bluuebunny/crossref_metadata_2025_split huggingface dataset:
Models and Methods of Applied Mathematics in Supply Chain Management and Optimization
Authors: Liu Ji , Li Juan , Ma Fengmin 
Published: June 2024

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.