bluuebunny/crossref_metadata_2025_split huggingface dataset:
A real-time risk assessment model for cross-border financial transactions based on big data technology
Authors: Bao Mengrui 
Published: November 2024

Abstract
The study applies the method of resampling to deal with unbalanced financial transaction data, which is resampled by the method of majority class weighted minority class oversampling. After data processing, the VaR-GARCH financial transaction risk assessment model is constructed. The financial transaction risk assessment method of this paper is compared with other risk assessment methods to test its assessment performance. Subsequently, taking the carbon financial market as an entry point, the trading price data of seven global carbon financial markets from 2021 to June 28, 2024, are selected for the study to assess the risk of the carbon transnational trading market in real-time. The risk assessment efficacy of this paper’s risk assessment model on both the AP and LC datasets has an overall advantage. Among the seven global carbon markets, the EU has the most drastic fluctuation in transaction prices, while the Chinese carbon market is the smoothest. The transaction price averages from highest to lowest are California-Quebec (85.59), South Korea (72.49), U.S. Regional Greenhouse Gas Emission Reduction Program (47.24), U.K. (44.80), China (37.26), New Zealand (34.35), and EU (34.34). California-Quebec had the highest average transaction price, while the EU had the lowest average transaction price. Transaction prices in China are the most stable, and log yield trends in the UK and South Korea are similar. The top three markets in terms of value-at-risk VaR are California-Quebec, South Korea, and the EU, and the smallest is the UK market.
Optimizing large-scale data processing in the digital economy using high-performance computing techniques
Authors: Dong Fei 
Published: November 2024

Abstract
In order to solve the current issues of not making the most of scheduling strategies, uneven load, and using too much energy in high-performance computing for the digital economy, this paper creates a task scheduling algorithm that is based on both a genetic-ant colony algorithm and an energy-aware task scheduling algorithm. The first algorithm leverages the robust search space solution capability of the genetic algorithm to transform the optimal solution into an initial pheromone, thereby enhancing global searchability and feedback. The latter reduces overall energy consumption through ETF and NCM operations and allocates appropriate computing nodes for each computing task. The CSI 300 index is used as the research data to carry out empirical analysis of large-scale data processing optimization. Under the scheduling strategy optimized by the algorithm in this paper, the performance of the task scheduling strategy is improved by 2.6 times compared with that of the unoptimized one, and the difference between the capacity share of the memory storage layer and the SSD storage layer is no more than 6%, so as to achieve the load balance between the storage layers. The average CPU utilization remains low at 18.12%, with the running total job power consumption primarily concentrated in the 10w-38w range, which accounts for about 56% of the overall power consumption.
Optimization path of blockchain technology in enterprise economic and financial management
Authors: Hu Haibo , Nordin Nurhaiza Binti , liujinbo  
Published: November 2024

Abstract
In the face of the rising heat of the blockchain concept, many enterprises compete to be the first to develop. However, the excessive enthusiasm of enterprises will inevitably lead to a large amount of waste of resources, therefore, it is particularly important to study the impact of the application of blockchain on the enterprise economy and finance. This paper first puts forward relevant assumptions on the behavioral characteristics of the participating subjects in the supplier order financing system, calculates the expected return function based on the return matrix and the initial ratio between the supplier, the company and the financial institution, and derives the replicated dynamic equation through further projection, according to which the evolutionary stabilization strategy is analyzed. Numerical simulation experiments are used to analyze how blockchain technology, receivable period, and guarantee rate affect the three-party gaming system. The concept of blockchain+supply chain finance model is proposed, and the method of empirical analysis is utilized to study the performance impact it has on enterprises. The thresholds of blockchain technology cost, accounts receivable period, and guarantee rate are [170,000 yuan, 190,000 yuan], [1.9a,2.0a], and [0.12,0.14], respectively. The operating revenue growth rate of the FT enterprise shows an increase in 2019 and 2020, which is 15.25% and 23.54%, respectively, and both of them are beyond the industry average in the same period, while the operating profit growth rate grows steadily and reached 102.54% in 2022, which can quickly respond to market changes and challenges.
Analysis of Artificial Intelligence-Based E-Commerce Transaction Anomaly Monitoring Strategies and Their Implementation Effects
Authors: Huang Yuqing , Nie Jialin 
Published: November 2024

Abstract
The rapid development of e-commerce is accompanied by the problem of a large number of abnormal transactions, and the development of artificial intelligence models has brought the possibility of large-scale real-time monitoring of transaction anomalies. The study integrates the transaction anomaly monitoring method based on the Light GBM model and multi-instance learning model, and trains the constructed combined model based on Taobao data. The average accuracy, precision, recall, and F1 values of the combined model are 0.977, 0.971, 0.973, and 0.972, respectively. The monitoring effect is higher than that of a single model, which indicates that the combined model is more effective in identifying anomalies in e-commerce transactions. The relevant technicians of A e-commerce platform who have used the model said that the implementation effect of the combination model constructed in this paper is more satisfactory.
Image Enhancement Network Architecture for Multidimensional Fusion of Medical Imaging Data under Intense Light Interference
Authors: Li Jiuhan 
Published: November 2024

Abstract
Due to the limitations of imaging equipment and environment, the acquired medical images usually have a certain degree of noise and artifacts, which leads to the degradation of the quality of medical images and affects the doctors’ clinical diagnosis of the condition. In this paper, the Gauss-Laplace operator is used to perform normalized filtering on medical images to reduce the influence of noise and improve the convolution effect of images. Through the CLAHE algorithm, the histogram is optimized for equalization, and the network architecture of the image is designed in this way. The quality of the enhanced image is evaluated through experimental design and dataset processing. In the evaluation of subjective and objective metrics, the PSNR and SSIM metrics of the images in SR × 2 are improved by 1.576 dB and 0.997 dB, respectively, on the BraTS dataset. This algorithm’s subjective score is the most high among the four enhancement algorithms, with an average score of 8.25, which aligns with the objective evaluation results. Among the image enhancement results, this paper’s algorithm better adjusts the histogram distribution with h(k) distribution ranging from 0.526-4.215, which is better than other enhancement algorithms in detail enhancement.
Research on intelligent identification algorithm for preventing external damage behavior of power cable based on machine learning
Authors: Li Qian , Guo Kang , Wang Siying , Zhang Jun , Zhang Zexin 
Published: November 2024

Abstract
As the key carrier of power distribution line power transmission and the supporting artery of urban functioning, power cables are widely used in electric power engineering construction. The number of users is increasing, and the distribution of locations is becoming more complex. However, if a power cable failure occurs, if not eliminated in time, it will threaten the safety of the power grid, seriously affecting the production of enterprises and the lives of residents. The article first on the power cable vibration signal recognition algorithm design, extraction of power cable external damage fault signal characteristics, proposed based on PSO-SVM power cable external damage fault waveform recognition algorithm, and the design of power cable anti-external damage intelligent early warning system, to achieve a certain period according to the trend of signal changes in the vibration event to discriminate. Research results show that under the same computer operating conditions, the average running time of the algorithm proposed in this paper is only 3.43. Compared to the other two algorithms, the algorithm has the highest recognition accuracy, the fastest convergence speed, and the optimal comprehensive performance. In the external damage signal recognition and warning test, the recognition rate of the excavator passing through defense zone one and two at a distance of 3 meters is 100%, which is greater than 80%, in line with the expected effect.
A Study on the Relationship between Digital Presentation of Intangible Cultural Heritage in Libraries and Visitors’ Interactive Experience
Authors: Liu Chunhui 
Published: November 2024

Abstract
Under the guidance of the theory related to the KANO demand model, this paper constructs the user demand hierarchy of the digital display of intangible cultural heritage and carries out the division of functional demand item categories through classification experiments. A questionnaire survey was set up to evaluate the satisfaction of the virtual reality technology of intangible cultural heritage crafts, and 30 participants’ interactive behaviors were coded so as to analyze the data results and explore the relationship between digital display and interactive behaviors. The findings indicate that both social and gamified interaction are charismatic needs of users when it comes to the digital display of NRM. In the practical case of virtual technology combined with non-heritage skills, the satisfaction level of design, content, and other aspects exceeded 60%. Non-heritage digital displays have 115 more interactive behaviors than traditional display methods. The interactive experience of participants can be enhanced and their needs can be satisfied by the digital display of NRLs.
Information flow and data-driven analysis of cross-cultural communication in the Chinese cultural community
Authors: Su Linting , Wang Cheng , Su Jian 
Published: November 2024

Abstract
The rapid development of the Internet also provides the possibility of information flow for cross-cultural communication, and the network not only breaks through the boundaries of time and space but also breaks through the limitations of cultural communication barriers. The study analyzes the influence mechanism of cross-cultural communication through the medium of social networks. A series of characteristics are proposed to characterize the flow of information, and a simulation experiment is carried out on the influencing factors of cross-cultural communication using the infectious disease model (SEIR). We utilize the Twitter dataset of real social networks to examine the effectiveness of the proposed SEIR model of information dissemination and verify its accuracy. The simulation experiment analyzes the influence of the containment mechanism, the time lag mechanism, and the forgetting mechanism on the model of information dissemination in cross-cultural communication, and information dissemination does not occur when R
0 <1. When R
0 >1, the cross-cultural communication information will be continuously and stably spread in the social network. This paper’s SEIR model has a positive effect on the dissemination of cross-cultural communication information, as evidenced by the results.
Research on the Influence Mechanism of Resilience of Agricultural Supply Chain Based on Mobile APP under the Perspective of New Development
Authors: Wu Tianjiao 
Published: November 2024

Abstract
The development of mobile apps provides a more convenient means of data collection, transmission and individual identification. The injection of new kinetic energy into the development of the agricultural supply chain is crucial for accelerating the construction of a modernized agricultural industry system. For this reason, this paper first analyzes the impact of the application of mobile apps in agricultural products enterprises on the output of agricultural products enterprises based on the theoretical model, and obtains fuzzy conclusions. Subsequently, the benchmark regression model was applied to examine the impact of mobile APP application on supply chain toughness, while the VAR model was used to study the direct effect of mobile APP application on the toughness of agricultural products supply chain and combined with the mediator model to verify the impact path of mobile APP application on the toughness of agricultural products supply chain. The model finally shows a positive response and gradual smooth convergence after giving shocks, indicating the positive effect of the development level of mobile APP applications on the toughness of the agricultural supply chain. The coefficients of the correlation between the level of trade development of mobile APP applications and innovation ability, human capital and supply chain resilience are all positive, indicating that the level of trade development of mobile APP applications has a significant positive impact on the resilience of agricultural supply chain, innovation ability and human capital. The investigation of the mechanism of the influence of mobile apps on the toughness of agricultural supply chains based on mobile apps has been completed.
Research on Visual Communication Design Strategies for Non-Heritage Cultural Creations in Shandong Yellow River Basin Based on Deep Learning Optimization
Authors: Yang Qiulu 
Published: November 2024

Abstract
As a brilliant treasure of human civilization, the non-legacy of the Shandong Yellow River Basin has always been an important carrier for people’s pursuit of beauty and unique expression. The rapid development of science and technology has resulted in the introduction of artificial intelligence and digital media technology, which have brought new vitality to cultural and creative visual communication design. The article introduces deep learning technology to analyze and extract non-legacy elements of the Yellow River Basin in Shandong, and deep learning technology mainly includes image recognition and image style migration technology. After studying the image recognition algorithm based on the migration of the generative adversarial model, the article proposes an image style migration model that integrates multi-scale discriminators, applies the decoupling training strategy so that the encoder is trained only when the adversarial loss is the largest, and finally sums up the visual communication design strategy optimized based on deep learning. Through the survey and study of 10 users’ satisfaction with the cultural and creative products designed by using the method of this paper, the total score rates of the four evaluation indexes, namely, content design, presentation design, element design and structural design, are 0.82, 0.83, 0.84 and 0.85, respectively, which the users highly praise. As a result, the visual communication design method proposed in this paper is effective for non-heritage cultural creations in the Shandong Yellow River Basin.
Research on Virtual Reality Art Creation Mode and Visual Perception under the Integration of Art and Technology
Authors: Zhang Nan 
Published: November 2024

Abstract
The use of cutting-edge information technology, such as virtual reality, has had a disruptive impact on the traditional model of visual perception art design. In this paper, we design a virtual reality modeling algorithm based on visual perception and QEM for creating virtual reality services. When the triangle feature constraint is combined with the QEM algorithm, it creates a triangle folding visual, geometric multi-feature metric function that guides the simplification order of the triangle face piece. Scene alignment and stitching using visual perception and QEM algorithms are examined, and the algorithms are employed to recreate the virtual reality of three selected artworks. This paper's algorithm achieves an average alignment rate of 90.93% for virtual scenes, outperforming the traditional algorithm by 15.64%. As for the quality of the scenes that were built, the mean standard deviation (SD) went from 11.92184 in the old way of doing things to 61.46286 in the new way. Similarly, the mean gradient (AG) went from 2.81938 to 15.58646, and the value of information entropy (EN) went from 5.40276 to 7.76728. Among the 12 dimensions scored by the experts for the first traditional artwork, only the light and shadow scored 51.13 points, while the rest were below 50 points. After the re-creation using the virtual reality technology in this paper, all the scores reached more than 50 points, with the highest being 88.8 points for visual aesthetics. With the exception of the background comparison, the virtual reality technology recreated the second work, improving the experts' scores on all dimensions, with the highest and lowest scores being 85.84 and 42.94 points, respectively. The third work also showed improvement in all aspects. In this paper, the algorithm optimizes the operation efficiency, reduces the number of rejections of the error transformation matrix and the running time of the model computation, and verifies that the virtual reality art creation mode can significantly improve the texture and level of artworks.
English Listening Prediction Strategies Based on Deep Learning and Ways to Cultivate Them
Authors: Luo Liang 
Published: November 2024

Abstract
This study proposes a strategy for predicting English listening learning performance and English listening achievement and constructs a corresponding model, which provides a basis for proposing a personalized cultivation pathway for English listening by implementing an English listening prediction model. The learners’ memory forgetting and learning behaviors are integrated into the Bayesian knowledge tracking model to obtain the BF-BKT model, and its corresponding CPT and Bayesian formula is updated to realize the prediction of English listening learning performance. With the help of the K-means algorithm to mine the relationship between learning behavior and learning performance, the gradient progressive regression tree in deep learning is used to analyze the connection between key influencing factors and performance, and the English listening learning performance is predicted. In this paper, the English listening learning performance prediction model ROC area AUC=0.826, and the relative error of the performance prediction model is always lower than that of the traditional model, with better prediction performance. Applying the personalized English listening cultivation pathway based on English listening prediction for English listening teaching, the total listening score of the experimental class was 36.88, which was higher than that of the control class by 5.62, showing a difference (P<0.01). The mean value of each specific strategy in English listening learning strategies was higher than that of the control class, and the overall listening teaching satisfaction reached 3.87, which was greater than that of the control class by 0.66.
Research on Mining and Analyzing Tourists’ Consumption Habits in Tourism Management
Authors: Ma Lijuan 
Published: November 2024

Abstract
The rapid development of the tourism industry has led to a continuous change in the way of tourism consumption. This paper takes the information of tourists’ consumption behavior as the research object and investigates their consumption habits. This paper adopts the fuzzy clustering (FCM) algorithm to analyze tourists’ consumption habits and clustering validity indexes and then uses the association rule algorithm on the basis of the FCM algorithm to mine the factors affecting tourists’ consumption habits in tourism management. In this paper, tourists are divided into five categories: free youth, couples, parents and children, families, and explorers. In tourism consumption, the top three primary concerns of tourists are “attraction characteristics, safety and consumption, which account for 30.34%, 18.04% and 12.07%, respectively. In the process of tourists’ tourism consumption, 93.47% of the concern factors are attraction features, with a confidence level of 98.76% and rule enhancement of 1.245, respectively. In addition, the probability of security and consumption appearing in the text at the same time is high, with rule support, confidence level, and enhancement of 89.52%, 75.59%, and 1.045, respectively. Attention should be given to the characteristics of attraction, safety, consumption, and service simultaneously. The results of this paper help to identify tourists’ consumption preferences and provide suggestions for tourism management centers to accurately understand tourists’ consumption habits.
Design of Real-Time Monitoring System for Tunnel Structure Based on Novel Sensor Technology
Authors: Meng Lingchen , Liu Yujia 
Published: November 2024

Abstract
This paper proposes a new wireless sensor tunnel monitoring system, comprising TAM sensors, a 5G network, a Zigbee network, and a monitoring center, based on the environmental characteristics of construction tunnels. The ZigBee algorithm enables the tunnel monitoring system to achieve real-time monitoring and fast transmission. We conducted detailed testing on the accuracy of the monitoring system, the validity of historical data, the response speed of the blasting control, the displacement, and the positioning accuracy, and analyzed the test results. According to the results, the new wireless sensor tunnel monitoring system has an accuracy rate of 94.79%, and the test value is comparable to the actual value. In the real-time monitoring of the stress of the support structure at the entrance and exit of the highway tunnel, a displacement ductility coefficient of 2.3 indicates the lowest bearing capacity, making it susceptible to damage. The T4 sensor was found to produce a maximum settlement of 1mm in the vertical direction during the highway tunnel blasting test.