Qianshu Yang, Shuangxi Liu^*, Xianyu Wu, Wei Zhao

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079895 - 08 May 2026

Abstract Path planning for unmanned systems in complex environments must simultaneously satisfy safety, kinematic feasibility, and real-time performance requirements. Monte Carlo Tree Search (MCTS) offers advantages such as model-free operation, strong interpretability, and anytime planning capability, but it suffers from large branching factors, excessive search depths, and poor convergence under sparse reward conditions in high-dimensional state spaces. To address these challenges, this paper proposes a Heuristic Rolling Monte Carlo Tree Search (HRMCTS) framework. First, the path planning problem is formulated as a constrained Markov decision process, where the state consists of position and heading, and actions… More >

Jinsung Park^1,#, Jaehyuk Lee^1,2,#, Eunchan Kim^1,3,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079734 - 08 May 2026

Abstract Accurate short-term load forecasting is essential for reliable power system operation, particularly under the increasing uncertainty caused by abnormal weather and socio-economic fluctuations. This study presents a month-conditioned boosting framework that integrates SHapley Additive Explanations (SHAPs) into model refinement. A baseline XGBoost model was first compared with linear and tree-based regressors, followed by enhancements through lagged and rolling-window features as well as loss weighting for vulnerable months. To further improve the performance, SHAP analysis was employed to identify the dominant error-contributing features, which guided the construction of targeted month-specific interaction terms for retraining. Experimental results More >

Uzma Anis Takkalki¹, Sayed Ahmed Imran Bellary², Sher Afghan Khan³, Abdul Aabid^4,*, Muneer Baig⁴

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.4, 2026, DOI:10.32604/fdmp.2026.073927 - 07 May 2026

Abstract The rapid development of space transportation systems and high-speed military aircrafts have intensified interest in turbulent separated flows, particularly under transonic and supersonic conditions. Such flows commonly arise downstream of sudden expansions, where separation and subsequent reattachment generate strong shear layers, increased drag, and a low-pressure recirculation region at the base. In this study, the control of base pressure downstream of a sudden expansion is investigated numerically using a passive bullet-shaped rib. A jet issuing from a nozzle is discharged abruptly into a duct of 25 mm diameter, producing a separated flow with pronounced recirculation.… More >

Yuting Tang^1,2,3,4, Yu Li^2,3,*, Xingyu Xiang^2,3,5, Jiaxiang Luo^1,2,3,4, Weien Zhou^2,3, Wen Yao^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079578 - 27 April 2026

Abstract Topology optimization is widely used in lightweight structural design to determine optimal material distributions. However, density-based results are represented in an implicit pixel-wise form with blurred boundaries and jagged contours, which limits their direct use in engineering design and manufacturing. This study proposes a two-stage post-processing framework to reconstruct topology optimization results into explicit parametric geometries while preserving structural performance. The framework first extracts and processes contour points from the optimized density field and reconstructs the geometry using Non-Uniform Rational B-Splines (NURBS). A subsequent shape optimization step based on the fixed-grid finite element method (FG-FEM) More >

Jaeseung Lee¹, Jehyeok Rew^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.076888 - 27 April 2026

Abstract The engine serves as the primary component that generates power and drives vehicle movement. Given its critical role, accurately diagnosing engine faults is essential for ensuring vehicle safety and reliability. Recent advances in machine learning (ML) have enabled the development of artificial intelligence (AI)-based diagnostic models with strong predictive performance. However, the lack of transparency in these models constrains user confidence in their diagnostic outcomes. While explainable AI (XAI) methods such as local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP) have been introduced to improve interpretability, their reliance on visual outputs requires manual… More >

Álvarez-Hostos Juan C.^1,2,3,*, Zambrano-Carrillo Javier A.⁴, Sarache-Piña Alirio J.⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.076279 - 27 April 2026

Abstract The Element-Free Galerkin (EFG) method was originally developed for linear solid mechanics problems, using Moving Least Squares (MLS) approximations to construct shape functions for the numerical approximation of the displacement field and its variations within the weak form of the equilibrium equations. Over the past decades, it has evolved into a versatile meshfree framework applicable to a broad spectrum of engineering and scientific problems. This review provides a comprehensive account of the main advances in EFG, tracing its development from the original formulation and early challenges to the strategies devised to overcome them. Subsequent improvements More >

Dmytro Tymoshchuk^1,*, Oleh Yasniy¹, Iryna Didych², Pavlo Maruschak^3,*, Yuri Lapusta⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077062 - 09 April 2026

Abstract This article presents an approach to predicting the hysteresis behavior of shape memory alloys (SMAs) using a Temporal Convolutional Network (TCN) deep learning model, followed by the interpretation of the results using Explainable Artificial Intelligence (XAI) methods. The experimental dataset was prepared based on cyclic loading tests of nickel-titanium wire at loading frequencies of 0.3, 0.5, 1, 3, and 5 Hz. For training, validation, and testing, 100–250 loading-unloading cycles were used. The input features of the models were stress σ (MPa), cycle number (Cycle parameter), and loading-unloading stage indicator, while the output variable was strain… More >

Oskar Kapuśniak¹, Adam Piórkowski^2,*, Julia Lasek³, Karolina Nurzyńska⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076062 - 09 April 2026

Abstract The efficacy of Active Shape Models (ASM) for automated ventricular segmentation was evaluated to address the computational demands of manual segmentation and the interpretability limitations of deep learning. A statistical shape model was constructed using a limited cohort of 19 Coronary Computed Tomography Angiography (CCTA) scans derived from patients with diverse cardiac abnormalities. Principal Component Analysis (PCA) was employed to encapsulate morphological variability, and strict point correspondence was enforced to maintain topological consistency. Validation was conducted via leave-one-out cross-validation, benchmarking automated segmentations against expert-delineated ground truths using the Dice Similarity Coefficient (DSC) and Hausdorff Distance More >

Sarmad Dheyaa Azeez¹, Muhammad Ilyas^2,*, Saadaldeen Rashid Ahmed^3,4

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074930 - 09 April 2026

Abstract The rapid evolution of 5G-enabled Software Defined Networks (SDNs) has transformed modern communication systems by enabling ultra-low latency, massive connectivity, and high throughput. However, the increased complexity of traffic flows and the rise of sophisticated cyber-attacks such as Distributed Denial of Service (DDoS), Botnets, Fake Base Stations, and Zero-Day exploits have made intrusion detection a critical challenge. Traditional Intrusion Detection System (IDS) approaches often suffer from poor gen-eralization, high false positives, and lack of interpretability, making them unsuitable for dynamic 5G environments. This paper presents a novel Graph Neural Network (GNN) with Multi-Head Attention (MHA)… More >

Zakaria S. M. Abdelhalim^*, Nahla Belal, Mohamed Seifeldin

Journal of Cyber Security, Vol.8, pp. 153-169, 2026, DOI:10.32604/jcs.2026.079097 - 06 April 2026

Abstract The rapid expansion of IoT and cloud services has increased the scale and complexity of modern networks, making intrusion detection challenging. Although deep learning-based Network Intrusion Detection Systems (NIDS) often report high accuracy, such metrics can be misleading on highly imbalanced datasets, where performance is dominated by majority classes and rare attacks remain poorly detected. This issue stems from global optimization strategies that encourage models to rely on dominant feature patterns, limiting their ability to capture the class-specific features required to identify infrequent attack types. To address this limitation, this work proposes a domain knowledge-guided… More >