bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploration of blended teaching and learning of Introduction to Nursing by integrating curricular politics in the Internet era
Authors: Liu Wei 
Published: February 2024

Abstract
The article improves the algorithm’s efficiency by constructing a pattern of interest level sequences and a method of calculating the support value using sequences through the knowledge of nursing. The study utilizes the leverage rate to filter valuable data and improves the leverage rate for sequence length, while introducing pruning strategies. The article proposes a TPKCEE teaching model of Civics integration into Introduction to Nursing and analyzes its practical effects. The study results showed that after the education of Civics Integration, the mean value of professional identity of most students in the studied nursing classes was more than 60 points. In the analysis of interest preference, students showed significant interest in “Concepts of Nursing”, especially in the knowledge points related to “Outlook on Life” in the Civics teaching. The TPKCEE blended teaching strategy effectively realizes the integration of Civics and Introduction to Nursing, and helps enhance medical students’ humanities. The TPKCEE blended teaching strategy effectively recognizes the integration of Civics and Introduction to Nursing courses, and helps to improve the humanistic quality of medical students. It provides practical guidance and methods for medical schools to educate in integrating medical technology and humanities, which is of great significance to enhance the comprehensive quality of medical students.
Exploring the Application of Computer Visual Arts in the Context of Digital Media
Authors: Liu Zhiming 
Published: February 2024

Abstract
This paper provides an in-depth discussion on the critical technologies of computer visual arts in digital media, mainly focusing on virtual character modeling and human body tracking technologies. The study merges the models into the same coordinate system by combining point cloud data from different angles and calculating the transformation matrix. Accurate 3D human body models were reconstructed using point cloud alignment and fusion techniques. A particle filtering method combined color, motion and shape information for effective tracking. The experimental results show that the success rate of hand tracking reaches 95.35% in the case of no background interference, while the success rate is 90.97% in the environment with background interference. In addition, the study shows that low resolution images (584×388) outperform high resolution (968×676) in terms of tracking accuracy and speed. The study confirms that the combination of advanced computer vision techniques can effectively construct and follow virtual character models and realize high-precision human motion capture under different environmental conditions.
A Logical Rationale for Sports Tourism to Help Rural Revitalization under Integrated Transfer Learning Strategy
Authors: Liu Chen , Yang Ning 
Published: February 2024

Abstract
As an emerging driving force to promote rural revitalization, sports tourism provides tourists with rich experiences and effectively promotes the development of local sports industry and economy. In this study, an integrated transfer learning model is constructed to assess the development level of rural revitalization by combining a constrained LightGBM-based learner, a Gaussian distribution and an optimized sampling function. Applying this model, this paper calculates the integrated development index of rural revitalization for 21 provinces, municipalities and autonomous regions in China between 2019 and 2023. Particularly for Tianjin, the study finds that its comprehensive development index for rural revitalization is relatively low, only 0.244 in 2023. Analyzing the cases of six typical villages in Tianjin, the study finds that sports tourism significantly impacts rural revitalization. During the five years, the average annual growth of tourist arrivals amounted to 125,400 visitors, and the average tourism revenue in 2023 reached 9.686 billion yuan. Meanwhile, these villages had an average of 3.79 points in the development level of living affluence, which is a high level. The study provides empirical support for sports tourism to help rural revitalization. It provides practical references for the future development direction and planning of rural sports tourism, emphasizing the critical role of sports tourism in promoting the comprehensive development of villages.
Teaching Core Literacy in College Physical Education and Health Courses Based on Multiple Data Integration
Authors: Yu Qi 
Published: February 2024

Abstract
The study used cluster analysis and decision tree modeling to construct a multivariate data fusion physical fitness evaluation model, and also built a physical fitness core literacy evaluation model through factor analysis and fuzzy comprehensive evaluation method. These two evaluation models were subsequently applied to physical education and health teaching in colleges and universities to measure the quality of physical education teaching in colleges and universities and students’ physical health status. By analyzing the practice of physical education teaching in colleges and universities, it was found that the pull-up program had the worst performance in students’ physical fitness test, with a score of 287. In addition, the lung capacity and 50-meter dash programs contributed the most to students’ total performance, with scores of 1,291 and 1,245, respectively. From the perspective of physical education and health teaching quality, the scores of teaching situation, teaching management and biological education school effectiveness were 4.68, 4.01 and 4.52, respectively, and all of these evaluation grades were good. This study provided an accurate and scientific analysis of the overall quality of physical education and health teaching in universities and the health status of students.
Exploration and Practice of Educational Teaching Evaluation Reform in Higher Vocational Colleges Based on Big Data Technology
Authors: Zhang Yamei 
Published: February 2024

Abstract
This paper constructs an evaluation model of education and teaching in higher vocational colleges based on big data technology, which focuses on students’ individual development and adapts to the “double-high program” goal. The evaluation index system of education and teaching quality established by the study contains 5 first- and 22 second-level indicators. The empirical Analysis takes QH College in R province as an example and analyzes the educational teaching data from 2018-2023. During this period, the mean values of secondary indicators of education teaching quality evaluation in QH College ranged from 2.7083 to 3.2886. The highest mean value of the rating of teaching objectives A1 and the lowest mean value of teaching standards A2. The coefficient of variation of teaching scenario B3 was the highest at 0.4887, and the coefficient of variation of building teaching resources was the smallest at 0.2587. In terms of the comprehensive evaluation of the quality of teaching and learning in higher vocational colleges and universities, the evaluation index system of QH College was categorized as “strong”. In addition, the study also analyzed the vulnerability of the evaluation of education and teaching in higher vocational colleges. From 2010 to 2023, the mean values of the vulnerability index of teaching quality evaluation of five higher vocational colleges in R province were 0.9693, 1.0536, 1.0507, 0.9993, 1.0407, respectively, with a significant turnaround in 2013, when the risk was significantly reduced. Synthesizing these data, this study provides an effective teaching model and evaluation method for higher education institutions to achieve high quality educational development.
Development of Civics Course for College Students Based on Integrated Transfer Learning Strategy
Authors: Zhang Li 
Published: February 2024

Abstract
This paper explores how to effectively predict and analyze students’ Civics course grades and their correlation with students’ behaviors based on the integrated transfer learning strategy to improve the teaching quality of Civics courses for college students through the integrated transfer learning strategy. The feature engineering method is used to extract the achievement features, and the achievement analysis and prediction model is established by Stacking integrated learning method combined with various algorithms such as KNN, linear regression and decision tree. By predicting and analyzing the Civics grades of college A students, the results show that the average error between the expected grades and the measured grades of computer science majors is 4.00 points. In contrast, biological science majors underestimate it by 5.41%. In addition, the cluster analysis of students’ behaviors using the k-mediods algorithm revealed significant differences between students with excellent academic performance and those with poorer performance in behaviors such as frequency of consumption and number of books borrowed. The integrated transfer learning strategy has a better application effect in the prediction and analysis of the performance of the Civics course, which can provide a powerful support to improve the quality of teaching.
Research on Intelligent Recommendation of Computer Course Items Combining Augmented Learning Algorithms and Data Mining Techniques
Authors: Zhao Yanfei , Jing Xia , Guo Hongjian 
Published: February 2024

Abstract
This paper combines augmented learning algorithms and data mining techniques for an intelligent recommendation system for computer course projects to optimize the allocation of learning resources and improve learning efficiency and effectiveness. The research methodology includes an improved association rule mining algorithm and an augmented learning model applying deep Q-network and adaptive reward function. The research results show that the system can effectively mine knowledge point associations and achieve personalized recommendation. In particular, the accuracy of knowledge point associations and the customized level of recommendation are improved by multi-support strategy improvement and knowledge point association correction. Experiments show that on the edX dataset, the recommendation recall reaches 0.347 at k=10 and 0.433 at k=20, significantly better than the traditional recommendation algorithm. This intelligent recommendation system can effectively improve the learning effect of computer course projects, and it has a positive impact on both learning attitude and independent learning ability, especially helping the backward students significantly and reducing the learning differences between classes.
Artificial intelligence empowers the construction of first-class financial management system
Authors: Wang Mingdong 
Published: February 2024

Abstract
With the rapid development of artificial intelligence technology, its application in financial management is increasingly widespread. This study aims to explore how to build a first-class financial management system with the help of artificial intelligence technology. We adopt the improved BP algorithm and ant colony algorithm to conduct in-depth research on financial risk early warning, financial analysis and financial forecasting. In terms of financial Risk early warning, a neural network-based risk early warning model is studied, and the improved model increases the prediction accuracy by 15%. The complex correlation between corporate financial status and market performance is found in economic analysis of multiple financial indicators. In terms of financial forecasting, a combination forecasting model is introduced and the weights are optimized by the ant colony algorithm, which improves the accuracy and reliability of the forecast and reduces the forecasting error by 20%. Artificial intelligence technology can effectively improve the efficiency and accuracy of financial management, provide powerful support for enterprise economic decision-making, and provide new ideas for building a first-class financial management system.
Impact toughness analysis of offshore wind power structures under the influence of long-period waves
Authors: Jiang Senhui , Wang Qing 
Published: February 2024

Abstract
In this paper, the performance of offshore wind turbine structures under long-period wave impacts is investigated, and a numerical model of long-period waves is developed to simulate the wave motion and fluid seepage in the pore medium by using the VARANS equation with the OlaFlow solver, and various turbulence models such as the model, RNG model, and the VOF method is applied to capture free surfaces, which can accurately simulate wave generation, propagation, reflection, breaking, and fluid seepage in the pore medium. These methods can accurately simulate the wave generation, propagation, reflection, breaking, and fluid seepage in the pore medium, and the accuracy of the numerical simulation is verified by comparing the results with those of the physical experiment. The results show that the wind farm exhibits good impact toughness under the influence of long period waves, and its overturning stability and slip stability are better than the safety coefficient required by the specification.
An applied study of industrial enterprise engineering and management based on human factors engineering
Authors: Niu Lixia , Ji Renzhou 
Published: February 2024

Abstract
This paper proposes an innovative management optimization method for the challenges facing the management of industrial enterprises in the context of the current rapid development of the industrial economy. The article combines human factors engineering and lean production theory to optimize the management efficiency and effectiveness of industrial enterprises. Considering the complexity of industrial environments, a personnel identification system and a tracking system are constructed in the study to achieve more effective staff management. To comprehensively assess the management effectiveness, the article combines the fuzzy synthesis method to build an evaluation model for the management effectiveness of industrial enterprises. The empirical study shows that the optimization method significantly improves the productivity: the working hours are reduced from 210 hours to 150 hours, the torso rotational and bending forces are reduced by about 54% and 45%, respectively, and the troops on each joint are reduced by about 59% on average. The overall optimization of the model has an affiliation value of 0.53, indicating that the management effect is evaluated as good. The data fully proved the effectiveness of the management optimization method for industrial enterprises proposed in this paper in increasing productivity and improving working conditions.
Innovative Research on Cultivation Mechanism and Introduction Mechanism of Top Talents in Colleges and Universities under the Background of Big Data
Authors: Qian Jibing 
Published: February 2024

Abstract
In the context of fierce competition for talents in colleges and universities, cultivating and introducing outstanding young talents are crucial to the construction and sustainable development of talents in colleges and universities. The article utilizes the IPO model to construct an evaluation system for the quality of cultivation of top talents and the implementation effect of talent introduction policy in colleges and universities. The study takes eight colleges and universities in the university city of Province P as examples, uses the EWM method to determine the weights of the indicators, and carries out a comprehensive evaluation through the improved TOPSIS method and the Data Envelopment Analysis Model (DEA). The results show that in talent cultivation, the weight of the result quality is 18.09% higher than the essential quality. In contrast, in implementing the talent introduction policy, the maximum difference of each weight value is only 1.24%.The effective decision-making unit of the DEA reaches the optimal value of 1.00 in the comprehensive technical efficiency, pure technical efficiency and scale efficiency. The study reveals the shortcomings of implementing talent cultivation and introduction policy in universities. It guides the universities to improve the mechanism of cultivation and introduction of top talents, to enhance the core competitiveness.
Novel technology for industrial VOCs exhaust treatment under dual-carbon target based on multivariate statistical analysis
Authors: Chen Yong , Xie Guangyuan 
Published: February 2024

Abstract
Against the background of the dual-carbon target, the treatment of volatile organic compounds (VOCs) emissions in the industrial sector, especially in the chemical industry, has become an urgent environmental issue. To provide scientific and objective guidance for technology selection, this study constructs a comprehensive evaluation model based on the combination of entropy weighting method and hierarchical analysis method for the complete progress of VOCs treatment technologies, taking into account the four main factors of environment, economy, management and technology and the thirteen secondary factors under them. The model provides in-depth quantitative analysis and evaluation of ten common VOCs end-of-pipe treatment technologies. The evaluation results show that thermal combustion, catalytic combustion, adsorption-concentration-combustion, and photocatalysis exhibit high comprehensive efficacy in VOCs emission treatment, with relative weight values of 0.1273, 0.1252, 0.1214, and 0.1184, respectively. These findings provide essential references and guidance for chemical companies in selecting suitable VOCs treatment technologies, which help to realize the win-win situation of environmental protection and economic development. This study fills the gap in evaluating VOCs emission management technologies and provides a practical decision-making tool for relevant decision makers.
Research on personalized recommendation of teaching resources based on joint probability matrix decomposition model and CNN improvement algorithm
Authors: Ma Junxia , Liu Qilin , Zhang Zhifeng , Gu Peipei 
Published: February 2024

Abstract
This study proposes a teaching resource recommendation method (TRRDLMF) based on deep learning and probabilistic matrix decomposition, aiming to improve teaching resource recommendation’s accuracy and personalization level. The study combines hybrid neural network feature extraction of teaching resources and user feature extraction based on extended noise-reducing self-encoder and extended probabilistic matrix decomposition-based teaching resource recommendation technique. The study validates the model’s effectiveness by conducting empirical analyses on four online learning platform datasets of different sizes. The recommendation system can effectively track learners’ knowledge mastery status and learning preferences, and provide corresponding teaching resources. Test question similarity and difficulty analysis results show that the model can accurately capture the correlation between test questions and provide complementary data in education. The personalized recommendation analysis reveals the learners’ knowledge preference states that change with learning, demonstrating the model’s ability to give explanations at the level of learners’ knowledge preferences. The analysis of the impact of data size on recommendation results shows that the recommender system can achieve up to 73.12% accuracy and 97.89% recall for different TOP-K recommendation lists, with the best F-value at TOP-6. The personalized recommendation system for teaching resources based on the joint probability matrix decomposition model and CNN improvement algorithm proposed by the research demonstrates significant effects in improving the accuracy and personalization level of teaching resources recommendation, which helps learners to acquire suitable learning resources more efficiently, and has high practical value and promotion potential.
Innovative Teaching of College Vocal Music Courses Aided by Information Integration Technology
Authors: Chen Shuangshuang 
Published: February 2024

Abstract
The article adopts the correlation function method to extract the fundamental frequency features of vocal speech signals and processes this information through the cepstrum method. Then, the audio signals are corrected by linear smoothing to match the features of the students’ audio signals with the features of the musical score to determine the accuracy of the vocal music. The study also includes quantization, frame-splitting, and windowing preprocessing of the audio signals, and constructing an unwritten audio signal feature set based on correlation feature selection. The article also proposes an innovative metaphorical method for improving the teaching mode of vocal music courses in colleges and universities. It analyzes the auxiliary role of information technology in teaching vocal music courses by combining simulation experiments with the effects of practical application. The study results show that the students’ vocal singing fundamental frequency range is close to the standard, roughly 123 to 200, with a maximum error value of 24. With the assistance of information fusion technology, the average score increment of the students’ vocal singing reaches 6.088 points, proving this method’s effectiveness in improving the quality of verbal music teaching.
Application of Information Integration Technology to Help Minority Traditional Arts in Chinese Painting Art Designs
Authors: He Yaru 
Published: February 2024

Abstract
This study utilizes the application of information fusion technology in the artistic design of Chinese paintings, especially exploring how to incorporate the elements of traditional ethnic minority art to enrich and develop the spatial aesthetic expression of Chinese paintings. The study first analyzes the differences in spatial artistic expression between Chinese and Western painting. It focuses on the spatial expression methods of Chinese painting and their application in ethnic minority traditional art. The mathematical model of minority art plane is constructed through the method of coordinate space and its transformation. The PAD emotional attribute model is used to quantitatively analyze the dynamic characteristics of Chinese painting art design works. The results show that the traditional elements of ethnic minorities incorporated in Chinese painting art design can effectively enrich the emotional expression of the paintings and enhance the aesthetic value of the art works. In 2022, the content of traditional elements of ethnic minorities in Chinese paintings increased by about 20 times compared with that in 2015, showing the increasing popularity of this art form. This study reveals the potential application value of information fusion technology in traditional art design and provides a new development direction for Chinese painting art design.