Higinio Ramos^1,2,*, M Chandru^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.083154 - 27 April 2026

Abstract This article has no abstract. More >

Nishu Gupta^1,*, Manuel J. C. S. Reis²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.082568 - 27 April 2026

Abstract This article has no abstract. More >

Hafeez Rahman¹, Naveed Butt¹, Naila Sammar Naz¹, Fahad Ahmed¹, Muhammad Saleem¹, Adnan Khan^2,3,4, Khan Muhammad Adnan^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.081316 - 27 April 2026

Abstract Gastrointestinal polyps are well-known precursors to colorectal cancer (CRC), making their accurate detection and segmentation during colonoscopy essential for early diagnosis and cancer prevention. Deep learning–based segmentation models trained on publicly available datasets such as Kvasir-SEG have demonstrated promising performance; however, two key challenges remain: limited robustness across diverse polyp morphologies and endoscopic imaging conditions, and the lack of interpretable decision-making mechanisms that support clinical trust and validation. Many existing centralized segmentation approaches are primarily optimized using overlap-based metrics such as the Dice coefficient and intersection over union (IoU), without adequately analyzing challenging cases such… More >

Jaekyung Lee^1,2, Youngjun Kim², Byungsung Ko², Taewon Kim², Jaeheon Park², Jiwon Lee², Wonhee Kim^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080624 - 27 April 2026

Abstract Automated reading of analog gauges in industrial environments is essential for predictive maintenance and safety monitoring. However, conventional computer vision approaches encounter two fundamental bottlenecks: polar unwrapping techniques induce severe nonlinear scaling distortions under oblique viewing angles and axis-aligned bounding boxes (AABBs) are geometrically inefficient for encapsulating high-aspect-ratio rotating needles. To overcome these limitations, this paper proposes a novel end-to-end framework that innovatively redefines gauge reading as a structural pose estimation task. We model each gauge as a topological five-keypoint skeleton (kstart,kmid,kcenter,kend,ktip), and localize these landmarks using a customized P2-YOLO-Pose architecture. By integrating a high-resolution… More >

Rafael de Oliveira Silva^1,2,*, Roberto Outa³, Fábio Roberto Chavarette⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080607 - 27 April 2026

Abstract The reliable detection of cracks in engineering materials remains a fundamental challenge in nondestructive testing, especially in applications that require automated inspection, reduced instrumentation costs, and robustness under noisy operational conditions. Traditional nondestructive evaluation techniques often rely on complex sensing setups or expert-dependent interpretation, which can limit scalability and real-time applicability. In this context, this study addresses the scientific problem of achieving reliable and automated crack detection using simplified sensing architectures combined with intelligent data-driven analysis. This work proposes an intelligent signal classification framework for crack detection in polymeric materials based on machine learning and… More >

Tianzhe Jiao, Yuming Chen, Xiaoyue Feng, Chaopeng Guo, Jie Song^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080595 - 27 April 2026

Abstract Knowledge distillation bridges the performance gap between camera-based and LiDAR-based 3D detectors by leveraging the precise geometric information from LiDAR. However, cross-modal knowledge transfer remains challenging due to the inherent modality heterogeneity between LiDAR and camera data, which often leads to instability during training. In this work, we find that these instabilities are closely related to distribution mismatch in the cross-modal feature space and noisy teacher signals. To address this issue, we propose a novel distribution-aware cross-modal distillation framework, named DA-T3D. Specifically, we first explicitly model the LiDAR teacher’s Bird’s-Eye-View (BEV) feature distribution and use… More >

Jingjie He^1,*, Yuhui Jing^2,3, Xiaopeng Zhang²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080458 - 27 April 2026

Abstract In high-heat-flux environments, traditional cooling channels often fail to satisfy concurrent requirements for high heat transfer efficiency, temperature uniformity, and minimal pumping power. This study proposes an engineering-oriented topology optimization method for fluid-solid conjugate heat transfer to address the conflict between thermal performance and flow resistance under non-uniform heat sources. We introduce a pseudo-three-dimensional conjugate heat transfer model governed by Darcy’s law. This formulation retains three-dimensional effects, such as sidewall conduction and non-uniform surface heat flux. Moreover, the governing equations are reduced to two dimensions, thereby significantly enhancing computational efficiency. To resolve the discrepancy between More >

Anh-Vu Nguyen¹, Tien-Chuong Vu¹, Ba-Duan Nguyen¹, Hoang-Anh Pham^1,*, Ravipudi Venkata Rao²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080453 - 27 April 2026

Abstract Structural optimization is essential for finding optimal designs in practical engineering tasks. Metaheuristic algorithms have been widely applied in structural optimization problems in recent years, especially when dealing with discrete design variables, the nonlinearity of the objective function and constraints. Unlike gradient-based algorithms, which rely on the slope variation of a function, metaheuristic algorithms do not require derivative calculations and thus avoid being trapped in local optimum. However, metaheuristic algorithms often require numerous function evaluations, involving costly structural analyses, thus increasing computational load considerably. This paper investigates a method to reduce computational load, specifically by… More >

Faten S. Alamri¹, Noor Ayesha², Afia Zafar³, Adil Ali Saleem^4,*, Amjad R. Khan⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080335 - 27 April 2026

Abstract The use of automated skin lesion classification is still a disadvantage, since there is a great visual similarity between benign and malignant lesions. The majority of deep learning methods utilize dermoscopic images only, without taking into account clinical metadata employed by dermatologists on a regular basis. The following paper proposes a vision-graph multimodal framework that links Image encoding to graph neural networks based on metadata representation through the fusion of learnable attention. The framework focuses on three limitations, which are underutilization of clinical context, absence of interpretability, and suboptimal incorporation of modalities. Gradient-weighted Class Activation… More > Graphic Abstract

Maram Fahaad Almufareh^1,*, Mamoona Humayun², Sadia Din^3,*, Khalid Haseeb⁴, Amr Munshi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080287 - 27 April 2026

Abstract The real-time systems perform key functionalities in various fields to automate the communication and response in critical events. The Internet of Things (IoT), integrated with numerous physical objects, gathers environmental data, processes it at the edge, and provides intelligent decisions while routing health records to processing units. However, the dynamic and resource-constrained nature of IoT-based healthcare environments introduces significant challenges related to latency, transmission costs, and the reliable interaction of devices amid uncertain activities. In this work, we propose a framework for a consistent and trustworthy system that uses a weighted trust aggregation model to More >