bluuebunny/crossref_metadata_2025_split huggingface dataset:
The practice of multi-criteria decision analysis modeling in decision support for landscape planning
Authors: Xu Yiwen , Chang Jiang 
Published: May 2024

Abstract
In this paper, after constructing the index system of garden landscape planning, the entropy weight method and hierarchical analysis method are used to combine and assign weights to each index. Using a multi-criterion decision analysis model to establish the decision matrix of landscape planning alternatives, the weighted normative value of each indicator is obtained to obtain the weighted normalization matrix for landscape planning. According to the matrix analysis to get the positive and negative ideal solution of each index, calculate the utility function value between each alternative and the perfect solution, and get the optimal decision-making scheme of landscape planning. The analysis of the three options for landscape planning found that Scheme S2 has the largest utility function value of 0.642, which is considered the optimal scheme. The analysis of the application effect of this scheme found that the proportion of garden green space in the Xinghua garden landscape area increased by 16.23 % compared with 2010, and the proportion of enterprise and institutional land decreased by 5.04 %. The analysis results of this paper show that the multi-criteria decision analysis model plays a highly supportive role in the decision-making of landscape planning, and lays a foundation for the decision-making of the optimal scheme in future landscape planning.
Effectiveness Analysis and Optimization Path of AI Tutoring Models in Foreign Language Learning Applications
Authors: Yan Jing 
Published: May 2024

Abstract
The advent of informatization and intelligent systems in education holds substantial practical importance. This study focuses on evaluating the application effects of an AI tutoring model in extralinguistic learning, using a middle school as a case study. Two parallel classes were selected to design teaching experiments aimed at assessing the AI tutoring model’s efficacy by comparing students’ language performance before and after its implementation. Furthermore, factors influencing the willingness to use the AI tutoring model were identified, leading to the construction of a structural equation model and the formulation and testing of related hypotheses. Results indicate that the AI tutoring model significantly enhances students’ language learning outcomes, with a performance increase of 12.02 points post-instruction, which is 10.19 points higher than that observed in the control group. Variables such as performance expectation, hedonic motivation, willingness to communicate, and interaction with AI demonstrated a positive and significant impact on attitudes (p < 0.05). They indirectly fostered a sustained desire to engage with the AI tutoring model. This research offers valuable insights for the future effective integration of AI into extralinguistic educational contexts.
An Economic Analysis of the Impact of Digital Currencies on Land Asset Securities Transactions
Authors: Gao Fan 
Published: May 2024

Abstract
Dilemmas in financing, risk control, and liquidity have arisen in the face of the current land system, and land securitization can provide a way of thinking to solve these problems. This paper proposes four financial instruments for land asset securitization. It calculates the transaction pricing of that land asset security by splitting it into a composite of independent financial derivative values based on the risk-free arbitrage theory in financial engineering. Finally, based on the multiple fractals and volatility spillover index method, the impact of digital currency on the trading dependence and risk spillover of land asset securities when land is securitized as an asset is studied in depth. The results show that the fractal characteristics of land stocks are 0.756, and land bonds are 0.737. The multiple fractal characteristics of the cross-correlation of different land security assets are differentiated, which indicates that the impact of digital currency on the land security assets among them is stronger in the long memory of small fluctuations. The inverse persistence of large fluctuations is stronger. The risk spillover results show that there is a small risk correlation between digital currencies and land securities transactions.
A study on the application of quantum computing in improving the efficiency of English language learning
Authors: Lei Fei 
Published: May 2024

Abstract
The rapid development of information technology has brought new opportunities to the field of language learning and formed new learning methods. In this paper, a content-based quantum recommendation algorithm is first proposed to calculate the preference attributes of English learners using the Hamming distance and then combined with the constructed dynamic interest model of learners to complete the personalized recommendation of English language learning resources. The algorithm is employed to build an English-language customized learning system that incorporates learning, resource management, and proficiency assessment functions, and performance testing is carried out simultaneously. Furthermore, the study utilizes the system to conduct application experiments to evaluate its improvement in English language learning efficiency. The results showed that the mean value of perceived satisfaction with the improvement of English language efficiency in classes using the system was 7.48, and the ANOVA results (F=3.88, p<0.5) indicated a significant difference from the traditional method. Among them, the performance enhancement rates of superior and intermediate students were more than 45% and 62%, respectively, which effectively enhanced the efficiency of English language learning. After this study, it can provide a high reference value for the application of quantum machine algorithms in the field of education.
Accurate Building Energy Management Based on Artificial Intelligence
Authors: Li Qiang , Zhu Jingjing , Xiao Qiyan 
Published: May 2024

Abstract
Artificial intelligence (AI) plays a pivotal role as a technical facilitator in the domain of building energy management. This paper presents the construction of a sophisticated model for building energy management, leveraging data-driven techniques and machine learning methodologies inherent to AI. The model encompasses three integral modules: characterization of building energy consumption, screening for anomalies in energy consumption, and forecasting of energy usage. Characterization is performed through a data analysis approach. At the same time, anomaly detection utilizes a Gaussian mixture model for data mining and modeling, enabling dynamic and precise identification of states of high energy consumption following clustering analysis. Additionally, the model employs cyclic features and DEEM to accurately predict future energy consumption patterns. The efficacy of this robust energy management model was validated through a case study at Hospital A, located in Changsha, Hunan Province, China. Notably, the total energy consumption at this facility witnessed a modest increase of 1.65% in 2023 compared to the previous year, with a projection of a substantial 24.62% rise in 2024. The surgery building, among various functional units of the hospital, was identified as the most energy-intensive, anticipated to utilize 4.03 million kgce in 2024. Furthermore, the disparity between actual electrical energy and oil consumption in 2023 and those forecasted was contained within 8% and 10%, respectively, underscoring the model’s high predictive accuracy.
The use and effective analysis of vocal spectrum analysis method in vocal music teaching
Authors: Zhang Bo 
Published: May 2024

Abstract
As computer science and technology continue to evolve and become more pervasive, their application in analyzing the audio spectrum of vocalizations offers valuable insights for vocal music education. This study introduces a method utilizing Fourier transform analysis to examine time-frequency domain signals in vocal teaching. Initially, voice frequencies are collected during vocal music instruction. Subsequently, these frequencies are processed to extract characteristic sequences, which are then reduced in scale to develop a model for voice spectrum recognition tailored to vocal music education. This model facilitates detailed spectral analysis, enabling the investigation of its auxiliary benefits in vocal music teaching, particularly in identifying prevalent instructional challenges. Our findings indicate that during training on vowels “a” and “i,” professional singers’ pitch at 4kHz declined to between −15 and −18 dB, whereas students’ pitch varied around ±6dB, trending upwards. In cases of air leakage, significant gaps were observed at frequencies of 5500Hz, 10500Hz, and 14500Hz. At the same time, students exhibited missing frequencies at 7kHz, 12kHz, and 14kHz during glottal tone production, with pronounced, abrupt peaks occurring when vocal folds were tightly constricted and devoid of excessive links. This research substantiates the theoretical and practical benefits of digital spectrum technology in enhancing vocal music education, thereby providing a scientific and supportive role.
Research on Information Technology Innovation and Rural Revitalization Strategies of Agricultural Complex Teaching and Research Bases in Colleges and Universities
Authors: Liu Xiqian 
Published: May 2024

Abstract
The construction of agricultural composite teaching and research bases plays an important role in promoting rural revitalization and strengthening agricultural education. This paper emphasizes the role of agricultural information technology innovation in rural revitalization and proposes the impact of agricultural technology innovation on the development of rural revitalization. The variables of agricultural technology innovation as well as rural revitalization are selected, the benchmark regression model is set, the local impact model is set for the seven secondary indicators under agricultural technology innovation, and the evaluation index model is constructed. Through the empirical analysis method, the model is sequentially subjected to preliminary analysis and nonlinear characterization. Benchmark regression test using the fixed-effects model, model (7) shows that agricultural technological innovation at the 1% significant level of β =2.6548, P < 0.01, indicating that agricultural technological innovation has a positive and positive impact on rural revitalization. The threshold test shows that when the level of financial activity is lower than the threshold effect of 0.0965, the coefficient of the impact of agricultural technological innovation on rural revitalization is 0.0458. The effect of agricultural technological innovation on rural revitalization is affected by the intensity of financial activity, and there is a moderating effect of economic activity on both.
The Legal Effect and Application of Smart Contracts under the Administrative Law System
Authors: Ma Wenzhe 
Published: May 2024

Abstract
As an important cornerstone of future digital civilization, smart contracts are being increasingly used in blockchain finance in innovative practice, and whether smart contracts have legal effect has become a question that academia must answer. This paper discusses the legal impact and application of smart contracts under the contract layer in the blockchain architecture, with a vision of an administrative legal system. Based on the operation principle of smart contracts, a method for detecting smart contract vulnerabilities that combines Glove word embedding and the Shapelet-Transform algorithm is proposed to improve data security during the fulfillment process. Finally, the smart contract model constructed in this paper is applied to the supply chain of agricultural products, and the legal effects of the smart contract are analyzed using practical examples. The transaction volume of unqualified agricultural products decreased from 19.4545 to 13.4655, which is lower than the production volume. The credit index of the smart contract model has increased, resulting in a shortened node performance time from 0.25s to 0.2s.
The Intersection of Art and Technology: Innovative Thinking and Practice in the Perspective of New Media
Authors: Mao Xiangyi 
Published: May 2024

Abstract
The swift advancement of science and technology has emerged as a pivotal catalyst for artistic innovation, with immersive art displays becoming increasingly prevalent as a favored mode of creative expression. This paper conducts a thorough analysis of the flow dynamics within immersive art display spaces, utilizing the eight-channel cardio fluid flow model as a foundation. By integrating the immersive communication effect model, this study further investigates the communicative aspects of these spaces. The implementation of human-computer interaction technologies not only enhances visual presentation but also facilitates the design of immersive art spaces through advanced scene modeling techniques. This design process includes optimizing the viewer’s perspective by incorporating visual perception parameters and the grid saliency MS algorithm. Additionally, this research undertakes a quantitative evaluation of interactive applications at the nexus of art, science, and technology, focusing on audience demand, interactive experiences, and recognition. The findings reveal a significant increase in audience demand for digital media interaction, from 12.64% to 31.29%, and an impressive average tolerance score of 84.32 for immersive art spaces. Comments related to these spaces uniformly express positive recognition, underscoring the successful integration of science and technology in art displays. This integration, as demonstrated, fosters a diverse evolution of artistic expression forms.
Construction of evaluation system for students’ practical ability in sports training in colleges and universities
Authors: Guan Huiyin 
Published: May 2024

Abstract
Sports training is the principal pedagogical approach to achieving educational objectives in college-level sports courses, where the demands on students’ practical sports skills are increasingly stringent. This study formulates an evaluation index system for the sports practice abilities of college students tailored to the contemporary requirements of sports training in higher education institutions. The entropy value method was employed to determine the weights of the indices in this system. Using colleges as a case study, the gray correlation model was applied to assess the comprehensive practical abilities of students, leading to the development of targeted sports training optimization strategies. Additionally, the effectiveness of these strategies was evaluated. The findings indicate that the weight coefficient for sports character is the most significant at 0.41659, highlighting its substantial role in overall practical ability. Beyond basic physical skills, significant improvements were observed in the experimental class’s sports cognition, health behavior, and sports character scores (P<0.05). The practical ability system proposed in this paper serves as a valuable guide for instruction and has proven effective in enhancing students’ practical sports skills.
The Application of SPSS Correlation Analysis in the Study of Precision Teaching of English in Universities
Authors: He Lirong 
Published: May 2024

Abstract
Amidst the swift advancements in information technology, university-level English education encounters both challenges and opportunities for innovation. This study is dedicated to the development of a system tailored for precise teaching strategies in college English, employing students from M-school as the primary research subjects. Subsequently, the relationship between various teaching strategies and the outcomes of precision teaching was investigated through correlation analysis using SPSS. Furthermore, the factors influencing English performance were analyzed using Pearson’s correlation coefficient and grey correlation analysis within multiple regression analyses. The results show that the overall mean score for English precision teaching is 3.5162, which is in the middle of the teaching level. Furthermore, the significant (two-sided) p-values of the factors affecting the level of English precision teaching and English achievement are less than 0.05, which is significantly correlated with English achievement. College students’ English achievement = 0.534+0.142*Teaching objectives+0.195*Learning situation analysis+0.238*Teaching content+0.271* Teaching methods+0.187*Teaching organization+0.185*Teaching evaluation. The study shows that the precise teaching of English in colleges and universities can be optimized in terms of teaching objectives, learning situation analysis, teaching content, teaching methods, teaching organization form, and teaching evaluation.
Study on the development status of the coupling of the digital innovation ecosystem and the digital economy
Authors: Huang Shan , Zhang Jingwen 
Published: May 2024

Abstract
In recent years, the level of China’s digital economy development has been increasing, but the problem of low coordination between the digital innovation ecosystem and digital economy development still exists. This paper constructs the evaluation index system of the digital innovation ecosystem and digital economy development, respectively, and analyzes the development level of the digital innovation ecosystem and digital economy according to the entropy value method evaluation model. To explore the coupling development status of the digital innovation ecosystem and digital economy, the coupling development of the two is analyzed through the coupling degree model and the coupling coordination degree model. According to relevant calculations, from 2017 to 2023, the average development composite index of China’s digital innovation ecosystem 0.3189 increased by 0.4920, and the average development composite index of the digital economy rose from 0.1622 to 0.3524. The coupling degree between the national digital innovation ecosystem and the digital economy increased from 0.2125 to 0.64319, with an obvious increase in the coupling degree. The degree of coupling coordination between the two regions is specifically characterized by a gradual decrease from the eastern areas to the western regions, with certain regional differences.
Using Data Analysis to Improve the Teaching Quality of Civics in College English Courses
Authors: Qi Shulan , Huang Tiebing 
Published: May 2024

Abstract
College English courses, serving both instrumental and humanistic purposes, are a crucial component of general education at colleges and universities. This underscores the pressing need to advance the integration of civics and politics within these courses. This study develops a blended teaching model for incorporating civics and politics into college English courses, employing a structured pedagogical framework of teaching objectives, implementation, and evaluation. It proposes several hypotheses concerning factors that influence the quality of civics and politics instruction within English courses. Using School Y as a case study, a comparative experiment was conducted, and various data analysis techniques— including one-way ANOVA, multiple linear regression, and stepwise regression—were applied to substantiate the hypotheses. The experimental results revealed that students in class A showed an improvement ranging from 5.59 to 9.5683 points over class B. Correlation and regression analyses demonstrated that the selected predictor variables accounted for 88.3% of the variance in teaching quality, with an F-value of 231.158 and a p-value of 0.000, indicating a robust fit and statistical significance of the regression model. All hypotheses were confirmed except for H2, H6, and H7. This research delineates the varying impacts of each factor on teaching quality. It offers concrete recommendations for enhancing civics and politics education in university English courses through a blended learning approach.
Improvement Path of Legal System Related to ChatGPT Application Combined with Decision Tree Algorithm
Authors: Guo Yan , Wang Chengzhen 
Published: May 2024

Abstract
Artificial Intelligence (AI) has been widely used in the social and legal fields, and ChatGPT, after AI painting, has once again set off a wave of discussion on whether AI and its generated works can obtain legal protection. Starting from the theoretical orientation of the origin of ChatGPT legal governance, this paper proposes the legal positioning and layered governance framework of ChatGPT application. It explores the role mechanism of ChatGPT empowering legal modernization and combs through the realistic dilemmas of ChatGPT-generated content data compliance legalization. To effectively analyze legal risks in the process of the ChatGPT application, data crawling technology and SMOTE oversampling technology are utilized to obtain ChatGPT application data and produce datasets. The Stacking integration strategy is introduced to combine the Random Forest in the Decision Tree Algorithm, GBDT algorithm, and Support Vector Machine to construct the legal risk prediction model of the ChatGPT application. For the effectiveness of the model, the ChatGPT application dataset is used to analyze the accuracy, ROC curve, and AUC value, which provides a reference for improving the legal system related to the ChatGPT application. The results show that the accuracy of the SVM classifier reaches 0.839, the correctness of the GBDT model is 0.947, and the AUC value of ChatGPT legal risk prediction based on the Stacking integration strategy is 0.947. Based on the inspiration of the decision tree algorithm, the improvement of the legal system related to the ChatGPT application should be improved in terms of generating content and allocating risk. Based on the insights of the decision tree algorithm, the improvement of the legal system related to the ChatGPT application should be made based on the dimensions of generation content and risk allocation.
Analysis of health state before and after fault self-healing based on a high percentage of distributed distribution grid systems
Authors: Zhou Zhongqiang , Zhang Yun , Ma Jianwei , Jiang Dan , Deng Ruifeng 
Published: May 2024

Abstract
In this paper, we preprocess the equipment data using by exponential smoothing method, fit the fault information by using the distribution model, and evaluate the fitting degree of the function with the help of the K-S test to derive the equipment fault information function. In accordance with the correlation mapping between the distribution network health index and the existence of faults, the health index of the distribution network equipment in the region before and after the self-healing of faults is comprehensively calculated, and the state assessment of the equipment is carried out. Before and after the fault self-healing of the IEEE33 node system, the system loss is reduced by 58.89%, as evidenced by the results. Before and after the fault self-healing, Condition 1: the line health index ranges from 1.317 to 3.777, and the transformer health index ranges from 2.011 to 4.646. Condition 2: the line health index ranges from 2.284 to 4.059, and the transformer health index ranges from 1.653 to 4.047, respectively. And Condition 3: the health indices all increase by varying from 0.711 to 2.067. Localized equipment degradation has a significant impact on the overall health of the network, with lines affecting the system to a greater extent than transformers. To summarize, the proposed health index state assessment method can effectively determine the health state of distribution networks before and after fault self-healing.