IEEE Transactions on Network and Service Management最新文献_第2页

A Comprehensive Evaluation of Networked Music Performance Using LEO Satellite Internet: The Starlink Use Case 使用LEO卫星互联网的网络音乐表演的综合评估：Starlink用例

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-07-15 DOI: 10.1109/TNSM.2025.3589226

Luca Borgianni;Davide Adami;Marina Bosi;Stefano Giordano;Chris Chafe

{"title":"A Comprehensive Evaluation of Networked Music Performance Using LEO Satellite Internet: The Starlink Use Case","authors":"Luca Borgianni;Davide Adami;Marina Bosi;Stefano Giordano;Chris Chafe","doi":"10.1109/TNSM.2025.3589226","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3589226","url":null,"abstract":"Networked Music Performance (NMP) is one of the most challenging real-time applications in which musicians can play together using the Internet without being physically together. Nowadays, the connection technology can have performance that guarantees an adequate Quality of Experience (QoE) for NMP. On the other hand, there are millions of musicians who live in remote and rural areas without access to high-speed network connections. The promising LEO satellite Internet technology could fill this gap and democratize NMP in remote places. This paper aims to provide an analysis of Starlink’s capability to meet the stringent requirements of NMP. We present an analysis of network metrics that are relevant to NMP applications and assess the NMP software JackTrip in order to test a real NMP application with LEO satellite Internet. The evaluation of the RTT, One-way delay, Packet loss, and jitter in different scenarios poses some challenges for the NMP scenario. We present the results of two live NMP jamming sessions with musicians, proposing an adapted QoE model that incorporates both objective and subjective metrics. With a tailored buffer strategy and the ability of a musician, we were able to obtain an adequate QoE, overcoming the challenges introduced by LEO satellite Internet.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 5","pages":"3947-3963"},"PeriodicalIF":5.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315464","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

α-Fair Mobility Management in 5G Networks α- 5G网络中的公平移动性管理

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-07-14 DOI: 10.1109/TNSM.2025.3588554

Anna Prado;Wolfgang Kellerer;Fidan Mehmeti

{"title":"α-Fair Mobility Management in 5G Networks","authors":"Anna Prado;Wolfgang Kellerer;Fidan Mehmeti","doi":"10.1109/TNSM.2025.3588554","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3588554","url":null,"abstract":"Mobility management in 5G is challenging due to the usage of high frequencies and dense cell deployments. As a result, users experience frequent handovers that cause an interruption in transmission/reception and diminish network capacity. In the common handover algorithm, the target Base Station (BS) is selected based solely on the signal strength, while the available resources are not considered, leading to overloaded cells, especially for macro cells with large coverage. Advanced handover techniques are needed in 5G to perform smooth network operation. In this paper, we formulate an optimization problem, whose goal is to provide <inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>-fairness in data rates among users and to reduce handovers. To accomplish that, we jointly perform user assignment and resource allocation while accounting for the interruption due to handovers. This is an integer nonlinear program and, by relaxing it, an upper bound is obtained. Further, because of the time complexity of the original problem, we propose a Deep Reinforcement Learning (DRL)-based algorithm, which finds near-optimal user-to-BS assignments and the amount of resources that should be allocated to a user. Our approach outperforms considerably state of the art in terms of fairness and handover rate while being within at most 12% of the optimum in most cases.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 5","pages":"5118-5136"},"PeriodicalIF":5.4,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11079667","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting Service Disruptions in Large BGP/MPLS VPN Networks 大型BGP/MPLS VPN网络业务中断检测

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-07-11 DOI: 10.1109/TNSM.2025.3588314

Alex Huang Feng;Pierre Francois;Maxence Younsi;Stéphane Frénot;Thomas Graf;Wanting Du;Paolo Lucente;Ahmed Elhassany

{"title":"Detecting Service Disruptions in Large BGP/MPLS VPN Networks","authors":"Alex Huang Feng;Pierre Francois;Maxence Younsi;Stéphane Frénot;Thomas Graf;Wanting Du;Paolo Lucente;Ahmed Elhassany","doi":"10.1109/TNSM.2025.3588314","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3588314","url":null,"abstract":"This paper presents the result of three years of experience in research, design, and deployment of a complete architecture aimed at automatically identifying service disruptions in large BGP/MPLS VPN networks. We present the main components of a comprehensive architecture that can be operated in production environments, highlighting the requirements that led to their design. We describe the data that are collected from the network using IETF standard protocols, the processing that is performed onto them to detect anomalies, and the scaling aspects that need to be considered when ingesting the large amounts of data that is necessary for the purpose at hand. We report on two and a half years of deployment experience on the Swisscom BGP/MPLS VPN Network services, by analyzing the behavior of our system in the face of actual network incidents. After each incident, we systematically performed post-mortem analyzes. These investigations led us to conclude that the rule-based approaches that are currently used in deployment, supported by a profiling of the VPN customers to fine-tune rule parameters, enables the detection of service disruptions with the required accuracy.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 5","pages":"3964-3977"},"PeriodicalIF":5.4,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315474","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

Achieving Efficient SFC Proactive Reconfiguration Through Deep Reinforcement Learning in Programmable Networks 在可编程网络中通过深度强化学习实现有效的SFC主动重构

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-07-03 DOI: 10.1109/TNSM.2025.3585590

Huaqing Tu;Ziqiang Hua;Qi Xu;Jun Zhu;Tao Zou;Hongli Xu;Qiao Xiang;Zuqing Zhu

{"title":"Achieving Efficient SFC Proactive Reconfiguration Through Deep Reinforcement Learning in Programmable Networks","authors":"Huaqing Tu;Ziqiang Hua;Qi Xu;Jun Zhu;Tao Zou;Hongli Xu;Qiao Xiang;Zuqing Zhu","doi":"10.1109/TNSM.2025.3585590","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3585590","url":null,"abstract":"Service function chain (SFC) consists of multiple ordered network functions (e.g., firewall, load balancer) and plays an important role in improving network security and ensuring network performance. Offloading SFCs onto programmable switches can bring significant performance improvement, but it suffers from unbearable reconfiguration delays, making it hard to cope with network workload dynamics in a timely manner. To bridge the gap, this paper presents OptRec, an efficient SFC proactive reconfiguration optimization framework based on deep reinforcement learning (DRL). OptRec predicts future traffic and places SFCs on programmable switches in advance to ensure the timeliness of the SFC reconfiguration, which is a proactive approach. However, it is non-trivial to extract effective features from historical traffic information and global network states, while ensuring efficient and stable model training. To this end, OptRec introduces a multi-level feature extraction model for different types of features. Additionally, it combines reinforcement learning and autoregressive learning to enhance model efficiency and stability. Results of in-depth simulations based on real-world datasets show the average prediction error of OptRec is less than 3% and OptRec can increase the system throughput by up to 69.6%~72.6% compared with other alternatives.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 5","pages":"4917-4932"},"PeriodicalIF":5.4,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230026","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

Evolutionary Multi-Objective Deep Reinforcement Learning for Task Offloading in Industrial Internet of Things 面向工业物联网任务卸载的进化多目标深度强化学习

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-07-02 DOI: 10.1109/TNSM.2025.3585148

Xu Liu;Zheng-Yi Chai;Yan-Yang Cheng;Ya-Lun Li;Tao Li

{"title":"Evolutionary Multi-Objective Deep Reinforcement Learning for Task Offloading in Industrial Internet of Things","authors":"Xu Liu;Zheng-Yi Chai;Yan-Yang Cheng;Ya-Lun Li;Tao Li","doi":"10.1109/TNSM.2025.3585148","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3585148","url":null,"abstract":"Mobile Edge Computing (MEC) plays a pivotal role in optimizing the Industrial Internet of Things (IIoT), where the Industrial Task Offloading Problem (ITOP) is crucial for ensuring optimal system performance by balancing conflicting objectives such as delay, energy consumption, and cost. However, existing approaches often oversimplify multi-objective optimization by aggregating conflicting goals into a single objective, while also suffering from limited exploration and robustness in uncertain MEC scenarios within IIoT. To overcome this limitation, we propose EMDRL-ITOP, an Evolutionary Multi-Objective Deep Reinforcement Learning algorithm that synergizes evolutionary algorithm with deep reinforcement learning (DRL). Firstly, we formulate a multi-objective task scheduling model for IIoT-MEC and design a three-dimensional vector reward function within a Multi-Objective Markov Decision Process framework, enabling simultaneous optimization of delay, energy, and cost. Then, EMDRL-ITOP integrates evolutionary mechanisms to enhance exploration and robustness: a dynamic elite selection strategy prioritizes high-quality policies, a distillation crossover operator fuses advantageous traits from elite strategies, and a proximal mutation mechanism maintains population diversity. These components collectively improve learning efficiency and solution quality in dynamic environments. Extensive simulations across six instances demonstrate that EMDRL-ITOP achieves a superior balance among conflicting objectives compared to state-of-the-art methods, while also outperforming existing algorithms in several key performance metrics.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 5","pages":"5074-5089"},"PeriodicalIF":5.4,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230068","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

Blockchain-Assisted Secure Embedding of Virtual Networks in Multi-Domain Elastic Optical Network 多域弹性光网络中区块链辅助的虚拟网络安全嵌入

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-07-02 DOI: 10.1109/TNSM.2025.3583898

Huanlin Liu;Bing Ma;Jianjian Zhang;Yong Chen;Bo Liu;Haonan Chen;Di Deng

{"title":"Blockchain-Assisted Secure Embedding of Virtual Networks in Multi-Domain Elastic Optical Network","authors":"Huanlin Liu;Bing Ma;Jianjian Zhang;Yong Chen;Bo Liu;Haonan Chen;Di Deng","doi":"10.1109/TNSM.2025.3583898","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3583898","url":null,"abstract":"With the continuous advancement of network virtualization (NV) technology, virtual network embedding (VNE) has played a crucial role in solving network resource allocation problem. However, multi-domain elastic optical networks (MD-EONs) are increasingly facing privacy and security challenges. The centralized VNE methods lead to significant communication overhead due to their excessive reliance on central servers. Additionally, network attacks, such as eavesdropping, pose severe threats to data security. So, we propose a blockchain-assisted virtual network secure embedding (BA-VNSE) framework MD-EONs. This framework employs quantum key distribution (QKD) technology to ensure data security during transmission and leverages the blockchain technology to enhance the transparency and security of the VNE process. Furthermore, we propose a blockchain-assisted minimum cost virtual network secure embedding (BAMC-VNSE). During the virtual node embedding (VNM), the multidimensional resources of nodes are comprehensively considered to ensure effective embedding. In the virtual link embedding (VLM), the QKD paths are allowed to differ from the encrypted data transmission paths, ultimately resulting in the selection of the most cost-effective valid embedding scheme. The simulation results demonstrate that the BAMC-VNSE effectively reduces request blocking probability, embedding cost and average number of message while improving the key utilization ratio.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 5","pages":"3838-3848"},"PeriodicalIF":5.4,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315537","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

TraceDAE: Trace-Based Anomaly Detection in Microservice Systems via Dual Autoencoder 基于跟踪的双自编码器微服务系统异常检测

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-06-26 DOI: 10.1109/TNSM.2025.3583213

Junjun Li;Shi Ying;Tiangang Li;Xiangbo Tian

{"title":"TraceDAE: Trace-Based Anomaly Detection in Microservice Systems via Dual Autoencoder","authors":"Junjun Li;Shi Ying;Tiangang Li;Xiangbo Tian","doi":"10.1109/TNSM.2025.3583213","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3583213","url":null,"abstract":"Microservice systems have become a popular architecture for modern Web applications owing to their scalability, modularity, and maintainability. However, with the increasing complexity and size of these systems, anomaly detection emerges as a critical task. In this paper, we introduce TraceDAE, a trace-based anomaly detection approach in microservice systems. The approach initially constructs a Service Trace Graph (STG) to depict service invocation relationships and performance metrics, subsequently introducing a dual autoencoder framework. In this framework, the structure autoencoder employs Graph Attention Networks (GAT) to analyze the structure, while the attribute autoencoder leverages the Long Short-Term Memory Network (LSTM) for processing time series data. This approach is capable of effectively identifying Service Response Abnormal and Service Invocation Abnormal. Moreover, the final experimental results on datasets show that TraceDAE is an efficient anomaly detection approach which outperforms the SOTA(State of The Arts) trace-based anomaly detection methods with F1-scores of 0.970 and 0.925, respectively.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 5","pages":"4884-4897"},"PeriodicalIF":5.4,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255902","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

FR-SFCO: Energy-Aware Offloading on Data Plane for Delay-Sensitive SFC FR-SFCO：延迟敏感SFC的数据平面能量感知卸载

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-06-24 DOI: 10.1109/TNSM.2025.3582223

Bo Pang;Deyun Gao;Xianchao Zhang;Chuan Heng Foh;Hongke Zhang;Victor C. M. Leung

{"title":"FR-SFCO: Energy-Aware Offloading on Data Plane for Delay-Sensitive SFC","authors":"Bo Pang;Deyun Gao;Xianchao Zhang;Chuan Heng Foh;Hongke Zhang;Victor C. M. Leung","doi":"10.1109/TNSM.2025.3582223","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3582223","url":null,"abstract":"Service Function Chaining (SFC) is widely deployed by telecom operators and cloud service providers, offering traffic QoS guarantees and other additional functions for various applications. The network state at the time of SFC deployment can differ significantly from the runtime conditions, leading to excessive resource allocation and consequent energy waste. The existing SFC reconfiguration methods face the challenge of meeting the latency requirements of delay-sensitive applications while achieving significant energy savings. This paper proposes FR-SFCO, a flow rate-aware SFC offloading framework on programmable data planes for delay-sensitive flows. Specifically, we designed a TCAM-friendly table matching method for FR-SFCO to reduce the flow entries needed for SFC offloading in programmable switches and support larger numbers of offloaded SFC. Then, we proposed a dual-threshold-based offloading trigger mechanism that, according to the real-time traffic arrival rate, can fast offload SFC flows before they default to servers. Building on this, we propose DQN-AOTA, an adaptive offloading thresholds adjustment algorithm based on Deep Q-Learning, which can wisely change the offloading thresholds by interacting with a dynamic network traffic environment to minimize the packet loss and long-term energy consumption. Finally, we build a testbed using BMv2 software switches and Docker containers for extensive evaluation. The experimental results demonstrate the effectiveness of our solution which not only meets the latency constraints for delay-sensitive SFC flows but also reduces energy expenditure by at least 14.6%.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 5","pages":"3823-3837"},"PeriodicalIF":5.4,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315473","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

Topology-Driven Configuration of Emulation Networks With Deterministic Templating 基于确定性模板的仿真网络拓扑驱动配置

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-06-23 DOI: 10.1109/TNSM.2025.3582212

Satoru Kobayashi;Ryusei Shiiba;Shinsuke Miwa;Toshiyuki Miyachi;Kensuke Fukuda

引用次数: 0

Black Hole Prediction in Backbone Networks: A Comprehensive and Type-Independent Forecasting Model 主干网黑洞预测：一种综合、类型无关的预测模型

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-06-20 DOI: 10.1109/TNSM.2025.3581557

Kiymet Kaya;Elif Ak;Eren Ozaltun;Leandros Maglaras;Trung Q. Duong;Berk Canberk;Sule Gunduz Oguducu

{"title":"Black Hole Prediction in Backbone Networks: A Comprehensive and Type-Independent Forecasting Model","authors":"Kiymet Kaya;Elif Ak;Eren Ozaltun;Leandros Maglaras;Trung Q. Duong;Berk Canberk;Sule Gunduz Oguducu","doi":"10.1109/TNSM.2025.3581557","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3581557","url":null,"abstract":"Network backbone black holes(BH) pose significant challenges in the Internet by causing disruptions and data loss as routers silently drop packets without notification. These silent BH failures, stemming from issues like hardware malfunctions or misconfigurations, uniquely affect point-to-point packet flows without disrupting the entire network. Unlike cyber attacks and network intrusions, BHs are often untraceable, making early detection vital and challenging. This study addresses the need for an effective forecasting solution for BH occurrences, especially in environments with unlabeled traffic data where traditional anomaly detection methods fall short. The Type-Independent Black Hole Forecasting Model is introduced to predict BH occurrences with high precision across various anomalies, including contextual and collective anomaly types. The three-stage methodology processes unlabeled time-series network data, where the data is not pre-labeled as anomaly or normal, using machine learning and deep learning techniques to identify and forecast potential BH occurrences. The ‘Point BH Identification and Segregation’ stage segregates point BH traffic using Density-Based Spatial Clustering of Applications with Noise(DBSCAN), followed by Reintegration and Time Series Smoothing. The final stage, Advanced Contextual and Collective BH Detection, leverages Convolutional AutoEncoder(Conv-AE) with window sliding for advanced anomaly detection. Evaluation using a dual-dataset approach, including real backbone network traffic and a time-series adapted public dataset, demonstrates the adaptability of the model to real backbone BH detection systems. Experimental results show superior performance compared to state-of-the-art unsupervised anomaly forecasting models, with a 98% detection rate and 90% F-1 score, outperforming models like MultiHeadSelfAttention, which is the main building block of Transformers.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 5","pages":"4983-4997"},"PeriodicalIF":5.4,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230022","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