Open Access
ISSN:1526-1492 (print)
ISSN:1526-1506 (online)
Publication Frequency:Monthly
This journal publishes original research papers of reasonable permanent intellectual value, in the areas of computer modeling in engineering & Sciences, including, but not limited to computational mechanics, computational materials, computational mathematics, computational physics, computational chemistry, and computational biology, pertinent to solids, fluids, gases, biomaterials, and other continua spanning from various spatial length scales (quantum, nano, micro, meso, and macro), and various time scales (picoseconds to hours) are of interest. Papers which deal with multi-physics problems, as well as those which deal with the interfaces of mechanics, chemistry, and biology, are particularly encouraged. Novel computational approaches and state-of-the-art computation algorithms, such as soft computing, artificial intelligence-based machine learning methods, and computational statistical methods are welcome.
Science Citation Index (Web of Science): 2024 Impact Factor 2.5; Current Contents: Engineering, Computing & Technology; Scopus Citescore (Impact per Publication 2024): 4.4; SNIP (Source Normalized Impact per Paper 2024): 0.693; RG Journal Impact (average over last three years); Engineering Index (Compendex); Applied Mechanics Reviews; Cambridge Scientific Abstracts: Aerospace and High Technology, Materials Sciences & Engineering, and Computer & Information Systems Abstracts Database; CompuMath Citation Index; INSPEC Databases; Mathematical Reviews; MathSci Net; Mechanics; Science Alert; Science Navigator; Zentralblatt fur Mathematik; Portico, etc...
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078312 - 30 March 2026
Abstract Metal additive manufacturing (AM) technology has promising applications across many fields due to its near-net-shape advantages. The quality of the as-built component is closely linked to the temperature evolution during the metal AM process, which exhibits strong nonlinearities, localized high gradients, and rapid cooling rates. Therefore, real-time prediction of the temperature field is essential for effective online process control to achieve high fabrication quality, which poses surprising challenges for numerical methods, as traditional methods suffer from the inherent time-consuming nature of fine time-space discretizations. In this study, we proposed an isothermal surface imaging and transfer… More >
Open Access
EDITORIAL
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.080415 - 30 March 2026
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
Abstract This article has no abstract. More >
Open Access
EDITORIAL
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.080244 - 30 March 2026
（This article belongs to the Special Issue: Cutting-Edge Security and Privacy Solutions for Next-Generation Intelligent Mobile Internet Technologies and Applications)
Abstract This article has no abstract. More >
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.074688 - 30 March 2026
Abstract Medical imaging is essential in modern health care, allowing accurate diagnosis and effective treatment planning. These images, however, often demonstrate low contrast, noise, and brightness distortion that reduce their diagnostic reliability. This review presents a structured and comprehensive analysis of advanced histogram equalization (HE)-based techniques for medical image enhancement. Our review methodology encompasses: (1) classical HE approaches and related limitations in medical domains; (2) adaptive schemes like Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogrma Equalization (CLAHE) and their advance variants; (3) brightness-preserving schemes like BBHE and MMBEBHE and related algorithms; (4) dynamic and More >
Graphic Abstract
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2025.074164 - 30 March 2026
Abstract Over the past decade, the landscape of cybersecurity has been increasingly shaped by the growing sophistication and frequency of malware attacks. Traditional detection techniques, while still in use, often fall short when confronted with modern threats that use advanced evasion strategies. This systematic review critically examines recent developments in malware detection, with a particular emphasis on the role of artificial intelligence (AI) and machine learning (ML) in enhancing detection capabilities. Drawing on literature published between 2019 and 2025, this study reviews 105 peer-reviewed contributions from prominent digital libraries including IEEE Xplore, SpringerLink, ScienceDirect, and ACM… More >
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078940 - 30 March 2026
（This article belongs to the Special Issue: The Evolution of Cybersecurity and AI: Surveys and Tutorials)
Abstract Illicit web ecosystems, encompassing phishing, illegal online gambling, scam platforms, and malicious advertising, have rapidly expanded in scale and complexity, creating severe social, financial, and cybersecurity risks. Traditional rule-based and blacklist-driven detection approaches struggle to cope with polymorphic, multilingual, and adversarially manipulated threats, resulting in increasing demand for Artificial Intelligence (AI)-based solutions. This review provides a comprehensive synthesis of research on AI-driven threat detection for illicit web environments. It surveys detection models across multiple modalities, including text-based analysis of Uniform Resource Locator (URL) and HyperText Markup Language (HTML), vision-based recognition of webpage layouts and logos,… More >
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.075882 - 30 March 2026
（This article belongs to the Special Issue: Next-Generation Intelligent Networks and Systems: Advances in IoT, Edge Computing, and Secure Cyber-Physical Applications)
Abstract The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has enabled Artificial Intelligence of Things (AIoT) systems that support intelligent and responsive smart societies, but it also introduces major security and privacy concerns across domains such as healthcare, transportation, and smart cities. This Systemic Literature Review (SLR) addresses three research questions: identifying major threats and challenges in AIoT ecosystems, reviewing state-of-the-art security and privacy techniques, and evaluating their effectiveness. An SLR covering the period from 2020 to 2025 was conducted using major academic digital libraries, including IEEE Xplore, ACM Digital Library, ScienceDirect, More >
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078672 - 30 March 2026
Abstract The contemporary smart cities, smart homes, smart buildings, and smart health care systems are the results of the explosive growth of Internet of Things (IoT) devices and deep learning. Yet the centralized training paradigms have fundamental issues in data privacy, regulatory compliance, and ownership silo alongside the scaled limitations of the real-life application. The concept of Federated Deep Learning (FDL) is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings. It is an overview of the privacy-preserving… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078151 - 30 March 2026
（This article belongs to the Special Issue: Topology Optimization: Theory, Methods, and Engineering Applications)
Abstract Lattice-type ultra-tall wind turbine towers are popular in China for their modular benefits in fabrication, transportation, and installation. Nonetheless, their conceptual design remains predominantly dependent on engineering experience, and a generally applicable approach is still absent. This study proposes a self-similar modular topology optimization framework for lattice-type wind turbine support structures and develops software for its application. A minimum weighted compliance formulation with a prescribed volume fraction is developed utilizing the variable density approach, wherein modular constraints and their corresponding sensitivity expressions are explicitly included. The method is applied to a reference wind turbine model More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077492 - 30 March 2026
（This article belongs to the Special Issue: Advances in Numerical Modeling of Composite Structures and Repairs)
Abstract Adhesively bonded joints are widely used in modern lightweight structures due to their high strength-to-weight ratio and design flexibility. However, the reliable non-destructive evaluation of bond integrity remains a significant challenge. This study presents a numerical investigation of adhesively bonded joints with different adhesive properties using ultrasonic guided waves. The main focus of the investigation is to evaluate the feasibility of using guided waves to assess bond integrity, particularly for detecting challenging weak bonds. For this purpose, a theoretical analysis of dispersion curves was conducted, revealing that the S0 Lamb wave mode is significantly sensitive… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.075929 - 30 March 2026
（This article belongs to the Special Issue: Advances in Numerical Modeling of Composite Structures and Repairs)
Abstract Functionally graded material (FGM) plates are widely used in various engineering structures owing to their tailor-made mechanical properties, whereas cracked homogeneous plates constitute a canonical setting in fracture mechanics analysis. These two classes of problems respectively embody material non-uniformity and geometric discontinuity, thereby imposing more stringent requirements on numerical methods in terms of high-order field continuity and accurate defect representation. Based on the classical Kirchhoff–Love plate theory, a numerical manifold method (MLS-NMM) incorporating moving least squares (MLS) interpolation is developed for bending analysis of FGM plates and fracture simulation of homogeneous plates with defects. The… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.073689 - 30 March 2026
（This article belongs to the Special Issue: Advances in Computational Fracture Mechanics: Theories, Techniques, and Applications)
Abstract Slope failures, particularly in railway embankments during intense rainfall, are a major cause of economic damage and humanitarian loss. To forecast how shear cracks develop in slopes under heavy precipitation, we present a novel modeling framework: the Extended Cohesive Damage Element enhanced by soil moisture (SMECDE). The method first translates forecasted rainfall into soil moisture levels via an established correspondence. Then, recognizing that rainfall infiltration lowers soil cohesion—particularly at varying depths—we introduce a Soil Moisture Decoherence Model (SMDM) based on experimental data, which quantifies how cohesion degrades with moisture and how depth affects this process. More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.076624 - 30 March 2026
（This article belongs to the Special Issue: Structural Reliability and Computational Solid Mechanics: Modeling, Simulation, and Uncertainty Quantification)
Abstract This paper introduces a computationally efficient global sensitivity analysis method for quantifying the influence of uncertain clamp support conditions on the natural frequencies of aero-engine pipe systems. The dynamic model is based on a three-dimensional Timoshenko beam finite element formulation, with clamps represented as distributed spring elements possessing anisotropic stiffness. To overcome the prohibitive cost of traditional Monte Carlo simulation, the multiplicative dimensional reduction method (M-DRM) is integrated with variance decomposition theory. This approach approximates the high-dimensional frequency response function as a product of univariate components, enabling rapid computation of Sobol’ sensitivity indices with a More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078230 - 30 March 2026
（This article belongs to the Special Issue: Multiscale, Multifield, and Continuum-Discontinuum Analysis in Geomechanics )
Abstract To address the complex seismic response of long tunnels longitudinally crossing heterogeneous geological formations, this study proposes a three-dimensional SV-wave oblique-incidence input method that accounts for the initial disturbance of the wave field induced by geological heterogeneity. The method transforms equivalent two-dimensional free-field responses into equivalent nodal forces applied at the boundaries of a 3D numerical model. A longitudinally heterogeneous “hard-soft-hard” site and tunnel system is established, in which the surrounding rock is modeled using the Mohr-Coulomb constitutive law, while the concrete lining is described by the concrete damaged plasticity model. The deformation patterns and… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078415 - 30 March 2026
Abstract Burden is one of the main parameters in blast design. However, field tests, either single- or multi-hole blasts, used to determine an appropriate burden, are difficult to capture crack propagation, evolution of breakage angle, and the mechanism governing these processes in the rock. In this study, a single-hole bench blasting model is developed using LS-DYNA software to comprehensively investigate the relationship between burden and rock breakage. The simulation results show that the breakage angle decreases with the increase in burden, and the blasted volume reaches a peak value with a burden of 4 m. Meanwhile,… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078945 - 30 March 2026
Abstract The failure of liquid storage tanks, one of the most critical infrastructure systems widely used, during severe earthquakes can have direct or indirect impacts on public safety. The significance of their safe performance even after destructive earthquakes and their potential for operational use underscores the necessity of appropriate seismic design. Hence, seismic isolation, specifically base isolation, has gained attention as a seismic control method to reduce damage to these infrastructures by increasing their vibration period. One prevalent type of seismic isolator used for tanks and other structures is the friction pendulum system (FPS) isolator. However,… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.076734 - 30 March 2026
（This article belongs to the Special Issue: Numerical Modeling in Technical Diagnostics and Predictive Maintenance)
Abstract The presented study analyses the impact of hysteresis on the response of mechanical systems. The main objective is to determine how the hysteretic models influence the system behaviour and if they can be utilised to describe a damaged or a faulty system. The hysteretic models are able to describe various types of nonlinear behaviour that can reflect the wear or damage of the system components. The data obtained from these models can possibly serve as a basis for the advanced approaches, such as digital twin modelling and predictive maintenance. All the results presented in this… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078250 - 30 March 2026
（This article belongs to the Special Issue: Machine Learning Applications in Earthquake Engineering: Advances, Challenges, and Future Directions)
Abstract Rapid quantification of seismic-induced damage immediately following an earthquake is critical for determining whether a structure is safe for continued occupation or requires evacuation. This study proposes a novel damage identification method that utilizes limited strain data points, significantly reducing installation, maintenance, and data analysis costs compared to traditional distributed sensor networks. The approach integrates finite element (FE) modeling to generate capacity curves through pushover analysis, incorporates noise-augmented datasets for Artificial Neural Network (ANN) training, and classifies structural conditions into four damage levels: Operational (OP), Immediate Occupancy (IO), Life Safety (LS), and Collapse Prevention (CP).… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.079302 - 30 March 2026
（This article belongs to the Special Issue: AI-Driven Numerical Methods: Theories and Applications in Geotechnical Engineering)
Abstract Accurate soil classification is essential for pavement design; however, the traditional American Association of State Highway and Transportation Officials (AASHTO) classification system relies on extensive laboratory testing and subjective judgment. This study presents an artificial intelligence (AI) enhanced framework for AASHTO soil classification. A synthetic dataset of 349,015 samples was generated using parameter ranges for five AASHTO input variables to support model development. Four machine learning models were trained, analyzed, and compared where the random forest (RF) consistently achieved the highest accuracy of 100% among the four models in predicting AASHTO soil groups. Feature importance More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077300 - 30 March 2026
（This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-III)
Abstract In the last decade, the importance of sustainable construction and artificial intelligence (AI) in civil engineering has been underlined in many studies. Numerous studies highlighted the superiority of AI techniques over simple and mathematical regression analyses, which suffer from relatively poor generalization and an inability to capture highly non-linear relationships among inputs and output(s) parameters. In this study, to evaluate the compressive strength of concrete with glass powder (GP) and recycled aggregates, 600 concrete samples were tested in the laboratory, and their results were evaluated. For intelligent assessment of concrete compressive strength (CCS), the study… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.079127 - 30 March 2026
（This article belongs to the Special Issue: Machine Learning, Data-Driven and Novel Approaches in Computational Mechanics)
Abstract This study develops a surrogate super-resolution (SR) framework that accelerates finite element method (FEM)-based computational fluid dynamics (CFD) using deep learning. High-resolution (HR) FEM-based CFD remains computationally prohibitive for time-sensitive applications, including patient-specific aneurysm hemodynamics where rapid turnaround is valuable. The proposed pipeline learns to reconstruct HR velocity-magnitude fields from low-resolution (LR) FEM solutions generated under the same governing equations and boundary conditions. It consists of three modules: (i) offline pre-training of a residual network on representative vascular geometries; (ii) lightweight fine-tuning to adapt the pretrained model to geometric variability, including patient-specific aneurysm morphologies; and… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078776 - 30 March 2026
（This article belongs to the Special Issue: Recent Developments in SPH and CFD Methods for Complex Flow Simulations)
Abstract Granular flow, such as hopper discharge and debris flows, involves complex multi-scale, multi-phase, and multi-physics coupling, posing significant challenges for numerical simulation. Over the past two decades, methods like the Discrete Element Method (DEM), Smoothed Particle Hydrodynamics (SPH), and Depth-Averaging Method (DAM), have been developed to address these problems. However, their applicability across different scales remains unclear due to differences in physical assumptions and numerical algorithms. Therefore, a comprehensive evaluation is critically needed. This study selects three typical methods (DEM, SPH, and DAM) to examine their convergence behavior, boundary condition implementation, and limitations in physical More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.076446 - 30 March 2026
（This article belongs to the Special Issue: Modeling and Applications of Bubble and Droplet in Engineering and Sciences)
Abstract Separation bubbles forming on airfoils significantly influence aerodynamic behavior, particularly at low Reynolds numbers, making their accurate prediction a critical challenge in transition modelling. This study investigates numerical modeling of a separation bubble and the effects of airfoil thickness and camber variation on the formation of the bubble dynamics at low Reynolds numbers. The numerical results were compared with the experimental results obtained from surface pressure distribution measurements, oil flow visualisation, and surface shear measurements to analyse the detailed flow behavior. The combination of pressure and flow visualisation techniques provided complementary insights, enabling a detailed… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.075694 - 30 March 2026
（This article belongs to the Special Issue: Mathematical and Computational Modeling of Nanofluid in Biofluid Systems)
Abstract Electroosmotic transport and entropy generation play a decisive role in regulating efficiency, stability, and energy cost of non-Newtonian nanoblood flows in stenosed arteries, particularly with tapered geometries. This study develops a unified model to analyze ZnO–Williamson nanoblood flow through a stenosed artery with converging, diverging, and non-tapered configurations, incorporating electroosmosis, viscous dissipation, and entropy production. The arterial walls are assumed to be electrically charged with a no-slip condition to induce electroosmotic propulsion along the endothelial surface. The partial differential equations are nondimensionalized to a coupled system of nonlinear ordinary differential equations, which are solved numerically… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078060 - 30 March 2026
（This article belongs to the Special Issue: Mathematical and Computational Modeling of Nanofluid in Biofluid Systems)
Abstract Tangent hyperbolic fluids characterized by shear-thinning behavior, are widely utilized in diverse industrial and scientific fields such as polymer engineering, inkjet printing, biofluids modeling, thermal insulation materials, and chemical manufacturing. Additionally, double-diffusive convection involving simultaneous heat and mass transfer driven by temperature and concentration gradients plays a critical role in many natural and industrial systems, including oceanic circulation, geothermal energy extraction, crystal solidification, alloy formation, and enhanced oil recovery. The current work examines the peristaltic transport of a tangent hyperbolic nanofluid under the concurrent effects of thermal radiation, electroosmotic forces, slip boundary conditions, and double… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2025.070788 - 30 March 2026
（This article belongs to the Special Issue: Intelligent Control and Machine Learning for Renewable Energy Systems and Industries)
Abstract This paper presents an artificial intelligence (AI)-based observer that combines fuzzy logic and neural networks to detect abnormalities in sensors embedded in an electrolyser. Electrolysers are hydrogen production plants that require effective maintenance to guarantee suitable operation, prevent degradation, and avoid loss of efficiency. In this sense, predictive maintenance arises as one of the most advisable techniques for maintenance in electrolysers by using sensor data to predict potential abnormalities. However, if the sensor fails, there will be an incorrect forecasting of abnormalities. Among the different types of operational faults that sensors can present are drift-related… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077236 - 30 March 2026
Abstract Ground water is a crucial ecological resource and source of drinking water to a great percentage of the world population. The quality of groundwater in an area with industrial emission and air pollution is an especially important issue that requires proper evaluation. This paper introduces a spatiotemporal deep learning model that incorporates the use of metaheuristic optimization in predicting groundwater quality in various pollution contexts. The given method is a combination of the Spatial–Temporal-Assisted Deep Belief Network (StaDBN) and a hybrid Whale Optimization Algorithm and Tiki-Taka Algorithms (WOA–TTA) that would model intricate patterns of contamination.… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077480 - 30 March 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 Leakage events occurring at multiple locations simultaneously generate overlapping and topology-dependent pressure signatures, making reliable detection and subsequent restoration planning a persistent challenge in water distribution systems (WDSs). While recent data-driven techniques have improved the ability to identify anomalous hydraulic behavior, most approaches remain limited to the detection stage and offer little guidance on how utilities should prioritize repairs once multiple failures are identified. To bridge this gap, this study proposes an integrated framework that links topology-aware leakage detection with quantitative restoration prioritization. First, a multi-task learning framework based on Graph Attention Networks (GAT) is… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.076371 - 30 March 2026
Abstract This paper introduces a novel fractional-order model based on the Caputo–Fabrizio (CF) derivative for analyzing computer virus propagation in networked environments. The model partitions the computer population into four compartments: susceptible, latently infected, breaking-out, and antivirus-capable systems. By employing the CF derivative—which uses a nonsingular exponential kernel—the framework effectively captures memory-dependent and nonlocal characteristics intrinsic to cyber systems, aspects inadequately represented by traditional integer-order models. Under Lipschitz continuity and boundedness assumptions, the existence and uniqueness of solutions are rigorously established via fixed-point theory. We develop a tailored two-step Adams–Bashforth numerical scheme for the CF framework More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.079067 - 30 March 2026
（This article belongs to the Special Issue: Advances in Mathematical Modeling: Numerical Approaches and Simulation for Computational Biology)
Abstract Bacterial growth requires strategic allocation of limited intracellular resources, especially under cold stress, where stabilized messenger ribonucleic acid (mRNA) secondary structures slow translation by impairing ribosome binding. Escherichia coli (E. coli) counters this bottleneck by inducing the cold-shock protein A (CspA), an RNA chaperone that remodels inhibitory structures. However, synthesizing CspA diverts biosynthetic capacity from ribosome production and metabolism, creating a fundamental resource-allocation trade-off. In this work, we develop a dynamical model capturing the interplay between metabolic precursors, ribosomes, and CspA, and use it to examine how growth and allocation patterns shift with temperature. Steady-state analysis shows… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.075080 - 30 March 2026
（This article belongs to the Special Issue: Advanced Image Segmentation and Object Detection: Innovations, Challenges, and Applications)
Abstract Human pose estimation is crucial across diverse applications, from healthcare to human–computer interaction. Integrating inertial measurement units (IMUs) with monocular vision methods holds great potential for leveraging complementary modalities; however, existing approaches are often limited by IMU drift, noise, and underutilization of visual information. To address these limitations, we propose a novel dual-stream feature extraction framework that effectively combines temporal IMU data and single-view image features for improved pose estimation. Short-term dependencies in IMU sequences are captured with convolutional layers, while a Transformer-based architecture models long-range temporal dynamics. To mitigate IMU drift and inter-sensor inconsistencies, More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.079062 - 30 March 2026
Abstract Real-time multi-person pose estimation (MPE) built upon neural network architectures aims to simultaneously detect multiple human instances and regress joint coordinates in dynamic scenes. However, due to factors such as high model complexity and limited expression of keypoint information, both the efficiency and accuracy of real-time MPE remain to be improved. To mitigate the adverse impacts caused by the aforementioned issues, this work develops FSEM-Pose, a real-time MPE model rooted in the YOLOv10 framework. In detail, first, FSEM-Pose upgrades the backbone module of the baseline network by introducing the Feature Shuffling-Convolution (FS-Conv), which effectively reduces More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077356 - 30 March 2026
Abstract This paper presents an automated imaging-to-CAD reconstruction system that combines telecentric vision and deep learning for high-accuracy digital reconstruction of printed circuit boards (PCBs). The framework integrates a telecentric camera with a Cartesian scanning platform to capture distortion-free, high-resolution PCB images, which are stitched into a single orthographic composite. A YOLO-based detection model, trained on a dataset of 270 PCB images across 23 component classes with data augmentation, identifies and localizes electronic components with a mean average precision of 0.932. Detected components are automatically matched to corresponding 3D CAD models from a part library and More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077293 - 30 March 2026
Abstract Recognizing frontal faces from non-frontal or profile images is a major problem due to pose changes, self-occlusions, and the complete loss of important structural and textural components, depressing recognition accuracy and visual fidelity. This paper introduces a new deep generative framework, Modified Multi-Scale Fused CycleGAN (MMF-CycleGAN), for robust and photo-realistic profile-to-frontal face synthesis. The MMF-CycleGAN framework utilizes pre-processing and then the generator employs a Deep Dilated DenseNet encoder-based hierarchical feature extraction along with a transformer and decoder. The proposed Multi-Scale Fusion PatchGAN discriminator enforces consistency at multiple spatial resolutions, leading to sharper textures and improved More >
Graphic Abstract
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078169 - 30 March 2026
Abstract The healthcare field is fraught with challenges associated with severe class imbalance, wherein such critical conditions like sepsis, cardiac arrest, and drug adverse reactions are rare but have dire clinical consequences. This paper presents a new framework, Deep Reinforcement Adaptive Gradient Optimization Network to Mining Rare Events (DRAGON-MINE), to demonstrate how deep reinforcement learning can be used synergistically with adaptive gradient optimization and address the inherent weaknesses of current methods in the prediction of rare health events. The suggested architecture uses a dual-pathway consisting of a reinforcement learning agent to dynamically reweigh samples and an… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2025.074109 - 30 March 2026
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract Currently, thyroid diseases are prevalent worldwide; therefore, it is necessary to develop techniques that help doctors improve their diagnostic skills for such diseases. In previous studies, 2-dimensional convolutional neural network (2D CNN) techniques were employed to classify thyroid nodules as benign and malignant without detecting the presence of thyroid nodules in the obtained ultrasound images. To address this issue, we propose a 3-dimensional convolutional neural network (3D CNN) for thyroid nodule detection. The proposed CNN exploits the 3D information and spatial features contained in ultrasound images and generates distinctive features during its training using multiple… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.076187 - 30 March 2026
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract Artificial Intelligence (AI) in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease, which include hemoglobin A1c (HbA1c), oral glucose tolerance test (OGTT), and fasting plasma glucose (FPG) screening techniques, which are invasive and limited in scale. Machine learning (ML) and deep neural network (DNN) models that use large datasets to learn the complex, nonlinear feature interactions, but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy. Conversely, DNN models are more robust, though the ability to reach a high accuracy rate consistently on… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077334 - 30 March 2026
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract Ovarian cancer (OC) is one of the leading causes of death related to gynecological cancer, with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers. Machine learning (ML) has the potential to process complex datasets and support decision-making in OC diagnosis. Nevertheless, traditional ML models tend to be biased, overfitting, noisy, and less generalized. Moreover, their black-box nature reduces interpretability and limits their practical clinical applicability. In this study, we introduce an explainable ensemble learning (EL) model, TreeX-Stack, based on a stacking architecture that employs tree-based learners such as Decision… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2025.073501 - 30 March 2026
Abstract Federated learning is a decentralized model training paradigm with significant potential. However, the quality of Federated Network’s client updates can vary due to non-IID data distributions, leading to suboptimal global models. To address this issue, we propose a novel client selection strategy called FedPA (Performance-Based Federated Averaging). This proposed model selectively aggregates client updates based on a predefined performance threshold. Only clients whose local models achieve an F1 score of 70% or higher after training are included in the aggregation process. Clients below this threshold receive the updated global model but do not contribute their… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077837 - 30 March 2026
（This article belongs to the Special Issue: Artificial Intelligence for 6G Wireless Networks)
Abstract Artificial Intelligence (AI)-native sixth-generation (6G) wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel variations and hardware imperfections to support secure and reliable device-level authentication under highly dynamic environments. In such networks, massive device heterogeneity and time-varying channel conditions pose significant challenges, as reliable authentication must be achieved with limited labeled data and constrained edge resources. To address this challenge, this paper proposes an Artificial Intelligence (AI)-assisted few-shot physical-layer modeling framework for channel robust device identification, formulated within the paradigm of Specific Emitter Identification (SEI) based on radio… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.074936 - 30 March 2026
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract Many machine learning–based Android malware detection often suffers from concept drift, where models trained on historical data fail to generalize to evolving threats. This paper proposes SCAN (Structural Clustering with Adaptive thresholds for iNtelligent Android malware detection), a hybrid intelligent framework designed to mitigate concept drift without retraining. SCAN integrates Gaussian Mixture Models (GMMs)-based clustering with cluster-wise adaptive thresholding and supervised classifiers tailored to each cluster. A key challenge in clustering-based malware detection is cluster-wise class imbalance, where clusters contain disproportionate distributions of benign and malicious samples. SCAN addresses this issue through adaptive thresholding, which dynamically… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077784 - 30 March 2026
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract As cyberattacks become increasingly sophisticated and intelligent, demand for machine-learning-based anomaly detection systems is growing. However, conventional systems generally assume a trusted server environment, where traffic data is collected and analyzed in plaintext. This assumption introduces inherent privacy risks, as privacy-sensitive information may be exposed if the server is compromised or misused. To address this limitation, privacy-preserving anomaly detection approaches have been actively studied, enabling anomaly detection to be performed directly on encrypted traffic without revealing privacy-sensitive data. While these approaches offer strong confidentiality guarantees, they suffer from significant drawbacks, including substantial computational overhead, high… More >
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