Shuai Zhao, Zhiwei Zhang^*, Junkai Wang, Ye Yuan, Guoren Wang

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073315 - 12 January 2026

Abstract With the development of sharded blockchains, high cross-shard rates and load imbalance have emerged as major challenges. Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates. Account partitioning based on historical transaction graphs is effective in reducing cross-shard rates but suffers from load imbalance and limited adaptability to dynamic workloads. Meanwhile, because of the coupling between consensus and execution, a target shard must receive both the partitioned transactions and the partitioned accounts before initiating consensus and execution. However, we observe that transaction partitioning and subsequent consensus do not require… More >

Sun Park^*, JongWon Kim

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072865 - 12 January 2026

Abstract In real-world autonomous driving tests, unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur. Conducting actual test drives under various weather conditions may also lead to dangerous situations. Furthermore, autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS. Driving simulators, which replicate driving conditions nearly identical to those in the real world, can drastically reduce the time and cost required for market entry validation; consequently, they have become widely used. In this paper, we design a virtual driving test environment capable of More >

Yuanyuan Wang^1,*, Yemeng Zhu¹, Xiuchuan Chen¹, Tongtong Yin¹, Shiwei Su²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072404 - 12 January 2026

Abstract To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects, similar defects and background features, and similarities between different defects, this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network (MSESE), which is built upon the You Only Look Once version 11 nano (YOLOv11n). To address the difficulty of locating defect edges, we first propose an edge enhancement module (EEM), apply it to the process of multiscale feature extraction, and then propose a multiscale edge enhancement… More >

Leyu Zheng¹, Mingming Xiao^1,*, Yi Ren², Ke Li¹, Chang Sun¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072036 - 12 January 2026

Abstract In a wide range of engineering applications, complex constrained multi-objective optimization problems (CMOPs) present significant challenges, as the complexity of constraints often hampers algorithmic convergence and reduces population diversity. To address these challenges, we propose a novel algorithm named Constraint Intensity-Driven Evolutionary Multitasking (CIDEMT), which employs a two-stage, tri-task framework to dynamically integrates problem structure and knowledge transfer. In the first stage, three cooperative tasks are designed to explore the Constrained Pareto Front (CPF), the Unconstrained Pareto Front (UPF), and the ε-relaxed constraint boundary, respectively. A CPF-UPF relationship classifier is employed to construct a problem-type-aware… More >

Sai Xu^1,*, Jun Liu^1,*, Shengyu Huang¹, Zhi Li²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071865 - 12 January 2026

Abstract In scenarios where ground-based cloud computing infrastructure is unavailable, unmanned aerial vehicles (UAVs) act as mobile edge computing (MEC) servers to provide on-demand computation services for ground terminals. To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs, this paper presents PER-MATD3, a multi-agent deep reinforcement learning algorithm with prioritized experience replay (PER) into the Centralized Training with Decentralized Execution (CTDE) framework. Specifically, PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution, while leveraging a shared replay buffer with More >

Ahmet Ezgi¹, Aytuğ Onan^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071301 - 12 January 2026

Abstract Knowledge distillation has become a standard technique for compressing large language models into efficient student models, but existing methods often struggle to balance prediction accuracy with explanation quality. Recent approaches such as Distilling Step-by-Step (DSbS) introduce explanation supervision, yet they apply it in a uniform manner that may not fully exploit the different learning dynamics of prediction and explanation. In this work, we propose a task-structured curriculum learning (TSCL) framework that structures training into three sequential phases: (i) prediction-only, to establish stable feature representations; (ii) joint prediction–explanation, to align task outputs with rationale generation; and (iii)… More >

Moyi Zhang, Yixin Wang^*, Yu Cheng

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071021 - 12 January 2026

Abstract Modern intelligent systems, such as autonomous vehicles and face recognition, must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations. However, when neural networks learn new classes sequentially, they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes. This challenge, which lies at the core of class-incremental learning, severely limits the deployment of continual learning systems in real-world applications with streaming data. Existing approaches, including rehearsal-based methods and knowledge distillation techniques, have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features… More >

Muhammad Asim^1,*, Abdelhamied A. Ateya¹, Mudasir Ahmad Wani^1,2, Gauhar Ali¹, Mohammed ElAffendi¹, Ahmed A. Abd El-Latif¹, Reshma Siyal³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070684 - 12 January 2026

Abstract The growing developments in 5G and 6G wireless communications have revolutionized communications technologies, providing faster speeds with reduced latency and improved connectivity to users. However, it raises significant security challenges, including impersonation threats, data manipulation, distributed denial of service (DDoS) attacks, and privacy breaches. Traditional security measures are inadequate due to the decentralized and dynamic nature of next-generation networks. This survey provides a comprehensive review of how Federated Learning (FL), Blockchain, and Digital Twin (DT) technologies can collectively enhance the security of 5G and 6G systems. Blockchain offers decentralized, immutable, and transparent mechanisms for securing More >

Jun Xiong^*, Peng Yang, Bohan Chen, Zeming Chen

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069438 - 27 December 2025

Abstract The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system. However, various defects could be produced in the secondary equipment during long-term operation. The complex relationship between the defect phenomenon and multi-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods, which limits the real-time and accuracy of defect identification. Therefore, a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed. The defect data of secondary equipment is… More >

Mingming Huang¹, Yunfan Ye^1,*, Zhiping Cai²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.072399 - 09 December 2025

Abstract As a fundamental component in computer vision, edges can be categorized into four types based on discontinuities in reflectance, illumination, surface normal, or depth. While deep CNNs have significantly advanced generic edge detection, real-time multi-class semantic edge detection under resource constraints remains challenging. To address this, we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection. Our model simultaneously predicts background and four edge categories from full-resolution inputs, balancing accuracy and efficiency. Key contributions include: a multi-channel output structure expanding binary edge prediction to five classes, supported by a deep supervision More >