IEEE Transactions on Services Computing最新文献_第9页

RMDF-CV: A Reliable Multi-Source Data Fusion Scheme With Cross Validation for Quality Service Construction in Mobile Crowd Sensing RMDF-CV：一种可靠的多源数据融合方案，带交叉验证功能，用于移动人群感知中的优质服务构建

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-11-25 DOI: 10.1109/TSC.2024.3506482

Kejia Fan;Jialin Guo;Runsheng Li;Yuanye Li;Anfeng Liu;Jianheng Tang;Tian Wang;Mianxiong Dong;Houbing Song

{"title":"RMDF-CV: A Reliable Multi-Source Data Fusion Scheme With Cross Validation for Quality Service Construction in Mobile Crowd Sensing","authors":"Kejia Fan;Jialin Guo;Runsheng Li;Yuanye Li;Anfeng Liu;Jianheng Tang;Tian Wang;Mianxiong Dong;Houbing Song","doi":"10.1109/TSC.2024.3506482","DOIUrl":"10.1109/TSC.2024.3506482","url":null,"abstract":"Mobile Crowd Sensing is a prevalent and efficient paradigm for multi-source data collection, where Multi-source Data Fusion (MDF) plays a crucial role in constructing quality data collection services. Current MDF methods often require the majority of participating sensing sources to be credible, or assume that the workers’ credibility is either prior known or easily calculable. However, due to the presence of uncredible environments and the problem of Information Elicitation Without Verification (IEWV), these methods are impractical. It may lead to a vicious cycle where the recruitment of uncredible workers affects the quality of the estimated truth, which can further lead to misjudgments of worker credibility, thereby exacerbating the quality of subsequent recruitment. In this article, a Reliable Multi-source Data Fusion scheme with Cross Validation (RMDF-CV) is proposed to obtain reliable truth for service construction. Specifically, we first introduce the Combinatorial Multi-Armed Bandit (CMAB) model to recruit high-credibility workers by balancing exploration and exploitation. Then, we establish three-stage truth data through three different data sources: Unmanned Aerial Vehicles, credible workers, and Deep Matrix Factorization. Theoretical analyses and extensive simulations confirm the excellent performance of our RMDF-CV scheme.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"399-413"},"PeriodicalIF":5.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142712801","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

Learning-to-Adaptation for Security Service in Industrial IoT: An AI-Enabled Slice-Specific Solution 工业物联网安全服务的 "从学习到适应"：基于人工智能的片式解决方案

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-11-22 DOI: 10.1109/TSC.2024.3505785

Zhiwei Wei;Bing Li;Rongqing Zhang;Lingyang Song

{"title":"Learning-to-Adaptation for Security Service in Industrial IoT: An AI-Enabled Slice-Specific Solution","authors":"Zhiwei Wei;Bing Li;Rongqing Zhang;Lingyang Song","doi":"10.1109/TSC.2024.3505785","DOIUrl":"10.1109/TSC.2024.3505785","url":null,"abstract":"Network slicing is the key enabler for the 5G Industrial Internet of Things (IIoT), allowing tailored services and security guarantees for vertical industries. With the advent of 5G-Advanced (5G-A) and 6G era, the number of slices will increase significantly, leading to more diverse security requirements given different slice features. To provide adaptive security management spanning multiple slices in IIoT, this paper proposes a novel slice-specific secure IIoT (SSIOT) architecture with an AI-enabled solution. The SSIOT architecture separates the control and data planes, where the control plane orchestrates the Security Service Function Chains (SSFC) across network slices and the data plane analyzes the slice-specific features like traffic patterns, resource SLA guarantees, and Virtual Security Network Function (VSNF) dependencies. To extract these spatial-temporal features from the dynamic IIoT environments, we facilitate the powerful deep reinforcement learning (DRL) methods and propose a structural GS2L approach. GS2L is maliciously designed with the core principles of graph convolutional network (GCN) and Gated Recurrent Unit (GRU), enabling a thorough understanding of physical resource distribution and the request dynamics across slices. Extensive experiments are conducted in diverse IIoT slices with the real-world USNet and fat-tree topologies. Simulation results demonstrate that GS2L outperforms state-of-the-art learning and heuristic benchmarks, showcasing an overall 15.2% improvement with efficient and stable resource utilization.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"239-252"},"PeriodicalIF":5.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690762","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

Online Layer-Aware Joint Request Scheduling, Container Placement, and Resource Provision in Edge Computing 边缘计算中的在线层感知联合请求调度、容器放置和资源提供

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-11-21 DOI: 10.1109/TSC.2024.3504237

Zhenzheng Li;Jiong Lou;Zhiqing Tang;Jianxiong Guo;Tian Wang;Weijia Jia;Wei Zhao

{"title":"Online Layer-Aware Joint Request Scheduling, Container Placement, and Resource Provision in Edge Computing","authors":"Zhenzheng Li;Jiong Lou;Zhiqing Tang;Jianxiong Guo;Tian Wang;Weijia Jia;Wei Zhao","doi":"10.1109/TSC.2024.3504237","DOIUrl":"10.1109/TSC.2024.3504237","url":null,"abstract":"Containers have emerged as a pivotal tool for service deployment in edge computing. Before running the container, an image composed of several layers must exist locally. Recent strategies have utilized layer-sharing in images to reduce deployment delays. However, existing research only focuses on a single aspect of container orchestration, like container placement, neglecting the joint optimization of the entire orchestration process. To fill in such gaps, this article introduces an online strategy that considers layer-aware container orchestration, encompassing request scheduling, container placement, and resource provision. The goal is to reduce costs, adapt to evolving user demands, and adhere to system constraints. We present an online optimization problem that accounts for various real-world factors in orchestration, including container and server expenses. An online algorithm is proposed, integrating a regularization-based approach and stepwise rounding to address this optimization problem efficiently. The regularization approach separates time-dependent container placement and server wake-up costs, requiring only current information and past decisions. The stepwise rounding process generates feasible solutions that meet system constraints, reducing computational costs. Additionally, a competitive ratio proof is provided for the proposed algorithm. Extensive evaluations demonstrate that our approach achieves about 20% performance enhancement compared to baseline algorithms.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"328-341"},"PeriodicalIF":5.5,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684347","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

Low-cost Data Offloading Strategy with Deep Reinforcement Learning for Smart Healthcare System 利用深度强化学习为智能医疗系统提供低成本数据卸载策略

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-11-19 DOI: 10.1109/tsc.2024.3404347

Qiang He, Zheng Feng, Zhixue Chen, Tianhang Nan, Kexin Li, Huiming Shen, Keping Yu, Xingwei Wang

引用次数: 0

ADSS: An Available-but-Invisible Data Service Scheme for Fine-Grained Usage Control ADSS：用于细粒度使用控制的可用但不可见数据服务方案

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-11-11 DOI: 10.1109/TSC.2024.3495498

Hao Wang;Jun Wang;Chunpeng Ge;Yuhang Li;Lu Zhou;Zhe Liu;Weibin Wu;Mingsheng Cao

{"title":"ADSS: An Available-but-Invisible Data Service Scheme for Fine-Grained Usage Control","authors":"Hao Wang;Jun Wang;Chunpeng Ge;Yuhang Li;Lu Zhou;Zhe Liu;Weibin Wu;Mingsheng Cao","doi":"10.1109/TSC.2024.3495498","DOIUrl":"10.1109/TSC.2024.3495498","url":null,"abstract":"The demand for mobile terminals to participate in data services is increasingly vital. The General Data Protection Regulation (GDPR) has established several principled requirements for data services. Existing studies focusing on data service put emphasis on data privacy and accessibility. However, they face challenges in achieving data forgetability and portability on mobile devices under GDPR and lack consideration of usage control. In this article, we propose ADSS, an app-level data service scheme for mobile devices that can be <italic>available-but-invisible</i> and guarantee fine-grained usage control. ADSS addresses the challenges by executing the logic of data usage in the Trusted Execution Environment (TEE) and managing the TEE states (i.e., data usage states) in the blockchain smart contracts. It not only satisfies the requirements of GDPR, ensuring strong security and confidentiality guarantees, but also enables the functionality of “pay-per-use”. We implement a prototype of the ADSS framework based on ARM Trustzone and conduct experimental evaluations. The results demonstrate that our scheme brings high efficiency compared with other data service schemes and exhibits feasibility on mobile-grade devices.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"43-56"},"PeriodicalIF":5.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599257","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

A Reinforcement Learning Based Framework for Holistic Energy Optimization of Sustainable Cloud Data Centers 基于强化学习的可持续云数据中心整体能源优化框架

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-11-11 DOI: 10.1109/TSC.2024.3495495

Daming Zhao;Jiantao Zhou;Jidong Zhai;Keqin Li

{"title":"A Reinforcement Learning Based Framework for Holistic Energy Optimization of Sustainable Cloud Data Centers","authors":"Daming Zhao;Jiantao Zhou;Jidong Zhai;Keqin Li","doi":"10.1109/TSC.2024.3495495","DOIUrl":"10.1109/TSC.2024.3495495","url":null,"abstract":"The widespread adoption of cloud data centers has led to a rise in energy consumption, with the associated carbon emissions posing a further threat to the environment. Cloud providers are increasingly moving towards sustainable data centers powered by renewable energy sources (RES). The existing approaches fail to efficiently coordinate IT and cooling resources in such data centers due to the intermittent nature of RES and the complexity of state and action spaces among different devices, resulting in poor holistic energy efficiency. In this paper, a reinforcement learning (RL) based framework is proposed to optimize the holistic energy consumption of sustainable cloud data centers. First, a joint prediction method MTL-LSTM is developed to accurately evaluate both energy consumption and thermal status of each physical machine (PM) under different optimization scenarios to improve the state space information of the RL algorithm. Then, this framework designs a novel energy-aware approach named BayesDDQN, which leverages Bayesian optimization to synchronize the adjustments of VM migration and cooling parameter within the hybrid action space of the Double Deep Q-Network (DDQN) for achieving the holistic energy optimization. Moverover, the pre-cooling technology is integrated to further alleviate hotspot by making full use of RES. Experimental results demonstrate that the proposed RL-based framework achieves an average reduction of 2.83% in holistic energy consumption and 4.74% in brown energy, which also reduces cooling energy consumption by 13.48% with minimal occurrences of hotspots. Furthermore, the proposed MTL-LSTM method reduces the root mean square error (RMSE) of energy consumption and inlet temperature predictions by nearly half compared to LSTM and XGBoost.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"15-28"},"PeriodicalIF":5.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599217","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

Efficient Hierarchical Federated Services for Heterogeneous Mobile Edge 为异构移动边缘提供高效的分层联合服务

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-11-11 DOI: 10.1109/TSC.2024.3495501

Shengyuan Liang;Qimei Cui;Xueqing Huang;Borui Zhao;Yanzhao Hou;Xiaofeng Tao

{"title":"Efficient Hierarchical Federated Services for Heterogeneous Mobile Edge","authors":"Shengyuan Liang;Qimei Cui;Xueqing Huang;Borui Zhao;Yanzhao Hou;Xiaofeng Tao","doi":"10.1109/TSC.2024.3495501","DOIUrl":"10.1109/TSC.2024.3495501","url":null,"abstract":"As 6G networks actively advance edge intelligence, Federated Learning (FL) emerges as a key technology that enables data sharing while preserving data privacy and fostering collaboration among edge devices for intelligent service learning. However, the multi-dimensional heterogeneous and hierarchical network architecture brings many challenges to FL deployment, including selecting appropriate nodes for model training and designing effective methods for model aggregation. Compared with most studies that focus on solving individual problems within 6G, this paper proposes an efficient deployment scheme named hierarchical heterogeneous FL (HHFL), which comprehensively considers various influencing factors. First, the deployment of HHFL over 6G is modeled amid the heterogeneity of communications, computation, and data. An optimization problem is then formulated, aiming to minimize deployment costs in terms of latency and energy consumption. Subsequently, to tackle this optimization challenge, we design an intelligent FL deployment framework, consisting of a hierarchical aggregation deployment (HAD) component for hierarchical FL aggregation structure construction and an adaptive node selection (ANS) component for selecting diverse clients based on multi-dimensional discrepancy criteria. Experimental results demonstrate that our proposed framework not only adapts to various application requirements but also outperforms existing technologies by achieving superior learning performance, reduced latency, and lower energy consumption.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"140-155"},"PeriodicalIF":5.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599216","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

A Novel Knowledge Search Structure for Android Malware Detection 用于安卓恶意软件检测的新型知识搜索结构

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-11-11 DOI: 10.1109/TSC.2024.3496333

Huijuan Zhu;Mengzhen Xia;Liangmin Wang;Zhicheng Xu;Victor S. Sheng

{"title":"A Novel Knowledge Search Structure for Android Malware Detection","authors":"Huijuan Zhu;Mengzhen Xia;Liangmin Wang;Zhicheng Xu;Victor S. Sheng","doi":"10.1109/TSC.2024.3496333","DOIUrl":"10.1109/TSC.2024.3496333","url":null,"abstract":"While the Android platform is gaining explosive popularity, the number of malicious software (malware) is also increasing sharply. Thus, numerous malware detection schemes based on deep learning have been proposed. However, they are usually suffering from the cumbersome models with complex architectures and tremendous parameters. They usually require heavy computation power support, which seriously limit their deployment on actual application environments with limited resources (e.g., mobile edge devices). To surmount this challenge, we propose a novel Knowledge Distillation (KD) structure—Knowledge Search (KS). KS exploits Neural Architecture Search (NAS) to adaptively bridge the capability gap between teacher and student networks in KD by introducing a parallelized student-wise search approach. In addition, we carefully analyze the characteristics of malware and locate three cost-effective types of features closely related to malicious attacks, namely, Application Programming Interfaces (APIs), permissions and vulnerable components, to characterize Android Applications (Apps). Therefore, based on typical samples collected in recent years, we refine features while exploiting the natural relationship between them, and construct corresponding datasets. Massive experiments are conducted to investigate the effectiveness and sustainability of KS on these datasets. Our experimental results show that the proposed method yields an accuracy of 97.89% to detect Android malware, which performs better than state-of-the-art solutions.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3052-3064"},"PeriodicalIF":5.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599258","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

Delay-Prioritized and Reliable Task Scheduling With Long-Term Load Balancing in Computing Power Networks 计算动力网络中具有长期负载平衡的延迟优先和可靠任务调度

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-11-11 DOI: 10.1109/TSC.2024.3495500

Renchao Xie;Li Feng;Qinqin Tang;Tao Huang;Zehui Xiong;Tianjiao Chen;Ran Zhang

{"title":"Delay-Prioritized and Reliable Task Scheduling With Long-Term Load Balancing in Computing Power Networks","authors":"Renchao Xie;Li Feng;Qinqin Tang;Tao Huang;Zehui Xiong;Tianjiao Chen;Ran Zhang","doi":"10.1109/TSC.2024.3495500","DOIUrl":"10.1109/TSC.2024.3495500","url":null,"abstract":"In the era driven by big data and algorithms, the efficient collaboration of pervasive computing power is crucial for rapidly meeting computing demands and enhancing resource utilization. However, current mainstream end-edge-cloud collaboration faces challenges of computing isolation, adversely affecting resource efficiency and user experience. The Computing Power Network (CPN) is a novel architecture designed to sense and collaborate ubiquitous computing resources through networks. Nevertheless, the expansion of its scope and the integration of networks complicate task scheduling. To address this, we design a collaborative scheduling system that considers the joint selection of computing nodes and network links, aiming to reduce delay, enhance reliability, and ensure long-term load balance. First, we propose a delay-prioritized reliable scheduling policy based on a dual-priority mechanism for forwarding and computing. Second, we define the scheduling problem as a Constrained Markov Decision Process (CMDP) and introduce Lyapunov optimization to transform constraints into instantaneous optimizations, achieving a long-term balanced load of computing and network resources. Lastly, we employ an enhanced Deep Reinforcement Learning (DRL) approach to solve the problem. Performance evaluation demonstrates that compared to standard DRL, the proposed algorithm effectively reduces delay and improves reliability while maintaining long-term load balance, resulting in an overall performance improvement of 54.7%.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3359-3372"},"PeriodicalIF":5.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599219","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

MADRL-Based Order Dispatching in MoD Systems With Bipartite Graph Splitting 基于 MADRL 的国防部系统订单调度与双向图分割

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-11-11 DOI: 10.1109/TSC.2024.3495538

Shuxin Ge;Xiaobo Zhou;Tie Qiu

{"title":"MADRL-Based Order Dispatching in MoD Systems With Bipartite Graph Splitting","authors":"Shuxin Ge;Xiaobo Zhou;Tie Qiu","doi":"10.1109/TSC.2024.3495538","DOIUrl":"10.1109/TSC.2024.3495538","url":null,"abstract":"Mobility on-demand (MoD) systems widely use machine learning to estimate matching utilities of order-vehicle pairs to dispatch orders by bipartite matching. However, existing methods suffer from overestimation problems due to the complex interactions among order-vehicle pairs in the global bipartite graph, leading to low overall revenue and order completion rate. To fill this gap, we propose a multi-agent deep reinforcement learning (MADRL) based order dispatching method with bipartite splitting, named SplitMatch. The key idea is to split the global bipartite graph into multiple sub-bipartite graphs to overcome the overestimation problem. First, we propose a bipartite splitting theorem and prove that the optimal solution of global bipartite matching can be achieved by solving multiple sub-bipartite matching problems when certain conditions are met. Second, we design a spatial-temporal padding prediction algorithm to generate sub-bipartite graphs that satisfy this theorem, where the spatial-temporal feature of orders and vehicles is captured. Next, we propose a MADRL framework to learn the matching utility, where multi-objective, e.g., immediate revenue and quality of service (QoS), are taken into account to deal with varying action space. Finally, a series of simulations are conducted to verify the superiority of SplitMatch in terms of overall revenue and order completion rate.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3914-3927"},"PeriodicalIF":5.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599220","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