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 >

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 >

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 >

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 >

Chi Zhong¹, Bo Ye¹, Xiao Wang², Yang Liu^1,*, Linmin Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082583 - 30 June 2026

Abstract With the sharp increase in the heat flux of high-power electronic devices, efficient thermal management has become critically important. Boiling heat transfer in parallel small channels, which utilizes latent heat efficiently, has emerged as a key enabling technology for next-generation cooling solutions. However, parallel channel systems are extremely susceptible to flow instabilities, resulting in severely uneven distributions of flow rate and heat transfer among the channels. This unevenness often leads to local overheating, which in turn restricts the system’s reliability and limits its practical application. In this paper, a three-dimensional transient numerical simulation method was… More >

Menghua Li^1,2,3, Bin Ou^1,2,3,4, Jiahao Li^1,2,3, Sitong Jin^1,2,3, Yanming Zhang^1,2,3, Shuyan Fu^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082514 - 30 June 2026

Abstract Accurate prediction of seepage discharge in earth-rock dams remains challenging due to the strong non-stationary and nonlinear characteristics, limited robustness of individual models, and poor interpretability of black-box approaches. To address these issues, this paper proposes an interpretable hybrid model that integrates Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM) networks, and Support Vector Machine (SVM). The model first decomposes the seepage discharge sequence and relevant lagged features using VMD. The LSTM network then captures temporal dependencies of the decomposed components, while the SVM performs regression on the original sequences and features. An adaptive fusion… More >

Hyunjun Park¹, Hee-Gook Jun², Seongyong Kim³, Dong-Hyuk Im^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082506 - 30 June 2026

Abstract Time-series forecasting can significantly aid decision-making in fields in which immediate action is required, such as power demand forecasting, financial market analysis, and traffic flow management. Transformer-based models achieve high forecasting accuracy by learning complex temporal patterns; however, their extensive parameters and substantial computational costs make practical deployment difficult in latency-sensitive environments. Therefore, lightweight models based on linear layers have recently been studied for improved efficiency. However, existing linear-based models have difficulty capturing local patterns and fail to reflect sudden volatility or fine-grained local trends, limiting their overall representational capacity. In this paper, SegTSF is… More >

Weiguo Chen¹, Xiaobin Hong^1,*, Kai Chen¹, Yunyun Deng¹, Bin Zhang^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082376 - 30 June 2026

Abstract Structural health monitoring (SHM) of ship piping systems is a core component of predictive maintenance strategies for complex marine engineering systems. During the detection of ship T-shaped pipes using ultrasonic guided waves, signal overlap arises from the diffusion of guided wave branches. To address this issue, an intelligent wave-guidance mechanism based on acoustic metamaterials is proposed for dynamic propagation control of ultrasonic guided waves. First, a metamaterial unit composed of a stainless steel substrate and a copper column is designed. The control of bandgap characteristics by lattice constant, column diameter, and column height is systematically… More > Graphic Abstract