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  • Open Access

    REVIEW

    Navigating the Blockchain Trilemma: A Review of Recent Advances and Emerging Solutions in Decentralization, Security, and Scalability Optimization

    Saha Reno1,#,*, Koushik Roy2,#

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2061-2119, 2025, DOI:10.32604/cmc.2025.066366 - 03 July 2025

    Abstract The blockchain trilemma—balancing decentralization, security, and scalability—remains a critical challenge in distributed ledger technology. Despite significant advancements, achieving all three attributes simultaneously continues to elude most blockchain systems, often forcing trade-offs that limit their real-world applicability. This review paper synthesizes current research efforts aimed at resolving the trilemma, focusing on innovative consensus mechanisms, sharding techniques, layer-2 protocols, and hybrid architectural models. We critically analyze recent breakthroughs, including Directed Acyclic Graph (DAG)-based structures, cross-chain interoperability frameworks, and zero-knowledge proof (ZKP) enhancements, which aim to reconcile scalability with robust security and decentralization. Furthermore, we evaluate the trade-offs More >

  • Open Access

    ARTICLE

    Machine Learning and Explainable AI-Guided Design and Optimization of High-Entropy Alloys as Binder Phases for WC-Based Cemented Carbides

    Jianping Li, Wan Xiong, Tenghang Zhang, Hao Cheng, Kun Shen, Miaojin He, Yu Zhang, Junxin Song, Ying Deng*, Qiaowang Chen*

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2189-2216, 2025, DOI:10.32604/cmc.2025.066128 - 03 July 2025

    Abstract Tungsten carbide-based (WC-based) cemented carbides are widely recognized as high-performance tool materials. Traditionally, single metals such as cobalt (Co) or nickel (Ni) serve as the binder phase, providing toughness and structural integrity. Replacing this phase with high-entropy alloys (HEAs) offers a promising approach to enhancing mechanical properties and addressing sustainability challenges. However, the complex multi-element composition of HEAs complicates conventional experimental design, making it difficult to explore the vast compositional space efficiently. Traditional trial-and-error methods are time-consuming, resource-intensive, and often ineffective in identifying optimal compositions. In contrast, artificial intelligence (AI)-driven approaches enable rapid screening and… More >

  • Open Access

    ARTICLE

    Multi-Level Subpopulation-Based Particle Swarm Optimization Algorithm for Hybrid Flow Shop Scheduling Problem with Limited Buffers

    Yuan Zou1, Chao Lu1,*, Lvjiang Yin2, Xiaoyu Wen3

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2305-2330, 2025, DOI:10.32604/cmc.2025.065972 - 03 July 2025

    Abstract The shop scheduling problem with limited buffers has broad applications in real-world production scenarios, so this research direction is of great practical significance. However, there is currently little research on the hybrid flow shop scheduling problem with limited buffers (LBHFSP). This paper deeply investigates the LBHFSP to optimize the goal of the total completion time. To better solve the LBHFSP, a multi-level subpopulation-based particle swarm optimization algorithm (MLPSO) is proposed, which is founded on the attributes of the LBHFSP and the shortcomings of the basic PSO (particle swarm optimization) algorithm. In MLPSO, firstly, considering the… More >

  • Open Access

    ARTICLE

    Physics-Informed Gaussian Process Regression with Bayesian Optimization for Laser Welding Quality Control in Coaxial Laser Diodes

    Ziyang Wang1, Lian Duan1,2,*, Lei Kuang1, Haibo Zhou1, Ji’an Duan1

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2587-2604, 2025, DOI:10.32604/cmc.2025.065648 - 03 July 2025

    Abstract The packaging quality of coaxial laser diodes (CLDs) plays a pivotal role in determining their optical performance and long-term reliability. As the core packaging process, high-precision laser welding requires precise control of process parameters to suppress optical power loss. However, the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise. To address this challenge, a physics-informed (PI) and data-driven collaboration approach for welding parameter optimization is proposed. First, thermal-fluid-solid coupling finite element method (FEM) was employed to quantify the sensitivity of welding parameters to physical characteristics, including… More >

  • Open Access

    ARTICLE

    A Multi-Objective Joint Task Offloading Scheme for Vehicular Edge Computing

    Yiwei Zhang, Xin Cui*, Qinghui Zhao

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2355-2373, 2025, DOI:10.32604/cmc.2025.065430 - 03 July 2025

    Abstract The rapid advance of Connected-Automated Vehicles (CAVs) has led to the emergence of diverse delay-sensitive and energy-constrained vehicular applications. Given the high dynamics of vehicular networks, unmanned aerial vehicles-assisted mobile edge computing (UAV-MEC) has gained attention in providing computing resources to vehicles and optimizing system costs. We model the computing offloading problem as a multi-objective optimization challenge aimed at minimizing both task processing delay and energy consumption. We propose a three-stage hybrid offloading scheme called Dynamic Vehicle Clustering Game-based Multi-objective Whale Optimization Algorithm (DVCG-MWOA) to address this problem. A novel dynamic clustering algorithm is designed… More >

  • Open Access

    ARTICLE

    QHF-CS: Quantum-Enhanced Heart Failure Prediction Using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data

    Prasanna Kottapalle1,*, Tan Kuan Tak2, Pravin Ramdas Kshirsagar3, Gopichand Ginnela4, Vijaya Krishna Akula5

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3857-3892, 2025, DOI:10.32604/cmc.2025.065287 - 03 July 2025

    Abstract Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide, exacerbated by the COVID-19 pandemic. Age, cholesterol, and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators. These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult, and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles. Modern medical datasets’ complexity and high dimensionality challenge traditional prediction models like Support Vector Machines and Decision Trees. Quantum approaches include QSVM, QkNN, QDT, and others.… More >

  • Open Access

    ARTICLE

    Research on Adaptive Reward Optimization Method for Robot Navigation in Complex Dynamic Environment

    Jie He, Dongmei Zhao, Tao Liu*, Qingfeng Zou, Jian’an Xie

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2733-2749, 2025, DOI:10.32604/cmc.2025.065205 - 03 July 2025

    Abstract Robot navigation in complex crowd service scenarios, such as medical logistics and commercial guidance, requires a dynamic balance between safety and efficiency, while the traditional fixed reward mechanism lacks environmental adaptability and struggles to adapt to the variability of crowd density and pedestrian motion patterns. This paper proposes a navigation method that integrates spatiotemporal risk field modeling and adaptive reward optimization, aiming to improve the robot’s decision-making ability in diverse crowd scenarios through dynamic risk assessment and nonlinear weight adjustment. We construct a spatiotemporal risk field model based on a Gaussian kernel function by combining… More >

  • Open Access

    ARTICLE

    AI-Integrated Feature Selection of Intrusion Detection for Both SDN and Traditional Network Architectures Using an Improved Crayfish Optimization Algorithm

    Hui Xu, Wei Huang*, Longtan Bai

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3053-3073, 2025, DOI:10.32604/cmc.2025.064930 - 03 July 2025

    Abstract With the birth of Software-Defined Networking (SDN), integration of both SDN and traditional architectures becomes the development trend of computer networks. Network intrusion detection faces challenges in dealing with complex attacks in SDN environments, thus to address the network security issues from the viewpoint of Artificial Intelligence (AI), this paper introduces the Crayfish Optimization Algorithm (COA) to the field of intrusion detection for both SDN and traditional network architectures, and based on the characteristics of the original COA, an Improved Crayfish Optimization Algorithm (ICOA) is proposed by integrating strategies of elite reverse learning, Levy flight,… More >

  • Open Access

    ARTICLE

    An Energy Optimization Algorithm for WRSN Nodes Based on Regional Partitioning and Inter-Layer Routing

    Cui Zhang1, Lieping Zhang2,*, Huaquan Gan3, Hongyuan Chen3, Zhihao Li3

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3125-3148, 2025, DOI:10.32604/cmc.2025.064499 - 03 July 2025

    Abstract In large-scale Wireless Rechargeable Sensor Networks (WRSN), traditional forward routing mechanisms often lead to reduced energy efficiency. To address this issue, this paper proposes a WRSN node energy optimization algorithm based on regional partitioning and inter-layer routing. The algorithm employs a dynamic clustering radius method and the K-means clustering algorithm to dynamically partition the WRSN area. Then, the cluster head nodes in the outermost layer select an appropriate layer from the next relay routing region and designate it as the relay layer for data transmission. Relay nodes are selected layer by layer, starting from the… More >

  • Open Access

    ARTICLE

    Behavior of Spikes in Spiking Neural Network (SNN) Model with Bernoulli for Plant Disease on Leaves

    Urfa Gul#, M. Junaid Gul#, Gyu Sang Choi, Chang-Hyeon Park*

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3811-3834, 2025, DOI:10.32604/cmc.2025.063789 - 03 July 2025

    Abstract Spiking Neural Network (SNN) inspired by the biological triggering mechanism of neurons to provide a novel solution for plant disease detection, offering enhanced performance and efficiency in contrast to Artificial Neural Networks (ANN). Unlike conventional ANNs, which process static images without fully capturing the inherent temporal dynamics, our approach represents the first implementation of SNNs tailored explicitly for agricultural disease classification, integrating an encoding method to convert static RGB plant images into temporally encoded spike trains. Additionally, while Bernoulli trials and standard deep learning architectures like Convolutional Neural Networks (CNNs) and Fully Connected Neural Networks… More >

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