bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on the Path of Integrating Curriculum Ideological and Political into Physical Education Teaching in Colleges and Universities in the Era of Artificial Intelligence
Authors: Liu Zhongliang 
Published: March 2024

Abstract
In the era of artificial intelligence, how to effectively integrate curriculum ideology into college physical education teaching has become an essential way to improve students’ comprehensive quality, and this research area has been increasingly emphasized. The study first analyzes the dialectical relationship between curriculum Civics and college physical education teaching, pointing out that the two have consistent educational goals and concepts and can form a good synergy effect. Regarding methodology, the item group theory is used to analyze the teaching mode of civics and politics, revealing the advantages of improving the homogeneity of physical education curriculum civics and politics. Particular emphasis is placed on the importance of enhancing physical education teachers’ awareness in practicing course ideology and politics, and it is believed that teachers are the key to guiding students to form correct ideological values. The study utilizes the pull-out gearing method and the material element expandable theory to quantitatively evaluate the effect of integrating curriculum ideology and politics into physical education teaching in colleges and universities. The results showed that after integrating curriculum ideology and politics, students’ mastery of physical education skills increased by 25%, and the awareness of ideological content increased by 30%. In addition, teachers’ self-efficacy in implementing the Civic Politics in PE teaching increased by 40%, and students’ social adaptability scores increased by an average of 2.3 points out of 10. The effective integration of curriculum ideology can significantly improve the quality of physical education teaching in colleges and universities, and positively impact the enhancement of students’ ideology and morality as well as their skills and techniques.
Exploration of High-level Apprenticeship Talent Cultivation Mode with Chinese Characteristics Based on SWOT Analysis
Authors: Lv Zhixia 
Published: March 2024

Abstract
High-level apprenticeship with Chinese characteristics is becoming increasingly important in the modern vocational education system, and its development is affected by many factors. SWOT analysis provides an effective tool for exploring and optimizing this training model. The research methods include literature review, fuzzy comprehensive evaluation and linear regression analysis. The results show that internal strengths include establishing theoretical and practical foundations and the implementation of regional pilots. In contrast, internal weaknesses are mainly reflected in the misunderstanding of high-level apprenticeship and the lack of professionalization standards. External opportunities include industrial transformation and policy support, while external threats involve social recognition of vocational education and enterprise participation. By analyzing the data from 643 questionnaires, the R-square value of the model was found to be 0.73, indicating that the selected factors explained 73.8% of the changes in talent cultivation. Developing a high-level apprenticeship system with Chinese characteristics should consider the comprehensive influence of internal and external factors and develop corresponding optimization strategies.
An applied study of multi-layer decision tree optimization algorithms in machine learning
Authors: Nie Jingjing 
Published: March 2024

Abstract
This paper investigates the multi-layer decision tree optimization algorithm in machine learning and explores its efficiency and accuracy in applying complex data classification. The focus is on the optimization strategy of the algorithm when dealing with large datasets with multiple types of records. The research adopts methods such as data set Discretization, fuzzification processing and hierarchical fuzzy decision tree construction algorithm. Through experimental verification, our constructed multilevel fuzzy decision tree performs well in two application scenarios: rainfall prediction and fingerprint identification. In the rainfall prediction experiment, using 5200 sample data, the algorithm achieves a classification accuracy of 61%. In the fingerprint recognition experiment, from 3,000 fingerprint images, a 100% recognition rate was performed, and the rejection rate was 25.2%. The results show that the multi-layer decision tree optimization algorithm can effectively handle large data sets of multiple types and significantly improve the accuracy and efficiency of classification. The algorithm offers high adaptability and accuracy when dealing with continuous type attributes. This study provides a new perspective and methodology for the application of multi-layer decision trees in machine learning, which is of great significance for the future development of data processing and classification techniques.
Construction of Online Interactive Teaching Platform for Drama, Film and Television Majors Based on Fuzzy Multi-Objective Decision Making Algorithm
Authors: Song Chi 
Published: March 2024

Abstract
This study introduces a multi-objective decision-making model tailored for developing an educational platform, blending economic considerations with the platform’s operational constraints. It employs a fuzzy set approach for algorithm design, utilizing a method that minimizes deviations in membership values for solving. The research culminates creating an online interactive teaching platform for drama and film studies. This platform is architecturally founded on the B/S framework, incorporates functional modules derived from its educational objectives, and utilizes MySQL for data flow design. Comparing it to conventional teaching methods, a comprehensive evaluation demonstrates its practical application in real-world teaching scenarios. Performance tests reveal module response times between 2.7 to 3.0 seconds, with compatibility tests ranging from 0.11 to 0.36 seconds. Under a load of 200 simultaneous users, the database connection pool maintains an optimal size of about 35 connections, with thread counts near 50 and zero idle database connections. The study also quantifies the platform’s impact on various educational aspects, showing correlation coefficients for learning attitude (0.88), motivation (0.50), behavior (0.72), and teacher-student (0.15) as well as student-student interactions (0.27) with the learning effectiveness of the platform.
The Heterogeneous Impact of Changes in the Age Structure of China’s Elderly Population on Regional Economic Development
Authors: Zhang Yi 
Published: March 2024

Abstract
Currently, the impact of population aging on economic development is mainly explored from the dimension of time, ignoring the Heterogeneity between the two. In this paper, we start by measuring the regional economic development level index, use the entropy value method to solve the index weights, and calculate the regional economic development level index using the linear weighting method. The regional development level index is used as an explanatory variable to construct the baseline regression model and the dynamic panel model for the change in the age structure of the elderly population. The panel data of 31 provinces (cities/autonomous regions) in China are used as examples to explore the heterogeneous effects of age structure changes on regional economic development of the elderly population. The results show that when the age structure change of the elderly population increases by 1%, the regional economic development level rises by 0.782%, and the age structure change of the elderly population is positively and significantly affecting the quality of economic development in the central and western regions at the 1% level. The regression coefficient of population aging in the low level stage is 0.007. The effect on the regional economic development level is significantly positive at the 1%. The age structure change of the elderly population affects the regional economic development by mediating health consumption expenditure. The age structure change of the elderly population has Heterogeneity in regional economic development in terms of period, region, and level at different stages.
Study on the Implementation Path of Integrating Jing-Chu Culture into the Living Regionalization of Aesthetic Education in Hubei Colleges and Universities in the Age of Informatization
Authors: Zhang Kain 
Published: March 2024

Abstract
Exploring effective ways to improve aesthetic education in colleges and universities is one of the hotspots of current educational research. Starting from the three dimensions of school theory, practical activities and social practice, this study successfully constructed a model of living and regionalizing aesthetic education in Hubei colleges and universities, integrating the elements of Jingchu culture using informatization tools. The study puts forward a series of hypotheses around the four key dimensions of school, society, policy and students. It uses factor analysis to explore the influencing factors behind these hypotheses and their interactions, ensuring the rationality of the influencing factors. This paper’s hypotheses are empirically supported by structural equation modeling verification. The model has been proved to be theoretically sound and practically feasible. The study results reveal that the mean values of all the influencing factors exceeded the median value of 3, and the standard deviations ranged from 0.7 to 1.0, showing a small degree of dispersion, which proved the reasonableness of the factor selection. Specifically, school support directly influences the living regionalization of aesthetic education in colleges and universities (0.628), while the indirect influence effects of social promotion, policy guarantee, and students’ attitudes are 0.240, 0.122, and 0.285, respectively. In addition, the combination of the Jingchu culture with aesthetic education in colleges and universities not only enhances the cultural self-confidence of college students, but also effectively carries forward the traditional culture, and provides a new perspective and practical path.
Self-Information Forgery Mining for Face Forgery Detection
Authors: Wang Xiaozhuo , Wei Jianyu 
Published: March 2024

Abstract
In the face of rapid advances in face forgery technology, effective detection methods have become crucial to maintain the authenticity of digital media. Deep learning technology has provided new strategies for recognizing and preventing face forgery in recent years. In this study, a new face forgery detection technique is proposed by utilizing self-information theory, which improves the accuracy and robustness of detection by mining forgery traces, especially in diverse forgery scenarios. The study extracts face features through an improved high-resolution network HRNet and optimizes identity information extraction by combining facial reenactment techniques to detect forged faces efficiently. Experiments have been conducted on several mainstream forged face datasets, and the method presented in this paper can effectively improve the detection performance with an average accuracy of 74.75% on C40 recompressed images. Comparison experiments show that this research method’s frame-level and video-level detection accuracy on the Celeb-DF dataset are 0.9846 and 0.9985, respectively, which are higher than those of existing techniques. Cross-library tests validate the method’s generalization performance, and the AUC metric remains at 0.7305 even in low-quality video environments, which shows good resistance to environmental interference. This study proposes a self-information forgery mining technique that enhances forgery detection accuracy while demonstrating superior generalization ability.
Research on Multimodal Teaching Model of English Writing in Colleges and Universities Based on Knowledge Mapping
Authors: Hu Zhenzhen 
Published: March 2024

Abstract
This study investigates the effectiveness of multimodal teaching methods in college English writing courses, contrasting these innovative approaches with traditional teaching methods. By integrating multimodal knowledge mapping into an English teaching platform, we aimed to enrich students’ learning experiences and enhance their writing skills. A comparative analysis involving two classes—one employing the traditional teaching method and the other utilizing the multimodal approach—revealed significant improvements in the multimodal group. Students in the experimental class demonstrated heightened interest, improved attitudes and cognition towards English writing, and their average writing scores increased by 2.68 points, from 12.66 to 15.34. In contrast, the control group showed no significant score changes. Our findings underscore the multimodal teaching method’s capacity to effectively elevate students’ English writing abilities, marking a successful adaptation to modern educational demands.
A Study on Influencing Factors of Dynamic Chinese Character Stroke Writing Behavior in CFL Beginners Using Digital Ink Technology
Authors: Lai Yun , Zhang Xiwen 
Published: March 2024

Abstract
Exploring the intricacies of learning Chinese, this study leverages digital ink technology to observe the stroke writing behaviors of novice learners. We delved into the nuances of stroke order, direction, and the fluidity of writing, scrutinizing 2,131 Chinese characters to uncover the relationship between character structure and writing behavior. Our findings illuminate beginners’ challenges, offering a novel perspective on teaching Chinese characters that caters to the dynamic nature of writing and learning.
Bin Packing Problem with Item Time Window Conflict Dependent Profits
Authors: Ren Jiayue 
Published: March 2024

Abstract
The problem of packing with items with conflicting time-window dependence on profit considering departure time is studied in the context of consolidation of goods in transport, which is a practical problem in the logistics industry. The objective is to select the loading plan that is most profitable based on the volume and time windows of the items. The time windows of items in the same box must intersect, and the intersection of the items’ time windows affects the box’s departure time and hence the profit-dependent time. The problem is identified as an integer programming model for decision making, and two modeling strategies are proposed to address it by improving the GRASP heuristic. Multi-class examples are used in experiments to confirm that the improved algorithm can solve the problem efficiently, find the near-optimal solution in a shorter time, and enhance customer satisfaction.
Dynamic analysis of digitally-driven technology upgrades and environments based on DAG-SVAR models-a case study of the United States of America
Authors: Lyu Yifei , Gao Jie , Wang Jie , Zhang Yuhua 
Published: March 2024

Abstract
Digitalization is a fundamentally disruptive force triggered by the fourth Industrial Revolution and the Internet of Things, driving technology upgrades, so what impact does digitalization have on the environment? In this paper, DAG-SVAR model was constructed to verify it. Through impulse response analysis, it was found that digitization drives technological upgrades, and environmental pollution is reduced through intelligent production, improved information environment, and ameliorated governance level. This paper suggests that, on the one hand, the technology upgrade is the key to digital empowerment in new fields and new spaces. Enterprises need to constantly explore diversified application scenarios, help the digital and intelligent transformation of energy, transportation, manufacturing and other industries, and greatly promote the integration and interaction of technology and various fields and sustainable development; on the other hand, it is necessary to strengthen digital ecological governance, promote the interconnection of everything, human-computer interaction, and the integration of space and land, so that information resources can fully flow, to create a digital green ecology.
Thermoelectric Optimization of Integrated Energy System Based on Bidirectional Electricity-to-Hydrogen Technology
Authors: He Cai , Cheng Shan , Cheng Ying , Tao Yuning 
Published: March 2024

Abstract
Addressing the challenges of high energy consumption and low efficiency in integrated energy systems, this research develops an optimal scheduling strategy that takes into account the dynamic operational conditions of the system and the complementary nature of electricity, hydrogen, and heat energies. Through the innovative application of variable efficiency gas turbines and solid oxide batteries, our model adeptly converts between these forms of energy. Moreover, we introduce a demand response strategy that seeks to balance efficiency with low-carbon generation and integrates a tiered carbon trading scheme to influence user electricity use and curb system-wide carbon footprints. Empirical results validate the effectiveness of our method in significantly enhancing energy use efficiency, cost savings, and emissions reductions.
Blockchain event ordering algorithm based on high-dimensional multi-objective differential evolution
Authors: Li Kuan 
Published: March 2024

Abstract
Since the birth of Bitcoin, blockchain technology has been widely used in many fields, such as finance, supply chain management, smart contracts, etc.and has been highly valued for its decentralized and tamper-proof characteristics. With the expansion of application scenarios, blockchain faces inefficient data processing challenges, particularly in event sequencing and processing. Developing efficient event sequencing algorithms is a research priority to enhance the performance of blockchain systems. This study addresses the efficiency problem in blockchain event sequencing by introducing a high-dimensional multi-objective differential evolution strategy to improve blockchain technology’s performance in various fields. This paper uses the NSGA-III-WA algorithm to efficiently sort blockchain events by combining differential evolution strategies. Compared to the traditional sorting algorithm, this algorithm reduces latency by 30% and improves memory usage by about 20% when dealing with events of the same size, ensuring sorting accuracy. By optimizing the weight vector adjustment strategy, the convergence speed and distribution uniformity of the algorithm and the solution can be optimized. In this paper, the algorithm can effectively improve the efficiency and performance of blockchain event processing, which is critical in optimizing blockchain systems.
Analysis of the application effect of ecological adaptability concept in rural landscape design based on TACSI-IPA evaluation
Authors: Mao Yantao , Sun Yihui 
Published: March 2024

Abstract
In this research, we apply the innovative TACSI-IPA system to study how ecological adaptability can transform rural landscape design, focusing on elevating design excellence and customer contentment. We innovatively blend the TACSI model—centered on capturing tourist attraction nuances—with a sophisticated latent variable approach for a deeper dive into customer satisfaction metrics. The introduction of IPA analysis allows us to intricately map out tourists’ satisfaction levels and interests, leading to more targeted evaluation outcomes. Our practical work in Zhangzhou Wuqiao Village has shown remarkable improvements in the appreciation of both its natural and crafted landscapes. Specifically, the plant and water landscapes achieved coordination scores of 2.25 and 4.46, respectively, with the crafted amenity landscape receiving widespread acclaim and an impressive satisfaction average above 4. A comparative lens against traditional villages underscores Wuqiao Village’s standout visual appeal.
Optimization of Digital Inheritance of Meishan Folk Woodcarving Skills in the Information Age
Authors: Peng Bo 
Published: March 2024

Abstract
This study delves into the digital preservation of Meishan folk woodcarvings, emphasizing the use of virtual reality (VR) technology to enhance the transmission of this traditional art. Utilizing 3D modeling and VR platforms, the research scans and rebuilds wooden carvings for digital exhibition. The realism and interactivity of the virtual environment are improved through collision detection algorithms such as Orientation Bracketing Box (OBB) and Axial Bracketing Box (AABB), complemented by high-precision 3D models created with LIDAR scanning technology. Findings indicate that the VR system significantly enhances the efficiency and quality of digital preservation, offering a robust solution for safeguarding Meishan folk woodcarvings.
Virtual Reality Enables the Dissemination and Preservation of Early Works of Art
Authors: Cai Chunming 
Published: March 2024

Abstract
With the rapid development of digitalization and information technology, virtual reality (VR) technology has shown unique potential in disseminating and conserving early works of fine art, changing people’s traditional ways of appreciating and conserving fine art. The article examines the use of virtual reality technology in disseminating and preserving early works of art. The objective is to improve the accessibility and conservation efficiency of art works by using digitization methods. The study uses VR technology to digitize works that are more vivid and easy to disseminate in terms of visual communication, with a 35% increase in audience acceptance, and digital protection effectively reduces the rate of work attrition to about 20%. Through empirical analysis, it has been found that the digital platform that relies on VR technology improves art works’ dissemination effect and protection efficiency. Virtual reality technology is effective at protecting and disseminating early works of fine art, promoting the public’s appreciation and understanding of art, and providing a new method for the digital transformation of fine art works.
A study of key issues in parallel algorithms for face recognition based on genetic neural networks
Authors: Guo Kai , Li Biao , Li Hao , Bai Zhi 
Published: March 2024

Abstract
This study examines the effectiveness of Genetic Neural Networks (GNN) in face recognition, particularly in optimizing parallel algorithms to overcome the challenges posed by complex data. We have significantly improved recognition accuracy and computational efficiency by employing an adaptive genetic algorithm that fine-tunes neural network weights through Selection, crossover, and mutation. Our approach was tested across diverse datasets, covering variations in posture, age, ethnicity, and lighting conditions. The results demonstrate outstanding recognition rates: 99.82% on LFW, 97.94% on AgeDB-30, 95.11% on CFP-FP, 95.87% on CALFW, and 89.44% on CPLFW, showcasing exceptional robustness against complex lighting and occlusions. Additionally, our algorithm maintains balanced accuracy across different ethnicities with an overall recognition rate of 96.77% and boasts a substantial reduction in processing time to an average of 4.15 seconds. These advancements underscore the potential and practicality of our method in enhancing face recognition technology.