bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on the Multimodal Integration of Visual Communication Design and Public Art in Digital Perspective
Authors: Chang Li , Cai Dongxue , Liu Zhaohui 
Published: May 2024

Abstract
With the rise of digital media, the visual design of public art has gradually added multimodal interactive experience from purely conforming to information communication. Starting from the multimodal integration of public art visual communication design, this study takes several groups of public art landscape projects in a certain region as research objects to explore the relationship between the multimodal visual design of public art landscape and its public satisfaction. Firstly, the evaluation standard for public art visual communication design is constructed. The sample visual communication design is analyzed using the semantic difference method and then combined with the factor analysis method to provide a comprehensive evaluation. Based on determining the satisfaction effect indexes, the AHP method is used to assign weights to each index. Finally, the satisfaction effect results of the sample visual communication designs are obtained. After utilizing the result equation, the relationship between public art visual communication design and landscape satisfaction effect is explored. The overall appearance of public art visual communication design is presented better, and the impact of multimodal fusion visual communication design is especially prominent in samples 3, 2, and 10. The satisfaction effect of the sample visual communication design is also more satisfactory, with 70% of the sample landscapes scoring above 1.5. Meanwhile, the study verified the positive and significant influence of public art visual communication design on the satisfaction effect of landscapes. The theoretical basis and methodological exploration for the multimodal integration research of public art can be provided by this study.
Optimization Strategies and Efficacy Evaluation of Cross-Language Embedding Model in Teaching English in Colleges and Universities
Authors: Liu Jie 
Published: May 2024

Abstract
With the trend of information technology, contemporary English language teaching is moving positively in the direction of intelligence. This study utilizes the text embedding model to represent language and convert it into a format that computers can process. Through the methods of adversarial training and self-learning training and adding the fine-tuning process to improve the effect of cross-linguistic word vectors, the machine reading comprehension and translation model is constructed to ensure the semantic consistency between it and the input source text. Meanwhile, this study combines the model to implement an innovative teaching model of English in colleges and universities and evaluates its effectiveness. The results show that the effectiveness of the proposed method in this paper on this indicator has been narrowed to a gap of 11.48%, which significantly confirms the effectiveness of the proposed model. In the direction of E2C, the cross-linguistic embedding model has the highest average F1 score (0.894). The average score of class 1 results is 82.6843, corresponding to a critical confidence level of 0.036<0.05, which indicates that there is a significant difference between the results of class 1 and class 2. The reference value of this study is important for English teaching and related system development in colleges and universities.
Research on the application of vocal music teaching methods and techniques in colleges and universities based on proportional integral differentiation algorithm
Authors: Ye Jing 
Published: May 2024

Abstract
With the continuous development of modern information technology, the combination of intelligent audio processing technology and vocal music teaching has gradually become a research hot spot. In this paper, we first build a vocal music teaching system based on music emotion and instrument recognition, optimize the support vector machine using the PSO algorithm, construct the music emotion recognition and instrument recognition method based on SVM, and control and optimize the vocal music teaching system through multi-objective proportional integral differentiation algorithm. Through the comparison experiments of different models of music emotion recognition and musical instrument recognition, the performance of music emotion recognition and musical instrument recognition of this paper’s model is explored. Then, the application effect analysis of the vocal music teaching system is carried out. The results show that the SVM model optimized by PSO has a more satisfactory effect on music emotion recognition, with a recognition accuracy 16.67% higher than the comparison model and an average adaptability of 70%–90%. In addition, this model has a higher instrument recognition rate of 18.17% and 7.45% compared to the other two models. After using the vocal teaching system, 63.04% of the students thought that it could promote learning, 47.83% of the student’s classroom interest increased, and more than 70% of the students were more satisfied with its functions. In this paper, the vocal teaching system can be applied to college vocal teaching to promote the improvement of the vocal teaching effect.
Characteristics and Optimization of University English Translation from a Cross-cultural Perspective-Based on Functional Translation Theory
Authors: Zhou Xi 
Published: May 2024

Abstract
Drawing on the theoretical framework of functional translation, this study presents a multifaceted exploration of university English translation through functional, constructive, evolutionary, and cultural lenses. Positioned within a cross-cultural context, the paper categorizes university English translation into three primary attributes: the frontier nature of translation, linguistic and cultural divergences, and the professionalism required in translation activities. Utilizing Super Decision software, these attributes serve as input variables to construct a Network Hierarchical Analysis model. This model is further integrated with the Interpretive Structural Modeling (ISM) recursive structure to assess the influence of various factors on translation quality. The study employs a multidimensional approach to dissect the impact of cultural and professional variances on the quality of university English translation. The results reveal that 54 out of 65 cultural dimensions (83.08%) significantly affect translation quality. In the realm of professional characteristics, four out of six dimensions (66.67%) are found to have a considerable influence on translation quality. Based on these comprehensive findings, the paper proposes four strategic optimizations aimed at enhancing the quality of university English translation, thereby offering valuable insights and practical recommendations for the field.
Research on the Application of Knowledge Graph in Demand-Side Flexible Resource Profiling and Aggregation Techniques
Authors: Yang Chao , Wang Libin , Feng Lida , Xu Lei , Yao Tao , Lv Yuntong , Lei Shuya 
Published: May 2024

Abstract
This paper commences by assessing the current landscape of power system development, focusing on the theory, principles, and structures of demand-side flexible resources and their aggregation technology. Utilizing network crawler technology within a knowledge graph framework, the research data pertinent to demand-side flexible resources and aggregation technology are extracted. These data undergo a meticulous cleaning process before being stored, culminating in the development of a knowledge graph tailored to the imaging and technology of demand-side flexible resources. The findings reveal a response rate of 7.25% ± 1.15%, with an uncertainty interval of 2.33%. Variations in air-conditioning load states appear to exert minimal impact on the response time lag. Following the issuance of a response signal, all systems can rapidly initiate appropriate response actions, demonstrating an uncertainty interval of approximately 52s±11s and 22s. The duration of the responses averages around 75s±11s, with an uncertainty interval of about 30s. This study fulfills the power system criteria for standards of demand-side flexible resources and augments the competitiveness of China’s power market.
5G Network Performance Optimization Strategies for Smart Grids
Authors: Yao Honglei , Liu Xin , Sun Mengqian , Zhao Fuhui , Xu Shanjie , Liu Donglan 
Published: May 2024

Abstract
In this paper, a network slice resource allocation algorithm based on delay sensing is proposed for the problems of service delay and QoS reduction, and resource utilization reduction caused by unreasonable network slice resource allocation. MIMO technology is being studied to establish a communication system that supports multiple inputs and outputs for channels. A resource preallocation method for 5G edge computing is proposed by reasonably allocating resources in the 5G edge computing environment. Then, the performance of the 5G network is optimized, and the optimized network performance is analyzed based on case studies. The results show that the buffer depth of the optimized 5G network is reduced by 1.25, the network delay is reduced by 0.02s, and the network throughput is significantly improved. This study provides the theoretical basis and technical support for the further development of a smart grid in a 5G environment.
Applying Knowledge Mapping Techniques to Enhance Contextual Understanding and Accurate Correspondence in Translating Metaphorical Information in Japanese Literature
Authors: Yue Shengting 
Published: May 2024

Abstract
Metaphorical information is prevalent in literary texts, and understanding the cultural and historical contexts of Japanese literature can significantly enhance the translation of its metaphors. This paper introduces a knowledge graph specifically designed for translating metaphorical content in Japanese literary works. This graph facilitates the categorization, extraction, and storage of symbolic information. To refine the knowledge graph further, the RAEA model is employed, coupled with a distinct attention mechanism assigned to each node of symbolic information. This approach significantly improves the entity alignment in translations, thereby enhancing translational accuracy. In constructing a dataset that includes background symbolic information, the knowledge graph demonstrated a precision of 0.9256 and a recall of 0.9136. Additionally, the graph achieved an average Mean Reciprocal Rank (MRR) index of 0.810, markedly surpassing traditional translation models. This paper’s knowledge graph thus offers an effective methodology for the precise translation and interpretation of symbolic information in Japanese literature, aiding in the deeper comprehension and accurate representation of such content.
The Construction of Teaching System of Foreign Language Courses in Colleges and Universities Based on the GFKE Model
Authors: Bian Yuzhu , Li Yanfang 
Published: May 2024

Abstract
Under the guidance of foreign language curriculum design theory, foreign language courses in colleges and universities should construct a foreign language course teaching system from the aspects of reconfiguring the course objectives, updating the course content, and designing a diversified evaluation system. This paper combines the 5Ds framework with the GFKE model to establish the teaching system of foreign language courses in colleges and universities, designs the blended teaching mode of foreign language courses in colleges and universities, and constructs the corresponding course evaluation index system. Taking Z University in H province as the research object, the relevant teaching process of foreign language courses was designed, the coefficient of variation method solved the weights of evaluation indexes, and the teaching effect of foreign language courses in universities was comprehensively evaluated by set-pair analysis. On this basis, a validation analysis was carried out using the dimensions of teaching effect and teaching evaluation. The reading scores of students in the Experiment 1 class went up by 3.31 points, and the average score of students’ writing test scores went up by 53.86%, as per the findings. In the posttest of the depth of knowledge of English vocabulary, the experimental 1 class scored 25.83 points, which was 45.28% more than the control 2 class’s score. Blending foreign language courses based on the GRKE model can promote the improvement of students’ foreign language learning ability, and teaching evaluation results will be more accurate.
Improvement of Students’ Sports Performance in VR-Based Smart Sports Teaching in Colleges and Universities
Authors: Li Dongpeng , Sun Yan 
Published: May 2024

Abstract
In the complex landscape of educational information, the integration of Virtual Reality (VR) technology into secondary school physical education presents a promising avenue to enhance both teaching quality and efficiency. This article delineates the construction of a VR-based smart sports classroom, focusing on the overarching structure of the teaching program and the pivotal teaching processes. It discusses methods for analyzing and editing sports teaching videos and details the integration of VR with sports posture estimation. This includes the development of a virtual human body motion imaging model and corresponding motion-solving algorithms. An empirical study was conducted within a secondary school sports classroom to assess the feasibility and impact of VR technology on physical education. The study involved a comparative analysis of physical quality between an experimental group taught using VR technology and a control group taught using traditional methods. Additionally, the study examined changes in learning interest pre- and post-intervention in both groups. Statistical analysis revealed that the p-values for the three evaluated physical tests—20-meter sprint, shuttle run, and 30-second rope skipping—were all above 0.05, suggesting no significant difference in physical performance due to the VR intervention. However, significant improvements were observed in the dimensions of sports participation (p < 0.05), positive interest (p = 0.013 < 0.05), and focus on sports (p > 0.05), indicating an enhancement in students’ engagement and attitudes toward learning. Conversely, the negative interest dimension also showed significant changes (p < 0.05). These findings underscore the beneficial role of VR technology in enriching the learning experience and potentially improving academic outcomes in college-level physical education.
Design of physical function monitoring system for athletes in soccer training in college sports
Authors: Ji Bin , Sun Xiaolong , Wang Zhen 
Published: May 2024

Abstract
A comprehensive assessment of the physical capabilities and attributes of soccer players is essential for effective college soccer training. The ability to implement targeted tactical training relies on a thorough understanding of each player’s physical condition. This paper presents a monitoring system for soccer players’ bodily functions, developed using inertial sensor technology alongside Beer’s Law, tailored to recognize human body movement states and to measure blood oxygen and heart rate. The system’s efficacy in movement recognition and physiological monitoring was evaluated through comparative testing, assessing its performance against established algorithms. Notably, this system demonstrated a 9.75% improvement in the average accuracy for recognizing walking and running actions compared to the Juha Parkka decision tree algorithm and a 6.5% increase in accuracy over the D.M. Karantonis algorithm for walking action recognition. Heart rate measurements incurred an error of approximately ±2 BPM, with a maximum error of 1.7, and oxygen saturation monitoring displayed an error margin of ±2%, with a maximum deviation of 1.6. These deviations fall within acceptable limits. The results of this study furnish valuable data to support the customization of training content and intensity for college soccer athletes and offer insights for optimizing player selection and tactical strategies during matches.
Digital finance and regional systemic financial risk forecasting
Authors: Tang Enlin , Bai Chunsong , Qin Teng 
Published: May 2024

Abstract
The abrupt and destructive nature of systemic financial risks underscores the imperative for modern societies to prioritize early detection and prevention over post-crisis management. This paper introduces a dual-perspective early warning indicator system for regional systemic financial risks, encompassing both macro and market dimensions. It further enhances this system by incorporating the ReliefF algorithm into the feature selection phase of a modified random forest model aimed at predicting regional systemic financial risks. The model’s efficacy was assessed using data spanning from 2009 to 2023, enabling dynamic early warning evaluations of regional systemic financial risks. The model was also employed to project the risk landscape for 2024. The results demonstrate superior performance metrics for the random forest model, with an accuracy of 0.9909, precision of 0.9847, recall of 0.9871, and an F1 score of 0.9785—outperforming three comparative models. Notably, during years of significant systemic risk (2008, 2010, and 2013), the model’s predictions exceeded 0.8, while in 2015 and 2020, they surpassed 0.9. For 2024, the model predicts a higher likelihood of maintaining a “normal” state of systemic financial risk in China, with probabilities ranging between 0.3 and 0.5. This study thus offers robust theoretical support for forecasting regional systemic financial risks.
Information technology-assisted analysis of college students’ physical fitness test data and research on physical education teaching reforms
Authors: Xie Bing 
Published: May 2024

Abstract
Enhancing the physical quality of students in higher education institutions is crucial for ensuring the successful completion of their academic pursuits. This article begins by analyzing the fundamental algorithms of decision trees, subsequently building upon this theoretical foundation to develop an ID3 decision tree model. It employs the decision tree model for data classification, extraction, and other related processes. By integrating the C4.5 algorithm and further segmentation techniques, a more stable decision tree is derived, allowing for a comprehensive analysis of physical fitness test data. The study’s findings indicate that using rope skipping test data as an example, the analytical model maintains an error margin of 3%. Over four years of physical test data, there was a notable classification of 1% of students into the “excellent” category in 2020, with nearly 20% achieving “good” status. The proportions of “excellent” and “good” students in 2021 saw a significant rise from the previous year. Furthermore, the percentage of students categorized as “failing” in 2023 decreased by 0.88%. To enhance the effectiveness of physical education, reforms could focus on multi-faceted collaborative efforts, reinforcing the application of the ID3 decision tree model in analyzing physical fitness data and targeting interventions to boost physical fitness outcomes.
Translation of Metaphorical Information in Japanese Literature Combined with Text Mining Techniques
Authors: Wang Tingting 
Published: May 2024

Abstract
Japanese literature is renowned for its conciseness and the profound, multi-layered meanings conveyed through its economical use of language and rich symbolic content. This paper explores the creative impact of these linguistic features in Japanese literature by analyzing the deep meanings and varied classifications of literary metaphors. The construction of a Japanese literature corpus involved the collection, cleaning, and organization of Japanese literary texts, followed by the selection of specific linguistic features. The corpus was then aligned using a dynamic programming approximation algorithm. A semantic representation model of the Japanese literature text corpus was developed by integrating the Attention Word Embedding Model with the Extended Topic Information Model, and metaphoric corpus features were fused using the static fusion strategy of Extended Topic Information and Attention Word Embedding. This study also examines the translation of symbolic information in Japanese literature, focusing on linguistic and stylistic features. The findings reveal that translations into Chinese tend to employ augmentation translation, resulting in 1,706 additional sentences compared to the original Japanese texts. Furthermore, the lexical density in the Chinese translation reference corpus is 2.45 percentage points higher than in the Japanese literature corpus. Analysis of verb modifications shows that among the 90 altered verbs in the Chinese translation corpus, only 24 have corresponding instances in the original Japanese texts. Text mining techniques were employed to conduct a deep analysis of symbolic content, enhancing the understanding of the nuanced connotations inherent in Japanese literature.
A study of applications in archive management empowered by artificial intelligence
Authors: Wang Yue , Wu Changjing , Li Wenjie , Wang Zhen 
Published: May 2024

Abstract
As a critical component of information management, the development of digital and information archive systems enhances both the efficiency and quality of enterprise operations, thereby increasing economic value. This paper employs the ReLU activation function to augment the non-linear capabilities of the neural network model. Additionally, we introduce the MP-CNN model, designed to extract and recognize textual content from archival images. These images are initially processed using the BP neural network algorithm, followed by grayscale conversion for clarity, and subsequently backed up for analytical purposes. The implementation of intelligent archive management resulted in a more than 35% improvement in efficiency. The distribution interval of the Return on Investment (ROI) values from archival data at the test site ranged between [0,5], displaying a concentrated pattern that aligns with the typical economic value distribution. The integration of AI technology in archive management not only streamlines processes but also maximizes resource utilization and enhances returns.
Study on the historical landscape evolution of the core reserve of Guangfu Ancient City based on computer simulation
Authors: Li Qinghong , Xu Jiaxuan 
Published: May 2024

Abstract
In the conservation of the historical landscape within the Guangfu Ancient City Core Protection Area, traditional descriptions via drawings and textual narratives often exhibit subjective biases. To address this, a theoretical model for the historical architectural landscape has been developed to objectively measure and characterize the spatial patterns and morphologies of the landscape. This study introduces Exploratory Spatial Data Analysis (ESDA) techniques, utilizing spatial weights and spatial correlation measures as primary analytical tools. Additionally, meta-cellular automata are employed to simulate the spatial and temporal evolution of the historical landscape. Geographical Information Systems (GIS) and Remote Sensing (RS) technologies have been leveraged to establish a spatial database and facilitate the visualization of the historical landscape’s evolutionary processes. This integrated model and technological approach enable a detailed analysis of the architectural landscape characteristics and the spatial structure’s evolution within the study area. The findings reveal distinct spatial distribution characteristics, with an undulation index ranging from 5 to 20 and a predominant index over 7, accounting for 15.67% of the land. Based on these insights, the study proposes strategic interventions to safeguard the historical integrity of Guangfu Ancient City’s core conservation area.