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ARTICLE

Shuoting Xiao^1,*, Nikita Igorevich Fomin¹, Kirill Anatolyevich Khvostunkov², Chong Liu¹
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2821-2847, 2025, DOI:10.32604/cmes.2025.071961 - 23 December 2025
（This article belongs to the Special Issue: Frontiers in Computational Modeling and Simulation of Concrete)
Abstract Precast concrete structures have gained popularity due to their advantages. However, the seismic performance of their connection joints remains an area of ongoing research and improvement. Grouted Sleeve Connection (GSC) offers a solution for connecting reinforcements in precast components, but their vulnerability to internal defects, such as construction errors and material variability, can significantly impact performance. This article presents a finite element analysis (FEA) to evaluate the impact of internal grouting defects in GSC on the structural performance of precast reinforced concrete columns. Four finite element models representing GSC with varying degrees of defects were… More >

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EDITORIAL

Ndolane Sene^1,*, Ameth Ndiaye²
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2849-2852, 2025, DOI:10.32604/cmes.2025.075915 - 23 December 2025
（This article belongs to the Special Issue: Analytical and Numerical Solution of the Fractional Differential Equation)
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REVIEW

Heng Cheng¹, Yichen Yang¹, Yumin Cheng^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2853-2894, 2025, DOI:10.32604/cmes.2025.073178 - 23 December 2025
Abstract The element-free Galerkin (EFG) method, which constructs shape functions via moving least squares (MLS) approximation, represents a fundamental and widely studied meshless method in numerical computation. Although it achieves high computational accuracy, the shape functions are more complex than those in the conventional finite element method (FEM), resulting in great computational requirements. Therefore, improving the computational efficiency of the EFG method represents an important research direction. This paper systematically reviews significant contributions from domestic and international scholars in advancing the EFG method. Including the improved element-free Galerkin (IEFG) method, various interpolating EFG methods, four distinct More >

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REVIEW

Mirza Murad Baig¹, Muhammad Rehan Faheem^2,*, Lal Khan^3,*, Hannan Adeel², Syed Asim Ali Shah⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2895-2935, 2025, DOI:10.32604/cmes.2025.073172 - 23 December 2025
Abstract With growing urban areas, the climate continues to change as a result of growing populations, and hence, the demand for better emergency response systems has become more important than ever. Human Behaviour Classification (HBC) systems have started to play a vital role by analysing data from different sources to detect signs of emergencies. These systems are being used in many critical areas like healthcare, public safety, and disaster management to improve response time and to prepare ahead of time. But detecting human behaviour in such stressful conditions is not simple; it often comes with noisy… More >

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REVIEW

Somayah Albaradei^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2937-2970, 2025, DOI:10.32604/cmes.2025.072584 - 23 December 2025
（This article belongs to the Special Issue: Advanced Computational Intelligence Techniques, Uncertain Knowledge Processing and Multi-Attribute Group Decision-Making Methods Applied in Modeling of Medical Diagnosis and Prognosis)
Abstract Cancer deaths and new cases worldwide are projected to rise by 47% by 2040, with transitioning countries experiencing an even higher increase of up to 95%. Tumor severity is profoundly influenced by the timing, accuracy, and stage of diagnosis, which directly impacts clinical decision-making. Various biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, contribute to cancer development. The emergence of multi-omics technologies has transformed cancer research by revealing molecular alterations across multiple biological layers. This integrative approach supports the notion that cancer is fundamentally driven by such alterations, enabling the discovery of molecular signatures… More >

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REVIEW

Deema Alzamil^1,2,*, Bader Alkhamees², Mohammad Mehedi Hassan^2,3
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2971-3027, 2025, DOI:10.32604/cmes.2025.070867 - 23 December 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 Breast cancer diagnosis relies heavily on many kinds of information from diverse sources—like mammogram images, ultrasound scans, patient records, and genetic tests—but most AI tools look at only one of these at a time, which limits their ability to produce accurate and comprehensive decisions. In recent years, multimodal learning has emerged, enabling the integration of heterogeneous data to improve performance and diagnostic accuracy. However, doctors cannot always see how or why these AI tools make their choices, which is a significant bottleneck in their reliability, along with adoption in clinical settings. Hence, people are adding… More >

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REVIEW

Khulud Salem Alshudukhi^1,*, Sijjad Ali², Mamoona Humayun^3,*, Omar Alruwaili⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3029-3085, 2025, DOI:10.32604/cmes.2025.073705 - 23 December 2025
Abstract Problem: The integration of Artificial Intelligence (AI) into cybersecurity, while enhancing threat detection, is hampered by the “black box” nature of complex models, eroding trust, accountability, and regulatory compliance. Explainable AI (XAI) aims to resolve this opacity but introduces a critical new vulnerability: the adversarial exploitation of model explanations themselves. Gap: Current research lacks a comprehensive synthesis of this dual role of XAI in cybersecurity—as both a tool for transparency and a potential attack vector. There is a pressing need to systematically analyze the trade-offs between interpretability and security, evaluate defense mechanisms, and outline a… More >

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Yingying Zhou, Wenlong Shen, Tianzhe Jiao, Chaopeng Guo, Jie Song^*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3087-3131, 2025, DOI:10.32604/cmes.2025.074378 - 23 December 2025
Abstract As consortium blockchains scale and complexity grow, scalability presents a critical bottleneck hindering broader adoption. This paper meticulously extracted 150 primary references from IEEE Xplore, Web of Science, Google Scholar, and other reputable databases and websites, providing a comprehensive and structured overview of consortium blockchain scalability research. We propose a scalability framework that combines a four-layer architectural model with a four-dimensional cost model to analyze scalability trade-offs. Applying this framework, we conduct a comprehensive review of scaling approaches and reveal the inherent costs they introduce. Furthermore, we map the artificial intelligence (AI)-enabled methods to the More >

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ARTICLE

Michał Guminiak¹, Marcin Kamiński^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3133-3163, 2025, DOI:10.32604/cmes.2025.071887 - 23 December 2025
（This article belongs to the Special Issue: Selected Papers from the 26th International Conference on Computer Methods in Mechanics (CMM2025))
Abstract The analysis of the dynamics of surface girders is of great importance in the design of engineering structures such as steel welded bridge plane girders or concrete plate-column structures. This work is an extension of the classical deterministic problem of free vibrations of thin (Kirchhoff) plates. The main aim of this work is the study of stochastic eigenvibrations of thin (Kirchhoff) elastic plates resting on internal continuous and column supports by the Boundary Element Method (BEM). This work is a continuation of previous research related to the random approach in plate analysis using the BEM.… More >

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ARTICLE

Guo-Zhi Li¹, Hua-Ping Wang^1,2,*, Si-Kai Wang¹, Jing-Cheng Zhou¹, Ping Xiang^3,4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3165-3195, 2025, DOI:10.32604/cmes.2025.072750 - 23 December 2025
（This article belongs to the Special Issue: Advances in Numerical Modeling of Composite Structures and Repairs)
Abstract Transportation structures such as composite pavements and railway foundations typically consist of multi-layered media designed to withstand high bearing capacity. A theoretical understanding of load transfer mechanisms in these multi-layer composites is essential, as it offers intuitive insights into parametric influences and facilitates enhanced structural performance. This paper employs an improved transfer matrix method to address the limitations of existing theoretical approaches for analyzing multi-layer composite structures. By establishing a two-dimensional composite pavement model, it investigates load transfer characteristics and validates the accuracy through finite element simulation. The proposed method offers a straightforward analytical approach… More >

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ARTICLE

Dan Jiao^1,2, Yan Dong^1,2,*, Hao Xie³, Yordan Garbatov^4,*, Jiancheng Liu⁵, Hui Zhang⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3197-3216, 2025, DOI:10.32604/cmes.2025.074239 - 23 December 2025
（This article belongs to the Special Issue: Advances in Fatigue Life Prediction and Reliability Assessment)
Abstract Floating offshore wind turbine platforms typically use stiffened tubular joints at the connections between columns and braces. These joints are prone to fatigue due to complex weld geometries and the additional stress concentrations caused by the stiffeners. Existing hot-spot stress approaches may be inadequate for analysing these joints because they do not simultaneously address weld-toe and weld-root failures. To address these limitations, this study evaluates the fatigue strength of stiffened tubular joints using the effective notch strain approach and the structural strain approach. Both methods account for fatigue at the weld toe and weld root… More >

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ARTICLE

Lei Zheng^1,*, Hong Pan^2,3, Yuelei Ruan^2,4, Guoxin Zhang¹, Lei Zhang^1,*, Jianda Xin¹, Zhenyang Zhu¹, Jianyao Zhang^2,5, Wei Liu¹
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3217-3241, 2025, DOI:10.32604/cmes.2025.074651 - 23 December 2025
（This article belongs to the Special Issue: AI-Enhanced Computational Methods in Engineering and Physical Science)
Abstract As a critical material in construction engineering, concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency. This study proposes a novel hybrid prediction method that integrates a heat conduction physical model with a multilayer perceptron (MLP) neural network, dynamically fused via a weighted strategy to achieve high-precision temperature estimation. Experimental results on an independent test set demonstrated the superior performance of the fused model, with a root mean square error (RMSE) of 1.59°C and a mean absolute error (MAE) of 1.23°C, representing a 25.3% RMSE reduction compared to More >

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ARTICLE

Siwei Zhang, Xiaozhou Xia^*, Xin Gu, Meilin Zong, Qing Zhang^*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3243-3278, 2025, DOI:10.32604/cmes.2025.073841 - 23 December 2025
Abstract This study presents a coupled thermo-hydro-mechanical-fatigue (THM-F) model, developed based on variational phase-field fatigue theory, to simulate the freeze-thaw (F-T) damage process in concrete. The fracture phase-field model incorporates the F-T fatigue mechanism driven by energy dissipation during the free energy growth stage. Using microscopic inclusion theory, we derive an evolution model of pore size distribution (PSD) for concrete under F-T cycles by treating pore water as columnar inclusions. Drawing upon pore ice crystal theory, calculation models that account for concrete PSD characteristics are established to determine ice saturation, permeability coefficient, and pore pressure. To… More >

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ARTICLE

Yassir M. Abbas^1,*, Ammar Babiker², Fouad Ismail Ismail³
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3279-3307, 2025, DOI:10.32604/cmes.2025.074069 - 23 December 2025
Abstract Accurately predicting the compressive strength of recycled aggregate concrete (RAC) incorporating supplementary cementitious materials (SCMs) remains a critical challenge due to the heterogeneous nature of recycled aggregates (RA) and the complex interactions among multiple binder constituents. This study advances the field by developing the most extensive and rigorously preprocessed database to date, which comprises 1243 RAC mixtures containing silica fume, fly ash, and ground-granulated blast-furnace slag. A hybrid, domain-informed machine-learning framework was then proposed, coupling optimized Extreme Gradient Boosting (XGBoost) with civil engineering expertise to capture the complex chemical and microstructural mechanisms that govern RAC… More >

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ARTICLE

Fatemeh Mobasheri¹, Masoud Hosseinpoor^1,*, Ammar Yahia^1,2, Farhad Pourkamali-Anaraki³
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3309-3347, 2025, DOI:10.32604/cmes.2025.072271 - 23 December 2025
Abstract Self-consolidating concrete (SCC) is an important innovation in concrete technology due to its superior properties. However, predicting its compressive strength remains challenging due to variability in its composition and uncertainties in prediction outcomes. This study combines machine learning (ML) models with conformal prediction (CP) to address these issues, offering prediction intervals that quantify uncertainty and reliability. A dataset of over 3000 samples with 17 input variables was used to train four ensemble methods, including Random Forest (RF), Gradient Boosting Regressor (GBR), Extreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM), along with CP techniques, More >

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ARTICLE

Ramazan Özkan^1,2, Mustafa Serdar Genç^1,3,*, İlker Kayali^1,4,5
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3349-3380, 2025, DOI:10.32604/cmes.2025.072519 - 23 December 2025
（This article belongs to the Special Issue: Computational Design and Modeling of Advanced Composites and Structures)
Abstract The optimization of turbine blades is crucial in improving the efficiency of wind energy systems and developing clean energy production models. This paper presented a novel approach to the structural design of small-scale turbine blades using the Artificial Bee Colony (ABC) Algorithm based on the stochastic method to optimize both mass and cost (objective functions). The study used computational fluid dynamics (CFD) and structural analysis to consider the fluid-structure interaction. The optimization algorithm defined several variables: structural constraints, the type of composite material, and the number of composite layers to form a mathematical model. The More >

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ARTICLE

Yunjun Lee¹, Sanggyu Cheon², Woo Chul Chung^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3381-3403, 2025, DOI:10.32604/cmes.2025.073729 - 23 December 2025
Abstract The application of carbon capture systems on ships is technically constrained by limited onboard space and the weight of the conventional absorption tower. The rotating packed bed (RPB) has emerged as a promising alternative due to its small footprint and high mass transfer performance. However, despite its advantages, the structural and vibration stability of RPBs at high rotational speed remains insufficiently studied, and no international design standards currently exist for RPBs. To address this gap, this study performed a comprehensive finite element analysis (FEA) using ANSYS to investigate the structural and dynamic characteristics of an… More >

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ARTICLE

Guang-Qi Liang¹, Peng-Nan Sun^1,2,*, A-Man Zhang³
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3405-3431, 2025, DOI:10.32604/cmes.2025.072496 - 23 December 2025
（This article belongs to the Special Issue: Recent Developments in Nonlocal Meshfree Particle Methods for Solids and Fluids )
Abstract A multi-resolution smoothed particle hydrodynamics and peridynamics (SPH-PD) coupling model is proposed in this study for simulating the fracture characteristics of ice plates exposed to underwater blast loads. The SPH model employs a volume adaptive scheme (VAS) and a multi-resolution particle technique to accurately simulate explosive charge detonation and shock wave propagation. This approach addresses numerical challenges from charge expansion and significant size disparity between the charge and the fluid particles. The model captures the full underwater explosion process, covering both the shock wave phase and the bubble expansion stage, by applying appropriate equations of More >

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ARTICLE

Quang-Thai Nguyen^1,2,#, Duong Ngoc Hai^3,4,#, The-Duc Nguyen^1,3,4,*, Van-Tu Nguyen^2,*, Jinyul Hwang², Warn-Gyu Park²
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3433-3452, 2025, DOI:10.32604/cmes.2025.070570 - 23 December 2025
（This article belongs to the Special Issue: Modeling and Applications of Bubble and Droplet in Engineering and Sciences)
Abstract This study presents a numerical analysis of the effects of a rigid flat wall with oscillating motion on the pressure wave propagation during a single spherical cavitation bubble collapse at different initial bubble positions. Different nondimensional distances S = 0.8, 0.9, 1.0, 1.1, 1.2 and 1.3 were considered to investigate the effects of initial in-phase and out-of-phase oscillations of the flat wall. Numerical simulations of cavitation bubble collapse near an oscillating wall were conducted using a compressible two-phase flow model. This model incorporated the Volume of Fluid (VOF) interface-sharpening technique on a general curvilinear moving… More >

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ARTICLE

Yongwei Li¹, Kaituo Jiao^2,*, Dongxu Han³, Bo Yu², Xiaoze Du¹
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3453-3479, 2025, DOI:10.32604/cmes.2025.073239 - 23 December 2025
（This article belongs to the Special Issue: Computer Modeling of Fluid Seepage in Porous Media with Ultra-low Permeabilities)
Abstract The permeability contrast between the Hot Dry Rock (HDR) reservoir and the surrounding formations is a key factor governing fluid loss in Enhanced Geothermal Systems (EGS). This study thus aims to investigate its impact on system performance under varying operating conditions, and a three-dimensional thermo–hydro–mechanical (THM) coupled EGS model is developed based on the geological parameters of the GR1 well in the Qiabuqia region. The coupled processes of fluid flow, heat transfer, and geomechanics within the reservoir under varying reservoir–surrounding rock permeability contrasts, as well as the flow and heat exchange along the wellbores from… More >

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ARTICLE

Arshad Riaz^1,*, Misbah Ilyas¹, Muhammad Naeem Aslam², Safia Akram³, Sami Ullah Khan⁴, Ghaliah Alhamzi⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3481-3509, 2025, DOI:10.32604/cmes.2025.072513 - 23 December 2025
（This article belongs to the Special Issue: Mathematical and Computational Modeling of Nanofluid in Biofluid Systems)
Abstract Peristaltic transport of non-Newtonian nanofluids with double diffusion is essential to biological engineering, microfluidics, and manufacturing processes. The authors tackle the key problem of Sisko nanofluids under double diffusion convection with thermal radiations and electroosmotic effects. The study proposes a solution approach by using Morlet-Wavelet Neural Networks that can effectively solve this complex problem by their superior ability in the capture of nonlinear dynamics. These convergence analyses were calculated across fifty independent runs. Theil’s Inequality Coefficient and the Mean Squared Error values range from 10⁻⁷ to 10⁻⁵ and 10⁻⁷ to 10⁻¹⁰, respectively. These values showed the proposed More >

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ARTICLE

Mujahid Islam¹, Fateh Ali^1,*, Xinlong Feng^1,*, M. Zahid², Sana Naz Maqbool¹
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3511-3561, 2025, DOI:10.32604/cmes.2025.073678 - 23 December 2025
Abstract Roll coating is a vital industrial process used in printing, packaging, and polymer film production, where maintaining a uniform coating is critical for product quality and efficiency. This work models non-isothermal Carreau fluid flow between a rotating roll and a stationary wall under fixed boundary constraints to evaluate how non-Newtonian and thermal effects influence coating performance. The governing equations are transformed into non-dimensional form and simplified using lubrication approximation theory. Approximate analytical solutions are obtained via the perturbation technique, while numerical results are computed using both the finite difference method and the BVP-Midrich technique. Furthermore, More >

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ARTICLE

Asgar Ali^1,*, Nayan Sardar², Poly Karmakar³, Sanatan Das⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3563-3626, 2025, DOI:10.32604/cmes.2025.074082 - 23 December 2025
（This article belongs to the Special Issue: Applications of Modelling and Simulation in Nanofluids)
Abstract Hybrid nanofluids have gained significant attention for their superior thermal and rheological characteristics, offering immense potential in energy conversion, biomedical transport, and electromagnetic flow control systems. Understanding their dynamic behavior under coupled magnetic, rotational, and reactive effects is crucial for the development of efficient thermal management technologies. This study develops a neuro-fuzzy computational framework to examine the dynamics of a reactive Cu–TiO₂–H₂O hybrid nanofluid flowing through a squarely elevated Riga tunnel. The governing model incorporates Hall and ion-slip effects, thermal radiation, and first-order chemical reactions under ramped thermo-solutal boundary conditions and rotational electromagnetic forces. Closed-form analytical… More >

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ARTICLE

Adnan Ashique^1,#, Khalid Masood², Usman Afzal¹, Mati Ur Rahman², Maddina Dinesh Kumar³, Sohaib Abdal³, Nehad Ali Shah^1,#,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3627-3699, 2025, DOI:10.32604/cmes.2025.072523 - 23 December 2025
（This article belongs to the Special Issue: Applications of Modelling and Simulation in Nanofluids)
Abstract This study outlines a quantitative and data-driven study of the mixed convection heat transfer processes that concern Cu-water nanofluids in a Γ-shaped enclosure with one to five rotating cylinders. The dimensionless equations of mass, momentum, and energy are solved using the finite element method as implemented in the COMSOL Multiphysics 6.3 software in different rotating Reynolds numbers and cylinder geometries. An artificial Neural Network that is trained using Bayesian Regularization on data produced by the COMSOL is utilized to estimate the average Nusselt numbers. The analysis is conducted for a wide range of rotational… More >

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ARTICLE

Hongrong Wang^1,2, Hang Geng^1,*, Jing Yuan¹, Wen Zhang², Hanmin Sheng¹, Qiuhua Wang³, Xinjian Li^4,5
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3701-3721, 2025, DOI:10.32604/cmes.2025.073513 - 23 December 2025
（This article belongs to the Special Issue: Incomplete Data Test, Analysis and Fusion Under Complex Environments)
Abstract Machine learning has emerged as a key approach in wildfire risk prediction research. However, in practical applications, the scarcity of data for specific regions often hinders model performance, with models trained on region-specific data struggling to generalize due to differences in data distributions. While traditional methods based on expert knowledge tend to generalize better across regions, they are limited in leveraging multi-source data effectively, resulting in suboptimal predictive accuracy. This paper addresses this challenge by exploring how accumulated domain expertise in wildfire prediction can reduce model reliance on large volumes of high-quality data. An active More >

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Changyu Jeon, Younghoon Kim^*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3723-3745, 2025, DOI:10.32604/cmes.2025.073030 - 23 December 2025
Abstract Accurate State-of-Health (SOH) prediction is critical for the safe and efficient operation of lithium-ion batteries (LiBs). However, conventional methods struggle with the highly nonlinear electrochemical dynamics and declining accuracy over long-horizon forecasting. To address these limitations, this study proposes CIPF-Informer, a novel digital twin framework that integrates the Informer architecture with Channel Independence (CI) and a Particle Filter (PF). The CI mechanism enhances robustness by decoupling multivariate state dependencies, while the PF captures the complex stochastic variations missed by purely deterministic models. The proposed framework was evaluated using the Massachusetts Institute of Technology (MIT) battery More >

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ARTICLE

Chen Pan, ChangGyoon Lim^*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3747-3781, 2025, DOI:10.32604/cmes.2025.073389 - 23 December 2025
Abstract Solar forecasting using ground-based sky image offers a promising approach to reduce uncertainty in photovoltaic (PV) power generation. However, existing methods often rely on deterministic predictions that lack diversity, making it difficult to capture the inherently stochastic nature of cloud movement. To address this limitation, we propose a new two-stage probabilistic forecasting framework. In the first stage, we introduce I-GPT, a multiscale physics-constrained generative model for stochastic sky image prediction. Given a sequence of past sky images, I-GPT uses a Transformer-based VQ-VAE. It also incorporates multi-scale physics-informed recurrent units (Multi-scale PhyCell) and dynamically weighted fuses… More >

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Mohamed Kouki¹, Nahid Osman², Mona Gafar³, Ragab A. El-Sehiemy^4,5,6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3783-3811, 2025, DOI:10.32604/cmes.2025.071964 - 23 December 2025
Abstract The growing integration of nondispatchable renewable energy sources (PV, wind) and the need to cut CO2 emissions make energy management crucial. Microgrids provide a framework for RES integration but face challenges from intermittency, fluctuating loads, cost optimization, and uncertainty in real-time balancing. Accurate short-term forecasting of solar generation and demand is vital for reliable and sustainable operation. While stochastic and machine learning methods are used, they struggle with limited data, complex temporal patterns, and scalability. Key challenges include capturing seasonal to weekly variations and modeling sudden fluctuations in generation and consumption. To address… More >

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Guo-Feng Fan^1,2, Jia-Jing Qian¹, Li-Ling Peng¹, Xin-Hang Jia¹, Ling-Han Zuo¹, Jia-Can Yan¹, Jiang-Yan Chen¹, Anantkumar J. Umbarkar³, Wei-Chiang Hong^4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3813-3837, 2025, DOI:10.32604/cmes.2025.073615 - 23 December 2025
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
Abstract Accurate prediction of perovskite photovoltaic materials’ optoelectronic properties is crucial for developing efficient and stable materials, advancing solar technology. To address poor interpretability, high computational complexity, and inaccurate predictions in relevant machine learning models, this paper proposes a novel methodology. The technical route of this paper mainly centers on the random forest-knowledge distillation-bidirectional gated recurrent unit with attention technology (namely RF-KD-BIGRUA), which is applied in perovskite photovoltaic materials. Primarily, it combines random forest to quantitatively assess feature importance, selecting variables with significant impacts on photoelectric conversion efficiency. Subsequently, statistical techniques analyze the weight distribution of More >

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Tariq Ali^1,2,*, Muhammad Ayaz^1,2, Mohammad Hijji², Imran Baig³, MI Mohamed Ershath⁴, Saleh Albelwi²
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3839-3858, 2025, DOI:10.32604/cmes.2025.073169 - 23 December 2025
Abstract The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind, solar, and other renewables. Accurate forecasting is crucial for ensuring grid stability, optimizing market operations, and minimizing economic risks. This paper introduces a hybrid forecasting framework incorporating fractional-order statistical models, fractal-based feature engineering, and deep learning architectures to improve renewable energy forecasting accuracy. Fractional autoregressive integrated moving average (FARIMA) and fractional exponential smoothing (FETS) models are explored for capturing long-memory dependencies in energy time-series data. Additionally, multifractal detrended fluctuation analysis (MFDFA) More >

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Carlos Torres-Aguilar^1,*, Pedro Moreno^2,*, Diego Rossit³, Sergio Nesmachnow⁴, Karla M. Aguilar-Castro¹, Edgar V. Macias-Melo¹, Luis Hernández-Callejo⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3859-3881, 2025, DOI:10.32604/cmes.2025.069996 - 23 December 2025
（This article belongs to the Special Issue: Innovative Computational Models for Smart Cities)
Abstract Solar chimneys are renewable energy systems designed to enhance natural ventilation, improving thermal comfort in buildings. As passive systems, solar chimneys contribute to energy efficiency in a sustainable and environmentally friendly way. The effectiveness of a solar chimney depends on its design and orientation relative to the cardinal directions, both of which are critical for optimal performance. This article presents a supervised learning approach using artificial neural networks to forecast the performance indicators of solar chimneys. The dataset includes information from 2784 solar chimney configurations, which encompasses various factors such as chimney height, channel thickness, More >

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Talha Farooq Khan¹, Majid Hussain¹, Muhammad Arslan², Muhammad Saeed¹, Lal Khan^3,*, Hsien-Tsung Chang^4,5,6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3883-3911, 2025, DOI:10.32604/cmes.2025.073021 - 23 December 2025
（This article belongs to the Special Issue: Applied NLP with Large Language Models: AI Applications Across Domains)
Abstract Text clustering is an important task because of its vital role in NLP-related tasks. However, existing research on clustering is mainly based on the English language, with limited work on low-resource languages, such as Urdu. Low-resource language text clustering has many drawbacks in the form of limited annotated collections and strong linguistic diversity. The primary aim of this paper is twofold: (1) By introducing a clustering dataset named UNC-2025 comprises 100k Urdu news documents, and (2) a detailed empirical standard of Large Language Model (LLM) improved clustering methods for Urdu text. We explicitly evaluate the… More >

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Jeongsu Park¹, Moohong Min^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3913-3948, 2025, DOI:10.32604/cmes.2025.072261 - 23 December 2025
Abstract Time series anomaly detection is critical in domains such as manufacturing, finance, and cybersecurity. Recent generative AI models, particularly Transformer- and Autoencoder-based architectures, show strong accuracy but their robustness under noisy conditions is less understood. This study evaluates three representative models—AnomalyTransformer, TranAD, and USAD—on the Server Machine Dataset (SMD) and cross-domain benchmarks including the Soil Moisture Active Passive (SMAP) dataset, the Mars Science Laboratory (MSL) dataset, and the Secure Water Treatment (SWaT) testbed. Seven noise settings (five canonical, two mixed) at multiple intensities are tested under fixed clean-data training, with variations in window, stride, and More >

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Lijuan Zhang¹, Yu Zhou², Jiawei Tian^3,*, Fupei Guo⁴, Xiang Zhang⁴, Bo Yang^4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3949-3968, 2025, DOI:10.32604/cmes.2025.071869 - 23 December 2025
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract In endoscopic surgery, the limited field of view and the nonlinear deformation of organs caused by patient movement and respiration significantly complicate the modeling and accurate tracking of soft tissue surfaces from endoscopic image sequences. To address these challenges, we propose a novel Hybrid Triangular Matching (HTM) modeling framework for soft tissue feature tracking. Specifically, HTM constructs a geometric model of the detected blobs on the soft tissue surface by applying the Watershed algorithm for blob detection and integrating the Delaunay triangulation with a newly designed triangle search segmentation algorithm. By leveraging barycentric coordinate theory, More >

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61

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Yi-Ting Peng^1,*, Chin-Laung Lei²
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3969-3992, 2025, DOI:10.32604/cmes.2025.073258 - 23 December 2025
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract The rapid advancement of Large Language Models (LLMs) has enabled their application in diverse professional domains, including law. However, research on automatic judicial document generation remains limited, particularly for Taiwanese courts. This study proposes a keyword-guided training framework that enhances LLMs’ ability to generate structured and semantically coherent judicial decisions in Chinese. The proposed method first employs LLMs to extract representative legal keywords from absolute court judgments. Then it integrates these keywords into Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback using Proximal Policy Optimization (RLHF-PPO). Experimental evaluations using models such as Chinese Alpaca More >

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240

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Shuangyuan Li^1,*, Zhengwei Wang², Jiaming Liang³, Yichen Wang⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3993-4011, 2025, DOI:10.32604/cmes.2025.073896 - 23 December 2025
（This article belongs to the Special Issue: Advanced Image Segmentation and Object Detection: Innovations, Challenges, and Applications)
Abstract Object detection plays a crucial role in the field of computer vision, and small object detection has long been a challenging issue within this domain. In order to improve the performance of object detection on small targets, this paper proposes an enhanced structure for YOLOv5, termed ATC-YOLOv5. Firstly, a novel structure, AdaptiveTrans, is introduced into YOLOv5 to facilitate efficient communication between the encoder and the detector. Consequently, the network can better address the adaptability challenge posed by objects of different sizes in object detection. Additionally, the paper incorporates the CBAM (Convolutional Block Attention Module) attention More >

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41

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Tanvir Fatima Naik Bukht^1,2, Yanfeng Wu¹, Nouf Abdullah Almujally³, Shuoa S. AItarbi⁴, Hameedur Rahman², Ahmad Jalal^2,5,*, Hui Liu^1,6,7,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4013-4036, 2025, DOI:10.32604/cmes.2025.071850 - 23 December 2025
（This article belongs to the Special Issue: Recent Advances in Signal Processing and Computer Vision)
Abstract Human activity recognition (HAR) is crucial in fields like robotics, surveillance, and healthcare, enabling systems to understand and respond to human actions. Current models often struggle with complex datasets, making accurate recognition challenging. This study proposes a quantum-integrated Convolutional Neural Network (QI-CNN) to enhance HAR performance. The traditional models demonstrate weak performance in transferring learned knowledge between diverse complex data collections, including D3D-HOI and Sysu 3D HOI. HAR requires better extraction models and techniques that must address current challenges to achieve improved accuracy and scalability. The model aims to enhance HAR task performance by combining… More >

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86

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Guangyu Xu^1,2, Siyuan Xu³, Siyu Lu^4,*, Yuxin Liu¹, Bo Yang¹, Junmin Lyu⁵, Wenfeng Zheng^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4037-4053, 2025, DOI:10.32604/cmes.2025.074901 - 23 December 2025
（This article belongs to the Special Issue: Recent Advances in Signal Processing and Computer Vision)
Abstract Robust stereo disparity estimation plays a critical role in minimally invasive surgery, where dynamic soft tissues, specular reflections, and data scarcity pose major challenges to traditional end-to-end deep learning and deformable model-based methods. In this paper, we propose a novel disparity estimation framework that leverages a pretrained StyleGAN generator to represent the disparity manifold of Minimally Invasive Surgery (MIS) scenes and reformulates the stereo matching task as a latent-space optimization problem. Specifically, given a stereo pair, we search for the optimal latent vector in the intermediate latent space of StyleGAN, such that the photometric reconstruction… More >

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Rıdvan Yayla, Hakan Üçgün^*, Onur Ali Korkmaz
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4055-4087, 2025, DOI:10.32604/cmes.2025.072703 - 23 December 2025
（This article belongs to the Special Issue: Environment Modeling for Applications of Mobile Robots)
Abstract Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems. Artificial intelligence enables real-time sensing, decision-making, and control on embedded platforms with improved efficiency. This study presents the design and implementation of an autonomous radio-controlled (RC) vehicle prototype capable of lane line detection, obstacle avoidance, and navigation through dynamic path planning. The system integrates image processing and ultrasonic sensing, utilizing Raspberry Pi for vision-based tasks and Arduino Nano for real-time control. Lane line detection is achieved through conventional image processing techniques, providing the basis for local path generation, while traffic sign classification employs a… More >

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Jia Mi¹, Zhikang Liu¹, Chang Li², Jing Wan^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4089-4106, 2025, DOI:10.32604/cmes.2025.074364 - 23 December 2025
Abstract Recognizing essential proteins within bacteriophages is fundamental to uncovering their replication and survival mechanisms and contributes to advances in phage-based antibacterial therapies. Despite notable progress, existing computational techniques struggle to represent the interplay between sequence-derived and structure-dependent protein features. To overcome this limitation, we introduce GLM-EP, a unified framework that fuses protein language models with equivariant graph neural networks. By merging semantic embeddings extracted from amino acid sequences with geometry-aware graph representations, GLM-EP enables an in-depth depiction of phage proteins and enhances essential protein identification. Evaluation on diverse benchmark datasets confirms that GLM-EP surpasses conventional More >

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42

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Damodharan Palaniappan¹, Tan Kuan Tak², K. Vijayan³, Balajee Maram⁴, Pravin R Kshirsagar⁵, Naim Ahmad^6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4107-4127, 2025, DOI:10.32604/cmes.2025.073387 - 23 December 2025
Abstract A phase-aware cross-modal framework is presented that synthesizes UWF_FA from non-invasive UWF_RI for diabetic retinopathy (DR) stratification. A curated cohort of 1198 patients (2915 UWF_RI and 17,854 UWF_FA images) with strict registration quality supports training across three angiographic phases (initial, mid, final). The generator is based on a modified pix2pixHD with an added Gradient Variance Loss to better preserve microvasculature, and is evaluated using MAE, PSNR, SSIM, and MS-SSIM on held-out pairs. Quantitatively, the mid phase achieves the lowest MAE (98.76 ± 42.67), while SSIM remains high across phases. Expert review shows substantial agreement (Cohen’s More >

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51

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Asma Batool¹, Fahad Ahmed¹, Naila Sammar Naz¹, Ayman Altameem², Ateeq Ur Rehman^3,4, Khan Muhammad Adnan^5,*, Ahmad Almogren^6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4129-4152, 2025, DOI:10.32604/cmes.2025.073149 - 23 December 2025
Abstract Early and accurate cancer diagnosis through medical imaging is crucial for guiding treatment and enhancing patient survival. However, many state-of-the-art deep learning (DL) methods remain opaque and lack clinical interpretability. This paper presents an explainable artificial intelligence (XAI) framework that combines a fine-tuned Visual Geometry Group 16-layer network (VGG16) convolutional neural network with layer-wise relevance propagation (LRP) to deliver high-performance classification and transparent decision support. This approach is evaluated on the publicly available Kaggle kidney cancer imaging dataset, which comprises labeled cancerous and non-cancerous kidney scans. The proposed model achieved 98.75% overall accuracy, with precision, More >

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57

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Mubashir Ali^1,2, Jingzhen Li¹, Zedong Nie^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4153-4177, 2025, DOI:10.32604/cmes.2025.073048 - 23 December 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 Diabetes imposes a substantial burden on global healthcare systems. Worldwide, nearly half of individuals with diabetes remain undiagnosed, while conventional diagnostic techniques are often invasive, painful, and expensive. In this study, we propose a noninvasive approach for diabetes detection using photoplethysmography (PPG), which is widely integrated into modern wearable devices. First, we derived velocity plethysmography (VPG) and acceleration plethysmography (APG) signals from PPG to construct multi-channel waveform representations. Second, we introduced a novel multiset spatiotemporal feature fusion framework that integrates hand-crafted temporal, statistical, and nonlinear features with recursive feature elimination and deep feature extraction using… More >

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144

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Sarfaraz Abdul Sattar Natha^1,*, Mohammad Siraj^2,*, Majid Altamimi², Adamali Shah², Maqsood Mahmud³
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4179-4201, 2025, DOI:10.32604/cmes.2025.072529 - 23 December 2025
（This article belongs to the Special Issue: Advanced Computational Intelligence Techniques, Uncertain Knowledge Processing and Multi-Attribute Group Decision-Making Methods Applied in Modeling of Medical Diagnosis and Prognosis)
Abstract A brain tumor is a disease in which abnormal cells form a tumor in the brain. They are rare and can take many forms, making them difficult to treat, and the survival rate of affected patients is low. Magnetic resonance imaging (MRI) is a crucial tool for diagnosing and localizing brain tumors. However, the manual interpretation of MRI images is tedious and prone to error. As artificial intelligence advances rapidly, DL techniques are increasingly used in medical imaging to accurately detect and diagnose brain tumors. In this study, we introduce a deep convolutional neural network… More >

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Rytis Maskeliūnas¹, Robertas Damaševičius^1,*, Audrius Kulikajevas¹, Kipras Pribuišis², Nora Ulozaitė-Stanienė², Virgilijus Uloza²
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4203-4223, 2025, DOI:10.32604/cmes.2025.072790 - 23 December 2025
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract This study introduces a novel voice cloning framework driven by Mordukhovich Subdifferential Optimization (MSO) to address the complex multi-objective challenges of pathological speech synthesis in under-resourced Lithuanian language with unique phonemes not present in most pre-trained models. Unlike existing voice synthesis models that often optimize for a single objective or are restricted to major languages, our approach explicitly balances four competing criteria: speech naturalness, speaker similarity, computational efficiency, and adaptability to pathological voice patterns. We evaluate four model configurations combining Lithuanian and English encoders, synthesizers, and vocoders. The hybrid model (English encoder, Lithuanian synthesizer, English More >

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144

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Manuel J. C. S. Reis^1,*, Nishu Gupta²
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4225-4243, 2025, DOI:10.32604/cmes.2025.073366 - 23 December 2025
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract The forthcoming sixth generation (6G) of mobile communication networks is envisioned to be AI-native, supporting intelligent services and pervasive computing at unprecedented scale. Among the key paradigms enabling this vision, Federated Learning (FL) has gained prominence as a distributed machine learning framework that allows multiple devices to collaboratively train models without sharing raw data, thereby preserving privacy and reducing the need for centralized storage. This capability is particularly attractive for vision-based applications, where image and video data are both sensitive and bandwidth-intensive. However, the integration of FL with 6G networks presents unique challenges, including communication… More >

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322

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Ahmed K. Ali¹, Jungpil Shin^2,*, Yujin Lim^3,*, Da-Hun Seong³
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4245-4278, 2025, DOI:10.32604/cmes.2025.073871 - 23 December 2025
Abstract Single-signal detection in orthogonal frequency-division multiplexing (OFDM) systems presents a challenge due to the time-varying nature of wireless channels. Although conventional methods have limitations, particularly in multi-input multioutput orthogonal frequency division multiplexing (MIMO-OFDM) systems, this paper addresses this problem by exploring advanced deep learning approaches for combined channel estimation and signal detection. Specifically, we propose two hybrid architectures that integrate a convolutional neural network (CNN) with a recurrent neural network (RNN), namely, CNN-long short-term memory (CNN-LSTM) and CNN-bidirectional-LSTM (CNN-Bi-LSTM), designed to enhance signal detection performance in MIMO-OFDM systems. The proposed CNN-LSTM and CNN-Bi-LSTM architectures are… More >

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Ana Hermosilla^1,2,*, Pedro Martinez-Julia³, Diego R. Lopez⁴, Antonio F. Skarmeta^1,2
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4279-4316, 2025, DOI:10.32604/cmes.2025.072607 - 23 December 2025
Abstract In this study, we analyzed the processes involved in the resolution and enforcement of multi-domain network intent policies for intent-based networking (IBN). Previous studies on IBN analyzed the basis of the network intent resolution processes. These processes produce the artifacts required by network intent policy enforcement. Thus, we continued such studies with the inclusion of network intent policy enforcement in the analysis, for which we constructed a model that predicts the accuracy of a multi-domain network intent policy enforcement system. We validated the model by designing a new multi-domain network intent policy enforcement system, and… More >

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127

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Gan Zhu¹, Yongtao Yu^2,*, Xiaofan Deng¹, Yuanchen Dai³, Zhenyuan Li³
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4317-4348, 2025, DOI:10.32604/cmes.2025.074349 - 23 December 2025
Abstract Existing deep learning Network Intrusion Detection Systems (NIDS) struggle to simultaneously capture fine-grained, multi-scale features and long-range temporal dependencies. To address this gap, this paper introduces TransNeSt, a hybrid architecture integrating a ResNeSt block (using split-attention for multi-scale feature representation) with a Transformer encoder (using self-attention for global temporal modeling). This integration of multi-scale and temporal attention was validated on four benchmarks: NSL-KDD, UNSW-NB15, CIC-IDS2017, and CICIOT2023. TransNeSt consistently outperformed its individual components and several state-of-the-art models, demonstrating significant quantitative gains. The model achieved high efficacy across all datasets, with F1-Scores of 99.04% (NSL-KDD), 91.92% More >

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Khalid Haseeb¹, Imran Qureshi^2,*, Naveed Abbas¹, Muhammad Ali³, Muhammad Arif Shah⁴, Qaisar Abbas²
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4349-4362, 2025, DOI:10.32604/cmes.2025.072326 - 23 December 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 rapid evolution of smart cities has led to the deployment of Cyber-Physical IoT Systems (CPS-IoT) for real-time monitoring, intelligent decision-making, and efficient resource management, particularly in intelligent transportation and vehicular networks. Edge intelligence plays a crucial role in these systems by enabling low-latency processing and localized optimization for dynamic, data-intensive, and vehicular environments. However, challenges such as high computational overhead, uneven load distribution, and inefficient utilization of communication resources significantly hinder scalability and responsiveness. Our research presents a robust framework that integrates artificial intelligence and edge-level traffic prediction for CPS-IoT systems. Distributed computing for More >

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