bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Importance and Practice of College Students’ Ideological and Political Education in Higher Education-Based on the Perspective of Principal Components Analysis
Authors: Shao Yexin 
Published: June 2024

Abstract
With the rapid development of artificial intelligence technology, the traditional mode of civic education has not adapted to the development of the times and is in urgent need of innovation. This paper proposes an evaluation study of civic education in colleges and universities based on the hierarchical analysis method (AHP)-principal component analysis (PCA). Starting from the current situation of civic education, based on the principle of evaluation index system construction, the evaluation index system of civic education is initially formulated. Then, the expert argumentation is used to modify and improve the initially formulated evaluation index system and then to determine the final version of the evaluation index system of civic education. On the basis of the evaluation index system, a combination of AHP and PCA algorithms is used to construct the evaluation model of civic and political education, and the statistical analysis software SPSS20.0 is used to analyze the constructed model. The results show that the Sig value between the evaluation indicators of the first level is less than 0.05, which means that the data are judged to be suitable for principal component analysis. The percentage of variance of the first three components after the rotation is 28.69%, 28.10%, and 25.48%, which indicates that the three principal components explaining the evaluation indicators of the first level are relatively average. This study promotes the development of civic education, thereby improving the level of civic education in colleges and universities.
Research on Image Classification and Recognition Technology Based on Machine Learning
Authors: Wang Yan 
Published: June 2024

Abstract
In the burgeoning field of computer vision, image classification and recognition have emerged as pivotal areas of research within computer science. This paper introduces a novel approach by amalgamating the Gramian Angular Field (GAF) method, recursive map technique, and Short-Time Fourier Transform (STFT) to devise a joint coding method for null spectrum analysis aimed at extracting spatial features from images. Subsequently, a hybrid classifier combining Deep Convolutional Neural Network (DCNN) and Support Vector Machine (SVM) algorithms—termed 3DCNN-SVM—is constructed to classify and recognize these extracted features. Further, this study conducts a comparative analysis between the proposed feature extraction and classification method and traditional algorithms. The focus of this evaluation is on the domain of vehicle label image recognition, aiming to ascertain the efficacy of the newly developed algorithm. This comprehensive comparison underscores the enhancements in accuracy and efficiency provided by the integration of 3DCNN-SVM in recognizing complex image features, thereby contributing significantly to the advancements in computer vision technology. The results show that after 35 iterations of training, the accuracy of 3DCNN-SVM reaches 99.5% with a loss value of 0.0877. Compared with ShffleNetV2 0.5×, ShffleNetV2 0.5×+ECA, ShffleNetV2 0.5×+ECA +Mish, ShffleNetV2 0.5×+ECA + Mish+SEP are 9.46%, 8.83%, 6.60%, and 5.52% higher on the average accuracy rate of 18 kinds of vehicle label images recognition, respectively. The 3DCNN-SVM classifier model constructed based on machine learning-related theory in this paper realizes the optimization of the traditional classification model and makes the classification and recognition of images more accurate, which has important practical significance.
Analysis of Influencing Factors on Consumers’ Purchase Intention in Cross-border E-commerce Live Streaming
Authors: Wu Xiaoli 
Published: June 2024

Abstract
Under the environment of rapid development of e-commerce platforms and Internet technology, consumers are more and more inclined to online shopping. This paper takes consumers’ perceived value as the main research object and adds platform attributes, such as function, content, brand, etc. The main factors affecting perceived usefulness, perceived entertainment, and perceived risk construct a theoretical model of the factors affecting consumers’ willingness to buy in cross-border e-commerce live streaming. In order to further analyze the importance of the factors influencing consumer purchase intention in cross-border e-commerce live streaming, this paper constructs a prediction model based on random forest according to the evolution of consumer purchase intention and the prediction of consumer purchase intention. Consumers’ perceived value of cross-border e-commerce platforms is not affected by perceived risk, as shown by the regression and prediction analyses. Perceived usefulness and perceived entertainment are affected by platform attributes accordingly. Cross-border e-commerce live streaming has the most significant impact on consumers’ willingness to buy, with platform features accounting for 17% of all influencing factors. The role of platform function in consumers’ purchase intention in cross-border e-commerce live streaming is demonstrated in this study.
Research and Practice on Strategic Planning and Resource Allocation Optimisation Model of Off-site Research Institute
Authors: Zhao Yingtao , Hua Zhilei , Liu Yiying , Hao Liqun 
Published: June 2024

Abstract
Off-site research institutes serve as crucial platforms for universities to facilitate the translation and application of their scientific research outcomes. Additionally, these institutes act as vital intermediaries for local governments to harmonize scientific inputs with economic development and play a key role in the industrial transformation and elevation of regional science and innovation levels. Drawing on the triple helix theory, this paper outlines strategic planning for off-site research institutes and develops a multi-objective linear programming model aimed at optimizing resource allocation. This model focuses on enhancing both the efficiency of resource utilization and the efficiency of resource allocation at these institutes. To address the issue of local minima commonly encountered in optimization algorithms, this study employs a simulated annealing algorithm to refine the performance of the particle swarm optimization algorithm. The resulting hybrid algorithm termed the simulated annealing particle swarm algorithm, is applied to solve the proposed model and investigate the determinants of optimal resource allocation. The findings indicate a significant improvement in resource allocation efficiency, with the coefficient for heterogeneous research institutes decreasing from an average of 0.84 in 2020 to 0.68. This optimization has led to a more effective and rational distribution of resources, better meeting the needs of the institutes. Furthermore, the analysis reveals that financial support and talent introduction and development account for approximately 69.7% of the variance in the optimized development of resource allocation at these institutes. The study provides actionable insights that could guide the optimal development of off-site research institutes, offering valuable references for future applications.
Application of embedded systems in electrical engineering automation and energy-saving design
Authors: Liu Yuxuan 
Published: June 2024

Abstract
With the advancement of embedded systems, their application across diverse sectors has notably expanded. This paper delineates the fundamental architecture of embedded systems and separately engineers the hardware and software components for an energy-efficient embedded system tailored for electrical engineering automation. The focus is placed on the energy-saving control module of the embedded system, which incorporates fuzzy logic with a PID control algorithm to produce definitive signal outputs. Building upon this foundation, the performance of this enhanced algorithm was evaluated and subsequently implemented in the tunnel lighting electrical engineering project. The findings reveal that the embedded system-controlled lighting setup can adjust the tunnel entrance and mouth lighting based on external luminance levels, maintaining daily energy consumption at approximately 550 kWh with an energy savings rate reaching as high as 51% per day. This study affirms the substantial potential of embedded systems in advancing energy-efficient solutions within electrical engineering automation.
Equilibrium Modelling of Cost Control in Civil Engineering Management in the Context of Carbon Neutrality
Authors: Ni Bijun 
Published: June 2024

Abstract
In civil engineering management, under the background of carbon neutrality, there is a strong correlation and mutual constraints between cost, duration, and quality, and all civil engineering management must grasp cost control and multi-objective equilibrium. In this paper, the objective functions of duration, cost, and quality are constructed, the multi-objective equilibrium model of duration-cost-quality of civil engineering management is designed, and an improved non-dominant ranking genetic algorithm with elite strategy is constructed for solving the mathematical problem of multi-objective optimization. Finally, the model is used to analyze the case of a three-story mixed-structure office building, and it is found that the total cost of the model is 1089048 yuan, and the calculation is stopped when the programming cycle is 10096 times. Among them, scheme 1 has 146 working hours, a cost of 1.765 million yuan, and a quality of 0.95. Scheme 2 has 161 working hours, a cost of 1.693 million yuan, and a quality of 0.94. Scheme 3 has 176 working hours, a cost of 1.917 million yuan, and a quality of 0.99, and the specific plan can be selected according to the preference of the decision-maker. The model and algorithm’s effectiveness and matching are verified by analyzing actual data. This study enriches the theory of multi-objective optimization problems in engineering projects and provides a practical approach to managing project objectives.
Exploring the Direction of English Translation of Environmental Articles Based on Emotional Artificial Intelligence Learning Models
Authors: Xian Wensen 
Published: June 2024

Abstract
In this paper, we improve the performance of machine translation emotion recognition by fusing word features and linguistic features of environmentally friendly text with LSTM neural networks. Multi-channel LSTM’s unbalanced emotion classification method can address the problem of unbalanced numbers of samples in each emotion category in the emotion classification task. The bilingual semantic similarity degree feature is added to the translation process of English environmental protection data so as to make the translation of English environmental protection data more accurate. The proposed method is validated through experiments, and the results show that the translation quality of the LSTM model is generally higher than that of other models, and the mean value of the translation quality of the LSTM on the test17 dataset is 50.4. In terms of environmental protection terminology, mistranslations, common word mistranslations, etc. There is a significant reduction of 2–3 times in the absolute number of the LSTM model compared to the traditional method. A total of 94,788 corpora were obtained from 10 environmental translations, such as Silent Spring and Solace of the Heart, and 2,311 favorite emotion words were identified. Comparative analysis of the samples reveals that most of the translation results of some environmental terms are in the range of 0.7–0.9, and the range of semantic overlay between the machine translation results of the LSTM model and the standard translations is large. Most users believe the model improves the translator’s translation level and diction accuracy.
Innovative design of digital sculpture art under 3D modeling technology
Authors: Liu Shaowei 
Published: June 2024

Abstract
3D modeling technology is one of the most important research directions in computer vision, which is increasingly linked with digital sculpture art. Combined with the point cloud, a new type of image data, this paper innovatively proposes a multi-view 3D digital sculpture reconstruction model to realize the 3D digital sculpture reconstruction of point cloud data.3D digital sculpture reconstruction is mainly divided into two parts: point cloud data processing and 3D reconstruction. The former uses radius, statistical filtering, and bilateral filtering algorithms for point cloud data denoising and proposes an NDT alignment algorithm to integrate point cloud data. The latter triangulates the unreconstructed observation points introduced by the new image, uses beam method leveling to globally optimize the point cloud structure, and proposes incremental recovery and global recovery reconstruction methods, respectively. In the point cloud data alignment performance test, the reconstruction plotting time of the reconstruction model in this paper possesses a large improvement, and the proposed NDT algorithm outperforms the traditional algorithm in terms of accuracy and execution efficiency. In the reconstruction of architectural digital sculpture, the average error of the parts of the digital sculpture is 0.00775, and the maximum residual difference is at about 4.5 pixels. Hence, the sculpture’s accuracy and precision performance are better.
Research on Coupling Model of Foundation Treatment and Geological Hazard Risk Assessment
Authors: An Kangyue , Zhang Jing 
Published: June 2024

Abstract
High-risk areas of geologic disasters need to select appropriate foundation treatment techniques according to different risk assessment levels to ensure the overall quality and safety of housing construction projects. In this paper, for the three foundation treatment technologies for different geologies, the geologic condition sensing measurement algorithm is designed according to the principle of InSAR technology, and the informativeness-logistic regression algorithm is proposed for vulnerability risk evaluation. The TRIGRS algorithm that has been improved is used to construct the geologic hazard risk assessment model. Then, the data preparation work is carried out in the practical application, and input data are connected through a series of preprocessing operations. Taking the landslide geohazards occurring in a certain place as an example, the geologic deformation curve generated by the system was used to analyze them, and the risk assessment of geohazard susceptibility was also carried out in a certain area. It was found that the cumulative deformation curve from the 20th week to the 30th week of 2020 decreased significantly by more than 100mm. The actual major landslide disaster occurred at the beginning of June 2020, and the results of the system operation are in line with the time of the actual disaster. For all 11 factors, logistic regression analysis yielded significant results. The proportion of 5-level susceptibility areas of geologic hazards is 31.5%, 24.6%, 20.2%, 14.7%, and 9%, respectively, and the distribution of assessment results is reasonable. This study provides new ideas and effective methods for defining the level of geohazard risk assessment and selecting foundation treatment technology.
A Study on the Internal Logic and Governance Effectiveness of Platform-Based Organizations from the Perspective of Digital Governance
Authors: Chen Rui , Zhang Rongjie 
Published: June 2024

Abstract
This paper first constructs a basic framework for digital governance of platform-type organizations and explores the application of digital governance of platform-type organizations and the social governance process from the three dimensions of the logic of efficiency, the logic of legitimacy, and the logic of social relations. Then, the driving elements of digital governance of platform-based organizations are studied using the fsQCA method and QCA analysis, and the influence of social synergy and people’s participation on the effectiveness of digital governance of platform-based organizations is analyzed. The results show that attention allocation, social governance resources, digital governance capacity, social digital environment, cultural openness, peer competition pressure, and innovation environment need to reach at least 94.5%, 79.53%, 75.4%, 81.5%, 77.5%, 78.35%, and 78.33% of the social governance quality bottleneck level, respectively. This research paper explores the drivers and influencers of platform governance performance, which helps to identify the patterns of platform operations.
Breakthroughs and Applications of Augmented Reality (AR) Technology in the Digital Media Field
Authors: Lin Muye , Yang Kangyu 
Published: June 2024

Abstract
This study pioneers the application of augmented reality (AR) in the realm of digital media, integrating AR technology with cultural education and artistic media concepts to forge a novel trajectory for digital media art creation. It delves into AR 3D scene construction technologies, employing calibration through the reconstruction of 2D image sequences, feature point extraction for spatial coordinate transformation, and 3D model development. Furthermore, it utilizes Level of Detail (LOD) models and texture mapping technologies to enhance visualization and interaction. The research culminates in a comparative analysis of traditional and AR-based media, evaluating differences in appeal and assessing functional capabilities. The findings indicate that AR-based media outperforms traditional media across several functions: media search (0.385), virtual design (0.424), immersive experience (0.466), real-time interaction (0.525), and user communication (0.396), with users expressing high satisfaction with these enhanced capabilities. This study not only diminishes the complexities associated with integrating digital media and AR technologies but also optimizes and innovates traditional methodologies.
Revealing or concealing: The impact of environmental penalty spillover effects on corporate environmental information disclosure
Authors: Dong Baoxin 
Published: June 2024

Abstract
As a way of environmental protection, environmental administrative punishment affects the environmental decision-making of enterprises to a certain extent. This paper proposes the industry spillover effect of environmental administrative punishment, utilizes the variable factors affecting the quality of corporate environmental information disclosure, and puts forward the hypothesis of the relationship between the spillover effect of environmental punishment and the level and quality of environmental information disclosure, whether it is “displaying” or “disguising”. Companies listed in the heavy pollution industry are selected as research objects to test the relevance of each influencing factor on environmental information disclosure. The double-difference method is used to construct the regression model to verify the influence of government regulation on the level of environmental information disclosure, analyze the impact of environmental information disclosure level on the market value of enterprises, and the relationship between environmental administrative penalties and environmental information disclosure. The regression equation passed the F-test with an adjusted R
2 of 0.425 and the coefficient of EDI of 0.362. It passed the 1% significance level test, indicating that the level of environmental information disclosure is positively related to enterprise value. The higher the level of disclosure of the environment, the higher the market value. Whether the listed company is engaged in legitimacy management or impression management, manipulating environmental disclosure during this period has an impact on the level and quality of environmental disclosure.
Design of a new fast heating device in wireless portable water dispenser in the age of intelligence
Authors: Qian Tao , Liu Huayu , Chen Chunquan 
Published: June 2024

Abstract
In our daily life, the quickness of water dispensers provides great convenience for our life. By examining the PID control system and fuzzy control system, a fuzzy PID controller has been designed. This system can be used for both automatic and manual parameterization. The problem that the stable operation of the temperature controller cannot be guaranteed in the PID controller has been solved. In this paper, the designed extreme heating and temperature regulation system is installed in the controller of the wireless portable water dispenser to analyze the effects of using this new product. Comparative experiments indicate that the wireless portable water dispenser with a heating and regulating system has an average hot water production capacity of 3.68L/H, which is higher than the standard value of 3.0L/H. The high-speed heating and regulating system in the wireless portable water dispenser shows that it has a better heating capacity than the ordinary wireless portable water dispenser. Based on the regression analysis of this new product on the basis of user satisfaction, the overall innovativeness of the product = 0.62*usability + 0.032*emotion + 0.056*functionality. The “Usability” construct has the best predictive power with 62% explanation. It indicates that the demand for the product is high among users.
Research on safety accident risk of construction engineering based on natural language processing technology and neural network technology
Authors: Wang Shuo , Meng Haohan 
Published: June 2024

Abstract
In recent years, China’s construction industry has achieved rapid development, while the problem of safety accident risks brought by construction projects has also arisen. In this paper, based on natural language processing technology, using a graph neural network algorithm, we constructed a graph data model of safety accident reports in construction engineering and generated a database of association rules of safety risk factors in construction engineering to identify and classify the risk of safety accidents in actual construction engineering. The pictures of safety risk behaviors at the construction site are processed with feature mapping and inputted into a convolutional neural network to build a construction engineering safety warning model. Through simulation experiments, the identification results of risk factors for the model constructed in this paper and the effective degree of safety warning are tested. The top five factors in the centrality ranking of risk factors of construction engineering safety accidents are S17, S6, S1, S9, and S8, with centrality levels of 1.9779, 1.8626, 1.8204, 1.6332 and 1.4534, and the factor attribute of S6 is the outcome factor. The warning model constructed in this paper is measured against the level of safety risk of the construction project under the non-adoption of the warning model. The safety risk is reduced by 35.8% after the adoption of the safety accident warning model, which shows that the model in this paper has a certain inhibition effect on the risk of safety accidents.