Lei Wang^1,2, Menghan Wei², Ziwei Huangfu³, Shunjie Zhu², Xuejian Ge^1,*, Zhengquan Li⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-32, 2026, DOI:10.32604/cmc.2025.071295 - 09 December 2025

Abstract Iterative Learning Control (ILC) provides an effective framework for optimizing repetitive tasks, making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems (ITS). This paper presents a systematic review of ILC’s developmental progress, current methodologies, and practical implementations across these two critical domains. The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows. Through case studies, it evaluates demonstrated improvements in positioning accuracy, surface finish quality, and production throughput. Furthermore, the study examines ILC’s applications in ITS, with particular focus on vehicular motion control More >

Dong Hyun Lee¹, Na Kyung Lee², Young Seo Lee^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.069976 - 09 December 2025

Abstract There has been an increasing emphasis on performing deep neural network (DNN) inference locally on edge devices due to challenges such as network congestion and security concerns. However, as DRAM process technology continues to scale down, the bit-flip errors in the memory of edge devices become more frequent, thereby leading to substantial DNN inference accuracy loss. Though several techniques have been proposed to alleviate the accuracy loss in edge environments, they require complex computations and additional parity bits for error correction, thus resulting in significant performance and storage overheads. In this paper, we propose FeatherGuard,… More >

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

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 >

Yassir M. Abbas^*, Abdulaziz Alsaif^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1789-1819, 2025, DOI:10.32604/cmes.2025.073088 - 26 November 2025

Abstract The rapid advancement of three-dimensional printed concrete (3DPC) requires intelligent and interpretable frameworks to optimize mixture design for strength, printability, and sustainability. While machine learning (ML) models have improved predictive accuracy, their limited transparency has hindered their widespread adoption in materials engineering. To overcome this barrier, this study introduces a Random Forests ensemble learning model integrated with SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) to model and explain the compressive strength behavior of 3DPC mixtures. Unlike conventional “black-box” models, SHAP quantifies each variable’s contribution to predictions based on cooperative game theory, which enables… More >

Hansam Cho¹, Seokho Moon¹, Sunhyeok Hwang¹, Seoung Bum Kim^1,*, Younghoon Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1941-1967, 2025, DOI:10.32604/cmes.2025.072455 - 26 November 2025

Abstract Accurately determining when and what to remanufacture is essential for maximizing the lifecycle value of industrial equipment. However, existing approaches face three significant limitations: (1) reliance on predefined mathematical models that often fail to capture equipment-specific degradation, (2) offline optimization methods that assume access to future data, and (3) the absence of component-level guidance. To address these challenges, we propose a data-driven framework for component-level decision-making. The framework leverages streaming sensor data to predict the remaining useful life (RUL) without relying on mathematical models, employs an online optimization algorithm suitable for practical settings, and, through More >

Haoyun Fan¹, Soon Poh Yap^1,*, Shengkang Zhang¹, Ahmed El-Shafie^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 17-65, 2025, DOI:10.32604/cmes.2025.070880 - 30 October 2025

Abstract Data-driven research on recycled aggregate concrete (RAC) has long faced the challenge of lacking a unified testing standard dataset, hindering accurate model evaluation and trust in predictive outcomes. This paper reviews critical parameters influencing mechanical properties in 35 RAC studies, compiles four datasets encompassing these parameters, and compiles the performance and key findings of 77 published data-driven models. Baseline capability tests are conducted on the nine most used models. The paper also outlines advanced methodological frameworks for future RAC research, examining the principles and challenges of physics-informed neural networks (PINNs) and generative adversarial networks (GANs), More >

Canlin Cui¹, Junyu Yao^1,*, Heng Xia^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4275-4306, 2025, DOI:10.32604/cmc.2025.069097 - 23 October 2025

Abstract High-quality data is essential for the success of data-driven learning tasks. The characteristics, precision, and completeness of the datasets critically determine the reliability, interpretability, and effectiveness of subsequent analyzes and applications, such as fault detection, predictive maintenance, and process optimization. However, for many industrial processes, obtaining sufficient high-quality data remains a significant challenge due to high costs, safety concerns, and practical constraints. To overcome these challenges, data augmentation has emerged as a rapidly growing research area, attracting considerable attention across both academia and industry. By expanding datasets, data augmentation techniques improve greater generalization and more… More >

Minjie Shao, Tielin Shi, Qi Xia^*, Shiyuan Liu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2685-2703, 2025, DOI:10.32604/cmes.2025.068078 - 30 September 2025

Abstract A data-driven model of multiple variable cutting (M-VCUT) level set-based substructure is proposed for the topology optimization of lattice structures. The M-VCUT level set method is used to represent substructures, enriching their diversity of configuration while ensuring connectivity. To construct the data-driven model of substructure, a database is prepared by sampling the space of substructures spanned by several substructure prototypes. Then, for each substructure in this database, the stiffness matrix is condensed so that its degrees of freedom are reduced. Thereafter, the data-driven model of substructures is constructed through interpolation with compactly supported radial basis More >

Cecilia Castro¹, Victor Leiva^2,*, Franco Basso^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1481-1543, 2025, DOI:10.32604/cmes.2025.067992 - 31 August 2025

Abstract Metaverse technologies are increasingly promoted as game-changers in transport planning, connected-autonomous mobility, and immersive traveler services. However, the field lacks a systematic review of what has been achieved, where critical technical gaps remain, and where future deployments should be integrated. Using a transparent protocol-driven screening process, we reviewed 1589 records and retained 101 peer-reviewed journal and conference articles (2021–2025) that explicitly frame their contributions within a transport-oriented metaverse. Our review reveals a predominantly exploratory evidence base. Among the 101 studies reviewed, 17 (16.8%) apply fuzzy multi-criteria decision-making, 36 (35.6%) feature digital-twin visualizations or simulation-based testbeds,… More > Graphic Abstract

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 >