bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on ESP Convergent College English Teaching - A Perspective of SMCR Communication Modeling
Authors: Wu Xian 
Published: June 2024

Abstract
Addressing the prevalent challenges in university-level English education, this study integrates the SMCR communication model’s attributes with the specifications of ESP (English for Specific Purposes)-integrated teaching, thereby devising an ESP-integrated model tailored for college English instruction from the SMCR perspective. The research objectives were clearly delineated, followed by the distribution of questionnaires to collect essential data for subsequent analysis. The gathered data underwent rigorous reliability and validity assessments to confirm the integrity of the research findings. Additionally, specialized data analysis software was employed to evaluate the effectiveness of the proposed instructional model. The analysis revealed that the average scores for the three dimensions of teacher knowledge—professional theoretical and practical knowledge, information technology knowledge and its applications, and knowledge of related disciplines and basic culture—were 3.947, 3.824, and 3.931, respectively. All dimensions demonstrated statistical significance with p-values less than 0.05 in the context of ESP-integrated English education. These results suggest a pressing need for university English instructors to enhance their understanding and proficiency in information technology, as well as to deepen their grasp of information technology teaching methodologies. Consequently, this investigation contributes novel approaches to university English teaching, leveraging information technology to enhance both educational outcomes and student engagement in the English learning process at the tertiary level.
Construction of Online Interactive Learning System for University English Driven by Artificial Intelligence
Authors: Wang Peipei 
Published: June 2024

Abstract
With the advent of rapid advancements in information technology, online learning has increasingly gained traction, prompting a multitude of colleges and universities to adopt innovative reforms in their educational delivery methods. This research undertakes the development of a college English online interactive learning system empowered by artificial intelligence. It primarily investigates the interactive behaviors of college students in online English learning environments. Initially, the study employs the K-means algorithm to categorize students based on their distinct learning behaviors. Subsequently, it leverages a recommendation algorithm to fine-tune the provision of English teaching resources online. Furthermore, the study integrates an enhanced Apriori algorithm to extract associations between student behaviors and learning outcomes. This analytical framework underpins the empirical evaluation of the system’s efficacy in actual educational settings, aiming to deepen the understanding of students’ online interactive behaviors. The findings reveal that over 95% of students consistently log into the learning platform on scheduled class days. The metrics for course recommendation success, such as the hit rate, the average inverse rank, and the range of normalized discounted cumulative gain, are reported between 18.37-21.94, 1.87-2.08, and 4.44-4.87, respectively. Additionally, system memory utilization remains stable at 57%-66%, corroborating the system’s operational effectiveness. This research contributes valuable insights and benchmarks for the development of robust online interactive learning systems.
Discussion on the Innovative Mode of Civic and Political Education by Integrating Students’ Behavioural Characteristics
Authors: Xu Dandan 
Published: June 2024

Abstract
How to effectively combine students’ behavioral characteristics and put forward an innovative civic education model is an important content of the current civic education module. In this paper, we collect relevant data from three aspects of students’ consumption, learning, and life behavioral characteristics and preprocess the data. Based on the data on students’ behavioral characteristics, a student profile is established, and a collection of their behavioral characteristics is constructed. Then, the entropy weight method is used to determine the weight value of each student’s behavioral characteristic, establish the stratification of student behavioral characteristics, and construct a model of student behavioral characteristics. In the student behavioral characteristic analysis, the students of University B in Guangdong Province, China, are used as the research object to develop the student behavioral characteristic analysis. In the cluster analysis of consumption behavior, 28% of students with low consumption levels may be experiencing economic poverty. During the winter and summer vacations, there was a decrease in the number of books borrowed, students entering the library, and seat borrowing appointments. The number of students who spent 14 hours a week on the internet and the frequency of accessing it more than three times was 62.01% and 89.76%, respectively. The average value of learning behaviors, except for information use behavior, is below 3.5, which is below average. Finally, by analyzing students’ behavioral characteristics, an innovative mode of civic education is proposed, which provides practical references to guarantee the creative development of civic education.
Application of zeroed neural networks to stability analysis of continuous dynamic systems
Authors: Huang Yun , Hu Jiaxin 
Published: June 2024

Abstract
Modern production processes frequently require steady-state analysis of continuous dynamic systems. Traditional numerical approaches, however, fall short in efficiency when tasked with addressing large-scale or dynamic problems. To tackle the inverse problem inherent in stability analysis, this study presents an innovative approach by integrating a combined excitation function into the foundational zeroing neural network (ZNN) model. This integration constrains the ZNN model, evolving it into an enhanced EZNN model specifically designed for solving the inverse of dynamic complex matrices. Additionally, this paper conducts a rigorous theoretical analysis of the robust performance of the EZNN model when excited by the combined function, both in the presence and absence of noise interference. The model solution process is promoted by using a class of high-dimensional continuous dynamic systems as an example, and numerical simulation experiments are used for validation. Considering the dynamic system satisfying

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\left\{ \matrix{ A(t) = \left( {4 + \sin (2t)\quad 4 - \cos (2t)\quad 5 + \sin (2t)} \right) \hfill \cr C(t) = \left( {\matrix{{\cos (t)} & {\sin (t)} & { - \cos (t)} \cr { - \sin (t)} & {\cos (t)} & {\sin (t)} \cr } } \right) \hfill \cr b(t) = 4 + \cos (4t),d(t) = {(\cos (2t),\cos (2t))^T} \hfill \cr} \right.

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1(x(t),t) obtained by the EZNN model with combinatorial function excitation always remains negative or tends rapidly to 0. The x (t) obtained by the model converges rapidly to an exact solution of the system. Through the discussion of parametric conditions, it is also found that increasing the value of parameter γ increases the rate of convergence of the ZNN model.
Analysis of Literature Education Strategies and Learning Behaviors in the Age of Big Data
Authors: Luo Chong 
Published: June 2024

Abstract
In the contemporary era of big data, the educational landscape is undergoing significant transformations. Literature education, as a vital component, faces both emerging opportunities and challenges. This study develops a framework for analyzing learning behaviors specific to literature education. It employs both an enhanced K-means clustering algorithm and a refined Apriori algorithm for mining data on student learning behaviors. Through cluster analysis and the investigation of association rules, this research explores the interconnections between students’ learning behaviors and their literary education. The findings categorize students into four distinct groups based on their learning behaviors. Students in Category 1 are identified as the most proficient learners, consistently achieving test scores above 85. Conversely, Category 2 students display the least motivation and effectiveness, with their examination scores not exceeding 70. Students in Categories 3 and 4 exhibit comparable levels of performance. Crucially, the analysis reveals that the most significant predictors of students’ literary achievement are their regular and examination scores, with correlation coefficients of 0.627 and 0.653, respectively. This segmentation and analysis of student behaviors facilitate the early detection of atypical learning patterns by educational practitioners, enabling timely intervention strategies to enhance academic outcomes.
Research on the Application of Image Feature Extraction in Mechanical Structure Recognition and Fault Diagnosis
Authors: Niu Zhenhua , Sun Shaokai 
Published: June 2024

Abstract
With the rapid development of modern industry and science and technology, in recent years the fault diagnosis method based on image processing has become a research hotspot in the field of mechanical fault diagnosis. In this paper, image characteristics are extracted from multiple aspects such as image texture, color, shape, etc. A grayscale symbiotic matrix image feature extraction method is proposed. On this basis, the algorithm for extracting gray symbiotic matrix time-frequency image features is designed. At the same time, the algorithm and parameters of mechanical structure identification are optimized to identify and diagnose mechanical faults. The results show that the grayscale symbiotic matrix time-frequency image feature extraction algorithm is able to accurately diagnose the wear-type faults, overwork-type faults, and short-circuit-type fault behavior of the mechanical equipment. All of them are able to obtain more than 80% accuracy, and all of them are able to reach 99.99% accurate detection of mechanical faults, which proves the effectiveness of the method of this research.
Design of online and offline teaching system for piano improvisation accompaniment course for preschool education majors integrating meta-learning recommendation algorithm and CDIO
Authors: She Tiantuo , Chen Chen 
Published: June 2024

Abstract
With the development of the Internet, educational data based on social networks and mobile applications are also emerging, and personalized teaching systems have become a topic that has gradually attracted attention. In this paper, after exploring the meta-learning algorithm based on deep learning, a dynamic graph neural network is used to predict user preference, and a recommendation algorithm model is constructed based on meta-learning. On this basis, combined with the teaching and training mechanism of CDIO, a teaching system for the piano improvisation accompaniment course of preschool education was built, and the performance of the construction system was tested to test its application effect in the piano improvisation accompaniment course of preschool education. It can be seen that the values of each index of the recommended algorithm model in this paper are between 0.382~0.956 under cold and warm start, and the effect is the best. And the MRR@10 accuracy of the model in this paper is up to 0.725. In the professional piano improvisation accompaniment course, the scores of all indicators of the system use effect in this paper are above 4 points, which are higher than those of the traditional system, which proves that the method can improve the use effect of the teaching system and provides practical value for the system design of professional piano improvisation accompaniment course.
Exploration and Practice of Digital Teaching Innovation Path for Art Majors in the Information Age
Authors: Sun Peng 
Published: June 2024

Abstract
This paper proposes a digital teaching innovation path for art majors and builds a digital teaching innovation technology framework with digital teaching resource management and online and offline blended teaching as the main modules. In digital teaching resource management, a SCORM digital teaching resource management model is constructed, and a resource recommendation algorithm is designed in combination with a collaborative filtering recommendation algorithm. In blended teaching, a meta-learning recommendation algorithm is utilized to complete the design of a teaching system for CDIO. Finally, experimental classes and control classes were set up to carry out digital teaching practices using the Fine Arts Painting major at University A in Zhejiang Province, China, as the research object. In the analysis of students’ learning behavior performance, the score of students in the experimental class was 4.373 points higher than that of the control class, with a significant difference (P<0.05). The total drawing performance score of the experimental class students was 78.45, and the overall drawing performance was satisfactory. In the analysis of students’ learning performance scale, all the index values of the experimental class were higher than those of the control class. In terms of teachers’ teaching behavior performance, except for teaching monitoring behavior, the experimental class showed significant differences from the control class in all other teaching behavior factors.
Integration and Innovation of Artificial Intelligence and Traditional English Translation Methods
Authors: Tang Hui 
Published: June 2024

Abstract
In this study, we employ autoregressive and neural machine translation models to process natural language. Our exploration of AI translation workflows encompasses three key aspects: the encoder-decoder framework, text feature representation, and translation derivation methodologies. Subsequently, we identify the core evaluation indices essential for assessing intelligent translation systems. An innovative translation model is constructed by amalgamating AI-driven and traditional English translation techniques. This model is scrutinized for its effectiveness through various lenses, including the quality of English-to-Chinese poetry translations and manual evaluations. Comparative analysis of the proposed translation method with other extant methods across diverse datasets reveals superior performance metrics: the BLEU score consistently exceeds 4.5 across all three test sets, and the METEOR score ranges between 4.3 and 4.6, surpassing competing methods. Additionally, translation accuracy for sentences of varying lengths in the source language is maintained between 85% and 96%. The objective of this research is to conduct a comprehensive examination of the integration of AI English translation with conventional methodologies, aiming to foster innovative theoretical contributions and practical advancements in the field.
Application of augmented reality technology in visual design of cultural product packaging
Authors: Tian Jing 
Published: June 2024

Abstract
Integrating intelligent terminal devices into product packaging to amplify brand influence represents a significant trend in contemporary packaging design research. This study introduces an augmented reality (AR) cultural product packaging design model predicated on the principles of AR packaging visual information interactivity and content interaction. This model facilitates a precise comprehension of shifts in user preferences for cultural product packaging. It primarily employs computer vision technology for AR tracking and registration, ensuring accurate overlay of virtual objects onto real-world spaces. Within the scope of this model, user experience metrics—ease of use, interactivity, and satisfaction—were quantitatively assessed, yielding scores of 14.523, 13.49, and 13.918, respectively, with variance values spanning from 0.031 to 0.1418. Notably, users rated the packaging design creativity of cultural product A, which utilizes this model, highest at 6.66. This demonstrates that the AR cultural product packaging design model delivers a stable and impactful user experience. Consequently, this research provides a robust and productive framework for advancing intelligent and interactive product packaging designs.
Construction and Application of University English Teaching Evaluation System - Based on the DPSIR Model
Authors: Wang Lixin 
Published: June 2024

Abstract
The establishment of the university English teaching evaluation system is to give full play to the guiding, diagnostic, and feedback role of evaluation, to realize the evaluation for teaching evaluation for learning, and to promote the overall development of students. In this paper, the drivers and pressures of teachers and students in the process of English teaching are taken as the basis of evaluation, and the evaluation index system of university English teaching is established in combination with the DPSIR model. Taking the English teaching data of Chinese universities from 2010 to 2023 as the research object, the entropy weighting method was used to calculate the weights of evaluation indexes, and the TOPSIS comprehensive evaluation model was combined to explore the comprehensive development level of university English teaching. The obstacle degree model was used to analyze the obstacle degree of university English teaching evaluation. It was found that the weight of the driving force subsystem in the evaluation system of university English teaching was 0.2036. The level of university English teaching increased nearly 1.3 times between 2010 and 2023. The highest cumulative degree of obstacle was the mean score of final English grades (R1), whose average degree of obstacle reached 18.15%. The construction of a university English teaching evaluation system requires teachers to optimize their teaching methods and enrich the teaching content so as to promote the internalization of students’ English knowledge and ensure a scientific and reasonable English teaching evaluation system.
The Role and Function of Artificial Intelligence and the Metaverse in Smart Libraries
Authors: Wang Xuetao , Gao Yan 
Published: June 2024

Abstract
The swift advancement of information technology has catalyzed the emergence of diverse new technical tools, presenting both significant opportunities and formidable challenges for university library service innovation. This study harnesses meta-universe technology to explore its integration with library services and to establish an architecture for an intelligent library system. It employs the trilateration positioning algorithm, among other technologies, to enhance the precision of user-targeted services within the smart library. Furthermore, the similarity of user preferences is calculated using a collaborative filtering algorithm, which is then utilized to refine the system’s recommendation mechanisms. A comprehensive research design was developed, incorporating empirical methods to evaluate the smart library system’s effectiveness from the perspectives of user satisfaction and willingness to engage with the technology. Among the ten higher education institutions evaluated, the ninth institution displayed the most favorable user experience, achieving a score of 0.757. Additionally, the study found a significant positive correlation between the variables measuring users’ willingness to use the system, ranging from 0.1 to 0.7. Notably, the strongest correlation was observed between concerns about privacy and behavioral intent, with a correlation coefficient of 0.6598. These findings underscore the critical importance of prioritizing user privacy in the development of intelligent library systems.
Study on the Relationship between Economic Growth and Environmental Pollution in Shanxi Province Based on EKC
Authors: Wang Yuxin 
Published: June 2024

Abstract
China’s rapid economic development since the reform and opening up has attracted the attention of the world, while a sharp deterioration of the environment accompanies the rapid economic development. Starting from the current situation of economic development and environmental pollution in Shanxi Province, the interaction between economic growth and environmental pollution in Shanxi Province is comprehensively analyzed, and the influence mechanism between the two is revealed. The EKC theoretical econometric model for the relationship between economic growth and environmental pollution in Shanxi Province is constructed and empirically analyzed by combining the multicollinearity test, correlation test, unit root test, and cointegration test to obtain the data required for this study, and at the same time, to determine the research indexes and parameters. The correlation coefficients between GDP per capita and industrial wastewater discharge per capita, industrial exhaust emissions per capita, and industrial solid waste generation per capita in Shanxi province reach more than 0.5, and the Sig values (0.001, 0.004, and 0.005) are less than 0.05, which is a significant correlation, and the R2 coefficient of determination of the fit of the EKC curves of GDP and exhaust emissions is 0.8532, and the expression is y = –0.1169 + 5×102
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2 –1.6×103
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3. This study profoundly reveals the relationship between economic growth and ecological environment and promotes Shanxi Province to achieve the goals of environmental improvement and sustainable economic development.
Artistic Guidance and Emotional Expression Pathways in Piano Performance - Based on Deep Score Characterization Fusion
Authors: Wu Lingcao 
Published: June 2024

Abstract
In the evolving domain of artificial intelligence and deep learning, the pedagogical applications of piano performance— specifically its artistic instruction and emotional expressiveness—have garnered increasing scholarly attention. This research leverages MIDI music file formats to mine necessary data, engaging in a detailed examination of the structural and characteristic elements of MIDI files. To optimize the precision of mathematical models applied in this study, MIDI music data underwent preliminary preprocessing steps, including hexadecimal encoding and feature filtering, which facilitated the transformation of MIDI features into vectors representing emotional attributes. The study introduced the development of a BiLSTM_BLS model tailored to articulate the nuances of piano performance and emotional expression. This model synthesis integrates features reflective of emotional states induced by piano playing. A mathematically derived loss function was utilized to gauge the model’s fidelity, thereby enabling an assessment of its efficacy in the realms of artistic instruction and emotional conveyance in piano performance. Findings indicate a proximity between the emotional states of Anger (0.392) and Stress (0.385), with Depressed (0.422) presenting a slightly elevated distance from these states. Moreover, the BiLSTM_BLS model achieved a performance score of 3.214 in the evaluation of artistic expression, which corresponds to 64.28 on a standardized 100-point scale. This investigation contributes to the enhancement of musical skills and theoretical knowledge among pianists, promotes cognitive development, augments emotional expression, and nurtures creativity.