Digital Communications and Networks最新文献_第10页

Joint user association and cooperative beamforming for multi-IRSs aided mmWave communication systems 多红外卫星辅助毫米波通信系统的联合用户关联和协同波束形成

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-02-01 Epub Date: 2025-12-04 DOI: 10.1016/j.dcan.2025.11.007

Qing Xue , Jiajun Mu , Fengsheng Wei , Meng Hua , Qianbin Chen

{"title":"Joint user association and cooperative beamforming for multi-IRSs aided mmWave communication systems","authors":"Qing Xue , Jiajun Mu , Fengsheng Wei , Meng Hua , Qianbin Chen","doi":"10.1016/j.dcan.2025.11.007","DOIUrl":"10.1016/j.dcan.2025.11.007","url":null,"abstract":"<div><div>This paper investigates a downlink millimeter-Wave (mmWave) communication system equipped with multiple cooperative Intelligent Reflecting Surfaces (IRSs), aiming to extend mmWave signal coverage and maximize system throughput. To fully exploit the potential of IRSs within a user-centric framework, this study delves into the joint optimization problem of user multiple association, transmit beamforming, and cooperative passive beamforming. Meanwhile, the impact of IRS locations on user association is analyzed. Given the non-convexity and complexity of the joint optimization problem, a low-complexity optimization algorithm is designed. The algorithm integrates iterative optimization, Lagrangian dual decomposition, and Fractional Programming (FP) techniques. Specifically, the user association problem is optimized using the Lagrangian dual decomposition method, while the joint beamforming is solved via the FP method. Simulation results demonstrate that, compared to traditional methods, the proposed algorithm significantly improves the system sum rate, validating its effectiveness and superiority.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 2","pages":"Pages 252-261"},"PeriodicalIF":7.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147423864","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

Deep learning aided CSI feedback optimization with robust error recovery 深度学习辅助CSI反馈优化与鲁棒错误恢复

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-02-01 Epub Date: 2025-11-10 DOI: 10.1016/j.dcan.2025.11.002

Bo Yang, Zhanglin Zhou, Nanqi Fan, Feng Ke, Jie Tang, Xiu Yin Zhang

{"title":"Deep learning aided CSI feedback optimization with robust error recovery","authors":"Bo Yang, Zhanglin Zhou, Nanqi Fan, Feng Ke, Jie Tang, Xiu Yin Zhang","doi":"10.1016/j.dcan.2025.11.002","DOIUrl":"10.1016/j.dcan.2025.11.002","url":null,"abstract":"<div><div>Driven by the increasing demand for efficient data transmission, massive Multiple-Input Multiple-Output (MIMO) systems have emerged as a key technology for future communication systems. However, effective utilization of MIMO relies heavily on accurate Channel State Information (CSI) that is fed back to the base station, which poses significant challenges due to the overhead associated with CSI feedback, especially with the increasing number of antennas. To overcome these drawbacks, this paper proposes a Deep Learning (DL) scheme to improve the CSI feedback, presenting a network named CsiDNet, which compresses CSI at the user end and decompresses it at the base station side. In addition, an auxiliary module is designed to restore CSI information under error-prone scenarios, enhancing the robustness of the system. Extensive performance analysis and simulations demonstrate that CsiDNet achieves an improvement of 2.7 dB and 0.1 dB in terms of Normalized Mean Square Error (NMSE) and Square Generalized Cosine Similarity (SGCS) respectively compared to other models, while significantly reducing computational complexity. The auxiliary module further improves the NMSE and SGCS performance by 4 dB and 0.1 dB respectively, reflecting its effectiveness in recovering error-prone CSI components. Overall, our research improves the accuracy and efficiency of CSI feedback while enhancing the system's robustness against real-world transmission challenges.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 2","pages":"Pages 354-363"},"PeriodicalIF":7.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147423857","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

Green scheduling for LLM workloads with model and data reuse across geo-distributed data centers 通过跨地理分布式数据中心的模型和数据重用实现LLM工作负载的绿色调度

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-02-01 Epub Date: 2025-12-03 DOI: 10.1016/j.dcan.2025.11.006

Hao Liu , Xiaonyu Hu , Ran Wang , Jie Hao , Qiang Wu , Hongke Zhang

{"title":"Green scheduling for LLM workloads with model and data reuse across geo-distributed data centers","authors":"Hao Liu , Xiaonyu Hu , Ran Wang , Jie Hao , Qiang Wu , Hongke Zhang","doi":"10.1016/j.dcan.2025.11.006","DOIUrl":"10.1016/j.dcan.2025.11.006","url":null,"abstract":"<div><div>The explosive proliferation of Large Language Models (LLMs) imposes significant energy and operational burdens on Geographically Distributed Data Centers (GDDCs), thereby demanding an efficient mechanism for LLMs task scheduling. While prior geo-distributed scheduling methods reduce cost and carbon emissions by exploiting regional heterogeneity, they largely overlook model and data reuse opportunities and the uncertainty of LLM execution times. In this paper, we introduce GCOS, to the best of our knowledge, the first green scheduling framework that incorporates a dual-cache system for both data and models, while jointly optimizing task assignment and cache migration. We firstly propose a dual-cache mechanism that decouples model and data caching to enable fine-grained reuse and minimize redundant transmissions. Subsequently, we propose the Multi-Agent Cache-aware Cooperative Scheduling (MACCS) algorithm, which leverages reinforcement learning to optimize task placement with a focus on minimizing both carbon emissions and cost. Additionally, we design a lightweight execution time predictor, DiPTree, to address the high variability in task execution times. Extensive experiments on real-world datasets demonstrate that GCOS reduces overall cost by up to 92.6 % and carbon emissions by 90.3 %, significantly outperforming existing baselines.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 2","pages":"Pages 236-251"},"PeriodicalIF":7.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147423866","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

Performance analysis of RIS-aided incremental-relaying wireless communication systems ris辅助增量中继无线通信系统性能分析

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-02-01 Epub Date: 2025-12-09 DOI: 10.1016/j.dcan.2025.12.003

Manlin Fang , Min Deng , Jun Yang , Saifullah Adnan , Zhen Chen

引用次数: 0

Low-complexity APSK demodulation algorithm based on K-means clustering in LEO satellite communication systems 低轨道卫星通信系统中基于k均值聚类的低复杂度APSK解调算法

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-02-01 Epub Date: 2025-08-27 DOI: 10.1016/j.dcan.2025.08.002

Guangfu Wu , Xiangrui Meng , Changlin Chen , Biqun Xiang

{"title":"Low-complexity APSK demodulation algorithm based on K-means clustering in LEO satellite communication systems","authors":"Guangfu Wu , Xiangrui Meng , Changlin Chen , Biqun Xiang","doi":"10.1016/j.dcan.2025.08.002","DOIUrl":"10.1016/j.dcan.2025.08.002","url":null,"abstract":"<div><div>Amplitude Phase Shift Keying (APSK) is more suitable for the nonlinear channels of Low Earth Orbit (LEO) satellite communication systems compared to Quadrature Amplitude Modulation (QAM). To tackle challenges posed by Direct Current (DC) interference and high demodulation complexity, we propose an APSK demodulation algorithm based on K-means clustering. Initially, static DC components are calculated and removed from the received APSK signals. Subsequently, the estimated APSK constellation points serve as initial centers for K-means clustering. These centers are refined through the K-means process and act as theoretical APSK constellation points for the Max-Log-MAP demodulation algorithm, effectively eliminating residual DC. We then introduce a low-complexity APSK demodulation algorithm that utilizes the symmetry of constellation points along with the Euclidean distance between DC-eliminated signals and these constellation points to minimize the set of constellation points. Simulation results indicate that for 32-APSK, our proposed demodulation submodule reduces computational complexity to approximately one-third that of the Max-Log-MAP algorithm while improving Bit Error Rate (BER) performance by about 0.23 dB. Furthermore, end-to-end simulation experiments conducted within LEO satellite communication systems demonstrate that our approach not only maintains this complexity advantage but also enhances BER performance by approximately 1.1 dB.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 2","pages":"Pages 343-353"},"PeriodicalIF":7.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147423856","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

Personalized federated learning for semantic communication with collaborative fine-tuning 通过协作微调实现语义通信的个性化联邦学习

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-02-01 Epub Date: 2025-08-18 DOI: 10.1016/j.dcan.2025.08.005

Maochuan Wu , Juan Li , Jing Xu , Bing Chen , Kun Zhu

{"title":"Personalized federated learning for semantic communication with collaborative fine-tuning","authors":"Maochuan Wu , Juan Li , Jing Xu , Bing Chen , Kun Zhu","doi":"10.1016/j.dcan.2025.08.005","DOIUrl":"10.1016/j.dcan.2025.08.005","url":null,"abstract":"<div><div>Semantic Communication (SemCom) is a promising paradigm for future 6G networks, where communication performance hinges on the effectiveness of SemCom models, particularly the source-channel encoder and decoder. However, training these models faces significant challenges. Firstly, the privacy-sensitive nature of communication data discourages users from uploading data to centralized servers. Secondly, heterogeneous local data distributions and diverse communication counterparts of different users necessitate personalized SemCom models. Specifically, a user's encoder must align with its receivers' decoders and the transmitted data distribution, while its decoder must adapt to the user's transmitters and received data distribution. To address these challenges, we propose FineFed, a personalized federated learning method with collaborative fine-tuning. Initially, a unified global model is trained distributively via federated learning, eliminating data uploads. Subsequently, users iteratively fine-tune encoders and decoders collaboratively, achieving SemCom model personalization. For encoder fine-tuning, decoders are fixed and shared with transmitters to address distributed loss calculation issues. Each encoder is fine-tuned using the idea of multi-task learning, treating communication with each receiver as a separate task. Then, encoders are fixed. A user shares its decoder with its own transmitters. These transmitters collaboratively fine-tune the user's decoder by the idea of federated multi-task learning. Experimental results demonstrate that FineFed improves the average performance of federated SemCom models by <span><math><mn>1</mn><mtext>%</mtext><mtext>-</mtext><mn>7</mn><mtext>%</mtext></math></span>, bringing it closer to the performance of centrally-trained models.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 2","pages":"Pages 306-318"},"PeriodicalIF":7.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147423860","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

TopoLLM: LLM-driven adaptive tool learning for real-time emergency network topology planning TopoLLM: llm驱动的实时应急网络拓扑规划自适应工具学习

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-02-01 Epub Date: 2025-10-17 DOI: 10.1016/j.dcan.2025.10.002

Yizhuo Ma , Rongzheng Wang , Shuang Liang, Guangchun Luo, Ke Qin

{"title":"TopoLLM: LLM-driven adaptive tool learning for real-time emergency network topology planning","authors":"Yizhuo Ma , Rongzheng Wang , Shuang Liang, Guangchun Luo, Ke Qin","doi":"10.1016/j.dcan.2025.10.002","DOIUrl":"10.1016/j.dcan.2025.10.002","url":null,"abstract":"<div><div>Communication infrastructure is often among the first casualties in natural or human-induced disasters, severely impairing the coordination and efficiency of rescue operations. Rapid deployment of Unmanned Aerial Vehicles (UAVs) and satellite systems has thus become essential for establishing robust communication links to support rescue-critical tasks. However, existing emergency communication networks rely heavily on domain expertise for topology design, thereby suffering from issues such as inefficient resource allocation and network congestion, among others. To address these challenges, we present TopoLLM, a framework that leverages Large Language Models (LLMs) for tool-driven optimization of emergency network topologies. This framework effectively combines the reasoning capabilities of the LLM with TopoTool, a domain-specific optimization toolkit engineered for high-precision and load-balanced network planning in disaster scenarios. Guided by an adaptive tool-selection mechanism, TopoLLM autonomously generates resilient topologies and allocates resources intelligently, reducing the need for extensive human interventions. Experimental evaluations on simulated disaster scenarios verify that TopoLLM can rapidly generate high-accuracy and robust topologies, achieving notable performance improvements compared with existing approaches.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 2","pages":"Pages 273-282"},"PeriodicalIF":7.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147423862","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 digital twin-based quadruped robot system with scene perception, fast communication, and holographic interaction 基于数字孪生的四足机器人系统，具有场景感知、快速通信和全息交互

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-02-01 Epub Date: 2025-11-27 DOI: 10.1016/j.dcan.2025.11.004

Chen Zhu , Jianrong Bao , Zhouxiang Zhao , Zhaohui Yang , Chongwen Huang , Jiawen Kang , Hao Xu , Zhaoyang Zhang

{"title":"A digital twin-based quadruped robot system with scene perception, fast communication, and holographic interaction","authors":"Chen Zhu , Jianrong Bao , Zhouxiang Zhao , Zhaohui Yang , Chongwen Huang , Jiawen Kang , Hao Xu , Zhaoyang Zhang","doi":"10.1016/j.dcan.2025.11.004","DOIUrl":"10.1016/j.dcan.2025.11.004","url":null,"abstract":"<div><div>This paper develops a quadruped robot virtual-real interactive control system based on digital twin technology. The system is designed to address key challenges in robotics technology, including real-time performance, low-latency control, high-precision multi-sensor data fusion, stable network transmission, data security, user-friendly interaction interface, system scalability, and maintainability. The system comprises a number of functional modules, including a 3D modeling module, a positioning perception module, a virtual interaction module, a wise sensing-transmission module, and a cloud server. The 3D modeling module is responsible for constructing the virtual quadruped robot and motion space scenarios. The positioning perception module integrates LiDAR and Inertial Measurement Unit (IMU) data, utilizing Point-LIO and HDL-localization algorithms for high-precision environmental perception and positioning. The virtual interaction module provides a user-friendly control interface through computer software and the HoloLens headset. The wise sensing-transmission module employs WiFi and 5G links to ensure low-latency and high-bandwidth data transmission, and employs <span>libhv</span> and <span>libssl</span> asynchronous IO and network security cryptographic libraries to guarantee data security. The system is designed to run on the Ubuntu 20.04 platform, offering excellent scalability and maintainability. This system has broad application prospects in industrial manufacturing, construction, disaster rescue, military applications, and educational training. It enhances the performance and reliability of quadruped robot systems and lays a solid foundation for the future development of the industrial metaverse.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 2","pages":"Pages 262-272"},"PeriodicalIF":7.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147423863","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

Risk-aware user satisfaction maximization in vehicle-assisted multi-access edge computing offloading: a game-theoretic approach 车辆辅助多址边缘计算卸载中的风险感知用户满意度最大化：一种博弈论方法

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-02-01 Epub Date: 2025-11-10 DOI: 10.1016/j.dcan.2025.11.001

Yu Dai , Jie Tian , Tiantian Li , Jing Wang , Chuanfen Feng

{"title":"Risk-aware user satisfaction maximization in vehicle-assisted multi-access edge computing offloading: a game-theoretic approach","authors":"Yu Dai , Jie Tian , Tiantian Li , Jing Wang , Chuanfen Feng","doi":"10.1016/j.dcan.2025.11.001","DOIUrl":"10.1016/j.dcan.2025.11.001","url":null,"abstract":"<div><div>Multi-access Edge Computing (MEC) enhances computational efficiency by enabling resource-constrained User Devices (UD) to offload tasks to edge servers. Compared to traditional edge servers fixed on the Small Cellular Base Stations (SBS), mobile vehicles with idle resources serve as mobile edge servers, which can reduce UD's task latency due to closer proximity to the UD. However, due to the limited computation resources of vehicles and highly competitive among UD, the available computation resources provided by vehicles for UD are uncertain, which poses a challenge for UD in making task offloading decisions. In this paper, we establish a risk-aware task offloading framework in vehicle-assisted MEC networks with computation resource uncertainty, where UD make offloading decisions by considering their risk-aware behavior. We first characterize and model UD's risk-aware behavior based on Prospect Theory (PT) and then formulate a user satisfaction maximization problem by optimizing the offloading strategy of UD. To solve it, we reformulate the above problem among multiple users as a non-cooperative game and prove the uniqueness of the Pure Nash Equilibrium (PNE). We also propose a low-complexity distributed iterative optimization algorithm to obtain the optimal offloading strategy. The simulation results demonstrate that the proposed scheme significantly enhances satisfaction utility of UD and reduces failure probability of vehicles compared to other benchmark methods.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 2","pages":"Pages 294-305"},"PeriodicalIF":7.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147423865","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

Spatio-temporal feature extraction with a global-local Transformer model for video scene graph generation 基于全局-局部Transformer模型的视频场景图生成时空特征提取

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-02-01 Epub Date: 2025-04-29 DOI: 10.1016/j.dcan.2025.04.010

Rongsen Wu, Jie Xu, Hao Zheng, Zhiyuan Xu, Zixuan Li, Shixue Cheng, Shumao Zhang

{"title":"Spatio-temporal feature extraction with a global-local Transformer model for video scene graph generation","authors":"Rongsen Wu, Jie Xu, Hao Zheng, Zhiyuan Xu, Zixuan Li, Shixue Cheng, Shumao Zhang","doi":"10.1016/j.dcan.2025.04.010","DOIUrl":"10.1016/j.dcan.2025.04.010","url":null,"abstract":"<div><div>In the field of video scene graph generation, spatio-temporal feature extraction and the long-tail effect in relationship classification are core research issues. This paper proposes extracting spatio-temporal features using the global–local Transformer model for video scene graph generation. Methods based on the Transformer architecture and attention mechanism enrich the semantic information of spatio-temporal features in videos, thereby improving the accuracy of relationship classification. In the feature processing module, pose features are introduced to strengthen the semantic representation of objects. In the spatial feature encoding module, a local spatial visibility matrix based on bounding boxes and key points of human pose features is proposed to add the issue of insufficient attention to local details in traditional Transformer encoders. In the temporal feature encoding module, a global random frame extraction strategy is proposed, which considers global temporal features while also taking computational complexity into account. In the relation classification module, to address the uneven distribution of object and relation categories in the Action Genome dataset, a relation classification loss function based on bipartite graph matching and Focal Loss is proposed, which alleviates the long-tail effect in relation classification and improves the accuracy.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 2","pages":"Pages 364-374"},"PeriodicalIF":7.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147423854","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