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

Learning-Augmented Online Minimization of Age of Information and Transmission Costs 信息时代和传输成本的学习增强在线最小化

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-18 DOI: 10.1109/TNSE.2025.3561736

Zhongdong Liu;Keyuan Zhang;Bin Li;Yin Sun;Y. Thomas Hou;Bo Ji

{"title":"Learning-Augmented Online Minimization of Age of Information and Transmission Costs","authors":"Zhongdong Liu;Keyuan Zhang;Bin Li;Yin Sun;Y. Thomas Hou;Bo Ji","doi":"10.1109/TNSE.2025.3561736","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3561736","url":null,"abstract":"We consider a discrete-time system where a resource-constrained source (e.g., a small sensor) transmits its time-sensitive data to a destination over a time-varying wireless channel. Each transmission incurs a fixed transmission cost (e.g., energy cost), and no transmission results in a staleness cost represented by the <italic>Age-of-Information. The source must balance the tradeoff between transmission and staleness costs. To address this challenge, we develop a robust online algorithm to minimize the sum of transmission and staleness costs, ensuring a worst-case performance guarantee. While online algorithms are robust, they are usually overly conservative and may have a poor average performance in typical scenarios. In contrast, by leveraging historical data and prediction models, machine learning (ML) algorithms perform well in average cases. However, they typically lack worst-case performance guarantees. To achieve the best of both worlds, we design a learning-augmented online algorithm that exhibits two desired properties: (i) <italic>consistency: closely approximating the optimal offline algorithm when the ML prediction is accurate and trusted; (ii) <italic>robustness: ensuring worst-case performance guarantee even ML predictions are inaccurate. Finally, we perform extensive simulations to show that our online algorithm performs well empirically and that our learning-augmented algorithm achieves both consistency and robustness.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3480-3496"},"PeriodicalIF":7.9,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891141","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

Formation and Obstacle Avoidance Control Based on Multi-Agent Reinforcement Learning 基于多智能体强化学习的编队与避障控制

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-17 DOI: 10.1109/TNSE.2025.3561744

Fuxi Niu;Xiaohong Nian;Chao Pan;Xunhua Dai;Haibo Wang;Hongyun Xiong

引用次数: 0

Effects of Cyberattacks on Regional Traffic Networks in a Connected Vehicle Environment 车联网环境下网络攻击对区域交通网络的影响

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-17 DOI: 10.1109/TNSE.2025.3560677

Liangwen Wang;Heng Ding;Xiaoyan Zheng;Weihua Zhang

{"title":"Effects of Cyberattacks on Regional Traffic Networks in a Connected Vehicle Environment","authors":"Liangwen Wang;Heng Ding;Xiaoyan Zheng;Weihua Zhang","doi":"10.1109/TNSE.2025.3560677","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3560677","url":null,"abstract":"Connected Vehicle (CV) technology aims to enhance information transmission and resource sharing. However, the open-access environment makes the CVs vulnerable to cyberattacks, causing concerns about traffic downgrading or even paralysis. Existing studies on the traffic impact of cyberattacks primarily focus on individual vehicles or queues, ignoring the macro impact of attacks on the road network. To analyze the impact of cyberattacks on road network at a regional level, this paper carries out three works. First, the traffic modelling under two cyberattack models from the queuing scale and the routing scale is provided, respectively. Second, we analyze the general analytical form of cyberattacks causing impacts on regional traffic based on Macroscopic fundamental diagram (MFD) theory and develop an evaluation index of road network efficiency under cyberattacks. Third, according to these two models and the evaluation index, the impacts of cyberattacks on the traffic performance of the road network are analyzed. The results showed that both scale models of cyberattacks affect the MFD, and the road network performance, which makes the network more vulnerable. The results also showed that the rerouting capability of CVs has a significant different effect on the road network performance under different cyberattacks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3434-3450"},"PeriodicalIF":7.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891067","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

User-Centric Clustering and Beamforming Design for Satellite-Assisted Cell-Free Networks 卫星辅助无蜂窝网络的以用户为中心的聚类和波束形成设计

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-16 DOI: 10.1109/TNSE.2025.3561505

Yunseong Lee;Chihyun Song;Donghyun Lee;Wonjong Noh;Sungrae Cho

{"title":"User-Centric Clustering and Beamforming Design for Satellite-Assisted Cell-Free Networks","authors":"Yunseong Lee;Chihyun Song;Donghyun Lee;Wonjong Noh;Sungrae Cho","doi":"10.1109/TNSE.2025.3561505","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3561505","url":null,"abstract":"The exponential growth in mobile traffic has driven significant interest in cell-free networks, particularly those integrated with satellites, to support high data rates and overcome geographic limitations. In this paper, we propose a novel dynamic clustering and beamforming control that maximizes the minimum data rate in satellite-assisted user-centric cell-free networks. Specifically, we formulate a nonconvex max-min fairness problem to optimize clustering and beamforming under a user-centric cluster size and transmit power constraints. To find a solution, this problem is decomposed into two subproblems: clustering and beamforming. A modified Gale-Shapley-based user-centric clustering algorithm is proposed for the clustering subproblem, which is solved on a long-term basis using statistic channel information. The beamforming subproblem transform is transformed into a semidefinite problem using linear approximation and a linear matrix inequality. We propose a robust beamforming algorithm that considers imperfect instantaneous channel state information, solved on a short-term basis. Lastly, we analyze the computational complexity, revealing that the proposed scheme has polynomial complexity. We evaluate its performance under user mobility in satellite-assisted cell-free networks. The proposed algorithm offers significant performance gains, achieving up to a 15.15% higher minimum data rate and a 14.14% higher average data rate than zero-forcing beamforming and distance-based clustering benchmark schemes.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3467-3479"},"PeriodicalIF":7.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891250","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

Dual-Agent DRL-Based Service Placement, Task Scheduling, and Resource Allocation for Multi-Sensor and Multi-User Edge Computing Networks 基于双agent drl的多传感器多用户边缘计算网络服务布局、任务调度与资源分配

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-14 DOI: 10.1109/TNSE.2025.3560402

Wenhao Fan;Xiongfei Chun;Zhiyu Fan;Ruimin Zhang;Siyang Liu;Yuan'an Liu

{"title":"Dual-Agent DRL-Based Service Placement, Task Scheduling, and Resource Allocation for Multi-Sensor and Multi-User Edge Computing Networks","authors":"Wenhao Fan;Xiongfei Chun;Zhiyu Fan;Ruimin Zhang;Siyang Liu;Yuan'an Liu","doi":"10.1109/TNSE.2025.3560402","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3560402","url":null,"abstract":"Multi-sensor and multi-user edge computing networks can support various data-intensive Internet of Things (IoT) applications, which exhibit the characteristic of a task-data-decoupled pattern. In this scenario, tasks generated by users can be scheduled to edge servers (ESs), where the ESs compute the task results and return them to the users. Meanwhile, a large number of sensors collect and upload data to the ESs to meet the requirements of task processing. However, existing works mainly consider the task-data-coupled pattern, overlooking the cost associated with sensor data collection processes. Therefore, we propose a joint optimization problem involving service placement, task scheduling, and resource allocation to minimize the total system cost, defined as the weighted sum of delay and energy consumption of each user and sensor. We jointly optimize service placement, user task scheduling, transmit power allocation for sensors and users, computing resource allocation for both the ESs and the cloud server (CS), and transmission rate allocation for ES-ES and ES-CS connections. Considering the differences in the update frequencies of the optimization variables, we propose a dual-agent Deep Reinforcement Learning (DRL) algorithm, which utilizes two SD3 (Softmax Deep Double Deterministic Policy Gradients)-based DRL agents to make service placement and task scheduling decisions asynchronously, while embedding two optimization subroutines to solve the optimal transmit power allocation, computing resource and transmission rate allocations using numerical methods. The complexity and convergence of the algorithm are analyzed and extensive experiments are conducted in 8 different scenarios, demonstrating the superiority of our scheme compared to three other reference schemes.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3416-3433"},"PeriodicalIF":7.9,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891060","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

Instantiating a Diffusion Network Model to Support Wildfire Management 实例化支持野火管理的扩散网络模型

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-14 DOI: 10.1109/TNSE.2025.3559681

Marc Demange;Alessia Di Fonso;Gabriele Di Stefano;Pierpaolo Vittorini

{"title":"Instantiating a Diffusion Network Model to Support Wildfire Management","authors":"Marc Demange;Alessia Di Fonso;Gabriele Di Stefano;Pierpaolo Vittorini","doi":"10.1109/TNSE.2025.3559681","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3559681","url":null,"abstract":"Wildfires require effective responses considering multiple constraints and conflicting goals. We provide a methodology and a tool enabling stakeholders to compute risk maps and use them in practical and realistic scenarios. The territory is modeled as a network where nodes are land patches subject to fire and links model the probability of fire spread from one patch to another. We discuss a risk function and show how to compute it effectively. We show how to instantiate the model on a real landscape. The methodology describes how to compute each patch's borders and probabilities of ignition and how to estimate the probability of fire spreading from one patch to a neighboring one. We embed the methodology into an ad-hoc modular tool-chain using geographical data, a fire simulator and geospatial tools. As a proof-of-concept, the tool-chain is applied in three different experiments on a region of Corsica, France, aiming at simulating a realistic scenario and measuring the sensitivity of the methodology with increasing wind speed or variable wind directions. We finally introduce the web application that incorporates the tool-chain and enables users to manipulate the model intuitively through an interactive map, evaluate what-if scenarios, and simulate the effects of different fire preventive measures.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3374-3388"},"PeriodicalIF":6.7,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492276","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

TEAM: Temporal Adversarial Examples Attack Model Against Network Intrusion Detection System Applied to RNN 团队：针对网络入侵检测系统的时间对抗实例攻击模型应用于RNN

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-11 DOI: 10.1109/TNSE.2025.3560027

Ziyi Liu;Dengpan Ye;Long Tang;Yunming Zhang;Jiacheng Deng;Wanrong Kuang

{"title":"TEAM: Temporal Adversarial Examples Attack Model Against Network Intrusion Detection System Applied to RNN","authors":"Ziyi Liu;Dengpan Ye;Long Tang;Yunming Zhang;Jiacheng Deng;Wanrong Kuang","doi":"10.1109/TNSE.2025.3560027","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3560027","url":null,"abstract":"With the development of artificial intelligence, neural networks play a key role in network intrusion detection systems (NIDS). Despite the tremendous advantages, neural networks are susceptible to adversarial attacks. To improve the reliability of NIDS, many research has been conducted and plenty of solutions have been proposed. However, the existing solutions rarely consider the adversarial attacks against recurrent neural networks (RNN) with time steps, which would greatly affect the application of NIDS in real world. Therefore, we first propose a novel RNN adversarial attack model based on feature reconstruction called <bold>Temporal adversarial <bold>Examples <bold>Attack <bold>Model <bold>(TEAM), which applied to time series data and reveals the potential connection between adversarial and time steps in RNN. That is, the past adversarial examples within the same time steps can trigger further attacks on current or future original examples. Moreover, TEAM leverages Time Dilation (TD) to effectively mitigates the effect of temporal among adversarial examples within the same time steps. Experimental results show that in most attack categories, TEAM improves the misjudgment rate of NIDS on both black and white boxes, making the misjudgment rate reach more than 97.65%. Meanwhile, the maximum increase in the misjudgment rate of the NIDS for subsequent original examples exceeds 95.57%.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3400-3415"},"PeriodicalIF":6.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492415","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

f-AnoGAN for Unsupervised Attack Detection in SDN Environment SDN环境下无监督攻击检测的anogan

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-10 DOI: 10.1109/TNSE.2025.3558936

Vitor Gabriel da Silva Ruffo;Luiz Fernando Carvalho;Jaime Lloret;Mario Lemes Proença Jr

{"title":"f-AnoGAN for Unsupervised Attack Detection in SDN Environment","authors":"Vitor Gabriel da Silva Ruffo;Luiz Fernando Carvalho;Jaime Lloret;Mario Lemes Proença Jr","doi":"10.1109/TNSE.2025.3558936","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3558936","url":null,"abstract":"Network management solutions remain essential for proper network service delivery. The software-defined networking (SDN) paradigm brought flexibility and programmability to today's large-scale networks, easing their governance. Another critical factor in the quality of network services is network security for protection against cyberattacks. This work proposes an unsupervised volume anomaly detection and mitigation system for securing SDN environments. We implement a fast AnoGAN (f-AnoGAN) to model legitimate user behavior and identify outlier samples. The generative network is trained on a low-dimensional representation of network traffic to reduce computational overhead. The f-AnoGAN model performance is further investigated through hyperparameter tuning and ablation study. The security system is evaluated on four public datasets: Orion, CIC-DDoS2019, CIC-IDS2017, and TON_IoT. We implement state-of-the-art alternative models for comparison analysis, namely Autoencoder, BiGAN, and FID-GAN. The f-AnoGAN presents improved class separation capacity and anomaly identification performance compared to the other models. The anomaly mitigation module can drop between 95% and 99% of malign traffic, supporting network resilience and correct functioning.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3271-3285"},"PeriodicalIF":6.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492428","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

Event-Triggered Zero-Gradient-Sum Distributed Constrained Optimization Over Jointly Connected Balanced Digraphs 联合连接平衡有向图上事件触发的零梯度和分布约束优化

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-10 DOI: 10.1109/TNSE.2025.3559905

Xinli Shi;Ying Wan;Guanghui Wen;Xinghuo Yu

{"title":"Event-Triggered Zero-Gradient-Sum Distributed Constrained Optimization Over Jointly Connected Balanced Digraphs","authors":"Xinli Shi;Ying Wan;Guanghui Wen;Xinghuo Yu","doi":"10.1109/TNSE.2025.3559905","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3559905","url":null,"abstract":"In the realm of distributed optimization (DO), it is expected to design a distributed algorithm that has a lower communication burden while handling general constraints over switching graphs. One promising approach is the zero-gradient-sum (ZGS) algorithm. However, existing ZGS-based discrete-time algorithms are limited to unconstrained DO on fixed network structures. This paper addresses this gap by first providing an event-triggered ZGS (ET-ZGS) algorithm for solving equality-constrained DO over uniformly jointly strongly connected (UJSC) and balanced digraphs. Sufficient conditions on the fixed step size are derived to guarantee the convergence for switching graphs. Specifically, when applied to fixed connected graphs, the proposed algorithm achieves linear convergence in solving equality-constrained DO with typical ET strategies; for UJSC graphs, it enables linear convergence in solving unconstrained DO. To further address inequality constraints, a distributed path-following ET-ZGS algorithm embedded with a finite-time max-consensus protocol is provided over UJSC digraphs, leveraging the barrier method akin to the interior-point method. Finally, two numerical examples are performed to verify the efficiency of the proposed algorithms.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3389-3399"},"PeriodicalIF":6.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492420","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

PPHMA: Privacy-Preserving Hybrid Multi-Task Allocation for Mobile Crowd Sensing 移动人群感知的隐私保护混合多任务分配

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-09 DOI: 10.1109/TNSE.2025.3559563

Xian Zhang;Xiaolin Qin;Haiwen Xu;Lin Li

{"title":"PPHMA: Privacy-Preserving Hybrid Multi-Task Allocation for Mobile Crowd Sensing","authors":"Xian Zhang;Xiaolin Qin;Haiwen Xu;Lin Li","doi":"10.1109/TNSE.2025.3559563","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3559563","url":null,"abstract":"With the widespread adoption of mobile smart devices, mobile crowd sensing(MCS) has provided better services for people. To meet the growing sensing demands within a limited budget, platforms have integrated two modes—opportunistic sensing and participatory sensing—to utilize their complementary strengths. However, location privacy issues may reduce workers' willingness to participate, thereby affecting task completion rates. Although existing methods have addressed privacy protection in a single sensing mode, there remains little focus on location privacy in hybrid sensing modes. There are two main limitations in privacy issues related to task allocation: (i) how to effectively preserve workers' location privacy in hybrid sensing modes, and (ii) the usual reliance on trusted third-party institution. To address these issues, we propose a privacy-preserving hybrid multi-task allocation for MCS (PPHMA). This approach preserves workers' location privacy without relying on a fully trusted third-party institution, while maximizing the number of tasks completed. Specifically, for opportunistic task allocation, we employ zero-knowledge range proofs to protect workers' location, thereby avoiding location privacy leaks. Subsequently, based on the performance capability indicator of opportunistic workers, we select appropriate workers for task allocation. For participatory task allocation, we employ a worker location obfuscation generation algorithm to locally generate and upload obfuscated locations, ensuring that both the worker's real and obfuscated locations satisfy <inline-formula><tex-math>$varepsilon$</tex-math></inline-formula>-Geo-Indistinguishability within the protected range. Then, based on the execution capability indicator of the participatory workers, we screen for candidate workers and use a greedy immune clone algorithm to optimize the workers' travel distances. Finally, we verify the effectiveness of the scheme through experiments using two real-world datasets.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3360-3373"},"PeriodicalIF":6.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492328","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