Yahya Ghazwani^1,2,3, Mohammad Alghafees^1,2,3,*, Mishari Alshasha^1,2,3, Fahad Brayan^1,2,3, Abdulrahman Alsayyari^1,2,3, Ali Alyami^1,2,3

Canadian Journal of Urology, Vol.33, No.2, pp. 291-308, 2026, DOI:10.32604/cju.2026.077411 - 20 April 2026

Abstract Objectives: The emergence of artificial intelligence (AI) and predictive modeling offers prospects for clinical, anatomical, and imaging factor combination, like radiomics, to help with stone-free status (SFS) estimation and peroperative decision-making. The goal of this study was, therefore, to define the present performance range, determine sources of heterogeneity, and determine methodological practices permitting reliable implementation by varied circumstances. Methods: We searched six bibliographic databases through 19 September 2025. Studies deriving or validating AI/predictive models for SFS after ureteroscopy were eligible. Independent dual screening, duplicate data extraction, and risk-of-bias consideration using QUADAS-AI were conducted. Results: Five retrospective… More >

Kai Dang^1,2,#, Teng Cui^1,2,#, Yongan Zhou^1,2, Jiayuan Ji^1,2, Yang Yang^1,2, Xiangyu Wang^1,2, Jing Xiao^1,2,*

Canadian Journal of Urology, Vol.33, No.2, pp. 403-415, 2026, DOI:10.32604/cju.2026.068291 - 20 April 2026

Abstract Objectives: Although renal colic is a well-known acute manifestation of urolithiasis, the relationship between its pain severity and a range of clinical parameters has not been clearly established by comprehensive studies. This study aimed to construct and validate a simple and accurate clinical nomogram for predicting the occurrence of more intense acute renal colic (ARC) in patients with urolithiasis. Methods: The development and validation of the prediction model followed the reporting standards outlined in the TRIPOD checklist. A retrospective analysis was conducted on 285 patients who visited the Department of Urology at Beijing Friendship Hospital,… More >

Eshita Dhar^1,2, Muhammad Ashad Kabir³, Divyabharathy Ramesh Nadar⁴, Li-Jen Kuo⁵, Jitendra Jonnagaddala^6,7, Yaoru Huang¹, Mohy Uddin^8,*, Shabbir Syed-Abdul^1,2,9,*

Oncology Research, Vol.34, No.4, 2026, DOI:10.32604/or.2026.074385 - 23 March 2026

Abstract Objectives: Decisions regarding CT after nCCRT for locally advanced rectal cancer (LARC) are challenging due to limited evidence guiding treatment. This study aimed to (i) evaluate the predictive performance of machine learning (ML) models in patients treated with neoadjuvant concurrent chemoradiotherapy (nCCRT) alone vs. those receiving nCCRT plus chemotherapy (CT), (ii) identify features associated with treatment improvement, and (iii) derive ML-based thresholds for treatment response. Methods: This retrospective study included 409 patients with LARC treated at three affiliated hospitals of Taipei Medical University. Patients were categorised into two groups: nCCRT alone followed by surgery (n =… More >

Zhuo Pan, Lin Zhu, Yi Xue^*, Hao Xu

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.2, 2026, DOI:10.32604/fdmp.2026.075809 - 04 March 2026

Abstract Fluid flow through fractured rock masses is a key process controlling the safety and performance of deep geoengineering systems, shaped by the complex interactions of thermal, hydraulic, mechanical and chemical (THMC) fields. This paper presents a systematic review of this subject with special emphasis on the multi-physics governing it. First, we elucidate the interdependent mechanisms and governing equations, highlighting the nonlinear, path-dependent, and evolving nature of the relationship between stress and permeability. Next, mainstream modeling approaches, including equivalent continuum, discrete fracture network (DFN), and dual-porosity/dual-permeability methods, are critically evaluated, and a strategy for model selection… More > Graphic Abstract

Huixin Wei, Baopeng Liao^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.012367

Abstract Friction behavior at soft-hard material interfaces plays a pivotal role in applications spanning biomedical devices, robotics, and tactile systems. While theoretical frameworks and experimental characterization methods have advanced, it is still difficult to unravel interfacial mechanisms. In this study, a theoretical model is firstly developed to predict static-to-sliding transitions by analyzing geometric evolution and stick-slip dynamics at soft material interfaces. The model quantitatively determines the threshold force for slip initiation, offering predictive insights into The sliding behavior of the interface. Second, an innovative tribometry platform is introduced, combining synchronized optical visualization, mechanical loading, and automated More >

Nguyen Dong Phuong¹, Nanthakumar Srivilliputtur Subbiah¹, Yabin Jin², Xiaoying Zhuang^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 263-303, 2025, DOI:10.32604/cmes.2025.067100 - 30 October 2025

Abstract Traditional inverse neural network (INN) approaches for inverse design typically require auxiliary feedforward networks, leading to increased computational complexity and architectural dependencies. This study introduces a standalone INN methodology that eliminates the need for feedforward networks while maintaining high reconstruction accuracy. The approach integrates Principal Component Analysis (PCA) and Partial Least Squares (PLS) for optimized feature space learning, enabling the standalone INN to effectively capture bidirectional mappings between geometric parameters and mechanical properties. Validation using established numerical datasets demonstrates that the standalone INN architecture achieves reconstruction accuracy equal or better than traditional tandem approaches while More >

Candan Tumer¹, Erdal Guvenoglu², Volkan Tunali^3,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5135-5158, 2025, DOI:10.32604/cmc.2025.067840 - 23 October 2025

Abstract Automated cartoon character recognition is crucial for applications in content indexing, filtering, and copyright protection, yet it faces a significant challenge in animated media due to high intra-class visual variability, where characters frequently alter their appearance. To address this problem, we introduce the novel Kral Sakir dataset, a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions. This paper conducts a comprehensive benchmark study, evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks (CNNs), including DenseNet, ResNet, and VGG, against a custom baseline model trained More >

Muhammad Salman Qamar^1,2,*, Muhammad Fahad Munir², Athar Waseem²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3557-3584, 2025, DOI:10.32604/cmes.2025.067447 - 30 September 2025

Abstract Air pollution, specifically fine particulate matter (PM2.5), represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems. Accurate forecasting of PM2.5 concentrations is essential for mitigating health risks; however, the inherent nonlinearity and dynamic variability of air quality data present significant challenges. This study conducts a systematic evaluation of deep learning algorithms including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and the hybrid CNN-LSTM as well as statistical models, AutoRegressive Integrated Moving Average (ARIMA) and Maximum Likelihood Estimation (MLE) for hourly PM2.5 forecasting. Model performance is… More >

You-Jyun Yeh¹, Edward T.-H. Chu^1,*, Chia-Rong Lee², Jiun Hsu³, Hui-Mei Wu³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3369-3391, 2025, DOI:10.32604/cmc.2025.065336 - 23 September 2025

Abstract Porters play a crucial role in hospitals because they ensure the efficient transportation of patients, medical equipment, and vital documents. Despite its importance, there is a lack of research addressing the prediction of completion times for porter tasks. To address this gap, we utilized real-world porter delivery data from National Taiwan University Hospital, Yunlin Branch, Taiwan. We first identified key features that can influence the duration of porter tasks. We then employed three widely-used machine learning algorithms: decision tree, random forest, and gradient boosting. To leverage the strengths of each algorithm, we finally adopted an… More >

Micheal Arockiaraj^1,*, Aravindan Maaran², C. I. Arokiya Doss²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 637-655, 2025, DOI:10.32604/cmc.2025.065674 - 29 August 2025

Abstract Covalent organic frameworks (COFs) are crystalline materials composed of covalently bonded organic ligands with chemically permeable structures. Their crystallization is achieved by balancing thermal reversibility with the dynamic nature of the frameworks. Ionic covalent organic frameworks (ICOFs) are a subclass that incorporates ions in positive, negative, or zwitterionic forms into the frameworks. In particular, spiroborate-derived linkages enhance both the structural diversity and functionality of ICOFs. Unlike electroneutral COFs, ICOFs can be tailored by adjusting the types and arrangements of ions, influencing their formation mechanisms and physical properties. This study focuses on analyzing the graph-based structural… More >