bluuebunny/crossref_metadata_2025_split huggingface dataset:
Relationship between athlete performance and training load in martial arts sparring: based on a gray rule model
Authors: Fu Zhongsheng 
Published: June 2024

Abstract
Wushu sparring is an important part of Chinese multi-sports, and the athletic performance of wushu sparring athletes has a crucial role in Chinese wushu sparring performance. In this paper, the relationship between athletes’ performance and the training load of wushu sparring athletes was investigated using gray correlation analysis, with wushu sparring athletes as the research object. According to the relevant standards to establish and determine the objective indicators to measure the performance of wushu sparring athletes, at the same time, the group of athletes with different intensity training experiments, the use of gray correlation analysis based on the data obtained from the training to calculate the gray correlation between the athlete’s performance and their training load, and as a basis for the establishment of the gray rule model through the GM (1, 1) algorithm. The relationship between training load and athlete performance was evaluated to correlate up to 0.975, and the algorithm’s model was 88% accurate in predicting athlete performance based on training intensity. The study of the relationship between training load and athletes’ performance can aid wushu sparring athletes in rationally scheduling their training intensity, which is crucial for the development of Chinese wushu sparring.
Analysis of Influencing Factors on the Improvement of Employment Quality of College Students in Private Colleges and Universities - Based on SEM Model
Authors: Li Fangyuan 
Published: June 2024

Abstract
The increasing number of graduates from private colleges and universities creates a great challenge to analyze the factors influencing the employment of graduates and to improve the quality. Based on the structural equation SEM model and combining the data of graduates of private colleges and universities in a city, this paper integrates and selects the factors influencing the employment quality of graduates of private colleges and universities. An evaluation model for the employment quality of graduated students has been established through the LSTM network to further study the degree of influence of each influential factor on graduates’ employment quality. In order to deal with the bidirectional dependence of time series data while considering the inputs before and after the current moment, this paper adds the reverse LSTM layer on top of the LSTM network to form a BiLSTM network, which better captures the correlation between the influencing factors. In the research process, it was found that, except for the first selected factor of graduates’ destination, which needs to be eliminated, the other six influencing factors passed the reliability and validity test and could make the model have a good fitting-out effect. In the algorithm analysis, it was found that the running accuracy of the BiLSTM network could reach 97.01% at the maximum. In comparison, the running accuracy of the LSTM network could only get 91.03% at the maximum.BiLSTM network is more outstanding than the LSTM network both in accuracy and stability.
A Study on the Application of Multi-sensory Interaction Technology in Enhancing Concert Audience Immersion
Authors: Li Feng 
Published: June 2024

Abstract
The progress of multi-sensory interaction technology provides technical support for creating immersive concerts full of participation and experience. Based on multi-sensory interaction technology, this study proposes the design idea of immersive concerts and outlines the application process of multi-sensory interaction technology in immersive concerts. It builds a concert virtual reality scene through emotion recognition and evocation in the virtual reality scene, designs a gesture interaction interface based on immersive flow field visualization, realizes the construction of an immersive concert, and carries out application evaluation. The model's emotion recognition rate is as high as 93.04%, and after the resting state adjustment, the subjects’ pleasantness and arousal are on the rise, and the ability to evoke emotions is consistent with the expectation. The experimental expectations were met as the mean values of realism and immersion scores were not below 7.6. The percentage of user agreement for clicking and pinching gestures is concentrated in rank 4 and rank 3, and the percentage of voting for comfort is concentrated in rank 2 and reaches 96% and 91%, respectively, which can meet the interaction needs of immersive concerts. This paper presents technical ideas for using multisensory interaction technology in immersive concerts.
The Development Trend of English Instant Translation Software in the Background of Mobile Internet
Authors: Gao Lili , Ma Yingjie , Ma Pengxiao 
Published: June 2024

Abstract
With the continuous development of the Internet industry, the function and technology of instant translation software can be continuously improved. This paper takes the machine translation model based on neural networks as the research basis and proposes a translation model suitable for the use of English instant translation software. Through word vector transformation, natural language is transformed into digitized symbols, and the n -gram model is used to improve the fluency of the processing. Using the neural probabilistic language model, the conditional probability of language processing is calculated. Introduce a recurrent neural network and optimize and adjust it based on the original model. Design the model framework for instant translation software for a series of operations, such as language signal processing. The case study revealed the factors that influenced users’ experience of using the software and their expectations of the instant translation software. The results show that the OR estimate of users’ satisfaction is 3.1655, and a one-unit increase in satisfaction will increase the advantage of instant translation software by 3.17 times. The error rate of translation software, as well as the learning cycle, have different degrees of influence on the advantage of the software. The period 2001-2022 was chosen as the study period, and the number of published patents for instant translation software research was examined. The overall trend during the study period showed a gradual increase. The number of patent publications increased by 40.9% to 62 in 2017, compared to the previous year. Instant translation software's future development trend is positive.
Research on animation performance based on virtual reality technology
Authors: Ma Xiaojing 
Published: June 2024

Abstract
In this study, we utilize the acquisition device from Open Pose to capture the joint points of human posture. Subsequent to data collection, each joint point undergoes a coordinate regularization process, using the neck coordinates as a reference, to derive human skeleton data. This processed data facilitates the extraction of angles and relative distances among all human body limbs, effectively capturing characteristic feature information. This information is subsequently imported into the Iclone software, where it is refined using a fitting tool to develop a bespoke character model. Following the character's construction, the study leverages NURBS curve modeling and Polygon polygon modeling technologies within the 3D software MAYA to achieve precise scene modeling. This approach culminates in the production of an animated performance that integrates virtual reality technology, with its effects thoroughly analyzed. During Action A, the animation method developed in this study yielded an average running frame rate of 96.54 frames per second, and the average texture size was 6.39 MB. These metrics significantly surpass those achieved by comparative methods. Moreover, the efficiency of animation scene production in this study (0.134339 seconds) substantially exceeds that of the control method (0.273984 seconds), thereby enhancing the level of animation design technology and presenting a more lifelike 3D virtual animation.
Strategies for Applying BOPPPS Model Supported by Intelligent Algorithms in Blended Teaching of College English
Authors: Ou Yi 
Published: June 2024

Abstract
Artificial intelligence technology is increasingly being employed within educational contexts, markedly enhancing the dynamics of instruction through the integration of intelligent algorithms. This study endeavors to revitalize the BOPPPS model in blended college English teaching by amalgamating it with the Bayesian knowledge tracking model and a reinforcement learning algorithm. The objective is to establish a refined, blended teaching framework based on the BOPPPS model and to investigate its efficacy along with the variables that influence its outcomes. The findings affirm a positive correlation between the effectiveness of blended English teaching at the university level and the student's cognitive abilities, skillsets, and affective attitudes, with all p-values demonstrating significance at the 0.001 level. Comparative analysis of pre-and post-test data from the blended teaching experiment revealed no initial significant differences between the experimental and control groups across six dimensions of English proficiency and five dimensions of interest in learning English. However, post-experiment results indicated substantial enhancements in the experimental group's overall English scores, listening, reading comprehension, writing, translation, and speaking abilities relative to the control group, with p-values falling below 0.05. Additionally, the p-values for the paired sample tests concerning the dimensions of interest in learning English in both groups were also below 0.05. These results suggest that blended English teaching at the university level not only significantly boosts students’ performance in English but also augments their interest in the language. This study underscores the potential of integrated teaching models that incorporate AI-driven algorithms to significantly enhance educational outcomes.
Exploring the development of athletic interests of students in physical education colleges and universities based on cluster analysis: physical education athletic training
Authors: Su Lulu , Zhang Tengyu 
Published: June 2024

Abstract
Sports interest is the internal motivation for students in physical education colleges and universities to participate in sports training enthusiastically. Based on the definition of fuzzy set and fuzzy clustering uncertainty measure, this paper utilizes the fuzzy C-mean clustering algorithm to evaluate and analyze students’ athletic training performance data by clustering, and based on the results of the analysis, it formulates the physical education teaching program based on the development of students’ athletic interest. At the same time, taking the physical education course of grade 2022 in College Q as an example, the IPA quadrant diagram was used to analyze the ‘importance-satisfaction’ of physical education teaching. The results showed that in the IPA quadrant, the five factors of teachers’ professional quality [4.89,4.13], teachers’ ability to explain [4.56,4.16], mastery of movement [4.64,4.03], novel content of teaching materials [4.46,3.99] and good image of the teacher [4.42,4.19] were in the region of high importance-satisfaction, which indicated that the continuation of these factors could improve students’ satisfaction of physical education teaching. The role can increase students’ interest in sports and overall satisfaction with physical education sports training. From the side, it verifies the feasibility of the teaching strategy proposed in this paper. It provides data support for the in-depth implementation of quality education and the cultivation of moral, intellectual, physical, and aesthetic talents of all-round development.
Construction of Ideological and Political Education Innovation Mechanism in the Era of Big Data - Based on Data Analysis Methods
Authors: Sun Haitao 
Published: June 2024

Abstract
In the era of big data, the constantly innovative technology provides a new model for the development of ideological and political education. In this paper, on the basis of the data mining process, regression analysis and clustering algorithms are used to construct a visual analysis model of ideological and political education data in order to better provide teachers with more information about students’ data and explore the factors influencing ideological and political education. On this basis, an innovative mechanism for ideological and political education has been constructed, and the Introduction to International Politics course at X University of G Province is used as an example to analyze its effectiveness. The results show that there is a significant difference between the two groups, except that there is no significant difference in the overall learning motivation level (p=0.241>0.05). Compared with the pre-test, students in Group A showed a small downward trend in national identity, learning motivation, academic level, and its sub-dimensions as a whole (0.028-0.162). In contrast, Group B showed an upward trend, with an enhancement of between 0.134 and 0.352. The adopted innovative mechanism of ideological and political education can significantly improve students’ national identity and academic level in all aspects and effectively stimulate students’ endogenous learning motivation.
Exploring the Influencing Factors of English Classroom Teaching Quality in Colleges and Universities Based on Structural Equation Modeling
Authors: Wei Fang , Lv Meiqin 
Published: June 2024

Abstract
With the acceleration of globalization, the teaching quality of English, as an important tool for international communication, is getting more and more attention. In order to explore ways to improve the quality of English classroom teaching, this paper combines the characteristics of English classroom teaching in colleges and universities. It summarizes the structure of STEP influencing elements of English classroom teaching quality in colleges and universities based on teaching objectives and resource constraints. Then, taking X school as an example, the influence dimension structure of English classroom teaching quality in colleges and universities was mathematically analyzed and empirically examined using normality test, factor analysis, and least squares structural equation modeling (PLS-SEM). The results of the study show that the standardized path coefficients of teaching atmosphere and process on students’ competence and students’ competence on the quality of teaching in English classroom are 0.985 and 0.818, respectively, which passed the test of significance at the level of 1%. The post-standardized path coefficients of teaching atmosphere and process on students’ ability, students’ ability on teachers’ ability, and teachers’ ability on the quality of teaching in English classroom are 0.222, 0.674, and 0.073, respectively, and all of them passed the significance test on level 5%. Teaching English in classrooms in colleges and universities involves the interaction between teachers’ and students’ behaviors, as well as the integration of teaching and learning resources. The improvement of the quality of English classroom teaching in colleges and universities should pay more attention to the management and control of the process of how to realize the integration of teaching resources on the basis of students’ ability and teachers’ abilities.
Research on Online Interactive Teaching Platform of College English Combined with Semantic Association Network Modeling
Authors: Wu Fengqin 
Published: June 2024

Abstract
The emergence of semantic association networks has injected a new impetus for the development of online English teaching and provided a new model reference for the design of online education platforms. In this paper, the research and design of an online interactive teaching platform for college English draws on the algorithmic advantages of the semantic associative network model and utilizes the self-operation of the semantic associative network to realize the functions of autonomous addition, deletion, modification, and checking. The text semantic similarity is predicted by word embedding model, convolutional neural network, and other algorithms so as to better achieve the integration of teaching resources, connecting English knowledge and highlighting the teaching focus in the online teaching process of college English. Dynamic load balancing algorithms are used to solve the problems of short-term surges in the number of visits and the concentration of call requests, and the optimization of load balancing algorithms is further realized through genetic algorithms to finally complete the design of the online teaching interactive platform. Comparison experiments concluded that the semantic association network proposed in this paper could hold a more stable repair effect when cleaning inconsistent data in the dataset, highlighting the effectiveness of the semantic association network model in this paper. The online interactive teaching platform designed in this paper also performs well in the performance test, with only a 0.01% abnormality rate in the concurrency performance test, and the load balancing ability test also achieves the expected effect.
Research on the method of fine-tuning teaching quality improvement in higher education under digital empowerment
Authors: Xia Xiaotian 
Published: June 2024

Abstract
With the rapid development of information technology, digital technology is also continuously applied to the teaching of higher education, and colleges and universities more and more emphasize the application of intelligent teaching guidance systems. A feature-weighted Bayesian algorithm is constructed in this study by assigning different weights to each attribute depending on its influence on the Bayesian classification results. On this basis, combined with the error-reversing BP neural network algorithm, it realizes refined intelligent instruction and develops an intelligent instruction system for higher education based on the ternary model. The transaction success rate reaches 98% when the number of concurrent users is below 4000, and the transaction success rate is 90% at 8000, and the system has good stability. The average classification accuracy of FWNB algorithm in this paper is 84.95%, which is larger than SWNB and HCWNB, and the average classification time is 3.17s, which is smaller than SWNB and HCWNB. The FWNB classifier achieves high classification accuracy at a low computational cost. The system in this paper can better mobilize learners’ enthusiasm, drive learners to study diligently and inspire learners to improve their learning ability, which leads to improved teaching quality.
Construction and Evaluation of KPI Performance Appraisal Model in Personnel Management System of Higher Vocational Colleges and Universities
Authors: Zhao Nan , Su Hui , Wang Bing , Liu Ran , Yang Qin , Huang Tengjiao , Wu Xiaolong 
Published: June 2024

Abstract
In recent years, enhancing the overall instructional quality in higher vocational colleges and universities through robust performance management has emerged as a pivotal concern in educational discourse. This study delves into the challenges inherent in the existing personnel performance appraisal systems within these institutions, employing both questionnaires and interviews for data collection. To address issues such as the vague delineation of appraisal objectives and the diffuse focus of evaluations, we propose the development of a Balanced Scorecard (BSC) model tailored for assessing faculty performance. This model integrates the four dimensions of finance, customer satisfaction, internal processes, and learning and growth, establishing causal linkages among them. The appropriateness of performance indicators for faculty evaluation is subsequently quantified using the fuzzy comprehensive evaluation method. Experimental validation reveals that the prevalent appraisal systems suffer from indistinct objectives and a lack of clear focus, with 41.1% of respondents indicating that the performance appraisal index system for counselors is suboptimal. Consequently, this research underscores the significant role that the construction of KPI-based performance appraisal models plays in refining personnel management practices within higher vocational colleges and universities.
Identification of traditional settlement landscape areas in Hainan based on unmanned aerial remote sensing imagery
Authors: Xu Minghui , Chen Zhanchuan 
Published: June 2024

Abstract
To enhance the accuracy of recognizing traditional settlement landscapes in Hainan, this study introduces a landscape recognition model predicated on a full convolutional neural network (FCN). The research delineates the selection of specific types and sensors for Unmanned Aerial Vehicles (UAVs), employing UAV remote sensing technology to capture image data of Hainan's traditional settlement landscapes. The collected initial image data, laden with interference information, underwent a preprocessing step. On this foundation, the landscape recognition model for Hainan's traditional settlements was developed utilizing the FCN. The empirical results derived from deploying the model reveal the presence of ancient buildings within a 400-meter radius of the principal river. Notably, these structures predominantly span a distance ranging from 100 to 350 meters. The spatial distribution pattern of these ancient edifices notably centers around the Zong ancestral hall. Furthermore, when compared to other benchmark models, the proposed FCN model exhibits superior performance in recognizing forest, grassland, and farmland within the Hainan settlement landscape, achieving average recognition rates of 88.66% and 84.91%, respectively. This investigation underscores the significant potential for applying UAV remote sensing technology in identifying traditional settlement landscapes. It provides pivotal technical support and a reference point for the survey of forest resources and ecological monitoring, thereby enhancing the applicability and dissemination of UAV technology in landscape recognition tasks.
Human resource management and artificial intelligence integration development and innovation
Authors: Yu Yang 
Published: June 2024

Abstract
Artificial Intelligence is one of the important tools to realize the effective management of human resources. The article firstly investigates and finds out the problems of the current salary satisfaction status of the employees of column A of a news center, designs the job salary prediction model based on a collaborative filtering algorithm and the separation prediction model based on standard Logistic regression based on the results of the questionnaire, and finally calculates the economic benefits of the employees of column A of the news center. The performance bonus of column A of the news center in 2019 showed a steady upward trend as a whole, with 107,908 yuan in January and 198,320 yuan in December. The market value of column A of the news center increased steadily in 2020, reaching 171,134 yuan in January and 230,370 yuan in December. In 2021, the performance bonus of column A of the news center in January ~ June has not changed much, which is 112,099 yuan in January and 122,076 yuan in June. It can be concluded that the salary prediction model proposed in this paper has high accuracy in predicting the performance bonus of the news center A column, and the market value shows an increasing trend year by year. It can be seen from the analysis of the economic benefits of the news center A column after operating the salary and separation prediction model proposed in this paper the financial benefits after performance management have been changed for the better.