bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multimodal Analysis and Optimisation Strategy of Teaching Behaviour in Physical Education Classroom
Authors: Liu Guannan 
Published: June 2024

Abstract
In the context of the new curriculum, physical education has become an important part of school teaching. In this paper, the edge extraction algorithm and TV-L1 optical flow method are utilized to extract key image features in physical education classroom teaching, and the improved TE network structure extracts the skeletal data features of teachers and students in the classroom based on the TEMS module. Then, the MTHA-MDCNN deep migration model is established to fuse the three features to identify multimodal teaching behaviors in the sports classroom. Experiments show that the accuracy of this paper’s method for recognizing teaching behaviors in physical education classrooms can reach 77.63%. In the actual behavior recognition, the average values of teachers’ and students’ behavioral coverage are 32.68% and 67.77% respectively, which is in line with the optimal division of teaching time in physical education classrooms. In contrast, the students’ behavioral coverage of physical practice is low, which is not conducive to their physical and mental health development. The construction of teachers and supervision of teaching should be strengthened in schools, and students’ learning paths should be expanded. The multimodal analysis method of teaching behavior in this paper provides a reference method for targeted improvement of physical education teaching, and the proposed optimization strategy is important for the successful optimization of physical education classroom teaching in schools in the future.
Implementation and Effectiveness Evaluation of Library Virtual Reference Service
Authors: Wang Liping 
Published: June 2024

Abstract
This study aims to elucidate the relationship between user demand and library virtual reference services, as well as to explore the development of such services across fifteen colleges and universities in China. Leveraging user research data, we established goals for virtual reference services. We introduced the VCRA-CP (Virtual Consultation Resource Allocation-Customer Profile) model for resource scheduling to better align with user needs. User clustering techniques were utilized to categorize users, and a hybrid push information service combining clustering service and collaborative filtering was adopted. This approach was further enhanced by integrating a “user-service evaluation model” to score and deliver services. The operational dynamics of the virtual reference consultation service were meticulously analyzed, particularly focusing on the real-time consulting function module. We proposed evaluating the effectiveness of virtual reference services based on three dimensions: consulting system performance, service impact, and information control. The evaluation metrics included average acceptance value, average perceived value, and average expected value from users. A critical finding concerning the “timeliness and accuracy of responses in virtual reference consultations” revealed a discrepancy, with the average perceived value minus the average acceptance value equaling −0.15, indicating a significant area for improvement in response timeliness and accuracy. User satisfaction was quantified at 65.86%, with a dissatisfaction rate of 6.93%. Although the overall satisfaction with the virtual reference consulting service was high, there remains a need to enhance the standardization of these services to better meet user expectations.
Research on the Integration Strategy of Student Management and Curriculum Education in Colleges and Universities Based on the Integration Model
Authors: Wang Xiaoxia , Xia Fan 
Published: June 2024

Abstract
Improving the efficiency of curriculum education and student management according to the changing characteristics of the social environment is of great significance to the construction of a perfect social and cultural system. This paper constructs a fusion model of student management and curriculum education, taking into account the weaknesses and outstanding problems in both areas. In order to accurately control the behavior of university students, a student behavior prediction method based on the fusion model is proposed, which first uses the k-means clustering method to cluster the daily activity frequency sequences of all users and then realizes the prediction of the individual mobility of students through the LSTM model based on the historical records, and extracts the collective location preference of each user cluster. The effectiveness of the proposed fusion model is assessed using BJ University Daxing Campus as a case study. The results show that after applying the model proposed in this paper, students’ perceptual, language expression, language comprehension, cognitive, social, emotional, and behavioral abilities are improved by 0.941~1.256 points compared with those before the application of the model, with a significant difference of P<0.01, which verifies that the fusion model has strong feasibility. It has positive significance for the work of student management and curriculum education. Research has a positive significance.
Research on the Application of Intelligent Sports Tracking System in Improving the Teaching Effect of University Physical Education
Authors: Yan Xinting , Luo Zhongjian , Wu Li 
Published: June 2024

Abstract
The enhancement of physical education’s stature necessitates imminent reforms in university physical education models. This study examines the deployment of an intelligent motion-tracking system within physical education, exploring facets such as the precise capture and real-time analysis of movement postures, movement evaluation, and optimization, as well as movement recording and tracking. It further investigates the system’s accuracy and convenience, assessing its impact on enhancing university physical education pedagogy. Our methodology involved constructing a framework for the intelligent motion tracking system, followed by developing a motion model. The design of the intelligent tracking model proceeded through initialization, sampling and prediction, observation, and resampling phases, concluding with the implementation of a complete human body tracking motion algorithm utilizing the VHMT algorithm. Our analysis revealed optimized posture and morphology of the upper arm and forearm during exercise, indicative of enhanced physical education teaching at the university level. Prior to implementation, in the volleyball class scenario, no significant difference was observed between the experimental and control groups in terms of motor skills, with a difference ranging from 1.82 to 4.4 (P>0.05). Post-implementation, however, the motor skill level of the experimental group in each class significantly surpassed that of the control group (P<0.05), with improvements ranging from 6.8% to 43.4%. The integration of an intelligent motion-tracking system in university physical education substantially fosters students’ motor skill acquisition and invigorates their enthusiasm for learning.
Automatic Identification and Tracking Method of Case-Related Vehicles Based on Computer Vision Algorithm
Authors: Wei Dan , Chen Bin , Lin Yujie 
Published: June 2024

Abstract
Amidst the rapid advancement of artificial intelligence, computer vision algorithms have found extensive applications across various societal sectors. This paper presents the development of an automatic vehicle identification algorithm for crime-related scenarios, leveraging enhancements in computer vision technology. Initially, we refine the multi-scale feature fusion within the YOLOv4 architecture, subsequently substituting the standard convolution in the feature extraction network with depth-separable convolution to minimize parameter computation. Furthermore, we replace the conventional CIOU target localization loss function with EIOU to expedite model convergence. To address the issue of target vehicle detection failures, we incorporate the Kalman filter algorithm, ensuring precise tracking. Our experimental analysis, which utilizes both target detection and multi-target tracking evaluation indices, demonstrates that the modified YOLOv4 algorithm excels in recall, precision, and average IOU metrics compared to other algorithms, with a mean Average Precision (mAP) of 95.68% and an average detection speed of 0.039 seconds per image, satisfying real-time operational criteria. Vehicle tracking efficacy, compared using the YOLOv4-based detector before and after the modifications, shows significant improvements, indicated by a reduction in Identification Switches (IDS) across all video samples. This study introduces a robust method for the accurate identification and localization of vehicles involved in criminal activities, significantly enhancing case resolution efficiency.
A dynamic planning method for satellite imaging mission based on improved genetic algorithm
Authors: Zhao Demin , Xiong Wei , Wang Yiran 
Published: June 2024

Abstract
The ongoing enhancement of imaging satellite platforms in terms of payload capacity, coupled with the proliferation of imaging satellites, introduces new complexities to the mission planning processes. These enhancements enable broader applications and significantly increase the societal benefits derived from imaging satellites. To address these challenges, a specific kinematic model for dynamic imaging attitudes is constructed, taking into account the dynamics of satellite imaging missions. This model uses information from satellite imaging observation tasks to design constraints that govern the planning of imaging tasks. Additionally, an optimization objective function is established to ensure compliance with these planning constraints. Building on the encoding method for relative imaging moments, an adaptive genetic algorithm tailored for satellite imaging task planning is introduced. This algorithm enhances the iterative efficiency of decision variables involved in satellite imaging tasks. Empirical validation through comparative simulation experiments, using a typical satellite imaging mission as a case study, demonstrates the effectiveness of the adaptive genetic algorithm. In various phases of imaging mission planning, the algorithm achieved a 100% task completion rate. The index function gain was enhanced by 21.47%, and the maximum synthetic angular velocity of attitude maneuvers between different targets peaked at the satellite’s maneuvering threshold of 7 degrees per second. By leveraging adaptive genetic algorithms, satellite imaging mission planning can optimize mission completion rates and effectively utilize the satellite’s maximum attitude maneuver capabilities.
The Impact of Artificial Intelligence and Consumer Behavior Interaction on Corporate Brand Management and Marketing Strategies
Authors: Luo Lei 
Published: June 2024

Abstract
The behavioral interaction between artificial intelligence and consumers can bring consumers multifaceted experiences, which are of great value for enterprises to improve brand management and adjust and optimize market strategies. In this paper, we construct a gray multiple linear regression model by combining the classical multiple linear regression model and gray system theory. Taking consumers as the research object, we design a questionnaire and carry out the reliability test to obtain the data related to the experience brought by behavioral interaction with consumers and the brand management and marketing strategy of enterprises. Finally, this paper takes AI and consumer behavioral interaction as the independent variable and corporate brand management and marketing strategy as the dependent variable. It carries out regression analysis to explore the influence of the independent variable on the dependent variable. The results show that: corporate brand management = 1.879 + 0.317*sensory experience + 0.283*emotional experience + 0.168*thinking experience + 0.221*personalized experience − 0.187*privacy concerns, and corporate market strategy = 1.943 + 0.285*sensory experience + 0.266*emotional experience + 0.191*thinking experience + 0.307*personalized Experience + 0.144*Privacy Concerns. The research results of this paper are of strategic significance to help enterprises understand the significance of the interaction between AI and consumer behavior, use AI to understand consumer groups, maintain good brand management, and adjust market strategies in a timely manner.
Workers and Safety Helmets Detection in Day and Night Scenes based on improved YOLOv5
Authors: Ma Guofeng , Jing Yiqin 
Published: June 2024

Abstract
Safety helmets, as crucial protective equipment, significantly contribute to the head safety of workers. Adherence to safety helmet regulations is integral to construction site safety management. Recognizing the limitations inherent in manual supervision methods, we have developed a vision-based framework for the detection of workers and their safety helmets. This framework features enhancements to the YOLOv5s model, resulting in the advanced YOLOv5-Pro. The enhanced YOLOv5-Pro model achieved a mean Average Precision (mAP) of 95.4% on the validation set, marking an improvement of 3.6% over the original model. Furthermore, we expanded the utility of the YOLOv5-Pro model by incorporating nighttime data augmentation. The augmented YOLOv5-Pro model demonstrated robust performance in both daytime and nighttime conditions, as evidenced by our experimental results.
Experimentation and analysis of network anti-mapping security access techniques for illegal scanning
Authors: Li Rui , Liu Zehui , Guo Min , Gao Wei , Liu Hengwang 
Published: June 2024

Abstract
With the rapid development of network technology, the increasing scale of the network, and the more complex network structure, network anti-mapping puts forward higher requirements. In this paper, based on the game theory of the network anti-mapping strategy selection method, the network mapping attack is divided into the reconnaissance stage and the mapping stage. According to the opacity of the information of both attackers and defenders, the attacker collects the defender’s information in the reconnaissance stage and introduces the signal game to construct the reconnaissance game model. The attacker and defender in the two-stage game utilize Bayesian equilibrium to solve the problem and select the strategy that maximizes their utility based on the assumed conditions. The results show that for the attack of illegal scanning, the CFE statistics of the attack data increase from 1.5~1.9. The game theory-based network anti-surveillance security access technology can effectively identify subnet and multi-IP devices and, at the same time, reduce the network load, and the network topology nodes can be up to 2134 degrees of freedom, which effectively improves the efficiency of network anti-surveillance.
Research on Quality Evaluation of Smart Education Schools Based on Fuzzy Clustering Algorithm and Strategies for Future Development
Authors: Lou Yingying 
Published: June 2024

Abstract
The burgeoning field of wisdom education has led to a disparity in the quality of educational institutions, thereby necessitating enhancements to the existing school quality evaluation systems. This study refines the traditional fuzzy C-mean clustering algorithm by incorporating a Gaussian mixture distribution and information entropy regularization. It then employs this enhanced algorithm to construct a quality evaluation system for smart education schools. The improved fuzzy C-mean clustering algorithm demonstrated in this paper significantly outperforms the standard C-means used at SCU and the K-means algorithm in terms of Mean Absolute Error (MAE) and Total Hit Rate (THR). Specifically, the optimized algorithm decreased the MAE to 0.572 from 0.619, a reduction of 16.01% compared to the K-means algorithm. It increased the THR to 0.158 from 0.147, marking an improvement of 17.04% over the Kmeans algorithm. Furthermore, the refined clustering algorithm achieved an impressive accuracy of 95.71%. Analysis through this clustering approach reveals that the developed quality evaluation system accurately reflects the operational level of smart education within schools. The establishment of such a system is anticipated to significantly guide the future progression of smart education and aid in the foundational development of smart education schools.
Innovative Application of Data Mining Technology in College Information System Based on Informatized Teaching Environment
Authors: Chen Liguan 
Published: June 2024

Abstract
With the rapid development of information technology, the development of information technology has become the main focus of national development, and the informationization of education in colleges and universities has also been mentioned as a strategic height, and various fields such as student management and teaching methods are also being continuously intelligentized and informative. In this paper, we obtain relevant data from the information systems of colleges and universities and use the K-Means clustering algorithm based on information entropy and density optimization to cluster and analyze educational data. The optimal clustering coefficient is used to determine major school behavioral characteristics, such as students’ academic performance, consumption amount, access control records, and so forth. It can be analyzed. Are respectively used as the clustering dimensions of K-Means, and the information entropy is used to determine the attribute weight values to classify these dimensions, and to find out the common prominent characteristics in their same class. Then analyze the laws of student behavior and cluster them into categories. The academic early warning model evaluation index is used to verify the model’s accuracy. Among them, the academic early warning model proposed in this paper has an accuracy rate of 95.2%, while the precision rate and recall rate at different warning levels reach more than 90%. The validity of the model has been verified. The study shows that the introduction of educational data mining technology can solve the pain points for modeling and analyzing diverse data and accurately predict student behavior.
A Study of the Challenges of Digital Currencies to the Traditional Financial System and Their Implications for Economic Policy
Authors: Shi Yue 
Published: June 2024

Abstract
Digital currencies, as an emerging monetary form, have profoundly transformed the landscape of financial transactions and redefined the structure of economic ecosystems, exerting an unprecedented influence on traditional financial systems. This study scrutinizes the ramifications of digital currencies on conventional financial systems, utilizing a theoretical framework as the foundation. It specifically employs Vector Autoregression (VAR) and Dynamic Stochastic General Equilibrium (DSGE) models to construct an impact model that examines the influence of digital currencies on monetary policies. The analysis utilizes empirical data to assess the efficacy of digital currencies in shaping economic policy, focusing particularly on their effect on monetary intermediation targets. The findings reveal that digital currencies significantly affect monetary policy, primarily through mechanisms of interest rate and exchange rate transmission. Moreover, the advent of digital currencies is associated with reductions in the money supply, exchange rates, and credit volumes of commercial banks. The analysis establishes a long-term stable equilibrium among the money supply, digital currency volume, cash leakage rate, fixed-to-floating rate ratio, and excess reserve ratio. Notably, the scale of digital currency exerts the most substantial impact on the money supply, contributing 10.89% to its variance. This study underscores the intricate relationships between digital currencies and economic policies, highlighting their critical role in contemporary financial systems.
Research on the design and application of wind turbine mechanical parts based on parametric modeling
Authors: Bi Yadong 
Published: June 2024

Abstract
Based on ANSYS finite element analysis software, a parametric modeling interface for wind turbine blades and pitch bearings is developed using parametric design language. The parametric modeling interface is used to quickly establish a simplified finite element model of the blade and pitch bearing so as to optimize the design of the blade structure, calculate the maximum tensile stress of the bearing and bolt connection, and analyze the relationship between the working stress of the bearing and bolt and the bolt preload. The analysis and calculation results guide the safety and reliability of wind turbine parts design. After analysis, the optimization of blade structure is mainly in the region of leaf root enhancement, the number of layers laid in the cross-section of No. 1-3 has no major modification, the bi-directional cloth in the cross-section of No. 3-6 is increased by 55 layers, 5 layers reduce the cross-section of No. 7-8, and the cross-section of No. 16-23, 1 layer of the bi-directional cloth is increased in order to improve the strength of the tip of the blade and the resistance to bending capacity. The preload force applied to the pitch bearing during the actual installation process is generally 50% to 70% of the yield limit of the bolt material. The pitch bearings have the best load-carrying capacity under ultimate loads, with the ditch curvature radius coefficient and clearance generally around 0.530 and −0.12mm.
Strategies for Integrating Historic District Preservation and Modern Functions in Urban Renewal
Authors: Bian Hongxu , Shi Yuefeng 
Published: June 2024

Abstract
Through the corresponding investigation and analysis, this paper derives the evaluation indexes of the coupling degree of coordination between the protection of historical neighborhoods and tourism development, establishes the weight of each index through the entropy method, conducts the mathematical and statistical analysis of the data, and applies them to the coupling relationship between the protection of historical neighborhoods and the development of modern functions, and constructs the coupling degree of coordination evaluation model to evaluate the relationship between the two. The results show that the commercial/residential interface ratios of Sanfangqixiang, Langzhong, and Zhouzhuang in the sample historic districts are 0.67, 0.88, and 049. In the type structure of the sample districts’ businesses, retail businesses account for 50% or less, catering businesses account for 36%-57%, and specialty businesses account for less at present. Langzhong and Nanluoguxiang have the best coupled and coordinated development of historic district protection and tourism development, with coupled coordination degrees of 1.968 and 1.956, respectively. Therefore, this paper uses the coupled coordination evaluation model to explore the coordinated development of historic district protection and modern functions and to continuously adjust and optimize the integration strategy for sustainable development.