bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rebranding and SWOT Analysis of Geographical Indication Agricultural Products in Jilin Province under Digital Transformation
Authors: Xu Hui 
Published: July 2024

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.