bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of Interactive Multimedia System in Early Childhood Dance and Artistic Creativity Cultivation
Authors: Bai Wen 
Published: June 2024

Abstract
This paper utilizes MPEG4 technology to design an interactive multimedia system and applies the system to instruct young children’s dance movements and techniques in their dance learning. The implementation process of the system is to use a multi-layer neural network to establish a coarse estimation method of dance posture, and then through the root node deep regression network to deeply estimate and analyze the 2D posture of young children in the process of dance. Finally, the multi-feature fusion module is used to fuse the skeletal points and key point features of the body to analyze the 3D posture of young children during the dance. After testing the recognition effect of the system and analyzing the application effect of the interactive multimedia system, the overall movement completion time of the spiral dance in the experimental group was significantly reduced, and the time required for the rotational movement in the dance was also reduced from the initial 1.22 s to 0.899 s. There were significant differences in the levels of artistic creativity in all dimensions of artistic creativity before and after the application of the interactive multimedia system (P<0.05). This paper provides a reference method for improving the ability of young children to dance and cultivate artistic creativity. Also, it lays the foundation for the educational application of interactive multimedia systems.
The Interaction between Dramatic Writing and the Construction of National Identity in the Southern National Society Period
Authors: Deng Hong 
Published: June 2024

Abstract
Starting from the background of the development of Chinese and Western cultures, this paper analyzes the textual themes and interprets the ideas of the dramas created during the period of the Nanguosha. Taking into account the specificity of the era and the national perception of Nanguosha’s theatrical works, the concept of theatrical cultural interaction is put forward to analyze the basis of cultural interaction in the cultural acceptance behavior of drama. Audience interaction behavior and audience emotional intelligence are chosen as intermediary and moderating variables respectively, and a four-variable research model is constructed for the creation of Nanguosha’s dramas, the construction of national identity, audience interaction behavior and audience emotional intelligence. Introduce the causal mediation effect model based on Nanguosha theater culture, explain the symbolic variables, and discuss the estimation method for the causal mediation effect. Combine the empirical data with the descriptive analysis and correlation analysis of the variables, and carry out the main effect test, mediation effect test, moderating effect test, and moderated mediation effect test of the research hypotheses in order to verify the interaction of the variables. After introducing the mediator variable of audience interaction behavior, the regression coefficient of Nanguosha’s drama creation on national identity construction is β =0.198, P<0.01, which confirms that there is a relationship of positive influence of Nanguosha’s drama creation on national identity construction, and that the audience interaction behavior plays a partly mediating role in the process of Nanguosha’s drama creation’s role in national identity construction.
Demonstration and Analysis of DRN Deep Learning Architecture for Step-by-Step Disassembly of Dahongquan
Authors: Tian Xixiang 
Published: June 2024

Abstract
The Dahongquan martial arts style is characterized by its intricate and varied maneuvers, posing challenges for traditional video demonstrations to fully satisfy learners’ needs. This study introduces a cutting-edge approach utilizing a visual neural network to deconstruct and map these martial arts movements in a 3D virtual environment. Initially, preprocessing involves extracting data from exercise videos, employing a combination of video annotation and skeleton feature extraction. Subsequently, a deep residual network (DRN) with multi-scale feature extraction and dense residual attention mechanisms is employed, facilitating a systematic breakdown of the martial arts movements. The third phase involves constructing a 3D action mapping and demonstration process, utilizing cubic B-spline curve fitting and cylinder deformation techniques to enhance the representation of movement based on the skeletal model. Remarkably, the DRN network, without the need for extensive training datasets, achieves flawless recognition of various gongfu combinations and records a recognition rate of 92.92% for the most challenging boxing movements. The efficacy of this model is further corroborated by its enhancement of the demonstration process, achieving an overall learner satisfaction rating of 5.26. The disassembly and demonstration strategy proposed in this study has proven effective during experimental sessions, demonstrating significant potential for advancing martial arts training methodologies.
Research and development of low-energy and high-efficiency wind-fed tobacco technology
Authors: Guo Yueping , Hu Dianguo , Lu Zhenyue 
Published: June 2024

Abstract
The current wind silk-feeding system in cigarette factories has defects such as randomness, uncertainty, and frequent changes in wind volume, which are the fundamental causes of the system’s non-energy saving and unstable silk-feeding wind speed. This paper proposes to construct a new wind silk feeding system based on fuzzy RBF by utilizing the functional equivalence of fuzzy inference systems and RBF neural networks from a global perspective. Then, according to the process characteristics of the wind balance control system, the use of fuzzy networks for PID control of the core system is proposed. The practical application of the new wind power silk feeding system confirms the energy-saving effect of the improved program. Measurement results show that after the improvement of the fan frequency from 50Hz down to 38Hz, power from 35kW down to 25kW, the system runs smoothly, the wind speed is stable, and the energy saving can be up to 25%~40%. This paper not only provides a new way of thinking for the optimal design of wind power wire feeding projects but also has reference value for guiding the selection and control scheme of the project.
The Transformation of Traditional Chinese Painting Elements in Modern Design Contexts
Authors: Ji Fan 
Published: June 2024

Abstract
To ensure the effective preservation and evolution of traditional painting art, it is imperative to integrate contemporary art design concepts and methodologies. This paper focuses on the reconstruction of traditional Chinese painting elements, distilling and adapting its pattern, ink, and calligraphy components into a modern design framework. Specifically, the features of the pattern elements are identified and transformed using a feature point extraction algorithm combined with K-Means clustering. For ink and calligraphy elements, a fusion of self-attention mechanisms and Generative Adversarial Networks (GANs) facilitates the stylistic migration and conversion of these elements through the construction of distinct discriminators. Subsequent evaluation of the transformed painting elements utilized Importance-Performance Analysis (IPA) within a contemporary design context, aiming to assess the practical value of traditional painting elements in modern design. The findings indicate that the clarity of information (C1) and its accuracy and efficacy (C3) in the context of contemporary design achieved importance scores exceeding 3.6. Furthermore, the satisfaction scores for modeling (C10), color (C11), and imagery (C12) each surpassed 3.5, denoting a high level of satisfaction in the IPA of their conversion. The study enriches traditional artistic methods and conceptualizations, thereby enhancing their cultural depth and significance.
Research on Innovative Models of Piano Education Driven by Artificial Intelligence
Authors: Ji Chen , Tong Meng 
Published: June 2024

Abstract
Piano education has been paid more and more attention to in recent years. How to be able to teach in a more targeted way and improve the efficiency of students’ daily practice has become an urgent problem to be solved in the popularization of piano education. This paper innovatively combines artificial intelligence technology with piano education and designs an innovative education model for smart piano based on the cloud-based score, intelligent analysis, and gradient teaching functions of smart piano. The model utilizes the piano transcription algorithm of machine learning and the alignment technology of HMM to determine the learner’s piano practice and introduces the attention mechanism in the bidirectional recurrent neural network to realize the function of automatic piano harmony orchestration while testing the performance of both. On this basis, the designed intelligent piano education model is implemented, and the innovative effectiveness of the model is tested through comparative experiments. The results show that the completion of piano music and the artistic expression of music of students using the intelligent piano teaching mode improved by 10.097 points and 11.048 points, respectively, compared with students using the traditional teaching mode, and their p-values are less than 0.05, which is a significant improvement. This study enhances the variety of piano education and enhances piano learning efficiency.
Deep Learning-based Computational Electromagnetics for Deep Exploration of Oil and Gas Reservoirs
Authors: Li Na , Liu Hongmei 
Published: June 2024

Abstract
The exploration potential of oil and gas is substantial, with considerable untapped resources available for future exploration and development. This paper investigates the application of time-frequency electromagnetic (EM) methods within the domain of computational electromagnetics, enhanced by deep learning techniques, for the profound exploration of oil and gas reservoirs. To identify zones favorable for hydrocarbon presence, it captures electric and magnetic field responses across time and frequency domains. From these responses, it extracts crucial parameters such as resistivity and excitation polarizability. The methodology initiates with a forward simulation to establish the most productive construction parameters for the time-frequency EM approach. Subsequently, data collected are processed and analyzed to qualitatively evaluate each physical parameter. This is followed by quantitative constraints on resistivity and polarizability, employing the adaptive differential evolution algorithm to execute a comprehensive parameter inversion using the horizontal E-field (Ex) data derived from the time-frequency EM method. This inversion facilitates the determination of resistivity and polarizability distributions at various depths, culminating in the generation of corresponding depth cross-sections. The inversion fitting and example analyses yielded that the apparent resistivity profiles of low-resistance backslope formations and backslope formations with high and low resistance interlayers form relative anomaly magnitudes of about 13% and 7.8%, respectively. In the real measurement from the depression to the uplift zone, the thickness of the giant inclined low-resistance conductive layer gradually decreases. The points of the extreme values and minima gradually move from the low-frequency band to the high-frequency band, which is identical to the simulation law. The anomaly amplitude is between [-0.4 Ω·m, 0.6 Ω·m], which can be accurately imaged. This study innovatively combines deep learning technology and electromagnetism, which provides a basis for the next decision on oil and gas exploration and target evaluation preference.
An analysis of the predictive efficacy of EDM, a model of emotional dynamics in second language acquisition, on anxiety and enjoyment in foreign language classrooms
Authors: Li Xueying 
Published: June 2024

Abstract
In recent years, foreign language classroom anxiety and enjoyment have received widespread attention for their significant effects on second language acquisition. Therefore, based on the theory of complex dynamic systems, this paper recruited five students in a second language classroom as research subjects and used the individual dynamics method and EDM modeling to follow up their foreign language anxiety and enjoyment in the second language classroom over a short window of time. After completing the data collection, correlation analysis and PLS regression analysis were combined in statistical analysis to explore the predictive efficacy of the emotion dynamic model on anxiety and enjoyment in the foreign language classroom. The results showed that there was a nonlinear dynamic correlation pattern between foreign language enjoyment and foreign language anxiety, which continuously alternated between zero, negative, and positive correlations, and the dynamic pattern was individualized. Learners’ emotional experiences and positive (intrinsic and external) emotional values together predict 44% and 67% of anxiety and enjoyment in foreign language classrooms. This study contributes to foreign language teachers’ further understanding of the mechanisms of positive and negative emotions and has implications for teaching second languages.
Model development for the application of sports data visualization techniques in sports science research
Authors: Tan Liang , Wu Jianwei , Chen Qin , Chen Weixiang , Liu Tianyu 
Published: June 2024

Abstract
With the continuous development of visualization technology and machine learning algorithms, sports data visualization is more and more widely used in sports training and management. In this paper, the visualization interface based on MFC and the drawing virtual environment of OpenGL is used to display the human motion data visualization function. Meanwhile, an algorithm based on a normalized Euclidean distance matrix is proposed in the process of mapping the sports data to virtual character models with different morphologies in order to maintain the specific relationship between body parts and between the body and the environment space. Applying motion data visualization to the individual performance of sportspersons, the similarity matching algorithm for motion data streams is investigated, and a DTW algorithm based on early stopping with lower complexity and less computation is proposed. The application of sports data visualization technology involves analyzing human movement patterns and proposing a gait prediction method that improves prediction accuracy by 84.8% compared to the prediction method proposed by Alexander. In conducting a comparative study of athletes’ movement flow, the scoring results of this paper’s sports data visualization technology have a P-value of 0.864 compared with the scoring results of the coach. Cohen’s d-value of the magnitude of the difference is 0.087, which is not a significant difference and is able to satisfy the requirements of sports training methods.
Digital Transformation Paths in English Language Teaching from an Eco-Linguistic Perspective
Authors: Tang Zhenzhen 
Published: June 2024

Abstract
This paper follows the guiding principles of eco-linguistics for English teaching, improves the encoder of the question generation model, uses the gated self-attention mechanism to refine the textual information, obtains the embedded representations of the articles, fuses them with the embedded representations of the answers, and constructs a new automatic generation model of the English teaching exercises as a way of assisting in the digital transformation of the English classroom. After the controlled test, it was found that the post-test control class scored 71.954 points, and the experimental class scored 84.309 points, which was 12.355 points higher than the control class, with Sig. The two-tailed test resulted in a coefficient of 0.035, which was less than 0.05, and there was a significant difference in the post-test scores. In the survey of interest in learning, the average number of respondents who chose “strongly agree” or “agree” was 89%, 8.5% said “uncertain”, and 2.5% “disagreed”. Overall, the interest of the experimental class students in English learning has improved significantly. They not only pay more attention in class but also have a higher interest in practicing English after class. The digital transformation of traditional English classroom teaching is addressed in this study through the provision of new ideas and methods.
BIM (Building Information Modeling) based collaborative design and construction process optimization
Authors: Ke Dandan 
Published: June 2024

Abstract
BIM technology is an important method to solve the problem of collaborative design and construction processes in the construction process of engineering projects. Based on BIM, this paper proposes a corresponding optimization model from the perspective of collaborative design and construction processes, respectively. In the collaborative design optimization model, the engineering collaborative management theory is utilized to extract indicators of schedule, cost, and quality to establish an IFC-based collaborative design optimization model. A multi-objective genetic algorithm is employed to solve the construction process optimization model by establishing the optimal scheduling model with schedule and cost minimization objectives. As instance, a second-class residential land use project in Chaoyang District, Beijing, is the subject of collaborative design and construction process optimization analysis. The optimization rate of the crossbeam floor cover solution compared with the slab floor cover solution without secondary beams is 13.5%, and the optimization effect of the co-design process is evaluated at 81.48 points, which reaches the standard of “good”. During the construction of the 6# building, the shortest duration of wall panel construction is 0.8416d, and the duration practice is shortened by 0.1467d. The overall construction quality score is 81.126, which reaches the level of “good”.
Research on the Integration of User Modeling and Design Strategies in the Design of Aging-Ready Products
Authors: Wu Qiongyao , Yao Yuan 
Published: June 2024

Abstract
This study introduces a novel product design strategy tailored for older people, premised on the physiological and psychological transformations associated with aging. It further develops an algorithm for designing age-appropriate products, utilizing a multilevel behavioral modeling approach. The algorithm employs a self-attention network and a recurrent neural network for serial data modeling and model training, respectively. Subsequently, this algorithm is harmonized with the initial design strategy to create a comprehensive product design scheme tailored to the user model. The efficacy of the proposed model is evaluated by comparing it with six other models using a designated test dataset. As a practical application, an online shopping application was redesigned specifically for older users. Comparative analysis between the original “Mobile Taobao” and the redesigned “Mobile Taobao Senior Edition” revealed that task completion times were significantly reduced from 162.64 seconds to 98.895 seconds and further to 63.745 seconds post-improvement. Additionally, user experience metrics such as flow, page layout, graphic harmony, page color, and text readability were enhanced by increments of 1.195, 0.47, 1.44, 1.07, 0.545, and 1.755, respectively. These results substantiate the effectiveness of the proposed design approach in enhancing the usability of age-friendly products. Ultimately, this research aims to pioneer new concepts and methodologies for the design of internet-based products that cater to the aging population, thereby enhancing their accessibility and user experience.
Research on Technical Approaches of Artificial Intelligence Algorithms to Simulate Complex Human Interactions in Virtual Reality Environments
Authors: Xun Wei , Zhang Songlin 
Published: June 2024

Abstract
In the era of rapid technological development, the integration of artificial intelligence and virtual reality technology has gradually become a research hotspot. Interpersonal interaction, as an important part of virtual reality, also presents opportunities and challenges. The study builds the virtual interpersonal interaction model using the emotion model and hierarchical emotion as the learning algorithm after exploring the key technology of virtual interpersonal interaction based on the UGC model. Finally, the complex interpersonal interaction in virtual reality is simulated to examine the use of virtual community users and the factors affecting interpersonal interaction in virtual communities. According to the results, the percentage of female users of virtual communities is 14.28 percentage points higher than that of male users. The majority of people aged 18-40 who use virtual communities account for 88.01% of the population. In terms of education distribution, the group of bachelor’s degree holders is the group that uses virtual communities the most frequently, accounting for 66.52%. The study concluded that 8 out of the 11 hypotheses it proposed were valid. Among them, hypothesis 3 and hypothesis 11 were supported by 0.01 significance, and hypothesis 9 had a significance of 0.017, which reached 0.05 significance level.
The Application of Multimedia Network Teaching Based on Big Data in Physical Education Teaching in Colleges and Universities
Authors: Yang Mingzhe , Meng Dan 
Published: June 2024

Abstract
The integration of educational information technology presents novel opportunities for revolutionizing traditional pedagogical approaches in college physical education (PE). This study leverages big data analytics through the application of the OpenPose algorithm to meticulously map human postural features, thereby refining instructional design within the college PE framework. In parallel, to align pedagogical resources more precisely with the unique demands of college education, a decision tree algorithm is employed to derive comprehensive user profiles from a cohort of college students, which aids in the targeted recommendation of educational materials. Moreover, this research incorporates online teaching methodologies to develop and implement a big data-driven multimedia network teaching platform tailored specifically for college sports education. Empirical evidence underscores substantial benefits following the platform’s deployment. Post-implementation metrics revealed a marked increase in both the duration and frequency of students’ independent physical activities; the proportion of students engaging in 91-120 minutes of exercise escalated by 15%, and those exercising between three to five times per week saw an increase of 16%. Additionally, the platform significantly enhanced the PE performance of students. Users of the platform attained an average score of 89.29 in their assessments, surpassing the control group’s average of 83.23, who did not utilize the platform. Consequently, this multimedia network teaching platform emerges as a formidable enhancement to college physical education, delivering significant pedagogical advancements and fostering increased physical activity among students.