Soran Manguri^1,2, Kasim Mermerdaş¹, Briar Esmail^3,4, Ahmed Manguri^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083537 - 30 June 2026

Abstract Geopolymer materials are increasingly recognized as sustainable alternatives to conventional cementitious materials due to their lower environmental impact and promising engineering performance. Recent studies have demonstrated that incorporating nanomaterials can further enhance the properties of geopolymer systems. In particular, nano-silica has been reported to significantly improve the mechanical performance of geopolymer materials. However, accurate prediction of compressive strength remains challenging because of the complex nonlinear interactions among mix design parameters, activator chemistry, and curing conditions. This study develops a machine learning framework to predict the 28-day compressive strength of nanosilica-modified hybrid geopolymer mortar using a… More >

Aminah Almehmadi¹, Ayman Noor¹, Aziza I. Noor², Hanan Almukhalfi¹, Talal H. Noor^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083503 - 30 June 2026

Abstract Fire emergencies have long posed a serious threat to people’s lives, real estate assets, and environmental sustainability in civilized societies, especially when combustible events are detected at late stages of development. Recent advancements in computer vision–based fire detection have enabled automated real-time monitoring; however, most solutions either detect the existence of fire/smoke or employ binary decision-making, which limits visual monitoring systems from being risk-aware. This work introduces a severity-aware fire/smoke detection model that supports intelligent monitoring systems in detecting visual hazards. The goal is to identify varying levels of fire intensity and smoke density and… More >

Yuyan Ning^1,2, Haiyun Huang¹, Legend Zhang³, Wei Wei⁴, Hao Quan⁵, Bo Yang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083345 - 30 June 2026

Abstract Hybrid CNN-Transformer models are widely used in medical image segmentation because they combine CNN-based local feature extraction with Transformer-based global context modeling. Despite their popularity, these models face several challenges, including computational complexity, noise blurring, and information loss. This paper proposes an enhanced convolutional attention network (ECANet) for liver segmentation. ECANet uses a U-shaped architecture with efficient channel-attention-based skip connections. Both the encoder and decoder are constructed using enhanced convolutional Transformer (ECT) blocks, where group convolution is integrated into the convolutional attention module for efficient Token embedding and channel disentanglement, and a Token-wise multi-layer perceptron More >

Sara Matheu^1,*, Pedro Ruzafa¹, Ilias Kalouptsoglou², Antonio Skarmeta¹, Dionysios Kehagias²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083308 - 30 June 2026

Abstract The evolution of the Future Mobile Internet, driven by large-scale connectivity and heterogeneous device ecosystems, introduces significant challenges for securely integrating devices into operational environments. Existing onboarding mechanisms primarily focus on authentication and credential provisioning, while security policy enforcement is typically deferred, creating a temporal gap during which devices may operate without appropriate constraints. This paper addresses this limitation by enabling policy enforcement during onboarding. To this end, we propose a model-driven approach that integrates the Device Security Passport (DSP) with the FIDO Device Onboard (FDO) protocol. The DSP is a lifecycle-aware model that aggregates… More >

Ouail Mjahed^1,*, Soukaina Mjahed²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083272 - 30 June 2026

Abstract Accurate aircraft engine diagnostics is essential for ensuring operational safety and enabling predictive maintenance under heterogeneous operating conditions. Although deep learning models can effectively capture high-dimensional multivariate sensor dynamics, purely data-driven approaches often entangle operating-condition variability with degradation-sensitive patterns, which limits robustness and generalization. This paper introduces PRIME, a physics-guided residual integrated framework for multi-task aircraft engine diagnostics. Rather than embedding explicit thermodynamic equations or physical constraints into the optimization process, PRIME relies on a physically motivated residual decomposition strategy that separates operating-condition-driven nominal behavior from degradation-sensitive sensor deviations. Specifically, nominal responses are estimated from operating-condition… More >

Wafa Alagal^1,*, Zanyar A. Ameen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083259 - 30 June 2026

Abstract Diabetes remains a major global health challenge and requires diagnostic systems capable of handling uncertainty and sometimes conflicting clinical evidence. In this study, a Disc T-Spherical Fuzzy (DT-SF) TOPSIS framework is proposed for diabetes risk assessment, where the radius parameter is used to encode the confidence associated with each diagnostic attribute. The methodology also integrates the Analytic Hierarchy Process (AHP) to determine the relative importance of several key risk factors, including blood glucose, body mass index, family history, lifestyle factors, and clinical symptoms. One important feature of the proposed approach is the ternary classification scheme,… More >

Qingxiang Meng^1,2,*, Zijie He^1,2, Yajun Cao^1,2, Weijiang Chu³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083227 - 30 June 2026

Abstract Constitutive modeling for geomaterials remains challenging because of limited data availability, strong nonlinearity, pressure sensitivity, and the non-smooth characteristics of commonly used yield surfaces. This study presents a deep-learning-based constitutive method for geomaterials that incorporates a neural stress-integration procedure based on the cutting plane algorithm (CPA). Two compact fully connected networks are trained to learn the yield function and its stress gradient from an augmented stress-state dataset. The trained networks are then incorporated into a cutting plane return-mapping procedure, in which only first-order information is required for the plastic stress return. This avoids explicit analytical More >

Adrian Soica, Bogdan Cornel Benea^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082801 - 30 June 2026

Abstract This study investigates the influence of autonomous vehicle (AV) front-end geometry on pedestrian injury biomechanics using PC-Crash multibody simulations. While emerging vehicles promise improved urban safety through automation and collision avoidance technologies, their unconventional front-end architectures introduce new passive safety challenges. The research compares classical passenger vehicles with van-type and symmetric flat-front autonomous platforms under standardized impact conditions at 40 km/h. Results reveal a clear redistribution of injury mechanisms depending on vehicle geometry. Conventional sloped front-end vehicles, super-mini and compact class, generate higher Head Injury Criterion (HIC) values due to wrap-around kinematics, where pedestrians rotate… More >

Pasquale De Luca^1,2,*, Livia Marcellino¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082664 - 30 June 2026

Abstract Osteosarcoma is the most common primary malignant bone tumor in pediatric populations. This work presents an extended Physics-Informed Neural Network framework that incorporates interferon-gamma (IFN-γ) as a fifth biological variable, complementing previous four-variable formulations with an explicit cytokine-mediated macrophage activation pathway. The model couples five biological fields with mechanical tissue response through Biot’s poroelastic theory over a two-dimensional domain. Four distinct initial macrophage distributions were investigated. Numerical stability was achieved across all scenarios, with total loss values between 0.056 and 0.158 and mechanical residuals below 3.2×10−5. The boundary-concentrated configuration yielded the lowest biological loss. More >

Yunlong Teng¹, Ying Liu², Jianhong Liang¹, Jinshang Luo^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082594 - 30 June 2026

Abstract Over the past decades, surrogate model-aided reliability analysis approaches grounded in active learning have undergone extensive development. However, Gaussian process models like Kriging suffer from severe computational burdens when handling high-dimensional problems or large samples. Conversely, machine learning algorithms such as extreme learning machines exhibit high computational efficiency but lack variance output and stability, making them difficult to employ for adaptive active learning strategies. To address these limitations, this study proposes a population Monte Carlo method based on an adaptive closed neuron extreme learning machine. First, a closed neuron strategy uses a consistency metric to… More >