Bo Yang^1,*, Chao Liu²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.083726 - 27 May 2026

Abstract This article has no abstract. More >

Arslan Qayyum Khan¹, Muhammad Dawood Rasheed², Muhammad Huzaifa Naveed², Amorn Pimanmas^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.083654 - 27 May 2026

Abstract The construction industry’s substantial carbon footprint, primarily attributed to the production of Ordinary Portland Cement, necessitates a transition toward more sustainable alternatives. Geopolymer concrete (GPC), an innovative binder synthesized from industrial by-products like fly ash (FA), offers a promising low-carbon solution but is hindered by performance variability and a lack of standardized design protocols. This research addresses this critical barrier by developing robust predictive models for the compressive strength of FA-based GPC. Six machine learning algorithms, including Bagging, Categorical Boosting (CatBoost), K-Nearest Neighbors (KNN), LightGBM, Random Forest Regressor (RFR), and eXtreme Gradient Boosting (XGBoost), were… More >

Marco Roccetti^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.082258 - 27 May 2026

Abstract Geospatial health risk signals, characterized by associations with high magnitude statistical significance, may frequently originate from circumscribed observational data streams. When these signals are fueled by massive N-size datasets, the large dimensional scale of the sample can induce a misleading interpretation of local evidence as a statistically significant risk inflation. The objective of this study is to verify whether such health risk configurations constitute geospatial structural artifacts: namely, stochastic distortions generated by the spatial information of local health repositories that, despite their massive scale, may remain fundamentally distant from broader contextual realities. To this aim,… More >

Hyeonsoo Yu¹, Hwankuk Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.082095 - 27 May 2026

Abstract The introduction of the Open Radio Access Network (O-RAN) architecture enhances network flexibility but introduces novel security threats targeting open interfaces and the RAN Intelligent Controller (RIC). Particularly in the Near-RT RIC environment, an effective Intrusion Detection System (IDS) that satisfies strict near-real-time constraints of within 1 s is essential to defend against cyber attacks. This paper proposes an Artificial Intelligence (AI)-based IDS xApp designed for real-time cyber attack monitoring in the O-RAN Near-RT RIC environment, and quantitatively analyzes its anomaly detection performance and inference latency characteristics against multi-layer security threats utilizing Open RAN Centralized… More >

Quynh-Anh Thi Bui^1,*, Son Hoang Trinh¹, Maryam Sayadi², Reza Khanali³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081794 - 27 May 2026

Abstract Geopolymer concrete has attracted increasing attention as a lower-carbon alternative to ordinary Portland cement concrete because it can utilize aluminosilicate-rich industrial by-products while still achieving satisfactory mechanical performance. However, the 28-day compressive strength of steel fiber-reinforced geopolymer concrete (SFGPC) is governed by multiple interacting mixture variables, which makes reliable prediction difficult, especially for medium-sized experimental datasets. This study developed an interpretable deep-learning framework to predict the 28-day compressive strength (CS28) of SFGPC using an original experimental dataset of 189 mixtures produced under a consistent laboratory protocol in Vietnam. The dataset covered nine mixture variables, including… More >

Zhenhua Yang^1,2, Yifan Li^1,2, Aimin Yang^1,2,*, Jie Li^2,3, Tao Xue^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081754 - 27 May 2026

Abstract Sinter quality prediction in iron ore sintering is a challenging computational modeling problem because of highly nonlinear process behavior, strong cross-variable interactions, and disturbances caused by changing operating conditions. This study develops a data-driven multi-index soft-sensing framework for sinter quality prediction by combining feature selection and hierarchical model optimization. An improved binary Greylag Goose Optimization algorithm is first employed to identify a compact subset of informative variables, reducing redundancy and multicollinearity in the original process data. A hierarchical two-stage Greylag Goose Optimization strategy is then designed to optimize the hyperparameters of a support vector regression… More >

Huilan Wu, Yijun Liu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081675 - 27 May 2026

Abstract Metasurface design often requires solving field distributions across varying structural parameters and frequencies, where neural operators offer a promising avenue for fast prediction. However, conventional neural operators have problems with degradation of the accuracy in multi-scale structural analysis. In this work, we propose a Generative Residual Enhanced Neural Operator (GRE-NO) framework that introduces a generative residual network to model the systematic bias of the main predictor. The core model retains the DeepONet architecture with both branch and trunk networks implemented using Fourier Neural Operators, combining strong generalization and efficient global representation. To handle the complexity More >

Chuanjiang Chen¹, Junyong Zhou^1,*, Binbin Du¹, Miaosi Dong^2,*, Liwen Zhang¹, Bitang Zhu³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081546 - 27 May 2026

Abstract Computer vision has been widely adopted in intelligent construction monitoring; however, existing studies primarily focus on identifying individual construction elements or isolated activities, with limited capability for integrated monitoring of complete construction workflows. Such workflow-level automation is a prerequisite for intelligent construction and unmanned job sites. To address the challenge of reliable visual recognition in drill-and-blast tunnel environments characterized by uneven illumination, localized glare, and dust interference, this study proposes a methodological framework for construction workflow recognition at the tunnel face using computer vision and context reasoning. The framework consists of three components: (1) a… More >

Basma Souayeh^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081163 - 27 May 2026

Abstract This study conducts a comprehensive numerical investigation of magnetohydrodynamic (MHD) mixed convection and entropy generation in a two-dimensional square cavity filled with a ternary hybrid nanofluid. The working fluid consists of Multi-Walled Carbon Nanotubes (MWCNT), Copper (Cu), and Ferric Oxide (Fe₃O₄) nanoparticles dispersed in water, selected for their superior thermal properties. Two vertically aligned, saw-tooth-shaped cooling structures are embedded along the left and right walls of the cavity, with four distinct configurations considered based on their vertical positioning. An externally imposed uniform magnetic field is applied to assess its influence on fluid flow, heat transfer, and… More >

Kiran Deep Singh¹, Prabh Deep Singh², Narinder Kaur³, Jawad Khan^4,*, Dildar Hussain⁵, Yeong Hyeon Gu^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080915 - 27 May 2026

Abstract In smart healthcare systems, Image data of critical patients is essential in controlling and diagnosing the disease development. To acquire the medical images, traditional methods encountered the difficulty of generating cost-effective data. This research work introduces a novel and innovative approach to collect high-quality image data from individuals with atypical clinical presentations. Initially, a new Internet of Medical Things (IoMT) image collection architecture is introduced. This design uses edge intelligence and motion-static synergy to make it easier to record both coarse-grained and fine-grained patient images. This study introduces an image acquisition technique that leverages edge… More >