Riad Mollik Babu¹, Md Shafiul Alam^2,*, Md. Hasibur Rahman³, Mohammad Ali², Md. Alamgir Hossain⁴, Md. Arifuzzaman⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082366 - 30 June 2026

Abstract The increasing penetration of solar photovoltaic (PV) systems into power grids poses challenges due to their inherent intermittency and variability, which can compromise grid stability and reliability. Hybridizing solar PV with wind energy and battery energy storage system (BESS) offers a promising solution by leveraging resource complementarity and providing fast frequency response. This study presents a techno-economic and environmental assessment of a hybrid renewable energy system. Wind turbines and a BESS are integrated with the existing 7.5 MW Sirajganj Solar PV Power Plant in Bangladesh. The proposed hybrid configuration is evaluated using real-world operational data… More >

Cristina Roxana Popa¹, Maria Tănase^2,*, Gheorghe Brănoiu³, Elena-Emilia Sirbu^4,5, Cătălina Călin⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082244 - 30 June 2026

Abstract Glass-fiber-reinforced polymer (GFRP) pipes are increasingly used in aggressive environments due to their high corrosion resistance and favorable mechanical properties. However, long-term exposure to saline environments and elevated temperatures can lead to degradation of their structural performance. This study investigates the influence of accelerated saltwater aging on the tensile behavior and structural characteristics of GFRP pipes and proposes machine-learning-based predictive models for the ultimate tensile strength (UTS). Experimental specimens were immersed in a 3.5% NaCl solution under controlled temperature and exposure time conditions. Tensile testing revealed that the unexposed samples exhibited a maximum UTS of… More >

Arvind Prasad^1,*, Ibrahim Aljubayri², Mohammad Zubair Khan^3,*, Abdulfattah Noorwali⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082021 - 30 June 2026

Abstract Intrusion detection in large-scale IoT deployments becomes particularly challenging during ongoing attack scenarios, where malicious traffic may temporarily dominate benign traffic. In such conditions, streaming network data exhibits severe class imbalance in favor of attack traffic, while device behavior remains heterogeneous, non-identically distributed (non-IID), and temporally evolving. Within federated learning environments, this imbalance can destabilize early aggregation rounds, dominant attack gradients bias the global model, distort decision boundaries, and degrade reliable discrimination of residual benign behavior. Since the server has no access to raw data, these effects can persist across communication rounds if not addressed… More >

Saeed Tavakoli^*, Ahmad Shakibaeinia, Najib Bouaanani

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081925 - 30 June 2026

Abstract Fluid–structure interaction (FSI) plays a critical role in civil engineering applications, directly influencing structural safety, resilience, and performance. However, the inherent multiphysics complexity of FSI problems presents significant challenges for numerical modeling, particularly under highly dynamic flow conditions. This study presents a fully Lagrangian mesh-free framework for FSI based on the moving particle semi-implicit (MPS) method. The approach couples an enhanced weakly compressible MPS (WC-MPS) fluid solver with a hybrid total–updated Lagrangian (TL–UL) MPS formulation for elastic solids. In the solid phase, strains are evaluated in the reference configuration, while momentum balance is enforced in… More >

Lilia Tightiz¹, L. Minh Dang^2,3, Hyosik Yang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081849 - 30 June 2026

Abstract We present QFedFormer, a federated transformer for dynamic electric vehicle (EV)-charging price prediction that combines quantization-aware training, SHAP-guided explainability, and blockchain-based incentives. The framework trains across distributed charging stations without centralizing user data, and programmable contracts set tariffs from forecasted demand and user-declared flexibility, while token rewards are derived from SHAP-based utility scores and anchored on-chain via Merkle proofs. On a real-world dataset, QFedFormer attains an energy-demand RMSE of 1.82±0.02 kWh and a tariff RMSE of 11.83±0.10 KRW/kWh (MAPE 2.7±0.2%) in the non-private baseline, outperforming FedAvg and Block-FeDL by 14.1% and 9.5More > Graphic Abstract

M. Saad Bin Ilyas¹, Sohail Masood Bhatti¹, Ghazanfar Latif^2,*, Sherif Abdelhamid³, Arfan Jaffar¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081720 - 30 June 2026

Abstract Federated learning (FL) has emerged as a promising approach for enabling collaborative model training across distributed Internet of Medical Things (IoMT) devices without sharing sensitive data. Existing FL frameworks face significant challenges in healthcare settings, including vulnerability to adversarial attacks, lack of verifiable update integrity, and limited robustness under heterogeneous data distributions. These limitations hinder reliable deployment in critical medical applications. To address these challenges, this paper proposes RP-IoMT, a robust and privacy-preserving FL framework that integrates secure multi-party computation (MPC), zero-knowledge proof-based gradient verification, and robust aggregation mechanisms. The objective of this work is… More >

Karim Gasmi^1,*, Afrah Alanazi², Inam Alanazi², Sahar Almenwer¹, Norah Alanazi¹, Sarah Almaghrabi³, Samia Yahyaoui⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081705 - 30 June 2026

Abstract Cardiac magnetic resonance imaging (MRI) segmentation is an essential aspect of quantitative cardiovascular analysis, facilitating accurate evaluation of ventricular volumes, myocardial mass, and functional parameters. Deep learning-based segmentation models have shown strong performance on benchmark datasets such as ACDC, but they remain challenging to deploy in real-world multi-centre settings. Data privacy laws make it hard to share data across institutions, and differences in imaging protocols and patient populations mean that data is not always distributed in the same way (non-IID). This can have a big impact on how well models work together and how well… More >

Naseer Muhammad Khan^1,2, Liqiang Ma^3,*, Majid Khan⁴, Sajjad Hussain⁵, Waleed Inqiad⁶, Tariq Feroze², Danial Jahed Armaghani^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081660 - 30 June 2026

Abstract Rock fracture behavior under stress is vital for risk evaluation in underground engineering excavation because the presence of water can significantly increase the extent of cracks and fractures in rock, leading to structural damage. This can result in catastrophic failures, including rock bursts, coal bursts, and water inrush. Hence, reliable prediction of rock damage and fracture processes is still lacking, which, in turn, enables the safe and efficient conduct of engineering projects in rock-mass environments. Thus, this study examines both dry and saturated sandstone samples under loading using Infrared Radiation (IR), Acoustic Emission (AE) monitoring,… More >

Antonio Gimeno, Emilio Larrodé^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081630 - 30 June 2026

Abstract This paper presents the development of a digital twin of a heavy-duty electric truck powered by a hybrid energy system based on a hydrogen fuel cell and a battery pack. The objective of the model is to analyze different energy management strategies and to determine how the power demand of a real route can be shared between both energy sources, while keeping the fuel cell within safe operating limits to preserve its service life. The digital twin simulates vehicle dynamics, traction, and regenerative braking, and the main operational constraints of the fuel cell, including minimum… More >

Joonhyeok Moon¹, Siheon Jeong¹, Byeonghyun Lee¹, Jeik Choi¹, Ki-Yong Oh^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081475 - 30 June 2026

Abstract Maintaining overhead distribution facilities inherently involves high risks for operators, where ensuring worker safety and operational efficiency remains a paramount challenge. In particular, automating the positioning of aerial work platforms is crucial to mitigate electrocution hazards during live-line maintenance tasks. This paper proposes a novel autonomous framework for detecting jumper lines that could be employed to estimate the optimal bucket position in live-line maintenance of overhead distribution systems. The proposed framework comprises three core modules to form a unified pipeline for autonomous field inspection: a 4D multi-modal map, Sparse-dense fusion network (SDFNet), and Rotational multi-pyramid… More >