bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploration of the standardization strategy of enterprise economic management mode in the context of the digital economy era
Authors: Wang Yuan 
Published: August 2024

Abstract
With the advent of the big data era, information flow and data sharing on a global scale have become the norm. This change has not only changed people’s way of life but has also had a profound impact on enterprises’ economic management mode. The purpose of this paper is to examine the enterprise economic management innovation mode in the digital context and then create a fuzzy, hierarchical, comprehensive evaluation model to examine its application advantages. This foundation is used to construct a standardized evaluation system for enterprise economic management. Specifically, combining the internal and external environments of the enterprise, the enterprise economic management standardization index system is constructed, the indexes are assigned through the hierarchical analysis method, Company A is taken as an example, and the questionnaires are issued to the internal personnel of the enterprise, and the indexes are rated comprehensively. From the comprehensive score of Company A, the standardized level of debt repayment, operation, profit, and development of the enterprise is 3.3125, 0.3242, 3.3588, and 3.2126, respectively, and the standardized level of economic management of Company A reaches 3.3415, which is in a stable state of development, but it can still be further improved. This paper’s use of the digital enterprise economic management model not only enhances Company A’s management operation but also serves as a model for standardizing economic management across other enterprises.
The Contribution of Music Information Retrieval System Optimization to Music Analysis in the Context of Big Data
Authors: Yin Junbo , Du Yuefeng 
Published: August 2024

Abstract
With the rapid popularization of Internet big data worldwide, people are able to transmit, download, and listen to huge amounts of music, which directly contributes to the demand for music information retrieval. In this paper, a music information retrieval system is constructed based on extracting music features. Both time and frequency domains characterize the music, and the transformation relationship between time domain, frequency domain, cepstrum domain, and power spectrum is proposed to extract music features. Further, the convolutional deep confidence network algorithm is applied to music information retrieval, an unsupervised greedy layer-by-layer algorithm carries out pre-training, and the network parameters are adjusted to improve the retrieval and recognition ability of the model. Functional validation of the system in this paper. In the music feature extraction experiments in this paper, the system’s accuracy for extracting feature points from different songs is more than 80%. In the music information retrieval experiments in nine different styles of music in the style of music in this paper, the average judgment of the system correct rate of 92.59%, in different proportions of the number of tracks in the retrieval success rate, is higher than 88%. In music analysis fields such as music recommendation and music soundtrack design, the music information retrieval system constructed in this paper plays a significant role.
Cooperative planning of new distribution system grid and energy storage system based on distribution robust optimization
Authors: Gao Chong , Zhang Junxiao , Li Hao , Xu Zhiheng , Hao Peng 
Published: August 2024

Abstract
With the increasing penetration of distributed power sources, the stochastic and fluctuating nature of distributed power sources poses a great challenge to the reactive power optimization of the distribution network system. In this study, a dynamic reactive power optimization model with two-stage robust optimization is established, proposing whether the energy storage is charged or discharged. The number of groups of group-switching capacitors is taken as the variables in the first stage. The power of the energy storage charging and discharging and the amount of static reactive power compensator compensation are placed in the second stage. The control strategy in the first stage ensures that the control strategy in the second stage can maintain the safe and stable operation of the distribution network under the worst scenarios. The grid-storage joint optimization technology based on distributed architecture establishes an optimization planning model for the distribution network energy storage system with the goal of optimal technical and economic performance of the transmission and distribution network and considering the constraints of safe and stable operation of the transmission and distribution network, respectively. The PG&E-69 node system arithmetic example is used to verify the effectiveness and feasibility of the proposed model and algorithm. The results of the arithmetic example show that the strategy obtained based on the robust optimization model can achieve voltage magnitude stability within the safety range of 1.0-1.05 p.u. in the simulation scenario that has the best economy. At the same time, the mismatches of each distribution system under cooperative planning are all 0, which indicates that the proposed optimization strategy can fully cooperate with the resources of transmission and distribution networks, promote the safe consumption of clean energy, effectively improve the economy of transmission and distribution networks, and achieve the goal of “mutual benefit and win-win”.
The Application of Artificial Intelligence Technology in Improving the Quality of Teaching and Learning in Computer Education
Authors: Zheng Yanyan , Pan Yifeng , Zhang Zhengmin , Chen Ying 
Published: August 2024

Abstract
The personalized recommendation of courses and exercises can realize more accurate, tailor-made teaching, significantly improve teaching efficiency, and stimulate students’ learning interest and motivation. In this paper, we first obtain the knowledge point data of computer specialties and then extract the course knowledge point entities using the TF-IDF algorithm. We then use artificial rules to extract the relationships between the entities, design an estimation method for students’ mastery of the knowledge points, combine the knowledge graph to generate different learning sequences for students, and recommend course resources for them. Then, we use a simple Bayesian classification algorithm to classify and characterize the text of exercises and recommend personalized exercises to learners by combining their mastery of knowledge points, degree of difficulty, and other characteristics. The system in this paper is applied to a computer program at a university in Guangzhou to compare the teaching effect. It was found that the average grade of the experimental class was 11.25 points higher than the average grade of the control class, an improvement of 11.92 points compared to the pre-test, and the distribution of the scores clearly progressed from the 75-80 point range to the 85-90 point range. In the three dimensions of perceived usefulness, perceived ease of use, and intention to use, the vast majority of the respondents chose to agree or strongly agree, and the average score of the survey was 4.31, 4.24, and 4.18, respectively, indicating that the system is easy to operate and has a reasonable functional design and that the results of the recommended course resources and exercises basically conform to the learners’ psychological expectations and practical needs. This study proposes a feasible path for the integration of artificial intelligence technology into computer teaching, which will improve the quality and efficiency of computer education and teaching.
Analysis and research on the efficiency improvement mechanism of automated invoicing process of outage ticket based on queuing theory
Authors: Bai Yiming , Shi Xiaojing , Zhang Bingsheng , Ruan Zhijie , Peng Zhenhua , Liang Shaoming , Chen Kunming , Liu Xinsheng 
Published: August 2024

Abstract
The electricity ticket system is an important basis for staff to carry out maintenance plan designation and execution. With the gradual progress of the work of the grid grassroots staff to reduce the burden, the need to simplify the process of electricity ticket invoicing and improve the efficiency of electricity ticket invoicing is increasing. This study first optimizes the scheduling and data capture problem of electricity based on queuing theory. Then, it constructs a recognition model for electric ticket data based on deep learning and combines the CTPN text detection algorithm with the DesNets text recognition algorithm to establish the CDNets algorithm model for recognition optimization. An automated generation model of the blackout ticket was created using the LSTM network. Taking into account the power company’s needs, combined with Intranet intranet software development concepts and Net software technology development and design of the ticket automation system. The AP of this paper’s blackout ticket recognition model under the mixed date of the ticket has a disadvantage of 0.7% compared with YOLOv4, but the reasoning speed is relatively improved by 2 times, and the overall algorithm is more cost-effective. The highest accuracy, F-value, and AP of this paper’s model for stamp data are 95.3%, 97.6%, and 99.2%, respectively. And in the login test and invoicing test, the system in this paper is faster than the traditional system by 298.4ms and 136.7ms, respectively. The electric ticket automation system in this paper is able to improve the automatic generation of electric tickets and the efficiency of electric ticket invoicing, and to enhance the level of lean management at the grassroots level of the grid, with wide adaptability.
Optimization of data model-driven design thinking in the software development process
Authors: Li Yun , Li Lianwei 
Published: August 2024

Abstract
Model-driven software development has become a hot research topic and discovery trend in the field of software engineering. Its core idea is to treat analysis and design models as equivalent to code. Better integration of models and code can greatly increase the chances of effective improvement and achieve automated software development through abstract models. In this paper, we first constructed a data model-driven architecture system based on the meta-modeling hierarchy, using a data dictionary for data storage. The mapping relation loader transforms the data extracted from the dictionary. Using the differential evolution algorithm, the model is defined as a metamodel that actually exists. At the same time, the MapReduce framework is combined to parallelize the computation of the DE algorithm based on the island model in order to solve the problem of poor optimization of the differential algorithm. Apply the model to actual software development and realize data visualization and display using Flex technology. Simulation experiments are set up to test the performance of the model and the platform. After CSI uncertainty estimation, the MSE index of the model is analyzed. This paper’s data model-driven method, with an MSE value of only 0.01084, stands out among the five methods in Case 4. Under the condition of 300 users concurrently, the user’s access response time is tested, and it can be seen that the user’s response time is within 1~2.9s, which passes the performance test.
Exploration of Interactive Teaching Models Integrating Augmented Reality Technology in University Science and Technology Parks
Authors: Yin Hongting , Miao Jianjun 
Published: August 2024

Abstract
Augmented reality technology is regarded as one of the modern information technologies that are most likely to change education and is widely used in education and teaching by virtue of its virtual, visual, and interactive features. In this paper, the skin color model and background model are used to recognize gestures in complex backgrounds during interactive teaching, and the optical flow features of the corner and edge points of the gesture image are extracted by the Harris method to recognize the occlusion gestures in interactive teaching. On this basis, the interactive teaching platform integrating augmented reality technology is established by combining the gesture recognition method with Unity 3D and EasyAR systems. After one semester of teaching in a university science and technology park, the average grades of the students in the experimental class applying the interactive teaching mode and the control class in the traditional multimedia teaching mode are significantly different (P=0.001<0.05). Students learning attitudes after the teaching experiment are significantly different from their learning attitudes before the teaching experiment. The teaching mode designed in this paper is of great significance for teaching and has a wide range of application prospects and use value in the field of education, and the optimization strategies proposed from the AR context design and other aspects lay the foundation for stimulating students’ interest in learning and improving the efficiency and effectiveness of teaching.
A multifactorial detection model of young children’s physical abnormality based on image recognition technology under the concept of physical and health integration
Authors: Chen Jundan , Zhang Lifang 
Published: August 2024

Abstract
This paper analyzes the influence of “the concept of integration of body and health” on the physical development of young children and proposes that scientific sports are an important way to intervene in the poor physical appearance of young children. In order to facilitate and accurately identify young children with poor posture, the boundary tracking algorithm is used to detect the human body contour, and based on the physical characteristics, a human posture recognition algorithm based on multi-feature fusion and image similarity is proposed. To model common toddler body postures and obtain toddler posture features, a star model is employed. Combined with the advantage of the SVM classifier, the principal component analysis algorithm is used to design a classification system for abnormal behaviors of toddlers, which combines the static and dynamic posture data images of toddlers to detect abnormalities of toddlers’ body postures. When the difference between the angle of the left elbow to the left shoulder and the angle of the right elbow to the right shoulder to the left shoulder is about 16.58°, the toddler has a high-low shoulder posture. The detection accuracy of the method in this paper meets the needs of young children’s posture detection, and the misjudgment rate reaches 10.25%, which can assist in detecting the abnormalities of young children’s posture and facilitate the teachers and staff to carry out the sports related to young children’s posture.
Application of Machine Vision Technology in Defect Detection of High Performance Phase Noise Measurement Chips
Authors: Deng Jianxun , Hu Chunxia 
Published: August 2024

Abstract
Defect detection plays a crucial role in chip quality control, and the chip field has widely researched and applied machine vision-based surface defect detection methods due to their high efficiency, accuracy, and real-time performance. In this paper, we utilize imaging equipment to collect images of high-performance phase noise measurement chips, and we use a mean filtering algorithm and a Sobel operator to preprocess the collected chip images. Then, the PCA method is applied to downscale the extracted chip shape and texture features, and the improved support vector machine algorithm using a genetic algorithm is used to classify and recognize chip defect features. The test results show that the error rate of the defect detection method for high-performance phase noise measurement of chip surface defects is only 1.82% at the highest, and the average error of the measurement of the chip pin width and pitch is much lower than the actual production of the specified error rate. Meanwhile, the design requirement of 3 pcs/s detection efficiency in the actual production of high-performance phase noise measurement chips is satisfied by the theoretical maximum defect detection efficiency of the method. The chip defect detection method presented in this paper has both practical application value and theoretical research significance.
Probabilistic-based Markov chains for behavioral prediction
Authors: Fang Xiaochao 
Published: August 2024

Abstract
Due to the wide application of Markov chains, it makes some models that cannot be computed due to a large amount of computation have an approximation. In this paper, based on Markov, combining probability theory with a state transfer probability matrix and using the ordered clustering method to divide the behavior into clusters, we construct a behavioral prediction model based on the probabilistic Markov chain to solve the problems that the model tends to have such problems as low overall prediction accuracy and limited applicability. By testing the model’s performance on the relevant dataset, we can predict the occupants’ in-room status. The Gowalla dataset has an MMP model that is 16% accurate and 21% recall. Classifying households and identifying indoor behavior patterns of different households is sufficient so that the indoor behavior patterns of the same type of households are closer to each other. The method is capable of considering various household characteristics parameters and their influence on in-room behavior comprehensively and classifying actual behavior reasonably.
Predictive modeling of soil water and salt dynamics for ecological restoration of mines in arid zones
Authors: Li Chengzhi 
Published: August 2024

Abstract
Ecological restoration of mines in arid zones is an important initiative to promote the harmonious coexistence of man and nature and realize green and low-carbon development, in which the prevention and control of soil secondary salinization is crucial for ecological restoration. This paper takes the soil and water environment (soil secondary salinization) of the Shendong mining area as the research object, and after systematically analyzing the ecological restoration of the study area, collects relevant soil water and salt data and processes them, and analyzes the characteristics of soil water and salt changes in the study area respectively. On this basis, combined with the basic mathematical model of soil water-salt transport, a 3-layer feed-forward BP network modeling method was invoked to predict the soil water-salt dynamics under the study’s comprehensive conditions, and then the sensitivity of soil salinity and soil alkalinity to each factor in the input layer was analyzed by using the default factor test method, and predictions were made for long-term ecological restoration. The sensitivity indices of soil water content to soil salinity as well as soil alkalinity were large, with R of 4.46 and 3.89, respectively, and the effects of each factor on soil water-salt dynamics should be considered comprehensively in the actual ecological restoration of mines in arid zones. In addition, through the prediction of soil water salinity dynamics, the crops in the study area were irrigated with 3.2g/L brackish water four times during the reproductive period, with an irrigation quota of 360mm, and with the measure of spring irrigation water volume of 148mm, which could achieve the purpose of reasonable utilization of salty water and water-saving irrigation, and effectively realize the ecological restoration of mines in arid zones.
Construction of Interactive Path Model of Digital Economy and Green Financing Based on Complex Network Theory
Authors: Li Dan 
Published: August 2024

Abstract
With the rapid development of science and technology and the in-depth popularization of the concept of environmental protection, green financing has become an important issue in the development of the digital economy nowadays. The research is based on the theory of digital economy and green financing, and the interaction model between digital economy and green financing is constructed using the complex network model. The digital economy and green financing network are constructed to carry out network characterization and prediction. To analyze the correlation coefficient matrix of the digital economy and green financing, the MIC statistic is chosen as the model basis. The average aggregation coefficient and network density of the sample countries and regions selected in this paper show a decreasing trend year by year between 2015 and 2020, while the average path length shows a gradual increase. The predicted results of the complex network model for the digital economy and green financing in 2021 are as follows: 165 projected network nodes, 865 edges, average degree value of 6.976, average clustering coefficient of 0.312, average path length of 2.782, network diameter of 6, and network density of 0.039. “The Belt and Road Initiative” policy was put forward after that, the MIC values between most of the sample countries have increased, and the digital economy exchanges and green financing cooperation between countries and regions have shown a thriving development.
Study on the Extensibility of Chinese Traditional Culture in College Education by Combining Gray Correlation Analysis Methods--Taking Miao Batik as an Example
Authors: Li Qishu 
Published: August 2024

Abstract
Miao batik’s unique cultural connotation suggests that it has important inheritance significance as a part of Chinese traditional culture. Combined with the discussion on the protection and inheritance of Miao batik, this paper proposes to utilize the entropy value method and the hierarchical analysis method to analyze and screen the influencing factors of the inheritance and development of Miao batik in college education and to calculate the weight coefficients of the four levels of human resources, material resources, financial resources, and intelligence. By using the greyscale correlation analysis method, the correlation between each influential factor and the development of the educational inheritance of Miao batik in colleges and universities can be calculated, and the degree of importance can be ranked. Based on the development of traditional culture courses in colleges and universities, the teaching path of Miao batik technique courses in colleges and universities is proposed, assisted by information technology. According to the ranking of the degree of relevance, the indicator “curriculum construction (0.9364)” has the greatest relevance to the development of the educational inheritance of Hmong batik culture in colleges and universities, and the relevance of “club construction (0.6008)” to the development of the educational inheritance of Hmong batik culture in colleges and universities is the smallest. Minimum. Colleges and universities can adjust the curriculum structure of Miao batik to improve the extension and development of traditional Chinese culture represented by Miao batik in colleges and universities.