IEEE Journal of Selected Areas in Sensors最新文献

A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues, and More

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-12-30 DOI: 10.1109/JSAS.2024.3523856

Vikas Hassija;Vinay Chamola;Rajdipta De;Soham Das;Arjab Chakrabarti;Kuldip Singh Sangwan;Amit Pandey

{"title":"A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues, and More","authors":"Vikas Hassija;Vinay Chamola;Rajdipta De;Soham Das;Arjab Chakrabarti;Kuldip Singh Sangwan;Amit Pandey","doi":"10.1109/JSAS.2024.3523856","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3523856","url":null,"abstract":"Digital Twins, sophisticated digital replicas of physical entities, have been gaining significant attention, especially after NASA's endorsement, and are poised to revolutionize numerous fields, such as medicine and construction. These advanced models offer dynamic, real-time simulations, leveraging enabling technologies, such as artificial intelligence, machine learning, IoT, cloud computing, and Big Data analytics to enhance their functionality and applicability. In the medical field, Digital Twins facilitate personalized treatment plans and predictive maintenance of medical equipment by simulating human organs with precision. In construction, they enable efficient building design and urban planning, optimizing resource use, and reducing costs through predictive maintenance. Startups are innovatively employing Digital Twins in various sectors, from smart cities—where they optimize traffic flow, energy consumption, and waste management—to industrial machinery, ensuring predictive maintenance and minimizing downtime. This survey delves into the diverse use cases, market potential, and challenges of Digital Twins, such as data security and interoperability, while emphasizing their transformative impact on industries. The future prospects are promising, with continuous advancements in AI, ML, IoT, and cloud computing driving further expansion and application of Digital Twin technologies.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"2 ","pages":"84-107"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818423","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105561","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

FallAware: An Explainable Learning Approach to Robust Fall Detection With WiFi

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-12-20 DOI: 10.1109/JSAS.2024.3520517

Sai Deepika Regani;Beibei Wang;Yuqian Hu;Guozhen Zhu;K. J. Ray Liu

{"title":"FallAware: An Explainable Learning Approach to Robust Fall Detection With WiFi","authors":"Sai Deepika Regani;Beibei Wang;Yuqian Hu;Guozhen Zhu;K. J. Ray Liu","doi":"10.1109/JSAS.2024.3520517","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3520517","url":null,"abstract":"Indoor falls have proved fatal to many people due to a lack of timely assistance. Existing approaches for fall detection using cameras and wearable devices intrude on privacy and cause inconvenience. Passive sensing approaches using radar have limited coverage and demand dense deployment. Current solutions using commercial off-the-shelf (COTS) WiFi devices are either environment-dependent or lack extensive testing in real environments to confidently assess false alarm rates. In this work, we propose a fusion approach to detect falls with COTS WiFi, where we leverage signal processing techniques to extract environment-independent features, and use a neural network to detect differentiating patterns in those features. We designed a lightweight long short-term memory-based neural network with only 21 k parameters that can easily be deployed on edge devices. We further provide a framework to explain the network's behavior that supports a calibration-free design. Our proposed <italic>FallAware system's detection performance has been extensively tested on <inline-formula><tex-math>$sim$</tex-math></inline-formula>2400 falls gathered from over 25 volunteers in 5 different environments. In addition, we conducted long-term false alarm testing in 6 diverse environments for a total duration of 21 months. The results show that <italic>FallAware can detect falls with an average detection rate of 94.1% in unseen environments with <inline-formula><tex-math>$< $</tex-math></inline-formula>5 false alarms per month in single-person occupancy homes.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"2 ","pages":"71-83"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10810750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105560","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

Nonparametric Multitarget Data Association and Tracking for Multistatic Radars 多基地雷达非参数多目标数据关联与跟踪

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-12-16 DOI: 10.1109/JSAS.2024.3517513

S. Sruti;K. Giridhar

{"title":"Nonparametric Multitarget Data Association and Tracking for Multistatic Radars","authors":"S. Sruti;K. Giridhar","doi":"10.1109/JSAS.2024.3517513","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3517513","url":null,"abstract":"Multistatic radar systems provide better detection performance for stealth airborne platforms and are resilient to single-point failures. However, when multiple targets are present over the radar surveillance region, incorrect target associations to the measurements could create ghost targets. Computationally efficient and accurate de-ghosting and tracking multiple targets are critical tasks in real-time distributed radar systems. By exploiting the geometry of the measurement model in the association process, we propose a novel and efficient data association approach followed by a tracking algorithm in this work. It utilizes the time-of-arrival and bistatic Doppler frequency measurements of the targets with respect to different transmitter–receiver pairs to accurately determine and track the 3-D positions and velocities of the targets. The proposed approach is nonparametric as it does not need any assumption on the initial states or the number of targets and their motion models, but only uses the knowledge of the geometry of the terrestrial radar sensors. This nonparametric data association and tracking (NPDAT) algorithm is tested with multiple targets in two significant scenarios. First, all the targets are simultaneously present in the region, and then, targets arrive and depart the region based on a random arrival pattern. Our approach precisely tracks targets even during crossover and also tracks fast-maneuvering targets. This NPDAT algorithm is compared with popular existing methods and is shown to exhibit superior performance in estimation accuracy and maneuvering target tracking ability, even while enjoying a significantly lower time and implementation complexity.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"2 ","pages":"28-39"},"PeriodicalIF":0.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10803016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912561","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

SrcSense: Robust WiFi-Based Motion Source Recognition via Signal-Informed Deep Learning SrcSense：基于信号的深度学习的基于wifi的鲁棒运动源识别

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-12-16 DOI: 10.1109/JSAS.2024.3517514

Guozhen Zhu;Beibei Wang;Weihang Gao;Yuqian Hu;Chenshu Wu;K. J. Ray Liu

{"title":"SrcSense: Robust WiFi-Based Motion Source Recognition via Signal-Informed Deep Learning","authors":"Guozhen Zhu;Beibei Wang;Weihang Gao;Yuqian Hu;Chenshu Wu;K. J. Ray Liu","doi":"10.1109/JSAS.2024.3517514","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3517514","url":null,"abstract":"As WiFi has become a ubiquitous medium for communication, its role in sensing applications has expanded. However, the current WiFi sensing applications are limited by their assumption that any detected motion signifies human activity, overlooking the potential impact of nonhuman subjects. Existing attempts to recognize the interference from nonhuman motion impose stringent requirements regarding device positioning, data quality, environmental complexity, and nonhuman subject categories. In this study, we design a robust deep learning framework, SrcSense (“<bold>Sou<bold>rce <bold>Sense”), to recognize the motion source with WiFi signals through the wall. SrcSense extracts environment-independent features from single-link WiFi. We investigate the performance of popular deep neural networks and explore the efficacy of transferring pretrained models to WiFi sensing tasks. We implement SrcSense and evaluate the performance in five real-world complex environments with commodity WiFi devices. With a challenging dataset considering large pets, diverse human activities and multiple subjects coexisting cases, SrcSense achieves an average validation accuracy of 95.84% across five distinct environments and an average testing accuracy of 91.71% in unseen environments without further model training or parameter tuning. By accumulating 20 s of WiFi data, SrcSense can achieve an elevated recognition accuracy of 99.77% with ResNet-50. These results underline the robustness of our approach and its readiness for integration into ubiquitous intelligent Internet of Things (IoT) systems and applications.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"2 ","pages":"40-53"},"PeriodicalIF":0.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10803907","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975940","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 Comprehensive Planning Framework for Connected Elevated LiDAR Sensors 互联高架激光雷达传感器的综合规划框架

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-11-25 DOI: 10.1109/JSAS.2024.3506478

Nawfal Guefrachi;Michael C. Lucic;Mohammad Yassen;Hakim Ghazzai;Ahmad Alsharoa

引用次数: 0

Assessing the Impact of Distractions Using a Virtual-Reality-Based GO/NOGO Task 使用基于虚拟现实的GO/NOGO任务评估分心的影响

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-11-25 DOI: 10.1109/JSAS.2024.3506476

Chun-Chuan Chen;Yan-Qing Chen;Tzu-Yun Yeh;Chia-Ru Chung;Shih-Ching Yeh;Eric Hsiao-Kuang Wu

{"title":"Assessing the Impact of Distractions Using a Virtual-Reality-Based GO/NOGO Task","authors":"Chun-Chuan Chen;Yan-Qing Chen;Tzu-Yun Yeh;Chia-Ru Chung;Shih-Ching Yeh;Eric Hsiao-Kuang Wu","doi":"10.1109/JSAS.2024.3506476","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3506476","url":null,"abstract":"The GO/NOGO task provides an objective assessment of a subject's attention and response inhibition and is typically given to subjects without any unexpected distractions. Studying the impact of distractions is important from the therapeutic viewpoint as distractions may occur during exposure therapy and degrade treatment efficacy. In this study, we utilized a virtual classroom integrated with electroencephalogram (EEG) for a GO/NOGO task with multimode environmental distractions to study the impact of distractions on behavioral and neuronal activities. Thirty healthy male adults were recruited. Statistical analysis and machine learning methods were employed to analyze the behavioral and neuronal data. The results demonstrated no significant behavioral differences between conditions with and without distractions. However, the impacts of distractions manifested in the enhancement of frequency-specific power, including theta, alpha, and gamma oscillations in GO trials, as well as beta power and the N200 peak in NOGO trials, highlighting their role in attention regulation and response inhibition. Finally, machine learning result analysis identified significant differences between conditions with and without distractions using EEG features, achieving an accuracy rate of 98.3%. In conclusion, we found that introducing distractions into a GO/NOGO task provides a deeper understanding of the neuronal correlates of distractions, and these findings can inform the development of therapeutic strategies for attention-related disorders.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"2 ","pages":"21-27"},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10767192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875144","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 Compact Home-Based Training System for Preventing Frailty Using a Mapping Model and Cross-Dataset Transfer Learning 基于映射模型和跨数据集迁移学习的家庭预防脆弱性训练系统

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-11-19 DOI: 10.1109/JSAS.2024.3502001

Lizheng Liu;Hsuan Hu;Shih-Ching Yeh;Eric Hsiao-Kuang Wu;Chun-Chuan Chen

{"title":"A Compact Home-Based Training System for Preventing Frailty Using a Mapping Model and Cross-Dataset Transfer Learning","authors":"Lizheng Liu;Hsuan Hu;Shih-Ching Yeh;Eric Hsiao-Kuang Wu;Chun-Chuan Chen","doi":"10.1109/JSAS.2024.3502001","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3502001","url":null,"abstract":"Frailty is becoming a more serious issue as the population ages. Numerous studies have shown that exercise can effectively slow the development of frailty. Compared with vigorous exercise, Baduanjin (BDJ), a kind of traditional Chinese Qigong with eight simple movements, is more suitable for frailty patients. BDJ has been used to train frailty patients by physical therapists. To provide an enhanced training method, we designed a lightweight family-based frailty training system via a virtual BDJ coach. To achieve a compact system, we use a webcam as the main device. The system also supports the Kinect framework. We use pose estimation and motion recognition methods to analyze the user's movements. In addition, a novel transfer learning method is proposed. We designed a mapping model called “Skeleton Mapnet” to convert skeletal data from different frameworks. This method enables datasets from different frameworks to share classification models. It can also mix skeletal data from different frameworks to solve the lack of webcam datasets. Such a design allows the system to be easily ported into other platforms. In addition, the system is also suitable for the use of the artificial intelligence of things. Our design ensures that frailty patients can easily learn and operate the system.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"2 ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10757399","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859185","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

Smart Pressure E-Mat for Human Sleeping Posture and Dynamic Activity Recognition 用于人体睡眠姿势和动态活动识别的智能压力电子垫

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-11-18 DOI: 10.1109/JSAS.2024.3501213

Liangqi Yuan;Yuan Wei;Jia Li

{"title":"Smart Pressure E-Mat for Human Sleeping Posture and Dynamic Activity Recognition","authors":"Liangqi Yuan;Yuan Wei;Jia Li","doi":"10.1109/JSAS.2024.3501213","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3501213","url":null,"abstract":"With the emphasis on healthcare, early childhood education, and fitness, noninvasive measurement and recognition methods have received more attention. Pressure sensing has been extensively studied because of its advantages of simple structure, easy access, visualization application, and harmlessness. This article introduces a Smart Pressure e-Mat (SPeM) system based on piezoresistive material, Velostat, for human monitoring applications, including recognition of sleeping postures, sports, and yoga. After a subsystem scans the e-mat readings and processes the signal, it generates a pressure image stream. Deep neural networks are used to fit and train the pressure image stream and recognize the corresponding human behavior. Four sleeping postures and 13 dynamic activities inspired by Nintendo Switch Ring Fit Adventure are used as a preliminary validation of the proposed SPeM system. The SPeM system achieves high accuracies in both applications, demonstrating the high accuracy and generalizability of the models. Compared with other pressure sensor-based systems, SPeM possesses more flexible applications and commercial application prospects, with reliable, robust, and repeatable properties.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"2 ","pages":"9-20"},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10756666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859235","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

Terahertz Metasurface With High-Q Fano Resonance for Bio-Sensing 用于生物传感的具有高 Q 值法诺共振的太赫兹元表面

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-10-29 DOI: 10.1109/JSAS.2024.3487487

Linda Shao;Zhihang Wang;Ning Mu;Tunan Chen;Weiren Zhu

{"title":"Terahertz Metasurface With High-Q Fano Resonance for Bio-Sensing","authors":"Linda Shao;Zhihang Wang;Ning Mu;Tunan Chen;Weiren Zhu","doi":"10.1109/JSAS.2024.3487487","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3487487","url":null,"abstract":"High-quality factor Fano resonances offer exceptional potential for the creation of ultrasensitive refractive index sensors owing to their capacity to facilitate robust interactions between electromagnetic waves and analytes. In this article, we introduce a general approach for designing sensitive metasurface sensors leveraging high-Q Fano resonances. The metasurface, composed of metallic strips varying in length, produces the characteristic Fano line shape through the interference of bright and dark modes. Our findings reveal a remarkable sensitivity of up to 0.473 THz/RIU at 2.37 THz, with a maximum resonance Q value attainment of 38.12. The tunable properties of Fano resonances can be fine-tuned by adjusting geometric parameters. As a demonstration of the practical applicability of these high-Q resonances, we conducted experimental assessments of the metasurface sensor's performance in detecting the concentrations of bovine serum albumin and glucose. Notably, both the resonance frequency and amplitude undergo significant changes in response to increasing analyte concentrations. This allows for rapid and precise determination of both the concentration and molecule type based on observed frequency shifts. Our strategy paves the way for the design of ultrasensitive real-time sensors operating in the terahertz regime.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"1 ","pages":"272-279"},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737402","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736235","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 Dynamic Bayesian Multichannel Fusion Scheme for Heart Rate Monitoring With Ballistocardiograph Signals in Free-Living Environments 自由生活环境中使用球心动图信号进行心率监测的动态贝叶斯多通道融合方案

IEEE Journal of Selected Areas in Sensors Pub Date : 2024-10-23 DOI: 10.1109/JSAS.2024.3485544

Jun Qi;Ruilin Cai;Qing Liu;Wei Wang;Jieming Ma;Jianjun Chen

{"title":"A Dynamic Bayesian Multichannel Fusion Scheme for Heart Rate Monitoring With Ballistocardiograph Signals in Free-Living Environments","authors":"Jun Qi;Ruilin Cai;Qing Liu;Wei Wang;Jieming Ma;Jianjun Chen","doi":"10.1109/JSAS.2024.3485544","DOIUrl":"https://doi.org/10.1109/JSAS.2024.3485544","url":null,"abstract":"Ballistocardiograph (BCG) stands out as a noncontact technology for heart monitoring, offering a wealth of cardiovascular parameter information. Its applications have overshadowed traditional electrocardiogram particularly for free-living environment, such as home monitoring, in recent years. However, challenges arise from the susceptibility of BCG signals to positional variations, bodily movements, and systemic noise, posing formidable obstacles for detection algorithms. In this article, we propose a novel interbeat interval detection approach with the dynamic Bayesian network for multichannel fusion, in terms of five unique indicators for the precise localization of cardiac activity from extracted features. We also introduce a peak detection method to locate the positions of all HIJK complex within BCG segment and evaluate the generalization of the proposed method in the simulated environment of different noise generation. The results from the dataset comprising 36 healthy subjects and four cardiovascular disease patients show that the proposed method exhibits average coverage rate up to 96.15%; the mean square error is 0.04 compared with single-channel measures, which suggest the potential of our method in assisting the long-term heartbeat monitoring in free-living environments.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"1 ","pages":"261-271"},"PeriodicalIF":0.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10729850","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713978","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