bluuebunny/crossref_metadata_2025 huggingface dataset:
Research and Application of Knowledge Graph Design for Interactive Teaching Platform Based on Artificial Intelligence
Authors: Bai Yajuan , Liao Xinhai
Published: July 2024

With the rapid development of education digitization, artificial intelligence (AI) and knowledge graph, their application in the field of education has received extensive attention and research by many scholars. In this paper, the design of knowledge graphs for AI teaching platforms is studied. Based on the construction process of a general knowledge graph, teaching resources’ text is processed through the steps of knowledge entity recognition and relationship extraction, which is applied to the intelligent interactive teaching platform. On this basis, the article designs a method for teaching resource retrieval based on semantic analysis to provide a path for further teaching applications of knowledge graphs. After completing its design, application and testing are carried out to test its feasibility and effectiveness. The score of the experimental class that used the knowledge graph increased by 17.68, which is about 22.47% year-on-year compared to the pre-class period; the score of the control class also rose, with a rise of 9.41, which is about 11.53% year-on-year. The results indicate that a knowledge graph-equipped teaching platform can better meet the needs of interactive teaching and enhance students’ learning abilities.
Exploration of the Path to Improve the Quality of Physical Education Teaching in Colleges and Universities with the Help of VR Technology
Authors: Dong Zhonglei , Sha Nu
Published: July 2024

VR technology, as an emerging frontier technology in recent years, has been applied in many fields, in which in the field of education, the technology has gradually entered the education classroom and played a great advantage. In order to promote VR technology comprehensively and help develop college education, this study explores the path to improve the quality of college physical education teaching using VR technology. Firstly, we review the relevant research data, select the relevant factors affecting the quality of physical education teaching, and explore the path to improving the quality of physical education teaching in colleges and universities through correlation analysis and structural equation modeling. On this basis, a VR-based innovation model of college physical education teaching is developed, and a comparison experiment is designed to study the effect of VR technology in helping to improve the quality of college physical education teaching. The five dimensions of the five factors: knowledge skills, teaching content, interest fit, VR input experience, and teaching method all have a significant positive correlation (p<0.01) to the quality of VR sports teaching in colleges and universities. The highest correlation coefficient is interest fit (r=0.345), followed by VR input experience (r=0.308). At the same time, through the teaching comparison experiment, it can be seen that the class based on the VR sports teaching of this paper is better than the traditional sports teaching mode in terms of physical quality and sports teaching effect, and P is less than 0.05. The VR sports teaching mode constructed in this paper can effectively improve the physical quality of the students, improve the level of sports teaching, achieve the effect of auxiliary teaching, and provide an effective path for the enhancement of the quality of sports teaching in colleges and universities.
Research on the method of predicting the trend of criminal activities based on time series analysis from the perspective of criminal procedure law
Authors: Liu Yiwen , Zou Mingzhu
Published: July 2024

Criminal activity has always been a problem that seriously affects people’s sense of security and well-being in life, and it is also the focus of public security departments in terms of security prevention and control. This study aims to use scientific methods to analyze the time series of existing historical data related to criminal activities and to advance inference or determination of the future trend of criminal activities. In this paper, after mining the spatiotemporal pattern of criminal activities using the centered moving average method, the spatiotemporal cycle characteristics and time distribution characteristics of criminal activities are analyzed. Based on the conclusions drawn, an improved LSTNet algorithm is proposed by combining deep learning spatio-temporal network, convolutional neural network CNN, recurrent neural network RNN, autoregressive AR, and specially designed Skip-RNN, and a GRU-gated recurrent unit structure is used to realize the optimization of the prediction accuracy of the criminal activities, to solve the problems related to the spatio-temporal dependencies, and to establish an improved ST- Res Net crime prediction model. Finally, the prediction performance of the model is examined. The RMSE, MSE, and MAPE values of this paper’s prediction model are 0.88, 5.12, and 5.12%, respectively, which are better than other models. The experiments demonstrate that the prediction model presented in this paper can enhance the accuracy of criminal activity prediction to a certain extent and can be employed in the prevention and management of criminal activities.
Optimization of Artificial Intelligence-based Dynamic Scene Generation and Perception Mechanism in Virtual Reality
Authors: Zhang Songlin , Xun Wei
Published: July 2024

Virtual reality, as a computer simulation system that can create virtual environments, is a fusion of multiple key technologies that are applied in different fields with important practical value and significance. This paper focuses on optimizing the artificial intelligence-based dynamic scene generation and perception mechanism in virtual reality. Firstly, a virtual dynamic scene generation method combining scene management technology and image rendering technology is proposed. In order to optimize the dynamic scene perception mechanism in virtual reality, a perception model based on spatial interaction perception and based on the analysis of user dynamic interaction is proposed. A two-layer perception management strategy is proposed. The paper finally uses the virtual reality technology proposed in this paper to create a Chinese classical garden attraction in a scenic spot. It carries out modeling accuracy assessment and comparison experiments and invites garden professionals and tourists to evaluate the overall effect of the virtual scene. The survey results show that more than 80% of the tourists are satisfied with the generation of the dynamic scene and think that it has spatial continuity and immersive experience.
Rebranding and SWOT Analysis of Geographical Indication Agricultural Products in Jilin Province under Digital Transformation
Authors: Xu Hui
Published: July 2024

This paper uses the DEA model and the Malmquist index model to look at how well building a geographical indication brand for an agricultural product works in prefecture-level cities in Jilin Province. It also looks at how the performance of building a geographical indication brand for an agricultural product changes over time in prefecture-level cities in Jilin Province. Then, a brand competitiveness evaluation system was made up of regional elements, brand elements, industrial elements, and support elements. The level of development of agricultural product brand competitiveness in Jilin Province was looked at using fuzzy comprehensive evaluation and hierarchical analysis. The results of the evaluation were used to find the factors that affect the improvement of its brand competitiveness, which helped to map out the next steps for reshaping agricultural product brands in Jilin Province. The comprehensive evaluation of the brand competitiveness of agricultural products in Jilin Province has a specific score of 81.4, and the regional element, brand element, product element, and support element have specific scores of 82.01, 83.2, 81.66, and 80.7, respectively. This study provides references to promote the development of agricultural products’ geographical indications and to enhance the competitiveness of the corresponding brands, and it enriches and improves theoretical research on the competitiveness of the brands of agricultural products’ geographical indications.
A Framework Study on the Application of AIGC Technology in the Digital Reconstruction of Cultural Heritage
Authors: Cong Lin
Published: July 2024

AIGC is currently a hot field and a future trend in AI applications, and addressing the challenge of digitally reconstructing cultural heritage under the influence of AI technology is a pressing issue that requires immediate resolution. The article proposes an application framework for AIGC technology that is based on refining its meaning and designing a specific process for applying it to the digital reconstruction of cultural heritage. A high-definition camera is used to acquire relevant images of cultural heritage. The image features are extracted by the SIFT algorithm optimized by the PROSAC algorithm. The color features are acquired by combining the color histogram, color moment, and color correlation diagram. The 3D laser scanning technology is used to obtain the 3D point cloud data of the cultural heritage; the KD-tree improved ICP algorithm is introduced to improve the efficiency of point cloud alignment; the dense reconstruction of the 3D point cloud data of the cultural heritage is realized based on CMVS/PMVS; and the immersive 3D experience system of the cultural heritage is constructed by combining with platforms such as Unity3D. The average matching rate of the optimized SITF algorithm to the image features of cultural heritage is about 74.91%, and the maximum alignment time of the ICP algorithm to the cultural heritage point cloud data based on KD-tree is 9.241 s. The cultural heritage immersive 3D experience system has a satisfaction rate of 56.75%, and the density reconstructed model’s surface has an average deviation of only 0.34 mm from the real surface. The user satisfaction rating for the immersive 3D experience system for cultural heritage is 56.75%. Based on AIGC technology, it can revitalize cultural heritage and achieve digital reconstruction and inheritance innovation of cultural heritage.
Risk assessment and electrical control of high-permeability renewable energy photovoltaic system under extreme weather conditions
Authors: Dong Qi , Yu Junfeng , Hao Shouli , Qiao Yanjun , Wang Zhijian , Fan Ke
Published: July 2024

High-permeability photovoltaic (PV) systems are a key technical way to bring about new energy changes in electric power networks. Risk assessment and electrical regulation during extreme weather can make PV systems with high permeability work much more efficiently during severe weather. We establish an operational risk model for a high penetration rate PV system using the PV probability model, load parameter model, and probabilistic trend calculation model. Based on the results of the tidal current calculation, the risk assessment indexes of PV systems are constructed, including the voltage overrun risk index and branch circuit tidal current overrun risk index. The Monte Carlo simulation method using the Latin hypercubic sampling method is used to simulate the operational loads of PV systems under extreme weather conditions such as windy, rainy, and snowy conditions and to form a fault simulation set. Finally, we implement the QMC method to assess the risk of PV systems with high permeability and use the RBF neural network design to achieve automatic control of PV electrical equipment. When the weather is the same extreme, the risk of overrun at each node goes up with the number of nodes. When the weather is different, the risk of voltage overrun at each node under ice-covered load goes up by about half compared to rain load, and the risk of active overrun at each branch goes up by almost six times compared to rain load.
Research on the Mechanism and Intervention of Cross-cultural Psychological Adaptation of International Students in the Context of Digitization
Authors: Dong Hao , Xingshuai Yu
Published: July 2024

The management and social problems caused by the psychological problems of international students have become more and more serious, and cross-cultural adaptation has a very important influence on the integration of international students into new environments and cultures, which has been increasingly concerned by the education circles at home and abroad. In this paper, we investigated the psychological adjustment ability and demographic characteristics of international students and analyzed the correlation between different factors and cross-cultural psychological adjustment ability based on the statistical results. After analyzing different groups of international students with different types of psychological adjustment abilities using the LPA method, a reasonable intervention plan is formulated. The mediation effect analysis method was also utilized to explore the impact and mechanism of interventions on psychological adaptation. The study’s results indicate that metacognition and cross-cultural psychological adjustment have a high correlation coefficient (0.773), which suggests that this factor can have a significant impact on the psychological adjustment of international students. It was also found that the intercultural psychological adjustment interventions for international students positively and significantly affected the intercultural psychological adjustment ability (B=0.085, P=0.032), indicating that the intervention program can improve the intercultural psychological adjustment ability of international students through psychological capital. This study provides a reference for the later targeted construction of intervention strategies to improve international students’ cross-cultural psychological adjustment and also paves the way for future studies to specifically analyze the mechanisms of international students’ cross-cultural psychological adjustment.
A way to combine music education and intelligent algorithms to cultivate innovative musical talents
Authors: Fang Liping
Published: July 2024

The arrival of the intelligent era makes intelligent music education a reality, which is a product of the combination of intelligent technology and music education. The article starts with the intelligent transformation of music education, analyzes the talent cultivation mode of music education, and establishes the intelligent classroom teaching mode of music education through the use of intelligent technology. Combined with the data of students’ online music learning behavior, a personalized music learning path model is established, and a nonlinear factor is introduced to optimize the SBPSO intelligent algorithm in order to realize the accurate recommendation of a personalized learning path. A DFN model for the automatic generation of music accompaniment was established based on the Flow model, and simulation analysis and teaching experiments were carried out to analyze the effectiveness of the above methods in music-intelligent classroom teaching. The convergence value of the NFSBPSO algorithm was in the range of 0.026~0.043, and the discrepancy between the mean value of the note length of the accompaniment generated by the DFN model and that of real data was only 0.35% and the total average score of the generated accompaniment was 7.0%. Accompaniment had an overall mean score of 7.23. The observation group scored 83.64 on their final music course grade, which was 11.46 points more than the control group’s average grade. Combining music education with intelligent algorithms can achieve intelligent transformations in music education and improve the quality of talent cultivation.
A Study on Learning Strategies and Styles Segmentation of Learner Groups in Online Education Platforms
Authors: He Qian
Published: July 2024

Online education is becoming a popular form of education day by day. In order to provide corresponding learning programs according to the types of learning styles and learning strategies of different learners, this paper proposes a learning style recognition algorithm based on the Felder-Silverman model. The Bayesian discriminant analysis algorithm is used to calculate the probability estimation of samples belonging to one category, and then the clustering center is calculated by k-means clustering analysis so as to complete the classification of learning styles. According to the results of learning style identification, the perceptual learning group has the highest level of cooperative communication strategies in the information perception style dimension, with a mean score of 3.575, the linguistic group is the least adept at all strategies in the information input dimension, with a mean score of only 3.55, the sequential group excels in a variety of strategies for information comprehension, and the active group is the most adept at performing information processing. The personalized learning experiment was conducted according to the characteristics of different style groups. It was found that students who received personalized teaching based on their learning styles improved their performance more significantly, with their average scores in each subject increasing from 73.174 to 82.247 in the pre-test. Their scores in the post-test were 5.729 points ahead of those of the control class. The application effects are significant when learning strategies and styles are classified, and teaching programs are improved accordingly.
Business model innovation of advertising operation based on RBM deep neural network
Authors: Li You
Published: July 2024

RBM, as an important structural component unit in the mainstream model of deep learning, is being widely used in various fields. The article first discusses the RBM deep neural network algorithm, gives an overview of the related function algorithm combining RBM and neural network, and then proposes a deep network method oriented to the prediction of advertisement click rate. At the end of the article, the algorithm proposed in this paper is compared with the model performance test. An advertising company is used as an object of empirical research to analyze the change in its market share and turnover in the years 2022-2023. Explore the significance and role of RBM-based deep neural networks in the innovation of advertising business models.
Research on the Path of Digital Transformation of Music Education in Colleges and Universities
Authors: Li Zhiyong
Published: July 2024

Digitalization is an important direction for the transformation of higher education, but the teaching of music majors still has problems, such as outdated methods and rigid models. In order to explore the digital transformation path of music education in colleges and universities, this paper designs and constructs a music emotion recognition model, which provides technical support solutions for the video-singing audition, appreciation of famous artists, and practice and correction of errors in the music classroom. Through feature extraction, the form of music expression emotion is transformed into music emotion features, using the wrapping method and filtering method for feature selection, using self-attention BiLSTM algorithm to establish the interoperability relationship between features and emotion model, and recognizing music emotion. The constructed model is utilized to aid in the teaching of music majors at a university as a teaching experiment object, and the teaching effect is examined. It was found that after the model-assisted teaching, the experimental class’s scores of music emotion comprehension ability, work element differentiation ability, emotion expression ability, and music psychology ability were all improved to different degrees, and compared with the pre-test scores showed a strongly significant difference (sig.<0.05). A significant improvement of 20.44% was seen in the total average score, which was 94.75. After the use of model-assisted teaching and the digitalization of music education, the quality of teaching in the experimental class was significantly improved, and this paper’s exploration of the digital transformation path of music education in colleges and universities was fruitful. This study has conducted an innovative exploration of the integration of modern information technology and music education, as well as the digital teaching reform of music majors in colleges and universities, and achieved good results.
Innovation of Innovation and Entrepreneurship Teaching Content and Methods under the Internet+ Education Mode
Authors: Liu Chao , Wang Xiaorui , Teng Zhiyuan , Guo Chence , Wang Zhuo , Zhang Xingxing
Published: July 2024

This paper proposes a way to innovate the teaching content and methods centered on building an online teaching system and information management system for innovation and entrepreneurship. In the J2EE-based innovation and entrepreneurship online teaching system, the genetic algorithm is used to improve the Pick-KX algorithm, and the resource allocation scheme is optimized and processed by the genetic algorithm, which effectively improves the efficiency of the Pick-KX algorithm on heterogeneous servers. In the innovation and entrepreneurship information management system, the TF-IDF algorithm and Word2vec are used to convert the text into document vectors, and K-Means clustering is used to realize document clustering, while the HyperLogLog algorithm is used to carry out the system website data statistics. The 2022 computer science and technology students of a teacher training school in Chongqing, China, were used as research subjects to carry out innovative and entrepreneurial teaching practices. The student’s performance on all dimensions of innovation and entrepreneurship practice ability was above 3.5, which was a good performance. In the evaluation of the online teaching system and information management system for innovation and entrepreneurship, the evaluation scores for each dimension are higher than 3.5, which meets the needs of students. After the practice, the average scores of satisfaction with the innovation and entrepreneurship practice course and their development were 9.2 and 9.02, which were improved by 0.42 and 0.68 compared to the pre-practice.
Enhancing the Visual Effectiveness of Overexposed and Underexposed Images in Power Marketing Field Operations Using Gray Scale Logarithmic Transformation and Histogram Equalization
Authors: Liu Kai , Wu Yidi , Ge Yunlong , Ji Shujun
Published: July 2024

In this paper, we propose an adaptive gamma transform that adjusts the local values of bright and dark parts to enhance the effect of low-illumination images, thereby improving the light component. We then apply diff texture enhancement to enhance the contrast of images processed by the Retinex algorithm, thereby optimizing the perception of overexposed and underexposed imagery. Analyze the effect of image brightness enhancement based on a nonlinear transformation combined with the LOL dataset. Use PSNR and SSIM image quality evaluation criteria to analyze the visual effect of improving low-illumination images based on Retinex theory. Create a dataset of power marketing field operation inspection images and examine the effects of overexposure and underexposure image processing on four types of images: high-voltage towers, transmission lines, high-voltage fixtures, and high-voltage wireframes, using the low-light image texture fusion algorithm based on Retinex theory. Overall, this paper’s algorithm and the three DeblurGAN and DMCNN models achieve the effect of deblurring overexposed and underexposed power marketing field operation inspection images. From the local details, the model in this paper has a better effect on the de-exposure of the image, which can provide effective help for the electric power staff to understand the situation of the electric power marketing operation site and has strong practicality.
Research on Image Analysis and Dynamic Early Warning Mechanism of Power Marketing Operation Site Based on SSD Networks
Authors: Wang Jinteng , Wu Guanghua , Zhang Xudong , Ma Xun
Published: July 2024

Safety production is an important way to ensure the personal safety of workers and the safety of national property. This project focuses on addressing the safety generation problem in the power marketing profession, designing a set of intelligent dynamic warning systems for power marketing operations. MobileNetV1 is used as the backbone feature extraction network of SSD to create a lightweight target detection network, and it is combined with OpenVINO and NCS2 neural computation bars to construct an image analysis and dynamic warning model for power operation based on the SSD MobileNetV1 algorithm. We use the images of power marketing operation sites in a city as a dataset to test and compare the constructed model with other target detection algorithms, analyzing its dynamic warning effect on power operations and exploring the effectiveness of the model’s practical application. The results show that the SSD network model can significantly improve the detection efficiency and accuracy of the dynamic early warning system for electric power operations, and its accuracy is 15.15% and 18.82% higher than that of the Faster⁃RCNN algorithm and the YOLO algorithm, respectively. The early warning system reduced the irregular behavior of electric power marketing operations by 135.71%, demonstrating the effectiveness of the SSD model in detecting and warning electric power targets. This model not only meets practical requirements but also effectively reduces the irregular production behaviors of electric power marketing operations, thereby enhancing the safety coefficient of marketing field operations.
Research on the application of high performance concrete and steel structure combination in civil engineering
Authors: Wu Taige
Published: July 2024

The increase in complexity and quality standards of civil engineering projects continues to drive the innovation of construction materials from the traditional ordinary concrete-steel structure combination to the combination of high-performance concrete and steel structure. In this paper, the application of the combination of high-performance concrete and steel structures in civil engineering projects is investigated based on the mechanical properties of the two structures. The structural reliability index and the corresponding functional function are used to measure the structural reliability of the combination of high-performance concrete and steel structures. After measuring its reliability, a finite element simulation is carried out to investigate the application value of the high-performance concrete-steel structure combination in civil engineering projects. Considering the reliability index of resistance and load time-varying effects decreases with the service time of the combined structure, if the reliability index of the components during the service period is not allowed to be lower than the target reliability index, the time to reach the limit state of the selected high-performance concrete-steel combined building in this paper is about 75 years. The ultimate load-carrying capacity of the high-performance concrete-steel structure combination is increased by 28.74% compared with that of the ordinary concrete-steel structure combination, and the end slip of the high-performance concrete-steel structure combination is smaller than that of the ordinary concrete-steel structure combination under the same load. It shows that the advantages of high-performance concrete-steel structures in civil engineering are much more than ordinary concrete-steel structures.
Study on the Efficacy of Recessed Green Space in Rainwater Harvesting and Utilization in Urban Roads
Authors: Yang Dan , Lv Juan
Published: July 2024

In order to confirm the performance of recessed green space in rainwater collection and utilization of urban roads, this paper proposes a corresponding experimental test program, which mainly covers the evaluation of rainwater infiltration, the evaluation of rainwater purification, and the simulation of rainfall-runoff reduction. Using the above test methods, the effectiveness of recessed green space in collecting and utilizing rainwater was analyzed. The results showed that in the one-way analysis of the compositional structure of the undercroft green space, the gravel layer, the media layer, and the vegetation layer had no significant difference in the infiltration rate sig<0.05, and all three had no significant difference with the infiltration rate. The order of rainwater infiltration effect of the four reactors was 3/7 mixed media reactor (23.08 ml/min) > red loam reactor (23.04 ml/min) > 2/8 mixed media reactor (23 ml/min) > black loam reactor (23.08 ml/min), while the order of purification effect was 3/7 mixed media > 2/8 mixed media > red loam > black loam. In addition, the combined high infiltration hypolimnetic green space had a better retention effect (flow yield range from 0 to 10 L) in rainfall-runoff with a reproduction period of 3 and 4 years. Based on the analysis results, the application effect of undercroft green space in urban road rainwater collection and utilization is comprehensively verified, and it is hoped that the investigation of this paper can bring certain references and references to the relevant personnel.
Multiple regression analysis of the mechanism of the role of infrastructure development in rural economic growth
Authors: Zhu Mingda
Published: July 2024

The improvement of China’s economic level makes the society’s requirements and standards for infrastructure constantly improve. In order to effectively promote the rapid development of the rural economy, it is necessary to strengthen the construction of rural infrastructure. This paper provides a comprehensive plan for rural infrastructure construction and analyzes its mechanism of action on rural economic development in depth. Taking rural economic growth as the explanatory variable and infrastructure construction as the explanatory variable, the multiple linear regression model is chosen to analyze the impact of rural infrastructure on rural economic growth. The unknown parameters are estimated by the least squares method. The model is tested and modified based on the diagnostic methods of covariance expansion factor and other covariates to obtain the final results. Through empirical analysis, rural economic growth = -15.1935 + 0.184*rural transportation + 0.0983*rural education + 2.4923*agricultural science and technology + 0.3652*agricultural water conservancy, and agricultural science and technology has the greatest impact on rural economic growth. The local area can improve rural infrastructure in three aspects: investment strength, investment focus, and investment and financing mechanisms.
The transformative role of computer-aided design technology in innovation in the apparel industry
Authors: Sun Ning , Cao Botao , Mu Xichan
Published: July 2024

With its unique advantages, computers are applied to various fields, and the garment industry is no exception. The study explores the role of computer-aided design technology in the apparel industry from the perspective of apparel design, production, and sales. We aim to evaluate the effect of computer technology on clothing design and production by constructing a clothing style migration model based on CycleGAN and evaluating its image style migration effect and aesthetic quality. Then, after completing the research design, we analyze the effect of virtual fitting rooms on consumers’ purchase intentions so as to support the role of computer technology in clothing sales. According to the findings, the clothing style migration model’s generated images are of excellent quality, with an average evaluation score of 6.68, and are acknowledged by nearly 80% of the respondents. All four characteristic elements of the virtual fitting room positively and significantly affect consumers’ purchase intention at the level of 0.01, and perceived risk and perceived value play a mediating role, which explains the process of generating consumers’ apparel purchase intention based on the virtual fitting room. Computer-aided design technology can be utilized effectively in apparel design to boost apparel sales and foster the transformation and advancement of the apparel industry.
Evaluation of the Effectiveness of Combining Virtual Reality and Augmented Reality in Children’s Pharmaceutical Packaging Designs
Authors: Wang Junyao , Abidin Sazrinee Zainal , Kamarudin Khairul Manami , Shaari Nazlina , Yu Mengyao
Published: July 2024

The rapid development of virtual reality and augmented reality technology has brought significant changes to many industries in modern society. In the field of product packaging design, the use of virtual reality and augmented reality technology in product packaging design is becoming more common in the field. Researchers are exploring the use of VR and AR technology in children’s drug packaging, employing augmented reality research that leverages natural features and artificial recognition to construct a model of children’s drug packaging that incorporates VR and AR technology. We analyze the impact of VR+AR on children’s drug packaging. Consumers’ main requirements for children’s drug packaging are bright, strong, practical, fun, novelty, and ease of understanding. The paper’s model, sample Z, ranks 5th in market competitiveness and 1st in technical competitiveness, outperforming competitors A, B, C, and D. This suggests that VR+AR children’s drug packaging still needs improvement in market competitiveness despite its high technical content. In descending order, the design evaluation dimensions score the VR+AR children’s drug packaging as follows: vivid picture (3.193)> texture (3.154) > easy to hold (3.142) > Moderate capacity (3.129) > simple and generous (3.105) > conform to the production process (3.047) > harmonious color (3.044) > convenient for transportation (3.028) > low production cost (3.012). The average score for each dimension of the VR+AR children’s drug packaging experience is approximately 4.46 points.
The interplay between the role of emerging technologies in the field of news communication and brand image building
Authors: Wang Lv
Published: July 2024

Emerging technologies such as big data, the Internet of Things and artificial intelligence are developing rapidly and entering into many fields and industries, while their role in the field of news communication and brand image shaping has an important role in influencing. This paper selects WeChat, a Tencent product, as the research object and divides brand image shaping into four dimensions, namely brand awareness, brand trust, brand loyalty, and brand value, to test the relationship between emerging technologies and brand image shaping. Then, we put forward the research hypothesis of this paper and use empirical analysis to test the hypothesis and the empirical results show that the r-value of emerging technology and brand loyalty is 0.591, p<0.01, which proves that there is a positive correlation between the two. Emerging technologies have a significant positive impact on brand awareness, trust, loyalty, and value.
Exploration of Digital Inheritance Ideas of Chinese Traditional Music Culture in the Information Age
Authors: Wu Wenjing , Tham Jacquline , Khatibi Ali
Published: July 2024

In the era of information technology, with the deepening of cultural heritage protection, the importance of digital technology intervention in traditional art and culture for protection and inheritance has become more significant, and the digital inheritance of Chinese traditional music culture has also received widespread attention. In this paper, we first preprocess the traditional music signal and extract the Meier multifrequency coefficients, then use the feature as the input of the deep confidence network, and then keep tuning the parameter training to get the optimal network model, and finally utilize the obtained optimal model to recognize the traditional music. Based on this basis, this paper focuses on the concept of the digital heritage of Chinese traditional music culture through three steps: recognition, processing, display, and dissemination. We also designed and developed a Chinese traditional music culture APP for Chinese traditional music lovers and inheritors and examined the effect and performance of the APP application based on the music recognition model in this paper. The modeling methods proposed in this paper meet the usage requirements by having accuracy rates above 90%. 73.25% of the audience expressed satisfaction and very satisfaction with the overall application effect of the Chinese traditional music APP. In this paper, the Chinese traditional music digital APP proposes an effective means of communication for the solution of digital inheritance and also provides a realizable path for the digital inheritance of Chinese traditional music culture.
Exploration of Intelligent Transformation Path of Traditional Cultural and Creative Products Based on Internet of Things Technology
Authors: Xu Jia , Zhang Leilei
Published: July 2024

More and more modern technology is applied to traditional cultural and creative industries, which enhances the technicality and novelty of traditional cultural products, and the fusion of culture and modern technology profoundly changes people’s way of life and learning. This paper explores the Internet of Things (IoT) technology and the intelligent transformation process of cultural and creative products and constructs cultural and creative products that integrate intelligent voice series. Specifically, a speech recognition model is integrated into the design process of cultural and creative products, and the designed model is applied to the design of intelligent cultural and creative products by analyzing the Hidden Markov Model (HMM), proposing a solution to the shortcomings of the HMM model, and choosing to perform noise reduction of speech signals through the fixed beam algorithm. Based on the speech recognition model designed in this paper, an intelligent cultural and creative product with a speech recognition function is designed, taking the ‘owl wine container’ from the Shanxi Museum as an example. The regression equation of the audience’s satisfaction with the product is Y (satisfaction) = -0.000263 + 0.208X1 (practicality) + 0.265X2 (innovation) + 0.253X3 (culture) + 0.271X4 (interactivity) - 0.296X5 (fun). This paper’s intelligent cultural and creative products have a greater impact on satisfaction due to their innovativeness and interactive function.
A study on the application of AI in modern interior design and its impact on design efficiency
Authors: Zhao Zijun
Published: July 2024

This paper focuses on the functional characteristics of BIM technology, emphasizing the need to develop a combination of BIM technology tailored to the user’s interior design process. We utilize the hardware and software to establish the BIM technology environment, construct the interior design model using the Autodesk Revit software platform, import it into Lumion for color rendering, and utilize Navisworks Simulate software to gather project data and monitor the interior design project’s progress. Autodesk Ecotect software was used to find out how lighting affected the interior design using BIM technology. Next, an interior layout optimization model based on the improved whale algorithm was made to solve the interior layout problem, and the impact of using AI was confirmed. The data show that the error range for the daylighting coefficient using BIM technology and the old method is between 0.04 and 0.91. The old method takes 131 minutes longer than BIM technology. This means that the BIM-based lighting simulation model pre-processing process is still much more efficient than the old method. Furthermore, this paper maintains all the model’s index values above 0.68, demonstrating the model’s ability to achieve a reasonable interior space layout while simultaneously enhancing both the layout speed and the layout effect.
Research on SVM Analysis Model of Influencing Factors of Employability of Graduates from Higher Vocational Colleges and Universities in Jiangxi Province
Authors: Chen Kaiqiong , Tham Jacquline , Khatibi Ali
Published: July 2024

With the expansion of higher vocational colleges and universities, the difficulty of employment of graduates has become an increasingly serious problem in the development of society. The reason for this phenomenon, in addition to the total pressure, lies in the difference between the knowledge, ability, and quality of college students and the needs of employers. This paper crawls the employment information data of graduates of higher vocational colleges and universities from relevant websites, combines the text classification method based on the SVM analysis model to mine the system of graduates’ employment ability, and takes it as a survey scale. Then Jiangxi Province’s higher vocational graduates were selected as the research object. After testing the reliability and validity of the scale, the scale, combined with independent samples, T-test method regression analysis, and other mathematical and statistical methods to explore the factors affecting the employment ability of graduates of higher vocational colleges and universities in Jiangxi Province. Among them, there is no significant difference in the overall employability in terms of gender and specialty category (P>0.05), while there is a significant difference in terms of having or not having work experience (P<0.05). The training objectives and strategies significantly contribute to the improvement of the professional competence of higher vocational graduates, which is a key factor in the influence of employability. Accordingly, students actively improve themselves at the same time. Colleges and universities should develop training objectives and strategies with the needs of enterprises to give students the necessary career guidance in a timely manner and other countermeasure suggestions.
The Role and Impact of Cross-Border Data Flows in International Business Digital Trade
Authors: Gao Zhongpo , Kang Chengwen , Zhang Xinjian
Published: July 2024

Data is an important production factor in the era of the digital economy, and cross-border data flow is a prerequisite for digital trade. The Digital Services Trade Restrictiveness Index (DSTRI) is the basis of this paper’s research on the impact of cross-border data flow on digital trade. Firstly, cross-border data flow and digital trade are refined into cross-border data flow restrictive measures and digital service trade, and the mechanism of cross-border data flow restriction on digital service trade is investigated. Then, the impact model is constructed based on the gravity expansion model, the variable selection is carried out, the model variables are tested from the perspective of Pearson correlation and multicollinearity, and the optimal regression model is selected. Finally, baseline regression results are derived based on the model, and heterogeneity analysis and robustness tests are conducted. Cross-border data flow restrictions have a correlation coefficient of −2.176 with total digital services, and they have both negative and positive effects on the total trade in digital services. The total trade in digital services in DSTRI is negatively impacted by all policy areas of restrictions, which suggests that restrictions in any policy area can hinder the development of digital trade. When conducting the robustness test, cross-border data flow restrictions still have a significant inhibitory effect on digital services trade, which is consistent with the results of the benchmark regression, indicating that the results of the benchmark regression in this paper are reasonable and valid.
Corporate Financial Risk Identification and Operation Control Analysis for XGBoost Modeling
Authors: Guan Yu , Zong Zhijuan
Published: July 2024

Risks in the financial market are omnipresent, and the operations of listed companies are affected by various factors, so the study of the financial risks of listed companies is also of great significance. In this paper, the statement data of listed companies and the text data of annual reports are used separately. The XGBoost model is used to analyze its classification effect, and the confusion matrix and ROC curve evaluation methods are used to compare the accuracy of the prediction results between the XGBoost model and the GBDT model, which helps corporate managers to identify the financial risks of enterprises in advance, and at the same time, improves the level of operation control. The results show that the accuracy of the XGBoost model fluctuates around 0.85, and the highest accuracy of the model is 0.883 when the number of its features is 21. The results of the confusion matrix assessment show that the accuracy of the prediction results of the risk-free company of the XGBoost model reaches 94.95%, and the accuracy of the prediction results of the XGBoost model increases by 5.15% compared with that of the GBDT model. This is in accordance with the ROC curve evaluation results. Obviously, the XGBoost model has a better prediction effect and a more stable early warning performance, and the use of the XGBoost model can help the managers of listed companies to be informed of the deterioration of the company’s financial situation as early as possible so that they can implement the corresponding operational control measures to reduce losses in time.
ChatGPT in orthodontics: limitations and possibilities
Authors: Lima Nara Grazieli Martins , Costa Lavínia , Santos Patrícia Bittencourt
Published: July 2024

Artificial Intelligence (AI) simulates human intelligence in machines capable of rational response and actions. AI’s growth in healthcare, particularly in dentistry, has been remarkable in aiding clinical practices from appointment scheduling to treatment outcome predictions. In orthodontics, AI effectively handles tasks such as automated landmark detection, diagnosing orthodontic needs, assessing maxillary constriction, and selecting appliances. ChatGPT, an AI-based program by OpenAI, excels in generating human-like responses, making it useful across many applications, including those in dentistry. AI enhances personalised patient communication, supports diagnosis and decision-making, digitises patient data, and aids in disease prevention and treatment. In addition, ChatGPT offers support in personalised care, billing, and patient health monitoring. Patients benefit from its help in finding providers, scheduling initial appointments, accessing health information, improving healthcare access and service effectiveness. However, there are limitations. ChatGPT sometimes produces nonsensical responses and poses privacy risks associated with patient data. Generated medical advice might not therefore match professional expertise. Despite these issues, if trained properly, ChatGPT could significantly enhance medical knowledge dissemination. Future studies should weigh the benefits of AI against its risks, and users must verify AI-generated information with reliable sources to ensure effective patient care.
Controlling a demographic wave in defined contribution pension systems
Authors: Molnár Sándor , Molnár Márk
Published: July 2024

A common problem in systems and control theory is to provide an approximation to non-linear systems. We provide a novel approach as a general solution to this problem originally conceived by Gamkrelidze. We consider and solve a general approximation problem which provides the fundamentals for various switching-type systems thus encompassing a wide range of systems theory problems.
Results on E-quasiconvex Functions for a linear map E
Authors: Benhadid Ayache , Brahimi Mahmoud
Published: July 2024

It is well known that convexity theory and its generalization play an important role and have many applications in mathematical programming. In the previous article [2], the authors studied E-convex and semi-E-convex functions. In the present article, we deal with a class of E-quasiconvex functions and give a weak condition for a lower semi-continuous function on ℝ
n
to be an E-quasiconvex function.