Irfan Saif Ud Din¹, Imran Siddique^2,3,4,5, Zohaib Zahid¹, Muhammad Nadeem⁶, Ibrahim Alraddadi^2,*, Taha Radwan^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081726 - 27 May 2026

Abstract This study examines the variable thermal conductivity and electroosmotic performance of Sutterby hybrid nanofluid (SBHNF) thin film flow over a stretched inclined sheet using an artificial neural network (ANN)-based on NARX (Multilayer Nonlinear Autoregressive Networks with Exogenous Inputs) multiple-layer backpropagation simulation with the Levenberg-Marquardt algorithm (LMA). AA7075 and AA7072 nanoparticles suspended in sodium alginate (SA) base fluid make up the hybrid nanofluid (HNF), which was selected due to its improved heat transfer properties and superior thermal conductivity. The model’s practical applicability is enhanced by melting heat, nonlinear thermal radiation, boundary slip, and Newtonian heating effects,… More >

Huilan Wu, Yijun Liu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081675 - 27 May 2026

Abstract Metasurface design often requires solving field distributions across varying structural parameters and frequencies, where neural operators offer a promising avenue for fast prediction. However, conventional neural operators have problems with degradation of the accuracy in multi-scale structural analysis. In this work, we propose a Generative Residual Enhanced Neural Operator (GRE-NO) framework that introduces a generative residual network to model the systematic bias of the main predictor. The core model retains the DeepONet architecture with both branch and trunk networks implemented using Fourier Neural Operators, combining strong generalization and efficient global representation. To handle the complexity More >

Ronak Patel¹, Miral Patel², Deep Kothadiya³, Noor A. Khan⁴, Shaha Al-Otaibi^5,*, Roaa Khalil Mohamed Ali Abed⁶, Tanzila Saba⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080819 - 27 May 2026

Abstract Magnetic resonance imaging (MRI) is widely utilized for brain tumor segmentation, yet significant challenges persist due to intensity variations, irregular boundaries, and substantial morphological heterogeneity. Current state-of-the-art deep learning methods often struggle to capture long-range spatial dependencies, delineate fine boundary details, and efficiently process 3D volumetric data. This study introduces a novel hybrid framework that integrates state-space models with frequency-domain learning to address these limitations. The proposed model offers four primary contributions: (1) incorporation of a morphological attention block in the encoder to enhance boundary localization via dilation-erosion gradient modeling; (2) a dual-domain bottleneck module… More >

Thao Nguyen-Trang^1,2,*, Hiep Ha-Hoang³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080808 - 27 May 2026

Abstract Solving differential equations (DEs), including ordinary differential equations (ODEs) and partial differential equations (PDEs), is fundamental to scientific computing and engineering. The development of deep learning has led to Physics-Informed Neural Networks (PINNs), in which physical laws are embedded directly into the loss function. However, PINNs inherit the intrinsic instability of deep neural networks (DNNs) and lack an effective mechanism for Uncertainty Quantification (UQ). This paper proposes a stochastic ensemble framework to address these limitations. The proposed method is a double-stochastic ensemble framework that combines bagging (via bootstrap resampling and randomized collocation points) with Monte… More >

Alanoud Al Mazroa¹, Fahad Masood², Bakri Hussain Awaji³, Mohammad Alhefdi⁴, Abeer Aljohani⁵, Jawad Ahmad^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080134 - 27 May 2026

Abstract The rapid integration of Internet of Things (IoT) devices and distributed energy resources into smart grids has improved monitoring, control, and energy efficiency. However, it also exposes the grid to cyberattacks and privacy risks, as increased connectivity and data exchange can significantly disrupt energy management and system stability. Studies focused on centralized cybersecurity mechanisms that lacked scalability and did not emphasize the inherent graph structure of power networks. This study proposes a privacy-preserving and cyber-resilient energy-optimization framework, FedGNN, for IoT-enabled smart grids that jointly integrates federated learning, graph neural network-based trust inference, and trust-aware energy dispatch.… More >

Roshana Mukhtar¹, Chuan-Yu Chang², Muhammad Asif Zahoor Raja^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079390 - 27 May 2026

Abstract Parkinson’s disease (PD) is a complex neurodegenerative disease associated with the accumulation of α-synuclein, which is linked to the dysfunctional ubiquitin–proteasome system. Fractional calculus has emerged as a powerful tool for modeling complex disease dynamics due to its promising features that inherently capture memory and hereditary effects. This paper presents a fractional-order Proteasome-Fibril interaction model (F-PFIM) for the dynamics of PD, represented by three fractional differential classes, showing concentrations of fibrils (F), proteasomes (P), and proteasome fibril complex (C). The three classes of the F-PFIM collectively make a controlling system that works for the clearance… More > Graphic Abstract

Zafer Serin^1,*, Cihan Karakuzu², Uğur Yüzgeç²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079254 - 27 May 2026

Abstract This study proposes SWAGE-3D (Spectral Wasserstein Attention Generative Ensemble), an enhanced 3D-VAE-GAN framework for single-view 3D object reconstruction using voxel-based representations. The proposed model integrates RGB-D encoding, Wasserstein adversarial learning with hybrid Lipschitz regularization, and a self-attention–augmented generator to improve structural coherence and training stability. By combining variational latent modeling with stabilized Wasserstein optimization, the framework aims to address common challenges in 3D generative modeling, including mode collapse, unstable convergence, and insufficient global consistency. The encoder employs a depth-aware feature extraction strategy, while the discriminator utilizes a hybrid spectral normalization and gradient penalty mechanism to More > Graphic Abstract

Kalim Sattar¹, Malik Muhammad Saad Missen², Syeda Zoupash Zahra^1,3, Najia Saher⁴, Rab Nawaz Bashir^3,5,6,*, Oumaima Saidani⁷, Shahid Kamal⁵, Muhammad I. Khan⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078569 - 27 May 2026

Abstract Tropical Cyclone (TC) genesis forecasting is an important aspect of early warning systems, as it allows the adoption of early warnings and mitigation plans. However, existing methods often rely on binary classification or fail to capture the complex spatio-temporal dependencies that govern TC formation. To address this limitation, this study introduces STAG-Net, a novel Spatio-Temporal Attention-Gated Network designed to directly predict the geographical coordinates of TC genesis. The model uses multivariate variables of meteorological factors such as u-wind, v-wind, relative humidity, temperature, and large-scale dynamic features using a Convolutional Neural Network (CNN), Gated Recurrent Units… More >

Amel Ksibi^1,*, Manel Ayadi¹, Hela Elmannai², Monia Hamdi², Ala Saleh Alluhaidan¹, Imen Ksibi³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078347 - 27 May 2026

Abstract Cerebral palsy is a prevalent neurodevelopmental syndrome that disrupts motor development in children, making early detection vital for effective intervention. Traditional clinical assessments rely on subjective observations, often missing minor motor abnormalities until they become severe, typically after 12 months of age. This article presents a novel deep learning model, TransCP-Net (Transformer-based Cerebral Palsy Network), designed for early detection of infant cerebral palsy through spatiotemporal pose representation learning. The architecture employs hierarchical spatial and temporal attention to analyze complex motion patterns in video sequences, integrating multi-modal data for improved accuracy. TransCP-Net incorporates specialized preprocessing, including More >

Shuai Ma¹, Xiang Fang^1,2,*, Liya Han¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078073 - 27 May 2026

Abstract Automated feature recognition (AFR) plays an important role in automated measurement path planning and metrological data processing in the manufacturing industry. Existing AFR methods face critical limitations, such as the loss of geometric-topological fidelity during Computer-aided design (CAD) model conversion and inadequate instance segmentation for dimensional metrology. To address these challenges, we propose an integrated multi-attention and multi-strategy network (MMNet) for feature recognition, which mainly includes the multi-attention geometric and attribute fusion module (MGAM) and the multi-strategy semantic and instance segmentation module (MSIM). Specifically, MGAM employs multi-attention mechanisms to synergize local geometric features with global More >