Zhihao Dong^1,2, Huakui Sun^1,2,*, Yueyue Tao², Daosen Zhai²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084301 - 23 July 2026

Abstract High-mobility Unmanned Aerial Vehicle (UAV) swarm networks suffer from fast-varying connectivity and interference, and therefore routing decisions must jointly account for link instability and topology changes. By leveraging mobile edge computing (MEC) capabilities, each UAV can perform online routing decisions locally without relying on centralized controllers. This paper develops a Predictive-Q learning framework for dynamic routing under interference and mobility, where the Q-value is trained by a multi-factor reward that explicitly models retransmission costs, predicts link lifetime from relative motion, and anticipates forward connectivity and neighbor redundancy. To further enhance reliability under harsh interference, we More >

Lijun Yang¹, Qiuwei Yang^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083976 - 23 July 2026

Abstract This study proposes a Multi-Stage Sparrow Search Algorithm (MS-SSA) for precise structural damage identification. Initially, the structural static displacement sensitivity formulation is derived via the Sherman-Morrison-Woodbury formula, and an objective function is constructed by integrating the sensitivity equations with the L2-norm penalty. Subsequently, MS-SSA is implemented to sequentially achieve preliminary damage localization and accurate quantification. In the localization phase, a constrained narrow-bound search space is predefined to identify potential damage regions. Leveraging this feedback, the sensitivity equations are condensed, and the search boundaries are adaptively refined for the quantification phase, where SSA is reapplied to… More >

Anton Surkov, Vera Ignatenko^*, Sergei Koltcov

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083643 - 23 July 2026

Abstract Large language models have recently demonstrated promising capabilities in mathematical reasoning; however, their performance on tasks requiring strict symbolic manipulation, such as solving differential equations, remains limited, especially for compact models. In this work, we investigate whether activation steering combined with reinforcement learning can improve the quality of solutions generated by pretrained language models without modifying their weights. In particular, we focus on relatively small-scale models, which exhibit limited baseline performance on symbolic mathematical tasks, and study whether their capabilities can be enhanced through activation-level interventions. The proposed approach introduces trainable steering vectors that are… More >

Qiwu Wu¹, Tao Yang^2,*, Yunchen Su², Lingzhi Jiang³, Tao Tong²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082757 - 23 July 2026

Abstract Deep reinforcement learning (DRL) has become an important method in Unmanned Aerial Vehicle(UAV) path planning, but the field still lacks a dedicated bibliometric review that summarizes its publication patterns, intellectual structure, and thematic evolution. This study analyzes 1402 Web of Science publications from 2010 to 2025 using CiteSpace, VOSviewer, and the Bibliometrix R package. Three main findings are reported. First, the bibliometric evidence suggests a four-phase evolution of the field—foundational exploration (2015–2016), continuous-control breakthrough (2017–2019), multi-agent collaborative coordination (2020–2022), and complex-scenario integration (2023–2025)—as reflected in publication trends, keyword bursts, and co-citation clusters. Second, co-citation and keyword More >

Suchang Yang, Hongtao Yu^*, Ruiyang Huang, Huansha Wang, Ran Li, Junzheng Li

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082651 - 23 July 2026

Abstract Modeling dynamic graphs in continuous time is critical for applications such as user behavior prediction and recommendation systems. These models can effectively capture fine-grained and long-term temporal dependencies. However, existing approaches often suffer from high computational costs and optimization difficulties, especially when handling time-sorted neighborhood sequences over long horizons. In this work, we propose DyG-Hyena, a novel continuous-time dynamic graph learning framework that combines conditional variational autoencoder (CVAE)-assisted temporal modeling with efficient feature fusion. Our approach has two main innovations: (i) Efficient temporal fusion—we replace the Transformer with an improved, lightweight Hyena module to model More >

Yiqiao Xiang¹, Jingchun Zhou^1,2,*, Ruijie Liu¹, Dehuan Zhang¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082357 - 23 July 2026

Abstract Underwater imagery is degraded by depth-dependent absorption and scattering, which often introduce color casts and contrast attenuation. Although recent Vision Mamba models provide efficient long-range dependency modeling, their conventional 2D scanning patterns are not explicitly designed to exploit the depth-correlated structure of underwater degradation and may therefore weaken geometry-aware feature dependencies. To address this limitation, we propose Isoline-Guided Evolutionary Mamba (IG-Mamba), a physics-inspired framework that uses a depth-correlated potential prior to organize state-space token propagation. Specifically, we introduce a Topology-Preserving Isoline Scanning mechanism. By leveraging a geometric prior, this mechanism quantizes the scene into discrete… More >

Ziyi Ju¹, Ping Yu¹, Rui Song¹, Tonglin Chen^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082060 - 23 July 2026

Abstract In modern high-performance chip design, achieving timing closure is essential to design success. With the increasing scale and complexity of modern chips, timing-driven placement has become increasingly important. Traditional placement methods primarily focus on minimizing wirelength, but lack timing optimization, making it difficult to meet the strict timing closure requirements of modern designs. Therefore, developing an efficient timing-driven placement method has become a critical challenge in modern chip design. This paper presents a novel timing-driven placement framework that integrates a variational graph autoencoder (VGAE) with a nonlinear mixed-size placement optimizer. The framework identifies timing-violation paths More >

Yimeng Liu¹, Xinyu Xu¹, Wangting Xue¹, Shigen Shen^1,*, Xiao-Zhi Gao²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081775 - 23 July 2026

Abstract The rapid growth of the Industrial Internet of Things (IIoT) has become a cornerstone of high-quality global economic development. By integrating sensor networks, edge computing, and cloud intelligence, IIoT has emerged as a key enabler for smart manufacturing and digital transformation across industries. However, this technological advancement introduces significant cybersecurity challenges that render traditional intrusion detection systems inadequate for IIoT environments. To address this critical gap, we propose a deep spiking Q-network (DSQN)-based intrusion detection system (DSQN-IDS) for the IIoT, formulating unknown intrusion detection as a Markov decision process (MDP). The system employs a hierarchical More >

Shasha Tian^1,2, Zhengyang Chen^1,3, Kai Ren^1,2, Na Li^1,2, Chongwei Ruan⁴, Zhijia Cui^1,3, Mian Wu^4,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081556 - 23 July 2026

Abstract To address the issues of low exploration efficiency and “geometric myopia” caused by the lack of high-level environmental structure modeling for mobile robots in complex indoor environments, this paper proposes an active SLAM object navigation method based on Situational Semantic Augmented Graph (SSAG). Unlike methods that learn policies solely on pixel-level semantic maps or exploit only object-level relations for implicit association, this work elevates local observations online into a room-level topological graph and performs explicit semantic reasoning over unobserved regions. First, an online room segmentation algorithm is employed to transform unstructured sensory data into a… More >

Chenying Yi¹, Yangjun Zhou^1,2, Wei Zhang¹, Like Gao¹, Hongwen Wu³, Yuanchao Zhou^4,*, Ke Zhou¹, Weixiang Huang¹, Juntao Pan⁵, Shan Li¹, Bin Feng⁵

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2025.073805 - 12 July 2026

Abstract Ensuring reliability in distribution networks is essential under increasing operational and economic constraints. Traditional planning models rely on power flow calculations, leading to high computational costs and poor scalability. This study proposes a quantitative decomposition framework that establishes a direct linkage among reliability improvement measures, reliability parameters, and reliability indices, enabling fast and analytical reliability evaluation without power flow analysis. A bi-objective optimization model is developed to minimize both reliability indices (SAIDI) and investment costs, solved using Pareto-based multi-objective PSO combined with the TOPSIS method. Case studies on a 519-node distribution network demonstrate that the More >