bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on rural landscape garden planning and plantscape construction method based on digital twin technology under rural revitalization strategy
Authors: Zhao Jiayu 
Published: July 2024

Abstract
This paper integrates the landscape information model, designs the digital twin rural landscape architecture, and analyzes the advantages of the digital twin rural landscape model. The practical application of digital twin technology is examined from the planning and design stage to the construction stage. Considering the actual construction site layout and the consumption of construction materials, the NSGA-II algorithm is selected to optimize the dynamic layout of temporary facilities by combining multi-objective optimization theory. Using the digital twin to drive the dynamic layout of the construction site, a digital twin-driven construction site layout mechanism is proposed in combination with BIM technology. The actual construction scheduling tasks are used to analyze the feasibility of the construction site layout scheme generated using digital twin technology. Analyze the construction of rural landscape gardens planned using digital twin technology in terms of ecological, social, and economic benefits. The convergence minimum is achieved at 0.15 and 0.09 when comparing the number of iterations between the initialized layout and the digital twin-driven layout. The corresponding scheme reaches its optimal state during the construction stage and can provide the most suitable site arrangement scheme during that stage. The rural landscape garden planned by the digital twin technology can generate an economic return value of 71.756 yuan/m² per year, which can further promote rural revitalization.
Research on the Reform and Development of Ideological and Political Theory Classes for College Students in the Context of Practical Parenting
Authors: Wang Quan 
Published: July 2024

Abstract
In the context of practical education, deepening the reform of quality education and implementing the fundamental task of cultivating moral integrity have become the guidelines and the basis for the development of curriculum reform in colleges and universities. This study conducts data mining based on the association rule model on the influencing factors of the reform of the Civic and Political Theory Course in colleges and universities to obtain the key influencing factors in the process of the reform of the theory course. Then, under the perspective of deep learning, the idea of practical parenting is integrated, and the structural framework of Civic and Political Theory course reform is designed from four directions: learning theme, learning objectives, learning activities, and evaluation feedback, and finally, the course reform method of this paper is examined through teaching practice. The results show that the influence weights of self-factors, school education factors, family factors, and social factors are 0.3022, 0.3521, 0.1381, and 0.2076, respectively, with self-factors and school education as the key influencing factors. After the teaching practice, the P-value of each dimension of the students under the curriculum reform based on this paper is less than 0.05, and the grade of the Civics and Political Science Theory course is improved from 81.47 to 90.45, which is an increase of 7.49 points compared with the class of the regular course. The reform method based on deep learning in this study can promote students’ development and improve their academic performance, which provides a reference for the research on the reform and development of the Civics and Political Science Theory course.
Research on the Development of New Sensors and Sensor Detection Technology in the Context of Big Data
Authors: Wang Jiahui 
Published: July 2024

Abstract
In the context of big data, traditional detection algorithms can no longer meet today’s needs. For traditional sensors, there are shortcomings such as long working times, high complexity, and high false detection rates. This paper proposes a new type of sensor and an algorithm for detecting anomalies in sensors based on Flink. The sensor network senses the monitoring targets in the monitoring area in real time and transmits them to the end users through wireless communication. Then, the fusion rule for target detection in sensor networks is studied. Then, the detection algorithm is computed using Flink and predicted using a sliding window and ARIMA model on the Flink platform. Then, the confidence intervals of the prediction results are calculated, and the outliers are evaluated. Finally, the K-Means++ algorithm is used for clustering and probability assessment of previously obtained outliers. Then, compared with traditional sensors, comparative analysis shows that the proposed method in this paper has some advantages in terms of both sensitivity (S) and quality factor (Q). The value of S is more than 320 than the 2D PhC sensor array. The value of Q is more than 5295 than the 2D PhC sensor array. This paper’s new sensor detection method is more diverse than the traditional method. The detection time is reduced. The detection accuracy is also improved compared to the traditional method. It can be summarized by comparison that the new sensor in this paper is more excellent than the traditional method of detection.
Research on the application method of agricultural machinery engineering automation based on multimodal characteristics
Authors: Wang Xianggeng , Fan Yujia 
Published: July 2024

Abstract
Agricultural operators can predict the yield of wheat at different stages of growth, development, and harvesting and take different measures to realize precise management. The purpose of this paper is to apply agricultural mechanical engineering automation to wheat yield prediction, and a UAV multimodal data wheat yield prediction model is developed using the RMGF algorithm. Different data sources, such as vertical distribution of terrain and spatial variability, canopy height and wheat plant height, canopy temperature difference, vegetation spectral characteristics, and vegetation index, were extracted using an agricultural UAV. Then GF decomposition algorithm based on MSD decomposes the multimodal image into an approximate image and detail image, and after optimization of the fused weight map using RSA, the fused image is obtained by IMST according to the optimized weight map. The model was used to carry out regression analysis of yield prediction for three types of wheat, heat-tolerant, medium heat-tolerant, and high-temperature-sensitive, and finally predicted the wheat yield from 2015 to 2024 in a production area. It was found that the R² of the RMGF multimodal model in this paper predicted the three kinds of wheat yields as 0.7936, 0.8609, and 0.9262 with excellent accuracy results. The predicted yields were basically in line with the actual yields in the high-yield portion, with large prediction errors above 9000 kg/ha. The prediction error for wheat was within 0-2.26%, and the predicted yield in a main wheat production area was 7050 kg/ha in 2024. This study provides a feasible method for large-scale yield estimation in the main production area, which contributes to high-throughput plant phenotyping and agricultural precision reform.
Audit Risk Assessment Model Construction by Incorporating Deep Learning Techniques
Authors: Yan Zhenzheng 
Published: July 2024

Abstract
While risk-oriented auditing has gradually emerged as a new trend in current internal auditing, the absence of specific risk assessment methods in related internal auditing standards has made it a common challenge for many enterprise internal auditing organizations and personnel to effectively implement and conduct risk assessments. This research focuses on integrating deep learning technology to construct an audit risk assessment model, first describing the problem of audit risk assessment and then validating it using the CNN-LSTM method. We select the appropriate indicators for audit risk assessment assignment after screening the sample data to ensure their qualitative and quantitative comparability. We construct a CNN-LSTM audit risk assessment model based on the above data. We use two input methods, the convolutional neural network and the long- and short-term memory network, to enhance the assessment model’s learning ability. We then choose 12 enterprise companies as data cases, with Company A serving as the representative for the CNN-LSTM-based assessment model’s performance simulation test and risk level assessment. We compare the four models, and the CNN-LSTM model’s AUC exceeds 0.5 in the confusion matrix and ROC curve graph, confirming the usefulness of CNN-LSTM in audit risk assessment.
Research and Practice of Digital Economy Helping Rural Industry Revitalization in the Information Age
Authors: Zhao Yixing 
Published: July 2024

Abstract
Based on the construction and development of digital economy-enabled rural industry revitalization in a region, this paper conducts theoretical analysis on the degree of promotion of rural industry revitalization from three aspects, namely, production conditions, capacity, and benefits. In order to verify the influence mechanism of the digital economy on rural industry revitalization, a regression measurement model oriented to the digital economy and rural revitalization is constructed based on the evaluation index system, and the entropy weight method is used to calculate the comprehensive evaluation index of rural industry revitalization and the comprehensive evaluation index of the digital economy. Determine the variables and data sources and conduct an empirical analysis of the digital economy contributing to rural revitalization from the perspective of informationization. The data show that the mean value of the digital economy index (Dige) is 0.2482, the minimum value is 0.0043, and the maximum value is 0.951, indicating that there are large differences in the development of the digital economy among different regions and that the eastern region has a more significant advantage in the rural industry (0.2258) as well as in the digital economy (0.3076), compared with the western region and the central region. In addition, the R² of the baseline regression (Model 1) is 0.5782, and the R² after replacing the explanatory variables using the entropy method index (Model 2) is 0.6881, based on the results suggesting that the development of the digital economy has a facilitating effect on the development of rural industries. The efficiency of resource utilization in rural areas is improved by this study, which releases the potential for agricultural development and further promotes the strategy of rural revitalization.
Multimodal biometric fusion sentiment analysis of teachers and students based on classroom surveillance video streaming
Authors: Zhang Tianxing , Dahlan Hadi Affendy Bin , Xie Zengsheng , Wu Jinfeng , Chen Yingping , Pan Qianying , Huang Ying 
Published: July 2024

Abstract
In the education system, teachers and students as the main body of the classroom; their emotional state in the classroom school is an important indicator of the effectiveness of the classroom. This study first explores biometric recognition, based on the needs of the classroom curriculum and the classroom monitoring as a sensor, to propose a multimodal biometric fusion detection method based on the fusion of face and gait recognition. The PCA algorithm is used to optimize the face recognition as well as the occlusion situation in the classroom to improve gait recognition, and then the face and gait are fused based on the decision layer to achieve the detection and recognition of the identity situation of teachers and students. On this basis, an expression recognition model is established using the attention mechanism, and an emotion analysis system is designed for the classroom curriculum. According to the empirical evidence of multimodal biometric fusion sentiment analysis, the mAP accuracy of this paper’s fusion method is 100% in Euclidean distance, and the accuracy is higher than 99% in cosine distance, which is obviously better than other methods, and the accuracy of this paper’s fusion recognition is above 95% under any condition limitations. At the same time, the correct rate of recognition of emotions such as listening, appreciation, resistance, doubt, and inattention are all higher than 85%, and the five indexes of average absolute error, Pearson correlation coefficient, Accuarcy5, Accuarcy2, and F12 score of this paper’s sentiment analysis have achieved the best results comparing with other sentiment analysis models, which proves the generalization and validity of this paper’s sentiment analysis.
Exploring the Impact and Innovative Application of Piano Art Instruction in the Context of Deep Learning
Authors: Zhao Qinghua 
Published: July 2024

Abstract
Social progress and technological development have brought sweeping changes to the field of art education. The innovative development of piano art instruction, as a part of art education, has a significant impact on students and the academic world. The study proposes a deep learning-based model for piano art instruction, taking into account the current state of piano art instruction, and investigates the impact of this model on students’ piano playing skills and appreciation of music art. The experimental group’s students improved their playing skills in the eight dimensions of fingering, timbre, pitch, rhythm, musical expression, emotional output, body language, and stage image. Except for timbre and stage image, the p-values of the experimental group and the control group in the other six dimensions of piano playing skills were less than 0.05, which effectively improved. In terms of music art appreciation, the total number of excellences in the three dimensions of aesthetic perception, artistic performance, and cultural understanding was 118, 131, and 126, respectively, and the total number of excellences in the experimental group was 32, 45, and 39. Obviously, piano art instruction based on deep learning has a positive impact on the students’ piano-playing skills and ability to appreciate music art.
Innovative Improvement of Business Laboratory Management Efficiency under the Internet+ Mode
Authors: Zhou Keyan 
Published: July 2024

Abstract
The laboratory is an important place for talent cultivation and scientific research activities. However, at present, there are problems such as backward management mode and low management efficiency in the laboratory. This paper combines the “Internet +” technology to study the effective means to improve the efficiency of business laboratory management. Through the Internet of Things, Android, and other key technologies, we can build an intelligent business laboratory management platform. The management level of the business laboratory management platform designed in this paper is evaluated and classified using the neural network algorithm. The designed and completed intelligent management platform for business labs contains functional modules such as intelligent control, equipment management, faculty management, and user management. The evaluation study found that the management level score of university laboratories after applying the intelligent management platform for business laboratories maintains more than 85 points in general, and the evaluation grade shows an excellent level of A grade.
Research on Teaching Quality Evaluation Model of Higher Mathematics Based on Hierarchical Analysis Approach
Authors: Deng Jin 
Published: July 2024

Abstract
The teaching quality of higher mathematics courses plays a pivotal role in the cultivation of students’ quality and the improvement of their abilities. In this paper, information entropy is added to the hierarchical analysis method to improve it and then combined with the fuzzy synthesis method to construct a model for evaluating the quality of mathematics teaching. The higher mathematics teaching course at a university is selected as the research object, and the constructed model for evaluating the quality of higher mathematics teaching is applied to the teaching of higher mathematics in classrooms. The evaluation index system for higher mathematics teaching quality is established by combining relevant literature, and the improved AHP is used for weighting. Then, the index data is collected through questionnaires to analyze the differences and assess the teaching quality of different genders and majors and then to propose strategies to improve the teaching quality of higher mathematics. The significant effects of teaching methods (0.184), teaching effectiveness (0.176), and teaching content (0.170) on the teaching quality of higher mathematics were revealed. The quality of teaching higher mathematics in the sample universities is located at a good level with scores between 8 and 9 on each evaluation index, with the best performance of 8.934 for teaching content. The evaluation model for higher mathematics teaching quality established by the research has validity and feasibility.
The Network Effect of Internet+Education Models in English Collaborative Learning Across Locations
Authors: Geng Cong 
Published: July 2024

Abstract
This paper establishes an English off-site collaborative learning system based on B/S architecture so that English learning is no longer restricted by time and space. This study investigates the influencing factors of network effects by introducing continuous learning behaviors in an online English off-site collaborative learning platform, and explores how course network size, course network diversity, and course strong linkage factors affect network effects. Two language classes in the College of Foreign Languages of a university were selected as research subjects to conduct an empirical study to test the effectiveness of the off-site collaborative learning teaching model. The results showed that the correlation coefficients of course network size, course network diversity, and course strong ties in the learning process were 0.341, 0.394, and 0.263, respectively, all of which were positively related to persistent behavior. In further testing the hypotheses, the main effects of course network size, course network diversity, and course strong ties were all significant (p<0.001). English off-site collaborative learning can improve students’ English performance and interest, which can develop their learning habits in the direction of making.
Research on the quantitative model of the communication effect of Civic and Political Education for social platform data
Authors: Tang Wenyan 
Published: July 2024

Abstract
Social platforms have revolutionized the lives of the public, provided a resourceful educational platform for the ideological and political education of college students, and become the distribution center of ideological and political education information and the amplifier of social opinion. In this paper, we concentrate on the dissemination of data related to ideological and political education on social platforms. We start by segmenting the text and then use the IF-IDF algorithm, the Text Rank algorithm, and the LDA theme model to process various text features. Simultaneously, we will employ a sentiment dictionary and simple Bayesian analysis, along with a convolutional neural network, to measure the impact of emotion propagation. The microblogging user’s comment data for the Civics communication emotion analysis concluded that the user group of the initial period of the Civics education activities will produce some negative emotions, while at the same time, the positive emotion has been maintaining an upward trend. While the negative emotions began to decline and slow down, the user group with the highest positive emotion reached a value of 6.35464.
Application of Digital RMB Smart Contracts in Grid Smart Payment Settlement
Authors: Hou Dongliang , Yang Qing , Hao Shanshan 
Published: July 2024

Abstract
The use of smart contract technology for contract execution and real-time payment can ensure the timely availability of funds, thus ensuring the security and authenticity of data in power grid transactions. In this paper, we design a digital RMB smart contract model based on blockchain technology and use the hexadecimal model to create, deploy, and execute smart contract functions. And through the DTSC algorithm, smart contracts can be applied to the grid smart payment settlement. At the same time, a privacy protection algorithm for transaction data is proposed, and finally, the grid smart payment settlement system is designed based on smart contracts and privacy protection algorithms. Simulation test results show that the cost required for the digital RMB smart contract proposed in this paper is lower than the existing schemes in terms of invocation and deployment costs. The average chain code invocation delay in smart contract technology decreases when the transaction sending rate increases from 250TPS to 300TPS. Moreover, the on-chain operation time of this system is only 2.69 seconds, which meets the demand of practical power grid smart payment settlement applications. This paper sets the foundation for the efficient operation of the grid smart payment and settlement system and provides a guarantee for payment and settlement security.
Remote sensing monitoring and early warning modeling of soil salinization process
Authors: Li Xiaoxiong , Ma Yanjun , Li Qiang , Yang Qingyi 
Published: July 2024

Abstract
In this paper, according to the process of remote sensing monitoring of soil salinity and alkalinity process as well as the conditions, the remote sensing images were radiometrically corrected and aligned, and the remote sensing images were enhanced by using digital models to change the gray structure relationship of the image elements and change the gray value of the image elements, and then the changes in the patches of the remote sensing images were analyzed to extract the soil salinity and alkalinity data. In this paper, we also used statistical methods to analyze the acidity and salinity characteristics of soil samples, the soil spectral reflectance characteristics, and the sensitive bands for estimating the soil acidity and salinity characteristics, and we performed the multispectral inversion analysis of soil salinity on the basis of the spectral data. The results show that the remote sensing monitoring and early warning model of the soil salinization process established in this paper has a coefficient of determination R
2 =0.697, RMSE=0.946, p=1.06*10-7 in the research calculations, and the root-mean-square error between predicted and measured values RMSE=2.33, which indicates that this model has a better performance in monitoring and prediction. The theoretical significance and practical value of this study are crucial for protecting the ecological environment and managing soil salinization.