Wael A. Altabey^*

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.079495 - 24 August 2026

Abstract The goal of this paper is to improve the monitoring of civil structures when we pair unmanned aerial vehicles (UAVs) technology with the current proposed algorithm, particularly to identify cracks in concrete structures. Typically, the current UAV methods are more about creating state maps of these structures, but they struggle with the impact of the drone’s movement on crack detection accuracy. This presents challenges for using intelligent systems for concrete crack detection. The current approach combines advanced technologies with a network of high-definition cameras mounted on inspection UAV systems and distributed in different parts of… More >

Ramya Narasimhan¹, Kikuye Sugiyama^2,*, Joanna Wang³, Ricardo Munarriz³, Carmen Sarita-Reyes¹

Canadian Journal of Urology, Vol.33, No.4, pp. 1007-1017, 2026, DOI:10.32604/cju.2026.072310 - 21 August 2026

Abstract Background: The incidence rate of prostate adenocarcinoma in the United States was 112 per 100,000 in 2019. Men aged 65 to 74 years had a higher incidence rate (638 per 100,000), and 70% of the cases were detected locally (confined to the primary site) per the United States Cancer Statistics. While rare, prostate adenocarcinoma metastasis to the penis has variable presentation and potentially atypical histology that may require immunohistochemical, biomarker, and genetic analysis for confident diagnosis. Case Description: We present a 56-year-old patient with advanced prostate cancer post-chemoradiation and androgen deprivation therapy with an uncommon site… More >

Aaron R. Hochberg^1,2, Monika Shirodkar³, Brian H. Im^1,2, Xiaoying Deng⁴, Fitsum T. Hailemariam⁴, Sohan S. Shah², Rasheed A. M. Thompson¹, Francisco Aguirre¹, Patrick T. Gomella¹, Mihir S. Shah¹, Costas D. Lallas¹, Adam R. Metwalli^1,*

Canadian Journal of Urology, Vol.33, No.4, pp. 931-941, 2026, DOI:10.32604/cju.2026.069461 - 21 August 2026

Abstract Objectives: Despite practice guidelines recommending hormonal testing for all cases of incidentally discovered adrenal adenomas (incidentalomas), only 30% of patients receive laboratory workup. This study sought to evaluate hormonal testing and adrenal surgery rates in incidentaloma patients seen by a specialist (endocrinologist, nephrologist, urologist, or general surgeon), compared to those not seen by a specialist. Methods: We identified incidentaloma cases by querying the TriNetX Research Network for all adult patients with an unspecified adrenal mass occurring within 1 month following abdominal imaging. We compared those seen by a specialist against those not following an incidentaloma… More >

Whitnee Hogan^1,2,*, Thomas S. Przybycien^3,4, Anita J. Moon-Grady^3,4

Structural and Congenital Heart Disease, Vol.21, No.3, 2026, DOI:10.32604/schd.2026.085038 - 31 July 2026

Abstract Improvements in fetal cardiac imaging have highlighted the need for large, multidisciplinary collaborations to further advance fetal cardiovascular care. The Fetal Cardiac Intervention Registry (IFCIR), established in 2011, aims to improve outcomes in fetuses with congenital heart disease (CHD) considered amenable to fetal cardiac intervention (FCI). The registry compiles data from multiple international centers to better define indications, selection criteria, procedural techniques, complications, and outcomes of FCI. The Fetal Heart Society (FHS), founded in 2014, is a non-profit organization dedicated to advancing the understanding of in utero cardiovascular physiology through collaborative research, education and mentorship. The… More >

Tanzila Saba¹, Muhammad Mujahid¹, Faten S. Alamri^2,*, Roaa Khalil Mohamed Ali Abed³

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084816 - 27 July 2026

Abstract In the era of artificial intelligence, pattern recognition techniques have become fundamental in advancing medical image processing, diagnosis, and automated disease classification systems. Among various clinical challenges, breast cancer is the second most dangerous leading cause of death in women worldwide. Early and accurate detection of breast cancer is crucial to develop advanced diagnostic methods to control further loss or reduce mortality rates. This study proposes a dynamic graph multi-scale network for breast cancer diagnosis, integrated with multi-scale convolutional feature extraction, a squeeze-and-excitation block, and a graph convolutional network to jointly model local spatial features… More > Graphic Abstract

Safa Alsafari¹, Ayman Yafoz^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084403 - 27 July 2026

Abstract Bearing fault diagnosis in industrial deployment must contend with two simultaneous distributional shifts: fault severity increases as damage progresses, and motors operate at loads unseen during training. We define this compound setting as the double domain shift and present a rigorous few-shot benchmark on the Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets. Six architectures spanning distinct learning paradigms—a multilayer perceptron (MLP), a capsule network (CapsNet), a residual capsule network (ResCaps), a prototypical network (ProtoNet), a modified residual convolutional network (MRCN), and Deep Correlation Alignment (Deep CORAL)—are evaluated under a strict three-way split… More >

Changwei Song¹, Jiaqi Qiang², Hongjun Liu¹, Jianqiang Li¹, Hui Pan², Qing Zhao^1,*, Jiuzuo Huang³, Shi Chen³

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083525 - 27 July 2026

Abstract Cushing’s syndrome (CS) is a rare endocrine disorder characterized by chronic hypercortisolism, and facial image-based intelligent diagnosis has emerged as a promising non-invasive approach. However, existing diagnostic models suffer from two core bottlenecks: inefficient fusion of deep semantic features and clinical prior features, and insufficient multi-view facial feature disentanglement without CS-specific pathophysiological constraints. To address these limitations, we propose a novel Multi-View Facial Feature Disentanglement Network (MVFFD-Net) for high-precision automatic CS diagnosis. The network takes five standard facial views (frontal, bilateral 45∘ oblique, and bilateral 90∘ lateral views) as input, with three key innovations:… More >

Junho Lee, Sanghyun Park, Younghun Lee, Namsu Kim^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083185 - 27 July 2026

Abstract Monitoring the conditions of electric motors in industrial applications is an essential step for ensuring safety and reducing maintenance costs. This paper deals with one of the most frequent winding failures—the inter-turn short fault of an interior permanent magnet synchronous motor. A novel high-fidelity co-simulation framework toward a digital twin-based approach combining Maxwell simulation in finite element method (FEM) for the motor and control system in system software for the inverter is presented. An analysis of the motor based on a 2D FEM model is performed considering the motor topology and non-linear properties, and inductance… More >

Lijun Yang¹, Qiuwei Yang^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083976 - 23 July 2026

Abstract This study proposes a Multi-Stage Sparrow Search Algorithm (MS-SSA) for precise structural damage identification. Initially, the structural static displacement sensitivity formulation is derived via the Sherman-Morrison-Woodbury formula, and an objective function is constructed by integrating the sensitivity equations with the L2-norm penalty. Subsequently, MS-SSA is implemented to sequentially achieve preliminary damage localization and accurate quantification. In the localization phase, a constrained narrow-bound search space is predefined to identify potential damage regions. Leveraging this feedback, the sensitivity equations are condensed, and the search boundaries are adaptively refined for the quantification phase, where SSA is reapplied to… More >

Israt Jahan¹, Afsana Begum¹, Bibhas Roy Chowdhury Piyas^1,*, Fahmid Al Farid^2,3,*, Fatama Jannat Tisha¹, Shahrin Islam¹, Abu Saleh Musa Miah⁴, Hezerul Abdul Karim^3,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081631 - 23 July 2026

Abstract Transforming underlying cardiovascular risk into actionable clinical decisions remains a major challenge in contemporary healthcare. Despite advances in cardiology, early-stage cardiovascular disease often remains undetected, which hinders timely intervention and leads to preventable deaths. To overcome this problem, this study presents an explainable machine learning framework for the early diagnosis of cardiovascular disease (CVD). Initially, this study examined several data-balancing strategies, for example, SMOTE (Synthetic Minority Over-sampling Technique), SMOTETomek (Synthetic Minority Over-sampling Technique + Tomek Links), Tomek Links, ADASYN (Adaptive Synthetic Sampling), and SMOTE-ENN (Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors) within the data-preprocessing pipeline. We… More >