bluuebunny/crossref_metadata_2025_split huggingface dataset:
Practical Reflections on Enhancing Cultural Confidence in English Education in Colleges and Universities under the Big Data Platform
Authors: Wang Hongyang 
Published: July 2024

Abstract
Day by day, we are realizing the goal of the great rejuvenation of the Chinese nation, which necessitates the development of sufficient cultural self-confidence. The amalgamation of traditional culture and English learning is an essential prerequisite for us to effectively communicate the Chinese story to the world. This paper proposes an optimized ridge regression model with regularization constraints to enhance the preprocessing of cultural data. Subsequently, we propose a statistical model for analyzing the teaching styles of English texts from four perspectives, taking into account the characteristics of English language teaching and the cardinal text statistics algorithm based on word frequency optimization. This model’s analysis revealed that textbook articles reflecting traditional Chinese culture accounted for only 1.8% of all types, resulting in an uneven distribution of grades. Following the implementation of improvements to tackle this issue, 74.86% of the students expressed their belief that enhanced teaching could bolster their cultural confidence. Similarly, 86.6% of the teachers expressed their belief that incorporating Chinese culture into English teaching could enhance students’ cultural awareness, and 90% of the teachers believed it could boost students’ identification with Chinese culture and cultural confidence. It shows that the new teaching method optimized by conducting textual statistical analysis helps to bring into play the role of English education in colleges and universities in enhancing cultural self-confidence.
Optimizing bank credit risk assessment models using big data analytics
Authors: Yang Feiyu , Xu Jing 
Published: July 2024

Abstract
With the strong promotion of financial globalization and marketization and the rapid development of financial industry innovation, credit risk management is becoming an important work that banks need to pay more and more attention to. This paper focuses on the construction of the bank credit risk model to carry out research. First of all, according to the credit characteristics selection method, the optimal indicators are combined with the indicators for the correlation test of variables. On this basis, the sample data is calculated using the up-sampling method of SMOTE to complete the algorithm and sampling. After obtaining the data, data preprocessing is carried out using the MinMaxScaler scaler method, and the processed data is inputted into the improved momentum BP neural network to complete the credit risk assessment. Profiling with risk feature data, it is obtained that each feature is not a separate individual from the other. They are interdependent and connected and have obvious correlations. For example, there are 47 features in the dataset with sample missing ratios greater than 0.97, which is too high and indicates an invalid feature. The results of the study show that SMOTE’s up-sampling method and Momentum BP algorithm can quickly utilize big data to provide a more accurate decision basis for bank credit risk assessment.
Innovative Development Mode of Tourism and Culture Industry in the Era of Smart Tourism
Authors: Zhang Pingping 
Published: July 2024

Abstract
In the era of smart tourism, its emergence not only provides crucial technical support to meet the needs of tourists but also prompts the reform and improvement of the development mode of the tourism and culture industries. This study begins by examining the current state of smart tourism and the development of the tourism and culture industries. It then conducts a thorough analysis of the mechanisms driving these developments and establishes an industry analysis model based on the RMP theory. The study then employs fractional regression to identify the factors that influence the development of the smart tourism and culture industry. It then constructs a model for smart tourism and culture development, which includes government and industry support, economic and cultural support, digital information technology support, personnel support, talent cultivation, cultural participation, and social participation. Finally, it analyzes the impact of this model’s application using the RMP analysis model. This paper’s development model sees annual increases in tourism and culture industry indexes, rising from 105.85 to 145.79, with an average growth ratio of 17.35%. The overall satisfaction rating for the product experience stands at 4.152, suggesting that under the development mode of the smart tourism and culture industry in this paper, both the market economy and the product experience exhibit excellent phenotypes. This paper supports the construction of a smart tourism cultural industry development model using digital technology and provides references for the tourism cultural industry model.
Efficiency Assessment of Policy Instruments for Green Finance Management in the Context of Low Carbon Economy Based on Panel Vector Autoregressive Modeling
Authors: Zhu Chenyang 
Published: July 2024

Abstract
This paper takes green finance policy as the research object and collects local government policy texts about green finance so as to study its policy structure in depth. Then, it studies the theory of green finance to support the low-carbon economy, empirically analyzes it in terms of measurement using the panel vector autoregressive model, and tests its smoothness and covariance to explore the impact of green finance in the development of the low-carbon economy. According to the findings, the proportion of demand-based green financial policies is the lowest among the five provinces, with a mean value of 22%. The extreme differences in development levels of a low-carbon economy and green finance are 1.52 and 1.46, respectively, showing different characteristics. The model regression coefficient of the level of green financial development is 0.08, and the development of a low-carbon economy positively influences it. Although the effectiveness of green financial policies will fluctuate, the overall trend is upward. This paper analyzes the panel vector autoregressive model and the effectiveness of policy tools to provide suggestions for improving the policy system.
Speech Network Analysis and Spoken English Teaching for 6G Wireless Transmission Technology
Authors: Wu Meimei 
Published: July 2024

Abstract
Recent years have seen intensive research and widespread application of 6G wireless transmission technology, providing a technical bridge for the application of speech network analysis to spoken English teaching. Thus, this paper suggests a method for improving 6G technology. This paper primarily focuses on the following areas: First, to prevent overfitting, the dropout technique under 6G is added to the model, and the speech feature extracted from the data by the coding network is directly utilized. We then utilize the feature for either reconstruction or classification purposes. Initially, we perform unsupervised reconstruction using unlabeled data, and once we obtain the network parameters, we use the labeled data for classification. The next step involves feeding both data simultaneously into the model to calculate the error. Finally, we use gradient descent to minimize the error and optimize the parameters until the model converges. This model can convert sound-related symbols or information into visual symbols, and it can also convert speech into text. Comparative analysis shows that the improved method in this paper is far better than other methods in terms of noise reduction, and the signal-to-noise ratio of this paper is 3.76 dB higher than that of the LMS algorithm and 1.36 dB higher than that of the spectral subtraction method, which can stimulate the students’ interest in learning spoken English and increase the student’s interest in learning by 13.64% as a whole. It can be summarized through the comparison that this paper’s improved method increases the clarity of speech network analysis, which helps students teach spoken English.
Marxist Internet Communication and Youth Ideology Education
Authors: Yan Xiaoyu 
Published: July 2024

Abstract
Enhancing the mainstream ideological identity of young people is an unavoidable issue in the ideological construction of socialism with Chinese characteristics in the new era, which is closely related to strengthening the national identity and expanding the mainstream ideological positions. In this paper, we refer to the E-learning platform framework to design a Marxism online learning platform to achieve the online dissemination of Marxism and ideological education for youth. The HRNN model is utilized to extract and identify the behavioral traits of youth on the Marxism learning platform and investigate the link between youth behavior and ideological identity. The results of the study show that the Marxist online learning platform has a better communication effect and can improve the identification of the youth group with Marxist ideology. Meanwhile, the correlation analysis shows that there is a significant correlation between video viewing completion, quiz completion, and ideological identity (P=0.000), which is highly positive. The suggestions put forward in this paper regarding communication management, communication technology, and publicity provide a reference for further online communication of Marxism and lay the foundation for the enhancement of youth ideology education.
Practical Research on the Reform of Social Practice Teaching in Colleges and Universities under the Background of Big Data
Authors: Yang Yang 
Published: July 2024

Abstract
Social practice teaching in colleges and universities combines theory and practice, transforms professional theory into practical behavior, and innovates students’ way of thinking, which can promote the teaching of colleges and universities as well as the cultivation of target talents. The big data era is innovating the traditional social practice teaching mode, and new opportunities are available for the development of social practice teaching. By analyzing the development form of social practice in colleges and universities in the era of big data, we propose to use the fuzzy C-mean clustering algorithm to deal with students’ social practice behaviors, to classify the type of students’ participation in social practice in colleges and universities, and to grasp the development status of the reform of social practice teaching in colleges and universities in the era of big data. The rating of dimensions of the social practice process in colleges and universities is used to determine the overall development level of social practice reform in colleges and universities. It also analyzes the differences in the process of carrying out social practice in colleges and universities, with the types of colleges and universities and categories of specialties as independent variables. When K is equal to 4, the contour coefficient is the largest, and the clustering effect is the best, so the students’ social practice participation can be reflected in four types: “need to improve”, “backward”, “hard-working” and “active”, combined with the overall score of social practice teaching reform in colleges and universities in the era of big data is 3.81±0.82 points, and the scores of all dimensions are above 3.75, and the overall development of social practice teaching reform in colleges and universities proposed in this paper is better.
Ideas for the construction of the curriculum system of calligraphy education in higher education institutions oriented to the future technology
Authors: Yuan Pingliang , Guo Dan 
Published: July 2024

Abstract
In view of the current situation that the curriculum construction of calligraphy education needs to be perfected, a more comprehensive and in-depth study is carried out for the curriculum setting and development of calligraphy education in higher teacher colleges and universities, starting from the core - curriculum system in calligraphy education. Based on information technology, this paper constructs a path of calligraphy education construction, from which the learning resources design, learning content design, learning activities design, and learning evaluation design of the practical calligraphy classroom are carried out. For the learning evaluation of the calligraphy education curriculum system, a calligraphy word evaluation model is proposed, which consists of three parts: overall structure, stroke form, and font layout. Ultimately, students can be scored according to the evaluation standard of calligraphy words. In the overall evaluation of the curriculum system constructed in this paper, 75%, 74%, and 72% of the students agreed strongly with the system in the cultivation of “knowledge”, “ability,” and “thinking and literacy,” respectively. The students indicated that they “strongly agree” with this system. With regard to the cultivation of “thinking and literacy”, this paper proposes corresponding improvement strategies to improve the calligraphy education program system.
RGB pixel information fusion segmentation of dense point cloud data for transmission and distribution 3D digital corridors
Authors: Li Duanjiao , Luo Jingbin , Zhang Jian , Liu Gao , Guo Qidi 
Published: July 2024

Abstract
Point cloud segmentation, as a key link in 3D point cloud data processing, can realize power transmission and distribution positioning, object identification, classification, and feature extraction, which helps to refine power grid management. In this paper, firstly, dense point cloud transmission and distribution 3D digital corridor modeling is carried out. Alignment splicing and noise reduction are carried out after obtaining the original dense point cloud. Contour line extraction, geometric modeling, and texture mapping are realized after processing the data to ultimately realize the transmission and distribution of 3D digitization. Then, the conversion formula for the pixel coordinate system and world coordinate system is derived to extract features from point clouds. Finally, a distance-based feature fusion method is designed to extract spatial features from point clouds and use the joint attention layer to segment them by fusing RGB pixel information. The original dense point cloud of a transmission and distribution digital corridor is segmented using the model presented in this paper for application after testing the dataset. It is found that the under-segmentation ratio of this paper’s algorithm is 0.96%, 3.44%, and 2.87% for the three scenarios of regular, irregular, and multi-targets, respectively, which is much lower than that of RANSAC+ECS with FCM + ECS. The intersection and concatenation ratios of this paper’s algorithm for the scenarios of irregular geometry as well as multi-target objects are 91.49% and 89.56%. It is much higher than 64.31% and 72.17% for RANSAC + ECS and 76.85% and 60.91% for FCM + ECS, which illustrates that this paper’s algorithm has a significant advantage in performance. In this study, the target point cloud can be segmented with high accuracy from the dense point cloud of a 3D model of power transmission and distribution with a large amount of data, effectively avoiding the phenomenon of under-segmentation and over-segmentation and contributing to the accurate control of power grid data.
Time domain analysis of catenary response characteristics caused by ambient wind
Authors: Gu Xuli , Liu Ting , He Yifan 
Published: July 2024

Abstract
The catenary system, as a framework within railway electrification projects, constitutes an exposed suspension structure deployed along railway lines. It serves the purpose of providing electrical power to electric locomotives through pantographs but lacks backup facilities and falls into the category of a specialized form of high-voltage transmission line. To ensure the safety and reliability of the catenary system and the stable current collection of the pantograph, thereby safeguarding railway operational safety, it is imperative to research the aerodynamic response of the catenary system induced by environmental winds. For this study, the catenary system in the 4m windbreak wall section of the Lanzhou-Xinjiang high-speed railway bridge has been selected as the research subject. To determine wind speed correction coefficients at the supporting cable and contact wire locations, a finite element model was created using wind tunnel experiments and numerical simulations. Samples of wind speed fluctuation have been obtained using the harmonic synthesis method. Based on quasi-steady aerodynamic theory, a temporal analysis has been conducted on the aerodynamic response of supporting cables and contact wires under the influence of both average and fluctuating winds. Temporal results, such as lift at positive and negative positioning locations and mid-span displacement, have been extracted to assess whether the existing structure complies with regulatory requirements. This study offers valuable insights for engineering applications that are similar.
Construction of Enterprise Green Accounting Information Disclosure System under the “Double Carbon” Goal
Authors: Xu Qian 
Published: July 2024

Abstract
Enterprises are the key participants in realizing the development of a green and low-carbon economy and are the main promoters of the environmental transformation and healthy development of the industry, and the green accounting information disclosure truly reflects the important indexes of green development of enterprises. In this paper, the relevant data of 15 listed companies in the paper industry from 2013 to 2023 are selected as samples to carry out empirical analysis, and their green accounting disclosure reports are used as the basis for calculating the GAID scores of the companies. A multiple linear regression model is created to test the disclosure-related research hypotheses, with the goal of discovering the factors that influence the current quality of enterprises’ green accounting disclosure. The fluctuation range of GAID scores of most of the fifteen listed enterprises in the paper industry is between 40 and 70, and there are also 20% of enterprises with GAID scores lower than 40. Enterprise size, proportion of outstanding shares, enterprise value, and enterprise development ability are significantly positively correlated with the quality of corporate green accounting disclosure at the 1% or 5% level, and the quality of disclosure increases by 0.151% for every 1% increase in enterprise size. In the enterprise green accounting information disclosure, enterprises need to be, based on their situation, reasonable and comprehensive information released in order to effectively enhance their corporate image and promote the green and sustainable development of enterprises.
Analysis of Strategies and Effectiveness of Big Data Technology for Business Decision Optimization
Authors: Wang Zhe , Xue Hongsong , Hu Junhua 
Published: July 2024

Abstract
Business decision-making is a decision-making process that relies heavily on business data, and with the continuous improvement of information technology, the level of intelligence of business decision-making has been increasing. The paper suggests relevant optimization strategies for business decision-making and creates an intelligent business decision-making process that utilizes big data. This paper mainly applies the association rule mining algorithm based on big data to business decision-making, extracting valuable information for business decision-makers from a large database of business information systems. In order to verify the operational efficiency of the improved algorithm, the traditional Apriori algorithm, FP-Growth algorithm, HBE-Apriori algorithm, and the improved algorithm are compared at the same time, and the results of the experiments show that the improved algorithm’s operational efficiency in the process of finding the maximal frequent itemset is significantly improved compared with the other three algorithms, which provides a basis for the analysis of the effectiveness of the optimization of business decision-making. The example of optimization of business decisions through big data technology found that after the optimization of business decision optimization, the asset quality and solvency of the enterprise are improved, and the operating capacity is unchanged due to the low operating capacity of the total assets of the enterprise. This paper’s method for optimizing business decisions based on association rules is shown to be feasible.
Future-oriented trends in intelligent building materials and applications
Authors: Xu Shifu 
Published: July 2024

Abstract
Nowadays, smart materials appear more and more frequently in people’s daily lives, and this paper investigates the development and current status of the application of smart materials in the construction industry in view of the high energy consumption status quo of the construction industry. The goal of the energy conversion mode of piezoelectric material is to be based on the basic principle of piezoelectric sensors. Study the monitoring function of buried piezoelectric sensors on the damage to the concrete main frame. The passive energy storage method of phase change material and its integration with the building are being investigated at the same time. The performance of these two materials is then tested by applying different forces to the piezoelectric sensors with the help of linear motors, and the output voltage of the piezoelectric materials is tested, which proves its good cyclic stability. The phase change material is split into multiple layers based on heat flow direction, and each layer’s temperature is calculated separately to analyze the thermal behavior inside the material in depth. Through experimental analysis, it has been found that the output voltage of the triangular piezoelectric transducer can reach 550 V, which can be applied to optimize pavement structure and pavement energy harvesting. The phase change material has excellent energy saving and thermal insulation effect in the summer, and the average heat flux decrease rate of the wall can reach 45.0%, which greatly reduces the energy consumption of air conditioners and other temperature-controlled appliances. The application of intelligent building materials is in line with the requirements of sustainable development planning, and there is ample room for growth.
Optimization study of anomaly detection algorithm in machine vision inspection technology
Authors: Zhou Guanghong , Wang Hairong , Zhuang Er-xing 
Published: July 2024

Abstract
In recent years, target detection algorithms based on machine vision have been a hotspot in computer vision research. The You Only Look Once (YOLO) algorithm, as an excellent target detection algorithm, has played an important role in improving detection speed and accuracy with the improvement of the network architecture in its development process. This paper introduces the concept of integrated learning to the YOLOv5 network architecture, incorporating deformable convolution and attention mechanisms. It also chooses the focal EIOU loss function to replace the GIOU loss function, thereby addressing the issue of localization loss, prioritizing abnormally detected targets, and enhancing the detection efficiency of these abnormal targets. Finally, we examine the practical value of the improved YOLOv5 algorithm by testing its performance and applying it to real-world anomaly detection. The results show that the improved YOLOv5 model outperforms the original YOLOv5 model in terms of performance and practical application advantages. In terms of performance, the classification accuracy of sea_person and earth_person in the improved YOLOv5 model is 37% and 26%, respectively, which is a significant performance gain overall. In actual application tests, the proposed method is more accurate than the traditional method. The accuracy is significantly higher.