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.144, No.1, pp. 1-27, 2025, DOI:10.32604/cmes.2025.065664 - 31 July 2025
（This article belongs to the Special Issue: Data-Driven and Physics-Informed Machine Learning for Digital Twin, Surrogate Modeling, and Model Discovery, with An Emphasis on Industrial Applications)
Abstract This study presents an interpretable surrogate framework for predicting pedestrian-leg injury severity that integrates high-fidelity finite-element (FE) simulations with a TabNet-based deep-learning model. We generated a parametric dataset of 3000 impact scenarios—covering ten vehicle types and various legform impactors—using automated FE runs configured via Latin hypercube sampling. After preprocessing and one-hot encoding of categorical features, we trained TabNet alongside Support-Vector Regression, Random Forest, and Decision-Tree ensembles. All models underwent hyperparameter tuning via Optuna’s Bayesian optimization coupled with repeated four-fold cross-validation (20 trials per model). TabNet achieved the best balance of explanatory power and predictive accuracy, More >
Graphic Abstract
Open Access
EDITORIAL
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 29-36, 2025, DOI:10.32604/cmes.2025.069309 - 31 July 2025
（This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
Abstract This article has no abstract. More >
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 37-81, 2025, DOI:10.32604/cmes.2025.067809 - 31 July 2025
Abstract The rapid advancement of artificial intelligence technology is driving transformative changes in medical diagnosis, treatment, and management systems through large-scale deep learning models—a process that brings both groundbreaking opportunities and multifaceted challenges. This study focuses on the medical and healthcare applications of large-scale deep learning architectures, conducting a comprehensive survey to categorize and analyze their diverse uses. The survey results reveal that current applications of large models in healthcare encompass medical data management, healthcare services, medical devices, and preventive medicine, among others. Concurrently, large models demonstrate significant advantages in the medical domain, especially in high-precision More >
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 83-146, 2025, DOI:10.32604/cmes.2025.067724 - 31 July 2025
Abstract Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems (ADAS) and autonomous driving, enabling robust environmental perception through precise range-Doppler and angular measurements. It plays a pivotal role in enhancing road safety by supporting accurate detection and localization of surrounding objects. However, real-world deployment of automotive radar faces significant challenges, including mutual interference among radar units and dense clutter due to multiple dynamic targets, which demand advanced signal processing solutions beyond conventional methodologies. This paper presents a comprehensive review of traditional signal processing techniques and recent advancements specifically designed to address… More >
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Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 147-199, 2025, DOI:10.32604/cmes.2025.066276 - 31 July 2025
（This article belongs to the Special Issue: AI and Optimization in Material and Structural Engineering: Emerging Trends and Applications)
Abstract Fiber-reinforced polymer (FRP) composites are renowned for their high mechanical strength, durability, and lightweight properties, making them integral to civil engineering, aerospace, and automotive manufacturing. Traditionally, the simulation and optimization of FRP materials have relied on finite element (FE) methods, which, while effective, often fall short in capturing the intricate behaviors of these composites under diverse conditions. Concrete examples in this regard involve modeling interfacial cracks, delaminations, or environmental effects that involve nonlinear phenomena. These degradation mechanisms exceed the capacity of classical FE models, as they are not detailed to the required level of detail.… More >
Graphic Abstract
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 201-237, 2025, DOI:10.32604/cmes.2025.066644 - 31 July 2025
（This article belongs to the Special Issue: AI and Optimization in Material and Structural Engineering: Emerging Trends and Applications)
Abstract Fatigue failure continues to be a significant challenge in designing structural and mechanical components subjected to repeated and complex loading. While earlier studies mainly examined material properties and how stress affects lifespan, this review offers the first comprehensive, multiscale comparison of strategies that optimize geometry to improve fatigue performance. This includes everything from microscopic features like the shape of graphite nodules to large-scale design elements such as fillets, notches, and overall structural layouts. We analyze and combine various methods, including topology and shape optimization, the ability of additive manufacturing to fine-tune internal geometries, and reliability-based More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 239-265, 2025, DOI:10.32604/cmes.2025.065461 - 31 July 2025
Abstract Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events, posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing, which frequently leads to the development of large and complex models. Inspired by the success of Large Language Models (LLMs), transformer-based foundation models have been developed for time series (TSFM). These models have been proven to reconstruct time series in a zero-shot manner, being able to capture different patterns that effectively characterize time series. This paper proposes the use of TSFM to generate… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 267-288, 2025, DOI:10.32604/cmes.2025.066596 - 31 July 2025
Abstract During the construction of bank slopes involving pile driving, ensuring slope stability is crucial. This requires the design of appropriate support systems and a thorough evaluation of the failure mechanisms of pile structures under dynamic loading conditions. Based on the Huarong Coal Wharf project, various support schemes are analyzed using numerical simulation methods to calculate and compare slope stability coefficients. The optimal scheme is then identified. Under the selected support scheme, a numerical model of double-row suspended steel sheet piles is developed to investigate the dynamic response of the pile structures under pile driving loads.… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 289-307, 2025, DOI:10.32604/cmes.2025.066716 - 31 July 2025
Abstract Gas-liquid two-phase flow in fractal porous media is pivotal for engineering applications, yet it remains challenging to be accurately characterized due to complex microstructure-flow interactions. This study establishes a pore-scale numerical framework integrating Monte Carlo-generated fractal porous media with Volume of Fluid (VOF) simulations to unravel the coupling among pore distribution characterized by fractal dimension (Df), flow dynamics, and displacement efficiency. A pore-scale model based on the computed tomography (CT) microstructure of Berea sandstone is established, and the simulation results are compared with experimental data. Good agreement is found in phase distribution, breakthrough behavior, and flow… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 309-343, 2025, DOI:10.32604/cmes.2025.068628 - 31 July 2025
Abstract This paper introduces a novel optimization approach called Recuperated Seed Search Optimization (RSSO), designed to address challenges in solving mechanical engineering design problems. Many optimization techniques struggle with slow convergence and suboptimal solutions due to complex, nonlinear natures. The Sperm Swarm Optimization (SSO) algorithm, which mimics the sperm’s movement to reach an egg, is one such technique. To improve SSO, researchers combined it with three strategies: opposition-based learning (OBL), Cauchy mutation (CM), and position clamping. OBL introduces diversity to SSO by exploring opposite solutions, speeding up convergence. CM enhances both exploration and exploitation capabilities throughout More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 345-357, 2025, DOI:10.32604/cmes.2025.066175 - 31 July 2025
Abstract Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation. This study proposes a deep learning framework based on Temporal Convolutional Networks (TCN) integrated with Adaptive Parametric Rectified Linear Unit (APReLU) to predict future road subbase strain trends. Our model leverages time-series strain data collected from embedded triaxial sensors within a national highway, spanning August 2021 to June 2022, to forecast strain dynamics critical for proactive maintenance planning. The TCN-APReLU architecture combines dilated causal convolutions to capture long-term dependencies and APReLU activation functions to adaptively model nonlinear strain More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 359-385, 2025, DOI:10.32604/cmes.2025.067099 - 31 July 2025
Abstract The study considers numerical findings regarding magneto-thermosolutal-aided natural convective flow of alumina/water-based nanofluid filled in a non-Darcian porous horizontal concentric annulus. Two equations are assumed to evaluate the thermal fields in the porous medium under Local Thermal Non-Equilibrium (LTNE) conditions, along with the Darcy-Brinkman-Forchheimer model for the flow. By imposing distinct and constant temperatures and concentrations on both internal and external cylinders, thermosolutal natural convection is induced in the annulus. We apply the finite volume method to solve the dimensionless governing equations numerically. The thermal conductivity and viscosity of the nanofluid mixture are determined utilizing… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 387-405, 2025, DOI:10.32604/cmes.2025.066917 - 31 July 2025
Abstract Effective water distribution and transparency are threatened with being outrightly undermined unless the good name of urban infrastructure is maintained. With improved control systems in place to check leakage, variability of pressure, and conscientiousness of energy, issues that previously went unnoticed are now becoming recognized. This paper presents a grandiose hybrid framework that combines Multi-Agent Deep Reinforcement Learning (MADRL) with Shapley Additive Explanations (SHAP)-based Explainable AI (XAI) for adaptive and interpretable water resource management. In the methodology, the agents perform decentralized learning of the control policies for the pumps and valves based on the real-time… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 407-427, 2025, DOI:10.32604/cmes.2025.066517 - 31 July 2025
Abstract Falls are a leading cause of injury and morbidity among older adults, especially those with Alzheimer’s disease (AD), who face increased risks due to cognitive decline, gait instability, and impaired spatial awareness. While wearable sensor-based fall detection systems offer promising solutions, their effectiveness is often hindered by domain shifts resulting from variations in sensor placement, sampling frequencies, and discrepancies in dataset distributions. To address these challenges, this paper proposes a novel unsupervised domain adaptation (UDA) framework specifically designed for cross-dataset fall detection in Alzheimer’s disease (AD) patients, utilizing advanced transfer learning to enhance generalizability. The… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 429-459, 2025, DOI:10.32604/cmes.2025.066415 - 31 July 2025
Abstract Reliable human action recognition (HAR) in video sequences is critical for a wide range of applications, such as security surveillance, healthcare monitoring, and human-computer interaction. Several automated systems have been designed for this purpose; however, existing methods often struggle to effectively integrate spatial and temporal information from input samples such as 2-stream networks or 3D convolutional neural networks (CNNs), which limits their accuracy in discriminating numerous human actions. Therefore, this study introduces a novel deep-learning framework called the ARNet, designed for robust HAR. ARNet consists of two main modules, namely, a refined InceptionResNet-V2-based CNN and… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 461-487, 2025, DOI:10.32604/cmes.2025.063973 - 31 July 2025
Abstract The exponential growth of over-the-top (OTT) entertainment has fueled a surge in content consumption across diverse formats, especially in regional Indian languages. With the Indian film industry producing over 1500 films annually in more than 20 languages, personalized recommendations are essential to highlight relevant content. To overcome the limitations of traditional recommender systems—such as static latent vectors, poor handling of cold-start scenarios, and the absence of uncertainty modeling—we propose a deep Collaborative Neural Generative Embedding (C-NGE) model. C-NGE dynamically learns user and item representations by integrating rating information and metadata features in a unified neural More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 489-509, 2025, DOI:10.32604/cmes.2025.065101 - 31 July 2025
Abstract Wireless Body Area Network (WBAN) is essential for continuous health monitoring. However, they face energy efficiency challenges due to the low power consumption of sensor nodes. Current WBAN routing protocols face limitations in strategically minimizing energy consumption during the retrieval of vital health parameters. Efficient network traffic management remains a challenge, with existing approaches often resulting in increased delay and reduced throughput. Additionally, insufficient attention has been paid to enhancing channel capacity to maintain signal strength and mitigate fading effects under dynamic and robust operating scenarios. Several routing strategies and procedures have been developed to… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 511-534, 2025, DOI:10.32604/cmes.2025.066514 - 31 July 2025
Abstract Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning, particularly when class overlap significantly deteriorates classification performance. Traditional oversampling methods often generate synthetic samples without considering density variations, leading to redundant or misleading instances that exacerbate class overlap in high-density regions. To address these limitations, we propose Wasserstein Generative Adversarial Network Variational Density Estimation WGAN-VDE, a computationally efficient density-aware adversarial resampling framework that enhances minority class representation while strategically reducing class overlap. The originality of WGAN-VDE lies in its density-aware sample refinement, ensuring that synthetic samples are positioned in underrepresented More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 535-580, 2025, DOI:10.32604/cmes.2025.067239 - 31 July 2025
Abstract Data hiding methods involve embedding secret messages into cover objects to enable covert communication in a way that is difficult to detect. In data hiding methods based on image interpolation, the image size is reduced and then enlarged through interpolation, followed by the embedding of secret data into the newly generated pixels. A general improving approach for embedding secret messages is proposed. The approach may be regarded a general model for enhancing the data embedding capacity of various existing image interpolation-based data hiding methods. This enhancement is achieved by expanding the range of pixel values… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 581-614, 2025, DOI:10.32604/cmes.2025.065188 - 31 July 2025
Abstract The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems (IDS) for enhanced network security. While conventional IDSs can be unsuitable for detecting different and emerging attacks, there is a demand for better techniques to improve detection reliability. This study introduces a new method, the Deep Adaptive Multi-Layer Attention Network (DAMLAN), to boost the result of intrusion detection on network data. Due to its multi-scale attention mechanisms and graph features, DAMLAN aims to address both known and unknown intrusions. The real-world NSL-KDD dataset, a popular choice among IDS researchers, is used to… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 615-641, 2025, DOI:10.32604/cmes.2025.067778 - 31 July 2025
Abstract Due to their resource constraints, Internet of Things (IoT) devices require authentication mechanisms that are both secure and efficient. Elliptic curve cryptography (ECC) meets these needs by providing strong security with shorter key lengths, which significantly reduces the computational overhead required for authentication algorithms. This paper introduces a novel ECC-based IoT authentication system utilizing our previously proposed efficient mapping and reverse mapping operations on elliptic curves over prime fields. By reducing reliance on costly point multiplication, the proposed algorithm significantly improves execution time, storage requirements, and communication cost across varying security levels. The proposed authentication… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 643-668, 2025, DOI:10.32604/cmes.2025.066313 - 31 July 2025
（This article belongs to the Special Issue: Theoretical and Computational Modeling of Advanced Materials and Structures-II)
Abstract This study proposes a three-dimensional (3D) coupled magneto-electro-elastic problem for the static analysis of multilayered plates embedding piezomagnetic and piezoelectric layers by considering both sensor and actuator configurations. The 3D governing equations for the magneto-electro-elastic static behavior of plates are explicitly show that are made by the three 3D equilibrium equations, the 3D divergence equation for magnetic induction, and the 3D divergence equation for the electric displacement. The proposed solution involves the exponential matrix in the thickness direction and primary variables’ harmonic forms in the in-plane ones. A closed-form solution is performed considering simply-supported boundary… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 669-691, 2025, DOI:10.32604/cmes.2025.068945 - 31 July 2025
（This article belongs to the Special Issue: Advanced Computational Modeling of Vehicle-Bridge Interaction with Practical Applications)
Abstract Much of the research has focused on identifying bridge frequencies for health monitoring, while the bridge damping ratio also serves as an important factor in damage detection. This study presents an enhanced method for identifying bridge damping ratios using a two-axle, three-mass test vehicle, relying on wheel responses captured by only two mounted sensors. Damping ratio estimation formulas are derived using both the Hilbert Transform (HT) and Wavelet Transform (WT), with a consistent formulation that confirms accurate estimation is achievable with minimal instrumentation, particularly when addressing the support effect. A comparative analysis of the two More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 693-715, 2025, DOI:10.32604/cmes.2025.068123 - 31 July 2025
（This article belongs to the Special Issue: Computational Fluid Dynamics Modeling for Fire and Explosion Safety in Confined Structures)
Abstract Road pavements in tunnels are usually made of asphalt mixtures, which, unfortunately, are flammable materials. Hence, this type of pavement could release heat, and more specifically smoke, in the event of a tunnel fire, thereby worsening the environmental conditions for human health. Extensive research has been conducted in recent years to enhance the fire reaction of traditional asphalt mixtures for the road pavements used in tunnels. The addition of the Flame Retardants (FRs) in conventional asphalt mixtures appears to be promising. Nevertheless, the potential effects of the FRs in terms of the reduction in consequences… More >
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Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 717-737, 2025, DOI:10.32604/cmes.2025.067891 - 31 July 2025
（This article belongs to the Special Issue: Advances in Numerical Modeling of Composite Structures and Repairs)
Abstract Steel cylindrical shells are widely used in engineering structures due to their high strength-to-weight ratio, but they are vulnerable to buckling under axial loads. To address this limitation, fiber-reinforced polymer (FRP) composites have emerged as promising materials for structural reinforcement. This study investigates the buckling behavior of steel cylindrical shells reinforced with inner and outer layers of polymer composite materials under axial compression. Using analytical and numerical modeling methods, the critical buckling loads for different reinforcement options were evaluated. Two-sided glass fiber reinforced plastic (GFRP) or carbon fiber reinforced plastic (CFRP) coatings, as well as… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 739-762, 2025, DOI:10.32604/cmes.2025.067442 - 31 July 2025
（This article belongs to the Special Issue: Peridynamic Theory and Multi-physical/Multiscale Methods for Complex Material Behavior)
Abstract This study investigates the dynamic compressive behavior of three periodic lattice structures fabricated from Ti-6Al-4V titanium alloy, each with distinct topologies: simple cubic (SC), body-centered cubic (BCC), and face-centered cubic (FCC). Dynamic compression experiments were conducted using a Split Hopkinson Pressure Bar (SHPB) system, complemented by high-speed imaging to capture real-time deformation and failure mechanisms under impact loading. The influence of cell topology, relative density, and strain rate on dynamic mechanical properties, failure behavior, and stress wave propagation was systematically examined. Finite element modeling was performed, and the simulated results showed good agreement with experimental… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 763-789, 2025, DOI:10.32604/cmes.2025.067363 - 31 July 2025
（This article belongs to the Special Issue: Topology Optimization: Theory, Methods, and Engineering Applications)
Abstract Lattice structures have attracted extensive attention in the field of engineering materials due to their characteristics of lightweight and high strength. This paper combines topology optimization with additive manufacturing to investigate how pore shape in Triply Periodic Minimal Surface (TPMS) structures affects mechanical properties and energy absorption performance. The periodic lattice structures (Triangle lattice, rectangle lattice and Rectangle lattice) and aperiodic mixed structures are designed, including a variety of lattice structures such as circle-circle and triangle-triangle (CCTT), triangle-triangle and rectangle-rectangle (TTRR), circle-circle and rectangle-rectangle (CCRR), triangle-circle-circle-triangle (TCCT), rectangle-triangle-triangle-rectangle (RTTR) and rectangle-circle-circle-rectangle (RCCR). The anisotropy of… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 791-809, 2025, DOI:10.32604/cmes.2025.066676 - 31 July 2025
（This article belongs to the Special Issue: Topology Optimization: Theory, Methods, and Engineering Applications)
Abstract The traditional topology optimization method of continuum structure generally uses quadrilateral elements as the basic mesh. This approach often leads to jagged boundary issues, which are traditionally addressed through post-processing, potentially altering the mechanical properties of the optimized structure. A topology optimization method of Movable Morphable Smooth Boundary (MMSB) is proposed based on the idea of mesh adaptation to solve the problem of jagged boundaries and the influence of post-processing. Based on the ICM method, the rational fraction function is introduced as the filtering function, and a topology optimization model with the minimum weight as More >
Graphic Abstract
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 811-839, 2025, DOI:10.32604/cmes.2025.065632 - 31 July 2025
（This article belongs to the Special Issue: Multi-Aspect Modelling in Rock Blasting)
Abstract The formation process of blasting craters and blasting fragments is simulated using the continuum-discontinuum element method (CDEM), providing a reference for blasting engineering design. The calculation model of the blasting funnel is established, and the formation and fragmentation effect of the blasting crater under different explosive burial depths and different explosive package masses are numerically simulated. The propagation law of the explosion stress wave and the formation mechanism of the blasting crater are studied, and the relationship between the rock-crushing effect and blasting design parameters is quantitatively evaluated. Comparing the results of numerical simulation with… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 841-872, 2025, DOI:10.32604/cmes.2025.065452 - 31 July 2025
（This article belongs to the Special Issue: Recent Advances on Smart MEE Composites: Testing, Modeling and Simulation)
Abstract This study aims to investigate the propagation of harmonic waves in nonlocal magneto-electro-elastic (MEE) laminated composites with interface stress imperfections using an analytical approach. The pseudo-Stroh formulation and nonlocal theory proposed by Eringen were adopted to derive the propagator matrix for each layer. Both the propagator and interface matrices were formulated to determine the recursive fields. Subsequently, the dispersion equation was obtained by imposing traction-free and magneto-electric circuit open boundary conditions on the top and bottom surfaces of the plate. Dispersion curves, mode shapes, and natural frequencies were calculated for sandwich plates composed of BaTiO3 and More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 873-898, 2025, DOI:10.32604/cmes.2025.066800 - 31 July 2025
（This article belongs to the Special Issue: Applications of Modelling and Simulation in Nanofluids)
Abstract In this study, we proposed a numerical technique for solving time-dependent partial differential equations that arise in the electro-osmotic flow of Carreau fluid across a stationary plate based on a modified exponential integrator. The scheme is comprised of two explicit stages. One is the exponential integrator type stage, and the second is the Runge-Kutta type stage. The spatial-dependent terms are discretized using the compact technique. The compact scheme can achieve fourth or sixth-order spatial accuracy, while the proposed scheme attains second-order temporal accuracy. Also, a mathematical model for the electro-osmotic flow of Carreau fluid over… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 899-925, 2025, DOI:10.32604/cmes.2025.066555 - 31 July 2025
（This article belongs to the Special Issue: Computational Methods in Mono/hybrid nanofluids: Innovative Applications and Future Trends)
Abstract Cooling system design applicable to more than one photovoltaic (PV) unit may be challenging due to the arrangement and geometry of the modules. Different cooling techniques are provided in this study to regulate the temperature of conductive panels that are arranged perpendicular to each other. The model uses two vented cavity systems and one L-shaped channel with ternary nanofluid enhanced non-uniform magnetic field. Their cooling performances and comparative results between different systems are provided. The finite element method is used to conduct a numerical analysis for a range of values of the following: the strength… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 927-944, 2025, DOI:10.32604/cmes.2025.067299 - 31 July 2025
（This article belongs to the Special Issue: Scientific Computing and Its Application to Engineering Problems)
Abstract The extended Kalman filter (EKF) is extensively applied in integrated navigation systems that combine the global navigation satellite system (GNSS) and strap-down inertial navigation system (SINS). However, the performance of the EKF can be severely impacted by non-Gaussian noise and measurement noise uncertainties, making it difficult to achieve optimal GNSS/INS integration. Dealing with non-Gaussian noise remains a significant challenge in filter development today. Therefore, the maximum correntropy criterion (MCC) is utilized in EKFs to manage heavy-tailed measurement noise. However, its capability to handle non-Gaussian process noise and unknown disturbances remains largely unexplored. In this paper,… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 945-968, 2025, DOI:10.32604/cmes.2025.067851 - 31 July 2025
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
Abstract Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems. Nevertheless, the inherently variable nature of wind and the intricacy of high-dimensional datasets pose major obstacles to reliable forecasting. To address these difficulties, this study presents an innovative hybrid method for short-term wind power prediction by combining a Long Short-Term Memory (LSTM) network with a Single Candidate Optimizer (SCO) algorithm. In contrast to conventional techniques that rely on random parameter initialization, the proposed LSTM-SCO framework leverages the distinctive capability of SCO to work More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 969-1011, 2025, DOI:10.32604/cmes.2025.063996 - 31 July 2025
（This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
Abstract In a world where supply chains are increasingly complex and unpredictable, finding the optimal way to move goods through transshipment networks is more important and challenging than ever. In addition to addressing the complexity of transportation costs and demand, this study presents a novel method that offers flexible routing alternatives to manage these complexities. When real-world variables such as fluctuating costs, variable capacity, and unpredictable demand are considered, traditional transshipment models often prove inadequate. To overcome these challenges, we propose an innovative fully fuzzy-based framework using LR flat fuzzy numbers. This framework allows for more… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1013-1044, 2025, DOI:10.32604/cmes.2025.066447 - 31 July 2025
（This article belongs to the Special Issue: Data-Driven and Physics-Informed Machine Learning for Digital Twin, Surrogate Modeling, and Model Discovery, with An Emphasis on Industrial Applications)
Abstract This paper proposes an automated detection framework for transmission facilities using a feature-attention multi-scale robustness network (FAMSR-Net) with high-fidelity virtual images. The proposed framework exhibits three key characteristics. First, virtual images of the transmission facilities generated using StyleGAN2-ADA are co-trained with real images. This enables the neural network to learn various features of transmission facilities to improve the detection performance. Second, the convolutional block attention module is deployed in FAMSR-Net to effectively extract features from images and construct multi-dimensional feature maps, enabling the neural network to perform precise object detection in various environments. Third, an… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1045-1070, 2025, DOI:10.32604/cmes.2025.066659 - 31 July 2025
（This article belongs to the Special Issue: Integration of Physical Simulation and Machine Learning in Digital Twin and Virtual Reality)
Abstract At present, noise reduction has become an urgent challenge across various fields. Whether in the context of household appliances in daily life or in the enhancement of stealth performance in military equipment, noise control technologies play a critical role. This study introduces a computational framework for simulating Helmholtz equation-governed acoustic scattering using a boundary element method (BEM) integrated with Loop subdivision surfaces. By adopting the Loop subdivision scheme—a widely used computer-aided design (CAD) technique—the framework unifies geometric representation and physical field discretization, ensuring seamless compatibility with industrial CAD workflows. The core innovation lies in the More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1071-1090, 2025, DOI:10.32604/cmes.2025.064775 - 31 July 2025
（This article belongs to the Special Issue: Data-Driven Artificial Intelligence and Machine Learning in Computational Modelling for Engineering and Applied Sciences)
Abstract The inherent limitations of 2D object detection, such as inadequate spatial reasoning and susceptibility to environmental occlusions, pose significant risks to the safety and reliability of autonomous driving systems. To address these challenges, this paper proposes an enhanced 3D object detection framework (FastSECOND) based on an optimized SECOND architecture, designed to achieve rapid and accurate perception in autonomous driving scenarios. Key innovations include: (1) Replacing the Rectified Linear Unit (ReLU) activation functions with the Gaussian Error Linear Unit (GELU) during voxel feature encoding and region proposal network stages, leveraging partial convolution to balance computational efficiency… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1091-1112, 2025, DOI:10.32604/cmes.2025.066165 - 31 July 2025
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
Abstract 3D medical image reconstruction has significantly enhanced diagnostic accuracy, yet the reliance on densely sampled projection data remains a major limitation in clinical practice. Sparse-angle X-ray imaging, though safer and faster, poses challenges for accurate volumetric reconstruction due to limited spatial information. This study proposes a 3D reconstruction neural network based on adaptive weight fusion (AdapFusionNet) to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images. To address the issue of spatial inconsistency in multi-angle image reconstruction, an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1113-1141, 2025, DOI:10.32604/cmes.2025.068726 - 31 July 2025
（This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
Abstract Arabic Sign Language (ArSL) recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing (DHH) community. Researchers have proposed multiple methods for automated recognition of ArSL; however, these methods face multiple challenges that include high gesture variability, occlusions, limited signer diversity, and the scarcity of large annotated datasets. Existing methods, often relying solely on either skeletal data or video-based features, struggle with generalization and robustness, especially in dynamic and real-world conditions. This paper proposes a novel multimodal ensemble classification framework that integrates geometric features derived from 3D skeletal joint… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1143-1164, 2025, DOI:10.32604/cmes.2025.066984 - 31 July 2025
（This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
Abstract Real-time surveillance is attributed to recognizing the variety of actions performed by humans. Human Action Recognition (HAR) is a technique that recognizes human actions from a video stream. A range of variations in human actions makes it difficult to recognize with considerable accuracy. This paper presents a novel deep neural network architecture called Attention RB-Net for HAR using video frames. The input is provided to the model in the form of video frames. The proposed deep architecture is based on the unique structuring of residual blocks with several filter sizes. Features are extracted from each… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1165-1196, 2025, DOI:10.32604/cmes.2025.067150 - 31 July 2025
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract Background: Accurate classification of normal blood cells is a critical foundation for automated hematological analysis, including the detection of pathological conditions like leukemia. While convolutional neural networks (CNNs) excel in local feature extraction, their ability to capture global contextual relationships in complex cellular morphologies is limited. This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification, laying the groundwork for future leukemia diagnostics. Methods: The proposed architecture integrates pre-trained CNNs (ResNet50, EfficientNetB3, InceptionV3, CustomCNN) with Vision Transformer (ViT) layers to combine local and global feature modeling. Four hybrid models were evaluated on… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1197-1224, 2025, DOI:10.32604/cmes.2025.066580 - 31 July 2025
（This article belongs to the Special Issue: Exploring the Impact of Artificial Intelligence on Healthcare: Insights into Data Management, Integration, and Ethical Considerations)
Abstract Accurate and efficient brain tumor segmentation is essential for early diagnosis, treatment planning, and clinical decision-making. However, the complex structure of brain anatomy and the heterogeneous nature of tumors present significant challenges for precise anomaly detection. While U-Net-based architectures have demonstrated strong performance in medical image segmentation, there remains room for improvement in feature extraction and localization accuracy. In this study, we propose a novel hybrid model designed to enhance 3D brain tumor segmentation. The architecture incorporates a 3D ResNet encoder known for mitigating the vanishing gradient problem and a 3D U-Net decoder. Additionally, to… More >
Graphic Abstract
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1225-1248, 2025, DOI:10.32604/cmes.2025.067098 - 31 July 2025
（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 increasing reliance on digital infrastructure in modern healthcare systems has introduced significant cybersecurity challenges, particularly in safeguarding sensitive patient data and maintaining the integrity of medical services. As healthcare becomes more data-driven, cyberattacks targeting these systems continue to rise, necessitating the development of robust, domain-adapted Intrusion Detection Systems (IDS). However, current IDS solutions often lack access to domain-specific datasets that reflect realistic threat scenarios in healthcare. To address this gap, this study introduces HCKDDCUP, a synthetic dataset modeled on the widely used KDDCUP benchmark, augmented with healthcare-relevant attributes such as patient data, treatments, and… More >
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