bluuebunny/crossref_metadata_2025_split huggingface dataset:
Digital Innovation of Red Cultural Resource Management Mode in Shanxi
Authors: Sun Chang 
Published: May 2024

Abstract
In this paper, we explore the mechanism of managing red cultural resources enabled by digital technology. We developed a digital communication model for red culture and proposed an innovative pathway for the management of digitally-enabled red cultural resources. Then, a regression analysis model was established to assess the effects of digital management of cultural resources through model estimation and significance testing. Based on the one-way ANOVA model, we explored the differences in the evaluation of digital management of red culture in Shanxi by different students’ genders. We tested and regressed the correlation between digital red resource management and students’ sense of political efficacy and political emotion. The results show that the correlation coefficients between digital red education and intrinsic political efficacy and extrinsic political efficacy are between (0.5 and 0.6). The correlation coefficients of digitalized red resource management with students’ sense of intrinsic political efficacy and extrinsic political efficacy r are all greater than 0, p=0.000. They are significantly positively correlated under the condition of α=0.01. This study effectively promotes the high-quality development of digital transformation in red culture and builds a new pattern of digital inheritance and innovation in red culture.
E-commerce Live Streaming Interaction Quality, Immersion Experience and Consumer Purchase Intention
Authors: Wang Dongfeng 
Published: May 2024

Abstract
With the rapid development of the digital economy, the wave of e-commerce live broadcasting has swept all walks of life and become the new force of online shopping. Based on the S-O-R model, this paper establishes a theoretical model affecting consumers’ purchase intention. It puts forward relevant research hypotheses from the dimensions of seller-consumer interaction, anchor-consumer interaction, and consumer-to-consumer interaction of e-commerce live broadcast interaction. Then, effective sample data are collected through the questionnaire survey method, and multiple regression analysis and correlation analysis models are used to explore the interrelationship between interactive quality, immersion experience, and consumers’ purchase intention in e-commerce live broadcasting. The analysis shows that the interactive quality, perceived usefulness, responsiveness, and mutual assistance of e-commerce live broadcast positively affect consumers’ immersion experience and purchase intention (Sig. <0.05), consumers’ immersion experience has a positive and significant effect on purchase intention (Sig. <0.05), and immersion experience has an important mediating role between perceived usefulness, mutual assistance, and purchase intention. This paper centers on the interactive quality and immersion experience. It puts forward corresponding enhancement suggestions for live e-commerce, which provides a basis for the development of marketing strategies for live merchants and has certain theoretical significance and practical significance.
A Study of the Impact of Integrating Artificial Intelligence with the Archival Profession under Data Mining
Authors: Wang Yue , Li Wenjie , Wu Changjing , Wang Zhen 
Published: May 2024

Abstract
This paper integrates data mining technology into the technical processing layer of the “Artificial Intelligence +” archive intelligent service system to provide technical support for massive archive data. Using association rules, archive classification, Mean-Shift clustering, and other techniques to reveal the relationship between archives and users, archives, archives, and various practical activities so as to play the role of the information of these archive data at a deeper level. Applying the “Artificial Intelligence +” archive wisdom service system to the actual operation of the business management of college archive professional teachers, the results show that the speed of “Artificial Intelligence +” database entry (4.83s) is significantly higher than that of the traditional database (38.62s) the speed is higher. The contour coefficients of the classification results of the experimental group are all above 0.80, realizing high-precision classification of text data, image data, and audio data in digitized archives.2018-2023 The number of business declarations is rising, the total score of the scientific research type varies considerably, and the total score of the teaching type is concentrated in the distribution, with most of them being between 70-80. Mean-shift clustering algorithms can make the scientific research type, the teaching type, and the Research and Teaching and heavy type three data types to achieve a good clustering effect.
Empirical analysis of the stability of labor contract relationship and its influencing factors from the perspective of civil law
Authors: Zhang Haoyue , Qiu Xiaofan 
Published: May 2024

Abstract
Stable employment is a pivotal foundation for contemporary economic development and transformation. The enhancement of labor contract relationship stability is crucial for the great rejuvenation of the Chinese nation. Drawing from civil law and various research materials, this study identifies key factors influencing labor contract stability and posits several research hypotheses. This paper constructs a regression analysis model to examine the relationships between these factors and labor contract stability, where the influencing factors serve as independent variables and the stability of the labor contract relationship acts as the dependent variable. The model’s fit is rigorously evaluated. A preliminary analysis employing Pearson’s correlation coefficient reveals significant correlations between all 14 identified factors and the stability of labor contract relationships, supporting the use of regression analysis. Empirical results from the regression analysis indicate a significant positive impact of education level on labor contract stability (p = 0.031 < 0.05). Similarly, employment income is shown to positively influence labor contract stability (p = 0.009 < 0.05). Apart from education level and urban integration challenges, other identified factors also significantly affect the stability of labor contracts. This research not only advances our understanding of the dynamics influencing labor contract stability but also provides a robust empirical basis for shaping relevant civil law regulations and policies.
The Practice and exploration of deep learning algorithm in the creation and realization of intangible cultural heritage animation
Authors: Tao Ran 
Published: May 2024

Abstract
The rapid development of modern technology and civilization has made the survival of non-heritage culture more and more serious, and the protection and inheritance of intangible cultural heritage is a heavy task and a long way to go. This paper takes the feasibility of non-heritage animation creation as an entry point, analyzes the ideological mechanism in the process of non-heritage animation creation, and explores the economic realization brought by deep learning technology assisting non-heritage animation creation. For the lens scene switching in the process of non-heritage animation creation, this paper utilizes the CNN network for the initial positioning of the lens boundary. It establishes the tangent detection model of a non-heritage animation lens by combining it with the 3D-CNN network. To understand the diversity of non-heritage animation creation styles, this paper establishes a model for style migration of non-heritage cultural images based on the VGG-Net network and conducts experimental investigations. The results show that when the hyperparameter value of the model is set to λ = 3, γ = 1.2, the model retains only 5.19% of the candidate boundary frames, and the accuracy of the detection of the non-heritage animation creation tangent shots is 0.939. The total loss value in the process of style migration fluctuates around 0.005, and the subjective evaluation score of the images generated by the style migration network is 4.82. The deep learning algorithm that promotes the creation of non-heritage animation can expand the content and characteristics of non-heritage animation creation and can also realize the economic realization of non-heritage animation.
Practical Application of Professional Group Curriculum Resource Library in Art Design Teaching
Authors: Zhang Ying , Luo Xiaobo 
Published: May 2024

Abstract
This paper studies the relevant functions and contents of the professional group course resource library and constructs the architecture of the professional group course resource library. In the specific construction, the Lian Li VSM model and LDA topic model establish the domain ontology of the resource library, retrieve the content of educational resources from the resource document based on the retrieval request for resource filtering, extraction, and integration, and through the information mining algorithm to browse the degree of interest in the learning resources to calculate, to achieve the personalized recommendation of educational resources. Finally, the satisfaction of teachers and students with the professional group course resource library was analyzed respectively, and the effect of the application of the professional group course resource library in art and design teaching practice was studied using t-test and regression analysis. The results show that the regression coefficient values of the professional group course resource library on teachers’ concept of teaching, teaching content, and teaching effect are 0.148, 0.145, and 0.059, respectively. The t-values are 2.342, 2.644, and 1.212, respectively, with p<0.05, and the professional group course resource library has a significant positive effect on the teaching of art design. This study provides a new direction for the development of art design teaching, and it offers important reference content for universities to build a professional group course resource library.
Heat transfer and flow characterisation of aircraft heat exchangers considering manufacturing constraints
Authors: Lei Mingjun , Wan Shuting , Yu Xiaoying 
Published: May 2024

Abstract
With the progress and development of the times, the traditional aircraft power system and difficult to meet the needs of the current aviation development, for the aircraft power and heat exchanger research has been more and more extensive attention. This study takes the heat exchanger of the aircraft loop control system as an example, constructs the thermal management system of the aircraft according to the mechanism of its heat transfer and flow characteristics, and optimizes it under the consideration of manufacturing constraints. Based on the theory of fluid mechanics, the aircraft heat exchanger is simulated, and control equations based on heat transfer and flow characteristics are established. In this paper, the temperature difference of the heat exchanger is increased by 6.5°C and 5.4°C, respectively, and the heat transfer coefficient is between 11 and 14 kW/m²k. Furthermore, the fluid temperature has risen from 0.05 to 0.5 to 0.30 to 0.87 with the increase in flow rate. The maximum deviation values were 8.3%, 6.7%, and 8.6%, respectively, and the confidence intervals were all above 90%. This study can effectively improve the efficiency of aircraft heat exchangers compared to traditional heat exchangers and has more advantages in heat transfer performance, which provides a reference value for the development of the aviation field.
Application of automatic driving task serialisation monitoring for operating robots
Authors: Bai Yiming , Ruan Zhijie , Peng Zhenhua , Ruan Dabing , Liang Shaoming , Zhang Bingsheng , Liu Xinsheng 
Published: May 2024

Abstract
With the increasing application of industrial robots, more and more simple and repetitive operations in industrial automated production lines are replaced by industrial robots. In this study, a path planning algorithm is proposed for the autopilot task of an operating robot to find the optimal path from the start point to the goal point under the constraints of safe obstacle avoidance, shortest distance, and fastest time. Forward kinematics is used to detect whether it will collide with environmental obstacles until the first feasible result is detected as the end position for navigation. Serialized monitoring is used to obtain the activity information of each computational node, the hardware state information of the system, and the state of the autopilot task processing. Finally, obstacle avoidance navigation experiments on a mobile operating robot are conducted to verify the effectiveness and feasibility of the obstacle avoidance navigation algorithm. The results show that the maximum error of the autopilot task of the operating robot is 2.91cm, and the average error is 0.13 2cm, which is in line with the trajectory tracking error requirements of the operating machine control method and verifies the validity and practicability of this study.
Research on Improved Particle Swarm Computational Intelligence Algorithm and Its Application to Multi-Objective Optimisation
Authors: Chen Lifei , Xiong Fang 
Published: May 2024

Abstract
Due to the pervasive generalization challenges in optimization technology, there is a noticeable trend toward planning and diversifying optimization techniques. This paper focuses on particle swarm optimization algorithms, particularly their application in multi-objective optimization scenarios. Initially, the study examines basic particle swarm, standard particle swarm, and particle swarm algorithms with a shrinkage factor. Subsequently, an enhanced particle swarm optimization algorithm is proposed, incorporating a hybridization model and a convergence factor model tailored to the specific characteristics of particle swarm algorithms. This improved algorithm is then applied to multi-objective optimization problems, establishing a novel algorithm based on the fusion of the enhanced particle swarm approach with constrained optimization. Simulation experiments conducted on this model reveal significant findings. In low-dimensional settings, the algorithm achieves a 100% optimization success rate, marking an average improvement of 53.80%, 40.78%, and 24.76% over competing algorithms. Moreover, in multi-objective optimization simulation experiments, this algorithm generates 142 and 135 optimal solutions, outperforming traditional algorithms by 112 and 107 solutions, respectively. These results validate the efficiency and enhanced performance of the improved particle swarm-based multi-objective optimization algorithm, demonstrating its potential as an effective tool for addressing real-world optimization challenges.
The Practice of SPSS-based Correlation Analysis in English Teaching Research: An Exploration of the Relationship between Classroom Teaching and Student Achievement
Authors: Gao Jie , Borras Dolores D. , Caiga Beverly T. 
Published: May 2024

Abstract
The quality of English language instruction in the classroom is a pivotal factor influencing students’ learning outcomes and performance in English. Grounded in this research context, this study draws upon relevant theoretical frameworks and examines a cohort of 80 students from the 2023 English class at Anhui Medical College. A multiple regression model was constructed using various dimensions of classroom instruction—namely, content, methodology, organization, teacher competence, instructional attitude, and linguistic features—as independent variables, with students’ English achievements serving as the dependent variable. Utilizing SPSS statistical software, both multiple regression and principal component analyses were employed to investigate the correlations between classroom teaching practices and student performance. The main conclusions of the article after the test are: in the rating of English teaching classroom performance, the teacher’s teaching ability (5.0952) scores high, and the teaching method (3.0324) scores the lowest, and the teacher’s teaching ability has a close relationship with the students’ English achievement. English achievement = 2.136 + 0.223*Teaching content + 0.41*Teaching style + 0.129*Classroom organization + 0.227*Teaching competence + 0.189*Teaching attitude + 0.449Language characteristics. There is a need to improve the performance related to classroom teaching and thus improve the performance of students.
Constructing an innovative system of management and education in colleges and universities based on artificial intelligence technology
Authors: Ge Shuangshuang , Zhu Peipei , Hao Huijuan 
Published: May 2024

Abstract
At this stage, the information literacy of teachers and students is constantly improving, and the “digital campus” represented by artificial intelligence is landing in various regions and schools. This paper accurately constructs a college management and education innovation system based on artificial intelligence technology, including institutional mechanisms, a one-stop service platform, and an early warning mechanism. The early warning mechanism has an early warning model constructed. The construction of an academic early warning model for students is based on multivariate linear regression. A model for early warning of student activity safety was constructed using multiclassification logistic regression. The impact of fusing the two models was evaluated, as well as the impact of practical application. The ROC value of the first-level warning is 0.9533, and the ROC value of the second-level warning is 0.9428, and both values are close to 1, which verifies that the model can be practically applied. By analyzing the F1-score value through practical application, the average value of the F1-score throughout the semester was as high as 84.68%, and the number of warnings of the early warning model was higher than the number of feedback from teachers. It has been verified that the early warning model in this study can effectively and efficiently manage student activities and academic anomalies in colleges and universities.
Big Data User Behaviour Prediction Model Incorporating Deep Learning
Authors: Huang Xiang 
Published: May 2024

Abstract
With the improvement of the quality of human life, various industries utilize deep learning technology to meet the needs of users. In this paper, after exploring the deep temporal model and deep forest algorithm (DF) model in extracting the characteristics of user behavior features, we propose a user behavior prediction model based on CNN-LSTM and add a front embedding layer as well as a feature fusion layer to improve it and increase the prediction accuracy. The weighted average method is used to integrate CNN-LSTM with DF for algorithmic model fusion, aiming to improve the robustness and stability of the model and achieve accurate predictions of user behavior. In the empirical analysis, the correct rate of the fusion model proposed in this paper exceeds the proper rate of several other models by 11.75-15.73%, and it can reach 11.2% recall at k=20. Meanwhile, the lower the user behavior level is, the higher the prediction accuracy of the CNNLSTM+DF algorithm is, which can reach up to 95.20%. The lower the average relative error and average absolute error are, which can reach 13.45% and 3.92min respectively, which verifies the validity of the fusion model proposed in this paper, and provides a reference for the research in the related fields.
The Role and Strategy of Big Data in Promoting Local Tourism Development
Authors: Du Yanyan 
Published: May 2024

Abstract
As science and technology advance, the tourism industry is increasingly pivoting towards digital and intelligent transformation. The pivotal role of big data in shaping tourism trends is becoming more apparent. This study leverages big data to explore its impact on tourism development and proposes a big data-based model for this purpose. An influence mechanism model tailored to local tourism’s unique characteristics was developed to guide this analysis. Employing data mining techniques, this research utilizes the association rule model to forecast local tourism trends. Additionally, it applies heat evaluation and hotspot analysis models to examine local tourism activities. Spatial characteristics of local tourism are investigated using the nearest neighbor algorithm. An empirical study conducted in Province H illustrates these concepts in action. Here, the entertainment and leisure segment aligns most closely with the overall clustering characteristics, achieving a Z-score of -200.01. The peak hotness months in Province H are May and October, with hotness indices of 0.0438 and 0.0468, respectively. Entertainment and leisure activities make the most significant contribution to this metric, with an average value of 0.0235. Moreover, the prediction error does not exceed 5%, underscoring the validity and practical utility of this study’s methodological approach in analyzing local tourism development. This provides a robust scientific basis for harnessing data to foster local tourism growth.
Research on the Impact of Digital Trade Barriers on International Business and Countermeasures
Authors: Guo Xiaping 
Published: May 2024

Abstract
In the contemporary digital economy, digital trade barriers have emerged as significant external risks for enterprises engaging in international business despite the digital economy’s role as a critical driver of international commerce. This study examines the relationship between digital trade barriers and international business through a theoretical model, investigating the influence mechanisms of digital trade barriers alongside other factors. Utilizing data from several countries spanning from 2015 to 2022, this research employs correlation analysis, benchmark regression, and robustness tests within a constructed benchmark regression model to explore the impact of digital trade barriers on international business. The findings indicate a significant negative correlation between digital trade barriers and international commerce, with a 1% increase in the trade restriction index of digital services resulting in a 0.217% decrease in international commerce (p<0.01). Additionally, while R&D intensity and total trade have positive effects on the development of international commerce, trade openness appears to have a negative impact. Given these results, it is recommended that governments enhance the construction of digital service trade systems and actively engage in the formulation and convergence of international regulations. Simultaneously, enterprises should elevate their technological capabilities related to digitization to effectively navigate and mitigate the impacts of digital trade barriers.