Kun-Lin Tsai¹, Shih-Ting Chiu², Chihhsiong Shih², Fang-Yie Leu^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.083958 - 28 August 2026

Abstract With the quick development of 5G networks, network slicing and Open Radio Access Network (O-RAN) have become key technologies for improving network resource-allocation efficiency and flexibility. However, network slicing also faces intrusion-detection challenges, particularly for detecting DDoS attacks, which are difficult to detect due to traffic being silently transmitted across multiple sub-slices. To address this problem, this paper proposes a 5G network slicing intrusion detection mechanism, called the DDoS Defense Model on 5G Network Slices (2D5NS) which integrates machine learning and real-time traffic monitoring techniques to detect and mitigate DDoS attacks within an O-RAN. This… More >

Chia-Hui Liu^*, Chen-Chuan Cheng

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.083813 - 28 August 2026

Abstract Industrial air quality forecasting remains challenging due to nonlinear pollutant formation, localized emissions, meteorological variability, and nonstationary spatiotemporal dependencies among monitoring stations. This study proposes FFTGNet, a frequency-aware spatiotemporal graph neural network for multi-pollutant forecasting in industrial air quality systems. It integrates an FFT-guided dominant-period estimation and period-folding module with a temporal-to-spatial graph backbone composed of TemporalGLU and Chebyshev graph convolution. The frequency-guided module reorganizes input sequences into intra-period and inter-period representations, TemporalGLU adaptively filters nonlinear temporal fluctuations and short-term spikes, and ChebGCN propagates information across inter-station spatial dependencies. Experiments were conducted using five years… More >

Shamsh Parveen¹, Mehtab Alam^2,*, Ashraf Ali³, Abdullah Alourani⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.083687 - 28 August 2026

Abstract Accurate prediction of complex failure modes in anisotropic composite structures—specifically matrix cracking, fiber rupture, and delamination (stratification)—remains a central challenge in computational fracture mechanics. The primary goal of this work is to bridge the gap between high-fidelity physical modeling and computational efficiency. While the extended finite element method (XFEM) enables mesh-independent crack modeling, its computational cost limits scalability. This work proposes a deep learning–accelerated extended finite element framework (DL-XFEM) that couples physically admissible XFEM fields with a neural network surrogate to predict incremental crack growth. XFEM is employed to generate stress-intensity factors and fracture-consistent state… More >

Pingan Peng^1,2, Qi Zhang^1,*, Ya Liu¹, Yue Han¹, Zhida Jiang¹, Linli Chen¹, Xuefeng Huo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.083624 - 28 August 2026

Abstract Unfilled goafs located beneath urban areas pose a significant threat to surface safety, and microseismic monitoring is an important tool for capturing rock-mass microfractures and supporting early warning. However, under field conditions, existing sequential models face challenges such as high computational complexity and deep feature degradation when deployed on edge devices, primarily due to the non-stationary characteristics of microseismic signals and the presence of complex ambient background noise. To address these issues, this paper proposes the Hierarchical Network with Manifold Hybrid Connection (H-NET-mHC), a lightweight manifold-aware state space model designed for edge computing. The model… More >

Eman Attallah H. Aljabarti, Mohd Yamani Idna Idris^*, Ainuddin Wahid Abdul Wahab

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.083479 - 28 August 2026

Abstract Facial emotion recognition (FER) aims to recognize and classify human emotional expressions accurately. Although there has been significant progress in developing FER models with respectable accuracy, the accuracy still has substantial room for improvement. These claims are supported by several factors, including poor parameter tuning, class imbalance, dataset bias, generalization limitations, and inefficient preprocessing. These factors make it more difficult to capture hierarchical and high-level features in training data. To address these limitations, therefore, this work develops and fine-tunes a deep convolutional neural network-based model to effectively learn discriminative facial features. First, the data are… More > Graphic Abstract

Juncheng Zhang^1,2, Xuhao Wei^1,2, Xiaolei Gu³, Haixing Zhao^4,*, Zhonglin Ye^1,2,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.083131 - 28 August 2026

Abstract Graph structure learning depends heavily on the integrity and reliability of graph data. However, real-world graphs often contain noise, missing information, and bias, thereby limiting the expressive capacity of existing models. Single-layer structure learning methods fail to simultaneously capture local interactions and the global structure. Furthermore, they rely excessively on high-quality labeled data, leading to label scarcity issues and high annotation costs. To address these challenges, we propose a self-supervised dual-layer structure-driven graph structure learning method, termed D2GSL. Specifically, D2GSL constructs a semantic similarity channel and a spectral feature channel to model node relationships from More >

Feng Gao¹, Guotao Meng², Yuepeng Sun^3,*, Xianglin Huang¹, Heyi Yang¹, Nuwen Xu^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.082323 - 28 August 2026

Abstract During flood seasons in hydropower expansion projects, reservoir level rise may hydraulically connect the excavation pit and shaft to the reservoir. The resulting high-head boundary can impose a strong hydraulic gradient across the unexcavated blocking section, while staged bench excavation further redistributes stresses. This study proposes a three-dimensional hydro-mechanical coupled numerical framework based on Fast Lagrangian Analysis of Continua in 3 Dimensions (FLAC3D), which explicitly accounts for pore water pressure evolution, asymmetric hydraulic boundary conditions, and staged bench excavation disturbance. The framework enables systematic evaluation of rock-plug stability under different retained lengths using plastic-zone connectivity,… More >

Matías Mariqueo^1,2, Rodrigo Valle³, César Garrido⁴, Sebastián Andrés Toro⁵, Víctor Tuninetti^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.078932 - 28 August 2026

Abstract Lifting and maintenance of existing Glass-Fiber Reinforced Polymer (GFRP) absorption towers pose significant structural risks, as these units were often not designed for such loads. Previous research has established that lifting these sections without reinforcement results in critically low factors of safety (1.9–2.5) due to high stress concentrations on the composite flanges. This paper presents the design, analysis, and validation of a novel bolt-on steel lifting system to mitigate these structural risks. A system comprising a 32 mm thick ASME A36 steel blind flange with integrated lifting lugs and half-moon stiffeners was designed to mount… More >

Sathishkumar Veerappampalayam Easwaramoorthy^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.087043 - 27 July 2026

Abstract This article has no abstract. More >

Xuan Kien Pham¹, Van Dat Nguyen^2,*, Van Thanh Phan^3,*, Duc Trien Nguyen^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.086196 - 27 July 2026

Abstract Accurate energy consumption forecasting faces two major challenges: limited historical data and complex consumption patterns. To address these challenges, this study proposes a new hybrid framework named the Decomposition-based Grey-Neuro-Fuzzy Architecture (DeGNA). The model first uses the Denton method to convert limited annual records into high-frequency monthly data. Next, it applies STL decomposition to separate the data into trend, seasonal and residuals components. A rolling-window GM(1,1) model is then used to predict the main growth trend, while a GWO-optimized ANFIS model uses economic indicators (IIP and FDI) to forecast complex seasonal changes. This study evaluates… More >