bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring English Translation Strategies Oriented by Big Data Technology
Authors: Fan Jinping 
Published: February 2024

Abstract
English translation is not a simple task, and it needs to be translated on the basis of different languages and cultures in order to get the ideal translation effect and promote cross-cultural communication between different languages. In this paper, with the help of web crawler technology to crawl the Chinese-English translation corpus data, and use the inverse document frequency and dynamic planning ideas to align the corpus data, the Chinese-English translation corpus is established. The LDA model is used to mine subject information in the Chinese-English translation corpus, and edge distribution estimation is used to construct the English translation model. For the application of the translation model and the Chinese-English translation corpus in improving the quality and effect of English translation, the word and sentence features, target word blocks, and the comparison of language features of human-computer translation from the Chinese-English corpus are verified in various aspects. The results show that the class/formant ratio of the source language in the corpus is between 56.73% and 75.84%, its average word length fluctuates in the range of [3.91,5.62], and the target word chunks of the English text are mainly based on nominal and verbal structures. The English translation model has a less significant discrepancy in other language features than human translation, except for the difference of 4.526 between sentence lengths. English translation needs to adopt a reasonable annotation method, semantic conversion, and technical tools to improve the quality of English translation.
Research on Clothing Art Innovation by Integrating Traditional Graphics and Intelligent Design in the Age of Intelligence
Authors: You Lingli 
Published: February 2024

Abstract
With the progress of the times, the aesthetics of the public have gradually become diversified, and clothing art design should also keep pace with the times, and carry out the innovation of clothing art design under the integration of traditional graphics. In this paper, the RGB and HSV values of traditional graphics are extracted by the method of color feature extraction, and each pixel is detected by scanning pixel by pixel, and the color characteristics of the graphics are judged according to the pixel points. The roughness and sharpness of the graphic texture are characterized by the gray symbiotic texture features. Combined with the central moments reflecting the distribution of gray levels in the object area relative to the gray level center of gravity metric, the size, shape, and other details of the image are described with the help of higher-order moments of more than three orders. The main direction of clothing art design innovation is analyzed by integrating different features of traditional images into clothing pattern design. The results show that the RGB values in the color design of sweet style clothing should be similar to (255,192,203), (135,206,250), (0,51,0), the entropy value of the graphic texture in the design of clothing art should be >1, and the degree of regularity should be >0.9, so that the regularity and regularity of the graphic is reflected as much as possible. The shape characterization value Φ6 and Φ7 of the image in clothing art design should be 0 to prevent the deformation caused by image scaling and rotation.
Research on Diversified Diversified Teaching Strategies for Music Courses in Colleges and Universities under the Background of Artificial Intelligence
Authors: Wan Lei 
Published: February 2024

Abstract
This paper proposes a music course resource recommendation algorithm by combining the similarity calculation of music courses with the user interest model. The ID3 algorithm in data mining is used to process the collected music course resource data, combined with the machine learning method to realize the parsing of the music course resource files and build a music course resource-sharing platform for colleges and universities based on artificial intelligence technology. Using the platform data, the correlation analysis of the factors affecting the learning effectiveness of music courses in colleges and universities was conducted, and the impact of the number of exercises on the learning effectiveness of music courses was explored. Ten colleges and universities in Guangdong Province were selected to conduct descriptive analyses of college music curriculum resources and music education teaching effectiveness based on the results of music curriculum resources development and utilization, respectively. The results show that the mean values of the dimensions of curriculum resources, text resources, and environmental resources of music education in Guangdong Province are close to the theoretical median, in which the minimum value of human resources is 1.24, the maximum value is 4.27, and the mean value is 2.765. The human resources situation is not good. Diversified teaching should focus on strengthening the human resource component, to improve the teaching effectiveness of music course resources in colleges and universities.
Combined with the Internet technology of college student training mode innovation and student management mechanism optimization
Authors: Yang Xiaolan , Zeng Xinyong 
Published: February 2024

Abstract
Based on the current situation of student cultivation mode and student management in colleges and universities, this paper puts forward six basic assumptions on student cultivation and management, and combines the assumptions to obtain the game payment matrix of student cultivation mode and student management. The dynamic equations of the cultivation and management process are reproduced by using mathematical calculus to derive the game payment matrix. Under the constraints, the game evolution model equilibrium point is tested for stability, the game evolution state is derived, and the game evolution model is completed to optimize the innovation of college student training mode and student management mechanism. Numerical simulation analysis is performed on the evolutionary game of college student cultivation and management based on the parameter settings of the evolutionary game model. The results show that the evolutionary game model will converge to a stable strategy point when the ratio of managers (teachers) and students’ initial participation in cultivation and management activities is set to (0.05, 0.95), (0.05, 0.05), (0.95, 0.05) or (0.95, 0.95). This study has a beneficial impact on cultivating exceptional talents, and it also provides a solid resource for society to deliver top-notch talent.
Research on the Path of Enhancing Students’ Political Identity in Teaching Ethics and Rule of Law in the Information Age
Authors: Zhang Jia , Bian Kaihong 
Published: February 2024

Abstract
Political identity directly affects the implementation of the nurturing function of politics and the enhancement of the teaching effect, this paper proposes a path to enhance students’ political identity in the teaching of Daofa based on the multimodal fusion model. The TF-IDF method is utilized to identify political emotions contained in students’ classroom expressions, and LBP is used to extract students’ facial expression features. Based on the above-extracted results, a multimodal feature learner emotion data model is designed, which is also combined with a deep RNN model to identify and analyze students’ learning behaviors in the actual Daofa classroom. Evaluating students’ political identity from five dimensions, the results show that all students have significant differences in the five dimensions, P<0.05, and their political identity is above 60, of which the highest political identity in the fourth year of college reaches 78.586. Among the states of political identity, the state of maturity type has the highest frequency of occurrence, which is 132 times. The results of the study provide data support for improving students’ political identity.
Enterprise Industrial Structure Transformation and Upgrading and Regional Innovation Capability Enhancement Strategies - An Empirical Analysis Based on VAR Models
Authors: Cui Hongkai 
Published: February 2024

Abstract
The transformation and upgrading of enterprise industrial structure and regional innovation ability are the concerns of social development in recent years. This paper introduces the global entropy method and VAR model to construct the indicators of enterprises’ industrial structure transformation and upgrading and regional innovation ability with the enterprises in X place as samples. The development level is measured through weight calculation and comprehensive evaluation, and after the smoothness test, the impulse response function and variance decomposition are applied to study the dynamic relationship between the two variables, to research the transformation and upgrading of enterprise industrial structure and the enhancement of regional innovation capacity. The study shows that the level of industrial structure transformation and upgrading of enterprises in the cities of X place has increased steadily from 2016 to 2020 as a whole, but only the level of the eastern cities is all over 0.3, which still needs to be further improved. Through the variance decomposition, the variance contribution rate of changes in regional innovation capacity to changes in the transformation and upgrading of enterprise industrial structure increases from 13% to 26.16%, and the variance contribution rate of changes in the transformation and upgrading of enterprise industrial structure to changes in regional innovation capacity decreases from 99.14% to 74.42%. The level of transformation and upgrading of the enterprise industrial structure can be improved to continuously promote the enhancement of regional innovation capacity and further promote the construction and development of the city.
Research on the coupling and coordination index of green finance and high-quality economic development based on matrix modeling
Authors: Hao Yao 
Published: February 2024

Abstract
The traditional way to calculate the coupling index of green finance and high-quality economic development has the problems of ignoring spatial spillover effects and regional heterogeneity. Based on the spatial measurement matrix, this paper calculates the coupling index of the two. Firstly, the index system is established according to the characteristics of green finance and high-quality economic development, then the entropy weight method is used to calculate the weight, combined with the Moran index to calculate the regional differences, and finally, the spatial measurement matrix is used to derive the final results. After the model was established, data on China’s green finance and high-quality economic development from 2016 to 2022 were collected as samples, and the coupling analysis was launched and tested using Granger causality. The calculation results show that Shanghai has the highest level of high-quality economic development from 2016 to 2022, steadily increasing from 0.635 to 0.819 in 2022. The global Moran index has been maintaining a level of 5% for all years. The LR index of SDM/SEM is 51.39. The coupling index of the measure of green finance is 0.1395, and the coupling index of the level of high-quality economic development is 0.1873. The Granger causality assumption chi-square for the uncorrelation of the two is 6.382 and 3.085, respectively, with p-values of 0.004 and 0.091. There is a correlation between them that is positive.
Application and research on the method of analyzing the composition of traditional Chinese medicine based on knowledge mapping
Authors: Wang Lin 
Published: February 2024

Abstract
Modern experimental analysis techniques have the problem of inefficiency in the analysis of traditional Chinese medicine composition. For the construction of a knowledge graph of traditional Chinese medicine ingredients, this paper first proposes a two-way graph convolutional aggregation network model, which is characterized by the node features of the knowledge graph being enhanced by the relationship features in the initial embedding of entities and relationships. The performer model can reduce the computation of the attention score and improve the computational efficiency of the model. Next, the two-way graph convolutional aggregation network model is used to perform the analysis in the TCM-related database. Finally, the results of the analysis are applied to a real case study of traditional Chinese medicine composition analysis. The construction of the knowledge graph for traditional Chinese medicine ingredients resulted in the acquisition of 14 high-frequency keywords. In the results of the compositional analysis of the herbs of C. minor, 13 batches of samples could be clustered into three groups, and 11 common peaks and 4 common components were identified for the 13 batches of samples, which were protocatechuic acid, vanillic acid, caffeic acid, and chrysanthemic acid, respectively. A cumulative variance contribution of 84.853% was obtained after extracting three principal components.
Artistic characterization of AI painting based on generative adversarial networks
Authors: Lu Weiwei , Qi Ruixing , Li Yuhui 
Published: February 2024

Abstract
Combined with the creation process of AI painting art, it analyzes the artistic design characteristics of AI paintings formed by generative adversarial networks. It utilizes a convolutional neural network to extract the artistic characteristics of AI paintings and combines the error of feature loss to calculate the features, which ensures the stable operation of the generative adversarial network model. To achieve the style migration of AI painting artworks, the Cycle GAN model was designed on this basis. Comparing the features of both AI paintings of generative adversarial networks and paintings of human artists, the perceptual complexity is taken as the dependent variable, and a regression model is established to analyze and calculate the complexity features of AI paintings, as well as to analyze the color matching art of AI paintings by combining the beauty calculation method. According to the comparison results, the AI paintings have a score of 3.71 for inspirational, 3.69 for aesthetic value, 3.52 for compositional rationality, and 3.38 for breakthrough. The AI paintings have a high level of thought and inspirational value.
Perceiving and Communicating the Value of Artistic Aesthetics Based on Multiple Intelligences Theory
Authors: Mao Yi , Jia Xiaojun 
Published: February 2024

Abstract
This paper establishes an evaluation system for the value of art aesthetics, and utilizes the hierarchical analysis method to determine the weights of each index. Then, based on the multiple intelligence theory and the affiliation function, we establish a fuzzy comprehensive evaluation method for the cognition and communication of artistic aesthetic value and use the degree of affiliation of each evaluation object in the comprehensive evaluation set as the evaluation value to establish a comprehensive evaluation matrix, to realize the comprehensive evaluation of the cognition and communication of artistic aesthetic value. In the practical exploration, traditional villages and cultural routes are taken as the objects of analysis to analyze the evaluation of all kinds of people on the cognition and communication of artistic and aesthetic values in both. It is found that the villagers have strong value cognition and conveyance identity to their native villages, and the villagers’ evaluation scores of the wholeness, specificity, and aesthetics of village B are higher than those of the experts and tourists, which are 3.6, 4.4, and 4.5, respectively. The five managers of the Meili Snow Mountain Transit Route achieved an average of 4.028 evaluation scores for the perception and communication of artistic aesthetic values in the route, and it was also found that tourists of all ethnicities focused on each evaluation index of artistic aesthetics differently, and Mongolian and Yi tourists evaluated the index of color richness more highly, with 4.93 and 4.99 scores, respectively.
An Exploration of Users’ Intention to Continuously Use English Social Platforms in Colleges and Universities Based on the SOR Model
Authors: Xu Yaling , Wang Wenyong 
Published: February 2024

Abstract
Nowadays, the development of social media tends to be saturated, and maintaining user stickiness is a common problem faced by developers. This paper constructs a research model of users’ continuous use intention of English social platforms in colleges and universities based on the SOR model and makes relevant research hypotheses based on the research model. The English social platforms constructed by M universities are selected as the research objects, questionnaires are designed and collected with the research model and hypotheses, and the data are analyzed with multiple linear regression model, and multiple regression analyses are conducted to analyze the four variables of opinion leader professionalism, content quality, social interaction, and visual design and users’ willingness to continue to use the platforms, as well as to test the hypotheses and mediation effects. The study shows that opinion leader professionalism, content quality, social interaction, and visual design have p-values less than 0.05, and all of them have a significant effect on users’ intention to continue using English social platforms. Except for the positive effect of visual design on trust, and the mediating role of trust in visual design and user’s intention to use did not pass the test, all other hypotheses and mediating effects satisfy the P-value of less than 0.05. Therefore, opinion leaders, content quality, and social interaction all have a positive effect on users’ trust and emotions, which in turn positively affect users’ intentions to continue using.
Construction of Satisfaction Evaluation System for Greenway Environmental Recreation Function in Tourist Attractions Based on Matrix Modeling
Authors: Luo Jinze 
Published: February 2024

Abstract
Greenways have a series of functions such as ecological protection and promotion of economic development, and exploring the needs of greenway tourists for greenway recreation functions has become an important way to promote the comprehensive development of green ecology. To meet this demand, this paper develops an intelligent model that objectively quantifies tourist satisfaction. The TF-IDF algorithm is used to mine tourists’ comments, and the keywords extracted by TF-IDF are used to compare the similarity with the theme words of the implied themes in the LDA model, to solve the defects of the implied themes in the LDA theme model that are not clear. Create a system to evaluate satisfaction for visitor recreation functions that utilize themes extracted from the LDA model. IPA satisfaction analysis is used to process the data mined by LDA to create visitors’ ratings. Subsequently, the model was put into practice greenway facilities maintenance, rest facilities along the greenway, and greenway diversity excursion program words accounted for small weight, respectively 0.0632, 0.0765, and 0.0833, that is, the tourists’ perception of these topics is low. The average value of the sentiment scores of the 10 themes is 174.852. The score for theme 10 is 85.27, which is significantly lower than the average value, which suggests that tourists are dissatisfied with the greenway recreation facilities in the scenic area. Optimizing the quality of greenway recreation and improving the development level of greenways can be achieved by utilizing this paper as a reference.
Bursting pressure estimation for pipeline containing crack-like flaws using a neural network model
Authors: Варшицкий Виктор Миронович , Козырев Олег Александрович , Фигаров Эльдар Намикович 
Published: February 2024

В настоящее время для прогноза величины давления разрушения трубопроводов с дефектами получают широкое распространение подходы, основанные на использовании искусственных нейронных сетей (ИНС). В статье представлен алгоритм и результаты прогнозирования разрушающего давления трубопровода с поверхностным продольным трещиноподобным дефектом на основе применения ИНС. Для автоматизации алгоритма рассматриваются возможности использования готового промышленного программного комплекса нейросетевого моделирования. В качестве исходных данных принимаются результаты натурных испытаний образцов труб с трещиноподобными дефектами, нагружаемых внутренним давлением до разрушения. Представлены основные аспекты обучения ИНС. Показаны преимущества и недостатки прогнозирования с применением ИНС по сравнению с расчетными критериями. Отмечается, что существующие расчетные критерии определения величины разрушающего давления включают размеры дефекта и трубопровода, механические свойства и вязкость материала, но не отражают связь между размером дефекта и напряженным состоянием в дефектной зоне. Применение ИНС позволяет учитывать влияние скрытых зависимостей между размерами дефекта и напряженным состоянием в зоне дефекта на прогноз разрушающих напряжений. По результатам тестирования нейросетевой модели установлено, что разброс значений при прогнозировании давления разрушения с использованием ИНС существенно меньше, чем в случае использования расчетного критерия.
Currently, approaches based on artificial neural networks (ANNs) are commonly used to predict the bursting pressure value for flawed pipelines. The paper presents an algorithm and results of bursting pressure prediction for a pipeline containing longitudinal surface crack-like flaw based on the use of ANNs. To automate the algorithm, the potential for use of the off-the-shelf neural network modeling software package is considered. The results of in-situ tests of pipe samples with crack-like defects exposed to internal pressure until bursting failure are taken as input data. The main aspects of ANN training are presented. Advantages and disadvantages of ANN-based prediction compared to use of predictive criteria are shown. It is noted that the existing predictive criteria for determining the burst pressure value include the flaw and pipeline dimensions, material mechanical properties and toughness, but do not take into account the relationship between the flaw size and the flaw area stress state. The ANN application allows to consider the effects of hidden relationships between the flaw size and the flaw area stress state on the bursting failure stress prediction. Based on the neural network model testing results, it is found that the ANN-based burst pressure prediction data scattering is considerably narrower than that for predictive criterion.