Viorel Mînzu^1,*, Iulian Arama²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.075619 - 10 February 2026

Abstract This work addresses optimality aspects related to composite laminates having layers with different orientations. Regression Neural Networks can model the mechanical behavior of these laminates, specifically the stress-strain relationship. If this model has strong generalization ability, it can be coupled with a metaheuristic algorithm–the PSO algorithm used in this article–to address an optimization problem (OP) related to the orientations of composite laminates. To solve OPs, this paper proposes an optimization framework (OFW) that connects the two components, the optimal solution search mechanism and the RNN model. The OFW has two modules: the search mechanism (Adaptive… More >

Zhen Wu^1,2, Hao Liu³, Linlin Zhang⁴, Zehui Zhang^5,*, Jie Wu¹, Haibin He¹, Bin Zhou⁶

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075156 - 10 February 2026

Abstract Recently, Internet of Things (IoT) has been increasingly integrated into the automotive sector, enabling the development of diverse applications such as the Internet of Vehicles (IoV) and intelligent connected vehicles. Leveraging IoV technologies, operational data from core vehicle components can be collected and analyzed to construct fault diagnosis models, thereby enhancing vehicle safety. However, automakers often struggle to acquire sufficient fault data to support effective model training. To address this challenge, a robust and efficient federated learning method (REFL) is constructed for machinery fault diagnosis in collaborative IoV, which can organize multiple companies to collaboratively More >

Yingli Liu^1,2, Weiyong Tang^1,2, Xiao Yang^1,2, Jiancheng Yin^3,*, Haihe Zhou^1,2

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.074681 - 10 February 2026

Abstract Quantitative analysis of aluminum-silicon (Al-Si) alloy microstructure is crucial for evaluating and controlling alloy performance. Conventional analysis methods rely on manual segmentation, which is inefficient and subjective, while fully supervised deep learning approaches require extensive and expensive pixel-level annotated data. Furthermore, existing semi-supervised methods still face challenges in handling the adhesion of adjacent primary silicon particles and effectively utilizing consistency in unlabeled data. To address these issues, this paper proposes a novel semi-supervised framework for Al-Si alloy microstructure image segmentation. First, we introduce a Rotational Uncertainty Correction Strategy (RUCS). This strategy employs multi-angle rotational perturbations… More >

Zhendong Du^*, Hanliu Wang, Kenji Hashimoto

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074520 - 10 February 2026

Abstract Planning in lexical-prior-free environments presents a fundamental challenge for evaluating whether large language models (LLMs) possess genuine structural reasoning capabilities beyond lexical memorization. When predicates and action names are replaced with semantically irrelevant random symbols while preserving logical structures, existing direct generation approaches exhibit severe performance degradation. This paper proposes a symbol-agnostic closed-loop planning pipeline that enables models to construct executable plans through systematic validation and iterative refinement. The system implements a complete generate-verify-repair cycle through six core processing components: semantic comprehension extracts structural constraints, language planner generates text plans, symbol translator performs structure-preserving mapping,… More >

Naresh Kumar¹, Anuj Kumar^1,*, Abhishek K. Mishra^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074430 - 10 February 2026

Abstract Density functional theory (DFT) calculations were employed to investigate the adsorption behavior of NH₃, AsH₃, PH₃, CO₂, and CH₄ molecules on both pristine and mono-vacancy phosphorene sheets. The pristine phosphorene surface shows weak physisorption with all the gas molecules, inducing only minor changes in its structural and electronic properties. However, the introduction of mono-vacancies significantly enhances the interaction strength with NH₃, PH₃, CO₂, and CH₄. These variations are attributed to substantial charge redistribution and orbital hybridization in the presence of defects. The defective phosphorene sheet also exhibits enhanced adsorption energies, along with favorable sensitivity and recovery characteristics, highlighting its potential More >

Chang Xu^1,*, Xianbo Yin², Zhiyong Zhou¹, Bomin Liu¹

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.074417 - 10 February 2026

Abstract This study presents a hybrid CNN-Transformer model for real-time recognition of affective tactile biosignals. The proposed framework combines convolutional neural networks (CNNs) to extract spatial and local temporal features with the Transformer encoder that captures long-range dependencies in time-series data through multi-head attention. Model performance was evaluated on two widely used tactile biosignal datasets, HAART and CoST, which contain diverse affective touch gestures recorded from pressure sensor arrays. The CNN-Transformer model achieved recognition rates of 93.33% on HAART and 80.89% on CoST, outperforming existing methods on both benchmarks. By incorporating temporal windowing, the model enables More >

Sara Tehsin¹, Inzamam Mashood Nasir^1,*, Wiem Abdelbaki², Fadwa Alrowais³, Khalid A. Alattas⁴, Sultan Almutairi⁵, Radwa Marzouk⁶

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074416 - 10 February 2026

Abstract Objective: Deep learning is employed increasingly in Gastroenterology (GI) endoscopy computer-aided diagnostics for polyp segmentation and multi-class disease detection. In the real world, implementation requires high accuracy, therapeutically relevant explanations, strong calibration, domain generalization, and efficiency. Current Convolutional Neural Network (CNN) and transformer models compromise border precision and global context, generate attention maps that fail to align with expert reasoning, deteriorate during cross-center changes, and exhibit inadequate calibration, hence diminishing clinical trust. Methods: HMA-DER is a hierarchical multi-attention architecture that uses dilation-enhanced residual blocks and an explainability-aware Cognitive Alignment Score (CAS) regularizer to directly align… More >

Tianxia Zou^1,*, Yilin Sun², Fuhao Fan¹, Zhen Zheng¹, Yanjin Xu², Baoshuai Han²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074121 - 10 February 2026

Abstract Ultra-high-strength aluminum alloy profile is an ideal choice for aerospace structural materials due to its excellent specific strength and corrosion resistance. However, issues such as uneven metal flow, stress concentration, and forming defects are prone to occur during their extrusion. This study focuses on an Al-Zn-Mg-Cu ultra-high-strength aluminum alloy profile with a double-U, multi-cavity thin-walled structure. Firstly, hot compression experiments were conducted at temperatures of 350°C, 400°C, and 450°C, with strain rates of 0.01 and 1.0 s⁻¹, to investigate the plastic deformation behavior of the material. Subsequently, a 3D coupled thermo-mechanical extrusion simulation model was established… More >

Ye Tian^1,*, Zhuokun Fan¹, Yifeng Zhang²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073935 - 10 February 2026

Abstract Amid the increasing demand for data sharing, the need for flexible, secure, and auditable access control mechanisms has garnered significant attention in the academic community. However, blockchain-based ciphertext-policy attribute-based encryption (CP-ABE) schemes still face cumbersome ciphertext re-encryption and insufficient oversight when handling dynamic attribute changes and cross-chain collaboration. To address these issues, we propose a dynamic permission attribute-encryption scheme for multi-chain collaboration. This scheme incorporates a multi-authority architecture for distributed attribute management and integrates an attribute revocation and granting mechanism that eliminates the need for ciphertext re-encryption, effectively reducing both computational and communication overhead. It More >

Sghaier Guizani^1,*, Tehseen Mazhar^2,3,*, Habib Hamam^4,5,6,7

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073883 - 10 February 2026

Abstract The advent of quantum computing poses a significant challenge to traditional cryptographic protocols, particularly those used in Secure Multiparty Computation (MPC), a fundamental cryptographic primitive for privacy-preserving computation. Classical MPC relies on cryptographic techniques such as homomorphic encryption, secret sharing, and oblivious transfer, which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries. This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI, IEEE Explore, Springer, and Elsevier, examining the applications, types, and security issues with the solution of… More >