Physical Communication最新文献

Secrecy analysis of IRS-assisted NOMA cognitive radio networks with full-duplex energy scavenging jammer 带有全双工能量清除干扰机的irs辅助NOMA认知无线电网络的保密性分析

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-07-29 DOI: 10.1016/j.phycom.2025.102784

Toi Le-Thanh , Cuong Tran-Minh , Khuong Ho-Van

引用次数: 0

Graph Reinforcement and Asynchronous Federated Learning based task offloading in fog computing 雾计算中基于图强化和异步联邦学习的任务分流

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-07-26 DOI: 10.1016/j.phycom.2025.102776

Nilesh Kumar Verma, K. Jairam Naik

{"title":"Graph Reinforcement and Asynchronous Federated Learning based task offloading in fog computing","authors":"Nilesh Kumar Verma, K. Jairam Naik","doi":"10.1016/j.phycom.2025.102776","DOIUrl":"10.1016/j.phycom.2025.102776","url":null,"abstract":"<div><div>Fog computing is an emerging and promising paradigm for handling the computational demands of the resource-constrained IoT devices, providing enhanced storage, bandwidth, and computing resources. Despite its potential, efficient task offloading in fog environments remains challenging due to dynamic network topologies, heterogeneous resource capabilities, and fluctuating task arrival rates. In order to address these challenges effectively, this study applies an integration of Graph Reinforcement Learning (GRL) with Asynchronous Federated Learning (AFL) and proposes a novel framework (AFedGRL). The GRL in this framework employs a Graph Neural Network (GNN) for capturing the topological features of the fog network and an Actor-Critic model for making informed offloading decisions in fog computing environments. By incorporating AFL, our framework efficiently updates global models without waiting for all nodes to synchronize, resulting in improved adaptability and reduced latency in dynamic network environments. The AFedGRL framework outperforms against state-of-the-art algorithms including GRL_without_AF, DROO, HEFT, and EG by dynamically balancing computational loads between local devices and fog nodes. Key performance metrics such as, task response latency, task completion rate, and offloading gain demonstrate its enhanced efficiency. Empirical evaluations across various scenarios show that the AFedGRL framework significantly outperforms compared to other approaches. AFedGRL achieves an offloading gain improvement of up to 15%, reduces task response latency by approximately 25%, and 13% higher task completion rate compared to the baseline methods. These results demonstrate the exceptional performance of AFedGRL in dynamic fog computing environments, highlighting its potential for real-time and resource-intensive applications.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102776"},"PeriodicalIF":2.2,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Vehicle positioning method based on UAV assisted RIS integrated sensing and communication system 基于无人机辅助RIS集成传感与通信系统的车辆定位方法

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-07-26 DOI: 10.1016/j.phycom.2025.102780

Junxia Li , Zhengwei Li , Mengyan Huang , Gaojian Huang , Gang Yang

{"title":"Vehicle positioning method based on UAV assisted RIS integrated sensing and communication system","authors":"Junxia Li , Zhengwei Li , Mengyan Huang , Gaojian Huang , Gang Yang","doi":"10.1016/j.phycom.2025.102780","DOIUrl":"10.1016/j.phycom.2025.102780","url":null,"abstract":"<div><div>Integrated sensing and communication (ISAC) systems are essential for advancing intelligent transportation, particularly as the increasingly complex transportation environment demands enhanced sensing capabilities. To improve the positioning accuracy of vehicles, this paper proposes a novel positioning framework that integrates unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS). In dense urban environments where obstacles disrupt signal transmission, RIS is deployed aerially to maintain reliable signal communication links. At the sensor’s location, a channel estimation method based on atomic norm minimization (ANM) is applied to accurately recover signal parameters. Furthermore, the theoretical Cramér-Rao lower bound (CRLB) of the estimation is derived to establish performance benchmarks. To refine angle estimation, RIS is strategically positioned beneath the UAV, enabling the extraction of reflection angle information. To minimize both estimation errors and CRLB, the RIS position is dynamically adjusted using the snake optimization (SO) algorithm. The simulation results demonstrate that mounting the RIS on the UAV significantly improves estimation performance compared to a fixed RIS deployment, thereby validating the effectiveness of the proposed approach.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102780"},"PeriodicalIF":2.2,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144749631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

An intelligent and lightweight physical layer authentication scheme for MEC-enabled IoT networks 针对支持mec的物联网网络的智能轻量级物理层认证方案

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-07-25 DOI: 10.1016/j.phycom.2025.102777

Ting Ma , Wei Yang , Feng Hu , Maode Ma , Chuang Qin

{"title":"An intelligent and lightweight physical layer authentication scheme for MEC-enabled IoT networks","authors":"Ting Ma , Wei Yang , Feng Hu , Maode Ma , Chuang Qin","doi":"10.1016/j.phycom.2025.102777","DOIUrl":"10.1016/j.phycom.2025.102777","url":null,"abstract":"<div><div>This paper proposes a lightweight Three-Layer Convolutional Neural Network(3L-CNN) based physical layer authentication (PLA) method for mobile edge computing-enabled IoT (MEC-IoT) networks. A novel channel state information (CSI) processing architecture is established where real and imaginary components are transformed into two-channel images with 64×64 resolution for network inputs. Two data augmentation techniques, Average Data Augmentation (ADA) and Exponentially Weighted Average(EWA) are developed, to enhance temporal correlation preservation and mobility pattern extraction in mobile scenarios, effectively mitigating training data scarcity for mobile devices. The core 3L-CNN architecture remains streamlined, employing progressive feature extraction through three convolutional layers with 64×8, 32×16, and 6×32 configurations, optimized by a hybrid loss function combining 50% Negative Log-Likelihood and 50% Cross-Entropy to refine classification boundaries. The architecture demonstrates 99% authentication accuracy for 10 devices configuration and maintains 96.6% accuracy for 30 devices respectively. It also exhibits superior robustness with 95% accuracy at 0 dB SNR. This lightweight solution achieves comparable performance to complex models while reducing training time by 66%, making it suitable for resource-constrained mobile edge computing-enabled IoT applications.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102777"},"PeriodicalIF":2.2,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144720791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Unveiling the power of features: A comparative study of machine learning and deep learning for modulation recognition 揭示特征的力量：调制识别中机器学习和深度学习的比较研究

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-07-23 DOI: 10.1016/j.phycom.2025.102791

Merih Leblebici , Ali Çalhan , Murtaza Cicioğlu

{"title":"Unveiling the power of features: A comparative study of machine learning and deep learning for modulation recognition","authors":"Merih Leblebici , Ali Çalhan , Murtaza Cicioğlu","doi":"10.1016/j.phycom.2025.102791","DOIUrl":"10.1016/j.phycom.2025.102791","url":null,"abstract":"<div><div>Wireless communication systems rely on amplitude, frequency, and phase parameters for signal transmission. Traditional modulation recognition (MR) techniques, employing likelihood-based (LB) and feature-based (FB) methods, struggle with accurate classification, particularly at low signal-to-noise ratios (SNR) and increasing modulation complexity. Machine learning (ML) and deep learning (DL) algorithms, which efficiently utilize in-phase/quadrature (IQ) and <span><math><mi>r</mi></math></span>-radius/<span><math><mi>θ</mi></math></span>-angle (<span><math><mrow><mi>r</mi><mi>θ</mi><mo>)</mo></mrow></math></span> data representations to enhance MR performance. DL, utilizing artificial neural networks (ANN), minimizes the need for extensive feature engineering, making it adept at handling diverse modulation types and challenging SNR conditions. This study systematically examines dataset generation parameters to reveal their impact on MR performance. By focusing on these underlying parameters, the analysis provides deeper insights into how data characteristics influence model performance, offering a foundational understanding for optimizing dataset configurations in MR tasks. Evaluating ML and DL models across datasets, results show DL model consistently outperforms ML models, achieving up to 79.41 % accuracy on IQ-based datasets. DL's hierarchical feature extraction enhances adaptability, particularly with larger datasets, reduced window lengths (WL), and specific <span><math><mi>θ</mi></math></span> ranges (e.g., radians or smaller degree intervals). For ML models, datasets based on IQ, <span><math><mrow><mi>r</mi><mi>θ</mi></mrow></math></span>, and IQ<span><math><mrow><mi>r</mi><mi>θ</mi></mrow></math></span> parameters yield better results but remain below 70 % accuracy. Overall, DL model exhibits robust adaptability to complex signal environments, highlighting their effectiveness in advancing modulation recognition for next-generation wireless communication systems.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102791"},"PeriodicalIF":2.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144720792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

On the optimal duplexing strategy for wireless-powered communication networks 无线通信网络的最优双工策略研究

IF 2 4区计算机科学

Physical Communication Pub Date : 2025-07-22 DOI: 10.1016/j.phycom.2025.102729

Arman Ahmadian , Hyuncheol Park

{"title":"On the optimal duplexing strategy for wireless-powered communication networks","authors":"Arman Ahmadian , Hyuncheol Park","doi":"10.1016/j.phycom.2025.102729","DOIUrl":"10.1016/j.phycom.2025.102729","url":null,"abstract":"<div><div>Due to its simplicity and lack of channel state information (CSI) feedback requirements, time division duplexing (TDD) has been the preferred duplexing method in wireless-powered communication networks (WPCNs), while the advantages of frequency-division duplexing (FDD) has remained largely unexplored. Yet, the decision between TDD and FDD goes beyond CSI considerations, as it depends on various system parameters and operational trade-offs not previously considered. In FDD, the transmitter remains active throughout the entire frame duration, enabling more effective utilization of the maximum instantaneous transmit power of the hybrid access point (HAP). In contrast, TDD exploits the full bandwidth (BW), thereby making better use of the feasible maximum power spectral density (PSD). Finally, while the constraint of maximum time-averaged transmit power in FDD closely resembles the effect of the maximum instantaneous transmit power constraint, they have different effects on the operation of the TDD-WPCN.</div><div>To analyze these effects, we thoroughly investigate both TDD-WPCN and FDD-WPCN and characterize their respective operating regions. Our extensive theoretical and simulation results reveal that selecting between the two schemes involves a complex, multidimensional trade-off, warranting careful consideration in system design. We demonstrate that, under certain assumptions, the throughput of an FDD-WPCN can be substantially greater than that of the same WPCN operating in TDD mode. Furthermore we prove that, under certain conditions, a single-node FDD-WPCN can achieve a two-fold increase in throughput compared to a single-node TDD-WPCN.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102729"},"PeriodicalIF":2.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Fractional-order Deep Neural Networks for Improved TDOA Localization in Wireless Sensor Networks 分数阶深度神经网络改进无线传感器网络TDOA定位

IF 2 4区计算机科学

Physical Communication Pub Date : 2025-07-19 DOI: 10.1016/j.phycom.2025.102767

Mehari Kiros , Kumlachew Yeneneh

{"title":"Fractional-order Deep Neural Networks for Improved TDOA Localization in Wireless Sensor Networks","authors":"Mehari Kiros , Kumlachew Yeneneh","doi":"10.1016/j.phycom.2025.102767","DOIUrl":"10.1016/j.phycom.2025.102767","url":null,"abstract":"<div><div>Accurate localization is critical for the effective operation of Wireless Sensor Networks (WSNs), yet traditional methods often struggle with noise and environmental variability. This research introduces a novel approach by incorporating fractional-order calculus into Deep Neural Networks (DNNs) to address two key technical challenges: (1) the sensitivity of Time-Difference-of-Arrival (TDOA) methods to noise and non-linearity in dynamic environments, and (2) the limitations of conventional gradient descent in DNN training, such as vanishing gradients and slow convergence. The proposed Fractional-Order (FODNNs) leverage the memory effects and non-local properties of fractional derivatives Grünwald–Letnikov definition) to enhance gradient computation and error propagation during backpropagation. Experimental results demonstrate that FODNNs reduce localization error by 50% (achieving a mean error of 0.21 ± 0.03 m vs. 0.42 ± 0.07 m for DNNs and converge 44.4% faster (<span><math><mrow><mn>50</mn><mo>±</mo><mn>3</mn></mrow></math></span> epochs vs. <span><math><mrow><mn>90</mn><mo>±</mo><mn>5</mn></mrow></math></span> epochs), while maintaining sub-0.25 m accuracy even in high-noise conditions (SNR <span><math><mo><</mo></math></span> 10 dB). The framework also shows superior robustness, with only 12.3% performance degradation under noise compared to 38.7% for DNNs.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102767"},"PeriodicalIF":2.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Three-dimensional trajectory optimization of rotary-wing UAV with cellular network connectivity based on modified DDPG 基于改进DDPG的蜂窝网络连接旋翼无人机三维轨迹优化

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2025-07-18 DOI: 10.1016/j.phycom.2025.102773

Weizhi Zhong, Xiang Liu, Haowen Jin, Qiuming Zhu, Zhipeng Lin, Kai Mao, Jie Wang

{"title":"Three-dimensional trajectory optimization of rotary-wing UAV with cellular network connectivity based on modified DDPG","authors":"Weizhi Zhong, Xiang Liu, Haowen Jin, Qiuming Zhu, Zhipeng Lin, Kai Mao, Jie Wang","doi":"10.1016/j.phycom.2025.102773","DOIUrl":"10.1016/j.phycom.2025.102773","url":null,"abstract":"<div><div>The integration of cellular network and unmanned aerial vehicles (UAVs) plays a critical role in the development of remote sensing and intelligent monitoring technologies. However, due to the limited onboard energy and the down-tilt characteristics of cellular base station (BS) antennas, UAVs navigating over urban areas still face practical challenges. By investigating the trade-off between UAV flight time and expected interruption time, this paper proposes a deep reinforcement learning (DRL) based joint optimization algorithm for UAV three-dimensional (3D) spatial cruising in dense urban areas. The algorithm enables the UAV to determine an optimal trajectory that navigates through designated waypoints within the cruising space while ensuring the completion of the journey under predefined energy constraints. Unlike traditional discretized trajectory optimization methods, our approach employs a deep deterministic policy gradient (DDPG) network to enable fully continuous and omnidirectional action selection, allowing the UAV to navigate more efficiently while avoiding low-coverage areas. Moreover, the algorithm is further modified through the incorporation of a prioritized experience replay (PER) mechanism and N-step learning method, aimed at enhancing overall performance. Numerical results verify that our proposed method significantly outperforms benchmark algorithms in connectivity-aware UAV path planning, demonstrating clear advantages in achieving robust and reliable aerial communication coverage in dynamic 3D environments.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102773"},"PeriodicalIF":2.2,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Distributed channel selection, relay assignment, and UAV deployment for delay minimization via game framework 通过游戏框架实现延迟最小化的分布式信道选择、中继分配和无人机部署

IF 2 4区计算机科学

Physical Communication Pub Date : 2025-07-18 DOI: 10.1016/j.phycom.2025.102785

Yuan He , Xie Jun , Yaqun Liu , Xijian Luo

{"title":"Distributed channel selection, relay assignment, and UAV deployment for delay minimization via game framework","authors":"Yuan He , Xie Jun , Yaqun Liu , Xijian Luo","doi":"10.1016/j.phycom.2025.102785","DOIUrl":"10.1016/j.phycom.2025.102785","url":null,"abstract":"<div><div>Due to the flexibility and rapid deployment characteristics, Unmanned Aerial Vehicles (UAVs) are widely used as relays to provide real-time data transmission services. In this paper, the network scenario of relay UAVs (RUs) assisting the communication between mission UAVs (MUs) and the ground base station is investigated. Considering the queuing delay that may be generated by real-time data transmission between UAVs, the total data transmission delay of MUs is minimized by optimizing the channel selection, the relay assignment and the deployment position. In order to solve the problem, the channel selection and deployment position optimization sub-problems are transformed into the potential games. A distributed best-response algorithm and a distributed RU deployment position optimization algorithm based on particle swarm optimization are separately designed to solve the games’ equilibrium. Meanwhile, the relay assignment sub-problem is transformed into a coalition formation game and a distributed relay assignment algorithm is designed based on the switch and swap rules. All algorithms optimize the global network performance based on local information. Simulation results show that the proposed algorithms have better performance than the baseline algorithms and can achieve a low delay in different environments.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102785"},"PeriodicalIF":2.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Generative AI-driven reinforcement learning for beamforming and scheduling in multi-cell MIMO-NOMA systems 多单元MIMO-NOMA系统波束形成与调度的生成式ai驱动强化学习

IF 2 4区计算机科学

Physical Communication Pub Date : 2025-07-18 DOI: 10.1016/j.phycom.2025.102771

Abuzar B.M. Adam , Elhadj Moustapha Diallo , Mohammed Saleh Ali Muthanna , Reem Ibrahim Alkanhel , Ammar Muthanna , Mohammad Hammoudeh

{"title":"Generative AI-driven reinforcement learning for beamforming and scheduling in multi-cell MIMO-NOMA systems","authors":"Abuzar B.M. Adam , Elhadj Moustapha Diallo , Mohammed Saleh Ali Muthanna , Reem Ibrahim Alkanhel , Ammar Muthanna , Mohammad Hammoudeh","doi":"10.1016/j.phycom.2025.102771","DOIUrl":"10.1016/j.phycom.2025.102771","url":null,"abstract":"<div><div>This article introduces a novel generative artificial intelligence-enhanced primal–dual proximal policy optimization (GAI-PDPPO) framework for joint user scheduling and beamforming in downlink multi-cell multiple-input and multiple-output non-orthogonal multiple access (MC-MIMO-NOMA) networks. Designed to address the challenges of interference-laden environments typical in beyond the fifth generation (B5G)/sixth generation (6G) systems, the proposed method formulates a complex mixed-integer nonlinear programming problem to minimize transmit power under stringent Quality-of-Service (QoS) constraints. Unlike conventional approaches, GAI-PDPPO incorporates an invertible transformer-based actor-critic architecture capable of modeling high-dimensional channel state information and unknown-source interference. Through the integration of generative pretraining and prioritized experience replay, the framework accelerates convergence and enhances policy generalization. Extensive simulations demonstrate that GAI-PDPPO consistently outperforms standard primal–dual PPO and benchmark solutions, achieving lower power consumption and higher spectral efficiency under varying signal-to-interference-plus-noise ratio (SINR) thresholds and interference conditions.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102771"},"PeriodicalIF":2.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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