bluuebunny/crossref_metadata_2025_split huggingface dataset:
ANALISIS KOREOGRAFI TARI HADRAH PADA MASYARAKAT DESA DALU X A KECAMATAN TANJUNG MORAWA KABUPATEN DELI SERDANG
Authors: Chairuna Chofifah , Prastiawan Inggit 
Published: November 2024

This study aims to describe the choreographic analysis of the Hadrah Dance in the community of Dalu X A village, Tanjung Morawa District, Deli Serdang Regency. Choreography Analysis refers to the Theory of Y. Sumandiyo Hadi (2012:41) that choreography is the skill of creating or arranging dance movements which includes the stages of cultivation as the first step consisting of the form of choreography and the principles of art formation, which consists of Unity (Intact Unity), Variation (Diversity), Repetition, Transition, Series, Climax. This type of research uses a qualitative descriptive method with data collection techniques, namely Observation, Interview, and Documentation. Qualitative descriptive data analysis technique by means of triangulation. The research was carried out at the Shield Studio, Dalu X A Village, Tanjung Morawa District, Deli Serdang Regency. The research was conducted from January 2024 to March 2024. Population and Sample consisted of 3 artists from Sanggar Prisai. 5 musicians and 5 male dancers at Sanggar Perisai. The results of this research include Unity (Unity), Diversity (Variation), Repetition (Repetition), Displacement (Transition), Hadrah Dance Series, and Climax (Climax).
Research on the Innovation of Education Management Informatization Based on Deep Learning Models
Authors: Hao Jie 
Published: November 2024

Abstract
In the digital era, the rapid development of information technology has not only changed the education model but also provided new tools and paths for education management. This paper establishes the two most commonly used metrics of support and confidence for association rule analysis. Using the Apriori algorithm, all frequent items in the dataset that satisfy the mining conditions are extracted, and through the constraints of the metrics, the eligible association rules are screened, and the association rule results are parsed. Add the interest degree model to reduce the generation of non-interesting rules. The cluster analysis algorithm and it are combined to apply it in the field of education management, which is empirically examined. The results of cluster analysis and association rule mining show that the support degree of rule 1 is 0.4925, and the confidence level is 1, indicating that if 49.25% of all students score between 2 and 4 in question 3, then the score in question 1 is similar to the score in question 3. Based on the weights calculated by the association rules, the teachers’ basic teaching literacy, classroom teaching ability, teaching and research ability, professional ability and after-school counseling were evaluated respectively, and the overall score of teacher 1 was the highest, with a score of 91.213, which accounted for 10% of the total number of students, and the evaluation method adopted in this paper was scientific and fair.
Abnormal sensing feature detection of DC high voltage power battery for new energy vehicles
Authors: Chen Yuanhua , Yang Yanping , Wang Lifeng 
Published: November 2024

Abstract
As a kind of clean energy transportation, new energy vehicles are widely respected. This topic focuses on the detection of abnormalities in power batteries in new energy vehicles. After combing the common faults of the battery management system, using the basic structure of RBF neural network and the advantages of the reduced clustering algorithm, for a single power battery, the power battery power abnormality detection scheme based on the improvement of reduced clustering algorithm is proposed, and the power battery abnormality detection process is designed. Taking the sensing feature data of the battery management system of a new energy vehicle as an experimental sample, through the battery state estimation experiment and the example application of the model, it is found that the RMSE (0.0018) and MAPE (0.0206) of the model training are lower than that of the comparison model, and the average error rate of the abnormal battery identification is 0.833%. The model’s abnormality detection results in both instances are consistent with the actual maintenance results. The analysis indicates that the RBF neural network model with reduced clustering algorithm has superior accuracy and feasibility for detecting abnormal battery power.
Research on the Relationship between Enterprise Innovation Capability and Industry-University-Research Collaboration in the Era of Digital Economy
Authors: Li Ting , Kong Yusheng , Tao Jingyan , Yu Guo 
Published: November 2024

Abstract
Industry-university-research cooperation has become one of the indispensable and important innovation modes in the construction of today’s innovation system. The article collects the enterprise information system from 2014 to 2021, screens 49 companies that have participated in industry-university-research cooperation as the research object and put forward hypotheses, measures the innovation quality by the number of large groups number and the number of small groups number based on IPC classification number, and measures the level of internal research and development by the two methods of the percentage of R&D investment and the percentage of scientific researchers to carry out empirical evidence to test the impact of the level of industry-university-research cooperation on the quality of innovation of the enterprise. Test. The results of the study show that there is a significant positive correlation between the level of University-Industry Cooperation and the quality of enterprise innovation, with a coefficient of 0.103 and significant at the 1% level, i.e., the higher the level of University-Industry Cooperation, the higher the quality of enterprise innovation. The stronger the driving effect of the level of university-industry research Cooperation on the innovation quality of enterprises in terms of improving the level of internal R&D, the stronger the driving effect.
Study on How Modern Public Art Expresses Digital Themes with the Help of Painting Creation
Authors: Li Maofen 
Published: November 2024

Abstract
Modern public art is an important characteristic form of painting creation, which is constantly given new connotations with the development of the times and the progress of science and technology. Based on the characteristics of modern public art, the article explores the digitalization practice, creation process, and corresponding techniques for painting creation. The TF-IDF algorithm is introduced to mine the digital theme keywords expressed in painting creation, and the BERTopic model is combined to mine the digital theme of painting creation. The K-Means clustering algorithm is used for clustering the digital theme keywords of painting creation, and the digital theme keyword co-occurrence network is integrated to analyze the digital theme research trend of painting creation and expression for modern public art. When the number of digitized themes of painting creation and expression is 50, the F1 value of the BERTopic model can reach 94.36%, and the AUC area for digitized themes of painting creation and expression is 93.37%. The mean value of the keyword clustering profile coefficient of the digitized themes of painting creation and expression is 0.945, and the frequency of “painting creation” in the keyword co-occurrence network is up to 304 times. Relying on digital technology to create modern public art paintings, the digital theme expression of public art can be better realized.
Goal Setting and Instructional Design in Music Education Based on Bloom’s Cognitive Hierarchy Theory
Authors: Li Tiantian 
Published: November 2024

Abstract
This paper first introduces Bloom’s cognitive hierarchy theory of music education goal classification and revises it to construct a new version of Bloom’s educational goal-setting system. After that, it adopts multi-objective hierarchical analysis (AHP) to calculate the weights of the music education objectives assessed by experts and optimizes the convergence of AHP based on Bergh’s improved particle swarm (PSO) optimization algorithm. New music education objectives and teaching contents were created based on the experimental results. The CR values of the six cognitive subprocesses of the second-level evaluation indexes were 0.0001, 0.0011, 0.0014, 0.0014, 0.0023, and 0.0029, respectively. The results of the consistency test of the first-level and second-level indexes were all CR<0.05, indicating that their consistency and trustworthiness were extremely high. The top three indicators in terms of the weights of the secondary indicators are inference, recall and differentiation, whose weights are 0.5723, 0.5276 and 0.5192, respectively; the three indicators with the lowest proportion are interpretation (0.2422), normalization (0.2661) and creation (0.3201). The purpose of this paper is to create a music course instructional design system with a 4-dimensional and 18-indicator system and evaluate the importance of each indicator. It fully embodies the basic spirit of quality education and reflects the common requirements of the new curriculum system of colleges and universities that combines epochal, basic, and selective. The teaching design system proposed in this paper is expected to serve as a reference for the current music teaching reform.
Design of Dynamic Monitoring and Prediction System for Energy Consumption in Public Organizations Based on Energy Efficiency Diagnosis
Authors: Gao Liangfang , Li Junwu , Zhang Li , Hu Pengtao , Yang Zhiping , Kang Zhenning 
Published: November 2024

Abstract
Energy is an important global issue at present, and reducing energy consumption of public organizations can promote the development of a low-carbon economy and low-carbon society, which is of immense significance to both economic and social development. In this paper, we gather dynamic energy consumption data from public institutions using the energy consumption dynamic monitoring platform, preprocess the information, and apply the 3-sigma criterion method to identify abnormal energy consumption points in these institutions. We then propose an energy consumption prediction model based on the PSO-BP neural network, use the Markov model to backtest historical energy consumption data, correct the model’s prediction results, and construct an energy consumption prediction system for public organizations. The study shows that the prediction effect of this system on energy consumption is better than that of the RS model and DS model for 1 hour and 24 hours in advance. This paper’s energy consumption prediction system effectively and timely detects and diagnoses energy consumption anomalies in public organizations’ operations, thereby supporting their energy conservation management. This paper lays the foundation for the establishment of an energy consumption prediction system and the study of energy-saving strategies. It can provide the basis and strong guidance for the optimization of energy-saving operations in public institutions.
Exploration of Monte Carlo Method for Optimization of Energy Consumption in Industrial Enterprises in Energy Efficiency Diagnosis
Authors: Gao Liangfang , Li Junwu , Zhang Li , Hu Pengtao , Yang Zhiping , Kang Zhenning 
Published: November 2024

Abstract
Energy consumption, as one of the most concerned parts of industrial manufacturing costs, has an important impact on the overall operation and development of industrial enterprises. The article takes an air-conditioning manufacturing enterprise as an example, constructs its energy consumption prediction model (ARIMA model), optimizes its parameter estimation method through Bayesian estimation and Markov Monte Carlo method, and finally constructs the ARIMAMCMC model for energy consumption prediction of industrial enterprises. The accuracy of its prediction effect is verified by comparing the load prediction results of this model with other prediction models. The effect of energy consumption optimization under fixed cooling demand and the effect of energy consumption optimization on continuous periods before and after model optimization are analyzed respectively. The model has the highest accuracy in predicting the cooling load of central air-conditioning, and the difference between its predicted and actual values is the smallest. The energy consumption is lower when there is high cooling demand above 70%. At lower levels of 60% and 50% of cooling demand, energy wastage is higher. About 6.09% of the optimized model’s energy consumption is reduced. During the duration period (9:00-20:00), the total energy consumption before and after optimization is 97.218kW and 90.706kW, respectively, and the system energy saving is 6.70%. The period with the worst energy-saving effect is 12:00-13:00 on the 21st, saving energy consumption by 2.27%. The best energy saving is 18:00-19:00 on the 21st, saving 21.05% of energy consumption. During the two days before optimization, the average water temperature was 26.31 and 23.66°C, and the average water temperature after optimization was 24.88 and 23.06°C, respectively.
Architecture Design of Virtual Power Plant Intelligent Scheduling System Based on Multi-Resource Fusion
Authors: Hu Pengtao , Fan Peilin , Xue Jianli , Gao Liangfang , Chen Xiaofang , Yang Zhiping 
Published: November 2024

Abstract
This paper proposes a distributed scheduling model for virtual power plants, and in order to realize the optimal scheduling of multiple virtual power plants, it proposes a reinforcement learning model and establishes a framework of reinforcement learning methods to subdivide the states, actions, and rewards that are generated by the grid scheduling center when it interacts with the power grid. An environment model including multiple virtual power plant operation models is constructed, and the objective function of the control center is set to allocate scheduling commands in the virtual power plants. The algorithmic analysis is based on the actual network framework of the regional power grid and divides the tariffs of power purchased/sold by the VPP to the power market. The centralized optimization method, distributed optimization method, and reinforcement learning optimization method are proposed respectively to solve the MVPP coordinated optimal dispatch model. The optimization objective, deviation control strategy, and carbon trading elements are exemplified, and different scenario scenarios are set to analyze the convergence of the reinforcement learning model (DDPG) and the scheduling results. Reinforcement learning real-time optimal scheduling focuses on the measured values of wind power and loads and avoids the increase of costs or decrease of revenues due to the fluctuation smoothing by the grid by coordinating the internal resources for smoothing or the complementary consumption through the inter-rate of VPP. It can take into account the impact of the current decision on future time, thus achieving scheduling optimization in multiple periods.
Optimization study of unmanned surface craft formation control by fusing BSM and ANN
Authors: Qi Xiaolong 
Published: November 2024

Abstract
With the continuous development of intelligent technology, the application of surface unmanned vessels (USVs) in the field of ocean engineering is becoming more and more widespread, and the cooperative cooperation of multiple USVs can improve the efficiency of task execution and expand the operation coverage, which has a broad application prospect and practical value. In this paper, the kinematic model of USVs formation under three degrees of freedom of longitudinal oscillation, transverse oscillation and bow rocking is established, and the dynamics model is built based on the internal structure of USVs, which lays the foundation for the study of the formation control and collision avoidance problems of USVs. Subsequently, the pilot-follower model is selected as the basis of the formation, the congestion control strategy based on the basic information model (BSM) is proposed to effectively alleviate the congestion of the fleet communication network, and the excellent approximation capability of the ANN-based RBF neural network is utilized to design the adaptive neural network controller. Finally, the event-triggered mechanism is combined with the adaptive dynamic planning algorithm to propose an adaptive optimal control strategy based on BSM event triggering, and at the same time, the neural network weight updating rules determined by the event triggering conditions are set to realize the optimization of unmanned surface boat formation control. Simulation experiments show that the unmanned boat can fulfill the formation task well, avoiding obstacles, maintaining formation, and maintaining target consistency. The proposed control method can significantly reduce the computational burden of the controller, ensuring the stability of the USV cluster system and alleviating the excessive utilization of system communication resources.
Requirements Analysis and Architecture Design of a Nursing Virtual Faculty Technology Support System
Authors: Zhang Meixia , Deng Junfen , Gao Lifang , Wang E , Yang Xuhong , Li Shuxiang , Cheng Beibei 
Published: November 2024

Abstract
Emerging technologies such as virtual reality can provide immersive and interactive experiences for relevant personnel by simulating real clinical environments so that they can obtain adequate professional training experiences. This paper proposes the functional requirements of the nursing virtual teaching and research center under the guidance of NLN Jeffries theory, then combines Marker-SLAM visual augmented reality technology and 3DMax software to construct the virtual interactive system of the nursing virtual teaching and research center, and uses cloud technology and other development platforms to design the nursing virtual teaching and research center technical support system. The results of the nursing practice experiment post-test showed that the nursing skills assessment scores of the students in the virtual teaching and research laboratory system group were higher than those of the students in the traditional model group, and the nursing skills assessment scores of the students in the virtual teaching and research laboratory system group were significantly different before and after the practice (P<0.05). The nursing students’ comprehensive score for the application effect of the system was 22.4, which indicates that the system received more satisfactory feedback from the entire group of nursing students. The nursing virtual faculty technology support system designed in this paper is innovative and practical, and can help nursing students get close to real clinical work, strengthen their comprehensive ability as well as provide more learning opportunities to enhance learning effects.
AI in teaching English writing: automatic scoring and feedback system
Authors: Sun Yiwei 
Published: November 2024

Abstract
Artificial intelligence technology, with its excellent data processing and analyzing ability, has become a new method for scientifically evaluating the quality of students’ writing. In this paper, we construct an automatic scoring model for English writing using a hybrid neural network, then use a random forest algorithm to categorize the assignment utterances and design an automatic writing feedback system. The results of the teaching case show that the mean posttest writing score of the experimental class is 16.832, and the mean value of the posttest writing error rate is 7.278, both of which perform significantly better than the control class. Moreover, the assignments from the experimental class demonstrated a higher level of word performance. As the experiment progressed, the students’ perceptions of this paper’s system grew more positive. This underscores the significance of the research conducted in this paper.
A Study on Synergistic Enhancement of Product Directive Optimization Strategies and Export Trade Performance in the Digital Era
Authors: Wang Guishuang , Dai Honglei 
Published: November 2024

Abstract
The topic of innovation appears again and again in the government work report. The state and enterprises also pay more and more attention to product optimization and the output of innovative products, and more innovative and technological content has emerged from the products. At the same time, China’s foreign trade is transitioning from the traditional big country of foreign trade to the modern foreign trade power. In this paper, we combine natural language understanding and interactive image command mapping networks to improve the command optimization of digital products. The improved Graph-LSTM model is utilized to process the natural language instruction vectors, and the ICF network is utilized to take the environment image and user instructions as inputs to construct a fusion network in which the environment image and the natural language instructions can complement each other to complete the instruction mapping and to generate the location heat map for the execution of the instructions. Using principal component analysis, panel regression modeling, and other methods, we extracted and screened indicators of the influencing factors of export trade performance evaluation and constructed the performance evaluation index system. The synergistic enhancement of product instruction optimization and export trade performance is analyzed through empirical analysis and enhancement effect evaluation. The regression results of the selection equation are analyzed, and in the second column of the model, the coefficients of marketing cost and financial cost are 35.254 and -2.568, respectively, and the P-value is greater than 0.1, which can be analyzed in the next stage. In terms of the level of economic development, the difference between the comprehensive performance of export trade between Guangdong and Heilongjiang is 55.11, which is a large gap, and the optimization of product instructions has a greater impact on the performance of export trade in the eastern region.
Quantitative analysis and model prediction of urban traffic flow optimization by geographic information technology
Authors: Wang Li , Pan Kunyou 
Published: November 2024

Abstract
With the development of intelligent transportation, urban traffic flow prediction faces more complex traffic situations and higher accuracy requirements. In this paper, we collected multi-day traffic data from local cities and used a k-means clustering algorithm to analyze the data by clustering. A BP neural network prediction algorithm combined with k-means clustering analysis is designed to construct an “hour-day-week” urban traffic flow prediction model. The model is trained and applied to real-time urban traffic flow prediction to further analyze the performance effect of this model. The training time of this model is the shortest, which is about 67 minutes, and the MAE value is 3.02. The training speed is ahead of the longest training time in model 2, which is about 163 minutes, and the error is reduced by 8.04 compared with that of the highest prediction error in model 1. With different levels of noise added, the R², ACC, and RMSE values of the model in this paper are maintained at about 0.45, 0.63, and 0.8, respectively, indicating that different levels of noise have a significant effect on the performance of this model. This indicates that different degrees of noise have little effect on the performance of the model in this paper. In addition, the prediction effect is better after clustering analysis. The MAPE value of this paper’s model is the lowest, only 2.64%, which is 3.65%-7.52% lower than the other three control models. The experimental results above fully demonstrate the superior performance of this paper’s model, which suggests that this paper’s model is more effective in predicting urban traffic flow.