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 >

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 >

Leyu Dai^1,2,3, Jindong Wang^1,2,3, Ming Zhou^1,2,3, Song Guo^1,2,3, Hengwei Zhang^1,2,3,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072449 - 10 February 2026

Abstract In recent years, with the rapid advancement of artificial intelligence, object detection algorithms have made significant strides in accuracy and computational efficiency. Notably, research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images (ORSIs). However, in the realm of adversarial attacks, developing adversarial techniques tailored to Anchor-Free models remains challenging. Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures. Furthermore, the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks. This study presents… More >

Qi Li, Yifan Wang^*, Yuxi Liu, Xingjing She, Yixuan Wu

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074066 - 29 January 2026

Abstract In data communication, limited communication resources often lead to measurement bias, which adversely affects subsequent system estimation if not effectively handled. This paper proposes a novel bias calibration algorithm under communication constraints to achieve accurate system states of the interested system. An output-based event-triggered scheme is first employed to alleviate transmission burden. Accounting for the limited-communication-induced measurement bias, a novel bias calibration algorithm following the Kalman filtering line is developed to restrain the effect of the measurement bias on system estimation, thereby achieving accurate system state estimates. Subsequently, the Field Programmable Gate Array (FPGA) implementation More >

Yewon Choi^1,2, Song Hyeon Ju², Jungsoo Nam^2,*, Min Ku Kim^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071817 - 29 January 2026

Abstract The composite material layering process has attracted considerable attention due to its production advantages, including high scalability and compatibility with a wide range of raw materials. However, changes in process conditions can lead to degradation in layer quality and non-uniformity, highlighting the need for real-time monitoring to improve overall quality and efficiency. In this study, an AI-based monitoring system was developed to evaluate layer width and assess quality in real time. Three deep learning models Faster Region-based Convolutional Neural Network (R-CNN), You Only Look Once version 8 (YOLOv8), and Single Shot MultiBox Detector (SSD) were… More >

Zhirong Jin^1,*, Xiaorui Hou¹, Yanrong Ge¹, Tiankui Guo², Ming Chen², Shuyi Li², Tianyu Niu²

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070750 - 27 January 2026

Abstract Hydraulic fracturing serves as a critical technology for reservoir stimulation in deep coalbed methane (CBM) development, where the mechanical properties of gangue layers exert a significant control on fracture propagation behavior. To address the unclear mechanisms governing fracture penetration across coal-gangue interfaces, this study employs the Continuum-Discontinuum Element Method (CDEM) to simulate and analyze the vertical propagation of hydraulic fractures initiating within coal seams, based on geomechanical parameters derived from the deep Benxi Formation coal seams in the southeastern Ordos Basin. The investigation systematically examines the influence of geological and operational parameters on cross-interfacial fracture… More >

Li Wang¹, Xuesong Xing¹, Yanan Hou¹, Heng Wen¹, Ying Zhu¹, Jingyu Zi¹, Qingwei Zeng^2,3,*

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.068653 - 27 January 2026

Abstract The deep coal reservoir in Linxing-Shenfu block of Ordos Basin is an important part of China’s coalbed methane resources. In the process of reservoir reconstruction, the artificial fracture morphology of coal seam with gangue interaction is significantly different, which affects the efficient development of coalbed methane resources in this area. In this paper, the surface outcrop of Linxing-Shenfu block is selected, and three kinds of interaction modes between gangue and coal seam are set up, including single-component coal rock sample, coal rock sample with different thicknesses of gangue layer and coal rock sample with different… More >

Herlina Marta¹, Novita Indrianti^2,*, Allifiyah Josi Nur Aziza³, Enny Sholichah⁴, Titik Budiati³, Achmat Sarifudin⁵, Yana Cahyana¹, Nandi Sukri¹, Aldila Din Pangawikan¹

Journal of Renewable Materials, Vol.14, No.1, 2026, DOI:10.32604/jrm.2025.02025-0145 - 23 January 2026

Abstract Corn starch (CS) is a renewable, biodegradable polysaccharide valued for its film-forming ability, yet native CS films exhibit low mechanical strength, high water sensitivity, and limited thermal stability. This study improves CS-based films by blending with poly(vinyl alcohol) (PVA) or glycerol (GLY) and using citric acid (CA) as a green, non-toxic cross-linker. Composite films were prepared by casting CS–PVA or CS–GLY with CA at 0%–0.20% (w/w of starch). The influence of CA on physicochemical, mechanical, optical, thermal, and water barrier properties was evaluated. CA crosslinking markedly enhanced the tensile strength, water resistance, and thermal stability More > Graphic Abstract