Soo-Hyun Sung¹, Seungwon Shin², Seok-Hwan Kim³, Minjung Park^4,*

International Journal of Mental Health Promotion, Vol.28, No.4, 2026, DOI:10.32604/ijmhp.2026.077710 - 28 April 2026

Abstract Objectives: Psychiatric inpatient care plays a critical role in stabilizing acute mental health crises, yet the optimal length of stay (LOS) and its impact on short-term clinical outcomes remain poorly defined across diagnostic groups. This study aimed to examine how LOS in psychiatric inpatient units is associated with clinical improvement at discharge and to determine whether this association differs across major diagnostic groups, using nationally representative hospital discharge data from Korea. Methods: A cross-sectional secondary analysis was conducted using the 2022–2023 Korea National Hospital Discharge In-depth Injury Survey. Adults whose primary discharge diagnosis was a mental… 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 >

Yue Su¹, Shukuan Zhang^1,*, Jinghao Jiao¹, Jiankang Zhong², Qianxi Zhao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079927 - 27 April 2026

Abstract This paper introduces a novel dual-layer optimization fault diagnosis framework for inter-turn short-circuit (ITSC) faults in permanent magnet synchronous motors (PMSMs). The synergistic of a SABO-optimized VMD for enhanced feature extraction and an MFO-optimized SVM for intelligent classification is proposed. Firstly, mathematical and simulation models of ITSC faults in PMSMs are established to obtain fault phase currents and motor electromagnetic torques as characteristic fault signals. Then, the SABO algorithm is used to optimize the VMD parameters, followed by VMD decomposition of the characteristic fault signals to obtain Intrinsic Mode Functions (IMFs), and the time-domain parameters More >

Jaeseung Lee¹, Jehyeok Rew^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.076888 - 27 April 2026

Abstract The engine serves as the primary component that generates power and drives vehicle movement. Given its critical role, accurately diagnosing engine faults is essential for ensuring vehicle safety and reliability. Recent advances in machine learning (ML) have enabled the development of artificial intelligence (AI)-based diagnostic models with strong predictive performance. However, the lack of transparency in these models constrains user confidence in their diagnostic outcomes. While explainable AI (XAI) methods such as local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP) have been introduced to improve interpretability, their reliance on visual outputs requires manual… More >

Jaeseung Lee¹, Jehyeok Rew^2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077691 - 09 April 2026

Abstract The accelerating transition toward electrified mobility has positioned electric vehicles (EVs) as a primary technology in modern transportation systems. In this context, ensuring the reliability of EV drive motors (EVDMs) becomes increasingly critical, given their central role in propulsion performance and operational safety. Accurate and interpretable fault diagnosis of EVDMs is therefore essential for enabling effective maintenance and supporting the broader sustainability and resilience of EVs. This study presents a novel framework that combines decision tree-based fault classification with a multi-agent large language model (LLM) interpretation architecture to deliver transparent and human-readable diagnostic explanations. The… More >

Daniyal Asif^1,*, Nabil Kerdid², Muhammad Shoaib Arif³, Mairaj Bibi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077334 - 30 March 2026

Abstract Ovarian cancer (OC) is one of the leading causes of death related to gynecological cancer, with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers. Machine learning (ML) has the potential to process complex datasets and support decision-making in OC diagnosis. Nevertheless, traditional ML models tend to be biased, overfitting, noisy, and less generalized. Moreover, their black-box nature reduces interpretability and limits their practical clinical applicability. In this study, we introduce an explainable ensemble learning (EL) model, TreeX-Stack, based on a stacking architecture that employs tree-based learners such as Decision… More >

Ye Ri Han^1,*, Sang Bong Lee^2,3,*

Oncology Research, Vol.34, No.4, 2026, DOI:10.32604/or.2026.075923 - 23 March 2026

Abstract Radiopharmaceuticals deliver diagnostic or therapeutic radionuclides to disease sites with molecular precision. Over the past five years, clinical adoption has accelerated, led by U.S. Food and Drug Administration approvals of ¹⁷⁷Lu-DOTA-TATE and ¹⁷⁷Lu-PSMA-617 and their complementary Positron Emission Tomography agents (⁶⁸Ga-DOTA-TATE, ⁶⁸Ga-PSMA-11), which have established radiotheranostics as a pillar of oncology care. The new generation of agents couples optimized radionuclides (β⁻, α, and Auger emitters) to antibodies, peptides, and small-molecule vectors that improve tumor uptake, residence time, and clearance profiles, thereby enhancing efficacy and safety. Beyond neuroendocrine tumors and prostate cancer, radiotheranostic strategies are advancing for diverse malignancies… More >

Fang Hao¹, Puyuan Hu², Yumo Jiang², Ruonan Liu^2,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075655 - 12 March 2026

Abstract Industrial fault diagnosis is a critical challenge in complex systems, where sensor data is often noisy and interdependencies between components are difficult to capture. Traditional methods struggle to effectively model these complexities. This paper presents a novel approach by transforming fault diagnosis into a graph recognition task, using sensor data represented as graph-structured data with the k-nearest neighbors (KNN) algorithm. A Graph Transformer is applied to extract node and graph features, with a combined loss function of cross-entropy and weighted consistency loss to stabilize graph representations. Experiments on the TFF dataset show that Graph Transformer More >

Yong Hu¹, Weifan Xu², Xiangtong Du^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074437 - 12 March 2026

Abstract Deep learning-based methods have shown great potential in intelligent bearing fault diagnosis. However, most existing approaches suffer from the scarcity of labeled data, which often results in insufficient robustness under complex working conditions and a general lack of interpretability. To address these challenges, we propose a physics-informed multimodal fault diagnosis framework based on few-shot learning, which integrates a 2D time-frequency image encoder and a 1D vibration signal encoder. Specifically, we embed prior knowledge of multi-resolution analysis from signal processing into the model by designing a Laplace Wavelet Convolution (LWC) module, which enhances interpretability since wavelet More >

Mingwei Zhan^1,#, Zhaokai Zhou^2,#, Jianpeng Zhang^3,#, Xin Wang⁴, Canxuan Li⁵, Bochen Pan⁶, Zhanyang Luo⁷, Wenjie Shi⁸, Yongjie Wang⁹, Minglun Li¹⁰, Weizhuo Wang^11,*, Run Shi^12,*, Jingyu Zhu^1,13,*

Canadian Journal of Urology, Vol.33, No.1, pp. 35-49, 2026, DOI:10.32604/cju.2026.076084 - 28 February 2026

Abstract Artificial intelligence (AI) is transforming the diagnostic landscape of malignant tumors in the urinary system, including prostate cancer, bladder cancer, and renal cell carcinoma (RCC). By integrating imaging, pathology, and molecular data, AI enhances the precision and reproducibility of tumor detection, grading, and risk stratification. In prostate cancer, AI-assisted multiparametric Magnetic resonance imaging (MRI) and digital pathology systems improve lesion localization and Gleason scoring. For bladder cancer, deep learning-based cystoscopy and radiomics models from Computed tomography/magnetic resonance imaging (CT/MRI) enable real-time lesion segmentation and non-invasive biomarker prediction, such as Programmed Cell Death-Ligand 1 (PD-L1) expression. More >