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 >

Ji Su Park^1,*, Pan Yi², Jong Hyuk (James) Park³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.083347 - 27 May 2026

Abstract This article has no abstract. 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 >

Irfan Saif Ud Din¹, Imran Siddique^2,3,4,5, Zohaib Zahid¹, Muhammad Nadeem⁶, Ibrahim Alraddadi^2,*, Taha Radwan^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081726 - 27 May 2026

Abstract This study examines the variable thermal conductivity and electroosmotic performance of Sutterby hybrid nanofluid (SBHNF) thin film flow over a stretched inclined sheet using an artificial neural network (ANN)-based on NARX (Multilayer Nonlinear Autoregressive Networks with Exogenous Inputs) multiple-layer backpropagation simulation with the Levenberg-Marquardt algorithm (LMA). AA7075 and AA7072 nanoparticles suspended in sodium alginate (SA) base fluid make up the hybrid nanofluid (HNF), which was selected due to its improved heat transfer properties and superior thermal conductivity. The model’s practical applicability is enhanced by melting heat, nonlinear thermal radiation, boundary slip, and Newtonian heating effects,… More >

Chaoshuai Duan, Yin Wang, Guohua Zhu^*, Xiaotian Zhang, Jiale Wang, Zhen Wang

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081676 - 27 May 2026

Abstract Hybrid fiber reinforced plastic (HFRP) composites, especially intra-layer carbon/glass hybrids, offer a promising balance of specific strength, impact resistance, and cost efficiency for thin-walled energy-absorbing structures. This study investigates the low-velocity impact response and energy absorption of intra-layer carbon/glass hybrid hat-shaped beams. Tensile and impact tests evaluated the effects of hybrid ratio and fiber orientation. A multiscale damage model based on micromechanical damage and failure criteria was established via Abaqus/VUMAT, integrating stress amplification factors to bridge micro-meso-macro scales. Experimental results show that carbon fibers aligned with the loading direction yield hybrid composites with superior tensile… 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 >