IEEE Transactions on Network Science and Engineering最新文献_第3页

Outage Probability Analysis of MISO-NOMA Downlink Communications in UAV-Assisted Agri-IoT With SWIPT and TAS Enhancement

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-24 DOI: 10.1109/TNSE.2025.3545148

Yixin He;Fanghui Huang;Dawei Wang;Ruonan Zhang

{"title":"Outage Probability Analysis of MISO-NOMA Downlink Communications in UAV-Assisted Agri-IoT With SWIPT and TAS Enhancement","authors":"Yixin He;Fanghui Huang;Dawei Wang;Ruonan Zhang","doi":"10.1109/TNSE.2025.3545148","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3545148","url":null,"abstract":"In the agricultural Internet of Things (Agri-IoT), the uncrewed aerial vehicle (UAV) can serve as a mobile relay to facilitate line-of-sight transmissions for agricultural sensors, especially in farmlands with complex terrain or numerous obstructions. Additionally, the integration of techniques such as simultaneous wireless information and power transfer (SWIPT) and multiple-input single-output (MISO), combined with non-orthogonal multiple access (NOMA) communications, not only supports a higher number of device connections but also provides an essential power supply to cell-edge sensors. Motivated by the above, we propose a collaborative MISO-NOMA communication mechanism in UAV-assisted Agri-IoT. Specifically, the UAV functions as a static relay, and the NOMA-enhanced decode-and-forward relay protocol and SWIPT technique are used in the cell-center relaying UAV. The selection combining technique is employed for cell-edge sensors to obtain the optimal quality signal from multiple antennas of the base station (BS). To further improve the channel capacity, we propose a transmit antenna selection (TAS) strategy for the base station equipped with multiple antennas. Different from existing strategies (such as maximizing harvested energy or direct-link performance), the proposed TAS strategy aims to achieve optimal outage performance at cell-edge sensors, rather than suboptimal performance. Then, we derive closed-form and approximate solutions for the outage probability of cell-edge sensors. These solutions can provide significant insights into the impact of MISO-NOMA communications in UAV-assisted Agri-IoT. Finally, the simulation results indicate that the proposed TAS strategy outperforms current state-of-the-art schemes in reducing the outage probability. Moreover, our simulation experiments verify that the derived approximate solution closely aligns with the closed-form solution.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2151-2164"},"PeriodicalIF":6.7,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial Disease Propagation With Hubs

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-24 DOI: 10.1109/TNSE.2025.3545386

Ke Feng;Martin Haenggi

引用次数: 0

Cyber-Physical Defense for Heterogeneous Multi-Agent Systems Against Exponentially Unbounded Attacks on Signed Digraphs

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-24 DOI: 10.1109/TNSE.2025.3545280

Yichao Wang;Mohamadamin Rajabinezhad;Yi Zhang;Shan Zuo

{"title":"Cyber-Physical Defense for Heterogeneous Multi-Agent Systems Against Exponentially Unbounded Attacks on Signed Digraphs","authors":"Yichao Wang;Mohamadamin Rajabinezhad;Yi Zhang;Shan Zuo","doi":"10.1109/TNSE.2025.3545280","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3545280","url":null,"abstract":"Cyber-physical systems (CPSs) are subjected to attacks on both cyber and physical spaces. In reality, attackers could launch any time-varying signals. Existing literature generally addresses bounded attack signals and/or bounded-first-order-derivative attack signals. In contrast, this paper proposes a privacy-preserving fully-distributed attack-resilient bilayer defense framework to address the bipartite output containment problem for heterogeneous multi-agent systems (MASs) on signed digraphs, in the presence of exponentially unbounded false data injection (EU-FDI) attacks on both the cyber-physical layer (CPL) and observer layer (OL). First, we design attack-resilient dynamic compensators that utilize data communicated on the OL to estimate the convex combinations of the states and negative states of the leaders. To enhance the security of transmitted data, a privacy-preserving mechanism is incorporated into the observer design. The privacy-preserving attack-resilient observers address the EU-FDI attacks on the OL and guarantee the uniformly ultimately bounded (UUB) estimation of the leaders' states in the presence of the eavesdroppers. Then, by using the observers' states, fully-distributed attack-resilient controllers are designed on the CPL to further address the EU-FDI attacks on the actuators. The theoretical soundness of the proposed bilayer resilient defense framework is proved by Lyapunov stability analysis. Finally, a comparative case study for heterogeneous MASs and the application in DC microgrids as a specific case study validate the enhanced resilience of the proposed defense strategies.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2165-2179"},"PeriodicalIF":6.7,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Network Abnormal Detection With NMF-SECNN: Leveraging Deep Feature Learning for High-Precision Traffic Analysis

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-21 DOI: 10.1109/TNSE.2025.3544251

Yazhou Yuan;Ning Yu;Zhuolin Zheng;Yong Yang;Kai Ma;Zhixin Liu;Cailian Chen;Jianmin Zhang

{"title":"Enhancing Network Abnormal Detection With NMF-SECNN: Leveraging Deep Feature Learning for High-Precision Traffic Analysis","authors":"Yazhou Yuan;Ning Yu;Zhuolin Zheng;Yong Yang;Kai Ma;Zhixin Liu;Cailian Chen;Jianmin Zhang","doi":"10.1109/TNSE.2025.3544251","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3544251","url":null,"abstract":"Detection of abnormalities in industrial network traffic plays a crucial role in maintaining network system security. However, current abnormal detection models suffer from low precision, and extracting deep-level feature information from industrial network traffic is difficult. This leads to the loss of partial feature information during the detection process, thereby affecting detection efficiency. To address this issue, this paper proposes an abnormal traffic detection framework for industrial networks. By employing a Non-negative Matrix Factorization (NMF)-based method for extracting abnormal traffic features and optimizing the NMF decomposition process through constructing label consistency constraints, we facilitate effective feature extraction. Additionally, the Squeeze-and-Excitation attention mechanism is introduced into a Convolutional Neural Network (CNN) to construct a classifier that enhances detection precision without increasing complexity, enabling efficient identification of complex network traffic patterns. This results in the NMF-Squeeze-and-Excitation-CNN (NMF-SECNN) model, which combines effective feature extraction capability with a lightweight structural design, achieving superior detection performance in industrial network environments. The proposed method achieves a detection accuracy of 99.4%, representing a 5.6% improvement over baseline methods, and a recall rate of 98.2%, showcasing the model's capability to identify abnormalities across diverse scenarios. Various classification metrics confirm the model's robustness and effectiveness, demonstrating its significant advantages over traditional methods.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2069-2080"},"PeriodicalIF":6.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Noncooperative Game-Based Composite Voltage Regulation for Microgrid With Switching Topologies and Prescribed Performance

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-21 DOI: 10.1109/TNSE.2025.3538603

Yuezhi Liu;Yong Chen;Longjie Zhang;Hongru Wang;Esam Hafez

{"title":"Noncooperative Game-Based Composite Voltage Regulation for Microgrid With Switching Topologies and Prescribed Performance","authors":"Yuezhi Liu;Yong Chen;Longjie Zhang;Hongru Wang;Esam Hafez","doi":"10.1109/TNSE.2025.3538603","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3538603","url":null,"abstract":"In this paper, the voltage regulation issue of the microgrid with the switching topologies, nonlinear coupling and prescribed performance is investigated. Firstly, to remove the requirement of the global topology information, a noncooperative game layer with the fully distributed Nash equilibrium seeking (FDNES) approach is devised. In this study, the noncooperative game is performed over the switching communication topologies. Furthermore, the voltage regulation layer is formulated, which integrates the adaptive coupling observer (ACO) and prescribed performance based composite voltage regulation (PPCVR) methods. Specifically, in the ACO, an adaptive observer gain is devised to attenuate the damaging peak dynamics. For the PPCVR, the prescribed performance function based dynamic surface control strategy is formulated such that the voltage tracking error can be restrained according to the specified constraints, which will enhance the voltage regulation performance. Additionally, the PPCVR utilizes the coupling estimation by the ACO to compensate the nonlinear coupling dynamics. It is shown that the signals of the closed-loop system are semi-global uniformly and ultimately bounded (SGUUB). Finally, comparative studies and hardware-in-loop experiment by RT-LAB platform are performed to illustrate the effectiveness of the proposed voltage regulation strategy.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"1720-1731"},"PeriodicalIF":6.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Progressive Community Merging Cooperative Coevolution Algorithm for Influence Blocking Maximization in Social Networks

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-21 DOI: 10.1109/TNSE.2025.3544429

Ming Gu;Tian-Fang Zhao;Jinghui Zhong;Wei-Neng Chen

{"title":"Progressive Community Merging Cooperative Coevolution Algorithm for Influence Blocking Maximization in Social Networks","authors":"Ming Gu;Tian-Fang Zhao;Jinghui Zhong;Wei-Neng Chen","doi":"10.1109/TNSE.2025.3544429","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3544429","url":null,"abstract":"The widespread adoption of online social networks (OSNs) has facilitated social interaction and knowledge dissemination while raising concerns about extensive negative information propagation. Competitive propagation of positive and negative information can mitigate negative impacts. Influence blocking maximization (IBM) identifies a set of nodes initiating positive information propagation in OSNs to maximize blocking negative influence. This paper first introduces an effective influence blocking estimator (DPADV) that replaces computationally expensive Monte Carlo simulations. DPADV can calculate the approximate diffusion value for nodes activated by negative information in any hop neighborhood. Meanwhile, to address the challenges of complexity and computational efficiency brought about by the continuous expansion of OSNs, we propose the progressive community merging cooperative coevolution (PCMCC) algorithm. PCMCC divides the search space into communities and initializes a subpopulation for each community. Each subpopulation is responsible for optimizing a community, thereby implementing a divide-and-conquer approach. To enhance collaboration among communities and global exploration, we employed a progressive community merging strategy, supplemented by multi-population evolution strategies to guide the search towards global optima. Additionally, we developed an efficient heuristic metric for evaluating node importance, which is used to design population crossover and local search in the evolutionary scheme. Experimental results on six real-world and four synthetic networks demonstrate that PCMCC exhibits competitive performance compared to state-of-the-art algorithms, achieving near-greedy performance with lower time complexity.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2093-2106"},"PeriodicalIF":6.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-20 DOI: 10.1109/TNSE.2025.3544313

Yao Wen;Guopeng Zhang;Kezhi Wang;Kun Yang

{"title":"Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning","authors":"Yao Wen;Guopeng Zhang;Kezhi Wang;Kun Yang","doi":"10.1109/TNSE.2025.3544313","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3544313","url":null,"abstract":"To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the <italic>edge computing and <italic>split learning to propose a model-splitting allowed FL (<monospace>SFL</monospace>) framework, with the aim to minimize the training latency without loss of test accuracy. Under the <italic>synchronized global update setting, the latency to complete a round of global training is determined by the maximum latency for the clients to complete a local training session. Therefore, the training latency minimization problem (TLMP) is modelled as a minimizing-maximum problem. To solve this mixed integer nonlinear programming problem, we first propose a <italic>regression method to fit the quantitative-relationship between the <italic>cut-layer and other parameters of an AI-model, and thus, transform the TLMP into a continuous problem. Considering that the two subproblems involved in the TLMP, namely, the <italic>cut-layer selection problem for the clients and the <italic>computing resource allocation problem for the parameter-server are relative independence, an alternate-optimization-based algorithm with polynomial time complexity is developed to obtain a high-quality solution to the TLMP. Extensive experiments are performed on a popular DNN-model <italic>EfficientNetV2 using dataset MNIST, and the results verify the validity and improved performance of the proposed <monospace>SFL</monospace> framework.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2081-2092"},"PeriodicalIF":6.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resilient Reliability/Loss-Based Distribution Network Reconfiguration: A Strategy Against FDI Attacks During State Estimation Procedure

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-20 DOI: 10.1109/TNSE.2025.3542632

Reza Behnam;Hamid Reza Baghaee;Gevork B. Gharehpetian;Roya Ahmadiahangar;Argo Rosin

{"title":"Resilient Reliability/Loss-Based Distribution Network Reconfiguration: A Strategy Against FDI Attacks During State Estimation Procedure","authors":"Reza Behnam;Hamid Reza Baghaee;Gevork B. Gharehpetian;Roya Ahmadiahangar;Argo Rosin","doi":"10.1109/TNSE.2025.3542632","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3542632","url":null,"abstract":"One of the intrinsic properties of Distribution networks, resilience, is the ability to resist, adjust, and recover from extreme, high-impact, low-probability events such as earthquakes, floods, hurricanes, thunderstorms, and cyber and physical attacks. Besides, the uncertainty of the network elements has a significant effect on the operation of the distribution system. Operators require methods and planning strategies to improve grid resilience. Distribution network reconfiguration (DNR) enhances reliability and reduces power losses. This paper proposes an application of DNR as a strategy to get a resilient configuration against false data injection (FDI) attack during state estimation (SE) procedure, minimize power losses, and improve the reliability of the distribution network simultaneously. In this paper, a driving training-based optimization (DTBO) method is exploited for DNR to demonstrate the effectiveness of the proposed strategy. The proposed strategy is tested on IEEE 33-bus, 69-bus, and 118-bus systems to reduce FDI attack impact on power measurements, power loss, and energy not supplied (ENS). The proposed DNR is evaluated by offline digital time-domain simulations on the distribution test systems in the MATLAB software environment. The simulations and comparisons of the proposed DNR strategy effectively prove the proposed strategy's effectiveness, accuracy, and authenticity.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"1994-2006"},"PeriodicalIF":6.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Motion Behaviour Based Communication Range Estimation of Adversarial Drone Swarms

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-19 DOI: 10.1109/TNSE.2025.3542401

Lan Mu;Tong Duan;Jiangxing Wu;Yawen Wang;Zhen Zhang

{"title":"Motion Behaviour Based Communication Range Estimation of Adversarial Drone Swarms","authors":"Lan Mu;Tong Duan;Jiangxing Wu;Yawen Wang;Zhen Zhang","doi":"10.1109/TNSE.2025.3542401","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3542401","url":null,"abstract":"Communication range is a crucial parameter that impacts the dynamic responses of traditional drone swarms, and accurate estimation of the communication range of adversarial drone swarms is essential to understanding the inner interaction of swarm members and designing more precise anti-swarm countermeasures. Especially when the drones in an adversarial swarm use short-range communications to exchange data, their internal communication behaviours are difficult to reconnoiter, and precisely estimating the swarm's communication range purely based on the sensed motion behaviours is a tough challenge. In this work, the principles and algorithms for communication range estimation of the artificial potential field based adversarial drone swarm are investigated. First, the attack and invasion based interaction approaches are proposed to trigger the swarm's dynamic responses, and it is found that the invasion based interaction approach is more effective when the adversarial swarm is under optimal steady-state; second, to adequately find the true communication range value while minimizing the impact on the adversarial swarm, an optimization framework is established to compute the intruder's optimal trajectory; finally, numerical simulations and comparative analyses are conducted, which demonstrate the effectiveness and advantages of the proposed motion behaviour based communication range estimation approaches.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"1953-1966"},"PeriodicalIF":6.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OVP-FL: Outsourced Verifiable Privacy-Preserving Federated Learning

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-19 DOI: 10.1109/TNSE.2025.3543601

Shilong Li;Xiaochao Wei;Hao Wang

{"title":"OVP-FL: Outsourced Verifiable Privacy-Preserving Federated Learning","authors":"Shilong Li;Xiaochao Wei;Hao Wang","doi":"10.1109/TNSE.2025.3543601","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3543601","url":null,"abstract":"Federated learning, a prominent method, eliminates the need for users to upload their original data, enabling collaborative model training through the transmission of only the gradient information of their models. However, a deeper exploration of federated learning has uncovered vulnerabilities wherein the gradient information uploaded by users can be exploited by adversaries to reconstruct users' original data. Additionally, ensuring the integrity of the aggregation result remains a primary focus of research to protect users' legitimate interests. To address these issues simultaneously, this study proposes a new framework called Outsourced Verifiable Privacy-Preserving Federated Learning. This framework aims to provide reliable privacy protection to users. Additionally, it includes a validation function to detect malicious aggregation models submitted by the server, providing an almost cost-free solution that accommodates the possibility of dropout. Finally, the paper concludes with a comprehensive security analysis that evaluates the reliability of the scheme using different datasets. In comparison to VerifyNet, our scheme demonstrates a significant advantage, with an approximate 100x improvement in the overall performance. And, the additional drop overhead is negligible. Simulation experiments demonstrate a significant improvement, including a reduction in communication and computation costs, showcasing the efficacy compared to existing verifiable privacy-preserving federated learning methods.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2057-2068"},"PeriodicalIF":6.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0