Pub Date : 2024-02-19DOI: 10.56028/aetr.9.1.837.2024
Wenbo Zhao
Accompanied by the continuous development of big data technology, various industries are well aware of the advantages of big data, which are widely used in customer service work, especially in the support of customer segmentation work, and have achieved good results. In this paper, for the problems of large fluctuation of clustering results and low clustering purity in the traditional data mining process, the big data precision mining technology with improved clustering algorithm is proposed. And it is applied in the field of customer segmentation, and the experimental results show that the improved clustering algorithm is applied in customer segmentation, the result curve fluctuation amplitude is small, and the clustering purity is significantly higher than the traditional algorithm.
{"title":"An Exploration of Customer Segmentation Methods Based on Clustering Algorithm in the Context of Big Data","authors":"Wenbo Zhao","doi":"10.56028/aetr.9.1.837.2024","DOIUrl":"https://doi.org/10.56028/aetr.9.1.837.2024","url":null,"abstract":"Accompanied by the continuous development of big data technology, various industries are well aware of the advantages of big data, which are widely used in customer service work, especially in the support of customer segmentation work, and have achieved good results. In this paper, for the problems of large fluctuation of clustering results and low clustering purity in the traditional data mining process, the big data precision mining technology with improved clustering algorithm is proposed. And it is applied in the field of customer segmentation, and the experimental results show that the improved clustering algorithm is applied in customer segmentation, the result curve fluctuation amplitude is small, and the clustering purity is significantly higher than the traditional algorithm.","PeriodicalId":355471,"journal":{"name":"Advances in Engineering Technology Research","volume":"292 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140451133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study aims to improve the efficiency of public vehicle evacuation during large-scale disasters by minimizing travel and waiting times for individuals and vehicles. To accomplish this, an S-curve behavior model was used to estimate evacuation demand, and a network model was developed to consider temporal and spatial factors of gathering points. A hybrid genetic algorithm and simulated annealing approach were utilized with an "enumerate then optimize" strategy and a step to temporarily retain optimal solutions for refinement. The effectiveness of the proposed model and algorithms was demonstrated in a case study of a typhoon evacuation in Chikan District, providing valuable insights for urban evacuation planning.
本研究旨在通过最大限度地减少个人和车辆的旅行和等待时间，提高大规模灾难期间公共车辆疏散的效率。为实现这一目标，使用了 S 曲线行为模型来估计疏散需求，并开发了一个网络模型来考虑聚集点的时间和空间因素。利用混合遗传算法和模拟退火方法，采用 "先枚举后优化 "的策略，并暂时保留最优解以进行改进。通过对赤坎区台风疏散的案例研究，证明了所提模型和算法的有效性，为城市疏散规划提供了有价值的见解。
{"title":"Research on Public Vehicle Evacuation Path Planning Model Based on Spatiotemporal Network","authors":"Wenxuan Zhang, Zhengwei Lin, Ziyang Wang, Yipu Huang, Haoyuan Shi, Ying Li","doi":"10.56028/aetr.9.1.826.2024","DOIUrl":"https://doi.org/10.56028/aetr.9.1.826.2024","url":null,"abstract":"This study aims to improve the efficiency of public vehicle evacuation during large-scale disasters by minimizing travel and waiting times for individuals and vehicles. To accomplish this, an S-curve behavior model was used to estimate evacuation demand, and a network model was developed to consider temporal and spatial factors of gathering points. A hybrid genetic algorithm and simulated annealing approach were utilized with an \"enumerate then optimize\" strategy and a step to temporarily retain optimal solutions for refinement. The effectiveness of the proposed model and algorithms was demonstrated in a case study of a typhoon evacuation in Chikan District, providing valuable insights for urban evacuation planning.","PeriodicalId":355471,"journal":{"name":"Advances in Engineering Technology Research","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140450758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-25DOI: 10.56028/aetr.9.1.534.2024
Haihui Huang, Shiyang Hu
In response to the low accuracy of Chinese short text classification in the current data mining field and the defects of existing deep learning models with more model parameters and higher time complexity, this paper proposes a new text classification model - short text classification model (ACBSM) based on pre-trained language model with feature expansion. In ACBSM, to address the problem of high dimensionality of text data without accurate text representation, the Bert model is used to train word vector representation to solve the problem of multiple meanings of a word. From the parallelization acceleration level, a parallel acceleration strategy of two-channel neural network is designed to improve the efficiency of the algorithm in processing massive data. To address the sparsity of text data and the more complex semantics, an attention mechanism is introduced and a CNN model is used to enhance the extraction of keyword information; secondly, BiSRU is used to capture the contextual features of the text, and finally, experimental validation is conducted on a news dataset. The experimental results show that ACBSM improves the accuracy of text classification to 95.83% under the same environment and dataset, and its classification performance is better than other text classification methods.
{"title":"Short Text Classification Model based on Pre-trained Language Model with Feature Fusion","authors":"Haihui Huang, Shiyang Hu","doi":"10.56028/aetr.9.1.534.2024","DOIUrl":"https://doi.org/10.56028/aetr.9.1.534.2024","url":null,"abstract":" In response to the low accuracy of Chinese short text classification in the current data mining field and the defects of existing deep learning models with more model parameters and higher time complexity, this paper proposes a new text classification model - short text classification model (ACBSM) based on pre-trained language model with feature expansion. In ACBSM, to address the problem of high dimensionality of text data without accurate text representation, the Bert model is used to train word vector representation to solve the problem of multiple meanings of a word. From the parallelization acceleration level, a parallel acceleration strategy of two-channel neural network is designed to improve the efficiency of the algorithm in processing massive data. To address the sparsity of text data and the more complex semantics, an attention mechanism is introduced and a CNN model is used to enhance the extraction of keyword information; secondly, BiSRU is used to capture the contextual features of the text, and finally, experimental validation is conducted on a news dataset. The experimental results show that ACBSM improves the accuracy of text classification to 95.83% under the same environment and dataset, and its classification performance is better than other text classification methods.","PeriodicalId":355471,"journal":{"name":"Advances in Engineering Technology Research","volume":"39 9-10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140496288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-25DOI: 10.56028/aetr.9.1.550.2024
Zheyu Xu
Market volatility has always been a focus of attention for investors and practitioners, as it is crucial for investment decision-making and risk management. Microeconomic factors, such as company financial conditions, macroeconomic factors, industry characteristics, and government policies, are considered to play a crucial role in the formation of stock market volatility. The contribution of this article lies in the in-depth exploration of the impact mechanism of microeconomic factors on stock market volatility, providing investors with more information on how to evaluate risks and formulate investment strategies. In addition, the research findings of this article can also guide financial practitioners to improve risk management tools and strategies to better adapt to market volatility. Governments and regulatory agencies can also develop more precise financial market policies based on research results to maintain market stability and fairness. In summary, this article emphasizes the importance of microeconomic factors in stock market volatility and provides in-depth insights on this key issue. This has important guiding significance for investment decision-making, risk management, and policy formulation in the financial field.
{"title":"Research on the Influence of Microeconomic Factors on Stock Market Fluctuation","authors":"Zheyu Xu","doi":"10.56028/aetr.9.1.550.2024","DOIUrl":"https://doi.org/10.56028/aetr.9.1.550.2024","url":null,"abstract":"Market volatility has always been a focus of attention for investors and practitioners, as it is crucial for investment decision-making and risk management. Microeconomic factors, such as company financial conditions, macroeconomic factors, industry characteristics, and government policies, are considered to play a crucial role in the formation of stock market volatility. The contribution of this article lies in the in-depth exploration of the impact mechanism of microeconomic factors on stock market volatility, providing investors with more information on how to evaluate risks and formulate investment strategies. In addition, the research findings of this article can also guide financial practitioners to improve risk management tools and strategies to better adapt to market volatility. Governments and regulatory agencies can also develop more precise financial market policies based on research results to maintain market stability and fairness. In summary, this article emphasizes the importance of microeconomic factors in stock market volatility and provides in-depth insights on this key issue. This has important guiding significance for investment decision-making, risk management, and policy formulation in the financial field.","PeriodicalId":355471,"journal":{"name":"Advances in Engineering Technology Research","volume":"113 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140495625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-25DOI: 10.56028/aetr.9.1.491.2024
Zhenkun Jin, Yiting Cai, Xiaomin Yu
The traditional training platform carries out experimental teaching for a specific course. The main content of experimental teaching is for each single knowledge point, not all experiments can be covered. Students can not rise from individual knowledge points to a systematic understanding of the course.This thesis addresses the problems of the traditional training platform and proposes that the construction of an innovative training platform based on Internet of Things intelligent hardware ,which covering multi-specialty courses is proposed.
{"title":"Construction of Innovative Comprehensive Training Platform Based on Internet of Things Intelligent Hardware","authors":"Zhenkun Jin, Yiting Cai, Xiaomin Yu","doi":"10.56028/aetr.9.1.491.2024","DOIUrl":"https://doi.org/10.56028/aetr.9.1.491.2024","url":null,"abstract":"The traditional training platform carries out experimental teaching for a specific course. The main content of experimental teaching is for each single knowledge point, not all experiments can be covered. Students can not rise from individual knowledge points to a systematic understanding of the course.This thesis addresses the problems of the traditional training platform and proposes that the construction of an innovative training platform based on Internet of Things intelligent hardware ,which covering multi-specialty courses is proposed.","PeriodicalId":355471,"journal":{"name":"Advances in Engineering Technology Research","volume":"38 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140495976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Flange bolt connection is one of the most important connection modes in bolt connection at present. In this paper, the finite element model of the bolt between the shell and the head flange is established to analyze the influence of different initial preload on the bolt stress state under external load. The results show that according to the bolt stress state, the range of initial preload is divided into the influence area of the external load, the influence area of the resultant force and the influence area of the initial preload. In the influence area of external load, the bending moment exerted on the bolt dominates the stress state of the bolt, and the stress state of the bolt is poor, which is not the safe value area of the initial preload；in the influence area of the initial preload, initial preloads dominates the stress state of the bolt, the average stress of the bolt section is larger, and the bolt safety is smaller, which is not the safe value area of initial preloads; in the influence area of the resultant force, the bolt has better stress state and higher safety coefficient, which is the safety value area of initial preload. The initial preload corresponding to the boundary point between the influence area of the resultant force and the influence area of the external load and the influence area of the external load is the limit of the safe value range of the bolt preload.
{"title":"Calculation Method of Flange Bolt Preload Based on Finite Element","authors":"Ziqiang Wang, Zhonggui Yang, Zhilei Zhao, Zhenqiang Liu, Jiahao Wen, Menghan Li","doi":"10.56028/aetr.9.1.515.2024","DOIUrl":"https://doi.org/10.56028/aetr.9.1.515.2024","url":null,"abstract":"Flange bolt connection is one of the most important connection modes in bolt connection at present. In this paper, the finite element model of the bolt between the shell and the head flange is established to analyze the influence of different initial preload on the bolt stress state under external load. The results show that according to the bolt stress state, the range of initial preload is divided into the influence area of the external load, the influence area of the resultant force and the influence area of the initial preload. In the influence area of external load, the bending moment exerted on the bolt dominates the stress state of the bolt, and the stress state of the bolt is poor, which is not the safe value area of the initial preload；in the influence area of the initial preload, initial preloads dominates the stress state of the bolt, the average stress of the bolt section is larger, and the bolt safety is smaller, which is not the safe value area of initial preloads; in the influence area of the resultant force, the bolt has better stress state and higher safety coefficient, which is the safety value area of initial preload. The initial preload corresponding to the boundary point between the influence area of the resultant force and the influence area of the external load and the influence area of the external load is the limit of the safe value range of the bolt preload.","PeriodicalId":355471,"journal":{"name":"Advances in Engineering Technology Research","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140494988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-25DOI: 10.56028/aetr.9.1.613.2024
JunHan Hu
Rescue robots can perform rescue missions in dangerous and complex environments, protect humans from harm, and improve the efficiency and effectiveness of rescue, thus playing an increasingly important role in disaster management and rescue. This article reviews the technologies and methods required to apply artificial intelligence to rescue robot teams. Firstly, the feasibility of motion control for swarm robots was explored from the perspective of biomimetic robots. Through the analysis of animal biomimetics and the comparison of commonly used topological structures, the nature of team rescue robot rescue is emphasized, and based on this, a scheme for optimizing topological networks by combining environmental intelligence is proposed. Secondly, several existing micro robots were introduced and their data loading capabilities were evaluated. On this basis, the process of robot vision and motion commands was outlined. At the meanwhile, researchers focus on the current mainstream robot motion trajectory algorithms, and study the algorithm optimization process from extending the motion path planning of a single robot to group coordinated motion. This includes traditional cell decomposition algorithms and algorithms combined with machine learning to improve path planning efficiency. Finally, the above methods were summarized, and the impact of other possible feasible methods in the field of artificial intelligence was explored and analyzed.
{"title":"Development and Review of Group Rescue Robots Based on Artificial Intelligence Technology","authors":"JunHan Hu","doi":"10.56028/aetr.9.1.613.2024","DOIUrl":"https://doi.org/10.56028/aetr.9.1.613.2024","url":null,"abstract":"Rescue robots can perform rescue missions in dangerous and complex environments, protect humans from harm, and improve the efficiency and effectiveness of rescue, thus playing an increasingly important role in disaster management and rescue. This article reviews the technologies and methods required to apply artificial intelligence to rescue robot teams. Firstly, the feasibility of motion control for swarm robots was explored from the perspective of biomimetic robots. Through the analysis of animal biomimetics and the comparison of commonly used topological structures, the nature of team rescue robot rescue is emphasized, and based on this, a scheme for optimizing topological networks by combining environmental intelligence is proposed. Secondly, several existing micro robots were introduced and their data loading capabilities were evaluated. On this basis, the process of robot vision and motion commands was outlined. At the meanwhile, researchers focus on the current mainstream robot motion trajectory algorithms, and study the algorithm optimization process from extending the motion path planning of a single robot to group coordinated motion. This includes traditional cell decomposition algorithms and algorithms combined with machine learning to improve path planning efficiency. Finally, the above methods were summarized, and the impact of other possible feasible methods in the field of artificial intelligence was explored and analyzed.","PeriodicalId":355471,"journal":{"name":"Advances in Engineering Technology Research","volume":"84 12-13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140496442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-25DOI: 10.56028/aetr.9.1.497.2024
Ming Huang, Yue He, Pingping Qiao, Siqi Zhang, Yongxiu Feng
For optimization problems minimizing the sum of two nonconvex and nonsmooth functions, we propose an alternate linearization method with inexact data. In many practical optimization applications, only the inexact information of the function can be obtained. The core idea of this method is to add a quadratic function term to the nonconvex function(called local convexification of nonconvex function), and then to construct an approximate proximal point model. In each iteration, a series of iteration points are obtained by solving subproblems alternately. It can be proved that, in the sense of inexact oracles, these iteration points converge to the stable point of the original problem, and theoretically show that the algorithm has good convergent properties.
{"title":"Solving a Class of Nonsmooth Nonconvex Optimization Problems Via Proximal Alternating Linearization Scheme with Inexact Information","authors":"Ming Huang, Yue He, Pingping Qiao, Siqi Zhang, Yongxiu Feng","doi":"10.56028/aetr.9.1.497.2024","DOIUrl":"https://doi.org/10.56028/aetr.9.1.497.2024","url":null,"abstract":"For optimization problems minimizing the sum of two nonconvex and nonsmooth functions, we propose an alternate linearization method with inexact data. In many practical optimization applications, only the inexact information of the function can be obtained. The core idea of this method is to add a quadratic function term to the nonconvex function(called local convexification of nonconvex function), and then to construct an approximate proximal point model. In each iteration, a series of iteration points are obtained by solving subproblems alternately. It can be proved that, in the sense of inexact oracles, these iteration points converge to the stable point of the original problem, and theoretically show that the algorithm has good convergent properties.","PeriodicalId":355471,"journal":{"name":"Advances in Engineering Technology Research","volume":"283 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140495482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-25DOI: 10.56028/aetr.9.1.588.2024
Muyang Li
The study of sorting algorithms has always been a key topic. This paper thoroughly explains and investigates the time complexity of six classic sorting algorithms through theoretical analysis and experimental comparison. We implemented insertion sort, selection sort, bubble sort, shell sort, quicksort, and heapsort. By controlling data scale and distribution, we systematically tested the performance of different algorithms under various scenarios. The results show that there are significant efficiency differences between algorithms on small-scale data, and the advantages of quicksort and heapsort become more obvious as data size increases. Through extensive comparative experiments, this paper identifies the application scenarios of each algorithm, providing a theoretical basis for algorithm design and selection.
{"title":"Balancing Performance Trade-offs in Modern Sorting Methodologies","authors":"Muyang Li","doi":"10.56028/aetr.9.1.588.2024","DOIUrl":"https://doi.org/10.56028/aetr.9.1.588.2024","url":null,"abstract":"The study of sorting algorithms has always been a key topic. This paper thoroughly explains and investigates the time complexity of six classic sorting algorithms through theoretical analysis and experimental comparison. We implemented insertion sort, selection sort, bubble sort, shell sort, quicksort, and heapsort. By controlling data scale and distribution, we systematically tested the performance of different algorithms under various scenarios. The results show that there are significant efficiency differences between algorithms on small-scale data, and the advantages of quicksort and heapsort become more obvious as data size increases. Through extensive comparative experiments, this paper identifies the application scenarios of each algorithm, providing a theoretical basis for algorithm design and selection.","PeriodicalId":355471,"journal":{"name":"Advances in Engineering Technology Research","volume":"100 1-2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140495513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-25DOI: 10.56028/aetr.9.1.567.2024
JunHan Hu
With the development of marine resources, image-based biological target detection technology has gradually become the core method of marine ecological monitoring. This paper adopts Faster R-CNN technology, combined with two deep learning models, VGG and ResNet50, to improve the efficiency of target detection and recognition of underwater organisms. By combining large-scale annotated seabed image datasets for training, accurate localization and recognition of biological targets in images can be achieved. Compared to ResNet50, VGG performs better in complex seabed environments, with its mAP 1.75% higher than ResNet50, indicating higher detection accuracy and robustness. Besides, this study provides a practical and feasible solution for underwater ecological monitoring, verifying the excellent performance of ResNet50 in marine biological target detection, and providing an important and reliable support tool for deep-sea scientific research and ecological protection.
{"title":"Object Detection Model for Marine Organisms Based on Faster R-CNN","authors":"JunHan Hu","doi":"10.56028/aetr.9.1.567.2024","DOIUrl":"https://doi.org/10.56028/aetr.9.1.567.2024","url":null,"abstract":"With the development of marine resources, image-based biological target detection technology has gradually become the core method of marine ecological monitoring. This paper adopts Faster R-CNN technology, combined with two deep learning models, VGG and ResNet50, to improve the efficiency of target detection and recognition of underwater organisms. By combining large-scale annotated seabed image datasets for training, accurate localization and recognition of biological targets in images can be achieved. Compared to ResNet50, VGG performs better in complex seabed environments, with its mAP 1.75% higher than ResNet50, indicating higher detection accuracy and robustness. Besides, this study provides a practical and feasible solution for underwater ecological monitoring, verifying the excellent performance of ResNet50 in marine biological target detection, and providing an important and reliable support tool for deep-sea scientific research and ecological protection.","PeriodicalId":355471,"journal":{"name":"Advances in Engineering Technology Research","volume":"356 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140495150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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