Intelligent and Converged Networks最新文献

{"title":"Toward Sustainable 6G Networks: A Taxonomy and Framework for Network Digital Twins","authors":"Teong Chee Chuah;Chun-Yeow Yeoh;Ryan Jeffery;Muhammad Sheraz;Manzoor Ahmed","doi":"10.23919/ICN.2026.0013","DOIUrl":"https://doi.org/10.23919/ICN.2026.0013","url":null,"abstract":"Network Digital Twins (NDTs) are emerging as key enablers of intelligent, data-driven automation in next-generation networks. However, existing definitions of NDTs remain generic and overlook telecom-specific requirements such as multi-granular synchronization, modeling fidelity, and integration with Operational and Business Support Systems (OSS/BSS). This paper addresses these gaps by introducing a capability-based taxonomy comprising four classes of NDTs, characterized along key dimensions including purpose, synchronization granularity, modeling detail, and contextual alignment. We further align this taxonomy with functional categories adapted from the TeleManagement (TM) Forum's Digital Twin for Decision Intelligence (DT4DI) initiative, clarifying how different NDT capabilities support decision intelligence across the 6G lifecycle. Building on this foundation, we present a standards-compliant architectural framework grounded in the TM Forum Open Digital Architecture (ODA) to enable deployment of diverse NDTs through OSS/BSS platforms. The paper's contributions are primarily conceptual, encompassing the proposed taxonomy, architectural framework, and representative 6G use cases, and are complemented by a survey that synthesizes recent research, industry developments, and quantitative economic assessments of NDT adoption, all aimed at informing academic and industry stakeholders on the state of the art and the key challenges associated with scalable and sustainable NDT deployment.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 3","pages":"238-271"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11701675","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solving the Elliptic Curve Discrete Logarithm Problem Using Quantum Tunneling Effects on the D-Wave Advantage2 Quantum Computer","authors":"Yu Lin;Shuaizhi Xiong;Zhi Pei;Yiliang Han;Shuaishuai Zhu;Shuxiao Yang;Chunlei Hong","doi":"10.23919/ICN.2026.0017","DOIUrl":"https://doi.org/10.23919/ICN.2026.0017","url":null,"abstract":"The computational hardness of the Elliptic Curve Discrete Logarithm Problem (ECDLP) directly determines Elliptic-Curve Cryptography (ECC) security. Quantum Annealing (QA) leverages its unique tunneling effects to effectively escape local optima. This paper proposes a quantum-annealing-based computational framework for attacking ECDLP by integrating QA with the index calculus method, achieving a quantum-solving attack on ECDLP. First, a relation-generation method based on Semaev summation polynomials is used to map the complex elliptic-curve point-addition constraints onto an Ising Hamiltonian. Next, by restructuring the penalty-term formulation and applying dynamic coefficient optimization, the parameter scale of the Ising Hamiltonian is compressed by 80%, thereby improving the QA success probability. Finally, the ECDLP is successfully modeled and solved on the D-Wave Advantage2 quantum computer. Experimental results demonstrate that the proposed quantum computing framework solves a 16-bit prime-field elliptic curve ECDLP on real quantum hardware, representing the highest experimental record publicly reported to date internationally, and further validating the practical attack potential and scalability of QA in cryptanalytic scenarios.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 3","pages":"272-285"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11701680","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physical-Model-Driven Compressive Sensing for High-Precision Off-Grid Multi-Target UAV Localization","authors":"Xiao Huang;Sicong Liu;Lingyu Chen;Ao Peng;Jianghong Shi;Zhu Han","doi":"10.23919/ICN.2026.0016","DOIUrl":"https://doi.org/10.23919/ICN.2026.0016","url":null,"abstract":"With the rapid development of advanced wireless networks and the growing reliance on location-based services, the demand for high-precision passive localization of unmanned aerial vehicles (UAVs) in fields such as emergency rescue and the industrial Internet of Things (IoT) has become increasingly urgent. Localization technologies based on received signal strength (RSS) have emerged as a preferred solution due to their low cost and ease of deployment. However, traditional on-grid compressive sensing-based localization in multi-target scenarios suffers from grid mismatch, resulting in limited localization accuracy and poor robustness. To address this issue, this paper proposes a high-precision off-grid multi-target localization method for UAVs based on physical-model-driven block-sparse compressive sensing. First, by employing a Taylor approximation of the RSS path loss model, an off-grid block sparse model with energy coefficients and geometric offset parameters is established, which jointly characterizes the target energy and position information while alleviating discretization errors at the model level. Second, a physical-model-driven fast iterative shrinkage-thresholding algorithm (FISTA) is proposed. The proposed physical-model-driven shrinkage operator replaces the proximal mapping operator, and incorporates physical boundary constraints and a differentiated penalty mechanism into the iterative process, thereby mitigating overfitting arising from higher-order model errors. Extensive Monte Carlo simulation experiments demonstrate that the proposed method achieves significantly superior average localization accuracy under various signal-to-noise ratio (SNR) and target number scenarios, with approximately 35% improvement over traditional on-grid methods and 25% over standard off-grid methods. Furthermore, it exhibits no accuracy saturation at high SNRs, accompanied by remarkably enhanced robustness.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 3","pages":"207-223"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11701682","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PolicyFRZ: Adaptive Layer Freezing via Policy Networks for Accelerating DNN Training in Edge Environments","authors":"Lisha Gao;Shuo Han;Xiaodan Wang;Xuyan Wang;Zonglin Zou;Leiyu Jiang;Jinghui Zhang","doi":"10.23919/ICN.2026.0008","DOIUrl":"https://doi.org/10.23919/ICN.2026.0008","url":null,"abstract":"Layer freezing can significantly accelerate the training of deep neural networks (DNNs) in edge environments. Existing freezing strategies typically rely on heuristic rules or intermediate model states such as activations, gradients, or loss values to assess parameter convergence. However, these methods often exhibit poor robustness to training fluctuations, which can result in degraded model accuracy. This paper proposes PolicyFRZ, a policy network based dynamic layer-freezing algorithm for accelerating DNN training. PolicyFRZ leverages a policy network within a reinforcement learning framework to dynamically determine effective freezing strategies during training. The policy network is trained using contextual information gathered throughout the training process, enabling it to make informed decisions and learn generalizable freezing patterns. Furthermore, a two-stage training scheme is designed to transfer the learned freezing patterns across different models and datasets. Extensive experiments on computer vision (CV) and natural language processing (NLP) tasks demonstrate that PolicyFRZ accelerates model training without compromising accuracy and outperforms existing layer freezing methods.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 3","pages":"224-237"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11701677","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trust Model and Privacy Protection in Edge Computing Environment","authors":"Zhaohong Guan;Yutong Song;Ruiduo Li;Xinglong Ouyang;Shaochen Sun;Dapeng Qu","doi":"10.23919/ICN.2026.0011","DOIUrl":"https://doi.org/10.23919/ICN.2026.0011","url":null,"abstract":"With the rapid increase in the number of terminals accessing Internet and the corresponding generated data, edge computing decreases the data sent to cloud center and reduces the pressure to bandwidth by processing data at the “edge” of network, either by the device or a local server. Meanwhile, the continuous evolution of Internet applications makes the content-centric mode be the trend of future Internet. Although the “content-centric” mode and innetwork caching make content-centric model more appropriate for solving the issues of resource discovery, compute reuse, and mobility management than the current IP address centric mode, some important security and privacy problems are still needed to be solved. Therefore, a hierarchical generalized trust model and corresponding privacy protection strategy are proposed for the edge computing environment. In the trust model, the direct trust between nodes for different interaction relationships within the intra-layers and across different layers are calculated, the indirect trust of the non-direct interaction node is obtained by the recommendation trust from the direct interaction nodes, and then the comprehensive trust is acquired by combining direct trust and indirect trust. Based on the proposed trust model, privacy is protected by encrypting transmission content between trusted interaction nodes. A symmetric encryption algorithm is applied between the interaction nodes in the intra-layer. For both of interaction nodes in cloud/edge layers, the key length is set based on their environment trust. Meanwhile, for terminals, the key length is set based on their environment trust and available resource. An asymmetric encryption is applied between the inter-layer interaction nodes. For an interaction from one node in higher layer to the other in lower layer, the key is calculated based on the environment trust of the node in lower layer and the similarity of the request content from nodes in lower layer. For the opposite interaction, the key is calculated based on the environment trust of the node in higher layer and the available resource of the node in lower layer. While the consideration of environment trust improves the interaction security and helps prevent privacy leakage, the consideration of available resource adjusts the key length adaptively, which reduces overhead and improves calculation efficiency. Experimental results show that the trust model has superior performance, in terms of interaction success rate, accuracy rate, recall rate, etc., than other trust models and the privacy protection policy has less overhead than fixed key length settings.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 3","pages":"306-326"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11701681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized Clustering and Deep Deterministic Policy Gradient Mechanism for Energy Efficient Sleep Scheduling in Green IoT","authors":"Nitin B. Raut;Shanmuga Sundaram Thangavelu","doi":"10.23919/ICN.2025.0027","DOIUrl":"https://doi.org/10.23919/ICN.2025.0027","url":null,"abstract":"Old industrial system is projected to become more intelligent and efficient with the help of the growing Internet of things (IoT) concept. Emerging as a new paradigm, the green IoT envisions the idea of connecting various equipment and lowering energy use. Allowing some sensor nodes to remain awake during a certain period of time while permitting others to sleep is a common technique for maintaining the sensor nodes energy. Using a sleep scheduling algorithm to dynamically arrange the active/sleep cycles (also known as duty cycles) of sensors is one typical method for extending the lifetime of a Wireless Sensor Networks (WSN). However, the existing algorithms are unable to guarantee a uniform consumption of network energy. So, in this proposed approach, an energy efficient optimized clustering and reinforcement learning based sleep scheduling algorithm is developed. The deployed sensor node initially goes through the clustering phase using the Horse Herd Optimization Algorithm (HOA) and Affinity Propagation (AP) algorithms. HOA is employed for the number of exemplar selection in AP. Following that, the Deep Reinforcement Learning (DRL) algorithm is used to adopt duty cycling in the second phase. This work separates the time period into many time slots that are allotted to each sensor node. To avoid data conflicts during transmission, every sensor node has a separate slot. In every slot, nodes adaptively change their mode such as sleep or wakeup using a reinforcement learning based on the Deep Deterministic Policy Gradient Algorithm (DDPGA). Then sleep cycle is computed by using Fuzzy Interference System based on some parameters. This proposed method achieves 8.56%, 1.26 ms of transmission delay, and 13.7% packet loss of packet delivery ratio. This methodology efficiently minimizes the energy consumed by sensor nodes in a green IoT environment.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 3","pages":"327-346"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11701676","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unified p-Norm Detection and Reliability-Weighted Fusion for Mobility-Robust CSS in Dynamic Clustered 5G NR Vehicular Network","authors":"Kenneth Okello;Elijah Mwangi;Ahmed Abd H. Malek","doi":"10.23919/ICN.2026.0014","DOIUrl":"https://doi.org/10.23919/ICN.2026.0014","url":null,"abstract":"This paper presents an analytical framework for clustered cooperative spectrum sensing (CSS) in dynamic 5G new radio (NR) vehicular networks, accounting for time-selective Nakagamim fading, mobility-induced decorrelation, and noisy reporting channels. We derive closed-form moments and detection probabilities for the p-norm detector under Jakes' Doppler model, extending prior static analyses to realistic vehicular mobility. At the cluster head, we compare two fusion schemes: reliability weighting (based on long-term secondary user (SU) performance) and adaptive weighting (based on instantaneous statistics, with finite-sample variance derived via delta method). Results show that adaptive weighting outperforms the benchmark equal-weighting scheme by up to 5 dB in required signal-to-noise ratio (SNR) to achieve probability of detection at the fusion center <tex>$P_{d}^{text{FC}}geqslant 0.7$</tex> under low-to-moderate SNR (< 10 dB) and high-mobility mmWave conditions (carrier frequency <tex>$f_{c}=28text{GHz}$</tex>, velocity <tex>$vgeqslant 110text{km}/mathrm{h}$</tex>), owing to its robustness to Doppler-induced channel de-correlation via self-normalised weight computation. Reliability weighting is superior at high SNR <tex>$(geqslant 10text{dB})$</tex> and under low-mobility, near-AWGN conditions <tex>$(f_{c}=3.5 text{GHz}, mgeqslant 6)$</tex>, where its lower-variance fixed-weight statistic yields more stable CFAR threshold calibration. Both proposed schemes consistently outperform the equal-weighting baselines across all three 5G NR configurations (3.5/28 GHz, 30/60/120 kHz sub-carrier spacing (SCS) and vehicular speeds (70–150 km/h), providing mobility-aware design guidelines for next-generation cognitive radio systems.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 3","pages":"286-305"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11701679","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Power Allocation and Residual U-Net Channel Estimation in Active IRS-Assisted Near-Field Multi-User Uplink Systems","authors":"Wei Gao;Xinji Guo;Yan Wang;Jingdie Xin;Jinbing Jiang;Feng Shu","doi":"10.23919/ICN.2026.0005","DOIUrl":"https://doi.org/10.23919/ICN.2026.0005","url":null,"abstract":"In this paper, the channel estimation (CE) problem in multi-user hybrid-field communication systems enhanced by an active intelligent reflecting surface (IRS) is investigated. To effectively characterize the spatially non-stationary characteristics of large-scale active IRS under hybrid-field propagation, the uniform linear array of the active IRS is partitioned into multiple sub-blocks. Each user is assumed to be in the far-field region of each sub-block while remaining in the near-field region of the entire IRS, which enables dimensionality reduction in channel modeling and alleviates computational complexity. Moreover, under the total power constraint between the users and the active IRS, a closed-form solution for the optimal power allocation factor is rigorously derived. Furthermore, a novel multi-user pilot transmission strategy is designed, based on which efficient least squares (LS) estimators are developed for both the direct and cascaded links. To further enhance estimation performance, a deep learning based CE framework is proposed by integrating residual blocks into a U-Net architecture. The proposed network leverages the encoder-decoder pathway and skip connections for hierarchical feature extraction, while the residual modules strengthen representational capability and facilitate gradient propagation. Simulation results demonstrate that the proposed deep learning based estimator significantly outperforms conventional LS and minimum mean square error (MMSE) methods in terms of estimation accuracy, while reducing pilot overhead by seven-eighths.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 2","pages":"166-182"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IIN-Health: A Dual-Chain Federated Learning Framework with Adaptive BFT Consensus for Auditable Medical Data Sharing","authors":"Saide Zhu;Chen Sun;Haijing Zhang;Lening Wang","doi":"10.23919/ICN.2026.0010","DOIUrl":"https://doi.org/10.23919/ICN.2026.0010","url":null,"abstract":"Federated Learning (FL) is increasingly deployed in healthcare to enable collaborative intelligence while keeping sensitive data privately at local institutions. However, existing healthcare-oriented FL frameworks still suffer from several limitations: they are vulnerable to adversarial model updates, provide limited transparency and verifiable auditability, and often lack predictable performance under constrained resources. We present IIN-Health, a blockchain-enhanced intelligent fusion network tailored for dependable healthcare FL. IIN-Health adopts a dual-chain architecture with policy-aware access control and auditable provenance tracking to integrate learning, security, and governance in a unified framework. Evidence-Carrying Access Tokens (ECATs), combined with zero-knowledge proofs, are introduced to enforce patient-defined policies and validate access decisions without disclosing sensitive information. In addition, we design MedBFT-Δ<inf>t</inf> a domain-specific Byzantine fault-tolerant protocol that ensures reliable system behavior in the presence of faulty or malicious participants. We conduct several experiments to validate its feasibility and accuracy on the MNIST dataset. The results demonstrate that IIN-Health achieves smooth and stable convergence, exhibits strong resilience against poisoning attacks, and maintains graceful performance degradation under resource constraints, while preserving verifiable auditability of model updates and data flows. These observations indicate that IIN-Health can provide a practical balance among performance, security, and regulatory compliance, and thus offers a promising foundation for trustworthy and scalable FL deployments in healthcare.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 2","pages":"146-165"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Enabled Near-Field Beam Focusing for Dynamic Metasurface Antenna Systems","authors":"Fenghao Sheng;Haiyang Zhang;Baoyun Wang","doi":"10.23919/ICN.2026.0012","DOIUrl":"https://doi.org/10.23919/ICN.2026.0012","url":null,"abstract":"In future 6G wireless communications, extremely large-scale antenna arrays and high-frequency bandwidths are expected to play a crucial role. Their utilization increases the likelihood that communication devices operate in the near-field region, where traditional transceiver architectures suffer from broadband beam-splitting effects that signifcantly degrade communication performance. To allieviate this issue, this paper studies the utilization of unique frequency selective properties of dynamic metasurface antennas (DMAs) in near-field uplink transmission. Specifically, we aim to configure the tunable parameters of the DMA to maximize the average beamforming gain across all subcarriers. Morever, we propose a deep reinforcement learning framework to efficiently learn the optimal tunable parameters of the DMA. Simulation results demonstrate that the proposed scheme achieves better beamforming gain performance over all subcarriers compared with existing benchmarks.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 2","pages":"194-205"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}