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  • Open Access

    ARTICLE

    Disturbed Dynamic Analysis and Robust Decision-Making Control of Complex Networks

    Xiusen Wang1,*, Zheng Fang2, Jie Chen2,*

    CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088952 - 15 September 2026

    Abstract Complex networks in cyber–physical, transportation, and information infrastructures operate under topology variations, unmeasured disturbances, and limited actuation. This paper proposes robust disturbance-aware data-driven decision control (R-D3C), which couples a sliding-window graph-regularized estimator, disturbance-envelope adaptation, sparse intervention allocation, receding-horizon optimization, and a robust safety projection. The theory directly bounds the dynamic prediction regret of the implemented sliding-window estimator. A checkable sufficient condition for safety-filter feasibility is coupled with an explicit slack-and-backup fallback when the strict projection is infeasible. Practical input-to-state stability and sparse-allocation risk reduction are established. The nominal comparison uses 30 paired runs with standard More >

  • Open Access

    ARTICLE

    5G-Aware Incremental Routing and Scheduling for Dynamic Time-Triggered Flow Admission in Time-Sensitive Networks

    Zhihao Liu1,2, Yi Zhang3, Wei Zhang1,2, Jian Wang4, Huiling Shi1,2, Xiaolong Wang1,2,*

    CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088018 - 15 September 2026

    Abstract Mobile edge services require deterministic communication across Time-Sensitive Networking (TSN) and 5G access, where the standardized integration architecture exposes the 5G System (5GS) to the TSN controller as a logical bridge. We study dynamic admission of time-triggered (TT) flows using reported 5GS bridge delay and TSN-to-5GS Quality of Service (QoS) mapping in route selection and Gate Control List (GCL) scheduling. Arrivals and departures can split available transmission time into noncontiguous windows. Online insertion preserves admitted schedules but may reduce subsequent schedulability, whereas full recomputation can restore schedulability but changes many routes and GCL entries, complicating… More >

  • Open Access

    ARTICLE

    Cross-View Geo-Localization via Dynamic Multi-Positive Mining from Unlabeled Data

    Long Yu1,2,3, Ma Zhu1,2,3,*, Xu Wang1,2,3, Yang Pei1,2,3, Chunfang Yang1,2,3

    CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087636 - 15 September 2026

    Abstract Cross-view geo-localization (CVGL) estimates the location of a street-level image by retrieving its matching GPS-tagged satellite tile. Semi-supervised methods reduce the need for dense annotations by mining pseudo labels, but most of them keep only one positive reference for each query. In real-world galleries, several overlapping satellite tiles may cover the same ground location. As a result, valid matches can be discarded as negatives, which gives the model conflicting supervision. To address this problem, we propose DMP-Geo, a semi-supervised cross-view geo-localization method that mines multiple positives for each query from unlabeled data. A bird’s-eye fusion More >

  • Open Access

    ARTICLE

    Intelligent Urban Transportation over Complex Vehicle Networks with YOLOv8 for Traffic Flow Monitoring

    Mohammed Alonazi1, Muhammad Adeel Ahmed Tahir2, Adnan Ahmed Rafique2, Maha Abdelhaq3, Raed Alsaqour4, Ahmad Jalal5,6, Jeongmin Park7,*

    CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086550 - 15 September 2026

    Abstract Accurate vehicle detection, tracking, and counting are fundamental components of Intelligent Transportation Systems (ITS) and urban traffic surveillance. However, real-world deployment remains challenging due to domain shifts, illumination variations, occlusions, dense traffic conditions, and heterogeneous data distributions. Existing studies often address detection, tracking, and counting as independent tasks, resulting in limited cross-domain generalization and inconsistent performance in complex traffic environments. To overcome these limitations, this paper proposes a unified cross-domain framework that jointly integrates vehicle detection, tracking, and lane-aware counting within a single intelligent traffic analytics pipeline. The proposed framework begins with image enhancement using… More >

  • Open Access

    ARTICLE

    An Improved Safe Soft Actor-Critic Path Planning Algorithm for Autonomous Vehicles Based on a Dual-Stream Q-Network and Dynamic Analytic Hierarchy Process

    Shengxuan Dong, Xiongwei Li*

    CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086535 - 15 September 2026

    Abstract To address the conflict between navigation performance and safety constraints in safe reinforcement learning, this paper proposes Dual Stream-Analytic Hierarchy Process-Safe Soft Actor (DS-AHP-SAC), a safe soft actor-critic algorithm based on a dual-stream Q-network and dynamic Analytic Hierarchy Process (AHP) stratified experience replay. The algorithm achieves a balance between reward maximization and constraint satisfaction through three synergistic designs: (1) decoupling the Q-network into independent navigation and safety value streams to eliminate gradient interference at the Critic level and mitigate gradient competition at the Actor level; (2) constructing a three-criterion dynamic sampling strategy based on AHP, More >

  • Open Access

    ARTICLE

    -FedVAE: Detached Posterior-Confidence Gating for Dimension-Wise KL Regularization in Personalized Federated Collaborative Filtering

    Jincheng Cai1, Li Feng1,*, Ni Zhao2

    CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086183 - 15 September 2026

    Abstract Federated Variational Autoencoders (VAEs) keep interaction data local, but existing federated VAE recommenders typically apply uniform KL regularization and do not adapt dimension-wise penalties to unreliable posteriors in sparse interaction scenarios. We propose α-FedVAE, which uses a detached, clipped normalized signal-to-noise ratio as a local confidence gate for each KL dimension of a fused user posterior, without extra communication. Across MovieLens-100K, MovieLens-1M, and Amazon Video, α-FedVAE improves mean HR@20 by 7.8%–55.3% and NDCG@20 by 8.0%–65.8% over FedDAE. These results indicate that α-FedVAE improves personalized recommendation under sparse and decentralized settings while preserving the communication More >

  • Open Access

    ARTICLE

    From Virtual Anchoring to High-Precision Station-Keeping: A Dynamic Virtual Guide-Point Strategy for Underactuated USVs

    Shigan Ding1,2, Zihe Qin1,3,*, Feng Zhang1,3, Mao Zheng2, Bowen Lin2

    CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086097 - 15 September 2026

    Abstract To address the challenge of precise station-keeping for underactuated unmanned surface vehicles (USVs) in unknown current environments, our team previously proposed a solution based on a “virtual anchoring” method. However, field tests revealed that the inherent “virtual anchor line” constraint limits positioning accuracy. This work introduces a novel control strategy to overcome the aforementioned issue, which enables accurate unmanned surface vehicle (USV) station-keeping by significantly reducing the distance constraint inherent to traditional virtual anchoring. The core innovation lies in a Dynamic Virtual Guide-Point, whose position is updated based on a real-time estimate of the current… More >

  • Open Access

    ARTICLE

    Physics-Informed Neural Networks for Hail-Impact Dynamics of Photovoltaic Panels: Multi-Condition Forward Modeling and Inverse Identification of Contact Stiffness

    Hassaan Idrees1,*, Pattabhi Ramaiah Budarapu2, Marco Paggi1,*

    CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085634 - 15 September 2026

    Abstract Hail impacts on photovoltaic laminates generate strongly nonlinear contact forces whose polynomial restoring form and coefficients govern the resulting damage pattern. Predicting the dynamic response across a range of impact velocities, and inferring substrate properties from post-event vibration measurements, are two tasks that classical time-integration schemes do not address in a unified manner. This work develops a physics-informed neural network (PINN) framework that handles both. For the forward problem, the network is conditioned on the initial velocity and trained simultaneously at four representative hail-impact speeds, i.e., v0{2,3,4,6} m/s, so that it learns… More >

  • Open Access

    ARTICLE

    A Dual-Neuron Memristor Hopfield Neural Network with Controllable Multiple Equilibrium Points: Dynamical Analysis, FPGA Implementation, and Image Encryption Application

    Yanyu Zhu1, Jie Jin2,*, Lv Zhao2,3, Fei Yu4

    CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085516 - 15 September 2026

    Abstract To address the issues of multi-neuron architectures, high parameter redundancy, and complex hardware implementation in existing memristive Hopfield neural networks (MHNN) for image encryption, a simple structure dual-neuron memristive Hopfield neural network (DNMHNN) modulated by multifrequency square waves is proposed in this study. The proposed DNMHNN model consists of only two neurons and one memristor, and by introducing dual-frequency square-wave external excitation into the memristor, the dynamical behavior of the DNMHNN model can be flexibly regulated. The simulation results verify that the proposed DNMHNN model can generate stable chaotic behavior over a wide parameter range.… More >

  • Open Access

    ARTICLE

    DMSALA: A Dynamic Multi-Subpopulation Artificial Lemming Algorithm for Feature Selection in IoT Intrusion Detection

    Hui Xu, Ruiqi Qu*, Xinlu Zong

    CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084624 - 15 September 2026

    Abstract With the rapid growth of the Internet of Things, intrusion detection systems face severe challenges in processing massive, high-dimensional, and redundant network traffic while satisfying strict low-latency and high-efficiency requirements. To address these challenges,this paper improves the original artificial lemming algorithm (ALA) and proposes a dynamic multi-subpopulation artificial lemming algorithm (DMSALA) for feature selection, and then constructs an intrusion detection framework for IoT based on DMSALA. The proposed DMSALA introduces an adaptive clustering-based dynamic multi-subpopulation structure to alleviate premature convergence during the search process. In addition, a cosine-based nonlinear weighting strategy is designed to achieve… More >

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