Pub Date : 2024-05-15DOI: 10.54254/2755-2721/64/20241349
Beichang Liu, Guoqing Cai, Zhipeng Ling, Jili Qian, Quan Zhang
Self-driving systems collect vast amounts of data through a variety of sensors, including cameras, lidar, millimeter-wave radar, and more. This data needs to be processed in real time to identify obstacles such as roads, vehicles, pedestrians and make decisions accordingly. Therefore, this paper discusses the importance of accurate positioning and prediction system in automatic driving technology, and analyzes the performance of various positioning technologies in automatic driving applications.In addition, the paper explores the application potential of AI technology in autonomous driving and the prospect of combining advanced positioning and prediction systems with generative AI. Overall, this study highlights the importance of algorithm performance improvement and artificial intelligence technology in the development of autonomous driving technology, and provides new ideas and directions for the innovation and development of intelligent transportation systems in the future.
{"title":"Precise positioning and prediction system for autonomous driving based on generative artificial intelligence","authors":"Beichang Liu, Guoqing Cai, Zhipeng Ling, Jili Qian, Quan Zhang","doi":"10.54254/2755-2721/64/20241349","DOIUrl":"https://doi.org/10.54254/2755-2721/64/20241349","url":null,"abstract":"Self-driving systems collect vast amounts of data through a variety of sensors, including cameras, lidar, millimeter-wave radar, and more. This data needs to be processed in real time to identify obstacles such as roads, vehicles, pedestrians and make decisions accordingly. Therefore, this paper discusses the importance of accurate positioning and prediction system in automatic driving technology, and analyzes the performance of various positioning technologies in automatic driving applications.In addition, the paper explores the application potential of AI technology in autonomous driving and the prospect of combining advanced positioning and prediction systems with generative AI. Overall, this study highlights the importance of algorithm performance improvement and artificial intelligence technology in the development of autonomous driving technology, and provides new ideas and directions for the innovation and development of intelligent transportation systems in the future.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"61 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140975142","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 : 2024-05-15DOI: 10.54254/2755-2721/64/20241334
Mingtian Xie, Ruifeng He, Aixing He
In recent years, cybersecurity issues have emerged one after another, with botnets extensively utilizing Domain Generation Algorithms (DGA) to evade detection. To address the issue of insufficient detection accuracy in existing DGA malicious domain detection models, this paper proposes a deep learning detection model based on multi-stage feature fusion. By extracting local feature information and positional information of domain name sequences through the fusion of Multilayer Convolutional Neural Network (MCNN) and Transformer, and capturing the long-distance contextual semantic features of domain name sequences through Bi-directional Long Short-Term Memory Network (BiLSTM), these features are finally fused for malicious domain classification. Experimental results show that the model maintains an average Accuracy of 93.26% and an average F1-Score of 93.32% for 33 DGA families, demonstrating better comprehensive detection performance compared to other deep learning detection algorithms.
{"title":"Deep learning DGA malicious domain name detection based on multi-stage feature fusion","authors":"Mingtian Xie, Ruifeng He, Aixing He","doi":"10.54254/2755-2721/64/20241334","DOIUrl":"https://doi.org/10.54254/2755-2721/64/20241334","url":null,"abstract":"In recent years, cybersecurity issues have emerged one after another, with botnets extensively utilizing Domain Generation Algorithms (DGA) to evade detection. To address the issue of insufficient detection accuracy in existing DGA malicious domain detection models, this paper proposes a deep learning detection model based on multi-stage feature fusion. By extracting local feature information and positional information of domain name sequences through the fusion of Multilayer Convolutional Neural Network (MCNN) and Transformer, and capturing the long-distance contextual semantic features of domain name sequences through Bi-directional Long Short-Term Memory Network (BiLSTM), these features are finally fused for malicious domain classification. Experimental results show that the model maintains an average Accuracy of 93.26% and an average F1-Score of 93.32% for 33 DGA families, demonstrating better comprehensive detection performance compared to other deep learning detection algorithms.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"121 49","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140977317","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 examines the development indicators of China's new energy vehicle industry using clustering and multiple regression methods. The indicators are divided into internal and external aspects: external factors, such as the degree of completeness of charging facilities, market demand, policies and regulations, and internal factors, mainly brand types and power costs. By comparing the forecasting models of its industry data, including the exponential smoothing model, grey forecasting model and Brownian forecasting model. The forecast results show that this industry in China maintains a positive development trend in the next ten years. It shows that the development prospect of electric vehicles is very bright.The population competition model is used to model the competitive situation between new energy and traditional energy vehicles, and it is concluded that new energy vehicles are replacing traditional fuel vehicles and promoting the transformation of the automotive industry to be environmentally friendly and efficient.Collect the key measures and points in time that countries have taken to target the development of this industry in China. Analysing the data on the development of the industry before and after these events, it is found that external factors, such as other countries' policies, may inhibit the industry's growth. If other countries take action to thwart this industry in China, it may temporarily break its growth or even lead to a short-term industry recession.
{"title":"Multi-dimensional analysis of the impact of new energy vehicles on the urban ecological environment and prediction of future trends","authors":"Xuanran Tang, Tianbing Yang, Chen Zhang, Zhenglin Xiong, Ruiqi Zhu","doi":"10.54254/2755-2721/64/20241363","DOIUrl":"https://doi.org/10.54254/2755-2721/64/20241363","url":null,"abstract":"This study examines the development indicators of China's new energy vehicle industry using clustering and multiple regression methods. The indicators are divided into internal and external aspects: external factors, such as the degree of completeness of charging facilities, market demand, policies and regulations, and internal factors, mainly brand types and power costs. By comparing the forecasting models of its industry data, including the exponential smoothing model, grey forecasting model and Brownian forecasting model. The forecast results show that this industry in China maintains a positive development trend in the next ten years. It shows that the development prospect of electric vehicles is very bright.The population competition model is used to model the competitive situation between new energy and traditional energy vehicles, and it is concluded that new energy vehicles are replacing traditional fuel vehicles and promoting the transformation of the automotive industry to be environmentally friendly and efficient.Collect the key measures and points in time that countries have taken to target the development of this industry in China. Analysing the data on the development of the industry before and after these events, it is found that external factors, such as other countries' policies, may inhibit the industry's growth. If other countries take action to thwart this industry in China, it may temporarily break its growth or even lead to a short-term industry recession.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"124 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140977651","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 : 2024-05-15DOI: 10.54254/2755-2721/64/20241375
Wentao Zhang
With the wide application of personalized recommender system in various fields, how to improve the accuracy and personalized level of recommender system has become a research hotspot. In this paper, a method of combining graph modeling and contrast learning is proposed to improve the performance of recommendation system by mining complex user project interaction and user preference. We first construct the user-project interaction graph, and extract the features of the graph structure by graph neural network (GNN) . In particular, graph convolution network (GCN) is used to update the node representation, and comparative learning is introduced to optimize the feature representation so as to improve the accuracy and personalization of recommendation. The experimental results show that the proposed method is superior to the traditional method in accuracy, recall and F 1 score. By analyzing the mechanism of combining graph modeling and contrast learning, this paper further expounds the theoretical basis and practical application of improving the performance of recommender system, and points out the limitations of existing methods and the future research direction.
随着个性化推荐系统在各个领域的广泛应用，如何提高推荐系统的准确性和个性化水平成为研究热点。本文提出了一种图建模与对比学习相结合的方法，通过挖掘复杂的用户项目交互和用户偏好来提高推荐系统的性能。我们首先构建了用户-项目交互图，并通过图神经网络（GNN）提取了图结构的特征。其中，使用图卷积网络（GCN）更新节点表示，并引入比较学习来优化特征表示，从而提高推荐的准确性和个性化。实验结果表明，所提出的方法在准确率、召回率和 F 1 分数上都优于传统方法。本文通过分析图建模与对比学习相结合的机理，进一步阐述了提高推荐系统性能的理论基础和实际应用，并指出了现有方法的局限性和未来的研究方向。
{"title":"Application of graph modeling and contrast learning in recommender system","authors":"Wentao Zhang","doi":"10.54254/2755-2721/64/20241375","DOIUrl":"https://doi.org/10.54254/2755-2721/64/20241375","url":null,"abstract":"With the wide application of personalized recommender system in various fields, how to improve the accuracy and personalized level of recommender system has become a research hotspot. In this paper, a method of combining graph modeling and contrast learning is proposed to improve the performance of recommendation system by mining complex user project interaction and user preference. We first construct the user-project interaction graph, and extract the features of the graph structure by graph neural network (GNN) . In particular, graph convolution network (GCN) is used to update the node representation, and comparative learning is introduced to optimize the feature representation so as to improve the accuracy and personalization of recommendation. The experimental results show that the proposed method is superior to the traditional method in accuracy, recall and F 1 score. By analyzing the mechanism of combining graph modeling and contrast learning, this paper further expounds the theoretical basis and practical application of improving the performance of recommender system, and points out the limitations of existing methods and the future research direction.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140975997","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 : 2024-05-15DOI: 10.54254/2755-2721/64/20241383
Jiaxin Huang, Yifan Zhang, Jingyu Xu, Binbin Wu, Bo Liu, Yulu Gong
AI and cloud native are mutually reinforcing and inseparable. Due to the huge storage and computing power requirements, most AI applications need cloud support, especially large model applications If cloud native has influenced the software industry to a considerable extent in the past few years, the big model boom means that cloud native has become a standard option for developers.This paper describes the rise of AI model applications and their integration with traditional development workflows, pointing out the challenges that enterprises and developers face when integrating large models. With the rise of cloud-native technologies, the combination of artificial intelligence and cloud computing is becoming increasingly important. Cloud-native technologies provide the infrastructure needed to build and run resilient and scalable applications, while distributed infrastructure supports multi-cloud integration, enabling a unified foundation of "one cloud, multiple computing." As an intelligent voice Assistant, Google Assistant achieves a more intelligent, convenient and efficient user experience through applications in smart home control, enterprise customer service and healthcare. Finally, this paper points out the advantages of combining Google Assistant with cloud computing, providing a more intelligent, convenient, and efficient user experience.
{"title":"Implementation of seamless assistance with Google Assistant leveraging cloud computing","authors":"Jiaxin Huang, Yifan Zhang, Jingyu Xu, Binbin Wu, Bo Liu, Yulu Gong","doi":"10.54254/2755-2721/64/20241383","DOIUrl":"https://doi.org/10.54254/2755-2721/64/20241383","url":null,"abstract":"AI and cloud native are mutually reinforcing and inseparable. Due to the huge storage and computing power requirements, most AI applications need cloud support, especially large model applications If cloud native has influenced the software industry to a considerable extent in the past few years, the big model boom means that cloud native has become a standard option for developers.This paper describes the rise of AI model applications and their integration with traditional development workflows, pointing out the challenges that enterprises and developers face when integrating large models. With the rise of cloud-native technologies, the combination of artificial intelligence and cloud computing is becoming increasingly important. Cloud-native technologies provide the infrastructure needed to build and run resilient and scalable applications, while distributed infrastructure supports multi-cloud integration, enabling a unified foundation of \"one cloud, multiple computing.\" As an intelligent voice Assistant, Google Assistant achieves a more intelligent, convenient and efficient user experience through applications in smart home control, enterprise customer service and healthcare. Finally, this paper points out the advantages of combining Google Assistant with cloud computing, providing a more intelligent, convenient, and efficient user experience.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"23 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140972655","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 : 2024-05-15DOI: 10.54254/2755-2721/64/20241367
Haoran Chen
Caused by the novel coronavirus SARS-CoV-2, COVID-19 is highly contagious via respiratory droplets from sneezing, coughing, or talking, and it can lead to severe respiratory issues, organ failure, and death. Early detection, treatment, and isolation of those at risk help slow its spread, it has challenged traditional diagnostic methods like RT-PCR due to limitations in sensitivity. CT imaging, aided by deep learning models, offers advantages in the early detection of lung abnormalities. This paper reviews the use of deep learning in analyzing CT images for COVID-19 diagnosis, highlighting advancements like image segmentation with U-Net and FPN, it also tracks the evolution of deep learning models in this domain, starting from initial applications focused on image classification and recognition to later advancements incorporating techniques like U-Net for image segmentation and feature pyramid networks. Novel techniques like multi-task learning and quantitative analysis show promise in improving accuracy. Future research focuses on enhancing training datasets, refining model architectures, and integrating methods to support clinical decision-making for COVID-19 management.
{"title":"Application and development direction of deep learning in COVID-19 identification based on Computed Tomography images","authors":"Haoran Chen","doi":"10.54254/2755-2721/64/20241367","DOIUrl":"https://doi.org/10.54254/2755-2721/64/20241367","url":null,"abstract":"Caused by the novel coronavirus SARS-CoV-2, COVID-19 is highly contagious via respiratory droplets from sneezing, coughing, or talking, and it can lead to severe respiratory issues, organ failure, and death. Early detection, treatment, and isolation of those at risk help slow its spread, it has challenged traditional diagnostic methods like RT-PCR due to limitations in sensitivity. CT imaging, aided by deep learning models, offers advantages in the early detection of lung abnormalities. This paper reviews the use of deep learning in analyzing CT images for COVID-19 diagnosis, highlighting advancements like image segmentation with U-Net and FPN, it also tracks the evolution of deep learning models in this domain, starting from initial applications focused on image classification and recognition to later advancements incorporating techniques like U-Net for image segmentation and feature pyramid networks. Novel techniques like multi-task learning and quantitative analysis show promise in improving accuracy. Future research focuses on enhancing training datasets, refining model architectures, and integrating methods to support clinical decision-making for COVID-19 management.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"45 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140975740","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 : 2024-05-09DOI: 10.54254/2755-2721/63/20240988
Alvin Xianghan Li
With the help of wind farms, wind energy is a vital renewable energy source that contributes significantly to the worlds energy balance. The lifespan and maintenance costs of wind turbines will be reduced with an accurate wind speed prediction. On the other hand, wind speed is highly volatile and unpredictable. Thus, it is essential to do research into creating complex models and algorithms for precise wind speed prediction. So far, some of the most promising models include Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Autoregressive Moving Average (ARMA). Python, as an advanced and versatile programming language, is exceptionally suited for scripting the algorithms of these sophisticated models. This paper will use the data from Austin Texas and apply a Support Vector Machine (SVM) for wind speed prediction involves several stages, including data collection, data preprocessing, model selection, model training, parameter optimization, model validation, and prediction. Wind energy resource optimisation, maintenance cost reduction, and total wind farm efficiency can all be significantly improved by incorporating these models into predictive analytics and continuously improving them against changing data.
{"title":"Wind speed prediction","authors":"Alvin Xianghan Li","doi":"10.54254/2755-2721/63/20240988","DOIUrl":"https://doi.org/10.54254/2755-2721/63/20240988","url":null,"abstract":"With the help of wind farms, wind energy is a vital renewable energy source that contributes significantly to the worlds energy balance. The lifespan and maintenance costs of wind turbines will be reduced with an accurate wind speed prediction. On the other hand, wind speed is highly volatile and unpredictable. Thus, it is essential to do research into creating complex models and algorithms for precise wind speed prediction. So far, some of the most promising models include Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Autoregressive Moving Average (ARMA). Python, as an advanced and versatile programming language, is exceptionally suited for scripting the algorithms of these sophisticated models. This paper will use the data from Austin Texas and apply a Support Vector Machine (SVM) for wind speed prediction involves several stages, including data collection, data preprocessing, model selection, model training, parameter optimization, model validation, and prediction. Wind energy resource optimisation, maintenance cost reduction, and total wind farm efficiency can all be significantly improved by incorporating these models into predictive analytics and continuously improving them against changing data.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":" 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140996262","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 : 2024-05-09DOI: 10.54254/2755-2721/63/20240993
Shitao Wu
The end-around taxiways have been proven to effectively reduce the risk of runway incursions caused by frequent aircraft crossings on closely spaced parallel runways, thus enhancing airport capacity. This practice has gained popularity in recent years, especially in large airports. However, there is currently limited experience in designing and operating end-around taxiways, particularly in high plateau airports facing challenging conditions such as a high water table and low obstacle clearance gradient. In this paper, we present a case study of the second runway project at a specific airport and propose various operational schemes for end-around taxiway construction, including straight, oblique, and controlled designs. We calculate aircraft payload under different obstacle clearance gradients using flight performance analysis. Taking into account both operational and groundwater levels, we determine the appropriate plane and vertical design of end-around taxiways. The findings of this research provide valuable references for the design of end-around taxiways.
{"title":"Plane and vertical design research on end-around taxiways at high plateau airports","authors":"Shitao Wu","doi":"10.54254/2755-2721/63/20240993","DOIUrl":"https://doi.org/10.54254/2755-2721/63/20240993","url":null,"abstract":"The end-around taxiways have been proven to effectively reduce the risk of runway incursions caused by frequent aircraft crossings on closely spaced parallel runways, thus enhancing airport capacity. This practice has gained popularity in recent years, especially in large airports. However, there is currently limited experience in designing and operating end-around taxiways, particularly in high plateau airports facing challenging conditions such as a high water table and low obstacle clearance gradient. In this paper, we present a case study of the second runway project at a specific airport and propose various operational schemes for end-around taxiway construction, including straight, oblique, and controlled designs. We calculate aircraft payload under different obstacle clearance gradients using flight performance analysis. Taking into account both operational and groundwater levels, we determine the appropriate plane and vertical design of end-around taxiways. The findings of this research provide valuable references for the design of end-around taxiways.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140997777","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 : 2024-05-09DOI: 10.54254/2755-2721/63/20240995
Zeyu Liu, Wenjie Dong, Zihao Shi, Jie Pei, Tengfei Ma, Kun Fu
With the continuous innovation and development of 3D printing technology, 3D-printed biomimetic coral bone scaffolds have demonstrated significant potential in the field of bone defect repair. This paper aims to explore in-depth the mechanistic study of 3D-printed biomimetic coral bone scaffolds in bone defect repair, by systematically reviewing relevant literature and analyzing their potential mechanisms in promoting bone growth and improving the success rate of bone defect repair. Firstly, this paper introduces the fabrication process and material characteristics of 3D-printed biomimetic coral bone scaffolds. Secondly, the paper discusses the mechanisms of 3D-printed biomimetic coral bone scaffolds in terms of biocompatibility, biomechanical performance, as well as their roles in vascularization and bone formation. Finally, the paper outlines future research directions for 3D-printed biomimetic coral bone scaffolds, including further optimization of material properties, improvement of printing precision, and expansion of clinical applications.
{"title":"Mechanistic study on the role of 3D-Printed biomimetic coral bone scaffolds in bone defect repair","authors":"Zeyu Liu, Wenjie Dong, Zihao Shi, Jie Pei, Tengfei Ma, Kun Fu","doi":"10.54254/2755-2721/63/20240995","DOIUrl":"https://doi.org/10.54254/2755-2721/63/20240995","url":null,"abstract":"With the continuous innovation and development of 3D printing technology, 3D-printed biomimetic coral bone scaffolds have demonstrated significant potential in the field of bone defect repair. This paper aims to explore in-depth the mechanistic study of 3D-printed biomimetic coral bone scaffolds in bone defect repair, by systematically reviewing relevant literature and analyzing their potential mechanisms in promoting bone growth and improving the success rate of bone defect repair. Firstly, this paper introduces the fabrication process and material characteristics of 3D-printed biomimetic coral bone scaffolds. Secondly, the paper discusses the mechanisms of 3D-printed biomimetic coral bone scaffolds in terms of biocompatibility, biomechanical performance, as well as their roles in vascularization and bone formation. Finally, the paper outlines future research directions for 3D-printed biomimetic coral bone scaffolds, including further optimization of material properties, improvement of printing precision, and expansion of clinical applications.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140997716","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 : 2024-05-09DOI: 10.54254/2755-2721/63/20241010
Yu Wang, Hao Lu
The study of particle deposition in ventilation ducts is crucial as it can have a significant impact on indoor air quality (IAQ) and human health. However, little research has been done on bends in ducts with different cross-sections. This study employs the Eulerian - Lagrange method to investigate particle deposition in a 90 elbow with gradually increasing and decreasing cross-sectional areas. The turbulence model used is based on the RNG k-, and the particulate phase is modelled by the discrete phase model (DPM). The study aims to discuss the effect of the cross-sectional asymptotic coefficient (K) and the Stokes number on particle deposition. The study found that as K increased, the particle deposition efficiency of the 90-degree bends decreased. Additionally, particles were primarily deposited on the outer curved surface of the bends. Specifically, when the particle size was 2 m, the pipe with K=0.75 had a particle deposition efficiency five times greater than that of K=1.25.
{"title":"Numerical study of deposition rates of monodisperse particles in curved pipes with different expansion or shrinkage variables","authors":"Yu Wang, Hao Lu","doi":"10.54254/2755-2721/63/20241010","DOIUrl":"https://doi.org/10.54254/2755-2721/63/20241010","url":null,"abstract":"The study of particle deposition in ventilation ducts is crucial as it can have a significant impact on indoor air quality (IAQ) and human health. However, little research has been done on bends in ducts with different cross-sections. This study employs the Eulerian - Lagrange method to investigate particle deposition in a 90 elbow with gradually increasing and decreasing cross-sectional areas. The turbulence model used is based on the RNG k-, and the particulate phase is modelled by the discrete phase model (DPM). The study aims to discuss the effect of the cross-sectional asymptotic coefficient (K) and the Stokes number on particle deposition. The study found that as K increased, the particle deposition efficiency of the 90-degree bends decreased. Additionally, particles were primarily deposited on the outer curved surface of the bends. Specifically, when the particle size was 2 m, the pipe with K=0.75 had a particle deposition efficiency five times greater than that of K=1.25.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140997135","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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