Published: June 2024

In the current teaching context, mathematics has become the most popular subject among all disciplines, and the establishment of mathematics teaching evaluation and feedback mechanisms is particularly important. The study constructs a feedback mechanism for mathematics teaching based on error analysis using the improved hierarchical analysis method and fuzzy comprehensive evaluation method. The mathematics teaching feedback mechanism and the traditional manual feedback mechanism are used to compare the performance of error analysis. Completely evaluating math teachers in Z-school using the math teaching evaluation index system. Compare the students’ math scores before and after the implementation of the math teaching feedback mechanism to explore the application effect of the feedback mechanism. The results show that the overall error of the math teaching feedback mechanism (1.12) is better than that of the traditional manual feedback mechanism (3.19), and the error fluctuation of the former is also smaller than that of the latter. The feedback results from the math teaching feedback mechanism are more accurate than the actual feedback results. The fuzzy comprehensive evaluation results of Teacher A are in the following order from highest to lowest: teaching method (93.069) > teaching content (92.531) > teaching objectives (91.645) > teaching effect (90.537) > teaching attitude (88.543). After the experiment of the math teaching feedback mechanism, the mean score of math achievement of the experimental group increased by 12 points. The control group decreased by 1.32 points, and the teaching feedback mechanism achieved significant results.
Models and Methods of Applied Mathematics in Supply Chain Management and Optimization
Authors: Liu Ji , Li Juan , Ma Fengmin
Published: June 2024

Amidst the swift expansion of the global trade economy, enterprises are confronted with escalating competition within their industries. Enhancing market competitiveness to boost economic efficiency emerges as a pivotal strategy for ensuring corporate viability. This study employs applied mathematics to refine the optimization of supply chain management, thereby offering a novel approach to augmenting management efficacy in enterprise supply chains. Initially, the research adopts mathematical models tuned to the traditional structure of enterprise supply chains, addressing prevalent profiling issues. Subsequently, it introduces a two-tier planning model to facilitate multi-objective decision-making in supply chain management. This model is rigorously applied to resolve various decision-making scenarios. An empirical analysis is conducted using Company M as a case study. Post-optimization, Company M's inventory exhibited only one instance of material shortfall, involving 132 items. The capital tied up in inventory was significantly reduced to 214,092 yuan—a decrease of 36,319 yuan compared to the capital occupied under traditional supply chain management methods. Furthermore, the optimized supply chain management system demonstrated a notable increase in the average inventory turnover rate to 12.3507%, marking an improvement of 3.6923% over the traditional model. The findings underscore the efficacy of the proposed optimization in enhancing the economic efficiency of enterprise supply chain management. This optimization not only streamlines operations but also provides robust support for the ongoing development of enterprise supply chains.
Blockchain-based E-commerce Transaction Verification Mechanism Design and Performance Evaluation
Authors: Ma Jun
Published: June 2024

With the development of blockchain technology and the increased demand for its application in e-commerce. However, the encryption algorithm of traditional blockchain technology poses a risk to data security protection. Therefore, this paper builds secure cryptographic protection by means of secondary encryption and interactive verification of the interaction process of e-commerce transaction data through a hash function. Then, for the transaction process of e-commerce, an electronic transaction protocol with privacy protection is proposed. The effectiveness of blockchain-based e-commerce transactions is analyzed according to correctness and security. The questionnaire of this study has good reliability based on the analysis of relevant data, where the overall reliability of all variables is 0.865. There is a positive correlation between the efficacy of e-commerce transactions and correctness, anonymity, resistance to simulation attacks, and accountability protocols at the 5% significance level. The correlation between traceability, non-repudiation, and resistance to replay attacks is not significant. E-commerce transaction effectiveness = 0.326* correctness + 0.455* anonymity + 0.354* traceability + 0.254* non-repudiation + 0.171* resistance to simulation attacks + 0.445* resistance to replay attacks + 0.552* accountability protocol. The accountability protocol factor has the greatest impact and contribution to the e-commerce transaction effectiveness of blockchain.
The Impact of Big Data-Driven Industrial Digital Unification System on Commercial Management Operational Efficiency
Authors: Maowu Mengyuan , Zhang Haidong , Wang Juncai , Wu Yi
Published: June 2024

Integrated management digitization is an important way to boost the competitiveness of commercial management enterprises. In this paper, we first design the functional architecture and system deployment of the commercial management enterprise digital system, including digital operation management, commercial operation data mining analysis, and other functional modules to meet the commercial operation management needs of commercial complexes, office buildings, and urban integrated operation business. The K-means clustering algorithm is then improved by using a particle swarm algorithm that is based on it. Specifically, the distribution estimation algorithm and stagnation perturbation strategy are used to update the population information and control the particle position boundary. Then, the greedy approach is used to select the advantageous particles. Then, the digital unified construction system of the commercial management enterprise is finally constructed to realize the data mining function of the operation and management and to assist the enterprise in carrying out customer management, property rights management, scientific decision-making, risk assessment, and so on. Finally, after testing the data analysis performance of the system, the business performance of the W commercial management group company that uses the system of this paper for digital transformation is analyzed. It is found that the accuracy of this paper's algorithm is the same as the PSO-Kmeans method. Still, the number of iterations of this paper's algorithm is the least; the lowest is only 14 times, and the optimization of efficiency is significant. The return on net assets and the net sales margin of W Commercial Management improved from 15.8% and 28.12% in 2016 to 20.20% and 28.12% in 2023. The debt repayment and operation ability are also optimized substantially, and the system designed in this paper The effectiveness of the developed system is proved. This study provides a proven solution for the digital transformation of commercial management enterprises and improves the operational efficiency of commercial management.
The Practice of Cloud Collaboration Tools in Enhancing the Interactivity of Computer-Based Instruction
Authors: Mei Yingying
Published: June 2024

Cloud collaboration tools are currently playing an increasingly important role in enhancing computerized teaching interactions. In this paper, we propose a method of mutual assessment of assignments based on cloud collaboration through the theoretical study of fuzzy set theory and further construction of the fuzzy cognitive diagnostic model, put forward the model assumptions, then combine them with the Fuzzy CDF of the computer for the design of teaching assignments to each other, and finally introduced the fuzzy cognitive diagnostic theory combined with the model to the computer classroom teaching, the overall scores of the overall of the pre-and post-tests of the mutual assessment of students’ assignments the average values of the scores are 3.79 and 3.87, the medians are 4.06 and 3.94, and the standard deviations are 1.23 and 1.15. In the statistics of the scores of the pre-and post-tests, the average score of the control class is higher than that of the experimental class by 1.13 points, and the average score of the experimental class is higher than that of the control class by 11.31 points. The interactivity of computerized teaching can be improved by using the homework mutual assessment method proposed in this paper, as can be concluded.
Optimization of school-enterprise cooperation path for engineering majors based on robust optimization algorithm
Authors: Meng Yuting , Lin Xinxin , Fan Yue , Liu Kang , Xu Jingwei
Published: June 2024

This research delineates the unique attributes of the industry-university-research collaboration network, formulating an innovation network specifically designed for engineering disciplines predicated on university-industry partnerships. It introduces a set of measurement indices aimed at evaluating the structural properties of this network. To address the prevalent dissatisfaction within conventional collaborative network decision-making scenarios, this study proposes a theoretical framework comprised of “baseline,” “target,” and “status quo” strategies. This framework systematically standardizes attribute values and constructs a decision-maker satisfaction function. Utilizing this function, the study identifies the optimal solution by ranking the satisfaction levels associated with various alternatives within the cooperative innovation network. Further, this paper examines the characteristics and evolutionary trends of the regional network structure of industry-university-research cooperation in the context of 5G technology, encompassing engineering departments, educational institutions, and corporate entities. It also analyzes the optimization outcomes of six preferred linkage types within the engineering majors’ university-enterprise industry-university-research innovation network under a scenario of enhanced network relationships. By comparing the weighted degree distributions between the optimally optimized network and the actual network and employing the entropy value method along with the TOPSIS method, the study evaluates the disparities in network structures between the two scenarios and identifies the optimal connection paths. The comprehensive score of the optimization effect of university-enterprise-industry-university-research cooperation and innovation network structure of engineering majors in the context of network relationship strengthening is ranked as Q
3 > Q
5 > Q
6 > Q
2 > Q
4 > Q
1, and the comprehensive score of the strong-weak connection type is the highest at 0.6774. That is, maintaining the connecting edges between “strong enterprise” nodes and “weak enterprise” nodes in the industry-university-research cooperation and innovation network has the best effect on the optimization of university-enterprise industry-university-research cooperation and innovation network for engineering majors.
Intelligent Algorithm-Driven Product Design Process Optimization: Intelligent Transformation of Product Design Processes
Authors: Qin Yue , Wang Chao
Published: June 2024

In this study, the Petri net model is initially employed to establish a framework for the product design process, subsequently enhanced by the integration of an ant colony algorithm to augment the intelligent capabilities of product design. Further refinement is achieved through the incorporation of a genetic algorithm, which optimizes the model by aligning the product design challenges more closely with real-world scenarios. This paper introduces a bespoke ant colony optimization algorithm tailored for discrete variable-oriented product design, focusing on optimizing the size and structural topology of products. A test enterprise is selected to demonstrate the efficacy of this approach. The analysis is conducted using a real case analysis method, assessing impacts on product manufacturing costs and input-output efficiency. Results from the implementation show a substantial reduction in the overall cost of intelligent product design by 15.819%. Following the adoption of smart product design strategies in 2017, there was a notable reduction in capital investment, amounting to 267,345,000 yuan, while maintaining the initial annual production volume. Over the period from 2016 to 2020, the employment of design engineers decreased by an average of 3,815 daily without compromising performance levels. The application of intelligent product process design not only achieved optimal input-output efficiency but also underscored the success of intelligent transformation in the product design process.
Model Exploration of Enhancing the Effectiveness of College Students’ Innovation and Entrepreneurship Based on Internet of Things Technology
Authors: Rong Xianqin
Published: June 2024

How to use the Internet of Things (IoT) technology to enhance the effectiveness of innovation and entrepreneurship of college students has become a topic of focus in undergraduate colleges and universities. For this reason, this paper proposes to integrate innovation and entrepreneurship education with IoT technology. Due to the inconsistent application direction of IoT technology, it can be broken down into system integration technology, wireless communication technology, and intelligent sensing technology. These three are working together to create an innovative entrepreneurship teaching model that is based on IoT technology. The empirical analysis shows that the experimental group and the control group at the end of the period show large differences in the factor of innovation and entrepreneurship knowledge, with the mean values of 4.822, 5.163, and T=6.47, respectively, and P=0.0004. In addition, the linear regression equation of IoT technology and innovation and entrepreneurship ability (Y2) is Y2=71.863+0.128X1, which shows that IoT technology can effectively improve the students’ college students’ innovation and entrepreneurship knowledge, ability, and thinking. This study confirms that the development of IoT will bring unlimited possibilities to innovation and entrepreneurship education and will become an important driving force for developing students’ creative thinking.
Application of RSM optimization model for slope stability analysis in geotechnical engineering
Authors: Shang Kai
Published: June 2024

Slope stability is a critical area of research pivotal to ensuring public safety and advancing societal development. A thorough examination of slope stability holds substantial practical value in augmenting geological disaster prevention and shaping engineering economic strategies. This study develops an optimized Response Surface Methodology (RSM) model by incorporating the main effect analysis to enhance the foundational response surface method. Subsequently, to address the requirements of slope stability analysis, a numerical model calculating the stress and reliability of anti-slip piles is formulated. Building on this foundation, the Kriging model is refined using a particle swarm optimization algorithm, which is then integrated with the RSM optimization model to evaluate slope stability in geotechnical engineering applications. The efficacy of the proposed method is underscored by its performance across five-speed boundaries, where it achieves a mean reliability value of 90.40%, with a minimal average error of 0.69%. Moreover, empirical validation of the method through conditional probability calculations yields a value of 19.95%. This figure closely aligns with the results derived from the narrow-boundary method, which estimates slope system failure probabilities between 19.28% and 22.54%. Comparatively, the system failure probability determined by the Probabilistic Network Evaluation Technique (PNET) stands at 20.75%. These results affirm that the RSM-based optimization model described in this study is not only precise but also productive, establishing it as a robust method for conducting slope stability analysis in geotechnical engineering.
Application of Big Data Analysis in Model Construction to Prevent Athlete Injury in Training
Authors: She Haiwen
Published: June 2024

Sports injury is a hot issue in the field of exercise science and sports medicine and a practical problem that needs to be solved urgently. Based on big data analysis, this paper proposes an Ada-RF prediction model that integrates the fusion of the Adaboost algorithm and Random Forest algorithm by studying five machine learning algorithms: logistic regression, k-nearest neighbor, plain Bayes, Adaboost algorithm, and Random Forest and evaluates experiments through the model evaluation criteria. The influence factors of injury risk in adolescent male basketball players were explored in terms of demographic information, training load, subjective perceived health, and assessment of athletic quality. By screening the important factors as independent variables, the risk of athlete injury and illness as dependent variables were applied to the model constructed in this paper and the models built by five traditional machine learning classification algorithms. The prediction effects of multiple models are compared. The experimental results show that the accuracy, recall, specificity, F1 score, sensitivity, and AUC value of the Ada-RF model constructed in this paper are 0.869, 0.885, 0.755, 0.683, 0.754, and 0.789, respectively, which indicate that the Ada-RF integrated model outperforms the single prediction model, and can be used for the early prevention and early treatment of athletes’ injuries and illnesses. It shows that the Ada-RF integrated model outperforms the single prediction model and can provide scientific and accurate auxiliary information for the early prevention and treatment of injuries and diseases in athletes.
Innovative Application of Heterogeneous Information Network Embedding Technology in Recommender Systems
Authors: Shi Jiaxin
Published: June 2024

This paper proposes a meta-structure-based heterogeneous personalized space-embedded recommendation system. The system algorithm uses a meta-structure-based random wandering strategy for heterogeneous personalized space for node sequence generation, which selects the next node type and chooses the next node through personalized probability. Then, node embedding learning is carried out through the heterogeneous Skip-Gram algorithm after obtaining node sequences. A nonlinear fusion function transforms the learned embedding vectors with different meta-structures and then integrates them into a matrix decomposition model for rating prediction. Pre-processing the user check-in datasets LastFM and Urban for a platform, the number of check-ins varies significantly, with some records exceeding 1,000 while others are only in the single digits. A comparison of recommender system performance shows that MPHSRec outperforms the comparison method in all metrics, with a recall of 0.2415 on the Top 20. This model analyzes the impact on cold-start users with a number of data and item interactions of less than 10 and verifies the validity as well as the feasibility of the methodology proposed in this paper.
Constructing an Innovative Interactive Experience of Stage Visual Design for Augmented Reality in Costume Performance
Authors: Shi Ruoqi
Published: June 2024

Augmented reality (AR) technology has rapidly advanced across various domains, propelled by its robust interactive immersion and the seamless integration of real and virtual environments. However, its exploration and deployment in theatrical contexts remain limited. This study leverages the Kinect system to capture images during costume performances, employing algorithms for dynamic frame difference merging and human-computer interaction to detect performers’ body movements. Building on this, the study constructs a visually innovative stage for costume performances that enhances the interactive experience for the audience. Additionally, a multimodal emotion analysis model is utilized to assess audience emotions, demonstrating significantly higher accuracy and F1 scores compared to other emotion analysis models. This model effectively integrates speech, expression, and action, surpassing the performance of unimodal analyses in emotion recognition. Furthermore, the audience's experiential perception of stage lighting effects notably exceeds expectations (P=0.013 < 0.05), underscoring an enhanced interaction experience. This research substantiates the transformative potential of AR technology in stage design, offering audiences a more innovative visual and interactive experience, and serves as a valuable reference for future applications in this field.
Application and Challenges of Information Theory in English Translation and Cross-cultural Communication
Authors: Wei Yan
Published: June 2024

With the deepening of China's foreign exchange, the importance of translation is becoming more and more prominent, but some scholars and translators still have great misunderstandings about translation, and translation has been in a neglected position. Aiming at this phenomenon, this paper introduces information theory into English translation and cross-cultural communication. It proposes an automatic translation control method based on the information entropy model to improve the accuracy and efficiency of translation. It expands the lexicon based on the information entropy model, which is divided into basic lexicon, specialized lexicon, and neologism lexicon. Then, it constructs a semantic model to complete the automated extraction of lexicon features and realize automatic translation. The transfer of language information and the modification of terminological structure are discussed in cross-cultural communication to improve the challenges of English translation and cross-cultural communication. During the translation process, the correlation between the entropy of Chinese and English corresponding unitary, binary, and ternary words - sentence entropy decreases gradually (0.993->0.977->0.972). The largest value of information entropy loss in different cultural contexts is caused by the tonal-phonetic contrast of combining yinping and declination. The relative degree of information loss is 5.300585%. This study has provided new perspectives for translation research through the use of redundant information theory and discrete information theory.
Reliability assessment and optimization of computer networks based on neural networks
Authors: Liu Shijin
Published: June 2024

Amidst the swift evolution of computer technologies, the prevalence of computer networks has become pivotal across diverse sectors, increasingly reliant on their robust functionality. This study rigorously evaluates and enhances the reliability of computer networks by leveraging an index system and an evaluation model devised through neural networks. To strengthen network dependability, this research employs a Hopfield neural network to address multi-constraint Quality of Service (QoS) multicast routing challenges, thereby elevating network reliability. Simulation experiments demonstrate that the Hopfield neural network effectively mitigates network latency and exhibits superior convergence performance compared to conventional QoS multicast routing methods. Further, this paper applies the neural network-based evaluation model to analyze the reliability of the air traffic network within the H-area after integrating the optimized computer network framework. It is observed that the most significant contributor to traffic flow loss is the network's degree value. An analysis of traffic flow density, employing actual sector flow data, reveals that high traffic volumes typically precipitate congestion. Nonetheless, the traffic flow density value consistently exceeds 100, suggesting that the enhanced computer network model holds practical applicability in real-world scenarios.