bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Fuzzy Logic Approach to Cost-Benefit Analysis of Multimodal Transportation for China-Europe Liner Game
Authors: Zhang Mengmeng , Xu Maozeng 
Published: May 2024

Abstract
The implementation of the “Belt and Road” initiative provides unprecedented development opportunities for China-Europe liner multimodal transportation. In this paper, we constrain the cost-effectiveness of multimodal transportation between China and Europe using linear games from three perspectives: transportation cost, transportation time, and cargo time value. We also establish a cost-effective decision-making model. At the same time, a simulated annealing algorithm is introduced on the basis of a genetic algorithm, an adaptive genetic algorithm based on simulated annealing is designed to solve the model, and the optimal cost-benefit path assessment of China-Europe liner multimodal transportation is carried out based on the fuzzy logic method. Example analysis is carried out with three demands, and the advantages and disadvantages of the set of transportation route schemes are obtained. The optimal cost-effective scheme is selected as Scheme III, which has the highest scheme sticker schedule of 0.841, which verifies the feasibility of the model. The sensitivity analysis of transportation costs and transportation time shows that different transportation costs and times have certain influences on the transportation route selection of other goods. The cost-benefit analysis method for China-European liner multimodal transportation constructed in this paper provides scientific and reasonable guidance for multimodal transportation operators on path selection, which is of certain practical significance.
Construction and Optimization of Dynamic Assessment Indicators for Distribution Grid Carrying Capacity with High Proportion of Distributed Resource Connections
Authors: Zhou Zhongqiang , Wen Yuan , Jiang Dan , Deng Ruifeng , Ma Jianwei , Meng Jingrong 
Published: May 2024

Abstract
In this study, we evaluate the enhanced carrying capacity of the modified IEEE 33-node distribution system, which incorporates a high percentage of distributed resource (DR) integration, utilizing the whale optimization algorithm. The evaluation employs a fuzzy comprehensive evaluation (FCE) method, wherein we construct FCE indices for factors influencing the distribution network’s carrying capacity. These indices involve calculating weight coefficients and establishing an affiliation function, culminating in the output of the final fuzzy evaluation result vector. The analysis identifies three nodes—11, 18, and 31—within the IEEE 33-node system for decentralized DR installation. Based on initial scenario data, the maximum carrying capacity of the distribution network is determined to be 40.68 MW. The analysis further reveals that the utilization rate of distributed resources reaches 100% at specific times, specifically at 2:00 AM and 6:00 AM. During the 3:00 to 5:00 AM interval, both the system voltage excursion index and the composite score for distribution network losses are calculated to be low. These findings suggest the feasibility of integrating a single regional distribution network with other energy networks, thereby facilitating multi-regional resource interconnection and enhancing the flexibility of system operations. This approach offers significant implications for improving the robustness and efficiency of electrical distribution systems.
A refined decision-making method for orderly power consumption in virtual power plants considering load characteristics
Authors: Guo Hao , Yang Junhong , Wang Weirong 
Published: May 2024

Abstract
In recent years, electricity consumption has been increasing, while the supply of electricity is relatively lagging, and power shortages often occur. Therefore, the management of organized power consumption is an urgent task for the current power industry. Based on the characteristics of power load, this paper constructs a decision-making management framework for orderly power consumption by considering four aspects: generation, grid side, demand side, and social benefit. Through the various types of flexible load resources aggregated within the virtual power plant, it participates in regulating its power, effectively reduces the peak-valley difference, and realizes refined, orderly power consumption management. Finally, based on the multi-objective optimization model, refined decision-making for orderly power consumption with the participation of virtual power plants is constructed and compared to traditional decision-making for orderly power consumption. The results show that compared with conventional decision-making, the peak-to-valley difference is reduced by 8.7789 WM, the economic benefit of the power enterprise is improved by 228,300 yuan, and customer satisfaction is enhanced by 0.1613, which verifies the effectiveness of the decision-making method of the sequential power consumption in this paper.
Exploring the Strategies of Civic Education in Higher Vocational Colleges and Universities Based on Deep Learning
Authors: Huang Jiping 
Published: May 2024

Abstract
Deep learning significantly enhances Civic and Political Education in higher vocational colleges and universities by facilitating a nuanced understanding of students’ cognition of learning content, engagement in the learning process, and perceptions of the educational environment. This study involved collecting behavioral data from students in Civic and Political Education classes and applying the YOLOv5s algorithm—an advancement in deep learning networks—to extract and detect student behavioral features. Additionally, the enhanced ShuffleNetV2 algorithm was employed to classify and identify these features. Based on these methodologies, a student behavior recognition system was developed for Civics classrooms, and its practical application was evaluated. The findings indicate that the implementation of this recognition system resulted in increased classroom activity and engagement, with 70% of the participants demonstrating hand-raising and speaking behaviors. There was a statistically significant enhancement in students’ Civic learning behaviors following the system’s application in teaching (P<0.05). This study underscores the potential of digital intelligence technology to foster precise and intelligent educational practices in Civics education.
A Study of Chinese Peking Opera Arithmetic Coding in Tan Dun’s Opera Marco Polo and the Art of Mixed Sexuality
Authors: He Man , Li Ying 
Published: May 2024

Abstract
Opera is an important part of Western music culture. Chinese Peking Opera and Western opera are a kind of art style focusing on expressiveness. Both of them are isomorphic to a certain extent, and they are different due to cultural differences. This paper takes Tan Dun’s opera “Marco Polo,” a hybrid work of art containing elements of Chinese Peking Opera and Western Opera, as the object of study and quantitatively analyzes the artistic characteristics and expressive forms of the work by means of arithmetic coding technology of audio signals and terminology acquisition and labeling model. According to the results, there are 15 labeled Beijing Opera singing tune fields in the work and 12 labeled locations, which indicate the destinations of Marco Polo’s travel process. The feasibility of the arithmetic coding quantization method, which was employed in the study of Tan Dun’s opera Marco Polo, is confirmed by the results.
The Path of Cultivating College Students’ Sense of Social Responsibility under the IMB Model--Taking Some Private Colleges and Universities in Anhui Province as an Example
Authors: Liu Jiang 
Published: May 2024

Abstract
In recent years, the sense of social responsibility among college students has garnered significant scholarly interest due to its impact on individual development and its implications for national modernization and strength. This study introduces the Information-Motivation-Behavioral Skills (IMB) model as an innovative approach to fostering social responsibility in students at private colleges in Anhui Province. By employing a questionnaire, we assessed the current state of social responsibility cultivation among these students. Subsequently, an intervention study was conducted to evaluate the effectiveness of the IMB model. The intervention group received a social responsibility intervention based on the IMB model, while the control group continued with traditional methods. The findings indicate that the intervention group outperformed the control group across various dimensions—information, motivation, behavioral skills, and social responsibility behaviors. Notably, the overall sense of social responsibility in the intervention group increased by 13.13% post-intervention, which is 14.87% higher than that observed in the control group. These results suggest that the IMB model is a potent strategy for enhancing social responsibility among college students.
Regression analysis of the effect of social support on rural teachers’ willingness to stay in the profession
Authors: Du Chenglin , Sun Ning , Yu Pei 
Published: May 2024

Abstract
The retention of rural teachers is crucial not only for enhancing the quality of education in rural areas but also for the cultivation of talent and broader social development. This article conducts a detailed analysis of rural teachers’ willingness to remain in their positions by constructing and comparing various statistical models, including multi-level linear models and logistic regression models. Based on the results of model fitting, it specifically derives a logistic regression model that identifies the factors influencing rural teachers’ intentions to stay at the conclusion of their terms. Finally, 250 rural teachers as a survey research object, using the research method of this paper to analyze and explore. After the study, it is concluded that social support will have a significant positive impact on the willingness to stay when B = 0.121, 95% confidence interval is [0.298, 0.434], β = 0.532, t = 18.326, p < 0.001. Work pressure and working hours negatively predicted rural teachers’ willingness to stay in the profession, while social support positively predicted rural teachers’ desire to remain in the profession. The higher the level of social support, the higher the degree of recognition of rural teachers’ profession in the family, school, and society.
Evaluation of the double-cycle benefits of international engineering cooperation and the Western economy - based on the SFA methodology
Authors: Li Yanfang , Bian Yuzhu 
Published: May 2024

Abstract
In the era of globalization and development, international engineering cooperation is crucial to the improvement of economic efficiency in the West. This paper selects panel data from six regions in the West from 2019-2022 and selects reasonable input-output indicators and environmental variable indicators. At the first stage of the three-stage DEA model, the static measurement of Western economic efficiency is first measured using the variable scale compensation (BCC) model. The SFA model is utilized to decompose the slack variables during the first stage and adjust the input variables without affecting environmental variables. In addition, the Malmquist index model is used to analyze the efficiency of the Western economy dynamically in order to have a more comprehensive evaluation of the double-cycle efficiency of the Western economy from the perspective of dynamic and static. The results of the study show that after removing the effects of environmental factors and random errors, the average values of technical efficiency, pure technical efficiency, and scale efficiency in the third stage of all western regions have increased by 0.0175, 0.0193, and 0.0194 compared with those in the first stage, and the annual average total factor productivity index has shown a decreasing and then increasing trend. The SFA method used in the study can effectively eliminate the influence of environmental factors and random factors and evaluate the double-cycle efficiency situation of international engineering cooperation and the Western economy more comprehensively.
A Study of Systemic Financial Risk Prediction Models in the Insurance Industry
Authors: Wang Haifei , Chen Xiaoxin 
Published: May 2024

Abstract
The influence of the stability of the insurance industry on the strength of the financial market cannot be ignored, and the healthy and strong growth of the insurance industry plays an indispensable role in maintaining the stability of the whole society. To fully analyze the influence of the stability of the insurance industry on the strength of the financial market, this paper proposes a prediction model of systemic financial risk. The insurance industry’s systemic financial risk prediction indicators are determined based on the principle of selecting systemic risk factors and indicators. After preprocessing the collected relevant research data, relevant research variables are determined, a Logit prediction model for the systemic risk of the insurance industry is constructed, and a case study is conducted. The data show that the average value of GDP during these 21 years is 379642.64, and it reaches about 2.4 times the average value in 2023. The insurance industry’s economy grows steadily and rapidly. It reached the maximum response in the 10th period (0.04616), i.e., the development of China’s life insurance industry, which has a more obvious role in promoting the development of the economy. It is predicted that China’s financial risk in the insurance industry in the second half of 2024 will have a great probability of being in a low-risk state. Its predicted probability value is close to 1. The insurance industry’s healthy development has a positive impact on economic development, and the insurance industry’s stable development is of great importance to economic development, as evidenced by this study.
Application and Implications of Artificial Intelligence Technology in Basketball Coaching Decision Making
Authors: Wang Bingyang , HoKun Yi , Xu Hua , Huang Yufei , Niu Yonggang 
Published: May 2024

Abstract
The proliferation of artificial intelligence (AI) technologies is significantly enhancing the precision and efficacy of decision-making in basketball. This study focuses on players from the men’s basketball team at School A, employing AI to model their sports postures, collect athletic performance data, and analyze physiological metrics in real time. By utilizing fuzzy gray correlation analysis, the research aims to identify the factors influencing basketball game outcomes, thereby informing tactical decisions for coaches. Moreover, this paper compares the proposed analysis method with other correlation techniques to evaluate its relative superiority. The findings indicate that post-training, the serum levels of all ten athletes exceeded 400 U/L. Yet, the creatine kinase (CK) levels remained below 6.8 mmol/L, and the urine specific gravity surpassed 1.05, suggesting that the training intensity was high but within the athletes’ physiological tolerances. With a correlation coefficient of 0.4, the study confirms a positive impact of game outcomes on the coach’s tactical adaptations. This underscores the utility of integrating AI systems into basketball to facilitate timely and effective decision-making by coaches.
Analysis of Factors Influencing the Quality of Teaching Mathematics and Applied Mathematics under the Orientation of OBE Philosophy
Authors: Wei Jinling , Zhang Tiecheng 
Published: May 2024

Abstract
Mathematics, recognized as a foundational subject, is marked by its high level of abstraction, rigorous logic, and extensive applicability, necessitating effective reforms. This article proposes a results-based teaching model for mathematics by integrating the Outcome-Based Education (OBE) concept with fundamental mathematical principles. The study utilizes a university in GD province as a case study to implement this OBE-based teaching model. Employing factor analysis and structural equation modeling, the research explores factors influencing the quality of teaching. Descriptive and correlational analyses were conducted on the variables involved, followed by structural equation modeling to test the proposed hypotheses. The findings revealed significant positive relationships among study habits, learning ability, interest motivation, learning resistance, teaching environment, and learning environment, with correlation coefficients positive at a 0.01 confidence level. The standardized path coefficients were 0.241, 0.143, 0.255, 0.153, 0.175, and 0.124 for learning habits, learning ability, interest motivation, learning resistance, teaching environment, and learning environment, respectively. These influences significantly and positively impact the effectiveness of the mathematics teaching model, demonstrating their beneficial effects on educational outcomes.
Modeling Personalized Smart Teaching for Learner Needs
Authors: Wu Juanjuan 
Published: May 2024

Abstract
With the deep integration of the Internet and education, the personalized development of education has become a new trend in education, and it also increasingly emphasizes the learner’s subject position in learning. In this study, a smart teaching model for learners’ individuality is developed by integrating WEB data mining technology, SOM neural network, and multiple recommendation mechanisms. The model achieves personalized recommendations for learning resources through the collection of user characteristics and then according to the recommendation algorithm. Then, using college English courses at Zhengzhou Shengda University as an example, the SOM neural network is utilized to diagnose the teaching cognition of the experiment. The experimental results show that the SOM neural network cognitive diagnosis results in a high judgment rate, with a high judgment rate of 84.842%. It has certain feasibility in teaching small sample diagnostics. In terms of efficiency, the time of cognitive diagnosis can be controlled within 1 second, which is real-time in teaching applications. The significance test of students’ performance after the experiment shows that the personalized wisdom teaching model constructed in this paper has a significant effect on improving teaching performance.
Kinematics and biomechanics analysis of classic 24-style taijiquan
Authors: Xia Hongwu 
Published: May 2024

Abstract
Inheriting and promoting Taijiquan, most of the process of oral teaching and teaching by words, the lack of scientific data support, there is an urgent need to standardize the interpretation level measurement standards. In this paper, based on the high-speed infrared motion capture technology, the HelenHayes method is used to select the bony landmarks of human anatomy, establish the coordinate system, and optimize the traditional posture-solving algorithm by using Mahony to design the kinematic and biomechanical analytical model used for the movements of the classical 24-step Taijiquan. The analytical model was used to analyze the kinematics and biomechanics of a total of 6 taijiquan practitioners in both professional and amateur groups. It was finally found that the center of gravity of the taijiquan champion was 0.077, 0.080, and 0.034 during the white crane bright wing movement, and the center of gravity of the three stages of the single whip was 0.062, 0.075, and 0.059, without excessive undulating movements, and basically remained at the same height. In the four stages of the movement, the left hip joint angle of the amateur group has been deviating from the standard, with a maximum deviation of 22.98 degrees, which is significantly different from that of the professional group. This study provides a theoretical basis for the improvement, rationalization, and simplification of Taijiquan movements and scientific experimental data for the standardized teaching and training of these movements.
Research on international trade flow analysis based on potential clustering network
Authors: Yi Pan 
Published: May 2024

Abstract
International trade is an important part of the world economy, and the rapid development of big data and complex network theory provides the possibility of an in-depth study of international trade networks. This study constructs the international trade flow gravity model and international trade flow index system on the basis of the clustered dynamic panel gravity model. It explores the influencing factors of national trade flow and the fitting effect of the international trade gravity model through factor analysis and regression analysis of variables in the system. The sample data’s KMO value is 0.834, according to the results, and there is a significant correlation between the variables. The degree of explanation of the public factors to the original variables is more than 0.5, and the variance contribution rate of trade structure, marketized structure, population and macroeconomics, international capital, and exchange rate reaches 71.832%, which makes the factor analysis more effective. The R² value of the trade gravity model reaches 0.837, with a remarkable effect. Among the sample countries, the highest trade flow potential is the United States (125,467.0), while the lowest is Portugal (512.4). The national GDP and population have a significant effect on international trade flows. The volume of each trading country should be used as a reference basis for conducting trade transactions when conducting international trade.
Intelligent Recognition and Classification of 3D Animation Styles - Based on AIGC Technology Application
Authors: Jiang Xinqu , Sun Jian 
Published: May 2024

Abstract
The study of style identification in 3D animation not only enhances the artistry of animation scenes but also has a profound significance on the development of 3D animation works. In this paper, we first sorted out the art style expressions of 3D animation and explored the innovation path of 3D animation art style and related influencing factors. Based on the generative adversarial network in AIGC technology, the discriminator and generator are trained. The 3D animation style intelligent recognition and classification model is established by combining the GAN network and SoftMax classifier, and the loss function of the model is optimized. Using crawler technology to obtain 3D animation-related images and crop them to 128*128 size for dataset construction, 3D animation style recognition and classification experiments were designed to verify the application effectiveness of the 3D-GAN model. It is found that the 3D-GAN model reaches the peak of IS index 9.222 after 60*103 iterations, which is 12.85% higher than the ALGAN model. The average classification accuracy of the 3D-GAN model is 91.42%, which is 6.01 percentage points higher than that of the sub-optimal CycleGAN model. Conducting intelligent recognition and classification of 3D animation styles based on AIGC technology can provide a new direction for innovating 3D animation styles and optimizing scene design.