bluuebunny/crossref_metadata_2025_split huggingface dataset:
Contribution of 3D reconstruction techniques to the animated restoration of historical scenes
Authors: Wang Zhirun , Li Wenwen 
Published: June 2024

Abstract
Amid the escalating demand for heightened realism across diverse sectors such as film, television, gaming, tourism, virtual display, and the preservation of cultural heritage, there has been a notable advancement in reverse modeling technology that facilitates the extraction of three-dimensional models from image sequences. This study focuses on the classification of 3D reconstruction for the animation restoration of historical scenes. It delves into the technical pathways for the 3D reconstruction of such scenes, employing a clustering-based Scale Invariant Feature Transform (SIFT) feature-matching acceleration algorithm for the extraction and matching of image features. Subsequently, it integrates the Bundler method of camera self-calibration with the Patch-based Multi-view Stereo (PMVS) algorithm for dense reconstruction, culminating in the assembly and testing of the reconstructed historical scene. The acceleration time of the SIFT algorithm is higher than that of the standard algorithm when the number of images is 10. The standard algorithm’s acceleration time is the longest when there are more than 10 images. When the number of images is greater than 50, the acceleration ratio of sigma=60 is greater than that of sigma=120. This indicates that the clustering-based SIFT algorithm is suitable for accelerated matching of large-scale image sets. The acceleration effect can be improved by appropriately decreasing the value of sigma. The maximum difference between the five independent vectors in model 1 and model 2 is 3.18. After scaling model 2, the difference is narrowed to[ 0.01,0.03]. The small error of the model in scenes 3~9 and the clear graphic texture indicate that the 3D reconstruction model of the historical scene designed in this paper has high accuracy and provides a model reference for the animation restoration of the historical scene.
The impact path of uncertain economic policy on the high-quality development of technology-based innovative enterprises
Authors: Xu Jing 
Published: June 2024

Abstract
In recent years, macroeconomic policies have been characterized by significant uncertainty. This context of uncertainty has made it imperative to explore strategies that enterprises can adopt to pursue high-quality development. This paper specifically investigates the impact of economic policy uncertainty on science and technology enterprises. We categorize the productive activities of these enterprises into two types: technology-oriented and market-oriented. The paper further preprocesses the samples based on these categories. For analytical purposes, we employ the Cobb-Douglas production function to assess the level of enterprise development. A fixed-effects regression model is utilized, incorporating both control variables and mediating variables. Our baseline regression analysis reveals that the fintech coefficient for science and technology enterprises stands at 0.0721 without control variables and 0.0775 with them, demonstrating significance at the 1% level. These results suggest a positive correlation between economic policy uncertainty and the technology innovation activities of enterprises. Moreover, our findings indicate that economic uncertainty exerts a moderating effect on enterprise innovation. Specifically, the regression coefficients of economic uncertainty are −0.301, −4.687, and −3.154, while the industry characteristics regression coefficient is positive and significant at the 1% level. This pattern indicates that economic uncertainty significantly inhibits R&D investment in high-tech enterprises, adversely affecting their high-quality development.
Regression utility analysis of rural red cultural tourism industry chain for rural revitalization
Authors: Yu Lu 
Published: June 2024

Abstract
A large number of red cultural sites preserved in the Chinese countryside, as well as the red spirit are combined with cultural tourism, and the red cultural tourism industry comes into being. This paper takes the service personnel of the rural red cultural tourism industry in County M as the research object, designs the relevant questionnaire, conducts the reliability test, and then obtains the relevant data of rural revitalization and rural red cultural tourism industry chain through the questionnaire survey method. Then, using the industrial environment and policies of the rural red cultural tourism industry chain as the independent variables and rural revitalization as the dependent variable, a round-off multiple regression analysis was carried out to explore the interrelationship between the two. The results show that rural revitalization = 1.374+0.086*tourism projects + 0.147*infrastructure + 0.169*tourism market + 0.177*tourist distribution = 1.074+0.246*history and culture + 0.259*red culture + 0.214*government policies + 0.268*funding inputs. This paper assists Chinese villages in improving the red cultural tourism industry chain and promoting rural revitalization by developing the red cultural tourism industry chain.
Research on the application of deep learning-based machine vision in automated inspection
Authors: Zhao Ranran 
Published: June 2024

Abstract
The continuous updating of deep learning algorithms and theories has laid a solid foundation for the development of the machine vision field. Automated detection technology has become a hot topic of research in the field of machine vision in recent years. In this paper, we first introduce the traditional one-dimensional and two-dimensional image segmentation algorithm and then optimize the image segmentation algorithm by combining the conventional pigeon flocking algorithm and chaotic search algorithm so as to obtain a more accurate detection target image. Then, on the basis of a deep learning network, an attention mechanism is introduced to construct a Swin-Transformer image detection model to realize automatic detection of machine vision. Finally, the performance of the model is tested, and it is applied to watermelon seedling quality detection to explore its application value. The results show that in the performance test experiment of the image segmentation algorithm of this paper, the three indexes of F1, IoU, and accuracy of the image segmentation algorithm designed in this paper on the ISIC-2020 dataset are 93.90%, 93.74%, and 98.37%, which are ranked the first among the algorithms participating in the experiment. The precision, recall, and mAP values of the image detection model designed in this paper are 92.87%, 77.13%, and 83.21% on the experimental data test set, which are higher than those of other models participating in the experiment. The image detection model designed in this paper was practically applied to watermelon seedling quality detection. The accuracy of the model in detecting the presence or absence of diseased spots and cotyledon area, two key characteristic parameters of seedling quality, reached 100%. The model showed high reliability in practical application. The image segmentation algorithm and image detection model developed in this paper are highly useful in automated detection.
Policy Perspectives on Big Data and AI in Translation Services
Authors: Yu Hui 
Published: June 2024

Abstract
With the rapid advancement of scientific and technological innovation, the significance of intelligent translation services has grown considerably. This study leverages policy support to integrate AI technology into the domain of grammar error detection and correction. It utilizes a Long Short-Term Memory (LSTM) neural network and employs a Transformer model grounded in a self-attention mechanism to address the task of grammar error correction. This approach is designed to balance local contextual nuances and long-range dependencies within the text, culminating in the development of an AI-based grammar error correction model. A thorough evaluation of this model’s performance involved comparative analysis with alternative models, assessments across various types of syntactic errors, and detection of collocation errors. The findings indicate that our model exhibits superior grammatical error correction capabilities, outperforming comparative models. Specifically, it achieves Grammar Learning Evaluation Understudy (GLEU) scores that are 2.31% to 7.95% higher than those of its counterparts. Moreover, it demonstrates overall recognition rates for different grammatical and collocational errors between 18.88% to 58.98% and 50% to 70%, respectively, which underscore its practical applicability. This methodology not only enhances grammatical error detection but also holds promise for broader application in AI-driven translation services.
Application and effect analysis of virtual reality technology in innovation and entrepreneurship education
Authors: Dai Keqiang , Jiao Huijie , Wang Huilin , He Liangchun 
Published: June 2024

Abstract
Adding virtual reality technology to innovation and entrepreneurship education in colleges and universities can help stimulate students’ learning interest and enhance their practical operation ability, thus improving the quality and effect of innovation and entrepreneurship education. In this paper, after exploring the demand for the application of virtual reality technology in innovation and entrepreneurship education, combined with the interactive environment of virtual reality technology, we constructed the innovation and entrepreneurship teaching mode based on VR. We used this teaching mode as the basis for the design of teaching cases in M school. During the implementation of teaching practice, relevant data are collected by distributing questionnaires. Then, the experimental data are analyzed by combining independent sample T-test, regression analysis, and other statistical methods to explore the application effect. The results demonstrate that there is a statistically significant difference between before and after the application of virtual reality technology (P<0.05), which can significantly improve students’ innovation and entrepreneurship. The perceived usefulness (0.69) and cognitive development (0.09) of virtual reality technology applications were the highest and lowest scores. In addition, financial support for innovation and entrepreneurship programs (0.131) had the greatest degree of influence on VR-based innovation and entrepreneurship education. This study is intended to contribute to the construction of innovation and entrepreneurship education for college students in colleges and universities.
The Construction of Higher Vocational English Language Bank Based on Neurolinguistic Knowledge
Authors: Deng Yuhua 
Published: June 2024

Abstract
With the advent of artificial intelligence and big data technologies, traditional approaches to teaching English in higher vocational education are increasingly inadequate for contemporary pedagogical demands. This study involved the collection and analysis of an extensive corpus of English learning materials facilitated by the use of neural network models to process this data. Consequently, we established an English language library tailored to the authentic language use and learning needs of higher vocational students. A substantial array of textual materials pertinent to higher vocational English instruction, including textbooks, online articles, and practical dialogues, was amassed using web crawler technology. These texts underwent natural language processing that included tokenization and the elimination of stop words. To develop a language model, we deployed a Transformer-based neural network for deep learning analysis of the processed data. Our findings indicate that the constructed language bank excels over traditional higher vocational English teaching resources in terms of vocabulary breadth, grammatical diversity, and practical utility. The language bank demonstrates notable benefits in analyzing vocabulary usage biases and enhancing students’ pragmatic language skills. This research substantiates the feasibility and effectiveness of constructing a language bank for higher vocational English utilizing neurolinguistic methodologies. The integration of advanced language modeling and big data processing technologies can significantly elevate the caliber of educational resources and instructional efficacy
A study on the modeling of shooting probability in basketball sports competition sport
Authors: Gao Quanzhong 
Published: June 2024

Abstract
In basketball sports competition sports, shooting percentage is an important indicator of basketball players. In this paper, the trajectory of basketball sports shooting is solved by mathematical modeling and simulation. In the first step, the hyperparameter estimation of the variance is obtained by using the SURE-Type Double shrinkage estimation method. In the second step, the variance of the SUREDSC method is determined by sampling from the hyperparametric distribution using the Bootstrap method. In the third step, the covariates were taken into account and combined with the Ghoreishi bi-level shrinkage estimation method to obtain the optimal shrinkage estimate of the probability of hitting a basketball shot. The results of the study yielded that α differs from β when α ≤ 75°. When α >75°, α and β are very close. When the angle of incidence is certain, the angle of the ball shot is negatively correlated with the height of the athlete, and the degree of reduction decreases with the increase of the angle of incidence. The prediction success rate of the shot probability model was 87.98%, and the overall error rate was small, and the prediction results of the seven games were basically consistent with the prediction results of the constructed shot probability model. The study offers a guide for basketball managers to weigh the pros and cons of player transfers.
Deep Learning-based Knowledge Graph and Digital Twin Relationship Mining and Prediction Modeling
Authors: He Fangzhou , Bai Wei , Wang Zhiqi 
Published: June 2024

Abstract
The era of big data produces massive data, and carrying out data mining can effectively obtain effective information in huge data, which provides support for efficient decision-making and intelligent optimization. The purpose of this paper is to establish a digital twin system, preprocess massive data using random matrix theory, and design the knowledge graph construction process based on digital twin technology. The BERT model, attention mechanism, BiLSTM model, and conditional random field of the joint deep learning technology are used to identify the knowledge entities in the digital twin system, extract the knowledge relations through the Transformer model, and utilize the TransE model for the knowledge representation in order to construct the knowledge graph. Then, the constructed knowledge graph is combined with the multi-feature attention mechanism to build an anomaly data prediction model in the digital twin system. Finally, the effectiveness of the methods in this paper is validated through corresponding experiments. The TransE model is used for knowledge representation. The accuracy of ternary classification is higher than 80% in all cases, and the MR value decreases by up to 64 compared to the TransR model. The F1 composite score of the anomaly data prediction model is 0.911, and the AUC value of the validation of knowledge graph effectiveness is 0.702. Combining deep learning with the knowledge graph, the knowledge information can be realized in the digital twin system’s accurate representation and enhance the data mining ability of the digital twin system.
Research on optimization of e-commerce supply chain logistics service model based on multi-source data fusion
Authors: Wu Junhong 
Published: June 2024

Abstract
Inefficiencies in supply chain logistics management, particularly in certain regions, often result in “broken chain” incidents that significantly impact the efficiency of e-commerce operations and degrade consumer experiences. This paper addresses the necessity of optimizing the logistics service model by integrating 12 heterogeneous data sources related to warehousing, transportation, processing, and sorting. We propose a site selection-inventory-path e-commerce logistics optimization model. For the first time, the model incorporates the Dempster-Shafer (D-S) evidence algorithm to quantitatively evaluate the support relationships among various pieces of evidence by adjusting the weight coefficients accordingly. This adjustment is further refined through the application of the Nash equilibrium in the evidence combination process. The optimized logistics model is then solved using a fusion of the model and an intuitive fuzzy set approach. A case study demonstrates that the optimized logistic supply capacity consistently outperformed the non-optimized scenarios, with statistical increases of 1.64%, 1.72%, 2.15%, 3.73%, 2.71%, 2.46%, 3.59%, 2.17%, and 3.98%. Furthermore, by refining the logistics model, the gap in logistics performance was narrowed by at least 5.13% and by as much as 12.9% across successive evaluations, significantly alleviating issues related to enterprise capacity constraints. This study offers novel insights and methodologies for enhancing the efficiency of regional e-commerce supply chain logistics, boosting the competitiveness of logistics enterprises, and elevating the consumer experience in e-commerce settings.
A study on the influence of network environment on college students’ ideological and political attitudes
Authors: Jing Hongbo 
Published: June 2024

Abstract
The network environment is gradually formed and developed under the impetus of information technology, and it plays an important role in transmitting political concepts, forming political attitudes, and cultivating political identity. After exploring the dilemma of ideological and political education in the network environment, this paper uses polynomial least squares approximation to construct polynomial regressions to solve various nonlinear regression problems. Then, the network environment factors such as bad guidance, network technology, cultural penetration, and false information are taken as independent variables. College students’ ideological and political attitudes are taken as dependent variables to construct a multivariate nonlinear regression model, observe the polynomial regression parameters according to the principle of least-squares estimation, and take 489 college students of the University of R as the research subjects to explore the influence of network environment on college students’ ideological and political attitudes. The results show that the correlation coefficients of four factors, namely, poor guidance of network environment, network technology, cultural penetration, and false information, with students’ ideological and political attitudes range from 0.178 to 0.531, which is a significant positive correlation. Meanwhile, compared with political cognition and political emotion, network environment factors explain the strongest tendency of political behavior (54.9%), and different network environment factors have various degrees of influence on college students’ ideological and political attitudes. The study offers data to support the cultivation and strengthening of college students’ ideological and political attitudes.
Research on the Value of Micro Animation Creation in Revitalizing Jilin Ice and Snow Tourism Industry
Authors: Liu Xiangyu , Xia Tian 
Published: June 2024

Abstract
Jilin Province, distinguished by its unique snow and ice resources, stands as a pivotal region for snow and ice tourism. This study delves into the innovative integration of micro-animation technology with the ice and snow tourism culture of Jilin, examining its influence on tourists’ travel intentions. A structural equation model was developed to articulate the attractiveness of Jilin’s ice and snow tourism, encapsulating the perceived symbolism of its attractions. Descriptive statistical analysis was employed to assess the impact of micro-animation on potential tourists’ perceptions and intentions. The study revealed that a majority of the viewers of the micro-animation on Jilin’s ice and snow tourism were female (54.7%), compared to males (45.3%). Additionally, a larger proportion of unmarried individuals (73.3%) engaged with the content relative to their married counterparts (26.1%). The demographic with the highest representation consisted of individuals earning a monthly income between 6001-9000 yuan (29.4%). Significantly, the attractiveness and symbolic perception of Jilin’s ice and snow tourism exhibited a robust positive correlation with the intention to travel, evidenced by a p-value of 0.001. This investigation underscores the potential of micro-animation technology as a transformative tool for enhancing the appeal of ice and snow tourism in Jilin Province.
Deep Forest-based Disease Prediction and Diagnosis under the Concept of Digital Health
Authors: Mei Xiangxiang , Shen Hao , Wu Fang , Cai Xiaodan , Chen Hongyun 
Published: June 2024

Abstract
With the continuous development of digital technology and the continuous improvement of medical information databases, effective mining methods for potentially useful information behind medical data have become one of the research focuses of digital development in the medical field. In this paper, based on the deep forest model, a deep forest classifier framework based on the federated learning algorithm is constructed for the privacy protection of medical data and optimized by gradient boosting decision tree. Then the feature vector of cardiovascular diseases is constructed from engineering features, and feature selection is carried out through the constructed classifier algorithm to realize the prediction and diagnosis of epidemic diseases, followed by the experimental analysis of the method of this paper. The empirical analysis shows that the diagnostic accuracy of this paper’s model in seven common cardiovascular diseases is higher than 80%. Among them, the accuracy rate of heart valve disease is as high as 87%, and the diagnostic accuracy rate of arrhythmia and coronary heart disease is tied for second place with 83%. It shows that the predictive diagnosis model based on deep forest in this paper has good performance, can meet the actual needs of predictive diagnosis of cardiovascular diseases, and provides an effective reference for the development of auxiliary diagnosis in the digital era.
Research on monitoring technology and application of conductor dancing condition of transmission line
Authors: An Shuhuai , Wei Zhen , Tang Lei , Guan Meng 
Published: June 2024

Abstract
In recent years, an increasing focus has been placed on the development of transmission line condition monitoring systems by academic and research institutions globally. This paper introduces a novel sensor-based system for monitoring the dynamic behavior—referred to here as ‘dancing’—of transmission line conductors. It hinges on the mathematical equations that describe the motion of a ‘dancing’ transmission line conductor. By computing the sets of distance and tension solutions, the system effectively determines the state of the conductor dance. This monitoring system integrates advanced sensor technologies and has been validated through simulation experiments. The experimental results are promising, showcasing an average error in coil measurements maintained within ±2.2%, with a maximum deviation not exceeding 2.9%. These figures underscore the system’s high accuracy and linearity, affirming that the design parameters adeptly satisfy the monitoring requirements of transmission line conductor dynamics. Furthermore, the system exhibits remarkable stability, as evidenced by average deviations of 0.636mm and 0.077mm on the X and Y axes, respectively, against input amplitudes of 120mm and 60mm. This paper demonstrates the efficacy of the proposed transmission line conductor dance state monitoring system in maintaining precise and reliable measurements.