bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Strategic Study of Digital Marketing Techniques to Enhance the Attractiveness of International Tourism Destinations
Authors: Meng Zhihong 
Published: November 2024

Abstract
This paper studies the current level of attractiveness of international tourist destinations by constructing an evaluation system and applying a fuzzy comprehensive evaluation method. Based on the evaluation results, digital marketing strategies are proposed in terms of customer management management systems and social media marketing. Using the entropy value method and other comprehensive evaluation models to quantify the level of digital marketing and international tourism destination attractiveness, and with the help of the coupling degree coordination model to calculate the coupling degree of coordination between digital marketing and international tourism destination attractiveness. Combined with the fuzzy comprehensive evaluation results before and after the implementation of the digital marketing strategy and the change in international tourist destination traffic, the effect of digital marketing strategy on the attractiveness of international tourist destinations is analyzed in depth. Taking the Yangtze River Delta region as an example, this paper calculates the coupling degree between digital marketing and the attractiveness of international tourism destinations in 41 cities in the Yangtze River Delta region and concludes that the coupling degree between the attractiveness level of international tourism destinations and the level of digital marketing is high. Meanwhile, the fuzzy comprehensive evaluation score of City A in the Yangtze River Delta region and the passenger flow of 10 international tourist destinations between 2012 and 2022 increased significantly after adopting the digital marketing strategy, which fully indicates that the digital marketing strategy has a significant driving effect on the attractiveness of international tourist destinations.
Research on Human Resource Management Optimization and Decision Support System Construction Based on Electric Power Enterprise Strategy
Authors: Shen Haihua 
Published: November 2024

Abstract
At the present stage, the development of electric power enterprises needs to update the concept of human resource management by fully combining it with their actual situation and constructing a systematic human resource management model so as to occupy a favorable position in the market competition. This paper brings the classification rule mining algorithm based on the ant colony algorithm into human resource planning decision-making, adds an adaptive mechanism to the ant colony algorithm, updates the algorithm strategy, and improves the quality of algorithmic decision rules. Establish a fuzzy comprehensive judgment model for performance appraisal and salary decision-making in electric power enterprises and design a comprehensive system of dynamic decision support for human resource management in electric power enterprises. Analyze the effect of α, β and ρ values on the performance of the adaptive ant colony algorithm and determine the utility of the classification rule mining algorithm based on the adaptive ant colony algorithm for human resources planning and decision-making. Combine the evaluation level and scoring factors of employees in electric power enterprises to obtain the employee performance appraisal rankings and determine their remuneration packages. The practical benefits of the HRM dynamic decision support system designed in this paper are examined by a system reliability test. The reliability of the human resource management decision-making system designed in this paper is maintained at 0.93~0.97, and the decision-making reliability of traditional system 1 and traditional system 2 is maintained at 0.90~0.92 and 0.90~0.91, respectively. The decision-making system for human resource management in electric power enterprises constructed in this paper is more advantageous for practical operation.
Optimization of Civic Education Paths in Colleges and Universities with the Application of Intelligent Computing Models and Assessment of Their Effectiveness
Authors: Wang Jing 
Published: November 2024

Abstract
Teachers and students find it difficult to find subject-related curriculum Civics resources from massive learning resources. Combining recommendation algorithms with the curriculum for civics learning can solve the problem and optimize the path of civics education. The collaborative filtering algorithm is the preferred method to normalize the sample data in the first step of this paper, and then the scoring matrix is constructed to calculate student similarity to determine user preference. In order to solve the problem of user cold start, based on the K-Means clustering algorithm given the objective function, iteratively update until the center no longer changes or reaches the threshold value, and finally output the clustering results, you can realize the personalized recommendation of the Civic and political education resources, completed the design of the model. Koch’s four-level assessment model is used to assess the teaching effect of students under the recommendation model at four levels. The mean square error and average error of the proposed method in this paper are the lowest and the best performance in the comparison experiments, which illustrates the excellent performance of the recommendation model in this paper. Students are highly satisfied with the smart classroom based on the recommendation model of Civic Education Resources, and 84% prefer the teaching design based on the recommendation model of Civic Education Resources. The recommendation model also promotes the improvement of students’ grades, learning interests, and learning motivation. It shows that the proposed optimization path for Civics education is feasible.
Carbon Emission Reduction Target Setting and Implementation for Power Grid Enterprises Based on Intelligent Decision Support System
Authors: Wang Yan , Zhang Ruizhi , Qiao Yu , Wang Yanli , Wang Yunfeng 
Published: November 2024

Abstract
The electric power industry is the most important basic energy industry in the development of the national economy, which is now listed as a key energy-saving and emission-reduction field and is the main force in realizing low-carbon development. The study is based on the carbon emission accounting method, constructs a multi-objective decision-making optimization model with carbon emissions and economic benefits as the objective function, and decouples the model using the improved particle swarm algorithm. Then the TOPSIS algorithm is used to rank the optimal solution set of decision-making Pareto and select the optimal carbon emission reduction target for grid enterprises. Finally, the distributed integrated energy system of power grid enterprises under an intelligent decision support system is taken as an example for arithmetic simulation. The particle swarm algorithm and TOPSIS algorithm have the ability to determine the target for reducing carbon emissions in power grid enterprises, with the optimal economic cost and carbon emission ranging from 160.465 billion yuan and 467.865 million tons, respectively. It can be seen that at this time, the carbon emissions are relatively low and the cost is relatively low, which can meet the overall implementation of the carbon emissions reduction target of the grid enterprise. The study provides reference significance for the formulation and implementation of low-carbon emission targets for power grid enterprises, and promotes the sustainable development of power grid enterprises.
Data Mining Methods for Educational Effectiveness in Higher Education Aesthetic Education Programs
Authors: Wei Qidan 
Published: November 2024

Abstract
This paper describes in detail the commonly used educational data mining algorithms and proposes a new data mining-based teaching model for aesthetic education in modern universities. Data mining technology is used to collect students’ online learning behavior and learning performance data, and collate them to lay the foundation for improving personalized teaching work and teaching resource management in aesthetic education. To evaluate the effectiveness of the application of the modern education model implemented in colleges and universities, the rough set evaluation method was also introduced. The evaluation scores for the modern education model of the aesthetic education program in higher education are all above 0.8, and the rating scale is exceptional, as shown in the results. Compared with the previous stage, the students’ learning behavior input and knowledge mastery in the latter stage have been significantly improved, and the average score of declarative knowledge has increased by 27.42. The curriculum resources for calligraphy, drama, film, and television should be improved.
Analysis of Higher Vocational English Learners’ Behavioral Characteristics and Teaching Content Optimization Strategies Based on Big Data Mining
Authors: Wen Lulu 
Published: November 2024

Abstract
In the context of the big data era, the study of the massive data accumulated in the information construction of higher vocational colleges and universities can provide convenience for the teaching and management of colleges and universities. By improving the K-means clustering algorithm and Apriori algorithm in big data mining technology, the article mines the English learning behaviors and laws of higher vocational English learners and explores the correlation between learners’ behavioral characteristics and teaching performance. Finally, through empirical testing, this paper proposes an optimization strategy for teaching content in higher vocational English education. In the comparative analysis of reading and writing pre-test and post-test scores between the experimental class and the control class, the pre-test score of reading comprehension of the students in the experimental class is 31.25, and the post-test score is 32.84, and the average score of reading comprehension has increased by 1.59, which can be obtained that the English reading comprehension of the students has been improved after teaching with the teaching content optimization strategy proposed in this paper.
Personalized Learning Path Design for Civic Education Content in Colleges and Universities Based on Cognitive Computing
Authors: Zhou Yulin 
Published: November 2024

Abstract
This paper introduces the cognitive computing model into Civic Education in colleges and universities and collects the multi-dimensional cognitive data of the learners through methods such as eye movement data measurement sequence data mining, which accurately reflects the individual differences in the interaction in the educational environment. Using algorithms such as personalized learning to achieve personalized learning path design based on learners. Using 42 students from a university as the research subjects, it was found that students prefer video-type learning resources with approximately 62% of them choosing them. Cognitive characteristics are portrayed through students’ behavioral data: some students are very active in exchanging learning activities, while others are relatively independent. Students’ behavior showed a multi-linear pattern. There was a significant difference between the high and low wind groups in terms of assigning learning activities with a p-value of less than 0.05. There were outliers and inattentiveness in the eye movement trajectories of students 3, 12 and 5. Personalized teaching based on the cognitive computing model is significantly better than the traditional teaching model’s teaching effect.
Research on Sales Dynamic Forecasting Method Based on Time Series Analysis in Global Supply Chain Environment
Authors: Zhu Huiru 
Published: November 2024

Abstract
This paper uses time series analysis to forecast sales dynamics in a global supply chain environment. The study selects supermarket chain data with typical time series characteristics and extracts features from it. We combine the delay operator’s difference operation with the ARMA model to construct the ARIMA model, which predicts and analyzes the sales volume of the supermarket chain dataset from 2016 to 2018. To address the issue of time series models being susceptible to nonlinear characteristics and random variables, this paper integrates the ARIMA model and the random forest RF model into a simple weighted average, forming the ARIMA-RF model. The accuracy of the model for sales volume prediction has greatly improved compared to the ARIMA model and RF model, with an accuracy rate as high as 99%. Meanwhile, the ARIMA-RF model has the smallest RMSE value among all the compared models and has the highest prediction accuracy among all the models for the sales volume in the 3rd quarter of 2018.
Research on Dynamic Monitoring and Evaluation Model of Cadre Building in Colleges and Universities
Authors: Yao Jiaqin 
Published: November 2024

Abstract
In today’s context of rapid development of higher education, college cadres as the core force to promote the development of the school, its construction and management is particularly important. This paper adopts B/S architecture and MVC design, introduces the dynamic monitoring mechanism of the construction of college cadres, and builds a dynamic monitoring system. The assessment model adopting the multiple regression method is used to evaluate the cadre force’s response to changes in work efficiency through real-time and comprehensive data collection and analysis. The dynamic monitoring system’s online situation on Saturday and Sunday is not as good as it is on Monday to Friday, as found in this paper’s research. And dynamic monitoring can increase the willingness of cadres to learn different subjects and improve their knowledge. Dynamic monitoring can also enhance the average grade of the cadres, and the average grade after the implementation of dynamic monitoring is approximately 9.88 points higher than before monitoring. Overall, the cadre’s satisfaction with dynamic monitoring is high, with an evaluated satisfaction score of 4.92 on the nine indicators of the survey. A one-unit increase in “Monitoring management” is associated with a 0.694 increase in the work efficiency of the cadres, which is the most influential factor in dynamic monitoring. In conclusion, dynamic monitoring helps to provide strong data support for the construction of college cadres and to build high-quality, professional college cadres.
A study on the relationship between career planning education strategies and student development based on big data analysis
Authors: Li Bing , Yang Ting 
Published: November 2024

Abstract
In an environment where the number of college graduates is increasing year by year, the education of college students in career planning is particularly important. However, the practical aspects of teaching pose additional challenges. Based on big data analysis technology, this paper designs an optimization strategy for career planning education. We have established a joint school-enterprise model of career management for college graduates to streamline career matching in career planning education. We then use the improved SARIMA-BP neural network model to predict the trend of career demand, providing students with personalized career planning advice and employment guidance. Clear career planning, setting clear development goals, studying diligently, understanding one’s strengths and weaknesses, and understanding the demand for professional competence in career positions all demonstrate significant positive correlations, with correlation coefficients greater than 0.5. This indicates the necessity of a career planning education strategy based on big data analysis and its positive promotion effect on students’ development.
Research on the process of pickling cucumber with compound pickling agent and its optimization
Authors: Shi Shouji 
Published: November 2024

Abstract
Pickled cucumber is a favorite pickle product of the general public, but the long-term or excessive consumption of traditional high-salt pickles seriously threatens people’s health. In this paper, after the subject samples were pickled according to the process flow, one-way and orthogonal tests were designed to experiment with the optimum additive amount of composite pickling agent for cucumber, and meanwhile, the amino acid content of cucumber in different pickling methods was calculated. The Gaussian process regression model’s learning process was optimized by choosing the squared exponential covariance function. The cuckoo search algorithm is proposed, and the improvement of the cuckoo search algorithm is achieved by combining step length adaptive change calculation and chaotic sequences. Jointly improve the cuckoo search algorithm and Gaussian process algorithm, construct the agent model of the pickled cucumber recipe, get the final pickled cucumber recipe parameter design results, and verify the optimization effect of the process through the control experiment. The experimental results show that the best pickling combination is the composite pickling agent with 0.1% addition of acetic acid, 1.5% addition of mannitol and 0.06% addition of calcium lactate, and the sensory score of the cucumber pickled from that place is 92.8564. After optimization, the amino acid content of the cucumber pickled using the composite pickling agent is only 0.03889g/100g, and the quality of the cucumber has been improved.
Visual Perception Modeling and Aesthetic Effect Evaluation of Interior Design Integrating Natural Elements
Authors: Liu Hui 
Published: November 2024

Abstract
The reasonable integration of natural elements in interior design needs to follow the principles of aesthetic color matching and visual perception theory. In this paper, we constructed an aesthetic assessment model for natural elements in interior design based on visual perception, using aesthetic color features, texture features, and higher-order semantic features. Firstly, we calculate the color histogram of the HSV distortion map to obtain the distortion’s description. The texture features are extracted using GLCM combined with LBP to capture the spatial relationships of different gray levels in the image within the global range. Smoothing the LBP features reduces the impact of noise on feature extraction, and the attention mechanism is employed to exclude redundant information in higher-order semantic features. The model is constructed and then tested on four datasets, including LIVE, to assess the aesthetic effect of visually perceived images, and it is found that the model in this paper performs at a better level in the four image libraries. Three practitioners applied the model to carry out interior design for a project, and the final work was rated by experts at 86 or above, with a mean score range of 82 to 92 for each dimension. In the process of using the model to evaluate the design draft for improvement and optimization, the model provides the designers with the functions of checking and filling the gaps in composition, light, and other aspects and optimizing the aesthetic color assessment so that they can complete a balanced and high-quality interior design work with natural elements, and all the dimensions of the aesthetic assessment have been paid attention to and optimized, and the overall interior design effect is more balanced, and this paper’s model meets the design expectations.
Legal and Ethical Considerations of Computer-Assisted Data-Legal Decision Support Systems in Digital Governance
Authors: Chen Long 
Published: November 2024

Abstract
Computer technology provides technical guarantee for legal decision-making and digital governance, and also brings related legal and ethical topics. The functional requirements of the data legal decision support system are examined in this paper, and the architecture design is completed by adopting the MVC pattern and three-layer B/S architecture. Based on this basis, the network topology and database design of the system have been completed. The analysis shows that there is a significant positive correlation (β=0.173, 0.127, 0.378) between the frequency of use, the length of use, the position of the user and the quality of decision-making of the legal decision-making support system and that the speed of approving a certain type of contract of Company M using this system has been increased by as much as 80.67%, and that litigation costs of 10.8 million yuan are saved after the system partially replaces the external lawyers. In addition, users have a positive evaluation of the system’s professionalism. The legal and ethical considerations related to this decision support system in digital governance are examined in this paper.
Exploring the Reform and Innovation Path of Physical Education in Colleges and Universities under the Background of Internet
Authors: Lv Pin 
Published: November 2024

Abstract
The Internet background and the rapid change in science and technology promote the innovation of traditional college physical education, and the study uses the OpenPose method to detect the key points of students’ bodies in physical education and evaluate their postures. Meanwhile, based on the extraction of feature information of students’ movement postures, the DTW algorithm solves the problem of the inconsistent length of the video sequence of movement postures, matches students’ movement postures, and determines whether the activities of various joints are standardized during students’ movement. The evaluation index system of college physical education is constructed to evaluate the college physical education teaching method proposed in this paper. Apply the method of this paper in practice, using the method of comparing and analyzing the students’ academic interest, physical fitness, motor skills and motor posture before and after the experiment to explore the effectiveness of this paper’s method. The evaluation of the physical education method scored the highest on the secondary index of technology application, the lowest on learning resources, and the highest on the tertiary index of “the ability to use modern educational technology” and “timely and accurate student feedback”. The lowest scores were for “reasonable layout of learning space” and “completeness of facilities”. Before the experiment, the P value of the tennis program of the two groups of male and female students in the 10 dimensions of academic interest, physical fitness and motor skills and the mobility specification of 4 types of joints was greater than 0.05. After the experiment, the two groups of male and female students performed better than the control group in academic interest, physical fitness, motor skills and the mobility specification of joints, and the P value of the two groups of male and female students was less than 0.05. This paper shows that the OpenPose-based approach to physical education in higher education has yielded positive outcomes.