Christina Stoycheva¹, Eva S. Savova², Rosen S. Sokolov², Ekaterina Kozuharova^1,*

Phyton-International Journal of Experimental Botany, Vol.95, No.8, 2026, DOI:10.32604/phyton.2026.087714 - 28 August 2026

Abstract Paeonia peregrina Mill. is a medicinal plant of conservation concern in Bulgaria, where its petals and roots are harvested from wild populations for pharmaceutical use. Developing effective propagation methods is therefore essential for its conservation and potential cultivation. This preliminary study investigated selected aspects of the reproductive biology of P. peregrina, including pollination, seed formation, seedling development, and vegetative propagation. Field observations and experiments were conducted during 2024–2025 in a natural population near the town of Boboshevo, southwestern Bulgaria, while propagation trials were performed under ex situ conditions. To assess the role of pollinators, flowers were isolated from… More >

Yan-Wei Li, David Chunhu Li^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.086216 - 28 August 2026

Abstract As urban transportation systems grow increasingly complex, accurate and interpretable traffic congestion forecasting is critical. While existing deep learning models utilize graph neural networks (GNNs) and attention mechanisms, they often struggle with physical consistency under extreme scenarios. To address this, we propose the Physics-Informed Explainable Spatial-Temporal Graph Attention Network (PI-X-STGAT). Our framework models road segments as graph nodes, integrating traffic, weather, and cyclical temporal features. The architecture comprises a Context-Aware Graph Attention Network (GAT) enhanced with Node Adaptive Parameter Learning (NAPL) for capturing dynamic spatial dependencies, a Gated Recurrent Unit (GRU) layer for temporal evolution,… 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 >

Xue-Wei Liu¹, Shao-Qiang Ye², Feng Qin³, Kai-Qing Zhou^1,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085316 - 13 August 2026

Abstract Weighted fuzzy production rules (WFPRs) provide superior expressiveness and interpretability in knowledge engineering area. However, manual construction of WFPRs is labor-intensive, time-consuming, and inherently subjective, which greatly restricts their practical application. The back propagation neural network (BPNN) has been widely adopted for automatic WFPR extraction. Nevertheless, its high sensitivity to initial weight configurations frequently results in premature convergence to local optima, generating redundant, poorly interpretable rule sets that compromise the inherent interpretability advantage of WFPRs. This paper proposes an elite dynamic scout-guided grey wolf optimizer (EDSG-GWO) and integrates it into a BPNN-based WFPR extraction framework… More >

Yujie Yan^1,2, Na An², Yanling Wang^1,*, Xiongwei Liu², Cheng Ji², Shu Jiang³

Energy Engineering, Vol.123, No.9, 2026, DOI:10.32604/ee.2025.070608 - 06 August 2026

Abstract Hydraulic fracturing technology is regarded as the most prevalent and effective means for unlocking tight natural fractured sandstone reservoirs and understanding the fracture and pre-existing natural fracture interaction is critical in the hydraulic fracturing design. Based on the global cohesive element model, the geological and engineering parameters were compared to explore the stimulation effectiveness. Numerical simulations demonstrate that when hydraulic fractures encounter natural fractures, various phenomena such as sliding, termination, and crossing occur, demonstrating the complex mechanical interaction. As the joint fracture energy (JFE) increases, a decrease in the total length and width of the… More >

Kiran Asma, Muhammad Asif Zahoor Raja^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083532 - 27 July 2026

Abstract The accelerated digital transformation of critical infrastructure has yielded unprecedented system interconnectivity, enhancing operational efficiency, simultaneously expanding the epidemiological propagation surface in heterogeneous networks. A novel machine learning-driven neuroarchitecture is designed in the present study, leveraging multilayer autoregressive exogenous neural networks (ARXNNs) iteratively trained with the Levenberg Marquardt (LM) algorithm, i.e., ARXNNs-LM, to address the intricate temporal dynamics of nonlinear fractional epidemiological computer virus propagation in the networks. The proposed ARXNNs-LM methodology effectively models the dynamic state transitions between susceptible, infected, and recovered systems. The dataset is synthesized through the application of the Grünwald–Letnikov (GL)… More >

V. V. Poplygin^1,*, A. Dieng², Min Wang³, Xian Shi³

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2025.074170 - 18 June 2026

Abstract Hydraulic fracturing in carbonate reservoirs presents unique challenges due to their complex pore structures and heterogeneous mechanical properties. This paper explores the application of statistical methods to improve fracture prediction and optimization in carbonate formations. Hydraulic fracturing is actively carried out on these formations. In order to properly plan hydraulic fracturing, it is necessary to identify the main factors affecting oil production after hydraulic fracturing. This study introduces an integrated framework combining information amount theory (IAT) and Gray relational analysis (GRA) to identify and rank the dominant parameters controlling hydraulic fracturing performance in heterogeneous carbonate… More >

Yu Yang, Yuheng Gu^*, Zhuoyun Yang, Shangjun Wu

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077275 - 09 April 2026

Abstract The performance of network traffic detection heavily relies on high-quality annotated data, yet the widespread presence of noisy labels in real-world scenarios severely undermines model reliability and generalization. Existing methods predominantly rely on training dynamics signals and struggle to distinguish between noisy labels and valuable hard samples, leading to diminished model sensitivity to emerging threats. To address this fundamental challenge, this paper proposes a noise-tolerant label recognition and correction framework based on graph-structured neighbor consistency (NCRT). The framework leverages the inherent clustering characteristics of network traffic in feature space, constructs an adaptive K-nearest neighbor graph… More >

Xiaole Jiang¹, Jingqi Huang^2,*, Xu Zhao^1,*, Wenlong Ouyang³, Xianghui Zhao⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078230 - 30 March 2026

Abstract To address the complex seismic response of long tunnels longitudinally crossing heterogeneous geological formations, this study proposes a three-dimensional SV-wave oblique-incidence input method that accounts for the initial disturbance of the wave field induced by geological heterogeneity. The method transforms equivalent two-dimensional free-field responses into equivalent nodal forces applied at the boundaries of a 3D numerical model. A longitudinally heterogeneous “hard-soft-hard” site and tunnel system is established, in which the surrounding rock is modeled using the Mohr-Coulomb constitutive law, while the concrete lining is described by the concrete damaged plasticity model. The deformation patterns and… More >

Najat Almutairi¹, Mohammed Messaoudi², Faisal Muteb K. Almalki³, Sayed Saber^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.076371 - 30 March 2026

Abstract This paper introduces a novel fractional-order model based on the Caputo–Fabrizio (CF) derivative for analyzing computer virus propagation in networked environments. The model partitions the computer population into four compartments: susceptible, latently infected, breaking-out, and antivirus-capable systems. By employing the CF derivative—which uses a nonsingular exponential kernel—the framework effectively captures memory-dependent and nonlocal characteristics intrinsic to cyber systems, aspects inadequately represented by traditional integer-order models. Under Lipschitz continuity and boundedness assumptions, the existence and uniqueness of solutions are rigorously established via fixed-point theory. We develop a tailored two-step Adams–Bashforth numerical scheme for the CF framework More >