IET Cyber-Physical Systems: Theory and Applications最新文献_第3页

A survey on detection and localisation of false data injection attacks in smart grids

IF 1.7

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2024-05-26 DOI: 10.1049/cps2.12093

Muhammad Irfan, Alireza Sadighian, Adeen Tanveer, Shaikha J. Al-Naimi, Gabriele Oligeri

{"title":"A survey on detection and localisation of false data injection attacks in smart grids","authors":"Muhammad Irfan, Alireza Sadighian, Adeen Tanveer, Shaikha J. Al-Naimi, Gabriele Oligeri","doi":"10.1049/cps2.12093","DOIUrl":"https://doi.org/10.1049/cps2.12093","url":null,"abstract":"In the recent years, cyberattacks to smart grids are becoming more frequent. Among the many malicious activities that can be launched against smart grids, the False Data Injection (FDI) attacks have raised significant concerns from both academia and industry. FDI attacks can affect the (internal) state estimation process—critical for smart grid monitoring and control—thus being able to bypass conventional Bad Data Detection (BDD) methods. Hence, prompt detection and precise localisation of FDI attacks are becoming of paramount importance to ensure smart grids security and safety. Several papers recently started to study and analyse this topic from different perspectives and address existing challenges. Data-driven techniques and mathematical modelling are the major ingredients of the proposed approaches. The primary objective is to provide a systematic review and insights into FDI attacks joint detection and localisation approaches considering that other surveys mainly concentrated on the detection aspects without detailed coverage of localisation aspects. For this purpose, more than 40 major research contributions were selected and inspected, while conducting a detailed analysis of the methodology and objectives in relation to the FDI attacks detection and localisation. Key findings of the identified papers were provided according to different criteria, such as employed FDI attacks localisation techniques, utilised evaluation scenarios, investigated FDI attack types, application scenarios, adopted methodologies and the use of additional data. Finally, open issues and future research directions were discussed.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 4","pages":"313-333"},"PeriodicalIF":1.7,"publicationDate":"2024-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253483","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

Cyber-physical-based welding systems: Components and implementation strategies 基于网络物理的焊接系统：组件和实施策略

IF 1.7

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2024-05-01 DOI: 10.1049/cps2.12092

József Szőlősi, Péter Magyar, József Antal, Béla J. Szekeres, Gábor Farkas, Mátyás Andó

{"title":"Cyber-physical-based welding systems: Components and implementation strategies","authors":"József Szőlősi, Péter Magyar, József Antal, Béla J. Szekeres, Gábor Farkas, Mátyás Andó","doi":"10.1049/cps2.12092","DOIUrl":"10.1049/cps2.12092","url":null,"abstract":"The conditions for a feasible Cyber-Physical System (CPS) in a welding environment are explored for the manufacturing technology components while also focusing on machine learning tools. Increasing manufacturing efficiency means making digitalisation feasible for all technologies, including welding, given today's challenges. Early versions of manufacturing management, such as Computer Integrated Manufacturing, are already leading the way, and one of the latest milestones in these developments is CPS. It can be shown that the digital migration of specific sub-domains (e.g. visual inspection of the weld seam during quality assurance) is significantly more challenging and unimaginable without artificial intelligence applications. However, it is also true that the full integration needed to achieve autonomous manufacturing has yet to be fully achieved, although there is a strong demand in the industry for these CPS to work. In some areas, the digital switchover has already been prepared. However, the interconnection of these subsystems requires modern information systems or, in the case of existing ones, their upgrading to the appropriate level. This research area is set to be addressed comprehensively by initiating several projects. In the initial phase, the aim is to develop an architecture that integrates the various Information Technology applications. In this work, the digital manufacturing environment under CPS is studied, the relevant components are explored, the conditions for the transition from traditional to CPS-based manufacturing are examined and examples of planned further specific studies on the components are listed.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 4","pages":"293-312"},"PeriodicalIF":1.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12092","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141049823","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

SEIR-driven semantic integration framework: Internet of Things-enhanced epidemiological surveillance in COVID-19 outbreaks using recurrent neural networks SEIR 驱动的语义整合框架：利用递归神经网络在 COVID-19 疫情爆发中加强物联网流行病学监测

IF 1.5

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2024-04-17 DOI: 10.1049/cps2.12091

Saket Sarin, Sunil K. Singh, Sudhakar Kumar, Shivam Goyal, Brij B. Gupta, Varsha Arya, Kwok Tai Chui

{"title":"SEIR-driven semantic integration framework: Internet of Things-enhanced epidemiological surveillance in COVID-19 outbreaks using recurrent neural networks","authors":"Saket Sarin, Sunil K. Singh, Sudhakar Kumar, Shivam Goyal, Brij B. Gupta, Varsha Arya, Kwok Tai Chui","doi":"10.1049/cps2.12091","DOIUrl":"10.1049/cps2.12091","url":null,"abstract":"With the current COVID-19 pandemic, sophisticated epidemiological surveillance systems are more important than ever because conventional approaches have not been able to handle the scope and complexity of this global emergency. In response to this challenge, the authors present the state-of-the-art SEIR-Driven Semantic Integration Framework (SDSIF), which leverages the Internet of Things (IoT) to handle a variety of data sources. The primary innovation of SDSIF is the development of an extensive COVID-19 ontology, which makes unmatched data interoperability and semantic inference possible. The framework facilitates not only real-time data integration but also advanced analytics, anomaly detection, and predictive modelling through the use of Recurrent Neural Networks (RNNs). By being scalable and flexible enough to fit into different healthcare environments and geographical areas, SDSIF is revolutionising epidemiological surveillance for COVID-19 outbreak management. Metrics such as Mean Absolute Error (MAE) and Mean sqḋ Error (MSE) are used in a rigorous evaluation. The evaluation also includes an exceptional R-squared score, which attests to the effectiveness and ingenuity of SDSIF. Notably, a modest RMSE value of 8.70 highlights its accuracy, while a low MSE of 3.03 highlights its high predictive precision. The framework's remarkable R-squared score of 0.99 emphasises its resilience in explaining variations in disease data even more.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 2","pages":"135-149"},"PeriodicalIF":1.5,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140694681","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

A machine learning model for Alzheimer's disease prediction 预测阿尔茨海默病的机器学习模型

IF 1.5

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2024-03-20 DOI: 10.1049/cps2.12090

Pooja Rani, Rohit Lamba, Ravi Kumar Sachdeva, Karan Kumar, Celestine Iwendi

{"title":"A machine learning model for Alzheimer's disease prediction","authors":"Pooja Rani, Rohit Lamba, Ravi Kumar Sachdeva, Karan Kumar, Celestine Iwendi","doi":"10.1049/cps2.12090","DOIUrl":"10.1049/cps2.12090","url":null,"abstract":"Alzheimer’s disease (AD) is a neurodegenerative disorder that mostly affects old aged people. Its symptoms are initially mild, but they get worse over time. Although this health disease has no cure, its early diagnosis can help to reduce its impacts. A methodology SMOTE-RF is proposed for AD prediction. Alzheimer's is predicted using machine learning algorithms. Performances of three algorithms decision tree, extreme gradient boosting (XGB), and random forest (RF) are evaluated in prediction. Open Access Series of Imaging Studies longitudinal dataset available on Kaggle is used for experiments. The dataset is balanced using synthetic minority oversampling technique. Experiments are done on both imbalanced and balanced datasets. Decision tree obtained 73.38% accuracy, XGB obtained 83.88% accuracy and RF obtained a maximum of 87.84% accuracy on the imbalanced dataset. Decision tree obtained 83.15% accuracy, XGB obtained 91.05% accuracy and RF obtained maximum 95.03% accuracy on the balanced dataset. A maximum accuracy of 95.03% is achieved with SMOTE-RF.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 2","pages":"125-134"},"PeriodicalIF":1.5,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140227957","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

Securing the Internet of Medical Things with ECG-based PUF encryption 利用基于心电图的 PUF 加密技术确保医疗物联网的安全

IF 1.5

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2024-03-08 DOI: 10.1049/cps2.12089

Biagio Boi, Christian Esposito

{"title":"Securing the Internet of Medical Things with ECG-based PUF encryption","authors":"Biagio Boi, Christian Esposito","doi":"10.1049/cps2.12089","DOIUrl":"10.1049/cps2.12089","url":null,"abstract":"The Internet of Things (IoT) is revolutionizing the healthcare industry by enhancing personalized patient care. However, the transmission of sensitive health data in IoT systems presents significant security and privacy challenges, further exacerbated by the difficulty of exploiting traditional protection means due to poor battery equipment and limited storage and computational capabilities of IoT devices. The authors analyze techniques applied in the medical context to encrypt sensible data and deal with the unique challenges of resource-constrained devices. A technique that is facing increasing interest is the Physical Unclonable Function (PUF), where biometrics are implemented on integrated circuits' electric features. PUFs, however, demand special hardware, so in this work, instead of considering the physical device as a source of randomness, an ElectroCardioGram (ECG) can be taken into consideration to make a ‘virtual’ PUF. Such an mechanism leverages individual ECG signals to generate a cryptographic key for encrypting and decrypting data. Due to the poor stability of the ECG signal and the typical noise existing in the measurement process for such a signal, filtering and feature extraction techniques must be adopted. The proposed model considers the adoption of pre-processing techniques in conjunction with a fuzzy extractor to add stability to the signal. Experiments were performed on a dataset containing ECG records gathered over 6 months, yielding good results in the short term and valuable outcomes in the long term, paving the way for adaptive PUF techniques in this context.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 2","pages":"115-124"},"PeriodicalIF":1.5,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140257509","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

A Petri net model for Time-Delay Attack detection in Precision Time Protocol-based networks 基于精确时间协议网络的时延攻击检测 Petri 网模型

IF 1.7

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2024-02-27 DOI: 10.1049/cps2.12088

Mohsen Moradi, Amir Hossein Jahangir

引用次数: 0

Leveraging graph clustering techniques for cyber-physical system analysis to enhance disturbance characterisation 利用图聚类技术进行网络物理系统分析，加强干扰特征描述

IF 1.7

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2024-02-17 DOI: 10.1049/cps2.12087

Nicholas Jacobs, Shamina Hossain-McKenzie, Shining Sun, Emily Payne, Adam Summers, Leen Al-Homoud, Astrid Layton, Kate Davis, Chris Goes

{"title":"Leveraging graph clustering techniques for cyber-physical system analysis to enhance disturbance characterisation","authors":"Nicholas Jacobs, Shamina Hossain-McKenzie, Shining Sun, Emily Payne, Adam Summers, Leen Al-Homoud, Astrid Layton, Kate Davis, Chris Goes","doi":"10.1049/cps2.12087","DOIUrl":"10.1049/cps2.12087","url":null,"abstract":"Cyber-physical systems have behaviour that crosses domain boundaries during events such as planned operational changes and malicious disturbances. Traditionally, the cyber and physical systems are monitored separately and use very different toolsets and analysis paradigms. The security and privacy of these cyber-physical systems requires improved understanding of the combined cyber-physical system behaviour and methods for holistic analysis. Therefore, the authors propose leveraging clustering techniques on cyber-physical data from smart grid systems to analyse differences and similarities in behaviour during cyber-, physical-, and cyber-physical disturbances. Since clustering methods are commonly used in data science to examine statistical similarities in order to sort large datasets, these algorithms can assist in identifying useful relationships in cyber-physical systems. Through this analysis, deeper insights can be shared with decision-makers on what cyber and physical components are strongly or weakly linked, what cyber-physical pathways are most traversed, and the criticality of certain cyber-physical nodes or edges. This paper presents several types of clustering methods for cyber-physical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyber-physical situational awareness. The collection of these clustering techniques provide a foundational basis for cyber-physical graph interdependency analysis.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 4","pages":"392-406"},"PeriodicalIF":1.7,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139960393","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

Status, challenges, and promises of data-driven battery lifetime prediction under cyber-physical system context 网络物理系统背景下数据驱动电池寿命预测的现状、挑战和前景

IF 1.7

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2024-01-31 DOI: 10.1049/cps2.12086

Yang Liu, Sihui Chen, Peiyi Li, Jiayu Wan, Xin Li

{"title":"Status, challenges, and promises of data-driven battery lifetime prediction under cyber-physical system context","authors":"Yang Liu, Sihui Chen, Peiyi Li, Jiayu Wan, Xin Li","doi":"10.1049/cps2.12086","DOIUrl":"10.1049/cps2.12086","url":null,"abstract":"Energy storage is playing an increasingly important role in the modern world as sustainability is becoming a critical issue. Within this domain, rechargeable battery is gaining significant popularity as it has been adopted to serve as the power supplier in a broad range of application scenarios, such as cyber-physical system (CPS), due to multiple advantages. On the other hand, battery inspection and management solutions have been constructed based on the CPS architecture in order to guarantee the quality, reliability and safety of rechargeable batteries. In specific, lifetime prediction is extensively studied in recent research as it can help assess the quality and health status to facilitate the manufacturing and maintenance. Due to the aforementioned importance, the authors aim to conduct a comprehensive survey on the data-driven techniques for battery lifetime prediction, including their current status, challenges and promises. In contrast to existing literature, the battery lifetime prediction methods are studied under CPS context in this survey. Hence, the authors focus on the algorithms for lifetime prediction as well as the engineering frameworks that enable the data acquisition and deployment of prediction models in CPS systems. Through this survey, the authors intend to investigate both academic and practical values in the domain of battery lifetime prediction to benefit both researchers and practitioners.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 3","pages":"207-217"},"PeriodicalIF":1.7,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140471755","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

Oversampling and undersampling for intrusion detection system in the supervisory control and data acquisition IEC 60870-5-104 用于监控和数据采集入侵检测系统的过采样和欠采样 IEC 60870-5-104

IF 1.7

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2024-01-04 DOI: 10.1049/cps2.12085

M. Agus Syamsul Arifin, Deris Stiawan, Bhakti Yudho Suprapto, Susanto Susanto, Tasmi Salim, Mohd Yazid Idris, Rahmat Budiarto

{"title":"Oversampling and undersampling for intrusion detection system in the supervisory control and data acquisition IEC 60870-5-104","authors":"M. Agus Syamsul Arifin, Deris Stiawan, Bhakti Yudho Suprapto, Susanto Susanto, Tasmi Salim, Mohd Yazid Idris, Rahmat Budiarto","doi":"10.1049/cps2.12085","DOIUrl":"10.1049/cps2.12085","url":null,"abstract":"Supervisory control and data acquisition systems are critical in Industry 4.0 for controlling and monitoring industrial processes. However, these systems are vulnerable to various attacks, and therefore, intelligent and robust intrusion detection systems as security tools are necessary for ensuring security. Machine learning-based intrusion detection systems require datasets with balanced class distribution, but in practice, imbalanced class distribution is unavoidable. A dataset created by running a supervisory control and data acquisition IEC 60870-5-104 (IEC 104) protocol on a testbed network is presented. The dataset includes normal and attacks traffic data such as port scan, brute force, and Denial of service attacks. Various types of Denial of service attacks are generated to create a robust and specific dataset for training the intrusion detection system model. Three popular techniques for handling class imbalance, that is, random over-sampling, random under-sampling, and synthetic minority oversampling, are implemented to select the best dataset for the experiment. Gradient boosting, decision tree, and random forest algorithms are used as classifiers for the intrusion detection system models. Experimental results indicate that the intrusion detection system model using decision tree and random forest classifiers using random under-sampling achieved the highest accuracy of 99.05%. The intrusion detection system model's performance is verified using various metrics such as recall, precision, F1-Score, receiver operating characteristics curves, and area under the curve. Additionally, 10-fold cross-validation shows no indication of overfitting in the created intrusion detection system model.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 3","pages":"282-292"},"PeriodicalIF":1.7,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139386040","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

Mobile detection of cataracts with an optimised lightweight deep Edge Intelligent technique 利用优化的轻量级深度边缘智能技术移动检测白内障

IF 1.7

IET Cyber-Physical Systems: Theory and Applications Pub Date : 2024-01-01 DOI: 10.1049/cps2.12083

Dipta Neogi, Mahirul Alam Chowdhury, Mst. Moriom Akter, Md. Ishan Arefin Hossain

{"title":"Mobile detection of cataracts with an optimised lightweight deep Edge Intelligent technique","authors":"Dipta Neogi, Mahirul Alam Chowdhury, Mst. Moriom Akter, Md. Ishan Arefin Hossain","doi":"10.1049/cps2.12083","DOIUrl":"10.1049/cps2.12083","url":null,"abstract":"Testing the visual field is a valuable diagnostic tool for identifying eye conditions such as cataract, glaucoma, and retinal disease. Its quick and straightforward testing process has become an essential component in our efforts to prevent blindness. Still, the device must be accessible to the general masses. This research has developed a machine learning model that can work with Edge devices like smartphones. As a result, it is opening the opportunity to integrate the disease-detecting model into multiple Edge devices to automate their operation. The authors intend to use convolutional neural network (CNN) and deep learning to deduce which optimisers have the best results when detecting cataracts from live photos of eyes. This is done by comparing different models and optimisers. Using these methods, a reliable model can be obtained that detects cataracts. The proposed TensorFlow Lite model constructed by combining CNN layers and Adam in this study is called Optimised Light Weight Sequential Deep Learning Model (SDLM). SDLM is trained using a smaller number of CNN layers and parameters, which gives SDLM its compatibility, fast execution time, and low memory requirements. The proposed Android app, I-Scan, uses SDLM in the form of TensorFlow Lite for demonstration of the model in Edge devices.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 3","pages":"269-281"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139394799","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