bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on financial innovation and Risk Management in the digital Economy Era
Authors: Xu Shiqin 
Published: August 2024

Abstract
This paper proposes four major characteristic trends for financial development in the era of the digital economy, utilizing provincial data, which are specifically characterized by the development of the provincial average of the total digital financial index and the three secondary indicators of the real economy, virtual economy, and coordinated development of the real and virtual economy and incorporated with the Kernel density estimation method to enhance the overall development level of digital finance. According to the risk assessment of big data finance, the comprehensive pressure index of digital financial risk is screened, and the data financial risk early warning model is constructed by combining the random forest algorithm and the kernel principal component analysis method. Financial data characteristic variables are extracted using the nuclear principal component analysis method, and the risk level is set to predict the risk of digital financial development. Combined with provincial data, it is obtained that a mean growth of 37.20% was realized in 2015 compared with 2014, and digital finance shows a rapid development trend from 2011-2021. The early warning result of the digital finance risk early warning model in 2022 is that the probability of being in the “risk” state is small, and the likelihood of maintaining the “normal” state is significant. The early warning results are valid, and the early warning model can be further developed.
Deep learning-based optimization method for detecting data anomalies in power usage detection devices
Authors: Shang Hang , Bai Bing , Mao Yang , Ding Jinhua , Wang Jiani 
Published: August 2024

Abstract
In this paper, the self-attention layer of a graph convolutional neural network is first constructed to output the important information in the network structure. The migration learning network model is established, and the sample data are preprocessed and trained sequentially. The final processing results are used as the initial data for abnormal power consumption detection. Introduce Bayes’ theorem to optimize the hyperparameters of the model. The optimized model is applied in the abnormal power consumption detection system to identify abnormal power consumption events and provide specific processing solutions. Through the detection of the system, it was found that the voltage of the test user dropped from a 100V cliff to about 20V in late November, which was determined by the system to be a power consumption abnormality, and, therefore, an operation and maintenance order was issued. The site survey revealed that the data was in line with the system detection. Calculating the power consumption information of another user, the phase voltage of this user stays around 85-100V, far below 150V, so the undercounting of power is verified for the user, and the amount of power that should be recovered is 201.22kW.
Research on the Protection and Dissemination of Cultural Heritage in Rural Landscapes Based on Image Processing
Authors: Ye Ziming 
Published: August 2024

Abstract
The countryside is an important part of the social development process, but with the acceleration of urbanization, the protection of rural landscapes as cultural heritage is facing an increasingly severe situation. In this study, image radiation correction, image fusion, cropping and mosaicing, geometric correction, band selection, and image enhancement are applied to rural landscapes using remote sensing image processing technology. A digital protection system for rural landscape cultural heritage is created using the processed rural landscapes. By comparing the accuracy of this paper’s method to other classification methods, we can explore the classification performance of this paper’s PCA method. By comparing the changes in rural landscape types before and after landscape protection, the effect of landscape protection is explored. Finally, the communication effect of rural landscapes on social media is explored by utilizing the evaluation index system. The PCA method employed in this paper achieves a landscape classification accuracy of 83%, which is significantly superior to the IHS transformation method (73.5%) and the Brovey transformation method (76%). After the rural landscape protection, the degree of landscape fragmentation in Village A was significantly improved compared to before the protection, and the effect of protection was remarkable. The scores of social media users on each dimension of the rural landscape communication effect in Village A were greater than 4, and rural landscape communication achieved a positive effect.
Quantitative analysis of the relationship between green technology innovation and environmental protection by ESG indicators
Authors: Zong Zhijuan 
Published: August 2024

Abstract
This paper defines the concepts of ESG performance, corporate green technology innovation, and the environment, as well as proposes research hypotheses. It determines the selection of research samples and related data sources, explains specific definitions and measurement methods for the selected variables, and constructs regression models for the corresponding variables. Through correlation analysis, benchmark regression, and robustness tests, we quantitatively analyze the relationship between ESG indicators, green technological innovation, and environmental protection. After analysis, it can be concluded that there is a significant positive correlation between ESG and green technology innovation (GTI) and environmental protection (EP) with P<0.05, and the corresponding correlation coefficients are 0.405 (0.05) and 0.317 (0.026). In the benchmark regression results analysis of corporate ESG and green innovation technology, environmental protection is significant at the 1% statistical level; its regression coefficient is 0.101, 0.0372, and the corporate risk-taking levels in ESG indicators play a mediating role in the relationship between green technological innovation and environmental protection.
The Role of Data Fusion in Contemporary Urban Image Design and Communication in the Context of Big Data
Authors: Wang Bo 
Published: August 2024

Abstract
The urbanization process is accelerating, the competition between cities is getting more and more intense, and the problem of urban image management is getting more and more attention from urban image researchers and city managers. Taking Nanchang City as the case study, this paper selects the city government, economy, tourism, and people image, as well as three data platforms: the official website, news positioning, and tourists’ diary, and crawls the comments of the four types of city image elements on the platforms, and carries out the data fusion and pre-processing to get the comment dataset. Subsequently, the TF-IDF algorithm is used to extract high-frequency words from the city image, and it is combined with the SnowNLP model and LDA theme analysis model to analyze and supplement the overall image of Nanchang City. Finally, after studying the mechanism of city image under data (media) fusion, the communication effect of Nanchang’s image is explored in terms of communication heat, recognition, and participation. Government image and tourism image aspects of the words appear most often, and the highest and lowest percentage of positive emotions are the city’s economic image and government image, respectively, which should integrate the existing resources, improve the level of service, and further create the city’s characteristics, to expand the city’s visibility and communication effect.
Application of mathematical modeling in analyzing and optimizing English teaching methods in vocational education
Authors: Chen Liailun , Wang Junpeng 
Published: August 2024

Abstract
In the era of big data, mathematical and scientific methods significantly contribute to educational reform, continuously revitalizing precision teaching. In this paper, we use the new Hawkes process to collect and vectorize data on how students learn. Then, we use the unique thermal coding method to combine the behavioral feature vector with the teaching feature vector. Finally, we classify the data and use the softmax function to predict the learning effect. Based on the prediction results, a mathematical model for precise teaching optimization of vocational education English has been established, and the decision function is used to solve the problem and optimize teaching methods. It was found that all the indicators of learning behavior characteristics of students in the experimental class applying the teaching method optimization model were significantly improved, and there was a significant difference at the level of 0.05 (P = 0.000<0.05). Furthermore, the students in the experimental class achieved higher academic performance in vocational education English than those in the control class under traditional teaching. This paper provides a scientific basis for teaching English to students in vocational education and provides methodological references and ideas for unfolding individualized and precise learning in classroom teaching.
Construction of risk warning model of agitated behavior of the elderly in Zhejiang pension institutions
Authors: Du Rongbing , Xia Junli 
Published: August 2024

Abstract
Alzheimer’s disease is considered the epidemic of the twentieth century, particularly the radical behavior produced by the patients, which can easily lead to an increase in mortality. This paper focuses on optimizing the search path algorithm to forecast the likelihood of aggressive behavior, enabling the implementation of targeted preventive measures. Additionally, it performs preprocessing procedures like data cleansing on the health data of the elderly. We propose a feature extraction optimization model to calculate the feature contribution of elderly radical behavior data, filter the feature data associated with radical behavior based on this contribution, and establish an interpolation model for association rule learning. Using Logistic Regression, Simple Bayes, and Support Vector Machine classification models, the risk warning model for aggravated behavior is constructed. After one month’s intervention with the risk warning model, the aggressive behavior of the elderly decreased by 58.83%, 43.06%, and 67.94%, respectively, compared to the pre-intervention period, and the intervention effect of the model was good.
Evaluation Method of Teaching Quality of Foreign Language Teachers in Colleges and Universities Based on Decision Tree Algorithm
Authors: Huang Yumei , Xin Rui 
Published: August 2024

Abstract
Teaching evaluation is an important part of the teaching process in colleges and universities and plays a very important role in the development of higher education and the improvement of education quality. Applying a decision tree algorithm to teaching evaluation can uncover the hidden intrinsic connection between teacher information and course quality evaluation results. This study combines the radar graph with the decision tree C4.5 algorithm to construct a foreign language teaching quality evaluation model, which explores the correlation between teacher information and radar graph attributes on teaching quality evaluation grades on the one hand and evaluates the predictive effect of the model on the other hand. The data mined by the radar chart-C4.5 evaluation model show that if the center of gravity of the radar chart is in the first quadrant, then the evaluation grade passes and has the highest support, which is 53.33%. If the radargram area is large, then the evaluation grade good has the highest confidence level of 77.27%. The evaluation result of the model is that the number of error samples of the prediction grade and the expert evaluation grade are 3 excellent, 5 good, 1 average, 4 qualified, and 1 poor, which proves that the performance of the radar map-C4.5 evaluation model is better.
Design of a land use change prediction model incorporating neural networks
Authors: Chen Shuiying , Wang Haibin 
Published: August 2024

Abstract
Land use change has gradually developed into a core area of global environmental change research. In this paper, we use object-orientated modeling to construct a model that combines Markov models, neural networks, and cellular automata. We extend the Markov model to the traditional CA, fully utilizing the advantage of ANN in simplifying the definition of land use transformation rules and obtaining a large number of spatial variable parameters of the model. This successfully simplifies the structure of the model and the definition of transformation rules. We apply the constructed Ann-CA-Markov land use change analysis model to the evolution and prediction of land use in County A. It has been found that the proportion of arable land area in County A decreased from 23.3% to 12.1%, and the proportion of construction land increased from 28.07% to 50.87%. From 2000 to 2020, other land continued to converge into construction land in large quantities, so the land area of County A increased from 1224.73km² to 1295.15km² in 2020. The area of arable land converted out is the largest among the five types of land, with an arable land area of only 308.11km² by 2020. The probability of conversion of four land types, namely, arable land, forest land, grassland, and watershed, to construction land is 21.7%, 10.5%, 10.9%, and 9.2%, respectively, by 2030, while the probability of conversion of construction land to arable land is 21.7%, 10.5%, 10.9%, and 9.2%, respectively. The probability of converting land to cropland is 13.9%. The model constructed in this paper shows strong performance in the analysis of land use change evolution and prediction of County A, which is in line with the design expectation and makes an innovative exploration for realizing the effective simulation of spatial and temporal land use changes.
Research on the Application of SVM in Ideology Analysis and the Enhancement of Ideological and Political Education Effectiveness
Authors: Huang Yueqin , Shen Tingting , Wang Huaijing 
Published: August 2024

Abstract
The consciousness and behavior of students in the Ideological and Political Education curriculum generate massive amounts of data information, how to allow educators to quickly obtain information from the massive amount of text data is very important to improve the energy efficiency of classroom education. SVM technology is employed in this paper for data mining and text classification. Firstly, using the web crawler method, the text of messages related to the Ideological and Political Science course posted by students on social networks is collected. The short text was preprocessed before performing sentiment analysis, which mainly included interaction information filtering, word segmentation, and lexical labeling. Then, the SVM model suitable for students’ ideology analysis is constructed, using the Gauss radial basis kernel function to accurately depict the distribution structure of the data, and the L1-SVM model with more stable computational performance is also proposed. The extension method of the classification algorithm in the real number domain is summarized at the end. This algorithm’s accuracy is 78%, and its F1 value is 80%, which is higher than the other three algorithms. DAG-SVM and recall are both optimized to a lesser extent. Overall, the classification efficiency of the algorithm in this paper has been improved. The positive effect of this paper’s algorithm on improving the effectiveness of Ideological and Political Education can be seen in the significant increase in the learning interest of the experimental class.
Innovative Strategies and Practical Effects of Supply Chain Management of Business Enterprises under the Framework of Smart City
Authors: Jin Jun 
Published: August 2024

Abstract
The change in modern information technology has brought new development opportunities to all walks of life, and the supply chain management mode of industrial and commercial enterprises has gradually shifted from management based on historical experience to management relying on information data. This paper combines the problems of inventory cost optimization and vehicle route scheduling optimization in the supply chain management of industrial and commercial enterprises and develops the supply chain innovation management system of industrial and commercial enterprises under the framework of a smart city. The proposed DES_RSA hybrid encryption method is proposed to ensure the timeliness and security of the information transmission process of the smart supply chain management system. During the interface test of the purchase order paging request, the system test revealed that the supply chain management system had a 0.00% error rate. The results of the application practice of Company H show that after the application of the supply chain innovation management system, the growth rate of Company H’s operating costs continued to decline, the expense margin rose by 8.50% in two years, and the trend of customer satisfaction also shows a significant increase. The application value of the business enterprise supply chain management system designed in this paper is high, which can achieve efficient management of the business enterprise supply chain.
Dynamic Optimization Research on Regional Security Planning of College Dormitory under Neural Network Perspective
Authors: Li Lan 
Published: August 2024

Abstract
A dormitory is a place where students live and live for a long time, and the safety of the dormitory can directly affect the quality of students’ studies and lives. In light of this, the study constructs a dynamic security planning system for college dormitory areas, enhancing their security and intelligence through the application of intelligent technology and multi-directional management coordination. Taking the access control system and fire monitoring system in the dormitory security prevention system as an example, based on the neural network perspective, we design the face recognition method of HOG-YOLO and the fire detection algorithm of improved YOLOv5 to realize, train, and test the two models, and evaluate the face recognition performance and fire target detection effect of the models. The results show that the face recognition method established in this paper is highly accurate and rapid, with a recognition accuracy of 95.7%, and it can complete recognition and detection under different facial angles. The fire detection algorithm’s FPS has been improved by 14.75%. The overall accuracy has improved by more than 1%, which has a good effect on fire monitoring. Planning the security system for the dormitory areas in higher education is a significant area of research for ensuring campus security, and access control and fire monitoring systems have significant application potential.
Semantic analysis and construction of English discourse based on neural network
Authors: Liu Junjie 
Published: August 2024

Abstract
Accurate recognition and analysis of English semantics is of great significance in the application field of computer and artificial intelligence technology. Traditional text analysis technology has poor real-time analysis accuracy and efficiency. This paper constructs an English semantic analysis model based on neural networks. By designing an English semantic framework that integrates LSTM and RNN and optimizing it on the basis of LSTM and RNN models, an English semantic analysis model with improved attention mechanisms and Bi-LSTM is constructed. By comparing the Bi-LSTM+Attention model proposed in this paper with other semantic analysis models, the classification accuracy of the model constructed in this paper reaches 98%. Through the practical application of the model to analyze the semantic coherence of English, the average score of the semantic coherence quality measured by the model is 15.76, and the average score of the manual judgment is 16.03, which indicates that the application of the semantic analysis model constructed in this paper is close to the effect of the manual judgment.
A Study on Modeling and Quality Evaluation Methods of Assignment Revision in English Education
Authors: Lu Xiaochun , Tian Shang 
Published: August 2024

Abstract
Assignment revision is an important part of English assignment teaching, which is of great significance in guiding the direction of English assignment teaching as well as improving students’ writing ability, so it is of great value to establish a scientific and reasonable assignment revision model and quality evaluation method. This paper proposes a model of revision in English education by evaluating the English revision ability of students in College A and then proposing a revision evaluation system. An empirical study of English assignment teaching in College A is the article’s conclusion. The article concludes that the student’s overall assignment review ability scores are around 78%, which is the highest score, but the students’ English assignment revision ability scores are only 37%. This article’s evaluation index system of assignment revision has a composite calculated value of (0.146, 0.104, 0.092, 0.021, 0.001)
The Aesthetic Value of Caravaggio’s Light and Shadow Technique from a Geometric Perspective
Authors: Sun Hanying 
Published: August 2024

Abstract
Caravaggio was an influential Italian painter of the 17th century who made important contributions to the development of later European art, both in terms of light and shadow techniques and in terms of ideas. This study first explores the characteristics of Caravaggio’s paintings and briefly explains Caravaggio’s painting style, light and shadow processing techniques, and their significant influence. Secondly, based on the visual perception art understanding method and aesthetic law, the curvelet geometric transformation is utilized to interpret the characteristics of different styles of Caravaggio’s light and shadow techniques in terms of three geometric features: multi-scale amplitude, coefficient distribution curve, and image non-smoothness. Finally, the aesthetic value of Caravaggio’s light and shadow techniques is interpreted from the geometric point of view by using six works from different periods: Caravaggio’s The Doubt of St. Thomas, The Supper at Emmaus, The Basket of Fruits, The Diviner, Narcasus and Judith Cutting down Hierophant as the research materials. The results show that Caravaggio’s light and shadow technique in the early stage of his creative career is characterized by a high low-scale coefficient and a low high-scaled coefficient, which indicates that there are more thick lines and fewer details. The maximum smoothness value of the works is 0.4035 respectively. The light and shadow technique in the later stage of his creative career is characterized by a low scale coefficient and a high scale coefficient, which means that he uses more fine lines to depict the details and less thick lines, reflecting the fact that the light and shadow technique in the later stage of his creative career is characterized by a low scale coefficient and a high high scale coefficient. Caravaggio’s light and shadow technique is characterized by a low scale factor, a high scale factor, and the use of fine lines to depict details and fewer thick lines, reflecting the light and shadow characteristics of Caravaggio’s light and shadow technique, which is depicted by the use of brushes and mineral pigments, and highly demonstrating the aesthetic value of his realist painting.