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REVIEW

Weiqiang Jin^1,2,#, Yang Liu^2,#, Yang Gao^1,#, Shixiang Tang², Yanghao Zhou³, Jinhu Qi⁴, Wentao Zhang⁴, Junli Wang⁵, Jing Gao², Yue Ma⁴, Ziwei Zhang^1,*, Biao Zhao^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083586 - 27 July 2026
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract Large Vision-Language Models (LVLMs) have achieved strong performance in multimodal perception, understanding, and generation, but their ability to perform complex logical reasoning remains insufficiently understood. In particular, it is still unclear whether current LVLMs can reliably conduct explicit logical operations, multi-step inference, abstract relational reasoning, and cross-modal evidence integration. Reasoning abilities such as deductive, inductive, abductive, multi-hop, and causal inference are fundamental to robust decision making, trustworthy interaction, and real-world deployment, yet they have not been systematically examined in the LVLM literature. Existing surveys mainly discuss mathematical reasoning, general multimodal intelligence, or benchmark progress, but… More >

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EDITORIAL

Sathishkumar Veerappampalayam Easwaramoorthy^*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.087043 - 27 July 2026
（This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)
Abstract This article has no abstract. More >

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REVIEW

Imran Ahsan¹, Muhammad Waseem Anwar², JungYoon Kim³, Mucheol Kim^1,4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.080382 - 27 July 2026
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract Graph neural networks (GNNs) have demonstrated promising results in enhancing machine learning and artificial intelligence techniques by addressing graph-related problems, including graph categorization, edge prediction, and node prediction. Despite the rapid growth of the field, a structured synthesis of recent application-oriented GNN research is necessary. This systematic literature review analyzes 363 peer-reviewed, application-oriented GNN studies published from 2019 through February 2026 from four scientific repositories. These studies are organized by GNN model variant, (including single and multimodel approaches), and four classification task categories: graph, link, node, and node and edge combined. In addition, the review… More >

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REVIEW

Ismail Hossain¹, Mridul Banik², Fahmid Al Farid^3,4, Jia Uddin^5,*, Hezerul bin Abdul Karim^4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.075507 - 27 July 2026
Abstract In recent years, the rapid advancement of Large Language Models (LLMs) has significantly transformed natural language processing (NLP), enabling impressive performance across a wide range of tasks. However, these developments have largely benefited high-resource languages, leaving many low-resource and underrepresented languages at risk of further digital marginalization. Addressing this imbalance is crucial to building more inclusive and culturally sustainable AI systems, which is motivating growing research interest in adapting LLMs for linguistically diverse and resource-scarce communities. This systematic review examines recent progress (2020–2025) in the pretraining and adaptation of LLMs for Low-Resource Languages (LRLs). Analysed… More >

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REVIEW

Yeog Kim, Changhoon Lee, Kiwook Sohn^*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083669 - 27 July 2026
（This article belongs to the Special Issue: The Evolution of Cybersecurity and AI: Surveys and Tutorials)
Abstract With the widespread adoption of encryption protocols, payload-based traffic analysis has become increasingly infeasible, posing significant challenges for intrusion detection systems (IDS). Consequently, AI-based approaches for encrypted traffic analysis have gained substantial attention. However, existing studies are often evaluated using inconsistent criteria, including heterogeneous attack labels, behavioral representations, and model architectures, making systematic comparison difficult. To address this limitation, this paper proposes a three-level analytical taxonomy for encrypted traffic analysis, structured around attack objectives (Level 1), observable network behaviors (Level 2), and detection models (Level 3). The proposed framework provides a structured perspective for analyzing… More >

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REVIEW

Malika Abid¹, Mohammed Kamel Benkaddour¹, Mohamed Benouis², Amine Khaldi¹, Monalisa Sahu³, Aditya Kumar Sahu^4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081055 - 27 July 2026
Abstract Federated Learning (FL) is a cutting-edge method in the medical imaging field that allows hospitals to collaboratively build models without revealing patient data. Nevertheless, FL is still vulnerable to numerous security and privacy issues, including, but not limited to, data poisoning, Byzantine attacks, and inference attacks. The existing literature has only partly dealt with this topic by focusing either on particular threats or on mitigation strategies, thus leaving the overall comprehension of the problems and their solutions in medical imaging as inadequate. The main threats to FL are systematically classified in this systematic review, with… More >

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ARTICLE

Zhongxue Li^1,*, Yu Peng¹, Jin Xu², Loc Vu-Quoc³, Bassam A. Izzuddin⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081240 - 27 July 2026
（This article belongs to the Special Issue: Advances in Modeling and Analysis of Complex Dynamics in Nonlinear Systems)
Abstract This paper proposes a computational theory for multibody dynamics based on a novel co-rotational formulation of a four-node quadrilateral shell element, designed to address nonlinear dynamic problems in flexible multibody systems undergoing arbitrarily large displacements and rotations. To circumvent the numerical inefficiency caused by the asymmetric tangent stiffness matrix in conventional co-rotational approaches, an incrementally additive vectorial rotational variable is introduced, which ensures the symmetry of the element’s tangent stiffness matrix in both global and local coordinate systems. The adoption of this vectorial rotational variable substantially improves computational efficiency and numerical stability. Hamilton’s principle is… More >

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ARTICLE

Teng Long¹, Leyu Wang^2,*, James D. Lee³, Cing-Dao Kan²
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.080866 - 27 July 2026
Abstract Titanium alloy Ti-6Al-4V has been widely applied in many industries, for example, aerospace, marine, automotive, and biomedical engineering systems, where accurate characterization of plastic deformation is important for evaluating material performance and potential failure under severe loading conditions. This material shows nonlinear plasticity and tension–compression asymmetry, which makes the strain hardening characterization important for computational failure analysis and crashworthiness-related simulations. However, conventional strain hardening models and parameter identification methods often rely on linear or extrapolation-based assumptions and are sensitive to initial guesses due to the non-convex nature of the optimization problem. In this study, a More >

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ARTICLE

Yi Chen, Xiaodan Ren^*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083966 - 27 July 2026
（This article belongs to the Special Issue: AI-Enhanced Computational Methods in Engineering and Physical Science)
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 >

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ARTICLE

Zhongjian Ling, Yongou Zhang^*, Yifan Li, Xianzhong Wang
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082363 - 27 July 2026
（This article belongs to the Special Issue: Recent Developments in Nonlocal Meshfree Particle Methods for Solids and Fluids )
Abstract Due to the complex and dynamic nature of multi-phase interfaces, accurately capturing interface evolution remains one of the key challenges in multi-phase flow simulations, particularly in modeling bubble rising. In this study, a fully Lagrangian method is developed by using the Generalized Finite Difference (GFD) scheme, which we refer to as Finite Difference Particle Method (FDPM), and the Continuum Surface Force (CSF) model to simulate bubble dynamics. In this framework, the fluid is represented by particles, and all partial differential terms in the Navier–Stokes equations are discretized into symmetric linear systems using the GFD scheme.… More >

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ARTICLE

Sachin Tom, Eva Gutheil^*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084886 - 27 July 2026
（This article belongs to the Special Issue: Modeling and Applications of Bubble and Droplet in Engineering and Sciences)
Abstract A single droplet heating, vaporization, and detailed combustion model is developed for pure p-xylene (p-C₈H₁₀) in hot air. p-C₈H₁₀ is a combustible solvent in precursor solutions, for instance, with titanium tetraisopropoxide (TTIP) for the production of TiO2 nanoparticles. In the present one-dimensional mathematical model, a spherically symmetric p-xylene droplet in hot air is considered, resolving both the droplet (liquid phase) and the ambience (gas phase). The calculation of the vaporization rate includes the Stefan velocity at the droplet surface. In the gas phase, a detailed chemical reaction scheme is used. Elementary reactions are… More >

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ARTICLE

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
（This article belongs to the Special Issue: Computational Advances in Nanofluids: Modelling, Simulations, and Applications)
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 >

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ARTICLE

José González-Cabrero^1,2, Carmelo Gómez^1,2, Manuel Paredes³, Francisco Cavas^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082643 - 27 July 2026
（This article belongs to the Special Issue: Advances in Modeling and Optimization of Biological and Bio-Inspired Systems)
Abstract The development of accurate digital twin models of the human cornea is a key factor for planning and monitoring eye treatments and clinical supervision. Corneal tissue can be simulated with the implementation of hyperelastic models based on strain energy density functions. However, the number of hyperelastic models and the parameters’ variation that define these models hinder comparison across different studies. Furthermore, parameter calculations based on a single test are an ill-posed problem. In this research, a novel sequential methodology based on collagen fibril crimping strain threshold has been implemented to calculate the corneal material’s parameters. More >

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ARTICLE

Bingqi Gui^1,2, Shuheng Lu^1,2, Yihui Zhu^1,2,*, Zhuangqing Fan^3,4, Haiwen Sun⁵, Xuefei Yan⁶, Ang Wang⁷, Wei Pang⁷, Wenchao Chen⁷
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084643 - 27 July 2026
Abstract Body armor prevents projectile penetration, but thoracic visceral injury may still result from pressure-wave transmission and back-face deformation. However, thoracic responses to single and multiple impacts remain insufficiently understood, numerical human thoracic models need further validation, and load transfer between the rib cage and cardiopulmonary system remains unclear. Therefore, this study combined live-fire blunt-impact tests on a biomimetic thoracic target with a human thoracic finite element model with filled thoracic cavity gaps. Load transfer and cardiopulmonary responses under non-penetrating rifle-bullet impact were investigated using a SiC/UHMWPE composite ballistic insert. The results showed that, first, under… More >

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ARTICLE

Yunus Celik^*, Burhan Necati Kiziloglu
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084744 - 27 July 2026
Abstract This study investigates the passive amplification of aerodynamic excitation forces through coordinated bluff-body geometric modifications to quantify the vortex-induced vibration (VIV) energy harvesting potential of stationary cylinders in the laminar regime. Two-dimensional laminar simulations on fixed bodies isolate geometric effects from structural feedback. Circumferential biomimetic grooves are first optimised on a circular baseline at Re=200 using a Taguchi orthogonal array (L9), identifying groove amplitude as the dominant control parameter and selecting N=24, Amp=5% as the optimal configuration, which yields a 21% increase in the root-mean-square lift coefficient (Cℓ,rms) and… More >

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ARTICLE

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
（This article belongs to the Special Issue: Numerical Modeling in Technical Diagnostics and Predictive Maintenance)
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 >

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ARTICLE

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 >

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ARTICLE

Junho Lee, Sanghyun Park, Younghun Lee, Namsu Kim^*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083185 - 27 July 2026
Abstract Monitoring the conditions of electric motors in industrial applications is an essential step for ensuring safety and reducing maintenance costs. This paper deals with one of the most frequent winding failures—the inter-turn short fault of an interior permanent magnet synchronous motor. A novel high-fidelity co-simulation framework toward a digital twin-based approach combining Maxwell simulation in finite element method (FEM) for the motor and control system in system software for the inverter is presented. An analysis of the motor based on a 2D FEM model is performed considering the motor topology and non-linear properties, and inductance… More >

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ARTICLE

Jiawang Zou, Annan Jiang^*, Xinzhi Wang, Hao Huang
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082647 - 27 July 2026
（This article belongs to the Special Issue: Artificial Intelligence and Advanced Numerical Modeling Integration Techniques in Tunnel and Underground Engineering)
Abstract Accurate forecasting of settlement in buildings adjacent to shield tunnels remains a critical challenge in underground engineering due to complex spatiotemporal interactions and nonlinear relationships among multi-source monitoring data and construction parameters. To address this issue, a Sparrow Search Algorithm (SSA)-optimized Convolutional Neural Network–Long Short-Term Memory–Transformer (CNN-LSTM-Transformer) hybrid framework is proposed, explicitly incorporating the relative spatial relationship between the shield excavation face and adjacent structures. In this framework, the Convolutional Neural Network (CNN) module extracts spatial features from monitoring data and tunneling parameters, capturing interdependencies among different construction indicators and reflecting local spatial heterogeneity of… More >

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ARTICLE

Alireza Norouzpour Shahrbejari¹, Nafiseh Pishbin², Mohammad Reza Maghami^3,*, Mazlan Mohamed^4,*, Mohammad Golmohammad¹
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083763 - 27 July 2026
Abstract This study presents a weighted-sum multi-criteria optimization framework using PSO for the optimal siting, sizing, and scenario-based operation of energy storage systems (ESSs) in renewable-integrated distribution networks. The proposed model concurrently addresses technical, economic, and reliability objectives—minimizing active power losses (PL), voltage deviation (VD), expected energy not supplied (EENS), and short-circuit level (SCL), while maximizing voltage sensitivity index (VSI) and power-loss sensitivity factor (PLSF). A Particle Swarm Optimization (PSO) algorithm with weighted-sum scalarization is employed to solve this complex, nonlinear optimization problem and effectively balance the conflicting operational goals. The framework is validated using IEEE… More >

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ARTICLE

Marta Leal, Verónica Abad-Alcaraz, María del Mar Castilla, José Domingo Álvarez^*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.080261 - 27 July 2026
（This article belongs to the Special Issue: Intelligent Control and Machine Learning for Renewable Energy Systems and Industries)
Abstract The effective control of parabolic trough collectors (PTCs) remains a significant challenge due to the inherent non-linearities of the system and the continuous impact of environmental disturbances. Although PTCs are a key technology for industrial process heat and large-scale electricity generation, classical control strategies often struggle to maintain optimal performance under fluctuating conditions. To address these limitations, this paper presents a novel reinforcement learning (RL)-based controller, designed specifically for solar thermal systems. The proposed RL agent is designed to learn directly from operational data, enabling it to adapt its control policy in real time to More >

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AL-Wesabi Ibrahim¹, Hassan M. Hussein Farh^2,*, Jiazhu Xu^1,*, Mohamad A. Alawad², Ahmed Alqurashi³, Abdullrahman A. Al-Shamma’a²
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081371 - 27 July 2026
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems, 2nd Edition)
Abstract Load frequency control (LFC) in interconnected power systems has always been a challenging task in the presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable energy sources and the impact of electric vehicles on the power system. Although various PI/PID and other advanced control strategies have been employed for LFC in power systems, the existing methods have shown some limitations in terms of dynamic flexibility and robustness in the presence of nonlinearities and couplings in the power systems. Moreover, the optimization methods employed for the tuning of… More >

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ARTICLE

Xuan Kien Pham¹, Van Dat Nguyen^2,*, Van Thanh Phan^3,*, Duc Trien Nguyen^4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.086196 - 27 July 2026
（This article belongs to the Special Issue: Intelligent Scheduling and Optimization in Engineering and Management)
Abstract Accurate energy consumption forecasting faces two major challenges: limited historical data and complex consumption patterns. To address these challenges, this study proposes a new hybrid framework named the Decomposition-based Grey-Neuro-Fuzzy Architecture (DeGNA). The model first uses the Denton method to convert limited annual records into high-frequency monthly data. Next, it applies STL decomposition to separate the data into trend, seasonal and residuals components. A rolling-window GM(1,1) model is then used to predict the main growth trend, while a GWO-optimized ANFIS model uses economic indicators (IIP and FDI) to forecast complex seasonal changes. This study evaluates… More >

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Mohammed Ansar Mohammed Manaz^1,*, Shang Ping Hong², Tzung-Lin Lee¹
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083270 - 27 July 2026
（This article belongs to the Special Issue: Stochastic Modeling and Reliability Assessment in Industrial Engineering Systems)
Abstract Silicon Carbide Metal Oxide Semiconductor Field Effect Transistors (SiC MOSFETs) have superior characteristics compared to traditional Silicon-based switching devices. SiC devices can support fast switching speeds and high blocking voltages. Due to limited historical data and rapid technological improvements, there is not enough field data to correctly evaluate the reliability of the state-of-the-art SiC MOSFETs. An accurate model of their reliability and aging characteristics is needed to expedite their rapid commercial adoption in mission-critical applications, such as offshore wind farms and electric vehicles. Classical handbook-based methods produce large errors due to their inability to correctly… More >

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Guglielmo D’Amico^1,*, Edoardo Lui², Filippo Petroni¹
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082608 - 27 July 2026
（This article belongs to the Special Issue: Stochastic Modeling and Reliability Assessment in Industrial Engineering Systems)
Abstract This paper develops an operational framework for short-horizon risk management in multistate stochastic systems, with application to wind farm performance. We focus on instantaneous mobility-based indicators derived from finite-state continuous-time Markov chains, which capture the local propensity of a system to transition between states. Unlike classical reliability and availability measures, these indicators provide a dynamic description of system behavior. The indicators are interpreted as policy signals to support decision-making under budget constraints. We introduce a state-conditional expected short-horizon loss, representing non-production risk, and use it to evaluate ranking-based intervention strategies. The framework is applied to More >

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Daniel Martín-Pérez¹, Francesc Rodríguez-Díaz¹, David Gutiérrez-Avilés², Alicia Troncoso¹, Francisco Martínez-Álvarez^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082712 - 27 July 2026
（This article belongs to the Special Issue: Quantum Machine Learning: Methods and Engineering Applications)
Abstract Hybrid classical-quantum architectures have emerged as a practical response to the data and compute demands of modern deep learning, since a pretrained classical backbone can carry the feature extraction while a compact quantum head provides the trainable component. Quantum transfer learning is the most active instance of this idea. However, existing quantum transfer learning pipelines have been evaluated in isolation, typically on a single software framework and without a structured treatment of noise or statistical significance, which makes it difficult to assess how this paradigm contributes over fair classical baselines. A methodological benchmark for quantum… More >

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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
（This article belongs to the Special Issue: Advances in Artificial Intelligence for Geotechnical Engineering)
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 >

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Ali Raza^1,*, Umar Shafique¹, Marek Lampart¹, Dumitru Baleanu², Emad Fadhal³, Hadil Alhazmi⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084939 - 27 July 2026
Abstract Kawasaki disease (KD) is an acute, self-limited pediatric vasculitis of unknown etiology and is one of the leading causes of acquired coronary artery complications in children. Endothelial dysfunction, vascular endothelial growth factor (VEGF) activity, adhesion molecule/chemokine activation, and inflammatory cytokine responses play important roles in its pathogenesis. This paper presents a delay differential equation model with stochastic perturbations to study lesion-level inflammatory mechanisms involved in Kawasaki disease pathogenesis. The model describes interactions among healthy endothelial cells, vascular endothelial growth factor (VEGF), adhesion molecules/chemokines, and inflammatory cytokine activity. Mathematically, endothelial-cell injury promotes VEGF production, VEGF contributes… More >

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Manal Ghannam¹, Bilgen Kaymakamzade^1,2, Muhammad Farman^1,3,4, Kottakkaran Sooppy Nisar^5,*, Mohammed Altaf Ahmed⁶
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081075 - 27 July 2026
（This article belongs to the Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics-III)
Abstract In Vitro Fertilization (IVF) has been a major medical advancement in the field of fertility treatment. It has helped millions of individuals and couples overcome infertility by providing a workable option. It involves removing eggs from the ovaries of a female, fertilizing those eggs with male sperm in a monitored lab condition. In this work, we developed a new model to show the success of In Vitro Fertilization rates in women through a fractional-order compartmental modeling framework by using real data. The developed model is analyzed statistically, and the biological feasibility of the model. The Lipschitz condition,… More >

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Emrah Altun^1,*, Christophe Chesneau², Atacan Erdis¹
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083459 - 27 July 2026
（This article belongs to the Special Issue: Computer Modeling in Statistics)
Abstract Modeling bounded response variables is an important problem in computational statistics, especially in applications involving skewed, heavy-tailed data. In such cases, the modal regression is a robust alternative to traditional mean-based modeling approaches. In this study, a new bounded distribution, called the extended Bradford distribution, is proposed as a flexible extension of the classical Bradford distribution. By incorporating an additional shape parameter, the corresponding model can capture various shape structures, such as left and right skewness, increasing, and bathtub hazard shapes. The new distribution provides an explicit expression for the mode, making it suitable for More >

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Rana Muhammad Adnan Ikram¹, Jing-Cheng Han^1,*, Ahmed A. Ewees², Mo Wang³, Ozgur Kisi^4,5,6,*, Salim Heddam⁷, Mohammad Zounemat-Kermani⁸
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083313 - 27 July 2026
（This article belongs to the Special Issue: Explainable AI, Digital Twin, and Hybrid Deep Learning Approaches for Urban–Regional Hydrology, Water Quality, and Risk Modeling under Uncertainty)
Abstract Two-phase optimized machine learning and deep learning models play a key role in enhancing the prediction accuracy of nonlinear time series modeling. This study assesses the performance of a novel two-phase optimized Long Short-Term Memory (LSTM) model with integration of Aquila Optimizer (AO) and Wild Horse Optimizer (WHO) in predicting monthly streamflow in a snow-fed catchment. The two-phase optimized LSTM-WHOAO model is compared with single-phase optimized models such as LSTM-GA (Genetic Algorithm), LSTM-GWO (Grey Wolf Optimizer), LSTM-WOA (Whale Optimization Algorithm), LSTM-AO, and LSTM-WHO. The outcomes acquired from the deep learning models were compared using four… More >

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Guangyu Xu^1,2, Yuxi Ban¹, Legend Zhang³, Junmin Lyu³, Feng Bao⁴, Wenfeng Zheng^1,3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.085484 - 27 July 2026
（This article belongs to the Special Issue: Recent Advances in Geospatial Artificial Intelligence (GeoAI) Models, Approaches, and Applications)
Abstract High-resolution remote sensing semantic segmentation is a fundamental task in Geospatial Artificial Intelligence (GeoAI). Existing CNN-based methods are effective for local and multiscale feature extraction but often lack progressive cross-scale semantic propagation, while attention- and Transformer-based methods improve global spatial modeling but generally ignore frequency-domain regularities. To address these limitations, this study proposes a Multiscale Long-Distance Feature Aggregation Network (MLFANet), a unified spatial-frequency segmentation framework for high-resolution remote sensing imagery. MLFANet introduces three key components: a Multiscale Global Dependency Extraction module for cascaded cross-scale contextual refinement, an FFT-based frequency-domain branch with learnable global filtering for… More >

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Adrián Bañuls-Arias, Cipriano Galindo, Ana Cruz-Martín, Manuel Castellano-Quero, Juan M. Gandarias, Juan-Antonio Fernández-Madrigal, Vicente Arévalo-Espejo^*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.085976 - 27 July 2026
（This article belongs to the Special Issue: Environment Modeling for Applications of Mobile Robots)
Abstract The basic operation of a mobile robot is navigating to some target, avoiding collisions and possibly minimizing other criteria. A diversity of methods have been developed since the past century, and the research is still active, but there is one aspect that is often neglected: the duration of the steps in which computational devices divide the navigation process. Usually, it is set heuristically to a small, constant value for sampling observations frequently enough to ensure safety; however, each robot and environment has particularities that can make such a fixed timestep sub-optimal under some criteria. This… More >

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Awais Qasim¹, Aniqa Iftikhar¹, Hanaa Nafea², Nay Chi Moe Oo³, Byung-Seo Kim^4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084691 - 27 July 2026
Abstract Optimal task assignment in holonic multi-agent systems has emerged as a pivotal problem in modern distributed systems. Despite substantial gains in agent coordination, many large-scale systems still suffer from poor job allocation, resulting in performance bottlenecks and resource waste. Effective task assignment is critical for these systems since it influences individual agent performance and overall adaptability. A significant challenge within holonic multi-agent systems is ensuring optimal task assignment while resolving performative inconsistencies, such as role conflicts and coordination failures among agents. This research proposes a novel optimization framework to address these inconsistencies, enabling more efficient More >

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Abeer S. Hadad¹, Fahman Saeed², Adeeb A. Ahmed^3,*, Jiangbin Zheng¹
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083873 - 27 July 2026
Abstract Loop invariant inference is fundamental to program verification, yet it remains particularly challenging for multipath loops, where different execution paths may exhibit incompatible behaviors across feasible executions. In such settings, invariants that are both sound and sufficiently precise often require disjunctive forms, whose automatic inference remains difficult. This paper presents an efficient, path-sensitive, counterexample-guided framework for automated loop invariant inference. Our approach leverages a Path Dependency Automaton (PDA) to systematically decompose the semantics of multipath loops by modeling feasible execution paths independently. Building on this decomposition, we introduce a localized, path-guided Counterexample-Guided Invariant Refinement (CEGIR) More >

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Mahander Kumar¹, Lal Khan^2,*, Mohammad Zubair Khan^3,*, Ibrahim Aljubayri⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081699 - 27 July 2026
（This article belongs to the Special Issue: Applied NLP with Large Language Models: AI Applications Across Domains)
Abstract Aspect-Based Sentiment Analysis (ABSA) is a fundamental Natural Language Processing (NLP) task that aims to determine fine-grained sentiment polarity toward specific aspects mentioned in text. With the emergence of Large Language Models (LLMs) and transformer-based architectures, significant improvements have been achieved in contextual representation learning for sentiment analysis. However, existing LLM-inspired and transformer-based ABSA frameworks often suffer from inadequate aspect-context alignment, redundant feature integration, limited interpretability, and insufficient coordination between contextual and sequential modeling components. To address these challenges, this paper proposes two hybrid architectures, namely HABSA and AAC-HABSA, centered on a novel Adaptive Aspect… More >

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Sahil Tripathi¹, Manaswi Kulahara², Abdul Khader Jilani Saudagar³, Hatoon S. AlSagri^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083755 - 27 July 2026
（This article belongs to the Special Issue: Applied NLP with Large Language Models: AI Applications Across Domains)
Abstract Spatial reasoning, defined as the ability to infer and compose relations among entities—is fundamental to geoinformatics applications such as spatial querying and map understanding. However, existing works such as Bidirectional Encoder Representations from Transformers (BERT)-based spatial Question Answering (QA) models and neuro-symbolic models rely on dataset-specific patterns, leading to shortcut learning, where reliance on superficial lexical cues rather than true relational understanding. Recent Large Language Models (LLMs)-based works, including fine-tuning and Chain-of-Thought (CoT) prompting, partially alleviate shortcut learning but remain limited by non-causal reasoning, where predictions depend on spurious correlations rather than stable relational structure. To address these… More >

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Jorge Gómez-Navalón, Ronghao Pan, Tomas Bernal-Beltrán, José Antonio García-Díaz^*, Rafael Valencia-García
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.085437 - 27 July 2026
（This article belongs to the Special Issue: Applied NLP with Large Language Models: AI Applications Across Domains)
Abstract Traditionally, speech emotion recognition has relied on supervised models that require task-specific training and annotated data. However, the recent emergence of audio-language models introduces a more flexible paradigm that enables multimodal reasoning through speech and natural language interaction. Nevertheless, their effectiveness for emotion recognition remains unclear. In this study, we evaluate audio-language models for speech emotion classification using the Spanish MEACorpus dataset and compare three approaches: prompt-based inference, embedding-based classification with lightweight classifiers, and instruction-tuned models with parameter-efficient fine-tuning plus a hybrid architecture based on class-specific confidence-driven routing. Our results show that the hybrid approach… More >

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Ki-Young Shin¹, Soonmo Kwon², Kyudong Park^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083410 - 27 July 2026
（This article belongs to the Special Issue: Explainable Multimodal AI: Interpretability, Generative Modeling, and Trustworthy Intelligent Systems)
Abstract Multimodal AI systems often suffer from “over-informing”, where excessive raw visual input introduces noise that distracts from task-relevant decisions. Motivated by selective human attention strategies, we propose ARS-MMT (Attention and Reasoning through Source Sentences for Multimodal Machine Translation), an architecture that operationalizes a “look-and-think” pipeline: a source-language encoder first builds contextualized linguistic representations, a relation reasoning network then produces a query-conditioned visual channel, and a multimodal decoder generates the translation conditioned in parallel on the encoded text and on this visual channel. We quantify the contribution of the visual modality through a controlled ablation: zeroing… More >

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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
（This article belongs to the Special Issue: Machine Learning and Data Fusion for Autonomous Control and Surveillance Systems)
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 >

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Changwei Song¹, Jiaqi Qiang², Hongjun Liu¹, Jianqiang Li¹, Hui Pan², Qing Zhao^1,*, Jiuzuo Huang³, Shi Chen³
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083525 - 27 July 2026
（This article belongs to the Special Issue: Advances in Computational Intelligence for Complex Systems)
Abstract Cushing’s syndrome (CS) is a rare endocrine disorder characterized by chronic hypercortisolism, and facial image-based intelligent diagnosis has emerged as a promising non-invasive approach. However, existing diagnostic models suffer from two core bottlenecks: inefficient fusion of deep semantic features and clinical prior features, and insufficient multi-view facial feature disentanglement without CS-specific pathophysiological constraints. To address these limitations, we propose a novel Multi-View Facial Feature Disentanglement Network (MVFFD-Net) for high-precision automatic CS diagnosis. The network takes five standard facial views (frontal, bilateral 45∘ oblique, and bilateral 90∘ lateral views) as input, with three key innovations:… More >

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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
（This article belongs to the Special Issue: Advances in Computational Intelligence for Complex Systems)
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 >

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Tanzila Saba¹, Muhammad Mujahid¹, Faten S. Alamri^2,*, Roaa Khalil Mohamed Ali Abed³
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084816 - 27 July 2026
（This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition, 2nd Edition)
Abstract In the era of artificial intelligence, pattern recognition techniques have become fundamental in advancing medical image processing, diagnosis, and automated disease classification systems. Among various clinical challenges, breast cancer is the second most dangerous leading cause of death in women worldwide. Early and accurate detection of breast cancer is crucial to develop advanced diagnostic methods to control further loss or reduce mortality rates. This study proposes a dynamic graph multi-scale network for breast cancer diagnosis, integrated with multi-scale convolutional feature extraction, a squeeze-and-excitation block, and a graph convolutional network to jointly model local spatial features… More >

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Muhammad Zaheer Sajid¹, Muhammad Fareed Hamid², Nauman Ali Khan^2,3,*, Imran Qureshi⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084724 - 27 July 2026
Abstract Digital pathology is rapidly transforming histopathological diagnosis, yet many existing deep learning models treat all spatial regions uniformly and do not exploit the multi-frequency structure of tissue, which limits both diagnostic accuracy and computational efficiency. This paper proposes WaSA-Net, an end-to-end architecture that integrates three complementary modules for histopathological image analysis. First, the Wavelet-Guided Tokenization (WGT) module decomposes input images into frequency-aware representations using learnable wavelet-like filters, so that both global tissue structures and fine-grained cellular patterns are exposed to attention from the first layer. Second, the Dynamic Sparse Attention with Pathology Priors (DSA-PP) module… More >

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Yiyan Zhang^1,*, Yi Xin², Qin Li²
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082841 - 27 July 2026
Abstract Medical data has specificity compared to other fields of data, and the description of medical data characteristics is still in a qualitative stage. This study included 293 sub-datasets of 138 independent datasets. First, data preprocessing was performed using methods such as incomplete data removal, inconsistent data normalization, and data integration. Then, the characteristics of 293 research datasets were quantified using 26 indicators in three categories: simple indicators, statistical indicators, and informational indicators. Furthermore, statistical analysis was performed on the above-mentioned quantitative characteristics, and stepwise regression and decision tree methods were used for modeling learning. The… More >

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Kiran Asma, Muhammad Asif Zahoor Raja^*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083532 - 27 July 2026
（This article belongs to the Special Issue: Emerging Technologies in Information Security: Modeling, Algorithms, and Applications)
Abstract The accelerated digital transformation of critical infrastructure has yielded unprecedented system interconnectivity, enhancing operational efficiency, simultaneously expanding the epidemiological propagation surface in heterogeneous networks. A novel machine learning-driven neuroarchitecture is designed in the present study, leveraging multilayer autoregressive exogenous neural networks (ARXNNs) iteratively trained with the Levenberg Marquardt (LM) algorithm, i.e., ARXNNs-LM, to address the intricate temporal dynamics of nonlinear fractional epidemiological computer virus propagation in the networks. The proposed ARXNNs-LM methodology effectively models the dynamic state transitions between susceptible, infected, and recovered systems. The dataset is synthesized through the application of the Grünwald–Letnikov (GL)… More >

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Karthick Raghunath K. M.¹, Manjula V.¹, Mahesh T. R.¹, Surbhi B. Khan^2,3,*, Ahmed Alyahya^4,*, Shakila Basheer⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081429 - 27 July 2026
（This article belongs to the Special Issue: Emerging Technologies in Information Security: Modeling, Algorithms, and Applications)
Abstract In general, medical biometric datasets, with the essential unique behavioral and physical traits for personalized healthcare, shape the patient identification process, but the tendency towards transparency and fairness is still far away. Most of the existing methods fail to integrate the latest mathematical techniques rigorously with the deep learning models, which eventually makes such models undesirable due to their lack of interpretability and potential bias. As such, in this study, a novel Contour Integrated Transparent Augmented Deep Learning (CITADL) methodology is introduced to bridge this gap. In this study, a structured framework, namely CITADL, combines… More >

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Md. Fahmid-Ul-Alam Juboraj¹, Fahmid Al Farid^2,3, Mahe Zabin⁴, Jia Uddin⁵, Muhammad Iqbal Hossain^1,*, Sarina Mansor^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081254 - 27 July 2026
Abstract The integration of Internet of Things (IoT) technologies in agriculture enables precision farming but introduces significant cybersecurity vulnerabilities. This paper presents FICNet (Feature Integrated Convolutional Network), a lightweight deep learning architecture for intrusion detection in agricultural IoT environments. Evaluated on the Farm-Flow AG-IoT security dataset, FICNet achieves 100% binary classification accuracy and 81.25% multiclass accuracy (macro F1: 80.43%, precision: 91.26%, ROC-AUC: 96.78%) across 8 traffic categories. A multi-dimensional component analysis confirms the contribution of each architectural component: multi-scale convolutions provide 5.3% noise robustness advantage, squeeze-and-excitation attention controls per-class detection trade-offs, and the full architecture achieves More >

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Kazutaka Toda¹, Yuntao Wang^1,*, Hyungrok Jo², Yang Li¹
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083407 - 27 July 2026
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract The rapid evolution of quantum computing poses a fundamental challenge to classical public-key cryptosystems, accelerating the adoption of lattice-based post-quantum cryptography in large-scale digital infrastructures, including Future Mobile Internet Technologies (FMIT) and their convergence applications (FMIT-CA). As lattice-based cryptography is expected to play an important role in such environments, accurate hardness estimation and parameter assessment of underlying lattice problems have become increasingly important. Since the security of these cryptographic schemes is closely related to the computational hardness of the Shortest Vector Problem (SVP), improving practical SVP-solving techniques contributes indirectly to the security evaluation of such… More >

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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
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
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 >

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