Jinha Kim¹, Hwankuk Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.082504

Abstract The advent of 5th Generation (5G) mobile networks has introduced Network Slicing as a core mechanism for supporting heterogeneous vertical services—such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC) over a shared physical infrastructure, thereby significantly expanding the attack surface at the User Plane Function (UPF). Securing this multi-slice environment requires intrusion detection systems that can simultaneously accommodate the statistical heterogeneity of per-slice traffic and the stringent Quality of Service (QoS) constraints of real-time slices, yet the practical cost of obtaining high-quality labeled traffic in operational 5G cores remains… More >

Samuel Martínez-Gutiérrez^1,*, Carlos Gutiérrez¹, Alejandro Merino¹, Diego García-Álvarez², Daniel Sarabia¹

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.081247

Abstract Accurate modelling of power production in wind power systems is essential for optimizing their real-time operation and meeting technical or economic objectives. However, the precise modelling of wind turbine power output remains challenging, particularly when relying on conventional parametric models, which often struggle to capture complex or non-linear behaviors. This paper compares three modelling approaches to estimate the power produced by a real wind turbine (a Senvion MM82/2050 located in France): one parametric, based on analytical expressions of the power coefficient C_P(λ, β); another nonparametric, which uses Gaussian processes (GP) to probabilistically model the relationship between… More >

David Amilo^1,*, Khadijeh Sadri¹, Evren Hincal^1,2, Chinedu Izuchukwu³, Mohamed Hafez^4,5, Muhammad Farman^1,6,7, Kottakkaran Sooppy Nisar^8,9

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.077921

Abstract Spinal disorders, such as disk hernia and spondylolisthesis, affect millions worldwide, leading to chronic pain and reduced quality of life due to disruptions in biomechanical alignment. Traditional diagnostic methods often overlook the viscoelastic memory effects in spinal tissues, necessitating advanced models that integrate machine learning with fractional calculus for improved accuracy and interpretability. The research introduces a new fractional-order machine learning system that analyzes vertebral column abnormalities through biomechanical motion analysis by using the University of California, Irvine (UCI) vertebral column dataset. The system selects the best machine learning model from Random Forest (RF), Gradient… More >

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

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.081794

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 >

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

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.081676

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 >

Yenjou Wang¹, Chihtan Cheng², Jia-Wei Chang^3,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.080888

Abstract As large language models (LLMs) become increasingly integrated into enterprise decision-making processes, structural pressures such as version drift, cross-source evidence integration, and regulatory accountability have shifted the primary challenge from isolated generative performance to system-level consistency, traceability, and governability. This paper systematically reviews key technological developments relevant to enterprise requirements, including document perception, retrieval-augmented generation (RAG), hybrid RAG-KG architectures, fine-grained attribution evaluation, and multi-agent coordination. The analysis demonstrates that the main obstacle to enterprise LLM adoption is not model capability, but rather the structural gap between fragmented technical modules and the need for high-reliability decision-making. More >

Trong-Thua Huynh^1,*, De-Thu Huynh², Cong-Sang Duong¹, Hong-Son Nguyen¹, Quoc H. Nguyen³, Lam-Thanh Tu⁴

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.080616

Abstract Deep iris recognition models are often trained on Cartesian grids, whereas iris texture follows a concentric structure with angular periodicity. This representational mismatch can weaken rotation robustness and limit pupil-to-limbus context modeling, while many pipelines still rely on accurate segmentation masks. We propose RadialFormer, an efficient mask-free iris recognition framework that performs representation learning directly in the polar domain. The pipeline first estimates pupil/iris parameters (cx,cy,rin,rout) using a percentile radial-gradient operator with anatomical constraints, and then applies a crop-based polar transform to obtain a compact 64×512 unwrapped iris map. To better match polar… More >

Shuaiqi Song, Lijie Grace Zhang, James D. Lee^*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.080567

Abstract The theory of thermomechanical-electromagnetic coupling was constructed. The finite element analysis of thermo-visco-elastic-plastic-electromagnetic continuum was formulated. Then the problem of wave propagation in this continuum was solved in two stages. In Stage I, a nearly static thermomechanical solution of a hollow cylinder, subject to twist and temperature gradient, was obtained. Then, in Stage II, the problem of wave propagation of scalar and vector potentials, due to deformation and temperature gradient, was solved. In the second approach, in Stage I, the static electric field and static magnetic field are obtained through static scalar and vector potentials, More >

Tanveer Ahmad^1,*, Tahani Alsubait², Amina Salhi³, Amani Ibraheem⁴, Muhammad Asim Saleem⁵

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.080285

Abstract The rapid adoption of Edge-AI in smart edge-IoT environments has dramatically led to an augmented vulnerability to cyber risks arising from distributed learning, data heterogeneity, and adversarial manipulation. This paper proposes a new risk-aware adaptive learning model that federated Edge-AI systems explicitly simulates cyber risk in the process of local training and global aggregation. The proposed solution combines stochastic optimization and adversarial risk bounding with adaptive gradient correction to develop strong learning in non-IID data distributions and malicious client behavior. Convergence guarantees are defined by the theoretical analysis in the case of limited adversarial perturbations.… More >

Alanoud Al Mazroa¹, Fahad Masood², Bakri Hussain Awaji³, Mohammad Alhefdi⁴, Abeer Aljohani⁵, Jawad Ahmad^6,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.080134

Abstract The rapid integration of Internet of Things (IoT) devices and distributed energy resources into smart grids has improved monitoring, control, and energy efficiency. However, it also exposes the grid to cyberattacks and privacy risks, as increased connectivity and data exchange can significantly disrupt energy management and system stability. Studies focused on centralized cybersecurity mechanisms that lacked scalability and did not emphasize the inherent graph structure of power networks. This study proposes a privacy-preserving and cyber-resilient energy-optimization framework, FedGNN, for IoT-enabled smart grids that jointly integrates federated learning, graph neural network-based trust inference, and trust-aware energy dispatch.… More >