Safa Alsafari¹, Ayman Yafoz^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084403 - 27 July 2026

Abstract Bearing fault diagnosis in industrial deployment must contend with two simultaneous distributional shifts: fault severity increases as damage progresses, and motors operate at loads unseen during training. We define this compound setting as the double domain shift and present a rigorous few-shot benchmark on the Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets. Six architectures spanning distinct learning paradigms—a multilayer perceptron (MLP), a capsule network (CapsNet), a residual capsule network (ResCaps), a prototypical network (ProtoNet), a modified residual convolutional network (MRCN), and Deep Correlation Alignment (Deep CORAL)—are evaluated under a strict three-way split… More >

Shu-Chuan Chu^1,2, Libin Fu², Jeng-Shyang Pan^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084391 - 27 July 2026

Abstract The Animated Oat Optimization Algorithm (AOO) is a novel evolutionary algorithm inspired by the behavior of animated oats. This paper proposes a Competitive Parallel Animated Oat Optimization Algorithm (CPAOO) comprising two components. First, a parallel strategy is employed in which inter-subpopulation communication is triggered at predefined iteration thresholds to balance exploration and exploitation. Second, a grouped competition strategy with incentive mechanisms is introduced, enabling the prioritized evolution of superior individuals to enhance the algorithm’s efficiency. Furthermore, building on the Prediction Error Expansion (PEE) algorithm, this paper proposes a Dual-Layer PEE (DLPEE) algorithm for reversible digital More >

Mutlu Seçer^*, Ali Alper Saylan

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084228 - 27 July 2026

Abstract Climate change modifies environmental exposure conditions and affects the corrosion-driven deterioration of steel bridges, thereby challenging conventional maintenance planning approaches. Thus, more advanced maintenance management strategies are required to address the challenges associated with varying corrosion rate projections. In this study, a novel adaptive maintenance management framework is proposed for steel truss bridges to address climate change-induced corrosion under evolving deterioration conditions. Adaptivity is achieved by updating corrosion rates to consider time-varying deterioration conditions associated with climate change. This enables time-dependent representation of corrosion progression under changing environmental conditions. The framework is demonstrated on a… More >

Minsu Kim¹, Daeho Choi¹, Younghyo Cho¹, Yeog Kim², Jun Lee³, Changhoon Lee¹, Kiwook Sohn^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084041 - 27 July 2026

Abstract Future mobile Internet and convergence applications increasingly rely on encrypted protocols, making security monitoring difficult because payload inspection is unavailable while traffic classes and threats evolve continuously. Encrypted traffic classification models must therefore adapt to newly emerging traffic classes without repeatedly overwriting or fully retraining large Transformer backbones. This study presents and extends an ET-BERT Adapter Fusion framework for AI/ML-driven encrypted-traffic security monitoring in future mobile Internet and convergence applications. The framework keeps the ET-BERT backbone frozen, trains a Base Adapter on USTC-TFC2016 classes 0–9, trains an Incremental Adapter for class 10, and composes them… More >

Huiguo Wu^1,2,3, Yuedong Wu^1,2,3,*, Jian Liu^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083977 - 27 July 2026

Abstract The resilient modulus (M_R) is a key mechanical parameter in geotechnical engineering, but conventional laboratory measurement is time-consuming and labor-intensive. Deep learning models provide an alternative for predicting M_R using easily obtainable soil properties, yet their performance is often limited by the small size of available datasets. To address this limitation, this study develops an interpretable data-enhanced deep learning framework for M_R prediction. In the proposed framework, a multilayer perceptron (MLP) is adopted as the base prediction model, a generative adversarial network (GAN) is used to generate synthetic samples from the limited training data, Optuna is employed… More >

Aoran Huang^1,2,*, Sinuo Zhang^1,2, Haoxiang Zhu^1,2, Xiaojing Fan^1,2, Huachun Zhou^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083972 - 27 July 2026

Abstract In Future Mobile Internet and convergence application scenarios, existing network attack traffic detection methods are insufficient in characterizing cross-flow correlations and structural dependencies during the attack process, and therefore still have limited generalization ability in complex scenarios and unknown attack identification tasks. To address this issue, this paper proposes a cross-flow modeling large language model framework, which extends the traditional detection paradigm based on single-flow features to joint modeling oriented toward cross-flow context and relational structure. Specifically, this paper constructs cross-flow context through flow sorting, grouping, and cross-group sampling, and combines an inter-flow relation matrix… More >

Yi Chen, Xiaodan Ren^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083966 - 27 July 2026

Abstract Concrete exhibits significant stochasticity and nonlinearity, making the calibration of nonlinear damage models challenging for high-precision structural analysis. To address the high computational cost of finite element model calibration and the influence of model bias, this study proposes a machine learning surrogate framework for Bayesian calibration of nonlinear concrete damage models. The framework integrates a bi-scalar damage constitutive model, support vector regression based surrogate modeling, response-level finite element model bias representation, and adaptive Markov Chain Monte Carlo sampling within a unified probabilistic setting. The surrogate models are constructed to approximate the nonlinear mapping from constitutive… More >

Waqas Ahmad¹, Shahzad Anwar², Abid Iqbal^3,*, Abuzar Khan⁴, Saad Arif⁵, Ali S. Alzahrani³, Mohammed Al-Naeem⁶, Fatimah Alhayan⁷, Syed Hashim Raza Bukhari³, Ghassan Husnain^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083950 - 27 July 2026

Abstract Accurate vehicle localization is essential for safety-critical vehicular ad hoc networks (VANETs), including emergency response, navigation, traffic monitoring, and cooperative driving. However, conventional GPS/GNSS positioning systems have often shown degradation in tunnels, dense urban corridors, and non-line-of-sight (NLOS) environments, where satellite visibility and signal reliability are limited. This paper proposes a calibrated Link Quality Indicator (LQI)-based closed-form localization framework for partially connected Roadside Unit (RSU)-assisted VANETs. The proposed framework first calibrates the LQI-to-range relationship using numerical regression parameters and then converts accepted LQI observations into distance estimates. The distances are processed through a variance-aware weighted… More >

Hyunghoon Kim¹, Wonsuk Choi², Kyungho Joo^3,*, Hyojin Jo^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083948 - 27 July 2026

Abstract As mobile networks evolve toward next-generation architectures in which cellular and IP-based voice services are increasingly converged, SIMBox-based call routing has emerged as an important issue in modern telecommunication networks. By converting Voice over IP (VoIP) traffic into local cellular calls, SIMBox appliances allow IP-originated calls to appear as domestic cellular calls. Although SIMBox usage is not inherently fraudulent, detecting SIMBox-routed calls is important for identifying abnormal call-routing behavior and supporting network-side and client-side security applications. In this paper, we propose a client-side framework for SIMBox-routed call detection. Calls routed through SIMBox infrastructure are identified… More > Graphic Abstract

Lei Yang^1,2, Yiyun Hu¹, Xianglong Yang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083905 - 27 July 2026

Abstract Accurate prediction of entropy generation in nanofluid turbulent convection is essential for optimizing thermal system efficiency, yet remains challenging due to complex near-wall phenomena and thermal property variations with temperature. This study applied an elliptic blending turbulence model (SST k-ω-φ-α) to numerically analyze entropy generation in alumina-water nanofluid flow through a uniformly heated circular tube. The model’s performance was validated using both experimental data and established heat transfer and fluid flow correlations at small wall-bulk temperature difference condition, and its superiority was rigorously evaluated against two widely adopted turbulence models (SST k-ω and realizable k-ε).… More >