Mustafa Turkyilmazoglu^1,2,*, Abdulaziz Alotaibi³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2847-2865, 2025, DOI:10.32604/cmes.2025.068031 - 30 June 2025

Abstract The variable salinity in stored reservoirs connected by a long channel attracts the attention of scientists worldwide, having applications in environmental and geophysical engineering. This study explores the impact of Navier slip conditions on exchange flows within a long channel connecting two large reservoirs of differing salinity. These horizontal density gradients drive the flow. We modify the recent one-dimensional theory, developed to avoid runaway stratification, to account for the presence of uniform slip walls. By adjusting the parameters of the horizontal density gradient based on the slip factor, we resolve analytically various flow regimes ranging… More >

Dong-Hyuk Shin¹, Jae-Jun Ha¹, Ieck-Chae Euom^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3795-3838, 2025, DOI:10.32604/cmes.2025.066727 - 30 June 2025

Abstract The accelerated global adoption of electric vehicles (EVs) is driving significant expansion and increasing complexity within the EV charging infrastructure, consequently presenting novel and pressing cybersecurity challenges. While considerable effort has focused on preventative cybersecurity measures, a critical deficiency persists in structured methodologies for digital forensic analysis following security incidents, a gap exacerbated by system heterogeneity, distributed digital evidence, and inconsistent logging practices which hinder effective incident reconstruction and attribution. This paper addresses this critical need by proposing a novel, data-driven forensic framework tailored to the EV charging infrastructure, focusing on the systematic identification, classification,… More >

Jiao Jia^1,*, Xin He², Zhenchen Liu³, Shiqing Wu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3215-3231, 2025, DOI:10.32604/cmes.2025.066489 - 30 June 2025

Abstract As primary load-bearing components extensively utilized in engineering applications, beam structures necessitate the design of their microstructural configurations to achieve lightweight objectives while satisfying diverse mechanical performance requirements. Combining topology optimization with fully coupled homogenization beam theory, we provide a highly efficient design tool to access desirable periodic microstructures for beams. The present optimization framework comprehensively takes into account for key deformation modes, including tension, bending, torsion, and shear deformation, all within a unified formulation. Several numerical results prove that our method can be used to handle kinds of microstructure design for beam-like structures, e.g., More >

Da Li¹, Yuqing Tian¹, Chengxin Weng^2,3, Fuyou Liang^1,4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2867-2882, 2025, DOI:10.32604/cmes.2025.066444 - 30 June 2025

Abstract Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is a life support intervention for patients with refractory cardiogenic shock or severe cardiopulmonary failure. However, the choice of cannulation strategy remains contentious, partly due to insufficient understanding of hemodynamic characteristics associated with the site of arterial cannulation. In this study, a geometrical multiscale model was built to offer a mathematical tool for addressing the issue. The outflow cannula of ECMO was inserted into the ascending aorta in the case of central cannulation, whereas it was inserted into the right subclavian artery (RSA) or the left iliac artery (LIA) in… More >

Tianwen Zhao¹, Guoqing Chen^2,3, Cong Pang⁴, Piyapatr Busababodhin^3,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2883-2917, 2025, DOI:10.32604/cmes.2025.066442 - 30 June 2025

Abstract Existing power forecasting models struggle to simultaneously handle high-dimensional, noisy load data while capturing long-term dependencies. This critical limitation necessitates an integrated approach combining dimensionality reduction, temporal modeling, and robust prediction, especially for multi-day forecasting. A novel hybrid model, SLHS-TCN-XGBoost, is proposed for power demand forecasting, leveraging SLHS (dimensionality reduction), TCN (temporal feature learning), and XGBoost (ensemble prediction). Applied to the three-year electricity load dataset of Seoul, South Korea, the model’s MAE, RMSE, and MAPE reached 112.08, 148.39, and 2%, respectively, which are significantly reduced in MAE, RMSE, and MAPE by 87.37%, 87.35%, and 87.43%… More >

Muhammad Amad Sarwar^1,*, Yuezheng Gong¹, Sarah A. Alzakari², Amel Ali Alhussan²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3523-3555, 2025, DOI:10.32604/cmes.2025.066356 - 30 June 2025

Abstract Global security threats have motivated organizations to adopt robust and reliable security systems to ensure the safety of individuals and assets. Biometric authentication systems offer a strong solution. However, choosing the best security system requires a structured decision-making framework, especially in complex scenarios involving multiple criteria. To address this problem, we develop a novel quantum spherical fuzzy technique for order preference by similarity to ideal solution (QSF-TOPSIS) methodology, integrating quantum mechanics principles and fuzzy theory. The proposed approach enhances decision-making accuracy, handles uncertainty, and incorporates criteria relationships. Criteria weights are determined using spherical fuzzy sets,… More >

Amani Baazeem¹, Muhammad Shoaib Arif^2,*, Yasir Nawaz³, Kamaleldin Abodayeh²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3463-3489, 2025, DOI:10.32604/cmes.2025.066299 - 30 June 2025

Abstract The numerical approximation of stochastic partial differential equations (SPDEs), particularly those including q-diffusion, poses considerable challenges due to the requirements for high-order precision, stability amongst random perturbations, and processing efficiency. Because of their simplicity, conventional numerical techniques like the Euler-Maruyama method are frequently employed to solve stochastic differential equations; nonetheless, they may have low-order accuracy and lower stability in stiff or high-resolution situations. This study proposes a novel computational scheme for solving SPDEs arising from a stochastic SEIR model with q-diffusion and a general incidence rate function. A proposed computational scheme can be used to… More >

Seongsoo Cho¹, Yeonwoo Lee^2,*, Cheolhee Yoon^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3769-3793, 2025, DOI:10.32604/cmes.2025.066298 - 30 June 2025

Abstract As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions, efficient clustering mechanisms are vital to ensure stable and scalable communication. Recent studies have emphasized the need for adaptive clustering strategies to improve performance in Intelligent Transportation Systems (ITS). This paper presents the Grasshopper Optimization Algorithm for Vehicular Network Clustering (GOA-VNET) algorithm, an innovative approach to optimal vehicular clustering in Vehicular Ad-Hoc Networks (VANETs), leveraging the Grasshopper Optimization Algorithm (GOA) to address the critical challenges of traffic congestion and communication inefficiencies in Intelligent Transportation Systems (ITS). The proposed GOA-VNET employs an… More >

Adéla Hamplová^*, Tomáš Novák, Miroslav Žáček, Jiří Brožek

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3329-3349, 2025, DOI:10.32604/cmes.2025.066025 - 30 June 2025

Abstract This study proposes a new component of the composite loss function minimised during training of the Super-Resolution (SR) algorithms—the normalised structural similarity index loss , which has the potential to improve the natural appearance of reconstructed images. Deep learning-based super-resolution (SR) algorithms reconstruct high-resolution images from low-resolution inputs, offering a practical means to enhance image quality without requiring superior imaging hardware, which is particularly important in medical applications where diagnostic accuracy is critical. Although recent SR methods employing convolutional and generative adversarial networks achieve high pixel fidelity, visual artefacts may persist, making the design of… More >

Fan Li¹, Hongxue Liu², Yongsong Li², Leilei Chen², Haojie Lian^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2785-2809, 2025, DOI:10.32604/cmes.2025.066001 - 30 June 2025

Abstract This study explores a sensitivity analysis method based on the boundary element method (BEM) to address the computational complexity in acoustic analysis with ground reflection problems. The advantages of BEM in acoustic simulations and its high computational cost in broadband problems are examined. To improve efficiency, a Taylor series expansion is applied to decouple frequency-dependent terms in BEM. Additionally, the Second-Order Arnoldi (SOAR) model order reduction method is integrated to reduce computational costs and enhance numerical stability. Furthermore, an isogeometric sensitivity boundary integral equation is formulated using the direct differentiation method, incorporating Cauchy principal value More >