bluuebunny/crossref_metadata_2025 huggingface dataset:
The Construction of Higher Vocational English Language Bank Based on Neurolinguistic Knowledge
Authors: Deng Yuhua
Published: June 2024

With the advent of artificial intelligence and big data technologies, traditional approaches to teaching English in higher vocational education are increasingly inadequate for contemporary pedagogical demands. This study involved the collection and analysis of an extensive corpus of English learning materials facilitated by the use of neural network models to process this data. Consequently, we established an English language library tailored to the authentic language use and learning needs of higher vocational students. A substantial array of textual materials pertinent to higher vocational English instruction, including textbooks, online articles, and practical dialogues, was amassed using web crawler technology. These texts underwent natural language processing that included tokenization and the elimination of stop words. To develop a language model, we deployed a Transformer-based neural network for deep learning analysis of the processed data. Our findings indicate that the constructed language bank excels over traditional higher vocational English teaching resources in terms of vocabulary breadth, grammatical diversity, and practical utility. The language bank demonstrates notable benefits in analyzing vocabulary usage biases and enhancing students’ pragmatic language skills. This research substantiates the feasibility and effectiveness of constructing a language bank for higher vocational English utilizing neurolinguistic methodologies. The integration of advanced language modeling and big data processing technologies can significantly elevate the caliber of educational resources and instructional efficacy
A study on the modeling of shooting probability in basketball sports competition sport
Authors: Gao Quanzhong
Published: June 2024

In basketball sports competition sports, shooting percentage is an important indicator of basketball players. In this paper, the trajectory of basketball sports shooting is solved by mathematical modeling and simulation. In the first step, the hyperparameter estimation of the variance is obtained by using the SURE-Type Double shrinkage estimation method. In the second step, the variance of the SUREDSC method is determined by sampling from the hyperparametric distribution using the Bootstrap method. In the third step, the covariates were taken into account and combined with the Ghoreishi bi-level shrinkage estimation method to obtain the optimal shrinkage estimate of the probability of hitting a basketball shot. The results of the study yielded that α differs from β when α ≤ 75°. When α >75°, α and β are very close. When the angle of incidence is certain, the angle of the ball shot is negatively correlated with the height of the athlete, and the degree of reduction decreases with the increase of the angle of incidence. The prediction success rate of the shot probability model was 87.98%, and the overall error rate was small, and the prediction results of the seven games were basically consistent with the prediction results of the constructed shot probability model. The study offers a guide for basketball managers to weigh the pros and cons of player transfers.
Deep Learning-based Knowledge Graph and Digital Twin Relationship Mining and Prediction Modeling
Authors: He Fangzhou , Bai Wei , Wang Zhiqi
Published: June 2024

The era of big data produces massive data, and carrying out data mining can effectively obtain effective information in huge data, which provides support for efficient decision-making and intelligent optimization. The purpose of this paper is to establish a digital twin system, preprocess massive data using random matrix theory, and design the knowledge graph construction process based on digital twin technology. The BERT model, attention mechanism, BiLSTM model, and conditional random field of the joint deep learning technology are used to identify the knowledge entities in the digital twin system, extract the knowledge relations through the Transformer model, and utilize the TransE model for the knowledge representation in order to construct the knowledge graph. Then, the constructed knowledge graph is combined with the multi-feature attention mechanism to build an anomaly data prediction model in the digital twin system. Finally, the effectiveness of the methods in this paper is validated through corresponding experiments. The TransE model is used for knowledge representation. The accuracy of ternary classification is higher than 80% in all cases, and the MR value decreases by up to 64 compared to the TransR model. The F1 composite score of the anomaly data prediction model is 0.911, and the AUC value of the validation of knowledge graph effectiveness is 0.702. Combining deep learning with the knowledge graph, the knowledge information can be realized in the digital twin system’s accurate representation and enhance the data mining ability of the digital twin system.
Research on optimization of e-commerce supply chain logistics service model based on multi-source data fusion
Authors: Wu Junhong
Published: June 2024

Inefficiencies in supply chain logistics management, particularly in certain regions, often result in “broken chain” incidents that significantly impact the efficiency of e-commerce operations and degrade consumer experiences. This paper addresses the necessity of optimizing the logistics service model by integrating 12 heterogeneous data sources related to warehousing, transportation, processing, and sorting. We propose a site selection-inventory-path e-commerce logistics optimization model. For the first time, the model incorporates the Dempster-Shafer (D-S) evidence algorithm to quantitatively evaluate the support relationships among various pieces of evidence by adjusting the weight coefficients accordingly. This adjustment is further refined through the application of the Nash equilibrium in the evidence combination process. The optimized logistics model is then solved using a fusion of the model and an intuitive fuzzy set approach. A case study demonstrates that the optimized logistic supply capacity consistently outperformed the non-optimized scenarios, with statistical increases of 1.64%, 1.72%, 2.15%, 3.73%, 2.71%, 2.46%, 3.59%, 2.17%, and 3.98%. Furthermore, by refining the logistics model, the gap in logistics performance was narrowed by at least 5.13% and by as much as 12.9% across successive evaluations, significantly alleviating issues related to enterprise capacity constraints. This study offers novel insights and methodologies for enhancing the efficiency of regional e-commerce supply chain logistics, boosting the competitiveness of logistics enterprises, and elevating the consumer experience in e-commerce settings.
A study on the influence of network environment on college students’ ideological and political attitudes
Authors: Jing Hongbo
Published: June 2024

The network environment is gradually formed and developed under the impetus of information technology, and it plays an important role in transmitting political concepts, forming political attitudes, and cultivating political identity. After exploring the dilemma of ideological and political education in the network environment, this paper uses polynomial least squares approximation to construct polynomial regressions to solve various nonlinear regression problems. Then, the network environment factors such as bad guidance, network technology, cultural penetration, and false information are taken as independent variables. College students’ ideological and political attitudes are taken as dependent variables to construct a multivariate nonlinear regression model, observe the polynomial regression parameters according to the principle of least-squares estimation, and take 489 college students of the University of R as the research subjects to explore the influence of network environment on college students’ ideological and political attitudes. The results show that the correlation coefficients of four factors, namely, poor guidance of network environment, network technology, cultural penetration, and false information, with students’ ideological and political attitudes range from 0.178 to 0.531, which is a significant positive correlation. Meanwhile, compared with political cognition and political emotion, network environment factors explain the strongest tendency of political behavior (54.9%), and different network environment factors have various degrees of influence on college students’ ideological and political attitudes. The study offers data to support the cultivation and strengthening of college students’ ideological and political attitudes.
Research on the Value of Micro Animation Creation in Revitalizing Jilin Ice and Snow Tourism Industry
Authors: Liu Xiangyu , Xia Tian
Published: June 2024

Jilin Province, distinguished by its unique snow and ice resources, stands as a pivotal region for snow and ice tourism. This study delves into the innovative integration of micro-animation technology with the ice and snow tourism culture of Jilin, examining its influence on tourists’ travel intentions. A structural equation model was developed to articulate the attractiveness of Jilin’s ice and snow tourism, encapsulating the perceived symbolism of its attractions. Descriptive statistical analysis was employed to assess the impact of micro-animation on potential tourists’ perceptions and intentions. The study revealed that a majority of the viewers of the micro-animation on Jilin’s ice and snow tourism were female (54.7%), compared to males (45.3%). Additionally, a larger proportion of unmarried individuals (73.3%) engaged with the content relative to their married counterparts (26.1%). The demographic with the highest representation consisted of individuals earning a monthly income between 6001-9000 yuan (29.4%). Significantly, the attractiveness and symbolic perception of Jilin’s ice and snow tourism exhibited a robust positive correlation with the intention to travel, evidenced by a p-value of 0.001. This investigation underscores the potential of micro-animation technology as a transformative tool for enhancing the appeal of ice and snow tourism in Jilin Province.
Deep Forest-based Disease Prediction and Diagnosis under the Concept of Digital Health
Authors: Mei Xiangxiang , Shen Hao , Wu Fang , Cai Xiaodan , Chen Hongyun
Published: June 2024

With the continuous development of digital technology and the continuous improvement of medical information databases, effective mining methods for potentially useful information behind medical data have become one of the research focuses of digital development in the medical field. In this paper, based on the deep forest model, a deep forest classifier framework based on the federated learning algorithm is constructed for the privacy protection of medical data and optimized by gradient boosting decision tree. Then the feature vector of cardiovascular diseases is constructed from engineering features, and feature selection is carried out through the constructed classifier algorithm to realize the prediction and diagnosis of epidemic diseases, followed by the experimental analysis of the method of this paper. The empirical analysis shows that the diagnostic accuracy of this paper’s model in seven common cardiovascular diseases is higher than 80%. Among them, the accuracy rate of heart valve disease is as high as 87%, and the diagnostic accuracy rate of arrhythmia and coronary heart disease is tied for second place with 83%. It shows that the predictive diagnosis model based on deep forest in this paper has good performance, can meet the actual needs of predictive diagnosis of cardiovascular diseases, and provides an effective reference for the development of auxiliary diagnosis in the digital era.
Research on monitoring technology and application of conductor dancing condition of transmission line
Authors: An Shuhuai , Wei Zhen , Tang Lei , Guan Meng
Published: June 2024

In recent years, an increasing focus has been placed on the development of transmission line condition monitoring systems by academic and research institutions globally. This paper introduces a novel sensor-based system for monitoring the dynamic behavior—referred to here as ‘dancing’—of transmission line conductors. It hinges on the mathematical equations that describe the motion of a ‘dancing’ transmission line conductor. By computing the sets of distance and tension solutions, the system effectively determines the state of the conductor dance. This monitoring system integrates advanced sensor technologies and has been validated through simulation experiments. The experimental results are promising, showcasing an average error in coil measurements maintained within ±2.2%, with a maximum deviation not exceeding 2.9%. These figures underscore the system’s high accuracy and linearity, affirming that the design parameters adeptly satisfy the monitoring requirements of transmission line conductor dynamics. Furthermore, the system exhibits remarkable stability, as evidenced by average deviations of 0.636mm and 0.077mm on the X and Y axes, respectively, against input amplitudes of 120mm and 60mm. This paper demonstrates the efficacy of the proposed transmission line conductor dance state monitoring system in maintaining precise and reliable measurements.
Application of Interactive Multimedia System in Early Childhood Dance and Artistic Creativity Cultivation
Authors: Bai Wen
Published: June 2024

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Challenges and Opportunities of Civic Education in Colleges and Universities in Digital Environment
Authors: Zhang Xingli
Published: June 2024

Civic education in colleges and universities plays a pivotal role in fostering students’ national identity and civic responsibilities. Enhanced by digital learning environments, it promises to cultivate exemplary citizens more effectively. This paper details the development of a MOOC interactive communication platform utilizing the open-source edX framework, which serves to enhance civic education. It delves into optimizing resource storage technologies on the platform. It introduces an innovative assignment mutual evaluation algorithm tailored for Civics and Political Science education, which accounts for rater bias and scoring patterns. The newly implemented resource storage and mutual assessment algorithms have notably improved the functionality of the civic education platform. Performance metrics from the platform indicate an encouraging trend in student achievement across three courses, with average scores of 78.32, 79.24, and 83.27, respectively, showcasing a consistent upward trajectory. Further analysis reveals a converging trend in the variance of data distribution, indicating a reduction in disparities in student learning outcomes as evidenced by a decrease in standard deviation values. Comprehensively, students’ overall test scores have shown significant improvement. Notably, mastery of subject matter knowledge saw an increase from 2.3% to 21.4%, and the pass rate escalated from 92.6% to 98.3%. Although there were initial discrepancies in the cognitive rating of civic education values between the experimental and control groups, the experimental group exhibited substantial gains, underscoring the efficacy of the platform and its methodologies.
Assessment of Electricity Demand Response Potential under Consideration of Uncertainties
Authors: Zhang Xudong , Zhang Ran , Cao Xiaobo , Shan Yajing , Dong Hao
Published: June 2024

The assessment of customer electricity demand potential is a critical part of further stimulating electricity demand growth and promoting utility development. The main aim of this paper is to investigate how electricity demand response potential can be assessed under uncertainty. The decision variables are set through a two-stage stochastic planning model, focusing on fuzzy decision-making through linear programming in interval planning, combining with consequent opportunity constraint planning and fuzzy opportunity constraint planning to realize artificial regulation of unfavorable factors, and finally completing the treatment of uncertain factors. Combining a Gaussian mixture model to construct a model for measuring electricity demand response potential. It has been found that different electricity pricing policies lead to other potential demand response values for peak electricity load. The optimal electricity pricing and revenue of each user will be greatly affected by the participation of different users in the electricity demand response. The optimal power pricing decreases from 3-4 when the number of users is 4 to 2.5 when the number of users is 20 because of the generation of scale rewards, and the benefits to the users increase substantially when the number of users participating in electricity demand response is 20. Evaluating customer electricity demand in an uncertain environment can help drive electricity system reform to some extent.
Deep belief networks in understanding and predicting changes in students’ political attitudes
Authors: Pang Daojing , Wang Yanhui
Published: June 2024

Leveraging data collection software, this study gathered and preprocessed text data reflecting students’ political attitudes across five dimensions: political cognition, ruling party cognition, positive political emotion, negative political emotion, and political behavioral tendency. The processed data were subsequently stored in Excel format. These dimensions of change in students’ political attitudes served as inputs for a deep belief network (DBN) model, with the changes in attitudes being the outputs. The model’s parameters were meticulously tuned to optimize its predictive capabilities, aimed at effectively constructing a prediction model for shifts in students’ political attitudes using the deep belief network. To refine the model’s parameters and minimize errors during inverse fine-tuning, the chimpanzee algorithm (ChOA) was employed. This novel approach, termed ChOA-DBN, facilitated the successful construction of the predictive model for students’ political attitude changes. The model’s efficacy was assessed using the collected data on students’ political attitudes. Notably, the DBN model optimized via the chimpanzee algorithm demonstrated robust predictive performance, particularly when processing sample points exhibiting significant changes in students’ political attitudes (ranging from 20 to 45). The model’s predictions predominantly fell within a ±15% error margin. This research not only showcases the accuracy of the ChOA-DBN model in predicting alterations in students’ political attitudes but also underscores its potential contribution to fostering the development of accurate political perspectives among students.
GAN-based Visual Creative Generation Process Automation and Its Application Efficiency in Brand Image Design
Authors: Wang Qian
Published: June 2024

New social, economic, cultural, and technological trends bring new challenges to designers, and the use of data intelligence analytics is a very promising way to improve the efficiency and quality of their results. In this paper, we use convolutional neural networks to improve and optimize the GAN model, resulting in a more stable DCGAN network model. Visual innovation-related features are inputted into the discriminator in the DCGAN model, and creative visual images are automatically generated in the generator under the constraint label restriction. The network parameters of the DCGAN model are optimized through the training process to construct the brand image design process of automated visual creativity. The performance test analysis of the DCGAN model reveals that the analysis results of its IS index and FID index values are much higher than those of the WGAN and CGAN models, indicating that the generated visual creative images have high quality. The analysis of the visual creativity of the designed brand image found that the subjects’ favorability of the brand image intended by the DCGAN model reached 3.57 points on average and brought a visual feast to the subjects. This paper provides support for the application of Generative Adversarial Networks in the field of brand image design and achieves automated brand creative image design.
Sentiment analysis of ethnic artworks using deep learning
Authors: Wang Yanfang
Published: June 2024

Deep learning models, characterized by their complex network layers, have demonstrated significant utility in the recognition and classification of ethnic artworks, particularly excelling in emotion recognition within such works. This study delves into the convolutional neural network (CNN) model underpinned by deep learning algorithms. The architecture of the model—encompassing the convolutional layer, pooling layer, fully connected layer, and activation layer—is meticulously constructed to extract emotional features from ethnic artworks. Subsequently, an activation function is employed to visualize these features, followed by the integration of emotional knowledge to optimize the network structure. The training of the model utilizes both the loss function and optimization function to enhance performance. The efficacy of the sentiment analysis is assessed through simulation experiments and practical applications, highlighting the model’s superior capability in identifying negative emotions such as Fear and Anger, achieving accuracies that are 0.101 and 0.137 higher, respectively, than those of the benchmark model. Additionally, a detailed analysis of the emotion scores of ethnic artworks reveals intriguing findings. Notably, Jiye Li’s “Four Seasons of Essays” registers the highest positive mean emotional score of 4.1026, with an overall mean of 3.5392, indicating a predominantly positive emotional response. Across the works of nine subjects analyzed, the aggregate mean emotional score stands at 1.0123, suggesting a generally positive emotional tone. This research underscores the nuanced capability of CNNs in the domain of emotional recognition in ethnic art, offering insights into both methodological advancements and interpretive analyses.
Research on Innovative Practice and Theoretical Exploration of Visual Communication Design for Future Media
Authors: Yang Yang
Published: June 2024

The rapid development of the new media era has brought brand new opportunities for visual communication design, and the creation concept and expression of visual communication design have undergone obvious changes. In this paper, the ant colony algorithm is applied to automatically arrange graphic text in visual communication design to achieve the purpose of visual communication design innovation. Additionally, an image synthesis model based on generative adversarial networks is proposed for visual communication design, and examples are analyzed to analyze visual communication design in the context of future media. As a result of the study, the average loss values of the generator and discriminator of its generative adversarial network model are 9.894 and 2.319, respectively, and the Inception score (3.74) corresponding to the GAN is undoubtedly the highest, which has a high communication effect. This study enhances the effectiveness of visual communication design, promotes efficient communication and interaction with the audience, and promotes the innovative practice and theoretical exploration of visual communication design.
Research on the Generative Mechanism of Intelligent Reconstruction for the Integration of Traditional Paper-cutting Art Symbols and Modern Pattern Designs
Authors: Zhang Zhe , Xu Jiang
Published: June 2024

In the realm of Chinese traditional arts, paper-cutting symbols embody deep-seated aesthetic and cultural values. Within the framework of artificial intelligence, the fusion of these conventional symbols with contemporary pattern design through intelligent reconstruction processes promotes innovation in modern design. This paper introduces a novel pattern feature extraction algorithm specifically designed for identifying and extracting the essence of traditional paper-cutting art. Utilizing Deep Convolutional Generative Adversarial Networks (DCGAN), an automated method for generating woven patterns is developed, integrating conventional motifs with modern design elements. Additionally, a segmentation model based on DeepLabV3+ semantics is constructed to facilitate this integration. The study focuses on the fusion of traditional paper-cutting symbols with fabric patterns, providing a new direction for textile design research. The empirical evaluation involved seven experimenters who predominantly favored patterns that blended abstract and traditional styles, with the average style rating exceeding 3.2. Moreover, the color performance analysis of the reconstructed fabric patterns across five regions showed color difference values greater than 4, indicating superior color fidelity. These findings underscore the potential of combining AI-driven techniques with traditional art forms to enhance and revolutionize modern design practices.