bluuebunny/crossref_metadata_2025_split huggingface dataset:
Big Data Knowledge Graph of Charging Safety Influencing Factors and Database Construction Method of Safety Features
Authors: Bai Shaofeng , Song Heng , Liu Zhibin , Chen Qian , Huang Wei , Yan Xinwei , Geng Deji 
Published: October 2024

Abstract
In this paper, we utilize big data to screen relevant data on charging safety influencing factors and perform data cleaning to constitute a charging safety influencing factors dataset. BERT is selected as the baseline model for the named entity recognition task, together with the CRF model, to exclude irrelevant features, resulting in an effective model for entity recognition in line with the knowledge graph. Introducing a security database, a graph attention network model that simultaneously obtains the structural features and textual description features of the security knowledge graph is proposed to improve the performance of knowledge graph relationship extraction. The dataset of high-frequency charging security composition, as well as the random dataset, are used as experimental samples, respectively, to compare and analyze the performance of the BERT-CRF named entity recognition model in terms of each index. The link prediction evaluation task is evaluated using the structure- and text-based graph attention network model, and experimental analysis is carried out using three benchmark models. From the overall results of the test, it can be seen that the BERT-CRF model learns 90% of the lexicon’s knowledge and passes the model test by keeping each evaluation metric in the range of 0.9 to 1.0 under the large data volume experimental environment. The proposed graph attention network model, which uses structure and text, has a better link prediction performance than other models and performs better in the FB15K-237 dataset.
Research on the Innovative Application of Artificial Intelligence in Cultural Heritage Services of Libraries
Authors: Chen Wei , Sun Cong 
Published: October 2024

Abstract
This paper applies artificial intelligence technology to libraries to promote service innovation in information resource construction, intelligent push and information analysis. Starting from college libraries, we collect relevant news reports and activity message texts from their libraries as textual materials for analysis and utilize the LDA subject model to reveal the current development status of the application of cultural inheritance in smart college libraries. A questionnaire was used to explore the tendency of teachers and students to promote cultural resources through RSS in smart college libraries. The results indicate that in smart libraries’ cultural heritage work, online services and knowledge resource services are the primary areas of focus, with their thematic strengths being 0.13874 and 0.12912, respectively. As time goes by, the intensity of the cultural theme of red gradually increases. The trend of RSS pushing resources in red culture, morality, philosophy, art, and religion is more than 60%.
Research on the Construction of Intelligent Evaluation System of State-owned Asset Management Performance in Higher Vocational Colleges and Universities Based on Data-Driven Approach
Authors: Chen Xiuchen 
Published: October 2024

Abstract
State-owned assets are an important guarantee for the daily teaching and research activities of higher vocational colleges and universities and serve an important role in ensuring the long-term development of higher vocational colleges and universities while also serving as an important task for talent cultivation and discipline development. In this paper, the construction process of the state-owned assets management performance assessment system is studied in depth, and the state-owned assets management level of higher vocational colleges and universities is analyzed by combining the hierarchical analysis method with the comprehensive fuzzy evaluation method. By constructing the index system and judgment matrix, the weights of “asset security level”, “asset management capacity”, “asset operation efficiency” and “asset management effect” are determined. The weights of “asset security level”, “asset management capacity”, “asset operation efficiency” and “asset management effect” are 0.41, 0.19, 0.18 and 0.22, respectively, and the judgment matrix satisfies the consistency test. The constructed index system is used to assess the management of state-owned assets of a higher vocational college, and the quantitative results show that its fuzzy comprehensive evaluation score is 66.6, reflecting the good level of comprehensive management of the school. The scores of specific level 1 indicators show that the school performs well in “asset management capacity” (70.08 points) but in “asset protection level” (66.72 points), “operational efficiency” (63 points) and “asset management capacity” (63 points), the school’s overall management level is good. However, there is still much room for improvement in “asset security level” (66.72 points), “operational efficiency” (63 points) and “management effectiveness” (66.32 points).
A real-time early warning method for electric vehicle fast charging safety based on multiple time scales
Authors: Song Heng , Huang Wei , Liu Zhibin , Li Lei , Luan Zhongfei , Liu Zhenyang , Sun Yuke 
Published: October 2024

Abstract
This paper puts forward the support technology of fast charging supply and demand matching in charging stations and analyzes the common large-capacity electrochemical energy storage technical parameters in charging stations. For the safety of electric vehicle charging, the thermal reaction and thermal runaway processes of power batteries are introduced. Design the electric vehicle charging state monitoring and safety warning methods, and select the multi-timescale ARIMA algorithm to build the electric vehicle charging safety warning model. The sliding window method is used to process the residual mean and residual standard deviation of electric vehicle charging data to improve prediction data and decrease the chance of misjudging pre- and alarms. Combined with the evaluation standard of the safety early warning model, set reasonable pre- and alarm thresholds using the residual analysis method. The safety warning model designed in this paper is verified by different charging fault warnings. Different charging fault warning examples show that the ARIMA-based charging safety early warning model proposed in this paper can be good for the charging facility’s output voltage, output current, and charging module temperature faults for early warning to ensure that the warning is carried out before the alarm of the actual fault information, to protect the charging safety of electric vehicles.
Research on the Strategy of Artificial Intelligence Technology in Enhancing the Quality of Students’ Civic and Political Education in Colleges and Universities
Authors: Wang Xia , Wu Meng 
Published: October 2024

Abstract
This study utilizes clustering algorithms, based on subject knowledge mapping, to construct a learning community that can support the comprehensive development of group wisdom, taking into account the learner portrait, initial learning community, and the learning path. In order to verify the validity of the E-GPPE-C model of wisdom learning, University X was selected to conduct a one-semester quasi-experimental study. Attitude data and outcome data from the pre- and post-teaching portraits were collected to analyze the effectiveness of teaching based on the amount of change and significance test. Using the E-GPPE-C model of smart learning proposed in this study, the number of learning achievers before and after teaching increased to 8, which can improve the learners’ academic performance in civics and politics (P<0.05) and their’ satisfaction with the learning environment. Many students believed that smart teaching had changed their ideal beliefs and knowledge base in civics and politics.
Study on the correlation between treatment outcome and quality of life of adolescent scoliosis based on big data analysis
Authors: Wei Qionge , Li Yongmei , Jin Lihua , Dong Juchuan , Wang Chuanhai 
Published: October 2024

Abstract
To evaluate the correlation between changes in spinal balance parameters and healthy quality of life after treatment of adolescent idiopathic scoliosis. The current study concentrated on the imbalance of the shoulder and pelvis in the coronal plane, along with the changes in thoracic and lumbar curvature and pelvic positional morphology in the sagittal plane in adolescent AIS patients. The aim was to summarize the characteristics of the morphological imbalance in the coronal and sagittal planes of AIS patients, and to investigate the mechanism of the influence between scoliosis deformity and the localized or overall morphological imbalance of the torso. Observe the changes in coronal and sagittal morphology indexes before and after rehabilitation treatment and analyze the effect of rehabilitation treatment on AIS. Based on the inclusion criteria, the Spine Disease Big Data Platform selected fifty patients with AIS and took pre-intervention full spine radiographs. The Cobb angle, clavicular angle, coronal pelvic tilt angle, and coronal sacral tilt angle were measured on the orthopantomographs, and the coronal torso offset was recorded. Pearson’s correlation coefficient was used to analyze the correlation between radiological parameters and quality of life score values. The mean value of ODI in the patients was 38.12±15.35 preoperatively and 19.11±10.43 after rehabilitation treatment, and the difference between preoperative and postoperative was statistically significant (p<0.05). The mean value of the patient’s Cobb angle decreased from 36.12±17.25° before rehabilitation therapy to 11.61±8.72° after rehabilitation therapy. The maximum isometric muscle strength of the subjects was measured in the standing position, prone position, and sitting position, respectively, and the strength of the low back muscles of the AIS patients who had undergone systematic treatment, compared with the observation group, was significantly improved (p<0.05). Improving spinal balance parameters alone does not indicate an improvement in quality of life after treatment in patients with AIS. It is more common for adolescent scoliosis patients to exhibit a high pelvic tilt angle and a low sacral incidence angle, and systematic treatment can effectively increase the sacral tilt and decrease the pelvic tilt angle, correcting spinal-pelvic imbalance in scoliosis patients. In AIS patients, the maximum isometric muscle strength of the low back muscles in a standing position significantly correlated negatively with dysfunction scores and positively with the patients’ health-related quality of life. The correlation was most significant with the dimensions of physiologic function and physiologic function, which are sensitive indicators of quality of life in patients with AIS.
Model Construction of the Impact of Digital Transformation of Non-Heritage Cultural Assets on Rural Revitalization in the Framework of Quantum Computing
Authors: Gao Xiaomei 
Published: October 2024

Abstract
In the new journey of building a socialist modernized country, how to empower rural revitalization in ethnic areas with non-heritage cultural assets is an important topic that requires in-depth thinking. In this paper, the development level of rural revitalization and the degree of digital transformation of non-heritage cultural assets are taken as explanatory variables and explanatory variables, and the gray correlation between them is calculated using the gray correlation analysis method. Then, based on the differential evolution operator and the improved quantum particle swarm algorithm with weighted coefficients as a framework, the impact prediction model of digital transformation of non-heritage cultural assets on rural revitalization is constructed. The study shows that the grey correlation between X2 “reliability”, X4 “quality” and X7 “diversity” and the development level of the rural revitalization of the three variables of digital transformation of non-heritage cultural assets exceeds 0.8 percent. The grey correlation between X2 “reliability”, X4 “quality” and X7 “diversity” and the development level of rural revitalization among the three variables is more than 0.850, and the grey correlation of the other variables is above 0.70, which indicates that the digital transformation of NRM cultural assets is a possible way out of rural revitalization. In addition, the MAE value of the impact prediction model constructed in this paper is 1.63%, which is much lower than other comparative models, indicating that it can accurately predict the impact of digital transformation of non-heritage cultural assets on rural revitalization. This paper studies the impact mechanism of digital transformation of non-heritage cultural assets to drive the development of regional high-rural revitalization. It is of great significance for other ethnic regions and even backward regions.
Research on the Application of Deep Learning Algorithm of Artificial Intelligence to 3D Clothing Design in Computerized Virtual Reality Environment
Authors: Hu Lingling 
Published: October 2024

Abstract
In this paper, point cloud data is used to construct a human body model, which in turn forms the transformation process of the human body. The corner point detection method is used to decompose the user-input two-dimensional garment shape contour lines, resulting in two types of data lines: shape lines and cross lines. Furthermore, the stereo configuration of the three-dimensional virtual garment is automatically generated using the void volume modeling method and displayed in real-time. The real graphic effect of 3D style is also studied by applying lighting and material processing methods. And take the actual case as the starting point to complete research on the use of 3D clothing design. After analysis, it can be seen that the standard deviation of point cloud data measuring human body parts is small, and the average errors of thickness, circumference, and volume are 1.21%, 2.64%, and 0.053%, respectively. The experimental study designed four garment models with the highest chest fit in terms of T-shirt styles’ fit measurement.
Study and Modeling of Nonlinear Dynamic Characteristics of DC-DC Converters with Amnesia Loads
Authors: Li Rong , Zhou Ling , Pan Haijun 
Published: October 2024

Abstract
The chaos phenomenon is a seemingly random irregular motion occurring in a deterministic system, which is an inherent characteristic and prevalent phenomenon of amnesia-loaded DC-DC converter nonlinear systems. Nonlinear dynamics and chaos theory guide the text, and we create an amnesia-loaded DC-DC converter system model using an amnesia resistor. We analyze the stability of the system using Filippov theory, and analyze the nonlinear dynamic characteristic behavior of the amnesia-loaded DC-DC converter using the time-domain waveform diagram, bifurcation diagram, and phase-track diagram. This paper talks about the slope compensation method and the parametric resonance perturbation method for control analysis. These methods are used to stop the chaotic behavior that happens in the amnesia-loaded DCDC converter. The system maintains a stable state when the reference current is less than 4.732A and the supply voltage exceeds about 17.966V, maintaining an amplitude of the maximum eigenvalue below 1. When the load resistance is 50Ω, the capacitance is 43 uF, the inductance is 2.8 mH, and the reference voltage is 2.05 volts, the amnesia-loaded DC-DC converter suffers from obvious chaos. To restore the system to a steady state by applying the ramp compensation method, the slope of the ramp signal will need to increase as the reference current increases. Loading amnesia onto the DC-DC converter achieves a stable cycle 1 state when the perturbation phase’s fluctuation range falls between 4.35 and 6.15. A nonlinear dynamic analysis of the amnesia-loaded DC-DC converter helps find the stable parameters of the amnesia-loaded DC-DC converter and improve the design of the converter.
Research on Real-Time Semantic Understanding and Dynamic Evaluation of Artificial Intelligence Techniques in Multilingual Environments
Authors: Li Wenyi , Li Gang 
Published: October 2024

Abstract
NPMI is used to describe the local co-occurrence properties of words, and the collected text data is constructed into a homogeneous graph composed of words. A word-granularity graph convolutional neural network (VGCN) is used as a graph encoder to achieve parallel training of graph convolutions. The output of the VGCN is used as a kind of embedding vector for BERT, and the VGCN is fused with other embedding using a transformer for feature fusion. The reprojection layer converts the model output to semantic representation vectors, and the post-projection layer converts the semantic representation vectors to vectors for loss computation. NT-Xent is chosen as the loss function of the model, while the real-time semantic understanding effect of the model is evaluated using the Spearman correlation coefficient in statistics. The CBOW model provided by Word2vec is learned from the training set and constantly updated for each parameter to calculate semantic similarity between words. The LDA model is used to extract topic keywords from each document and calculate the probability, which finally completes the dynamic assessment of the credibility of text content based on semantic understanding. The ROUGE-L index and BLEU-4 index of the VGCN-CL model in this paper are 66.47% and 63.86%, respectively, which are 23.20% higher than the baseline model ROUGE and 25.14% higher than BLEU-4. When the content assessment results are true, the accuracy and other metrics of this paper’s model are 90.36%, 89.87%, and 90.09%, respectively, which are the best performance among all models.
Research on the Integration and Innovation of Artificial Intelligence Technology in Modern Brand Design
Authors: Liu Xuelin 
Published: October 2024

Abstract
In the context of the artificial intelligence era, the application of artificial intelligence technology in modern brand design is increasingly widespread and is profoundly changing the design creation in this field. The purpose of this paper is to propose an intelligent image acquisition and automated brand design technology that utilizes AI technology to assist with brand design. The image acquisition and retrieval process of the online CBIR system is established, the average precision mean is used as the evaluation index of image retrieval, and the BoF model is constructed to realize RootSIFT feature extraction and image acquisition. Meanwhile, MultiStyleLogo GAN, a multi-style generative adversarial network, is designed to intelligently automate random output and generate brand design solutions through input sketches and style semantic descriptions to improve designers’ design efficiency. This paper utilizes technology to redesign the brand of A green plum wine in a province in China. Except for the pattern and font indicators, the average value of the newly designed brand image in each evaluation index of aesthetic design is greater than 4. The average values of the image aesthetics, cultural connotation, and emotional resonance indicators in the evaluation of brand narrative reach 4.25, 4.24, and 4.15, respectively, which achieve a high consumer satisfaction level.
Research on Cloud Computing-based Music Resource Sharing Platform Technology and Its Promotion of Employment and Entrepreneurship
Authors: Wei Wei , Du Yuelin , Li Wei , Wang Yan 
Published: October 2024

Abstract
Music resource-sharing platforms can increase students’ employment paths and enhance their employment ability. Load prediction and resource scheduling algorithms are designed in this paper to construct a music resource-sharing platform based on cloud computing technology. The mean predictor’s predictions are combined with the GRU-CNN model’s predictions, and the weighted average is calculated to enhance the accuracy and stability of the predictions. Then, the integrated migration scheduling method is designed to make the load of the cloud environment more balanced and the resource utilization more reasonable by the four processes of migration triggering, container selection, node selection, and container migration in a coarse-grained dynamic scheduling approach with nodes as objects. Following the completion of the construction, the algorithm that was designed is evaluated for load prediction and resource scheduling, and regression analysis is performed to examine the role of music resource-sharing platforms in promoting employment and entrepreneurship. It is found that after the resource scheduling by the algorithm in this paper, the node operation tension is greatly alleviated, and the CPU resource utilization and memory resource utilization of node Node1 are reduced from 80.96% and 62.69% to 58.03% and 43.36%, respectively. The resources of the low-load node Node4 are effectively utilized, the memory resource usage rate has increased from 6.86% to 49.52%, and the resource usage of each node in the cluster is more balanced and reasonable. At a statistical level of 1%, the impact of music resource-sharing platform development on employment quality is 0.194. In contrast, the industrial structure does not significantly contribute to the quality of employment, indicating that under the current economic situation, emerging economic forms such as music resource-sharing platforms are the main driving force to improve the quality of employment and the development of music resource sharing platforms generally improves the quality of labor force employment.
Research on the Informatization Strategy and Practical Effect of Chinese Traditional Culture Education
Authors: Wei Zhan'gang , Ma Xueying 
Published: October 2024

Abstract
The development of curriculum culture is a profound reflection on the evolution of school curriculum in the present era. The article proposes a Retina Student network to detect students’ classroom behaviors and then incorporates the implementation of a classroom behavior coding system using computer vision. Based on this, we established a student detection dataset for the classroom attention recognition task, utilizing the Retina Studen student detection algorithm to achieve optimal recognition accuracy of 0.92 and 0.93, and recall of 0.91 and 0.94 for both gaze and non-gaze states. Then, the visualization information of students’ learning behaviors was targeted, and the classroom status was discussed. The experimental results demonstrate that this paper’s algorithm outperforms other algorithms in identifying student learning behaviors. Finally, through the coding analysis of the teaching video of the cultural education smart classroom, we found that students’ behaviors in the calligraphy, painting, and opera smart classroom are rich, and the traditional way of “explaining-questioning-answering-feedback” is still the main behavior in the classroom.