Ndolane Sene^1,*, Ameth Ndiaye²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2849-2852, 2025, DOI:10.32604/cmes.2025.075915 - 23 December 2025

Abstract This article has no abstract. More >

Guangyu Xu^1,2, Siyuan Xu³, Siyu Lu^4,*, Yuxin Liu¹, Bo Yang¹, Junmin Lyu⁵, Wenfeng Zheng^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4037-4053, 2025, DOI:10.32604/cmes.2025.074901 - 23 December 2025

Abstract Robust stereo disparity estimation plays a critical role in minimally invasive surgery, where dynamic soft tissues, specular reflections, and data scarcity pose major challenges to traditional end-to-end deep learning and deformable model-based methods. In this paper, we propose a novel disparity estimation framework that leverages a pretrained StyleGAN generator to represent the disparity manifold of Minimally Invasive Surgery (MIS) scenes and reformulates the stereo matching task as a latent-space optimization problem. Specifically, given a stereo pair, we search for the optimal latent vector in the intermediate latent space of StyleGAN, such that the photometric reconstruction… More >

Lei Zheng^1,*, Hong Pan^2,3, Yuelei Ruan^2,4, Guoxin Zhang¹, Lei Zhang^1,*, Jianda Xin¹, Zhenyang Zhu¹, Jianyao Zhang^2,5, Wei Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3217-3241, 2025, DOI:10.32604/cmes.2025.074651 - 23 December 2025

Abstract As a critical material in construction engineering, concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency. This study proposes a novel hybrid prediction method that integrates a heat conduction physical model with a multilayer perceptron (MLP) neural network, dynamically fused via a weighted strategy to achieve high-precision temperature estimation. Experimental results on an independent test set demonstrated the superior performance of the fused model, with a root mean square error (RMSE) of 1.59°C and a mean absolute error (MAE) of 1.23°C, representing a 25.3% RMSE reduction compared to More >

Yingying Zhou, Wenlong Shen, Tianzhe Jiao, Chaopeng Guo, Jie Song^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3087-3131, 2025, DOI:10.32604/cmes.2025.074378 - 23 December 2025

Abstract As consortium blockchains scale and complexity grow, scalability presents a critical bottleneck hindering broader adoption. This paper meticulously extracted 150 primary references from IEEE Xplore, Web of Science, Google Scholar, and other reputable databases and websites, providing a comprehensive and structured overview of consortium blockchain scalability research. We propose a scalability framework that combines a four-layer architectural model with a four-dimensional cost model to analyze scalability trade-offs. Applying this framework, we conduct a comprehensive review of scaling approaches and reveal the inherent costs they introduce. Furthermore, we map the artificial intelligence (AI)-enabled methods to the More >

Jia Mi¹, Zhikang Liu¹, Chang Li², Jing Wan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4089-4106, 2025, DOI:10.32604/cmes.2025.074364 - 23 December 2025

Abstract Recognizing essential proteins within bacteriophages is fundamental to uncovering their replication and survival mechanisms and contributes to advances in phage-based antibacterial therapies. Despite notable progress, existing computational techniques struggle to represent the interplay between sequence-derived and structure-dependent protein features. To overcome this limitation, we introduce GLM-EP, a unified framework that fuses protein language models with equivariant graph neural networks. By merging semantic embeddings extracted from amino acid sequences with geometry-aware graph representations, GLM-EP enables an in-depth depiction of phage proteins and enhances essential protein identification. Evaluation on diverse benchmark datasets confirms that GLM-EP surpasses conventional More >

Gan Zhu¹, Yongtao Yu^2,*, Xiaofan Deng¹, Yuanchen Dai³, Zhenyuan Li³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4317-4348, 2025, DOI:10.32604/cmes.2025.074349 - 23 December 2025

Abstract Existing deep learning Network Intrusion Detection Systems (NIDS) struggle to simultaneously capture fine-grained, multi-scale features and long-range temporal dependencies. To address this gap, this paper introduces TransNeSt, a hybrid architecture integrating a ResNeSt block (using split-attention for multi-scale feature representation) with a Transformer encoder (using self-attention for global temporal modeling). This integration of multi-scale and temporal attention was validated on four benchmarks: NSL-KDD, UNSW-NB15, CIC-IDS2017, and CICIOT2023. TransNeSt consistently outperformed its individual components and several state-of-the-art models, demonstrating significant quantitative gains. The model achieved high efficacy across all datasets, with F1-Scores of 99.04% (NSL-KDD), 91.92% More >

Dan Jiao^1,2, Yan Dong^1,2,*, Hao Xie³, Yordan Garbatov^4,*, Jiancheng Liu⁵, Hui Zhang⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3197-3216, 2025, DOI:10.32604/cmes.2025.074239 - 23 December 2025

Abstract Floating offshore wind turbine platforms typically use stiffened tubular joints at the connections between columns and braces. These joints are prone to fatigue due to complex weld geometries and the additional stress concentrations caused by the stiffeners. Existing hot-spot stress approaches may be inadequate for analysing these joints because they do not simultaneously address weld-toe and weld-root failures. To address these limitations, this study evaluates the fatigue strength of stiffened tubular joints using the effective notch strain approach and the structural strain approach. Both methods account for fatigue at the weld toe and weld root… More >

Nader Karmous¹, Wadii Jlassi¹, Mohamed Ould-Elhassen Aoueileyine¹, Imen Filali^2,*, Ridha Bouallegue¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4363-4393, 2025, DOI:10.32604/cmes.2025.074178 - 23 December 2025

Abstract This paper introduces a robust Distributed Denial-of-Service attack detection framework tailored for Software-Defined Networking based Internet of Things environments, built upon a novel, synthetic multi-vector dataset generated in a Mininet-Ryu testbed using real-time flow-based labeling. The proposed model is based on the XGBoost algorithm, optimized with Principal Component Analysis for dimensionality reduction, utilizing lightweight flow-level features extracted from OpenFlow statistics to classify attacks across critical IoT protocols including TCP, UDP, HTTP, MQTT, and CoAP. The model employs lightweight flow-level features extracted from OpenFlow statistics to ensure low computational overhead and fast processing. Performance was rigorously… More >

Asgar Ali^1,*, Nayan Sardar², Poly Karmakar³, Sanatan Das⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3563-3626, 2025, DOI:10.32604/cmes.2025.074082 - 23 December 2025

Abstract Hybrid nanofluids have gained significant attention for their superior thermal and rheological characteristics, offering immense potential in energy conversion, biomedical transport, and electromagnetic flow control systems. Understanding their dynamic behavior under coupled magnetic, rotational, and reactive effects is crucial for the development of efficient thermal management technologies. This study develops a neuro-fuzzy computational framework to examine the dynamics of a reactive Cu–TiO₂–H₂O hybrid nanofluid flowing through a squarely elevated Riga tunnel. The governing model incorporates Hall and ion-slip effects, thermal radiation, and first-order chemical reactions under ramped thermo-solutal boundary conditions and rotational electromagnetic forces. Closed-form analytical… More >

Yassir M. Abbas^1,*, Ammar Babiker², Fouad Ismail Ismail³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3279-3307, 2025, DOI:10.32604/cmes.2025.074069 - 23 December 2025

Abstract Accurately predicting the compressive strength of recycled aggregate concrete (RAC) incorporating supplementary cementitious materials (SCMs) remains a critical challenge due to the heterogeneous nature of recycled aggregates (RA) and the complex interactions among multiple binder constituents. This study advances the field by developing the most extensive and rigorously preprocessed database to date, which comprises 1243 RAC mixtures containing silica fume, fly ash, and ground-granulated blast-furnace slag. A hybrid, domain-informed machine-learning framework was then proposed, coupling optimized Extreme Gradient Boosting (XGBoost) with civil engineering expertise to capture the complex chemical and microstructural mechanisms that govern RAC… More >