Guangyu Xu^1,2, Siyuan Xu³, Siyu Lu^4,*, Yuxin Liu¹, Bo Yang¹, Junmin Lyu⁵, Wenfeng Zheng^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4037-4053, 2025, DOI:10.32604/cmes.2025.074901 - 23 December 2025

Abstract Robust stereo disparity estimation plays a critical role in minimally invasive surgery, where dynamic soft tissues, specular reflections, and data scarcity pose major challenges to traditional end-to-end deep learning and deformable model-based methods. In this paper, we propose a novel disparity estimation framework that leverages a pretrained StyleGAN generator to represent the disparity manifold of Minimally Invasive Surgery (MIS) scenes and reformulates the stereo matching task as a latent-space optimization problem. Specifically, given a stereo pair, we search for the optimal latent vector in the intermediate latent space of StyleGAN, such that the photometric reconstruction… More >

C. M. Fu^a, M. Z. Ge^b, X. Q. Zhang^c, W. Yan^c,*

Chalcogenide Letters, Vol.22, No.8, pp. 665-677, 2025, DOI:10.15251/CL.2025.228.665

Abstract Constructing heterojunctions represents a crucial strategy for enhancing semiconductor photocatalysts. In this study, the C-MoO₂/Cd_0.9Zn_0.1S S-scheme heterojunction composite was successfully fabricated through a self-assembly approach. XPS analysis confirmed the spontaneous transfer of intrinsic electrons from Cd_0.9Zn_0.1S to C-MoO₂ in the dark, establishing an internal electric field at the C-MoO2/Cd_0.9Zn_0.1S interface. Under visible light irradiation, the C-MoO₂/Cd_0.9Zn_0.1S composite exhibited significantly enhanced hydrogen evolution activity, achieving a 6.3-fold improvement compared to pristine Cd_0.9Zn_0.1S. PL, TRPL, and electrochemical measurements collectively demonstrated that the incorporation of C-MoO₂ effectively suppressed the recombination of photogenerated electrons in Cd_0.9Zn_0.1S. The outstanding photocatalytic performance and improved charge More >

Byeongjun Kim^1,#, Seung-Hun Lee^2,#, Won-Du Chang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1237-1252, 2025, DOI:10.32604/cmes.2025.073530 - 26 November 2025

Abstract Side-scan sonar (SSS) is essential for acquiring high-resolution seafloor images over large areas, facilitating the identification of subsea objects. However, military security restrictions and the scarcity of subsea targets limit the availability of SSS data, posing challenges for Automatic Target Recognition (ATR) research. This paper presents an approach that uses Cycle-Consistent Generative Adversarial Networks (CycleGAN) to augment SSS images of key subsea objects, such as shipwrecks, aircraft, and drowning victims. The process begins by constructing 3D models to generate rendered images with realistic shadows from multiple angles. To enhance image quality, a shadow extractor and More >

Yassir M. Abbas^*, Abdulaziz Alsaif^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1789-1819, 2025, DOI:10.32604/cmes.2025.073088 - 26 November 2025

Abstract The rapid advancement of three-dimensional printed concrete (3DPC) requires intelligent and interpretable frameworks to optimize mixture design for strength, printability, and sustainability. While machine learning (ML) models have improved predictive accuracy, their limited transparency has hindered their widespread adoption in materials engineering. To overcome this barrier, this study introduces a Random Forests ensemble learning model integrated with SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) to model and explain the compressive strength behavior of 3DPC mixtures. Unlike conventional “black-box” models, SHAP quantifies each variable’s contribution to predictions based on cooperative game theory, which enables… More >

Faiza Benabdallah¹, Kaouther Ghachem¹, Walid Hassen², Haythem Baya², Hind Albalawi³, Lioua Kolsi^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1839-1861, 2025, DOI:10.32604/cmes.2025.071678 - 26 November 2025

Abstract Current developments in magnetohydrodynamic (MHD) convection and nanofluid engineering technology have have greatly enhanced heat transfer performance in process systems, particularly through the use of carbon nanotube (CNT)–based fluids that offer exceptional thermal conductivity. Despite extensive research on MHD natural convection in enclosures, the combined effects of complex obstacle geometries, magnetic fields, and CNT nanofluids in three-dimensional configurations remain insufficiently explored. This research investigates MHD natural convection of carbon nanotube (CNT)-water nanofluid within a three-dimensional cavity. The study considers an inclined cross-shaped hot obstacle, a configuration not extensively explored in previous works. The work aims… More >

José Velázquez, Dolores Ojados, Adrián Semitiel, Francisco Cavas^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1821-1837, 2025, DOI:10.32604/cmes.2025.071131 - 26 November 2025

Abstract This research establishes a methodological framework for generating geometrically accurate 3D representations of human crystalline lenses through scanning technologies and digital reconstruction. Multiple scanning systems were evaluated to identify optimal approaches for point cloud processing and subsequent development of parameterized solid models, facilitating comprehensive morpho-geometric characterization. Experimental work was performed at the 3D Scanning Laboratory of SEDIC (Industrial Design and Scientific Calculation Service) at the Technical University of Cartagena, employing five distinct scanner types based on structured light, laser, and infrared technologies. Test specimens—including preliminary calibration using a lentil and biological analysis of a human… More >

Mukesh Kumar Verma^1,2,*, Manohar Yadav¹

Revue Internationale de Géomatique, Vol.34, pp. 855-879, 2025, DOI:10.32604/rig.2025.069914 - 17 November 2025

Abstract Precision Agriculture (PA) is revolutionizing modern farming by leveraging remote sensing (RS) technologies for continuous, non-destructive crop monitoring. This review comprehensively explores RS systems categorized by platform—terrestrial, airborne, and space-borne—and evaluates the role of multi-sensor fusion in addressing the spatial and temporal complexity of agricultural environments. Emphasis is placed on data from LiDAR, GNSS, cameras, and radar, alongside derived metrics such as plant height, projected leaf area, and biomass. The study also highlights the significance of data processing methods, particularly machine learning (ML) and deep learning (DL), in extracting actionable insights from large datasets. By More >

Huaiqin Liu¹, Meng Li¹, Jianwen Shao², Weishen Zhang¹, Qifan Yang¹, Yutong Li¹, Tian Su^1,3,*, Xuefeng Mei⁴

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1563-1588, 2025, DOI:10.32604/sdhm.2025.073009 - 17 November 2025

Abstract Rock collapse is a significant geological disaster that poses a serious threat to life and property in mountainous regions worldwide. Investigating the response of protective structures to rockfall impacts can provide valuable references for the design and placement of such structures. In this study, RocPro3D and ABAQUS were employed to comprehensively analyze rockfall movement trajectories and the structural response upon impact. The results indicate that when the impact velocity of rockfall at the protective structure reaches 20–30 m/sec, the corresponding bounce height ranges from 5 to 8 m, and most rockfall accumulates at the slope More > Graphic Abstract

Jinsi Yuan, Haijiang Wang^*, Jiaming Bai^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-2, 2025, DOI:10.32604/icces.2025.011073

Abstract Solid oxide fuel cells (SOFCs) have attracted considerable attention for their high efficiency, environmental advantages, and versatility in fuel sources. Research has shown that optimizing the structure of SOFCs can lead to significant performance improvements. Additive manufacturing (AM) has emerged as a promising technology for geometrical optimization of SOFCs, owing to its capability to create complex and programmable structures. However, fabricating three-dimensional electrode structures with fine, highly resolved features remains a significant challenge. Herein, a vat photopolymerization (VPP) 3D printing process was developed for fabricating the Nickel Oxide-Yttria Stabilized Zirconia (NiO-YSZ) anode structure of SOFC.… More >

Xinwei Li^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-2, 2025, DOI:10.32604/icces.2025.010448

Abstract Advances in 3D printing have unlocked new opportunities for developing lattice structures tailored for enhanced sound absorption. This work explores methods to maximize sound absorption in microlattice designs by introducing heterogeneity, leveraging dual dissipation mechanisms, and reshaping cavity wall geometries. We present a multilayered Helmholtz resonance (MLHR) analytical model to predict and guide the design of broadband sound-absorbing lattices [1]. Through structural optimization, we demonstrate that heterogeneous microlattices with varying pore and cavity morphologies achieve broadband absorption [2–4], with experimentally validated absorption coefficients exceeding 0.75 across a wide frequency range from 1000 to 6300 Hz.
Beyond… More >