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Analysis of the SEIR mean-field model in dynamic networks under intervention

Infectious Disease Modelling 2025年第3期10卷 850-874页

作者： Li, Jiangmin Jin, Zhen Tang, Ming Complex System Research Center Shanxi University Shanxi Taiyuan 030006 China Key Laboratory of Complex Systems and Data Science of Ministry of Education Shanxi University Shanxi Taiyuan 030006 China Shanxi Key Laboratory for Mathematical Technology in Complex Systems Shanxi University Shanxi Taiyuan 030006 China School of Physics and Electronic Science East China Normal University Shanghai 200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai 200241 China

For emerging respiratory infectious diseases like COVID-19, non-pharmaceutical interventions such as isolation are crucial for controlling the spread. From the perspective of network transmission, non-pharmaceutical interventions like isolation alter the degree distribution and other topological structures of the network, thereby controlling the spread of the infectious disease. In this paper, we establish a SEIR mean-field propagation dynamics model for the synchronous evolution of dynamic networks caused by propagation and tracing isolation. We employ the reducing-dimension method to convert the mean-field model in networks into an equivalent and simpler low-dimension model, and then calculate the exact expression of the final size. In addition, we get the differential equations of the degree distribution over time in dynamic networks under tracing isolation and the relationships between the first and second moment of the dynamic network. While the degree of a node remains constant regardless of its state in many previous studies, this paper takes into account that the degree of each node changes over time whatever its state under the disease spread and intervention measures. © 2025 The Authors

关键词： Degree distribution Dynamic network The final size Tracing isolation

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NUDIF: A Non-Uniform Deployment Framework for Distributed Inference in Heterogeneous Edge Clusters

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Future Internet 2025年第4期17卷 168-168页

作者： Li, Peng Qing, Chen Liu, Hao National Key Laboratory of Complex Aviation System Simulation Chengdu610036 China Southwest China Institute of Electronic Technology Chengdu610036 China Beijing100876 China

Distributed inference in resource-constrained heterogeneous edge clusters is fundamentally limited by disparities in device capabilities and load imbalance issues. Existing methods predominantly focus on optimizing single-pipeline allocation schemes for partitioned sub-models. However, such approaches often lead to load imbalance and suboptimal resource utilization under concurrent batch processing scenarios. To address these challenges, we propose a non-uniform deployment inference framework (NUDIF), which achieves high-throughput distributed inference service by adapting to heterogeneous resources and balancing inter-stage processing capabilities. Formulated as a mixed-integer nonlinear programming (MINLP) problem, NUDIF is responsible for planning the number of instances for each sub-model and determining the specific devices for deploying these instances, while considering computational capacity, memory constraints, and communication latency. This optimization minimizes inter-stage processing discrepancies and maximizes resource utilization. Experimental evaluations demonstrate that NUDIF enhances system throughput by an average of 9.95% compared to traditional single-pipeline optimization methods under various scales of cluster device configurations. © 2025 by the authors.

关键词： Deep neural networks

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Few-Shot Source Separation for IoT Anti-Jamming via Multi-Task Learning and Meta-Learning

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IEEE Internet of Things Journal 2025年第11期12卷 16761-16776页

作者： Guo, Pengcheng Yu, Miao Li, Cheng An, Kang Sun, Yifu Chatzinotas, Symeon Niyato, Dusit National University of Defense Technology Sixty-Third Research Institute China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System China Kyung Hee University College of Electronics and Information Yongin-si17104 Korea Republic of Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

Malicious jamming attacks pose a significant threat to the integrity and performance of Internet of Things (IoT) networks. However, many jamming patterns are rare or infrequent, which makes them difficult to counter effectively. This paper addresses the critical issue of anti-jamming (AJ) under few-shot sample conditions in IoT networks. Source separation is a key component of AJ communication. Although deep learning-based source separation has demonstrated significant advantages, it typically requires a large amount of labeled data, which can be impractical in certain environments. To overcome this challenge, we propose two novel schemes that leverage multi-task learning (MTL) and meta-learning (ML) to enhance the model's signal separation capabilities within the constraints of limited sample scenarios. MTL enhances robustness by leveraging shared representations across tasks, while ML allows for rapid adaptation to novel jamming signals with minimal samples. Specifically, we employ a modified separation model, SepFormer, as our baseline and integrate MTL and ML schemes to enable the separation of unknown or few-shot jamming signals. Additionally, we have constructed two datasets encompassing both simulated and real-world environmental data to test and evaluate the performance of the proposed methods. simulation results demonstrate the superior AJ performance of our schemes, particularly when compared with a direct application of the separation model with few-shot samples. Furthermore, our evaluation of performance across various jamming scenarios and interference-to-signal ratios (ISR) further confirms the effectiveness of our proposed scheme. © 2014 IEEE.

关键词： Zero-shot learning

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Random Coefficient-Moving Product Based Wireless Key Generation

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China Communications 2025年第2期22卷 199-212页

作者： Lu Xinjin Lei Jing Li Wei College of Electronic Science and Technology National University of Defense TechnologyChangsha 410073China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang 471003China China South Industries Group Corporation Zhi Yuan Institute310012China

The physical layer key generation technique provides an efficient method,which utilizes the natural dynamics of wireless ***,there are some extremely challenging security scenarios such as static or quasi-static environment,which lead to the low randomness of generated ***,the coefficients of the static channel may be dropped into the guard space and discarded by the quantization approach,which causes low key generation *** tackle these issues,we propose a random coefficient-moving product based wireless key generation scheme(RCMP-WKG),where new random resources with remarkable fluctuations can be obtained by applying random coefficient and by moving product on the legitimate ***,appropriate quantization approaches are used to increase the key generation ***,the security of our proposed scheme is evaluated by analyzing different attacks and the eavesdropper’s mean square error(MSE).The simulation results reveal that the proposed scheme can achieve better performances in key capacity,key inconsistency rate(KIR)and key generation rate(KGR)compared with the prior works in static ***,the proposed scheme can deteriorate the MSE performance of the eavesdropper and improve the key generation performance of legitimate nodes by controlling the length of the moving product.

关键词： moving product random coefficient static environment wireless key generation

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Magnitude,distribution and temporal trend of nitrous oxide emissions from China's natural soils over 1980–2022

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science China Earth sciences 2025年第4期68卷 1074-1085页

作者： Songbai HONG Zimeng LI Mingsong TANG Fa LI Yitong YAO Yanzi YAN Mingzhu HE Xuhui WANG Hui ZENG Shilong PIAO School of Urban Planning and Design Shenzhen Graduate SchoolPeking UniversityShenzhen518055China Key Laboratory of Earth Surface System and Human-Earth Relations Ministry of Natural Resources of ChinaShenzhen Graduate SchoolPeking UniversityShenzhen518055China Institute of Carbon Neutrality Sino-French Institute for Earth System ScienceCollege of Urban and Environmental SciencesPeking UniversityBeijing100871China Department of Earth System Science Stanford UniversityStanfordCA94305USA Division of Geological and Planetary Sciences California Institute of TechnologyPasadenaCA91125USA Department of Soil and Environment Swedish University of Agricultural SciencesUppsala75007Sweden Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance Beijing Normal UniversityZhuhai519087China School of National Safety and Emergency Management Beijing Normal UniversityZhuhai519807China

Soils represent the largest contributor to global nitrous oxide(N_(2)O)***,current research efforts predominantly focus on N_(2)O emissions from agricultural soils,while studies on N_(2)O emissions from natural soils,another significant source,remain *** this study,we explored the magnitude,spatiotemporal dynamics,and drivers of N_(2)O emissions from China's natural soils over the period 1980–2022 via machine learning and a compiled novel dataset of 319 field *** results revealed that the N_(2)O flux per unit area was generally higher in the southeast compared to the northwest,and higher in forests than in *** spatial and biome heterogeneity was strongly regulated by local hydrothermal conditions and nitrogen *** averaged N_(2)O emissions over the study period were 646.2±27 Gg N_(2)O yr^(-1),with approximately equivalent contributions from forests and ***,there exhibited a significant and increasing trend from 1980 to 2022,with a rate of 2.7 Gg N_(2)O yr^(-2).Such an increase was primarily driven by the expansion of forested area and elevated nitrogen *** data-driven study presents a long-term and gridded estimate of N_(2)O emissions from China's natural soils,emphasizing the need for future research on changes of greenhouse gas emissions induced by land use change and nitrogen deposition.

关键词： Nitrous oxide Spatiotemporal dynamics Nitrogen deposition Forests Grasslands Data-driven estimates

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Enhancing Geolocation Accuracy of High-Altitude Airborne SAR Through Tropospheric Delay Compensation

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Xiang, Yaobing Sun, Yuli Lei, Lin Ji, Kefeng Kuang, Gangyao National University of Defense Technology State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology Changsha410073 China

Geolocation is a crucial step in the processing of synthetic aperture radar (SAR) images. High-altitude airborne SAR systems present unique geolocation challenges due to travelling long distances through the troposphere. However, the impact of tropospheric delay on geolocation is often overlooked in existing airborne SAR studies, which can lead to inaccuracies. To address this issue, we propose a new positioning method, the tropospheric delay-compensated range-Doppler (TDC-RD) model. The TDC-RD model leverages reference atmospheric models to estimate and compensate for the tropospheric delay in SAR images. This model effectively mitigates the impact of tropospheric delay in SAR geolocation. To further optimize the TDC-RD model solution, a digital elevation model (DEM)-assisted dual iteration method is proposed. This method iteratively adjusts the target's plane position and elevation in an alternating manner. The effectiveness of the TDC-RD model has been validated through both simulation experiments and actual flight experiments. The results show a significant improvement in geolocation accuracy compared to existing methods, with a maximum reduction of 11.39 m and 24.33% in the mean absolute error (MAE) of SAR geolocation. The TDC-RD model has a great advantage in long-range SAR geolocation. Our research enhances the accuracy and stability of high-altitude airborne SAR geolocation without requiring ground control points. © 2025 IEEE.

关键词： Troposphere

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Automatic Inverse Synthetic Aperture Radar Ship Targets Recognition using Simple Multiple Kernel Learning: Automatic ISAR Ship Targets Recognition using Simple-MKL 24

Automatic Inverse Synthetic Aperture Radar Ship Targets Reco...

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Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management

作者： Yuan Wu Fulin Su Xue Li Peipei Zhu Xunwei Xie National Key Laboratory of Complex Aviation System Simulation Southwest China Institute of Electronic Technology Chengdu Sichuan China School of Electronics and Information Engineering Harbin Institute of Technology Harbin Heilongjiang China

ISBN: (纸本)9798400709753

In radar automatic target recognition (RATR), inverse synthetic aperture radar (ISAR) image recognition shows its advantages. Due to the limited sample size of ISAR images, support vector machine (SVM), known for its robustness in small sample classification, is often used for ISAR image recognition. For ISAR images of different targets, the single kernel SVM algorithm might lose its robustness. Therefore, this paper applies multiple kernel learning (MKL) to ISAR ship target recognition. The process begins with the preprocessing of the ISAR images to suppress Gaussian white noise. Then, principal component analysis (PCA) is employed to extract features from the ISAR images. Finally, the Simple-MKL method is used to recognize the samples. Experiments based on simulation data indicate that the method used in this paper improves the accuracy compared to other single-kernel SVM algorithms with different kernel functions.

关键词： Inverse Synthetic Aperture Radar (ISAR)

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Aggregation Modeling of system Frequency Response Under Inertia Control of Grid-following Direct-drive Wind Turbine

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Dianli Xitong Zidonghua/Automation of Electric Power systems 2025年第10期49卷 39-48页

作者： Jiang, Chao Yang, Dongfeng Guo, Shujia Liu, Xiaojun Zhou, Shuyu Cao, Zhichong Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China School of Automation and Electrical Engineering Zhejiang University of Science and Technology Hangzhou310023 China State Grid Jilin Electric Power Research Institute Changchun130000 China

The inertia control of grid-following direct-drive wind turbines significantly influences the system frequency response pattern afrer disturbances. However, current research has not clearly identified the dominant response link of direct-drive wind turbines in frequency regulation, while the modeling is too complicated to be suitable for frequency characteristic analysis. Therefore, the linearized models under inertia control are derived for two typical types of direct-drive wind turbines according to the aerodynamic equations, generator models, power control link, and control circuit structure. The fast physical process of current control-boost circuit/stator voltage dynamic loop is clearly understood and reasonably ignored. It is revealed that the two types of direct-drive wind turbines have a common frequency response mechanism which can be described as the cascade effect of the speed regulator-wind rotor motion equation. Furthermore, combined with the frequency response model of synchronous unit, the aggregation model of system frequency response under the inertia control of grid-following direct-drive wind turbines is constructed, and the aggregation model is reduced to a second-order model by the balance truncation method for analytical calculation of frequency extremes. The accuracy and applicability of the aggregation model and its reduced-order model are verified by MATLAB/Simulink simulation, and the influence of control parameters and system states on frequency dynamic characteristics and stability margin is analyzed. © 2025 Automation of Electric Power systems Press. All rights reserved.

关键词： Electric power system control

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Object Detection and Multiple Objective Optimization Manipulation Planning for Underwater Autonomous Capture in Oceanic Natural Aquatic Farm

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Journal of Field Robotics 2025年第0期

作者： Hai, Huang Tao, Jiang Xinyu, Bian Hao, Zhou Xu, Yang Gang, Wang Hongde, Qin Xinyue, Han National Key Laboratory of Science and Technology on Underwater Vehicle Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Bei Jing China

Underwater autonomous capture operations offer significant potential for reducing labor and health risks in sea organism industries. This study presents a comprehensive solution for cross-domain underwater object detection and autonomous capture. A novel unsupervised domain adaptive learning method is proposed, integrating multiscale domain adaptive modules and attention mechanisms into a Faster Region-Convolutional Neural Network framework. This approach enhances feature alignment across diverse aquatic domains without parameter tuning. Additionally, an efficient, parameterless constrained multiobjective optimization algorithm is introduced for underwater autonomous mobile capture, integrating parameterized trajectory planning with innovative features, such as adaptive mutation strategies and constraint violation tolerance. The proposed approaches are extensively validated through simulations, tank experiments, and real-world oceanic trials in the Natural Aquatic Farm of Zhangzidao Island. Results demonstrate the system's robustness in complex underwater environments with varying currents, with experimental outcomes validating the accuracy and reliability of detection and capture capabilities. This research significantly advances autonomous underwater systems' capabilities in object detection and capture tasks, addressing complex challenges in realistic organism capture applications across diverse aquatic environments. © 2025 Wiley Periodicals LLC.

关键词： Health risks

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Late Paleozoic–Early Mesozoic tectonic evolution of the Zongwulong Shan-Qinghai Nanshan tectonic belt in the northern Qaidam continent， northern Tibet

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Journal of Earth science 2025年

作者： Yonghui Zhao Chen Wu Jie Li Peter J.Haproff Lin Ding State Key Laboratory of Tibetan Plateau Earth System and Resources Environment (TPESRE)， Institute of Tibetan Plateau Research， Chinese Academy of Sciences University of Chinese Academy of Sciences Chongqing Key Laboratory of Complex Oil and Gas Field Exploration and Development， Chongqing University of Science and Technology Department of Earth and Ocean Sciences， University of North Carolina Wilmington

The Zongwulong Shan-Qinghai Nanshan tectonic belt of the northern Tibet Plateau experienced a protracted tectonic history， including the openings and closures of the Proto- and Paleo-Tethyan Oceans. Although the tectonic belt has been extensively studied， details regarding the tectonic processes involved in its development remain controversial. To better constrain the tectonic processes of this tectonic belt， we conducted detailed field geological mapping， zircon U-Pb geochronology， and whole-rock geochemical and Sr-Nd isotopic analyses. Our results show that intrusive rocks in the tectonic belt crystallized in ca. 292–233 Ma， perhaps in an arc/subduction setting. Geochemical and Sr-Nd isotopic data suggest that early Permian–late Triassic ultramafic-intermediate intrusions were sourced from the enriched mantle， whereas intermediate-acidic rocks were sourced from mixed crust-mantle. We present the tectonic model that involves: (1) Early Devonian–early Permian intracontinental extension occurred in the northern margin of the Qaidam continent (ca. 416 Ma–292 Ma); (2) Early Permian–late Triassic northward subduction of the Paleo-Tethyan Ocean resulted in arc magmatism (ca. 292 Ma–233 Ma); and (3) subsequent late Triassic intracontinental extension (ca. 233 Ma–215 Ma). Our results suggest that the late Paleozoic–early Mesozoic development of the Zongwulong Shan-Qinghai Nanshan was related to the opening， subduction， and slab retreat of the Paleo-Tethyan Ocean， which has key implications for the tectonic evolution of the northern Tibetan Plateau.

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