Internet of Things and Cyber-Physical Systems最新文献_第2页

Data management method for building internet of things based on blockchain sharding and DAG 基于区块链分片和 DAG 构建物联网的数据管理方法

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 DOI: 10.1016/j.iotcps.2024.01.001

Wenhu Zheng, Xu Wang, Zhenxi Xie, Yixin Li, Xiaoyun Ye, Jinlong Wang, Xiaoyun Xiong

{"title":"Data management method for building internet of things based on blockchain sharding and DAG","authors":"Wenhu Zheng, Xu Wang, Zhenxi Xie, Yixin Li, Xiaoyun Ye, Jinlong Wang, Xiaoyun Xiong","doi":"10.1016/j.iotcps.2024.01.001","DOIUrl":"https://doi.org/10.1016/j.iotcps.2024.01.001","url":null,"abstract":"<div><p>Sharding technology can address the throughput and scalability limitations that arise when single-chain blockchain are applied in the Internet of Things (IoT). However, existing sharding solutions focus on addressing issues like malicious nodes clustering and cross-shard transactions. Existing sharding solutions cannot adapt to the performance disparities of edge nodes and the characteristic of three-dimensional data queries in building IoT. This leads to problems such as shard overheating and inefficient data query efficiency. This paper proposes a dual-layer architecture called S-DAG, which combines sharded blockchain and DAG blockchain. The sharded blockchain processes transactions within the building IoT, while the DAG blockchain stores block headers from the sharded network. By designing an Adaptive Balancing Load Algorithm (ABLA) for periodic network sharding, nodes are divided based on their load performance values to prevent the aggregation of low-load performance nodes and the resulting issue of shard overheating. By combining the characteristics of the KD tree and Merkle tree, a block structure known as 3D-Merkle tree is designed to support three-dimensional data queries, enhancing the efficiency of three-dimensional data queries in building IoT. By deploying and conducting simulation experiments on various physical devices, we have verified the effectiveness of the solution proposed in this paper. The results indicate that, compared to other solutions, the proposed solution is better suited for building IoT data management. ABLA is effective in preventing shard overheating issue, and the 3D-Merkle tree significantly enhances data query efficiency.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 217-234"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667345224000014/pdfft?md5=4cbb598cfddbffa06e124be5e2862437&pid=1-s2.0-S2667345224000014-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139718720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review 物联网网络入侵检测的多目标优化算法：系统综述

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 DOI: 10.1016/j.iotcps.2024.01.003

Shubhkirti Sharma , Vijay Kumar , Kamlesh Dutta

{"title":"Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review","authors":"Shubhkirti Sharma , Vijay Kumar , Kamlesh Dutta","doi":"10.1016/j.iotcps.2024.01.003","DOIUrl":"10.1016/j.iotcps.2024.01.003","url":null,"abstract":"<div><p>The significance of intrusion detection systems in networks has grown because of the digital revolution and increased operations. The intrusion detection method classifies the network traffic as threat or normal based on the data features. The Intrusion detection system faces a trade-off between various parameters such as detection accuracy, relevance, redundancy, false alarm rate, and other objectives. The paper presents a systematic review of intrusion detection in Internet of Things (IoT) networks using multi-objective optimization algorithms (MOA), to identify attempts at exploiting security vulnerabilities and reducing the chances of security attacks. MOAs provide a set of optimized solutions for the intrusion detection process in highly complex IoT networks. This paper presents the identification of multiple objectives of intrusion detection, comparative analysis of multi-objective algorithms for intrusion detection in IoT based on their approaches, and the datasets used for their evaluation. The multi-objective optimization algorithms show the encouraging potential in IoT networks to enhance multiple conflicting objectives for intrusion detection. Additionally, the current challenges and future research ideas are identified. In addition to demonstrating new advancements in intrusion detection techniques, this study attempts to identify research gaps that can be addressed while designing intrusion detection systems for IoT networks.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 258-267"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667345224000038/pdfft?md5=19510c1690405bb751695fbd58ac122c&pid=1-s2.0-S2667345224000038-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139812070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Credit card default prediction using ML and DL techniques 利用 ML 和 DL 技术预测信用卡违约情况

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 DOI: 10.1016/j.iotcps.2024.09.001

Fazal Wahab , Imran Khan , Sneha Sabada

{"title":"Credit card default prediction using ML and DL techniques","authors":"Fazal Wahab , Imran Khan , Sneha Sabada","doi":"10.1016/j.iotcps.2024.09.001","DOIUrl":"10.1016/j.iotcps.2024.09.001","url":null,"abstract":"<div><p>The banking sector is widely acknowledged for its intrinsic unpredictability and susceptibility to risk. Bank loans have emerged as one of the most recent services offered over the past several decades. Banks typically serve as intermediaries for loans, investments, short-term loans, and other types of credit. The usage of credit cards is experiencing a steady increase, thereby leading to a rise in the default rate that banks encounter. Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques. The application of DL approaches to credit card default prediction has not been extensively researched despite their considerable potential in numerous fields. Moreover, the current literature frequently lacks particular information regarding the DL structures, hyperparameters, and optimization techniques employed. To predict credit card default, this study evaluates the efficacy of a DL model and compares it to other ML models, such as Decision Tree (DT) and Adaboost. The objective of this research is to identify the specific DL parameters that contribute to the observed enhancements in the accuracy of credit card default prediction. This research makes use of the UCI ML repository to access the credit card defaulted customer dataset. Subsequently, various techniques are employed to preprocess the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hypertuned to evaluate the enhancement in prediction. We used standard evaluation metrics to evaluate all the models. The evaluation indicates that the AdaBoost and DT exhibit the highest accuracy rate of 82 % in predicting credit card default, surpassing the accuracy of the ANN model, which is 78 %.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 293-306"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667345224000087/pdfft?md5=f77f275cf416221418432e3c1d730036&pid=1-s2.0-S2667345224000087-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142168457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models 物联网安全的机器学习技术：使用生成式人工智能和大型语言模型的当前研究和未来展望

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 DOI: 10.1016/j.iotcps.2023.12.003

Fatima Alwahedi, Alyazia Aldhaheri, Mohamed Amine Ferrag, Ammar Battah, Norbert Tihanyi

{"title":"Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models","authors":"Fatima Alwahedi, Alyazia Aldhaheri, Mohamed Amine Ferrag, Ammar Battah, Norbert Tihanyi","doi":"10.1016/j.iotcps.2023.12.003","DOIUrl":"10.1016/j.iotcps.2023.12.003","url":null,"abstract":"<div><p>Despite providing unparalleled connectivity and convenience, the exponential growth of the Internet of Things (IoT) ecosystem has triggered significant cybersecurity concerns. These concerns stem from various factors, including the heterogeneity of IoT devices, widespread deployment, and inherent computational limitations. Integrating emerging technologies to address these concerns becomes imperative as the dynamic IoT landscape evolves. Machine Learning (ML), a rapidly advancing technology, has shown considerable promise in addressing IoT security issues. It has significantly influenced and advanced research in cyber threat detection. This survey provides a comprehensive overview of current trends, methodologies, and challenges in applying machine learning for cyber threat detection in IoT environments. Specifically, we further perform a comparative analysis of state-of-the-art ML-based Intrusion Detection Systems (IDSs) in the landscape of IoT security. In addition, we shed light on the pressing unresolved issues and challenges within this dynamic field. We provide a future vision with Generative AI and large language models to enhance IoT security. The discussions present an in-depth understanding of different cyber threat detection methods, enhancing the knowledge base of researchers and practitioners alike. This paper is a valuable resource for those keen to delve into the evolving world of cyber threat detection leveraging ML and IoT security.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 167-185"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667345223000585/pdfft?md5=d4450c6f6b2d36d74f0de919ecba7bd9&pid=1-s2.0-S2667345223000585-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139393793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Constructing immersive toy trial experience in mobile augmented reality 在移动增强现实技术中构建身临其境的玩具试用体验

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 DOI: 10.1016/j.iotcps.2024.02.001

Lingxin Yu, Jiacheng Zhang, Xinyue Wang, Siru Chen, Xuehao Qin, Zhifei Ding, Jiahao Han

{"title":"Constructing immersive toy trial experience in mobile augmented reality","authors":"Lingxin Yu, Jiacheng Zhang, Xinyue Wang, Siru Chen, Xuehao Qin, Zhifei Ding, Jiahao Han","doi":"10.1016/j.iotcps.2024.02.001","DOIUrl":"10.1016/j.iotcps.2024.02.001","url":null,"abstract":"<div><p>When consumers purchase toys from retail stores, the majority of toys are packaged, making it difficult for them to observe the toys comprehensively. This limitation may hinder their ability to make informed purchase decisions. To address this challenge, this paper introduces an immersive toy experience program utilizing augmented reality (AR) technology. The program utilizes the camera on mobile devices to scan and identify the toy's cover image, subsequently showcasing corresponding virtual toy models in a simulated environment. Additionally, interactive controls enable users to manipulate the viewing angles. In terms of methodology, we have specifically designed an expandable collection of toy images, allowing the recognition of recently introduced toys by adding them to the database, enhancing the scalability of our application. In comparison to previous research, our work transcends the constraints of traditional toy shopping, providing a more intuitive, interactive, and personalized experience through AR technology.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 250-257"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266734522400004X/pdfft?md5=1e3a50014cb3a60d1d2d0bd8be7c6312&pid=1-s2.0-S266734522400004X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139832026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Green buildings: Requirements, features, life cycle, and relevant intelligent technologies 绿色建筑：要求、特点、生命周期和相关智能技术

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 DOI: 10.1016/j.iotcps.2024.09.002

Siyi Yin , Jinsong Wu , Junhui Zhao , Michele Nogueira , Jaime Lloret

引用次数: 0

A self-configuration framework for balancing services in the fog of things 物联网中平衡服务的自配置框架

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 DOI: 10.1016/j.iotcps.2024.09.003

Edson Mota , Jurandir Barbosa , Gustavo B. Figueiredo , Maycon Peixoto , Cássio Prazeres

{"title":"A self-configuration framework for balancing services in the fog of things","authors":"Edson Mota , Jurandir Barbosa , Gustavo B. Figueiredo , Maycon Peixoto , Cássio Prazeres","doi":"10.1016/j.iotcps.2024.09.003","DOIUrl":"10.1016/j.iotcps.2024.09.003","url":null,"abstract":"<div><div>Fog Computing has been playing a pivotal role in the Internet of Things (IoT) ecosystem, offering benefits such as local availability, access facilities, and enhanced communication among devices. However, managing numerous gateways in an IoT network poses service distribution and network management challenges, leading to imbalances and inefficiencies. Within this context, this paper presents a novel self-organizing environment based on the Fog of Things approach, designed to address these challenges. Our key contributions include developing the FoT Balance Management service, which dynamically configures and optimizes the distribution of services across the network. This service utilizes advanced load-balancing algorithms to ensure the workload is evenly distributed among the available gateways, preventing any single node from becoming a bottleneck for the service distributions. Additionally, we integrate Apache Karaf Cellar for real-time monitoring and adaptive reconfiguration. This integration allows the system to continuously monitor the network state and automatically reconfigure the service distribution in response to changes, such as adding or removing nodes. This approach ensures seamless adaptation to network changes, maintaining high performance and load balancing. We validate our solution through planned experiments using ANOVA and a 2<sup><em>k</em></sup> factorial design. The experimental results demonstrate significant improvements in network performance, response time, and load balancing. Specifically, in scenarios with ten fog nodes, our approach increases average availability by 10 %–20 % and achieves 70 %–80 % load balancing. The analysis reveals that the absence of a balancing strategy can reduce availability by approximately 30 %. Our proposed solution effectively prevents infrastructure overload, balancing computation costs and node availability, thereby enhancing the efficiency and responsiveness of the IoT ecosystem.</div></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 318-332"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Metaverse for smart cities: A survey 智慧城市的元宇宙：一项调查

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 DOI: 10.1016/j.iotcps.2023.12.002

Zefeng Chen , Wensheng Gan , Jiayang Wu , Hong Lin , Chien-Ming Chen

{"title":"Metaverse for smart cities: A survey","authors":"Zefeng Chen , Wensheng Gan , Jiayang Wu , Hong Lin , Chien-Ming Chen","doi":"10.1016/j.iotcps.2023.12.002","DOIUrl":"10.1016/j.iotcps.2023.12.002","url":null,"abstract":"<div><p>The concept of a smart city is geared towards enhancing convenience and the efficient management of city areas through innovation. As Metaverse rises in the 2020s, providing the possible direction for a new generation of the Internet, it has a huge number of opportunities to promote smart cities. The Metaverse can empower smart cities in various aspects. In this article, we provide a detailed review of smart cities based on Metaverse technologies. Firstly, we introduce the Metaverse and smart cities and describe the future vision and applications of smart cities, which are based on the Metaverse. In addition, we discuss the essential technologies for smart cities in the Metaverse and the currently available solutions. Additionally, we have some concerns regarding the potential of Metaverse and there are still unresolved issues that should be addressed. The purpose of this article is to provide researchers and developers with essential guidance and opportunities to propel the development of the Metaverse and smart cities.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 203-216"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667345223000573/pdfft?md5=4b1fea1706d8c4603535fe4a90823712&pid=1-s2.0-S2667345223000573-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139456814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural network inspired efficient scalable task scheduling for cloud infrastructure 受神经网络启发的云基础设施高效可扩展任务调度

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 DOI: 10.1016/j.iotcps.2024.02.002

Punit Gupta , Arnaav Anand , Pratyush Agarwal , Gavin McArdle

{"title":"Neural network inspired efficient scalable task scheduling for cloud infrastructure","authors":"Punit Gupta , Arnaav Anand , Pratyush Agarwal , Gavin McArdle","doi":"10.1016/j.iotcps.2024.02.002","DOIUrl":"https://doi.org/10.1016/j.iotcps.2024.02.002","url":null,"abstract":"<div><p>The rapid development of Cloud Computing in the 21st Century is landmark occasion, not only in the field of technology, but also in the field of engineering and services. The development in cloud architecture and services has enabled fast and easy transfer of data from one unit of a network to other. Cloud services support the latest transport services like smart cars, smart aviation services and many others. In the current trend, smart transport services depend on the performance of cloud Infrastructure and its services. Smart cloud services derive <em>real</em> time computing and allows it to make smart decision. For further improvement in cloud services, cloud resource optimization is a vital cog that defines the performance of cloud. Cloud services have certainly aimed to make the optimum use of all available resources to the become as cost efficient and time efficient as possible. One of the issues that still occur in multiple Cloud Environments is a failure in task execution. While there exist multiple methods to tackle this problem in task scheduling, in the recent times, the use of smart scheduling techniques has come to prominence. In this work, we aim to use the Harmony Search Algorithm and neural networks to create a fault aware system for optimal usage of cloud resources. Cloud environments are in general expected to be free of any errors or faults but with time and experience, we know that no system can be faultless. With our approach, we are looking to create the best possible time-efficient system for faulty environments, Where the result shows that the proposed harmony search-inspired ANN model provides least execution time, number of task failures, power consumption and high resource utilization as compared to recent Red fox and Crow search inspired models.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 268-279"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667345224000051/pdfft?md5=52d4b3c6032dcde3e4d8b4568429050a&pid=1-s2.0-S2667345224000051-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140113276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blockchain sharding scheme based on generative AI and DRL: Applied to building internet of things 基于生成式人工智能和 DRL 的区块链分片方案：应用于构建物联网

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 DOI: 10.1016/j.iotcps.2024.11.001

Jinlong Wang , Yixin Li , Yunting Wu , Wenhu Zheng , Shangzhuo Zhou , Xiaoyun Xiong

{"title":"Blockchain sharding scheme based on generative AI and DRL: Applied to building internet of things","authors":"Jinlong Wang , Yixin Li , Yunting Wu , Wenhu Zheng , Shangzhuo Zhou , Xiaoyun Xiong","doi":"10.1016/j.iotcps.2024.11.001","DOIUrl":"10.1016/j.iotcps.2024.11.001","url":null,"abstract":"<div><div>When applying blockchain sharding technology in the building Internet of Things (IoT) domain to enhance the throughput performance of the blockchain, cross-shard transactions triggered by device collaborative tasks have increasingly become a prominent issue. Existing solutions base their shard division on historical transaction moments, using the outcomes for future transaction processing. However, since the historical interaction characteristics do not accurately reflect the interaction details within specific fine-grained time periods, this leads to poor system performance. Additionally, the parameter configuration in blockchain sharding systems is mostly based on arbitrary or default settings, which also results in unstable system performance. To address these two challenges, this paper proposes a blockchain sharding scheme called AI-Shard. Firstly, the system includes a module, G-AI, that utilizes generative AI to predict future node interaction relationships, enabling more proactive and adaptive shard division based on the predicted interaction matrix. Secondly, the system integrates a reinforcement learning module, DL-AI, specifically tailored for configuring parameters of the blockchain sharding system, such as the number of shards, block size, and block interval, to automatically optimize them, aiming to further enhance the system's throughput. Experimental results show that AI-Shard can reduce the proportion of cross-shard transactions and improve the system's throughput.</div></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 333-349"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0