bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Construction of Intelligent Platform in Ideological and Political Education in Colleges and Universities
Authors: Chen Tingting , Liang Jian 
Published: July 2024

Abstract
Based on the feasibility of a personalized teaching platform and user requirements, this paper puts forward the overall architecture design and database design scheme of a personalized teaching platform in ideological and political education, which mainly consists of three functional modules, namely, the knowledge mapping module, ideological and political education course recommendation module, and learning effect evaluation module. After crawling the initial data based on the LTP model, the ideological and political education course resources were extracted and integrated to complete the construction of the knowledge graph module. The ideological and political education course recommendation module is created using the KGCNN algorithm, and then the learning effect evaluation module is constructed by combining the online behavior of students. After testing the system’s performance, the application effect of the teaching platform is assessed. The results show that KGCNN aggregation layer space in the interval of 10~100 can embed data with power law distribution more effectively, and the KGCNN algorithm also has certain advantages in the field of modeling personalized teaching platforms for ideological and political education. The number of experimental classes and ordinary classes with final grades in the L1 band increased by 21.90% and 7.17%, respectively, compared to the midterm, indicating that the personalized teaching platform for ideological and political education courses can effectively promote the improvement of student’s academic performance, and the enhancement effect of students with high levels is more significant.
A Study on Coping with the Psychology of Employment and Entrepreneurship Stress among College Students under the Cognitive Interaction Model (CIM)
Authors: Dang Xiling 
Published: July 2024

Abstract
It is especially important to solve the psychological pressure of college students’ employment if they want to complete their employment and get better development. This paper proposes a cognitive interaction model based on the full information emotion theory and develops the psychological analysis of employment and entrepreneurial pressure on college students on this basis. In the construction of the cognitive interaction model, the basic assumptions of the all-information affective theory are first proposed, the concepts of the individual’s a priori information, a posteriori information, actual information, and expected information are introduced, the individual’s emotional model is established, and the assumption of information-emotional equivalence is proposed. Combining the PDA model with the information theory of emotional motivation completes the model construction based on this basic assumption. In the model performance test, the accuracy of emotion dimension recognition in this paper’s model is more than 95%, which is better than other models. Taking college students in 2 general colleges and universities in Hunan Province, China, as the research object, the study shows that the total psychological mean score of college students’ employment and entrepreneurship stress is 2.75, and the stress psychology is at a medium level. In the data of the randomly sampled student samples, 70% of the student’s emotional tendencies are anxiety, and anxiety is the mainstream emotion of college students. College students’ feelings of employment and entrepreneurship stress and negative coping styles are both at a high-intensity level.
Sentiment analysis of art and design works using deep learning
Authors: Dang Zhonghua 
Published: July 2024

Abstract
Deep learning technology has the characteristics of automatic learning, nonlinear modeling, etc., which makes the machine algorithm better adapt to the data so as to better complete the task of sentiment analysis. The purpose of this paper is to explore the application of deep learning technology in the emotional analysis of art design works. After combining the emotion construction mechanism of art design works, based on the superior performance of convolutional neural network image recognition, it is introduced into the emotion analysis of art design works, combined with the attention mechanism model, to construct the emotion analysis model of art design works based on deep learning. Art design works are collected as the experimental dataset. The emotion analysis effect of this paper’s model for art design works is evaluated through the comparison of emotion classification indexes. The comparison of ROC curves of different models, and the emotion instance analysis of art design works is carried out. The experiments show that the experimental accuracy, precision, recall, and F1 value of this paper’s model range from 81% to 86%, and the F1 and ROC values are improved by 2.3% to 7.0% and 1.7% to 4.2% compared with the comparison model, which reflects the high accuracy and precision of this paper’s model for the sentiment analysis of art design works. Seven emotional patterns were found in tea art design works, and the model can be used to analyze the emotions of art design works.
A Study on the Application of Social Network Analysis Based Approach in the Optimization of Cultural Communication Paths and Efficiency
Authors: Dong Rui 
Published: July 2024

Abstract
The optimization of cultural communication paths is an important part of Chinese culture’s continued dialogue with the world in the face of increasingly fierce international competition. In this paper, we use social network analysis to collect relevant data on the microblogging platform and analyze the overall network structure of “The Belt and Road Initiative” using network density, network distance, and clustering coefficients. Using social network analysis metrics, we calculate the node position of the network path of “The Belt and Road Initiative” and analyze the influence of each microblog account. We filter the cultural communication topics of “The Belt and Road Initiative”, scrutinize the participating regions, and establish the characteristics of the cooperation mode within this cultural communication. “The Belt and Road Initiative” has a network density of 0.247, 0.281, and 0.295, respectively, which indicates that the network density of these three topics is small and there is less information exchange, but the cohesive force of cultural communication is stronger. “People’s Daily,” “Central Committee of the Communist Youth League,” and “Phoenix Video” have high intermediary centrality (712.256, 700.425, and 695.253, respectively). The high intermediary centrality indicates that these nodes have a great influence on the flow of resources in the information dissemination network and can accelerate the efficiency of the “Belt and Road Initiative” cultural dissemination path.
An Exploration of Tourism Marketing Strategies of “Ethnicity + Folklore + Folk Lodging” in Guizhou Province Based on Echo State Network Modeling
Authors: Kang Ting , Lan Nan 
Published: July 2024

Abstract
Due to the improvement of national consumption levels, the Guizhou tourism industry plays an increasingly important role in the economy of Guizhou province, and the proportion of tourism revenue to GDP is increasing. We combine the improved FOA algorithm with an echo-state network, taking into account the characteristics of Guizhou tourism demand, to construct an AFOA-ESN prediction model for Guizhou tourism demand. The new algorithm fixes the problems with the old Drosophila algorithm by changing the number of Drosophila populations, the size of the search step, and the best place for the first iteration. This makes the new algorithm better at local searches and more efficient. We select the annual data on the number of travelers to Guizhou from ten provinces as the dataset and apply the simulation analysis method to test the effectiveness of the previously mentioned prediction model. The data show that among the ten sets of experiments, the AFOA-ESN prediction model has seven times the smallest MAPE value, while the AFOA-ESN model has six times the smallest MSE value. In addition, the consumption structure of Guizhou tourism tourists from 2010 to 2022 did not change much in this stage, showing the development of shock, sensitivity, and vulnerability. Based on the analysis results, we propose the tourism marketing strategy of “Ethnicity + Folklore + Folk Lodging.” This study selects forecast information suitable for the tourism industry and provides the necessary reference for decision-making in tourism-related departments.
The Promotion of Students’ Critical Thinking Skills through the Integration of English Civics Elements into EFL Classrooms
Authors: Lan Jing 
Published: July 2024

Abstract
As education advances with the times, traditional English EFL teaching can no longer fully adapt to the current educational situation. This paper studies the mining of the Civics element in English teaching integrates it into EFL classroom teaching, and constructs the morality cultivation EFL critical thinking ability teaching strategy based on the critical thinking ability model. The effects of applying this teaching strategy are analyzed and compared to the six dimensions of critical thinking ability: interpretation, analysis, evaluation, reasoning, explanation, and self-regulation ability. The exploration of critical thinking ability development and individual teacher factors is conducted. After the teaching experiment, the experimental group improved by 12.08 points, while the control group only improved by 0.46 points, indicating a significant difference between the two groups’ scores. The Sig. The values of the experimental group in the six dimensions are less than 0.05, and the Sig. The values of the control group are greater than 0.05. The teaching strategy of moral cultivation in EFL critical thinking ability has an obvious effect on improving students’ critical thinking abilities. There is a correlation between the development of critical thinking ability and teachers’ education, and teachers with master’s degrees are better than teachers with bachelor’s degrees.
Personalized Recommendation System for English Teaching Resources in Colleges and Universities Based on Collaborative Recommendation
Authors: Qu Zhihua 
Published: July 2024

Abstract
In recent years, with the rapid development of the Internet and education informatization, online teaching has become a popular education mode in the information age, providing learners with very rich teaching resources. In this paper, we construct a personalized system and co-recommendation technology for English teaching resources, and we improve the traditional co-recommendation algorithm and propose hybrid recommendations. The performance of the system is evaluated experimentally to compare the effectiveness of the performance of the four systems. The improved recommendation algorithm is superior to the other three recommendation algorithms in each dataset in the system. The average grade mean of the experimental class assisted by the hybrid recommendation system in teaching English in colleges and universities in the latter two experiments is 27.54, which is higher than that of the comparison class of 25.33, and the T-value is 1.81>1.645. The improved personalized recommender system has good validity and stability in both performance and practical application.
Modeling the Investment Efficiency and Risk Assessment of Great Health Industry in the Context of Digital Economy
Authors: Shen Lei 
Published: July 2024

Abstract
With the development of the economy and society, people pay more and more attention to physical health. In order to make the big health enterprises in the long term at the level of smooth development, it is necessary to carry out an in-depth study on the investment efficiency and potential risk of the big health industry. This study constructs an investment efficiency evaluation method based on the DEA model. Firstly, the comprehensive efficiency is decomposed through the CCR model to further obtain the output results. Then, the effectiveness of enterprise investment is evaluated. The changes in the investment efficiency of the big health industry and other sample decision-making units are analyzed through the DEA-Malmquist model to output the trend of the overall investment efficiency. Logistic regression, support vector machine, and random forest models are used to assess the risk of the large health industry, respectively, and several classifiers are trained. When predicting the final sample, the voting or mean value method is used to count the effect of classification. The overall mean value of big health enterprises hovered between 0.96 and 0.98 in five years, indicating that the comprehensive investment efficiency of the big health industry is relatively stable. The average AUC value of the random forest model is 0.635, which is 0.028 higher than the average AUC value of the support vector machine; thus, it is concluded that there is no great fluctuation in the investment efficiency of the big health industry under the background of the digital economy, and the random forest model is more suitable for the risk assessment of the big health industry.
Improvement of Inventory Management and Demand Forecasting by Big Data Analytics in Supply Chain
Authors: Tang Weiping 
Published: July 2024

Abstract
Inventory management plays a very important role in the process of business operation, providing favorable backing for the smooth operation of production and sales. In this paper, LightGBM and PSO-LSTM models in big data technology are combined to improve inventory management and demand forecasting in supply chains. Then, the relationship between inventory, order, and forecast is elaborated, the two-level inventory cost components and the relationship between them are analyzed, the model constraints are formulated, and a mathematical model for two-level multi-cycle inventory control is constructed. Finally, the single demand forecasting model is compared with the improved model to explore the optimization effect of inventory management after the application of the LightGBM-PSO-LSTM model. The LightGBMPSO-LSTM model is the best fit and can be used for actual demand forecasting. After the optimization of inventory management, the inventory turnover ratio of Company H increased from 8.2 in 2022 to the maximum value of 9.2 in 2023, and the OTIF achievement rate of sales orders increased from 97.9% in 2022 to 99.3% in 2023. This paper provides a successful example of optimizing supply chain inventory management using big data analytics.
Research on defect recognition technology of transmission line based on visual macromodeling
Authors: Li Yang , Li Yan , Wang Qi , Wang Wanguo , Liu Guangxiu , Li Zhenyu , Wu Hefeng , Jiang Shihao 
Published: July 2024

Abstract
In order to improve the defect recognition efficiency of transmission lines, the industry is currently using aerial images for automatic visual defect detection to ensure the safe operation of transmission lines. This paper proposes a method for defect recognition from coarse to fine, based on convolutional neural networks and connected domain algorithms, to improve recognition accuracy. The recognition speed is improved by using the knowledge distillation method of target detection networks based on decoupled features, adversarial features, and attention features. It has been found that the optimized recognition model improves the precision rate by 7%, the recall rate by 8%, and the average accuracy rate by 10%. The FPS of the model optimized by knowledge distillation is 62.5, and the average value of the FPS of other versions of this model is 47.35. It is believed that the two optimization ideas introduced in this paper can enhance the previous transmission line defect recognition algorithm in terms of accuracy and recognition speed.
Evaluation of the Benefits of an Intelligent Health Monitoring System in the Care of Geriatric Patients
Authors: Wang Yue 
Published: July 2024

Abstract
Collaborating with intelligent health monitoring in the clinical care of elderly patients can enhance understanding of changes in their body functions, thereby improving their quality of life and preventing complications. This paper focuses on the user characteristics and needs of geriatric patients, builds an intelligent health monitoring system based on C/S architecture, and develops a health data collection process for geriatric patients using smart wearables. We use the VMD algorithm to reduce the noise in the physiological signal data of elderly patients, then input it into a time-sequence convolutional network to extract the corresponding ECG signal features. We then combine this data with the LSTM model to classify the ECG signals, enabling us to diagnose the health of elderly patients. We established a continuity of care program based on the intelligent health monitoring system and designed a comparison experiment to evaluate the impact of the application. The VMD algorithm can recognize the insignificant signal peaks between 1.5s and 5.5s, and the health diagnosis model has the highest classification accuracy of 99.24% for ECG beats. In the continuity of care model, the elderly patients’ physiological function score was 68.42±4.76, and their serum ALB level was 35.79±6.72 g/L, which was 17.42% higher than the control group’s level after the intervention. It also helped the elderly patients’ mental health. The intelligent health monitoring system can dynamically acquire the physiological characteristics of elderly patients in real time and generate visualization results to help medical staff customize personalized care plans.
A time-series analysis of the evolution of scientific and technological concepts in the change of social thinking in modern China
Authors: Liu Yong 
Published: July 2024

Abstract
Exploring the evolutionary process of the concept of science and technology in modern Chinese society is conducive to the innovation of the current path of science education popularization and scientific and technological literacy enhancement. In this paper, we construct a sampling selection algorithm using Bayesian and MCMC and then design a parameter derivation model. Assuming the model prior distribution hyperparameters and using the likelihood function to derive the specific form of the posterior probability distribution, an approximate integral calculation is carried out in order to directly derive the parameters to be estimated, and the BVAR-MCMC model is constructed. Using the model to analyze the temporal changes in the evolution of scientific and technological concepts in modern China, it is finally found that the changes in social and technological concepts during the Republican period were even stronger than those in the late Qing period. Influenced by the increasing scale of cross-cultural exchanges due to the gradual opening up of the gateway, cross-cultural open exchanges have the highest impulse value and the longest duration on the evolution of scientific and technological concepts, with the impulse of the Late Qing and the Republic of China phases above 1, and the duration of the positive influence covers the whole time series. The impulse values of the late Qing and the Republic of China are 2.68 and 3.59, respectively, and the education popularization rate, economic development level, the speed of technological life progress, and the degree of civilization and freedom of the social atmosphere have different degrees of influence on the evolution of scientific and technological concepts in the time series of modern history. This study innovates the statistical research method for modern history and conducts a pioneering exploratory study on the evolutionary process of modern social ideas change.
Analyzing the Impact of ESP Teaching Models on Enhancing Non-English Majors’ English Application Skills
Authors: Wu Yanbei 
Published: July 2024

Abstract
The development of globalization has led to the rising demand of society and enterprises for the English application ability of students in colleges and universities, and the enhancement of the English application ability of non-English majors has become the key research direction of English teaching in colleges and universities under this trend. In this paper, we study this issue, highlighting the urgency of innovating the English teaching mode for non-English majors through the current situation of the passing rate of the English application ability examination and the main problems of the cultivation of English application ability of non-English majors. Gray correlation analysis is used to study the correlation between the English teaching mode, job requirements and other factors and the English application ability of non-English majors to provide data support for the introduction of ESP teaching mode. The ESP teaching mode is proposed, the teaching experiment is designed, and the results of the experimental and control classes are tested before and after participating in the experiment to assess the role of the ESP teaching mode in improving the English application ability of non-English majors as well. In the study of students’ satisfaction with the teaching of application abilities under the ESP mode, the overall satisfaction is high. Non-English majors are rated 11.87% more satisfied with the teaching effect under the ESP teaching model than English majors.
A Study of the Impact of Smart Contract Technology on the Understanding and Application of Contract Law from the Perspective of the Legal Profession
Authors: Yan Qingli 
Published: July 2024

Abstract
This paper constructs the logical framework of the smart contract model based on the sale and purchase contract transaction, integrating the legal framework and the smart contract. The smart contract model merges blockchain technology’s decentralized features with smart contracts to create a decentralized service mode for sale and purchase transactions. The classification of transaction attributes optimizes and adjusts the matching degree calculation method to meet the needs of both buyers and sellers. The matching degree determines the total satisfaction of both buyers and sellers. The simulation experiment model is designed to analyze the matching results, and the experimental results indicate that the overall utility value range is between 0.1 and 0.5, resulting in a higher level of satisfaction for both parties involved. Applying the model to actual purchase and sale transaction contracts, the expected profit of transaction users after using smart contracts is 26,300 yuan higher than that without using smart contracts. The accuracy and confidence scores of experienced users are 0,8886 and 4.0493, respectively, when evaluating their perceptions of the use of smart contracts. Smart contracts can help participants identify sales and purchase problems and provide innovative perspectives on the application of contract law.