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Dynamic and Distributed Intelligence over Smart Devices, Internet of Things Edges, and Cloud Computing for Human Activity Recognition Using Wearable Sensors 通过智能设备、物联网边缘和云计算实现动态分布式智能,利用可穿戴传感器识别人类活动
IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-01-02 DOI: 10.3390/jsan13010005
Ayman Wazwaz, Khalid Amin, Noura Semary, Tamer Ghanem
A wide range of applications, including sports and healthcare, use human activity recognition (HAR). The Internet of Things (IoT), using cloud systems, offers enormous resources but produces high delays and huge amounts of traffic. This study proposes a distributed intelligence and dynamic HAR architecture using smart IoT devices, edge devices, and cloud computing. These systems were used to train models, store results, and process real-time predictions. Wearable sensors and smartphones were deployed on the human body to detect activities from three positions; accelerometer and gyroscope parameters were utilized to recognize activities. A dynamic selection of models was used, depending on the availability of the data and the mobility of the users. The results showed that this system could handle different scenarios dynamically according to the available features; its prediction accuracy was 99.23% using the LightGBM algorithm during the training stage, when 18 features were used. The prediction time was around 6.4 milliseconds per prediction on the smart end device and 1.6 milliseconds on the Raspberry Pi edge, which can serve more than 30 end devices simultaneously and reduce the need for the cloud. The cloud was used for storing users’ profiles and can be used for real-time prediction in 391 milliseconds per request.
包括体育和医疗在内的各种应用都在使用人类活动识别(HAR)。使用云系统的物联网(IoT)提供了巨大的资源,但也产生了高延迟和巨大的流量。本研究利用智能物联网设备、边缘设备和云计算提出了分布式智能和动态 HAR 架构。这些系统用于训练模型、存储结果和处理实时预测。在人体上部署了可穿戴传感器和智能手机,从三个位置检测活动;利用加速计和陀螺仪参数识别活动。根据数据的可用性和用户的移动性,对模型进行了动态选择。结果表明,该系统可以根据可用特征动态处理不同的场景;在训练阶段,使用 LightGBM 算法,当使用 18 个特征时,其预测准确率为 99.23%。智能终端设备的每次预测时间约为 6.4 毫秒,Raspberry Pi 边缘设备的每次预测时间约为 1.6 毫秒,可同时为 30 多台终端设备提供服务,减少了对云的需求。云用于存储用户配置文件,每次请求的实时预测时间为 391 毫秒。
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引用次数: 0
Output Stream from the AQM Queue with BMAP Arrivals AQM 队列的输出流与 BMAP 抵达量
IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-01-02 DOI: 10.3390/jsan13010004
A. Chydzinski
We analyse the output stream from a packet buffer governed by the policy that incoming packets are dropped with a probability related to the buffer occupancy. The results include formulas for the number of packets departing the buffer in a specific time, for the time-dependent output rate and for the steady-state output rate. The latter is the key performance measure of the buffering mechanism, as it reflects its ability to process a specific number of packets in a time unit. To ensure broad applicability of the results in various networks and traffic types, a powerful and versatile model of the input stream is used, i.e., a BMAP. Numeric examples are provided, with several parameterisations of the BMAP, dropping probabilities and loads of the system.
我们分析了一个数据包缓冲器的输出流,该缓冲器采用的策略是以与缓冲器占用率相关的概率丢弃进入的数据包。结果包括特定时间内离开缓冲区的数据包数量、随时间变化的输出率和稳态输出率的公式。后者是缓冲机制的关键性能指标,因为它反映了缓冲机制在一个时间单位内处理特定数量数据包的能力。为确保结果在各种网络和流量类型中的广泛适用性,我们使用了一个功能强大、用途广泛的输入流模型,即 BMAP。我们提供了数字示例,其中包括 BMAP、丢包概率和系统负载的若干参数设置。
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引用次数: 0
Multi-Objective Optimization of Gateway Location Selection in Long-Range Wide Area Networks: A Tradeoff Analysis between System Costs and Bitrate Maximization 远距离广域网中网关位置选择的多目标优化:系统成本与比特率最大化之间的权衡分析
IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-01-02 DOI: 10.3390/jsan13010003
Charuay Savithi, Chutchai Kaewta
LoRaWANs play a critical role in various applications such as smart farming, industrial IoT, and smart cities. The strategic placement of gateways significantly influences network performance optimization. This study presents a comprehensive analysis of the tradeoffs between system costs and bitrate maximization for selecting optimal gateway locations in LoRaWANs. To address this challenge, a rigorous mathematical model is formulated to incorporate essential factors and constraints related to gateway selection. Furthermore, we propose an innovative metaheuristic algorithm known as the M-VaNSAS algorithm, which effectively explores the solution space and identifies favorable gateway locations. The Pareto front and TOPSIS methods are employed to evaluate and rank the generated solutions, providing a robust assessment framework. Our research findings highlight the suitability of a network model comprising 144 gateways tailored for the Ubon Ratchathani province. Among the evaluated algorithms, the M-VaNSAS method demonstrates exceptional efficiency in gateway location selection, outperforming the PSO, DE, and GA methods.
LoRaWAN 在智能农业、工业物联网和智能城市等各种应用中发挥着至关重要的作用。网关的战略布局对网络性能优化有重大影响。本研究全面分析了系统成本与比特率最大化之间的权衡,以选择 LoRaWAN 中的最佳网关位置。为应对这一挑战,我们制定了一个严格的数学模型,将与网关选择相关的重要因素和约束条件纳入其中。此外,我们还提出了一种被称为 M-VaNSAS 算法的创新元启发式算法,该算法可有效探索解决方案空间并确定有利的网关位置。我们采用帕累托前沿法和 TOPSIS 法对生成的解决方案进行评估和排序,从而提供了一个稳健的评估框架。我们的研究结果突出表明,由 144 个网关组成的网络模型非常适合乌汶叻差他尼府。在所评估的算法中,M-VaNSAS 方法在网关位置选择方面表现出卓越的效率,优于 PSO、DE 和 GA 方法。
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引用次数: 0
Robust ISAC Localization in Smart Cities: A Hybrid Network Approach for NLOS Challenges with Uncertain Parameters 智能城市中稳健的 ISAC 定位:针对参数不确定的 NLOS 挑战的混合网络方法
IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-12-29 DOI: 10.3390/jsan13010002
Turke Althobaiti, R. A. Khalil, Nasir Saeed
Accurate localization holds paramount importance across many applications within the context of smart cities, particularly in vehicular communication systems, the Internet of Things, and Integrated Sensing and Communication (ISAC) technologies. Nonetheless, achieving precise localization remains a persistent challenge, primarily attributed to the prevalence of non-line-of-sight (NLOS) conditions and the presence of uncertainties surrounding key wireless transmission parameters. This paper presents a comprehensive framework tailored to address the intricate task of localizing multiple nodes within ISAC systems significantly impacted by pervasive NLOS conditions and the ambiguity of transmission parameters. The proposed methodology integrates received signal strength (RSS) and time-of-arrival (TOA) measurements as a strategic response to effectively overcome these substantial challenges, even in situations where the precise values of transmitting power and temporal information remain elusive. An approximation approach is judiciously employed to facilitate the inherent non-convex and NP-hard nature of the original estimation problem, resulting in a notable transformation, rendering the problem amenable to a convex optimization paradigm. The comprehensive array of simulations conducted within this study corroborates the efficacy of the proposed hybrid cooperative localization method by underscoring its superior performance relative to conventional approaches relying solely on RSS or TOA measurements. This enhancement in localization accuracy in ISAC systems holds promise in the intricate urban landscape of smart cities, offering substantial contributions to infrastructure optimization and service efficiency.
在智慧城市的许多应用中,尤其是在车载通信系统、物联网和综合传感与通信(ISAC)技术中,精确定位至关重要。然而,实现精确定位仍然是一个长期存在的挑战,这主要归因于非视线(NLOS)条件的普遍存在以及关键无线传输参数的不确定性。受普遍存在的非视距条件和传输参数不确定性的严重影响,ISAC 系统中的多个节点的定位任务错综复杂,本文提出了一个专门用于解决这一问题的综合框架。所提出的方法整合了接收信号强度(RSS)和到达时间(TOA)测量,作为有效克服这些重大挑战的战略对策,即使在发射功率和时间信息的精确值仍然难以捉摸的情况下也是如此。为了解决原始估计问题固有的非凸和 NP-困难性质,本研究明智地采用了近似方法,从而实现了显著的转变,使问题适合于凸优化范例。本研究中进行的一系列综合模拟证实了所提出的混合合作定位方法的有效性,强调了其相对于仅依赖 RSS 或 TOA 测量的传统方法的卓越性能。在智能城市错综复杂的城市景观中,ISAC 系统定位精度的提高为基础设施优化和服务效率做出了巨大贡献。
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引用次数: 0
Reduction in Data Imbalance for Client-Side Training in Federated Learning for the Prediction of Stock Market Prices 在预测股市价格的联合学习中减少客户端训练的数据不平衡
IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-12-21 DOI: 10.3390/jsan13010001
Momina Shaheen, M. Farooq, Tariq Umer
The approach of federated learning (FL) addresses significant challenges, including access rights, privacy, security, and the availability of diverse data. However, edge devices produce and collect data in a non-independent and identically distributed (non-IID) manner. Therefore, it is possible that the number of data samples may vary among the edge devices. This study elucidates an approach for implementing FL to achieve a balance between training accuracy and imbalanced data. This approach entails the implementation of data augmentation in data distribution by utilizing class estimation and by balancing on the client side during local training. Secondly, simple linear regression is utilized for model training at the client side to manage the optimal computation cost to achieve a reduction in computation cost. To validate the proposed approach, the technique was applied to a stock market dataset comprising stocks (AAL, ADBE, ASDK, and BSX) to predict the day-to-day values of stocks. The proposed approach has demonstrated favorable results, exhibiting a strong fit of 0.95 and above with a low error rate. The R-squared values, predominantly ranging from 0.97 to 0.98, indicate the model’s effectiveness in capturing variations in stock prices. Strong fits are observed within 75 to 80 iterations for stocks displaying consistently high R-squared values, signifying accuracy. On the 100th iteration, the declining MSE, MAE, and RMSE (AAL at 122.03, 4.89, 11.04, respectively; ADBE at 457.35, 17.79, and 21.38, respectively; ASDK at 182.78, 5.81, 13.51, respectively; and BSX at 34.50, 4.87, 5.87, respectively) values corroborated the positive results of the proposed approach with minimal data loss.
联合学习(FL)方法解决了访问权限、隐私、安全和多样化数据可用性等重大挑战。然而,边缘设备是以非独立和同分布(non-IID)的方式生产和收集数据的。因此,边缘设备之间的数据样本数量可能会有所不同。本研究阐明了一种实施 FL 的方法,以实现训练准确性和不平衡数据之间的平衡。这种方法需要在数据分布过程中利用类估计和本地训练过程中的客户端平衡来实现数据增强。其次,在客户端利用简单的线性回归进行模型训练,以管理最佳计算成本,从而降低计算成本。为了验证所提出的方法,我们将该技术应用于由股票(AAL、ADBE、ASDK 和 BSX)组成的股票市场数据集,以预测股票的每日价值。所提出的方法取得了良好的效果,拟合度达到 0.95 及以上,误差率较低。R 平方值主要在 0.97 至 0.98 之间,表明该模型能有效捕捉股票价格的变化。在 75 至 80 次迭代中,可以观察到 R 平方值持续较高的股票具有较强的拟合能力,这表明了模型的准确性。在第 100 次迭代中,MSE、MAE 和 RMSE 值不断下降(AAL 分别为 122.03、4.89 和 11.04;ADBE 分别为 457.35、17.79 和 21.38;ASDK 分别为 182.78、5.81 和 13.51;BSX 分别为 34.50、4.87 和 5.87),证实了所提方法在数据损失最小的情况下取得了积极成果。
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引用次数: 0
Performance Evaluation of LoRa Communications in Harsh Industrial Environments 恶劣工业环境中 LoRa 通信的性能评估
IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-28 DOI: 10.3390/jsan12060080
L. Aarif, Mohamed Tabaa, Hanaa Hachimi
LoRa technology is being integrated into industrial applications as part of Industry 4.0 owing to its longer range and low power consumption. However, noise, interference, and the fading effect all have a negative impact on LoRa performance in an industrial environment, necessitating solutions to ensure reliable communication. This paper evaluates and compares LoRa’s performance in terms of packet error rate (PER) with and without forward error correction (FEC) in an industrial environment. The impact of integrating an infinite impulse response (IIR) or finite impulse response (FIR) filter into the LoRa architecture is also evaluated. Simulations are carried out in MATLAB at 868 MHz with a bandwidth of 125 kHz and two spreading factors of 7 and 12. Many-to-one and one-to-many communication modes are considered, as are line of sight (LOS) and non-line of Sight (NLOS) conditions. Simulation results show that, compared to an environment with additive white Gaussian noise (AWGN), LoRa technology suffers a significant degradation of its PER performance in industrial environments. Nevertheless, the use of forward error correction (FEC) contributes positively to offsetting this decline. Depending on the configuration and architecture examined, the gain in signal-to-noise ratio (SNR) using a 4/8 coding ratio ranges from 7 dB to 11 dB. Integrating IIR or FIR filters also boosts performance, with additional SNR gains ranging from 2 dB to 6 dB, depending on the simulation parameters.
作为工业 4.0 的一部分,LoRa 技术因其较远的传输距离和较低的功耗而被集成到工业应用中。然而,噪声、干扰和衰减效应都会对 LoRa 在工业环境中的性能产生负面影响,因此需要解决方案来确保通信的可靠性。本文评估并比较了 LoRa 在工业环境中使用和不使用前向纠错(FEC)时的数据包错误率(PER)性能。此外,还评估了在 LoRa 架构中集成无限脉冲响应(IIR)或有限脉冲响应(FIR)滤波器的影响。仿真在 MATLAB 中进行,频率为 868 MHz,带宽为 125 kHz,两个扩展因子分别为 7 和 12。考虑了多对一和一对多通信模式,以及视线(LOS)和非视线(NLOS)条件。仿真结果表明,与加性白高斯噪声(AWGN)环境相比,LoRa 技术在工业环境中的 PER 性能明显下降。不过,使用前向纠错(FEC)可积极抵消这种下降。根据所研究的配置和架构,使用 4/8 编码比的信噪比 (SNR) 增益范围在 7 dB 到 11 dB 之间。集成 IIR 或 FIR 滤波器也能提高性能,额外的信噪比增益从 2 dB 到 6 dB 不等,具体取决于模拟参数。
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引用次数: 0
Electric Vehicles Energy Management for Vehicle-to-Grid 6G-Based Smart Grid Networks 基于 6G 的智能电网网络的电动汽车能源管理
IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-27 DOI: 10.3390/jsan12060079
Rola Naja, Aakash Soni, Circe Carletti
This research proposes a unique platform for energy management optimization in smart grids, based on 6G technologies. The proposed platform, applied on a virtual power plant, includes algorithms that take into account different profiles of loads and fairly schedules energy according to loads priorities and compensates for the intermittent nature of renewable energy sources. Moreover, we develop a bidirectional energy transition mechanism towards a fleet of intelligent vehicles by adopting vehicle-to-grid technology and peak clipping. Performance analysis shows that the proposed energy provides fairness to electrical vehicles, satisfies urgent loads, and optimizes smart grids energy.
这项研究基于 6G 技术,为智能电网的能源管理优化提出了一个独特的平台。所提议的平台应用于虚拟发电厂,包括考虑到不同负载情况的算法,根据负载优先级公平调度能源,并对可再生能源的间歇性进行补偿。此外,我们还通过采用车联网技术和削峰技术,为智能车队开发了一种双向能源转换机制。性能分析表明,所提出的能源为电动汽车提供了公平性,满足了紧急负荷,并优化了智能电网能源。
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引用次数: 0
A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network 欧洲远程医疗网络中支持重症监护室患者决策过程的联合学习方法
IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-20 DOI: 10.3390/jsan12060078
Giovanni Paragliola, Patrizia Ribino, Zaib Ullah
A result of the pandemic is an urgent need for data collaborations that empower the clinical and scientific communities in responding to rapidly evolving global challenges. The ICU4Covid project joined research institutions, medical centers, and hospitals all around Europe in a telemedicine network for sharing capabilities, knowledge, and expertise distributed within the network. However, healthcare data sharing has ethical, regulatory, and legal complexities that pose several restrictions on their access and use. To mitigate this issue, the ICU4Covid project integrates a federated learning architecture, allowing distributed machine learning within a cross-institutional healthcare system without the data being transported or exposed outside their original location. This paper presents the federated learning approach to support the decision-making process for ICU patients in a European telemedicine network. The proposed approach was applied to the early identification of high-risk hypertensive patients. Experimental results show how the knowledge of every single node is spread within the federation, improving the ability of each node to make an early prediction of high-risk hypertensive patients. Moreover, a performance evaluation shows an accuracy and precision of over 90%, confirming a good performance of the FL approach as a prediction test. The FL approach can significantly support the decision-making process for ICU patients in distributed networks of federated healthcare organizations.
大流行病的一个结果是迫切需要数据合作,以增强临床和科学界应对快速发展的全球挑战的能力。ICU4Covid 项目将欧洲各地的研究机构、医疗中心和医院联合在一个远程医疗网络中,共享分布在网络中的能力、知识和专长。然而,医疗数据共享在伦理、监管和法律方面存在复杂性,对数据的访问和使用造成了一些限制。为了缓解这一问题,ICU4Covid 项目整合了一个联合学习架构,允许在跨机构医疗保健系统内进行分布式机器学习,而无需将数据传输或暴露在其原始位置之外。本文介绍了在欧洲远程医疗网络中支持重症监护室患者决策过程的联合学习方法。所提出的方法被应用于高危高血压患者的早期识别。实验结果表明,每个节点的知识是如何在联盟内传播的,从而提高了每个节点对高危高血压患者进行早期预测的能力。此外,性能评估显示准确率和精确率均超过 90%,证实了 FL 方法作为预测测试的良好性能。FL方法可为联合医疗机构分布式网络中的重症监护室患者决策过程提供重要支持。
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引用次数: 0
Enhancing Mental Fatigue Detection through Physiological Signals and Machine Learning Using Contextual Insights and Efficient Modelling 利用上下文洞察和高效建模,通过生理信号和机器学习增强精神疲劳检测
Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-03 DOI: 10.3390/jsan12060077
Carole-Anne Cos, Alexandre Lambert, Aakash Soni, Haifa Jeridi, Coralie Thieulin, Amine Jaouadi
This research presents a machine learning modeling process for detecting mental fatigue using three physiological signals: electrodermal activity, electrocardiogram, and respiration. It follows the conventional machine learning modeling pipeline, while emphasizing the significant contribution of the feature selection process, resulting in, not only a high-performance model, but also a relevant one. The employed feature selection process considers both statistical and contextual aspects of feature relevance. Statistical relevance was assessed through variance and correlation analyses between independent features and the dependent variable (fatigue state). A contextual analysis was based on insights derived from the experimental design and feature characteristics. Additionally, feature sequencing and set conversion techniques were employed to incorporate the temporal aspects of physiological signals into the training of machine learning models based on random forest, decision tree, support vector machine, k-nearest neighbors, and gradient boosting. An evaluation was conducted using a dataset acquired from a wearable electronic system (in third-party research) with physiological data from three subjects undergoing a series of tests and fatigue stages. A total of 18 tests were performed by the 3 subjects in 3 mental fatigue states. Fatigue assessment was based on subjective measures and reaction time tests, and fatigue induction was performed through mental arithmetic operations. The results showed the highest performance when using random forest, achieving an average accuracy and F1-score of 96% in classifying three levels of mental fatigue.
本研究提出了一种机器学习建模过程,用于使用三种生理信号:皮肤电活动、心电图和呼吸来检测精神疲劳。它遵循传统的机器学习建模管道,同时强调特征选择过程的重要贡献,从而不仅得到高性能模型,而且得到相关模型。所采用的特征选择过程考虑了特征相关性的统计和上下文方面。通过独立特征与因变量(疲劳状态)之间的方差和相关分析来评估统计相关性。上下文分析是基于从实验设计和特征中得出的见解。此外,采用特征排序和集合转换技术,将生理信号的时间方面纳入基于随机森林、决策树、支持向量机、k近邻和梯度增强的机器学习模型的训练中。使用从可穿戴电子系统(第三方研究)获得的数据集进行评估,其中包括三名受试者进行一系列测试和疲劳阶段的生理数据。3名受试者在3种精神疲劳状态下共进行了18项测试。疲劳评价以主观测量和反应时间测试为主,疲劳诱导采用心算方法。结果表明,在使用随机森林时,对精神疲劳的三个等级进行分类的平均准确率和f1得分达到96%,表现出最高的性能。
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引用次数: 0
Enhancing the Fault Tolerance of a Multi-Layered IoT Network through Rectangular and Interstitial Mesh in the Gateway Layer 通过网关层的矩形和间隙网格增强多层物联网网络的容错性
Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-10-16 DOI: 10.3390/jsan12050076
Sastry Kodanda Rama Jammalamadaka, Bhupati Chokara, Sasi Bhanu Jammalamadaka, Balakrishna Kamesh Duvvuri, Rajarao Budaraju
Most IoT systems designed for the implementation of mission-critical systems are multi-layered. Much of the computing is done in the service and gateway layers. The gateway layer connects the internal section of the IoT to the cloud through the Internet. The failure of any node between the servers and the gateways will isolate the entire network, leading to zero tolerance. The service and gateway layers must be connected using networking topologies to yield 100% fault tolerance. The empirical formulation of the model chosen to connect the service’s servers to the gateways through routers is required to compute the fault tolerance of the network. A rectangular and interstitial mesh have been proposed in this paper to connect the service servers to the gateways through the servers, which yields 0.999 fault tolerance of the IoT network. Also provided is an empirical approach to computing the IoT network’s fault tolerance. A rectangular and interstitial mesh have been implemented in the network’s gateway layer, increasing the IoT network’s ability to tolerate faults by 11%.
大多数为实现关键任务系统而设计的物联网系统都是多层的。大部分计算是在服务层和网关层完成的。网关层通过互联网将物联网的内部部分连接到云。服务器和网关之间任何节点的故障都将隔离整个网络,导致零容忍。服务层和网关层必须使用网络拓扑进行连接,以实现100%的容错。为了计算网络的容错能力,需要选择通过路由器将服务的服务器连接到网关的模型的经验公式。本文提出了一种矩形和间隙网格,通过服务器将业务服务器连接到网关,使物联网网络容错率达到0.999。本文还提供了一种计算物联网网络容错性的经验方法。在网络的网关层中实现了矩形和间隙网格,将物联网网络的容错能力提高了11%。
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引用次数: 0
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