bluuebunny/crossref_metadata_2025_split huggingface dataset:
The logic of change and its unfolding in Germany’s relations with countries in the Middle East driven by big data
Authors: Tian Yushi 
Published: August 2024

Abstract
Big data, as an advanced production technology, can, to a certain extent, explain the transformation of state-to-state relations. This paper uses Germany’s bilateral relations with the Middle East as an example to explore the specific application of big data technology in the transformation of state relations. Data-driven data visualization, automatic text analysis, social network analysis, and other analysis techniques of national relationship change are first summarized. Then, the change in Germany’s relationship with the Middle East countries is analyzed based on social networks. The social network data analysis process is designed, and the K-means temporal clustering algorithm is used to analyze Germany’s relationship with the Middle East countries from the time dimension. The data processing of the GDELT database, Germany and the Middle East countries during the period of 2004-2023 in the cooperation category of events and conflict category of events, Germany and the Middle East countries conflict relationship is divided into two phases of 2004~2013 and 2014~2023, and the cooperation relationship is divided into two phases of 2004~2016 and 2017~2023.
Research on Intelligent Decision Support System for Student Management and Mental Health Intervention in Higher Education Institutions
Authors: Yuan Zhuo 
Published: August 2024

Abstract
The intelligent management system of colleges and universities is an important display of the informationization construction of colleges and universities. Aiming at the life trajectory and life information of students in colleges and universities, this paper combines data mining and other technologies, adopts Python language and MySQL database technology, and improves on the Flask framework to develop an intelligent decision support system for student management and mental health intervention. The study takes student achievement abnormality detection and mental health intervention as an example, uses the improved K-Means algorithm and Apriori algorithm to mine and analyze the relevant data, and discusses the specific application of the intelligent decision support system. The sample students can be divided into three different types of academic performance: excellent, ordinary, and difficult. Precision teaching can be implemented according to the different types of students. Only 3% of the students had more obvious and serious psychological problems, while 80% of the students had a relatively healthy psychological state. Moreover, the correlation rules between student information and mental health status are analyzed, which can aid in the implementation of mental health interventions. The intelligent system of student management can provide the most direct and effective evidence for academic administrators in making decisions on teaching plans, thus making student education more targeted, personalized, and predictive.
A Study on Analyzing Symbolism in Literature by Combining Computer Vision Techniques
Authors: Chen Jiuen 
Published: August 2024

Abstract
The development of computer technology has greatly promoted the analysis and research of literary works, mainly in the aspects of information collection and data analysis. The analysis model of symbolism in literary works is constructed using the Word2vec algorithm and TextCNN text classification algorithm in this paper. Using computer vision-based OCR text recognition technology, text data is collected from poetic literary works related to a watershed. The dataset is analyzed using the constructed text analysis method to uncover the symbols and symbolic meanings in the sample literary works. Through the analysis of the sample poetic works, two categories of landscape symbols, natural and humanistic, are extracted, and the former is characterized by symbols such as “stream beach”, “shore tide”, “flowers and birds”, “fish and dragons”, etc. Fish and dragons” and other symbols, while the latter is represented by “city buildings” (0.11%), “boats” (0.41%), and “travelers” (0.59%). 0.59%). Five symbolic images are summarized, namely mountain climbing, hearing sounds in temples and monasteries, searching for antiquity in deep forests, boating in rivers and lakes, and fishing songs in idyllic gardens. The research in this paper explores a text-mining method for symbolism in literary works, which is conducive to the development of data-oriented and efficient analysis of literary works.
Exploring the Construction and Practice of Intelligent Teaching Environment for Civic and Political Education in Colleges and Universities
Authors: Chen Ying , Zhang Yingbo 
Published: August 2024

Abstract
In this paper, the basic framework of AI education is firstly constructed by combining educational AI technology, and then the technical architecture, including intelligent classroom, online learning platform, and interactive learning tools, is built based on the content of intelligent teaching environment of Civics education in colleges and universities. Then, we explored the students’ Civics learning under the intelligent teaching environment, analyzed the correlation between the Civics teaching effect and intelligent teaching, and investigated the influence of the intelligent teaching environment on the Civics teaching effect through a stepwise regression model. Intelligent teaching correlates with students’ learning interest, theoretical knowledge, professional ability, and learning attitude at the 5% level, with correlation coefficients of 0.798, 0.656, 0.712, and 0.553. In this study, theoretical references are provided for the integration of intelligent technology into Civics teaching.
Discrete Optimization Technology Helps Non-heritage Tourism Project Ecological Chain Construction Model Optimization
Authors: Huang Kirsty 
Published: August 2024

Abstract
The protection, inheritance, and innovation of non-heritage tourism projects need to find a suitable entry point so that more people can participate in the inheritance and innovation of non-heritage in order to promote the long-term stable development of non-heritage tourism projects. The article establishes the framework for the ecological chain of non-heritage tourism projects based on the cultural ecological chain and analyzes the problems that exist in the development process of the ecological chain of non-heritage tourism. The optimization of the ecological chain of non-heritage tourism projects is considered a dynamic multi-objective optimization problem, and a dynamic planning model for tourism paths is established. The tourism path dynamic planning model is solved using a two-stage parallel optimization ant colony algorithm and a two-stage heuristic algorithm based on the ant colony algorithm. A simulation experiment was conducted to analyze the data and evaluate the effectiveness of the algorithm. The results show that the DACS algorithm can solve the optimal path of the ecological chain of non-heritage tourism projects with an average gap value of GAP between [10.88% and 18.71%]. The IGD of the DACS algorithm in the dMOP test case varies within 0.005, the average running time of the DACS algorithm is 29.72s, and the maximum deviation rate between the optimal solution of the algorithm and the actual optimal solution is only 3.85%. The use of the DACS algorithm can optimize the ecological chain model of non-heritage tourism projects and help the healthy development of non-heritage tourism ecological chains.
Kinetic analysis of efficient energy conversion in table tennis sports
Authors: Qu Qingling , Gao Xiang , Zhang Jinqian 
Published: August 2024

Abstract
Improving the accuracy and stability of players’ stroke speed and stroke landing point and maximizing the energy conversion have become important tasks in table tennis skill practice. This paper analyzes the forces during ball collision and the energy conversion law of its motion state and proposes to measure and identify the key mechanical parameters of table tennis motion from the aspects of batting speed, rotation, and batting angle. The 3D coordinates of the ball are reconstructed, and the parameters are measured in the translational and rotational states of the ball, respectively. The UKF state estimator is used to construct the system process equations of the table tennis ball motion model, and the mathematical expressions are further derived to obtain the kinematic-based post-collision trajectory prediction model of the table tennis ball. The accuracy of the model in predicting the trajectory of the table tennis ball movement is evaluated by the distribution of the error points of the collision points of the table tennis ball, and it is known through experiments that the range of the error of the collision point prediction of the table tennis ball by the method used in this paper is in the range of (x±0.05, y±0.05) m, which is smaller than that estimated by the traditional table tennis ball physical model, and the model’s prediction accuracy is good. After analyzing the motion trajectories with different angular velocities, drag coefficients, initial velocities, and exit angles, the optimal hitting speed of a curved circle ball is obtained as V=15m/s.
Research on multidimensional impact assessment model of public cultural service facilities in Henan Province rural
Authors: Lou Xiaohong 
Published: August 2024

Abstract
The countryside plays a crucial role in constructing a robust socialist cultural nation, with rural public cultural facilities serving as the primary hub for public cultural activities and content. This paper utilizes each city and village in Henan Province as an example, with rural culture development serving as the primary research topic. The paper uses the influence of rural public cultural facilities as the main research index and constructs a relevant evaluation index system to assess their impact. Combine the subjective entropy value method and the objective hierarchical analysis method for the combination of weighting to judge the importance of the indicators. The TOPSIS method is employed to evaluate the complete impact, and the fuzzy set qualitative comparison method is employed to investigate the path of influence improvement. The government places greater importance on facility services and their social impact, and from 2015 to 2022, rural public cultural service facilities in Henan Province experienced a growth trend, transitioning from a risk level to a sensitive one. In addition, this paper proposes an influence strategy for rural public cultural service facilities. The consistency of the solution of the four combined paths is 0.997715, 0.97225, 0.97815, and 0.96524, all > 0.8, which illustrates that the influence factors such as resource construction, government intervention, and economic security will have a propelling effect on rural public cultural service facilities. It shows that influencing factors such as resource construction, government intervention, and economic security, all have the effect of promoting rural public cultural service facilities.
The integration path of new generation information technology and ideological and political education in colleges and universities
Authors: Tong Hui , Shi Chuanlin 
Published: August 2024

Abstract
As a representative of the new generation of information technology, reinforcement learning technology has received more and more attention, but no scholars have yet applied reinforcement learning recommendation technology to ideological and political education. In this paper, a diversity optimization recommendation algorithm for ideological and political education is constructed, and the vector representation model of the state is designed and optimized based on the Markov decision process. Through the actor-critic algorithm, to calculate the probability of recommendation sampling to generate a recommendation list and then establish a decision model in order to improve the ideological and political education recommendation algorithm on the user’s feedback of accurate judgement and recommendation decision-making effect. The experimental results show that the Diversity Opt performs well in terms of prediction error. The algorithm is ahead of the comparison algorithm by an average of 9.35% and 8.3%, respectively, and all other performances are also significantly better than the comparison algorithm. The average score of students’ performance in the experimental class applying the algorithm is 14.4 points higher than that of the control class. It shows that the recommendation algorithm helps to accurately recommend ideological and political education and exhibits beneficial practical effects.
Data-driven coordination of theoretical and practical education in higher education institutions
Authors: Wang Haiying , Liu Mingwei 
Published: August 2024

Abstract
It is an indisputable fact that there exists a non-integrated phenomenon between educational theory and practice, and educational theory and practice have the correlation of both interdependence and mutual deviation, so how to make the two coordinate and harmonize the development of the two has become a hotspot of the current research. In this paper, under the perspective of evidence-based education, we explore the coordinated development of the two, construct the coordinated index system of theoretical and practical education, and utilize the entropy weighting method to assign weights to the index system. Subsequently, taking School A as an example, the data-driven web crawler technology was utilized to obtain the relevant data, and the internal coordination level and future development trend of its theoretical and practical education were measured by the linear weighting method, the coupled coordination degree model, and the GM(1,1) model, respectively. The results show that the coupling coordination degree of its theoretical and practical education increases from 0.1489 in 2013 to 0.5609 in 2022, and although it maintains an upward posture, the overall values are lower than 0.6, and the coupling coordination level is low. In addition, the level of theory and practice education coordination is showing a continuous upward trend, with an average growth rate of 15.19%, but has shown a slowing trend. The coordinated and healthy development of theory and practice education in higher vocational colleges and universities can be realized from three aspects: enriching the form of theory and practice education activities, strengthening the atmosphere of theory and practice education, and school-enterprise cooperation.
Transparent Grid Visualization Surveillance and Fault Warning in High Density Distributed Power Access Areas
Authors: Zhou Zhongqiang , Xia Tian , Ma Jianwei , Liang Ling , Wan Huijiang 
Published: August 2024

Abstract
Transparent grid, as an advanced form of “Internet + smart energy”, is of great value in supporting energy transformation and promoting the development of new power systems. Based on the grid connection requirements of high-density distributed power supply, this paper provides specific grid connection methods and trend calculations and explores the impact of grid connection on high-density distributed power supply. Subsequently, the technical framework and blueprint of the transparent grid are proposed, combined with the grid visualization system to monitor the grid state visually, and based on the random matrix single-loop theory and ARIMA algorithm to realize fault identification and early warning of the transparent grid. The analysis of the algorithm shows that the fault identification has good rapidity, feasibility, and practicability, and while ensuring that no fault alarm is issued in the case of normal disturbance of distributed power supply, it is able to issue fault alarms for all locations and types of short-circuit faults in the system and determine the fault moment within 0.1s. It is “fast”, “accurate,” and “stable” in the research and judgment of active distribution network faults, and it is more intuitive in the assignment of risk levels, and it can identify the categories and levels of faults and realize the following Visualization of early warning.
Research on the application of cluster analysis in cross-border e-commerce customer segmentation and market strategy
Authors: Wang Xia , Cai Baoyu , Salleh Kalsom 
Published: August 2024

Abstract
E-commerce platforms are becoming increasingly competitive, and how to attract and retain users has become a problem that operators need to solve. The study improves the K-means clustering algorithm by introducing the concepts of mean sample distance and SSE and using the median to construct profile coefficients. Based on this, the user labels of cross-border e-commerce companies are selected, and clustering segmentation is performed after collecting the user data of Company T on cross-border e-commerce platforms to reveal the basic characteristics, purchasing preferences, and behavioral habits of different e-commerce users. A cross-border e-commerce marketing strategy based on customer segmentation has been formulated, and its effectiveness has been verified. Through clustering analysis, users are successfully classified into three types: high-value (4.01%), dynamic (7.35%), and growth (88.64%). After implementing the marketplace strategy, the sample companies gained significant growth in user growth, referral effect, and merchandise sales, as reflected in the number of first-buy users (63.46%), effective users (21.45%), ad click rate (18%~22%) and sales volume (166.43%). This study can help cross-border e-commerce enterprises better understand user needs and behavioral patterns and formulate more accurate marketing strategies to enhance their competitiveness and market share.
Intelligent Assessment and Feedback: Managing Student Learning States in Industrial Education
Authors: Chen Man , Zhang Xinyu , Sun Changzhong 
Published: August 2024

Abstract
Students are the main body of the classroom, and their learning status reflects the quality of classroom teaching to a certain extent. This paper aims to manage students’ learning status in industrial education classrooms by designing a student learning status assessment system. The collected video data from industrial education classrooms are processed by binarization and histogram equalization. The key technologies, such as face detection and eye movement analysis, are used to detect the learning status of students in the industrial education classroom, and the functional modules, such as data acquisition and statistical analysis, are combined to form this paper’s student learning status assessment system based on facial feature detection. It is found that the face detection algorithm of this paper’s system improves the detection accuracy by 11.35% compared with the baseline algorithm when the standard difficulty is difficult, and this paper’s algorithm is able to successfully detect and display the students’ facial features through 68 key feature points. The system in this paper is able to detect students’ learning states, such as concentration, fatigue, and doubt, by analyzing their eye movement frequency and other indicators. After applying the system discussed in this paper, the final grades of students in industrial education have significantly improved.
Exploring the machine learning approach to the cultural and ecological sustainable development of Fuzhou tea picking opera
Authors: Liu Fen , Yuan Mengjue 
Published: August 2024

Abstract
The article introduces machine learning based on a support vector machine to illustrate the scenario of Fuzhou Tea Casting Opera, a folkloric non-heritage research and ecologically sustainable development, from the perspective of economic return prediction. Fine-grained information recognition and extraction of massive, unstructured UGC data are used for data collection preprocessing in this paper. Mostly, the relationship is established by analyzing the training samples to establish the connection between input and output data. The limited sample information is used to find the best compromise between the model’s complexity and learning ability. Then, search for the parameters that make the geometric interval the largest so as to construct the function and obtain the optimal hyperplane. In this paper, the kernel function is introduced using a ɛ -insensitive loss function. To obtain the decision function in different cases, two cases are modeled using support vector machine regression. Finally, the support vector machine model is used to predict the economic benefits of the Fuzhou tea-picking operation. The SVM prediction model’s prediction error of the economic benefits of tea-picking opera for the nine years from 2010 to 2018 is always maintained at a safe value below 0.035. The accuracy and stability of the prediction effect of the decision tree model and the gray prediction model are not as good as this paper’s model. It shows that the model presented in this paper is more suitable for predicting the benefits of the cultural and ecological sustainable development of Fuzhou tea-picking opera.
Design of Customized Teaching Strategies Based on User Behavior Analysis in the Digital Transformation of Health Education
Authors: Liu Zhihan , Huang Jing 
Published: August 2024

Abstract
With the greater popularity of the Internet, on the one hand, online education platforms have flourished and education informatization has entered a new era, with massive learning behavior data generated by learners in different learning platforms. This paper constructs a portrait of student learning behavior based on the characteristics of their learning behavior in the health education class. Based on the clustering analysis method in the big data mining algorithm, the user’s behavioral characteristics are analyzed. On this basis, the algorithm is improved by using collaborative filtering personalized recommendation and introducing the LDA model, and a customized teaching model is constructed in the context of health education, and its application effect is explored. From the four dimensions of “course completion characteristics”, “teaching interaction characteristics”, “learning input characteristics,” and “learning achievement characteristics”, we analyzed different groups of students and investigated the effect of its application. “The behavioral characteristics of different groups of students were analyzed, and it was concluded that Group A and Group B performed better, but Group C accounted for a higher percentage of 24%. Finally, according to the collaborative filtering-based digital customized teaching on the students’ health education test scores for the pre and post-test, it was concluded that the average scores of the experimental subjects increased after the post-test. The mean scores for the third post-test were 4.27, 4.44, and 4.35, respectively. It can be concluded that the customized teaching model has a significant improvement in the students’ classroom teaching effectiveness.