Zhuangqiang Wen¹, Min Zhang², Zhekun Shou^3,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071300 - 08 January 2026

Abstract Roadbed disease detection is essential for maintaining road functionality. Ground penetrating radar (GPR) enables non-destructive detection without drilling. However, current identification often relies on manual inspection, which requires extensive experience, suffers from low efficiency, and is highly subjective. As the results are presented as radar images, image processing methods can be applied for fast and objective identification. Deep learning-based approaches now offer a robust solution for automated roadbed disease detection. This study proposes an enhanced Faster Region-based Convolutional Neural Networks (R-CNN) framework integrating ResNet-50 as the backbone and two-dimensional discrete Fourier spectrum transformation (2D-DFT) for… More >

Mohammed Debakla^1,*, Ali Mezaghrani¹, Khalifa Djemal², Imane Zouaneb¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-21, 2026, DOI:10.32604/cmc.2025.071927 - 09 December 2025

Abstract Magnetic Resonance Imaging (MRI) has a pivotal role in medical image analysis, for its ability in supporting disease detection and diagnosis. Fuzzy C-Means (FCM) clustering is widely used for MRI segmentation due to its ability to handle image uncertainty. However, the latter still has countless limitations, including sensitivity to initialization, susceptibility to local optima, and high computational cost. To address these limitations, this study integrates Grey Wolf Optimization (GWO) with FCM to enhance cluster center selection, improving segmentation accuracy and robustness. Moreover, to further refine optimization, Fuzzy Entropy Clustering was utilized for its distinctive features… More >

Lijuan Huang¹, Xianyi Liu², Jinping Liu^2,*, Pengfei Xu^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.068818 - 10 November 2025

Abstract The ubiquity of mobile devices has driven advancements in mobile object detection. However, challenges in multi-scale object detection in open, complex environments persist due to limited computational resources. Traditional approaches like network compression, quantization, and lightweight design often sacrifice accuracy or feature representation robustness. This article introduces the Fast Multi-scale Channel Shuffling Network (FMCSNet), a novel lightweight detection model optimized for mobile devices. FMCSNet integrates a fully convolutional Multilayer Perceptron (MLP) module, offering global perception without significantly increasing parameters, effectively bridging the gap between CNNs and Vision Transformers. FMCSNet achieves a delicate balance between computation… More >

Shuailong Guo^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.012841

Abstract The loading capacity, spatial arrangement, and structural stability of monatomic catalysts have significant effects on their performance. Traditional physical and chemical methods cannot precisely control the adsorption, reduction, and anchoring of metal salt ions, making it challenging to achieve accurate synthesis of metal single atoms in three-dimensional space. This project aims to use porous crystal materials as the adsorption carrier for metal salt ions and lasers as the energy source for accurate reduction. This approach facilitates the precise synthesis and customization of single atoms in multidimensional space. By designing the pore size, morphology, particle size,… More >

Shuai Xu¹, Congfei Zang¹, Yiming Zhang², Chun Li^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-2, 2025, DOI:10.32604/icces.2025.011525

Abstract The development of novel spintronic devices based on spin manipulation in magnetic nanostructures is crucial for achieving higher speed and miniaturization in future computing technologies. As a unique type of graphene quantum dot, triangulene nanoflakes (TNFs) exhibit nontrivial magnetic properties and excellent extensibility, making them highly promising for the design and application of spin logic units. In this study, we employ first-principles calculations to investigate experimentally synthesizable TNFs, in which transition metal (TM) atoms —namely Fe, Co, Ni, and Cu— are individually introduced at π-conjugated doping sites. The effects of different dopants and doping positions… More >

Tianfeng Li^*, Yonghua Fan

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-1, 2025, DOI:10.32604/icces.2025.011408

Abstract A fixed-time anti-saturation backstepping guidance law (FTABGL) is designed for interceptor under acceleration input constraints. Firstly, an adaptive fixed-time anti-saturation compensator (AFAC) is proposed to ensure the stability of saturated system and drive it to faster leave the saturated region. Compared with conventional anti-saturation compensators, the auxiliary variable of AFAC is able to realize faster response speed and higher convergent precision when saturation disappears, which avoids the impact on convergent characteristics of original tracking error. In addition, the novel adaptive law in AFAC can further shorten the duration of saturation and improve the convergent speed… More >

Carlos Quiterio Gómez Muñoz¹, Fausto Pedro García Márquez^2,*, Jorge Bernabé Sanjuán³

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3369-3386, 2025, DOI:10.32604/cmes.2025.069225 - 30 September 2025

Abstract Due to the continuous increase in global energy demand, photovoltaic solar energy generation and associated maintenance requirements have significantly expanded. One critical maintenance challenge in photovoltaic installations is detecting hot spots, localized overheating defects in solar cells that drastically reduce efficiency and can lead to permanent damage. Traditional methods for detecting these defects rely on manual inspections using thermal imaging, which are costly, labor-intensive, and impractical for large-scale installations. This research introduces an automated hybrid system based on two specialized convolutional neural networks deployed in a cascaded architecture. The first convolutional neural network efficiently detects More >

Alehegn Atalay Birlie^*

Journal of Renewable Materials, Vol.13, No.9, pp. 1783-1802, 2025, DOI:10.32604/jrm.2025.02025-0039 - 22 September 2025

Abstract This study explores vegan leather, an eco-friendly substitute for conventional animal-derived leather. Using materials like polyurethane, pineapple leaves, cork, and recycled plastics, vegan leather aims to transform the fashion industry and consumer products while addressing environmental concerns. Despite its advantages, challenges related to availability and durability persist. The booming market for vegan leather is expected to reach billions of dollars, reflecting a broader societal shift towards sustainable and cruelty-free alternatives. The review traces the historical development of vegan leather from its origins in Germany to modern innovations like Mylo and Piñatex. By comparing these materials More >

Shen-Haw Ju^*, Yi-Chen Huang

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1631-1654, 2025, DOI:10.32604/cmes.2025.067679 - 31 August 2025

Abstract This study focuses on determining the second-order irregular wave loads in the time domain without using the Inverse Fast Fourier Transform (IFFT). Considering the substantial displacement effects that Floating Offshore Wind Turbine (FOWT) support structures undergo when subjected to wave loads, the time-domain wave method is more suitable, while the frequency-domain method requiring IFFT cannot be used for moving bodies. Nonetheless, the computational challenges posed by the considerable computer time requirements of the time-domain wave method remain a significant obstacle. Thus, the paper incorporates various numerical schemes, including parallel computing and extrapolation of wave forces… More >

Peng Sun^1,2, Dechen Yao^1,2,*, Jianwei Yang^1,2, Quanyu Long^1,2

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1221-1239, 2025, DOI:10.32604/sdhm.2025.066098 - 05 September 2025

Abstract Supervised learning-based rail fastener anomaly detection models are limited by the scarcity of anomaly samples and perform poorly under data imbalance conditions. However, unsupervised anomaly detection methods based on diffusion models reduce the dependence on the number of anomalous samples but suffer from too many iterations and excessive smoothing of reconstructed images. In this work, we have established a rail fastener anomaly detection framework called Diff-Fastener, the diffusion model is introduced into the fastener detection task, half of the normal samples are converted into anomaly samples online in the model training stage, and One-Step denoising… More >