bluuebunny/crossref_metadata_2025_split huggingface dataset:
Strategic Analysis of Profit Structure and Quality Improvement in Digital Transformation of Advanced Manufacturing Industries
Authors: Li Lan , Li Yuping , Xiang Yu 
Published: October 2024

Abstract
Digital transformation is the engine to promote the high-quality development of the advanced manufacturing industry. This paper constructs a measurement model for the development level of digital transformation in the advanced manufacturing industry and proposes a measurement method for the development level of digital economy and digital transformation in the advanced manufacturing industry. The data are preprocessed through text mining, the vertical and horizontal pull-out grade method is used to determine the weights and calculate the final measurement value, and the time weights are obtained with the help of the nonlinear programming method to realize the measurement of the development level of the digital economy. Use the maximum-minimum value method to process the data related to digital transformation, utilize the entropy value method to calculate the index weights, and calculate the digital transformation development level through the linear weighting function method. The representative provinces of each region are selected to analyze the digital transformation of the advanced manufacturing industry in the east, middle, and west regions of China. The composite index of the digital economy development level in the East, Central, and West is 0.62, 0.45, and 0.37, respectively. The value of the digital transformation development level of Guangdong, Shandong, and Sichuan in 2023 reaches 0.9857, 0.3949, and 0.3747, respectively, ranking in the top three, whereas Jilin and Guizhou are only 0.1479 and 0.1128, and the level of digitalization of China’s advanced manufacturing industry is marked by obvious geographical imbalance.
Research on the Optimization Path of College Education and Teaching Based on Big Data Analysis
Authors: Liu Yan , Tuerdahong Nuerlan , Wang Bo 
Published: October 2024

Abstract
Educational big data analytics can provide data support and decision-making reference for the optimization of education and teaching in colleges and universities by mining the daily behaviors of college and university students, searching for the data features that affect college students’ academic performance, and establishing corresponding prediction and early warning models. In this paper, we constructed a college student performance prediction model based on the CNNBiLSTM-Attention algorithm, integrating the advantages of bi-directional long- and short-term memory neural networks and attention mechanisms, introducing the 1-tanh function after the forgetting gate to make the output value of the forgetting gate in a more obvious range, retaining the features of the input data as much as possible, and improving the learning ability of LSTM. In the context of teaching in colleges and universities, we observed that the model in this paper achieved an accuracy of 86.39% and an F1 value of 83.59% on two dataset tasks, Principles of Microcomputer and News Editing, respectively. When predicting the results of the 2022 academic year grade performance points, the prediction accuracy of seven different score levels exceeded 80%. Three students in the experimental class had midterm scores below 40, but after teacher communication and targeted counseling, there were no students with scores below 40 at the end of the semester. In the midterm prediction, no student scored higher than 90. After the teachers adjusted the teaching plan and optimized the teaching arrangement based on the prediction results, six students had scores above 90 at the end of the final term. Students’ scores in all subsections have improved, and the overall distribution of scores has moved significantly toward the higher-scoring sections. From 68 points in the midterm prediction to 72 points in the final test, the number of sub-segments increased the most. The use of big data technology has had an optimizing effect on existing universities’ traditional education and teaching models.
A Data-Driven Study of the Patterns of Influence of Transformational Leadership Styles on Worker Safety Behavior in the Construction Industry
Authors: Song Hongling , Khatibi Ali , Tham Jacquline 
Published: October 2024

Abstract
Safety behavior in the construction industry is the basic guarantee for the safe construction of buildings, and the leadership style of managers has an important impact on the safety behavior of construction workers. To elucidate the mechanism of transformational leadership’s impact on the safety behavior of construction workers, this study first examined the theoretical underpinnings of transformational leadership theory and safety behavior. It then developed a conceptual model of transformational leadership and the safety behavior of construction workers. Finally, it analyzed the correlation and strength of this model between transformational leadership, organizational equity, and safety behavior. The main effect of transformational leadership on construction workers’ safety behavior is analyzed. The mediating variable of organizational equity, which directly affects workers’ safety behavior, is introduced to enhance the mechanism of transformational leadership and construction workers’ safety behavior. The correlation between transformational leadership and safety behavior is 0.89, indicating that transformational leadership has a significant impact on safety behaviors. The regression coefficient of organizational equity was significant at the p<0.001 level of 0.217, testing the mediating role of organizational equity between transformational leadership and worker safety behavior. The data-driven monitoring of workers’ safety behaviors and the research results clarify that transformational leadership facilitates the emergence of safety behaviors among construction workers and ensures the normal operation of construction projects.
Exploring the Innovative Methods of Ideological and Political Education for College Students in the New Media Era
Authors: Bao Shanshan 
Published: October 2024

AbstractWith the continuous progress of social development and the times, ideological and political education faces newer challenges and needs to adapt to the requirements of the new era. Based on the statistical analysis method, this paper explores the innovation of ideological and political education in colleges and universities from the perspective of new media. By studying the impact of new media on ideological and political education in colleges and universities, six research variables were obtained. Using research to obtain research data and combining independent sample t-test, Spearman correlation coefficient to the linear regression model, and other methods to process the data, the innovation of ideological and political education in colleges and universities under the new media perspective is empirically analyzed. The results show that the faculty team, educational resources, educational methods, curriculum content, practical activity system, and teacher-student interaction have a significant positive correlation on the innovation of ideological and political education, with Spearman correlation coefficients of 0.097, 0.576, 0.322, 0.174, 0.756, and 0.708, respectively, and the regression equations are 0.167+0.148*faculty team + 0.116* Educational Resources + 0.159* Teaching Methods + 0.126* Practical Activity System + 0.135* Teacher-Student Interaction, and based on the results of the empirical analysis, we propose an innovative method of ideological and political education.
Structural performance and durability assessment of ultrafine powdered concrete in modern buildings
Authors: Cheng Xu 
Published: October 2024

Abstract
This paper investigates the impact of various ultrafine powders on the flow characteristics and mechanical properties of concrete, with the aim of enhancing its overall performance. Examine the compressive and flexural strengths of cement, establish distinct matching ratios, and clarify the actual mixing quantity of silica fume and ultrafine powder. Analyze the durability of concrete structures and propose causes for carbonation and formation of concrete. Obtain the durability evaluation indexes, propose the variable weight fuzzy comprehensive judgment method, and establish the durability evaluation system for ultrafine powder concrete buildings. When the on-site test results for reinforcement corrosion, concrete strength, carbonation depth, cracks, and damage degree are added together, the values of each assessment vector of the fuzzy comprehensive evaluation are found. This lets us figure out the bridge’s durability grade. The highest compressive degree was achieved by both ultrafine silica-aluminum material and ultrafine mineral fishery powder at a 20% dosage. The submicron silica material achieved the highest compressive strength with a 40% dosage. In the test and analysis of the ultrafine powder concrete bridge building structure, the fuzzy comprehensive judgment grade is III. That is, there is a certain degree of damage state, and measures must be taken, which is consistent with the field test results.
Comparative analysis of the effects of different physical education elective courses on college students’ physical fitness indices
Authors: Huang Zhuanzhuan , Luo Jieya 
Published: October 2024

Abstract
This paper uses mathematical statistics and one-way ANOVA for the data of each index between groups to conduct comparative experiments on the teaching of elective courses of basketball, aerobics, track and field, and badminton with undergraduate students majoring in physical education at University X. The comparative analyses of changes in students’ physical health index before and after the experiments are an attempt to systematically study the extent of the influence of the elective courses of physical education in general colleges and universities on the physical fitness of college students in order to seek a more scientific and more effective curriculum program. A more scientific and effective curriculum program is being sought. The conclusions are as follows: physical education elective courses have a positive impact on students’ physical fitness and provide different opportunities. Students who chose basketball, aerobics, and badminton elective courses improved their seated forward bending, 800m/1000m, and grip strength qualities to a greater extent with significant differences (P<0.05 or P<0.01). Basketball, aerobics, track and field, and badminton all significantly improve lung capacity for both boys and girls. Basketball had the highest impact on the students’ physical fitness index, followed by aerobics.
Quantitative Impact Analysis of Social Network Usage Patterns on College Students’ Mental Health and Preventive Countermeasures
Authors: Jia Zhiping 
Published: October 2024

Abstract
Along with the rapid development of the Internet, social networks have caused significant changes in people’s interaction patterns. The study chose students from a certain university to fill out a questionnaire. The questionnaire data was then analyzed using descriptive statistics, correlation analysis, and mediation effect validation to learn more about how college students use social networks. The study also looked at how college students’ social network use affects their mental health. College students produce positive psychology through friendship quality when using active social networks, and they can also produce partially positive and negative psychology through friendship quality when using passive social networks. There is a significant positive effect of passive social network use on students’ upward social comparison (t = 2.13, Sig. < 0.01) and a significant negative effect on friendship quality and positive psychology (t = -2.58/-2.77, Sig. < 0.01), and it can have a significant positive effect on students’ positive psychology through the chain of upward social comparison and friendship quality. Colleges and universities should provide mental health counseling courses and organize fun activities to guide students to use social networks reasonably, build self-knowledge, and prevent the negative effects of overusing social networks on students’ mental health.
The Promoting Role of Green Finance Reform and Innovation on the Sustainable Development of Low-Carbon Enterprises - An Analysis Based on Recursive Neural Networks
Authors: Shi Lulu , Shang Meng 
Published: October 2024

Abstract
The government has introduced a series of green financial support policies to support the low-carbon technological innovation of enterprises, and it has become an inevitable trend to take the road of high-quality development with clean, low-carbon efficiency and safety. In this paper, in order to further realize the quantitative analysis of green financial reform on the sustainable development of low-carbon enterprises, based on the principles of the evaluation index system construction on the sustainable development of low-carbon enterprises evaluation index system design, to determine the evaluation index level thesis domain, its data dimensionless processing, so that its evaluation indexes are composed of fuzzy relationship matrix. With the help of a recursive neural network, we analyze the low-carbon sustainable development of enterprises in the context of green financial reform. It is found that the squared error of the mean value of the enterprise low carbon sustainable development ability index based on a recursive neural network is 0.044. During the implementation of the green financial reform policy, the low carbon sustainable development ability of the sample enterprises has been improved, among which the growth trend of enterprise D (0.50415-0.44039=0.06376) is more rapid, which strongly confirms that the green financial reform and innovation promote the sustainable development of the low carbon enterprises. The promotion of green financial reform and innovation has been confirmed to have a strong effect on the sustainable development of low-carbon enterprises.
Prediction of Historical Development Trends of Traditional Wushu Culture Based on Data Mining
Authors: Zheng Mingjie , Kong Ruyu 
Published: October 2024

Abstract
This paper first introduces the use of data mining technology in the development of the traditional culture of martial arts. This paper begins with the optimized FCM algorithm, obtains the fuzzy pattern through the affiliation function, and builds a prediction model using the clustering algorithm of fuzzy time series. The historical development trend of Chinese martial arts traditional culture is predicted using this model. The results show that the process of defuzzification prediction divides the literature on the development of traditional culture of martial arts into five groups, and the five clustering centers are A1: 3055-4693, A2: 5603-6919, A3: 6388-7497, A4: 7984-8150, and A5: 8876-9483. The predicted values of the FCM algorithm model for the literature of the first group to the fifth group are respectively 3735.3, 5374.05, 6351.57, 7048.56, and 9144.31. The average error of prediction is 0.134, 0.062, 0.094, 0.126, and 0.025, respectively. The average prediction accuracies of the predictions are all >85%, and in particular, the accuracy of predicted values for the fifth group reaches 98%. It can be seen that the prediction model proposed in this paper is effective in predicting the historical development trend of the traditional culture of martial arts.
Multi-source data-driven technology research on carbon emission dynamics prediction in electric power industry
Authors: Wang Ruoyu , Li Jing , Li Zhipeng , Sun Qingchao 
Published: October 2024

Abstract
This paper analyzes the trend of power generation structure and carbon emission changes in the power industry and decomposes and analyzes the influencing factors of carbon emission in the power industry by using the LMDI decomposition method. Combined with the analysis of the influencing factors of carbon emissions in the power industry from 2016 to 2022, the carbon emissions of the power industry in the Yellow River Basin are simulated by the scenario analysis method. Four simulation scenarios were constructed based on the economic scale, industrial structure, industrial electricity consumption intensity, thermal power fuel conversion rate, and power supply structure. The IPSO-LSTM model for carbon emission prediction was created after optimizing the LSTM neural network prediction model. Combining the scenario analysis method to set the amount of changes in the high carbon, baseline, and low carbon scenarios of the influencing factors, the carbon emissions from the power sector in different scenarios are predicted for the years 2025-2035. From 2025 to 2035, the carbon emissions from the power sector in the three scenarios, except for the energy transition scenario, show a trend of increasing, then decreasing, and then increasing over the study period. The energy transition scenario shows a pattern of increasing and decreasing carbon emissions from the power sector.
Exploration of New Digital Media Art Forms under the Combination of Virtual and Augmented Reality Technology
Authors: Wei Yanyu 
Published: October 2024

Abstract
VR and AR not only expand the art field, but also enrich the form and content of digital media art and promote the development of digital media art. This paper proposes a 3D reconstruction of media art scenes based on the improved Neural Radiation Field (NeRF) technology and then completes the style migration, through which a unique scene art effect is formed, with a view to exploring new ways for the development of new digital media art forms. In the virtual orchard construction, the PSNR value of the optimized NeRF method is improved by 0.205, the SSIM value is improved by 0.015, and the LPIPS value is reduced by 0.018 compared with the state-of-the-art method, which can be concluded that the optimized NeRF method has a good image synthesis effect.
Integrated Application and Optimized Design Strategies of Automatic and Servo Control Systems for Industrial Robots
Authors: Xue Lan , Yang Shuai 
Published: October 2024

Abstract
At present, industrial robots have been widely used in industrial fields. This topic focuses on optimizing the system of servo control technology for the automatic control of industrial robots. Taking the optimization of robot automatic grasping technology with visual servo as an example, image-based and position-based visual servo control schemes are designed, respectively, and adaptive gain and first-order non-singular linear equations are used for visual servo performance enhancement. Open-loop control and closed-loop control are combined to create the visual servo-based automatic robot grasping docking method. The dynamic tracking effect of visual servo technology and the grasping performance of automatic grasping technology are evaluated through test experiments. The results can be obtained that the image-based and position-based visual servo control schemes can realize better dynamic tracking of objects, and the tracking accuracy is kept within 1.1mm and 8mm in the uniform and variable speed experiments, respectively. The robot’s automatic target grasping error gradually decreases to about 0 with the increase in the number of steps, and the system exhibits high grasping accuracy (over 96.67%) and speed (within 0.80s) at an operating speed lower than 20 m/s. Dynamic tracking performance and target grasping accuracy are certain features of the automatic robot grasping method based on the visual servo in this paper.
Construction and Application of Intelligent Optimization Model of Urban Resource Management for Sustainable Development
Authors: Yao Dachuan 
Published: October 2024

Abstract
In this paper, we first digitally design the urban management resource base and then construct a dynamic bargaining resource allocation model based on super-marginal analysis with corner point equilibrium and dynamic bargaining. In the context of sustainable development, an intelligent optimization model is used to analyze the problem of water resource allocation and optimize urban traffic infrastructure paths in order to achieve carbon reduction targets. Finally, six recommendations are made for optimizing and managing urban resources in the context of sustainable development. The results of the prioritization coefficients of each subarea are 0.186, 0.164, 0.153, 0.158, 0.153, 0.0769, and 0.111, respectively, and the results of the allocation of the optimal solution for the social objective, the optimal solution for the economic benefit, the optimal solution for the ecological benefit, and the optimal solution for the comprehensive benefit are very close to each other. In addition, the rates of water shortage for agricultural, industrial, and other uses. are 3.29%, 4.85%, 3.94%, 2.92%, and 3.87%, respectively. The variation range of the average passenger load factor and departure frequency under the carbon reduction target is between ±40%, the average vehicle area varies in the range of ±20%, and the variation range of the energy consumption factor and CO2 emission factor is -40%-0%. The intelligent optimization model for urban resource management proposed in this paper has practical significance for urban water resource planning and transportation structure optimization.
Research on the Innovation of University English Teaching Mode Driven by Artificial Intelligence
Authors: Yao Xiaochao 
Published: October 2024

Abstract
With the continuous deepening and development of information technology in the field of education, the combination of artificial intelligence technology and university English teaching has become a trend. This paper constructs a human-machine symbiotic English teaching model based on intelligent speech recognition, which takes into account university English learning abilities and learning objectives. Specifically, the multi-scale attention mechanism is used instead of the Conformer model to extract the global information of speech features and the local information of different scales and fuse them to learn more speech representations of different scales so as to better recognize the teacher and student behaviors in the English classroom. Meanwhile, combining the teaching mechanism of human-computer symbiosis, we construct a human-computer symbiosis English teaching model for speech recognition in an intelligent classroom and receive feedback after teaching experiments. The occupancy rate of teacher and student behaviors in the English classroom is 50.86% and 49.14%, respectively, and the conversion rate of teacher and student behaviors is 29.78%, which achieves a high level of teacher-student interaction in this model. In addition, the class utilizing the English teaching model of this paper was 5.02 points higher than the traditional class after one semester of teaching, P=0.038<0.05, and the results showed a significant difference. Through the comparison of cases and teaching experiments, the reasonableness of the English teaching model based on speech recognition is demonstrated, and the research content and problem areas of this paper are summarized.