bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of Influencing Factors on the Teaching Reform Quality of Visual Communication Design Courses in Colleges and Universities Combined with Entropy Weighted TOPSIS Modeling
Authors: Liu Yichang , Liu Yiming 
Published: March 2024

Abstract
In the fast-evolving field of information technology, visual communication design education must adapt through innovative curriculum reforms. This study employs the CIPP model to develop a robust index system for evaluating the effectiveness of these reforms within higher education institutions. Utilizing the entropy weight method for index weighting and the TOPSIS method for comprehensive evaluation, we assessed the reform quality across 12 colleges and universities. Our analysis, incorporating descriptive statistics and linear regression, reveals that a significant proportion (75%) of institutions score below 0.4, indicating average to low reform quality. Key factors impacting reform quality— teaching attitude, learning attitude, and course content—demonstrate substantial influence, with coefficients of 0.238, 0.648, and 0.658, respectively. All second-level factors showed a significant positive impact on reform quality, with significance coefficients below 0.01. These findings highlight the critical areas for enhancing curriculum reform efforts in visual communication design education.
Exploring the Inheritance and New Changes in Pre-Qin Literature in Cao Zhi’s “Luoshen Fu” Using Network Textual Analysis
Authors: Zhang Mina 
Published: March 2024

Abstract
Through network textual analysis, this paper explores the inheritance and development of Cao Zhi’s Luoshen Fu on pre-Qin literature, revealing its innovations in linguistic style, emotional expression and literary concepts. In-depth discussion of Cao Zhi’s Luoshen Fu on the inheritance and innovation of pre-Qin literature. The study firstly analyzes the main themes and emotional tendencies of Luoshen Fu by modeling textual themes through natural language processing techniques. In the word distribution analysis, we find that the frequency of verb and noun usage in Luoshen Fu is 221‰ and 178‰, respectively, which is significantly higher than the average level of pre-Qin literature (232‰ and 140‰). In addition, Luoshen Fu demonstrates more complex and rich emotional changes in the emotional analysis than Li Sao. By calculating the information entropy value of Luoshen Fu (10.5), compared with the average level of pre-Qin literature (9.8), it reflects its richness and innovation in vocabulary use. Luoshen Fu demonstrates remarkable innovation in language style, emotional expression, and literary concepts while demonstrating a profound ancestry in pre-Qin literature.
Research on School Uniform Style Design and User Experience Based on Virtual Technology
Authors: Hu Zhenhua 
Published: March 2024

Abstract
The development of Virtual Reality (VR) technology has revolutionized many fields, especially in the growing application in fashion and design. Through virtual technology, designers can instantly present the design effect of school uniforms without the need for physical prototypes, and at the same time, collect user feedback to optimize the design scheme. This study uses virtual reality technology to establish a platform for designing and presenting school uniforms. Through experimental design and user research, this technology’s application effect in the school uniform design field is evaluated. VR technology resulted in a 30% increase in the efficiency of designers in designing school uniform styles and a 25% increase in user satisfaction. The platform’s user experience was assessed using the gray fuzzy comprehensive evaluation method, and the score reached 3.56 (out of 5), indicating the overall recognition of the platform by users. The virtual technology used in this study can significantly improve the efficiency and quality of school uniform style design and enhance user experience.
Construction of Intelligent Financial Management Platform Based on Industry-Finance Integration
Authors: Yu Meng 
Published: March 2024

Abstract
Exploring the Construction of intelligent financial management platform based on integrating industry and finance is aimed at improving enterprises’ financial efficiency and management level and realizing the efficient Integration of finance and business. This study centers on an in-depth discussion on the Construction of an intelligent financial management platform that integrates industry and finance. The study finds that integration problems between enterprise finance and business management, such as separation of information systems and insufficient performance evaluation system, lead to interdepartmental cooperation and coordination problems. The proposed intelligent financial management platform is based on ERP and CRM systems and processes data through ETL tools to achieve information sharing. The functional Construction of the platform relies on value correction and knowledge transformation of financial big data, and uses multiple linear regression models to optimize data processing. Empirical studies show that Group T has significantly improved its financial efficiency and management level through the implementation of the platform, which is reflected in the reduction of accounting personnel by 60.22% and the significant shortening of the average audit time of finance. Furthermore, fund management capability was improved, management expenses and expenses were decreased, and performance assessment was more comprehensive. The study concludes that the effective implementation of the intelligent financial management platform can significantly improve the effect of the enterprise’s industry-finance Integration, and promote the enterprise’s overall operational efficiency and competitiveness.
Research on school social work methodology based on predictive control and architectural innovations
Authors: Ni Wei 
Published: March 2024

Abstract
This study focuses on innovations in school social work methods and explores applying predictive control-based models to effectively support and improve students’ mental health and behavioral development. The XGBoost algorithm and support vector machine were employed to predict students’ mental health. The study was influenced by physiological, self, family, school, social, and network factors, and a questionnaire was developed based on the SCL-90 Symptom Self-Rating Scale. A validity rate of 94.32% was achieved by collecting 432 valid questionnaires. The results indicated a significant positive correlation between the influencing factors and students’ mental health, with physiological and family factors being the most influential. An AUC value of 93.6% and an accuracy rate of 93.9% were achieved by the XGBoost-SVM model when predicting students’ mental health. The prediction model can update students’ mental health status in real-time and provide an effective predictive control tool for school social work.
Research on the Innovation Path of Music Education in Higher Vocational Colleges and Universities in the Context of the New Era
Authors: Shi Shuang 
Published: March 2024

Abstract
This study navigates the digital transformation challenges within music education at higher vocational colleges and universities, proposing an innovative approach that integrates modern technology to enhance student musicality. Employing a mixed-methods research framework that combines quantitative and qualitative analyses, we explore the impact of digital teaching tools, including artificial intelligence and multimedia resources, on students’ musical understanding and creativity. Our findings reveal that students engaged with our digital-enhanced educational model exhibit significant improvements in rhythmic sense (30% increase) and creativity (25% increase) compared to traditional settings. This synergy of music education and technology elevates teaching outcomes and fosters innovative thinking among students, suggesting new directions for the evolution of music education in higher education institutions.
Blockchain-based e-commerce supply chain logistics management model innovation in the context of big data analysis
Authors: Hou Dongdong , Zhou Mengke 
Published: March 2024

Abstract
Facing the triple challenges of information opacity, inefficiency, and security in e-commerce supply chain logistics, this study turns to blockchain technology for solutions. Blockchain’s decentralization, transparency, and security features present fresh avenues for overcoming these obstacles. By harnessing the Adaboost algorithm, we optimize logistics data processing to bolster decision-making efficiency. Moreover, using game theory, we analyze how information sharing strategies among enterprises affect logistics management, advocating for a paradigm shift towards cooperative logistics modes. Implementing blockchain technology in our study led to a significant leap in logistics transparency and efficiency, with experimental data revealing a 20% decrease in logistics costs and a 30% enhancement in efficiency. These findings affirm the role of blockchain as a catalyst for innovation in e-commerce supply chain logistics, promoting a more efficient and secure information-sharing ecosystem.
Optimized design research on daylighting performance of cold land buildings based on improved neural network
Authors: Liu Lei , Sun Cheng , Liu Ying , Leng Hong , Yang Yang 
Published: March 2024

Abstract
This study delves into optimizing daylighting in buildings in cold regions, employing an innovative neural network approach to enhance natural lighting efficiency. Cold climates present unique challenges for daylighting, making it essential to improve indoor lighting conditions, reduce energy usage, and enhance occupant comfort. Traditional design methods fall short in optimizing daylighting due to their inability to effectively navigate complex environmental factors and building configurations. We introduce an advanced neural network model that pioneers efficiency and innovation in the daylighting design of cold buildings. This model leverages the GA-PSO-BP framework, integrating Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Back-Propagation (BP) neural networks to create a potent optimization tool. Our approach focuses on refining key design parameters such as building orientation, floor height, plan depth, and external window design. Notably, specific adjustments to building orientation and floor height significantly boost daylight autonomy (DA) and helpful daylight illuminance (UDI) while maintaining the daylight glare probability (DGP) within optimal limits. Our findings reveal that optimizing building orientation can elevate DA and DGP values by 4.756% and 0.037325, respectively. Similarly, adjustments to floor height can enhance DA, UDI, and DGP values to 51.833%, 51.278%, and 0.361377, respectively. This refined neural network model demonstrates a robust capability to improve daylighting performance in cold-region buildings, offering fresh perspectives and methodologies toward the sustainable evolution of architectural design.
Research on the Reform of English Language Curriculum System in Colleges and Universities Based on SERVQUAL Model
Authors: Wang Weijia 
Published: March 2024

Abstract
This investigation assesses the efficacy of the SERVQUAL model in advancing the quality of English language education within higher education institutions. Confronted with the imperative to modernize teaching content and methodologies to enhance student satisfaction, the SERVQUAL model’s application provides a systematic framework for evaluating and improving service quality dimensions. Employing questionnaires and data analysis to compare teaching quality and student satisfaction before and after targeted curriculum reforms, this study documents substantial improvements, notably in “Responsiveness” and “Reliability,” where satisfaction levels rose by 20% and 15%, respectively. The successful implementation of the SERVQUAL model underscores its utility in elevating the caliber of English language instruction and student satisfaction, offering a practical blueprint for ongoing curriculum reforms in higher education.
Digital Financial Inclusion and Financial Vulnerability Analysis of the Household Economy - Empirical Evidence Based on CFPS
Authors: Fu Lingyan 
Published: March 2024

Abstract
The global landscape of digital financial inclusion aims to extend broader, more accessible, and cost-efficient financial services via digital channels to those marginalized by conventional financial systems. Particularly in developing countries, household economic and financial fragility remains a pressing concern. Economic vulnerability denotes the incapacity of households to withstand unforeseen difficulties in the wake of economic downturns, precipitating a significant deterioration in living standards. This research investigates the role of digital financial inclusion in diminishing household economic vulnerability and enhancing the efficacy of financial services to address this challenge. Employing a fuzzy rough set algorithm to examine the China Family Panel Studies (CFPS) dataset, our analysis identifies critical factors of household economic vulnerability and their interaction with digital financial inclusion services. Findings demonstrate that digital financial services markedly reduce household economic vulnerability, evidenced by a 12% improvement in household solvency and a 20% uplift in financial resilience. Rural and lower-income families derive the most significant benefits from digital financial inclusion. Furthermore, the study underscores the importance of advancing financial literacy and refining financial product design as pivotal strategies in mitigating household economic vulnerability. This research offers valuable insights for policymakers and financial service providers on leveraging digital technology to bolster household economic stability.
Risk prediction and control of strategic operation of e-commerce enterprises based on economic management science
Authors: Hong Qingyu , Luo Lei , Zhang Yanting 
Published: March 2024

Abstract
The burgeoning realm of Internet technology has ushered e-commerce into a pivotal economic role. However, navigating the myriad risks inherent in e-commerce operations is vital for the sustained growth of businesses in this sector. This study melds economic management principles with a deep dive into e-commerce risk management, focusing on predictive strategies and mitigation measures. We commence by dissecting the principal risk categories within e-commerce operations. Subsequently, we employ Structural Equation Modeling (SEM) and Particle Swarm Optimization-Generalized Regression Neural Network (PSO-GRNN) for quantitatively dissection of these risk factors. Our findings pinpoint internal, technological, and operational management risks as the critical triad influencing e-commerce strategic operations. Remarkably, the PSO-GRNN model’s risk prediction accuracy stands at 93.62%, outstripping conventional models significantly. Through this research, we offer a robust framework for e-commerce entities to enhance their strategic foresight and resilience, aiding in optimizing their strategic maneuvers.
An Informational Model of Mathematics Teaching and Research Activities Based on the ARCS Model
Authors: Hu Jun 
Published: March 2024

Abstract
Enhancing the quality of mathematics education is pivotal, with teaching and research activities playing a critical role. This paper explores integrating the ARCS model—a framework designed to boost and maintain learning motivation—within an informational mathematics teaching and research approach to elevate teaching effectiveness and student motivation. Over four months, an experimental study involving second and third senior classes from College X compared the impacts of an ARCS-based informational model against traditional teaching methods. Utilizing classroom observations, surveys, and achievement analyses, the study revealed that the experimental group outperformed the control group in employing motivational strategies, teaching efficacy, and fostering student motivation. Specifically, the experimental group more effectively utilized the ARCS model’s four dimensions: Attention, Relevance, Confidence, and Satisfaction. Moreover, the experimental group’s motivation scores surged by an average of 2.48 points post-test, significantly outpacing the control group’s marginal increase of 0.19 points (p<0.05). Additionally, mathematics scores in the experimental group saw a 12% average increase compared to the control group. These findings demonstrate that the ARCS-based informational teaching model significantly boosts student motivation and learning outcomes, offering a novel approach for high school mathematics education.
Virtual Reality-based Visualization of Optical Illusion in the Art and Design Field
Authors: Huang Xiaozhou 
Published: March 2024

Abstract
Virtual reality technology has transformed numerous fields, including art design, where its implications are particularly profound. This study explores the integration of optical illusion with virtual reality to offer novel expressions and perspectives in art. This approach generates unparalleled visual effects by leveraging visual discrepancies and augmented reality. It softens the divide between the virtual and tangible worlds, enriching art design’s creative domain.Our research demonstrates the effective visualization of three-dimensional objects and the streamlined post-processing of images. Employing optical illusion technology, we observed an average enhancement of 37% in the accuracy of categorizing illusions, alongside a notable increase in the perceived depth of images without special equipment. Specifically, the perceived three-dimensional effect of images processed through this technology scored an average of 67.57, significantly from the unprocessed images’ score of 54.83. Furthermore, a case study on ‘No Signal’ design illustrates how dynamic optical illusions can dramatically amplify the visual impact and novelty experienced by viewers.In summary, applying optical illusions grounded in virtual reality technology profoundly enhances artworks’ visual appeal and broadens art design’s expressive capabilities. This advancement holds considerable theoretical and practical relevance, underscoring its significance and potential in art and design.
Optimization of Innovative Path of Physical Education Teaching in Colleges and Universities under Information Integration Technology
Authors: Lai Songyang 
Published: March 2024

Abstract
In the era of swift advancements in information technology, the domain of physical education (PE) at colleges and universities stands at the cusp of transformative change, necessitating innovative approaches to teaching methodologies. This paper delves into integrating information fusion technology within PE to refine instructional strategies and bolster student physical health. Through the employment of surface electromyography (EMG) signal collection, Analysis of motion imagery features, and a comprehensive approach to multi-information fusion, this investigation analyzes the EMG data, heart rates, and motion images of 60 students. The findings reveal that information fusion technology serves as an effective tool for monitoring exercise states and physiological metrics in real-time. A notable aspect of the research is using a Dual Stream CNN-ReliefF algorithm, enhancing the precision of interpreting human motion intentions. The study confirms the method’s accuracy and reliability, as the monitored average heart rate, EMG signals, blood pressure, and body temperature align with readings from specialized medical devices. Consequently, adopting information fusion technology in PE teaching significantly enhances educational methodologies and outcomes. This research underscores the potential of technology-driven solutions in redefining physical education, fostering a more effective and scientifically informed learning environment.
Cultural Inheritance and Artistic Construction of Non-heritage Dance in Northern Anhui in the Era of Artificial Intelligence
Authors: Li Xiaona 
Published: March 2024

Abstract
This paper explores the integration of image processing, motion capture, and virtual reality technologies to digitize and visualize dance. We capture the core dynamics of dance movements by extracting key frames and movement features from dance videos. Our analysis of motion capture data, exemplified by the “Flower Drum Lantern” dance, reveals a maximum vertical foot displacement of 72 cm and hip displacement of 93 cm. Virtual display technology significantly enhances the visual representation and dissemination of dance performances. This innovative approach to documenting and showcasing dance not only aids in preserving and transmitting intangible cultural heritage but also boosts public awareness and appreciation for such heritage.
Informatization Exploration of Wushu Teaching Management Platform in Colleges and Universities under the Concept of Modern Education
Authors: Yuan Xiaohui 
Published: March 2024

Abstract
In the evolving landscape of higher education, integrating information technology into Wushu teaching management presents an opportunity to elevate teaching standards. This paper investigates the development of an innovative platform for Wushu instruction, utilizing cutting-edge video analysis techniques to enhance learning outcomes. By analyzing video data from Hikvision DS-2CD2051-1 webcams with advanced preprocessing methods, we demonstrate the effectiveness of the Single Shot MultiBox Detector (SSD) algorithm in improving action recognition over conventional CNN approaches. Our results reveal a notable enhancement in the quality of Wushu movement evaluation, evidenced by a 72.02% accuracy rate and a 0.3865 Mean Squared Error (MSE). The deployment of this technology in a university setting not only fosters a significant uplift in student performance but also boosts their engagement and satisfaction levels. This paper contributes to the discourse on educational technology by offering a successful model for the digital Transformation of Wushu teaching in academic institutions.
Diversified and Innovative Practices of Japanese Language Teaching Based on Multi-Cohort Structural Modeling
Authors: Tang Huiqin 
Published: March 2024

Abstract
The traditional teaching mode can no longer meet the diversified needs of current students, so exploring effective teaching methods has become an important issue in education. Language education, particularly Japanese language teaching, is facing unprecedented challenges and opportunities due to globalization. The technique of multi-group structural modeling is used to explore the diversified and innovative practices of Japanese language teaching in this paper. The study analyzes the perceived differences between different groups of students in learning Japanese, and discusses the key factors that affect the effectiveness of learning Japanese. The study utilizes accurate survey data and incorporates teacher-student support, teaching conditions, teacher quality, and language atmosphere. These factors greatly influenced Japanese language learning outcomes, with teacher and student support being the most prominent factor, which explained 22% of the variance in learning outcomes. Structural equation modeling analysis further verified the positive influence of teaching conditions and teacher literacy on teaching satisfaction. The study verified the validity of multicluster structural modeling in the practice of Japanese language teaching innovation through the comparative experiments between the experimental class and the control class, in which the experimental class’s performance in Japanese intensive reading was higher than that of the control class after adopting the innovative teaching path. So, the diversified and creative practice of teaching Japanese based on the multicluster structural model improves students’ learning effectiveness and teaching satisfaction.