bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on the application of neural network modeling in the assessment of traditional sports training and coaches’ quality in colleges and universities
Authors: Chen Dan , Lin Li 
Published: November 2024

Abstract
Scientific monitoring in sports training in colleges and universities is particularly important, which is an important symbol of the scientific level of sports training, and has a role in promoting the ability of athletes and coaches. In this paper, we collect data based on the evaluation index of coach quality and then preprocess the collected data of coach quality and athlete performance into the BP neural network model. The firefly algorithm is used to optimize the prediction performance of the BP neural network, and based on the model, a visualization system for sports training is constructed to display the predicted performance and quality assessment data in real-time. It has been found that the average prediction error of the BP neural network model based on the firefly algorithm is 0.73%, which can be used in sports training. The test scores of athletes in all aspects assisted by the visualization system were significantly better than those of the traditional training group, and the quality assessment scores of coaches in this model were significantly higher than those of the traditional group (P<0.05). This paper forms a systematic method for monitoring college sports training, which can enhance the scientific level of sports training monitoring and help improve the ability of athletes and the quality level of coaches.
Application of Data Analysis and Hierarchical Analysis in Project Management Supported by Resilience Theory
Authors: Chen Zhiwei , Dong Pingping 
Published: November 2024

Abstract
With the development of modern construction, the scale of construction has been expanding. Construction projects in the construction stage gradually exhibit high risk characteristics, and the complexity and difficulty of project management have increased greatly. Based on the perspective of resilience, this paper carries out an evaluation study of project management systems, taking four projects of building construction projects as cases to analyze the degree of resilience of construction project management systems. The study constructs an evaluation index system for project management that considers four aspects: stability, redundancy, efficiency, and adaptability. The network hierarchy analysis method is again proposed on the basis of the hierarchy analysis method, and the ANP-can-top cloud combination evaluation model applicable to project management is constructed by combining the advantages of the network hierarchy analysis method as well as the topable cloud model. The constructed resilience evaluation index system and evaluation model for project management are applied to the research cases to verify the practicality of the indexes and the rationality of the model. It has been found that stability has the greatest impact on building construction projects. In the reliability analysis of the topable cloud model, it is most reasonable when the number of cloud drops is taken in 1800. Based on the evaluation results, this paper proposes a strategy for improving the toughness level of the project management system, which provides a certain reference significance.
Quantitative Analysis and Strategy Research on the Improvement of Human Resource Allocation Efficiency in Big Data Environment
Authors: Gong Hui , Zhou Wenbin 
Published: November 2024

Abstract
In this paper, for the problem of optimal allocation of human resources in multiple R&D projects, the traditional genetic algorithm is introduced into the Tabu taboo search algorithm, which is able to jump out of the local optimal solution while ensuring the diversity of the population, so as to achieve the purpose of the global optimal solution. Three software system development projects of J Software Service Company have been selected as practice cases to explore the advantages of the improved genetic algorithm in optimizing human resource allocation. Fuzzy comprehensive evaluation and questionnaire surveys are also used to explore the efficiency and satisfaction of optimized solutions. According to the experiments, the enhanced genetic algorithm that is based on the enhanced genetic algorithm performs better than other algorithms. The efficiency value of human resource allocation in the project of J Software Service Company is 71.6, and more than 90% of the employees surveyed in the survey of the optimized scheme of staffing are considered to be satisfied.
A fast short circuit capacity calculation model based on regression neural network in complex grid environment
Authors: He Siyang , Zhao Shiqin , Wang Shuai , Huang Juncheng , Zhang Changzi , Wang Huaiyuan 
Published: November 2024

Abstract
The current short-circuit capacity level of local area power grids is close to the rated value of the existing equipment. To address this dilemma, this paper proposes a calculation method that most closely aligns with the principles of the short-circuit capacity calculation model and the short-circuit capacity calculation method in a pure AC power system, as well as the original definitions of the short-circuit ratio and the effective short-circuit ratio index. Next, we calculate the maximum short-circuit capacity of each bus in the system using the short-circuit capacity calculation method, which is based on the current. We select the generators and active output of the loads that contribute most to the short-circuit capacity by calculating their relevant sensitivity under the typical current. We then use these generators as feature vectors to establish the training samples. Finally, we train a generalized regression neural network to calculate the short-circuit capacity in a complex grid environment. Based on this, a fast calculation model for short-circuit capacity in a complex grid environment is constructed, which can be applied to quickly scan and calculate the short-circuit capacity level of buses in an operating grid.GRNN consistently achieves the lowest MAE and RMSE values in the two datasets, B0005 and B0006. The absolute time of the calculation of several methods, namely, ICA-GPR, LS-SVR, AST-LSTM NN, and ICA-RCV, is much longer than the absolute time of GRNN. GRNN, and the absolute time of GRNN in B0006 is only 0.0077 s. It shows that the GRNN model not only optimizes the speed of calculating short-circuit capacity in complex grids but also improves the accuracy and stability of the calculation. The feasibility and effectiveness of the fast calculation model for short circuit capacity based on a generalized regression neural network have been verified.
A Practical Study of Translation Wisdom Teaching Based on Semantic Association Network Modeling
Authors: Hu Juan , Cui Cui 
Published: November 2024

Abstract
The traditional translation teaching mode is obsolete and inefficient. This paper designs a model for assessing translation quality based on a semantic association network and innovates the translation teaching mode accordingly. Firstly, a special semantic association processing layer is introduced for the RNN translation quality assessment system based on a cross-language pre-training model with a self-attention mechanism. Then, the semantic correlation between the original text and the translated text is enhanced by the similarity-enhanced splicing mechanism to improve the accuracy of quality assessment so as to construct a translation quality assessment model based on a semantic correlation network. The model is employed in a university’s translation classroom. It is found that the semantic association network similarity enhancement splicing mechanism designed in this paper can improve more than 10 percentage points on the basis of the rest of the algorithms, and the Pearson correlation coefficients are 72.81% and 70.79% compared with the competing models, which are significantly better than the optimal models UNBA (EN-DE)/UNBA (EN-RU). After applying the model, the average score of the experimental class in the semester post-test was 69.19, which was 22.7 points higher than the pre-test. There is an obvious tendency for the distribution of scores to move to the high score range, and the densest distribution of scores becomes 68-70, with 22 students scoring 70 or more and 5 students scoring 80 or more, which is an obvious improvement compared with the semester pre-test. After practicing in a translation classroom, the model in this paper realizes intelligent teaching, saves teachers’ time, and improves the quality of translation teaching.
An Exploration of the Application of Natural Language Processing Technology in the Quality Enhancement of Cross-Cultural Language Conversion
Authors: Xie Nina , Zhao Hui 
Published: November 2024

Abstract
In the context of the globalization era, language conversion plays an important role in cross-cultural communication. Natural Language Processing (NLP) technology enables computers to automatically convert text from one language to another, i.e., to achieve cross-language communication and understanding through machine translation. In this paper, we first extract webpage content through distributed crawlers to construct a parallel corpus of cultural contexts in English and Chinese. We then conduct an empirical analysis of cross-cultural translation in English-Chinese bilingualism using the improved Transformer translation model. If you compare the PPL and HM values of the Transformer improved model to the baseline model, they are -54.18 and 24.79 points lower than the baseline model. This is better than the improved values of Transformer (+BD+DE), which were -38.41 and 15.63 points lower. This indicates that the Transformer improvement model outperforms other models in terms of language conversion quality, specifically the translation effect. Therefore, we have verified the effectiveness of NLP technology in enhancing the quality of cross-cultural language conversion.
Research on the Application of Intelligent Analysis Model for Educational Content in Higher Education Physical Training
Authors: Lin Li , Chen Dan 
Published: November 2024

Abstract
Intelligent technology brings brand new possibilities for education and teaching by virtue of its powerful computing and learning capabilities. Aiming at the problems of low efficiency and poor error correction effect of the current means of correcting sports training errors, this paper uses intelligent analysis technology to design a sports training error detection model to automatically measure the training process so as to realize the measurement and analysis of training errors. The model divides the video image as candidate foreground and candidate background by combining the Bayesian decision-making method and inter-frame difference method and sets the initial value of this background as the main component analysis method to obtain the real-time detection results of target posture. The analysis of the model’s sports posture detection shows that the recognition rate of this paper’s algorithm for different sports is more than 81%, and the error rate and accuracy are 0.038% and 95.9%, which has a better sports training error detection accuracy. After applying the intelligent analysis model to actual sports training, the 100-meter run and standing long jump scores of the students trained with the intelligent analysis model are 3.52 points and 0.9 points higher, which proves that the sports training error detection model promotes sports training.
Exploration of Bassoon Quartet Teaching Methods and Training Methods under the Background of Informationization
Authors: Zhang Yuzhe 
Published: November 2024

Abstract
The continuous development and progress of the times have promoted the diversification of teaching methods in colleges and universities. Based on the teaching and training methods of the bassoon quartet, this paper proposes an evaluation model for their teaching and training methods, in combination with an informational background. Specifically, after extracting features from the audio signal, a two-way LSTM neural network and Softmax classification algorithm are used to evaluate the quality of bassoon quartet training audio. The purpose of the teaching practice for the bassoon quartet teaching method is to explore its practical utility and support information technology conditions. The results show that the evaluation model of bassoon quartet training has a high evaluation accuracy, and the values of each index are between 0.80 and 0.94. The level of bassoon quartet of the students was significantly improved after the application of the teaching method, and the percentages of “excellent” and “good” in the formative evaluation were 12.5%-22.5% and 42.5%-50.0%, and the number of students with scores above 75 points increased by 18% compared with that of the conventional teaching form. The number of students with scores of 75 or above increased by 18.96% compared to the conventional teaching format. In this paper, the teaching method of the bassoon quartet based on information technology can promote the teaching level of the bassoon quartet and enhance students’ mastery of the bassoon quartet.
A Study of Data-Driven Optimization of Personalized Instructional Strategies for Physical Education Training
Authors: Shi Mingming 
Published: November 2024

Abstract
As a new type of sports training, the application of sports information technology can effectively improve the athletes’ sports level and sports performance. In this paper, based on the way and effect of sports information technology application, a system for sports teaching and training assistance is designed. Through sports training intensity monitoring, the training intensity of the athletes is obtained, and a high load alarm is triggered. At the same time, the improved hybrid Kalman filter is used to solve the posture of training movements, and the DTW algorithm is used to recognize the training movements and compare them with the standard movements to obtain the training scores. Coaches can be assisted in formulating personalized teaching strategies by using the system’s obtained sports training data. This paper’s sports teaching and training assistance system has a significantly shorter response time than the two comparative systems, which suggests it has better response performance and practicality. The RMSE values of the improved hybrid Kalman filter are 0.92, 0.84 and 1.47 for the roll, pitch and yaw angles, respectively, which are better than that of the extended Kalman filter, indicating that it is closer to the original data and has better data fusion performance. Compared with the control class using traditional teaching strategies, the students in the experimental class using the personalized teaching strategies of this paper have higher skill improvement, indicating that the strategies of this paper have an important guiding role for personalized physical education teaching.
A data-driven model for human resource strategy development in higher education in a machine learning framework
Authors: Yang Linlin , Liu Yuwang 
Published: November 2024

Abstract
Various fields in real life have increasingly utilized machine learning methods and data mining technology in recent years. This paper creates a data-driven model to implement intelligent human resource management in colleges and universities. The model utilizes the fuzzy decision tree algorithm to assist colleges and universities in selecting the most suitable talents and completing the talent recruitment and selection process within a short timeframe. Additionally, it utilizes the ID3 algorithm to filter out potential staff departures and factors, enabling targeted decision-making to prevent talent loss. After the implementation of the human resource management strategy based on the model, the completion rate of the recruitment plan increased by 15.1% compared with the pre-implementation rate, the turnover rate decreased by 3%, and 91% of the employees agreed with the management of the Human Resources Department. This paper’s model provides a reliable personnel decision-making and management strategy for human resource management in universities.
A study on visualization of kindergarten teaching content assisted by big data technology
Authors: Du Xiyao 
Published: November 2024

Abstract
Preschool education is an indispensable and important part of education in China. The study collects the educational content of kindergarten teachers from five kindergartens in Shanghai as survey data, uses text mining methods to visualize and analyze the teaching content of kindergartens, screens the core words of the collected text data, and then uses the FW-K-means clustering algorithm to analyze the correlation between the core words, and then uses the LDA theme model to realize the visualization of the content of the teaching theme of young children. The results show that kindergarten teaching themes are divided into three major clustering themes: game teaching theme, physical education teaching theme, and art teaching theme, among which “interest cultivation” has the highest high-frequency word probability of 0.0217 and the lowest high-frequency word probability of 0.0098, which indicates that kindergarten teaching themes have the highest high-frequency word probability and lowest high-frequency word probability. 0.0098, indicating that kindergarten teaching content lacks attention to “creativity”, so kindergarten teaching needs to take this as the main cultivation direction. Based on big data-assisted text mining technology, we can perform visualization research on kindergarten teaching content.
A Computerized Visual Analysis of Form and Technique under Constructivist Theory in Musical Theatre Music Performance
Authors: Yu Xiaolin 
Published: November 2024

Abstract
A computer vision model is constructed to recognize the expressions and movements of musical performers so as to analyze the changes in the forms and techniques of musical performances under the constructivist theory. The model needs to perform pre-processing, such as grayscale conversion, thresholding, background subtraction, noise reduction, and so on, on the related video before recognizing expressions and actions. The performer’s movements are recognized based on TC-HGCN, while the expressions are recognized based on CSACNN. The average accuracy of TC-HGCN in recognizing dance movements in musicals reaches 87.95%. The facial expression recognition method based on CSACNN can overcome the shortcomings of commonly used expression recognition models. The use of computer vision modeling analyzes the constructivist theory on the form and technique of the next musical performance, which enhances the performance effect and has more infectious and artistic charm.
Research on the Strategy and Implementation Path of Digital Transformation of Traditional Sports Games in Modern Tourism Industry
Authors: Li Ming , Gao Zixuan 
Published: November 2024

Abstract
This paper employs panel data regression analysis to conduct empirical research on the impact of digital transformation on the high-quality development of the sports and games tourism industry, thereby verifying the driving path and role of this development. The Yellow River Basin’s scenic spots undergo further research on the development of the tourism industry, market feedback, tourist satisfaction, and other related issues following digital transformation. The results show that digital transformation promotes the efficiency of the sports game tourism industry, and the enhancement dimension reaches 1.164. Digital transformation has a positive impact on the overall development level of the high-quality sports tourism industry. The regression coefficient is 0.6021. Following the digital transformation, the tourism industry in the Yellow River Basin scenic area continues to grow, with a year-over-year increase in tourists of 29.89% in 2023. About 77% of tourists display positive emotions in their emotional image analysis. Therefore, this paper proposes a strategy for the digital transformation of sports and games tourism, which is useful for the future development of sports and games tourism.
Transformation of the Accounting Function in Supply Chain Finance Innovation in the Perspective of Intelligent Algorithms
Authors: Sun Meijiao , Gong Chunyan , Pan Cheng , Ye Zhenxiong 
Published: November 2024

Abstract
In the era of artificial intelligence, the transformation and reshaping of financial functions have become inevitable for the development of the accounting industry. Based on the background of the intelligent era, the study analyzes the transformation of the accounting function in supply chain finance and integrates intelligent algorithms to showcase their application in the accounting function. We construct the enterprise credit risk assessment model based on RAGA-PP, combining the evaluation index system and the accelerated legacy algorithm to optimize the projection tracing model. Then, using the XGBoost model, design the method for identifying corporate financial fraud. Empirical analysis reveals that the sample enterprises’ average projection value is 0.8277, indicating a medium-high credit risk, thereby confirming the feasibility of the RAGA-PP model. The XGBoost model exhibits superior stability and recognition accuracy in identifying financial fraud, with its fitness value and loss value reaching 0.483 and 0.688, respectively. Its recognition accuracy surpasses that of comparative algorithms by 12.23% to 14.43%. With the support of intelligent algorithms, supply chain finance can use intelligent models to assist in management and decision-making and provide more value for enterprises.
Research on Physical Fitness and Health Improvement Strategies for Youth Basketball Players Based on Big Data Analysis
Authors: Ge Chenxuan 
Published: November 2024

Abstract
This paper aims to address the limitations of the traditional K-mean clustering algorithm, which does not account for the influence of both extremely poor and excellent physical fitness classmates on abnormality detection and the issue of high false detection rate. It bases its approach on three abnormality detection methods: K-mean clustering, distance, and density. These methods are used to determine the physical fitness test data outlying index (PFT-OI) and identify abnormal data. We used this algorithm to conduct research on the physical health of youth basketball players from a big data perspective. The differences between the mean values of height, weight, and BMI pre- and post-test scores of adolescent basketball players before and after the experiment were not significant (P > 0.05). The p-values of the pre- and post-test scores of 50 meters, standing long jump, and 1000 meters of male athletes after the experiment were 0.000**, 0.005**, and 0.029*, respectively. The absolute values of the pre- and post-test scores of female athletes differed by 1.05 seconds, 0.2 meters, 5 meters, and 8.12 seconds, respectively, except for the pre- and post-test differences of seated forward bends, which were not significant (P < 0.05). In the teaching of basketball, male adolescent basketball players can focus on practicing the 50-meter run and standing long jump and, at the same time, controlling body weight in order to maximize the possibility of achieving an excellent overall assessment score. Female athletes should prioritize 50 meters, 800 meters, and the standing long jump in their daily training regimen.