Shuoting Xiao^1,*, Nikita Igorevich Fomin¹, Kirill Anatolyevich Khvostunkov², Chong Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2821-2847, 2025, DOI:10.32604/cmes.2025.071961 - 23 December 2025

Abstract Precast concrete structures have gained popularity due to their advantages. However, the seismic performance of their connection joints remains an area of ongoing research and improvement. Grouted Sleeve Connection (GSC) offers a solution for connecting reinforcements in precast components, but their vulnerability to internal defects, such as construction errors and material variability, can significantly impact performance. This article presents a finite element analysis (FEA) to evaluate the impact of internal grouting defects in GSC on the structural performance of precast reinforced concrete columns. Four finite element models representing GSC with varying degrees of defects were… More > Graphic Abstract

Michał Guminiak¹, Marcin Kamiński^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3133-3163, 2025, DOI:10.32604/cmes.2025.071887 - 23 December 2025

Abstract The analysis of the dynamics of surface girders is of great importance in the design of engineering structures such as steel welded bridge plane girders or concrete plate-column structures. This work is an extension of the classical deterministic problem of free vibrations of thin (Kirchhoff) plates. The main aim of this work is the study of stochastic eigenvibrations of thin (Kirchhoff) elastic plates resting on internal continuous and column supports by the Boundary Element Method (BEM). This work is a continuation of previous research related to the random approach in plate analysis using the BEM.… More >

Lijuan Zhang¹, Yu Zhou², Jiawei Tian^3,*, Fupei Guo⁴, Xiang Zhang⁴, Bo Yang^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3949-3968, 2025, DOI:10.32604/cmes.2025.071869 - 23 December 2025

Abstract In endoscopic surgery, the limited field of view and the nonlinear deformation of organs caused by patient movement and respiration significantly complicate the modeling and accurate tracking of soft tissue surfaces from endoscopic image sequences. To address these challenges, we propose a novel Hybrid Triangular Matching (HTM) modeling framework for soft tissue feature tracking. Specifically, HTM constructs a geometric model of the detected blobs on the soft tissue surface by applying the Watershed algorithm for blob detection and integrating the Delaunay triangulation with a newly designed triangle search segmentation algorithm. By leveraging barycentric coordinate theory, More >

Tanvir Fatima Naik Bukht^1,2, Yanfeng Wu¹, Nouf Abdullah Almujally³, Shuoa S. AItarbi⁴, Hameedur Rahman², Ahmad Jalal^2,5,*, Hui Liu^1,6,7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4013-4036, 2025, DOI:10.32604/cmes.2025.071850 - 23 December 2025

Abstract Human activity recognition (HAR) is crucial in fields like robotics, surveillance, and healthcare, enabling systems to understand and respond to human actions. Current models often struggle with complex datasets, making accurate recognition challenging. This study proposes a quantum-integrated Convolutional Neural Network (QI-CNN) to enhance HAR performance. The traditional models demonstrate weak performance in transferring learned knowledge between diverse complex data collections, including D3D-HOI and Sysu 3D HOI. HAR requires better extraction models and techniques that must address current challenges to achieve improved accuracy and scalability. The model aims to enhance HAR task performance by combining… More >

Deema Alzamil^1,2,*, Bader Alkhamees², Mohammad Mehedi Hassan^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2971-3027, 2025, DOI:10.32604/cmes.2025.070867 - 23 December 2025

Abstract Breast cancer diagnosis relies heavily on many kinds of information from diverse sources—like mammogram images, ultrasound scans, patient records, and genetic tests—but most AI tools look at only one of these at a time, which limits their ability to produce accurate and comprehensive decisions. In recent years, multimodal learning has emerged, enabling the integration of heterogeneous data to improve performance and diagnostic accuracy. However, doctors cannot always see how or why these AI tools make their choices, which is a significant bottleneck in their reliability, along with adoption in clinical settings. Hence, people are adding… More >

Quang-Thai Nguyen^1,2,#, Duong Ngoc Hai^3,4,#, The-Duc Nguyen^1,3,4,*, Van-Tu Nguyen^2,*, Jinyul Hwang², Warn-Gyu Park²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3433-3452, 2025, DOI:10.32604/cmes.2025.070570 - 23 December 2025

Abstract This study presents a numerical analysis of the effects of a rigid flat wall with oscillating motion on the pressure wave propagation during a single spherical cavitation bubble collapse at different initial bubble positions. Different nondimensional distances S = 0.8, 0.9, 1.0, 1.1, 1.2 and 1.3 were considered to investigate the effects of initial in-phase and out-of-phase oscillations of the flat wall. Numerical simulations of cavitation bubble collapse near an oscillating wall were conducted using a compressible two-phase flow model. This model incorporated the Volume of Fluid (VOF) interface-sharpening technique on a general curvilinear moving… More > Graphic Abstract

Carlos Torres-Aguilar^1,*, Pedro Moreno^2,*, Diego Rossit³, Sergio Nesmachnow⁴, Karla M. Aguilar-Castro¹, Edgar V. Macias-Melo¹, Luis Hernández-Callejo⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3859-3881, 2025, DOI:10.32604/cmes.2025.069996 - 23 December 2025

Abstract Solar chimneys are renewable energy systems designed to enhance natural ventilation, improving thermal comfort in buildings. As passive systems, solar chimneys contribute to energy efficiency in a sustainable and environmentally friendly way. The effectiveness of a solar chimney depends on its design and orientation relative to the cardinal directions, both of which are critical for optimal performance. This article presents a supervised learning approach using artificial neural networks to forecast the performance indicators of solar chimneys. The dataset includes information from 2784 solar chimney configurations, which encompasses various factors such as chimney height, channel thickness, More > Graphic Abstract

Jawad Ahmad¹, Mujeeb Ur Rehman^2,*, Wadii Boulila³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1253-1257, 2025, DOI:10.32604/cmes.2025.074581 - 26 November 2025

Abstract This article has no abstract. More >

Tiantian Wang^1,2, Zhihua Hu^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2357-2381, 2025, DOI:10.32604/cmes.2025.073640 - 26 November 2025

Abstract Rain streaks introduced by atmospheric precipitation significantly degrade image quality and impair the reliability of high-level vision tasks. We present a novel image deraining framework built on a three-stage dual-residual architecture that progressively restores rain-degraded content while preserving fine structural details. Each stage begins with a multi-scale feature extractor and a channel attention module that adaptively emphasizes informative representations for rain removal. The core restoration is achieved via enhanced dual-residual blocks, which stabilize training and mitigate feature degradation across layers. To further refine representations, we integrate cross-dimensional spatial attention supervised by ground-truth guidance, ensuring that 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 >