Gang Xu¹, Yeda Lian^2,*, Leike Yang^2,*, Hao Li², Yonggang Yang³, Lanjie Niu⁴

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087556 - 15 September 2026

Abstract Hot-section nickel-based single-crystal superalloy components under isothermal cyclic loading often exhibit systematic life shifts when datasets span different stress ratios and notch severities, making it difficult to maintain a globally consistent parameter set using conventional models. Because the effects of temperature, stress ratio, and stress concentration on cyclic response and damage evolution are typically nonlinear and coupled, this study proposes a multi-level equivalent driving force framework for fatigue life prediction, in which condition-induced life differences are represented as comparable shifts on a unified engineering driving-force scale. The proposed framework links the nominal cyclic response, the… More >

Nashat Nawafleh^*, Faris M. Al-Oqla

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087255 - 15 September 2026

Abstract In order to mimic the thermal properties of various natural fibers, this research presents a novel prediction framework that combines Fuzzy Logic (FL) with Grey Wolf Optimization (GWO). While the GWO technique ensures mathematical correctness by fine-tuning membership function parameters, this research uses a hybrid fuzzy model to outline nonlinear relationships between fiber components and thermal performance, which significantly reduces the need for extensive, trial-and-error laboratory testing. In this study, moisture, cellulose, and hemicellulose levels are predicted to be used to identify the finest natural fibers for biomaterial uses. An optimization methodology is seen by More >

Yalong Liang¹, Xiaohui Yuan¹, Yuning Han², Pei Li^3,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086403 - 15 September 2026

Abstract NBT-based lead-free piezoceramics are promising alternatives to Pb-containing materials, yet their functional properties arise from complex and coupled composition–processing–structure–property relationships. Here, we develop a structure-informed machine learning framework to predict and interpret the piezoelectric coefficient d₃₃, depolarization temperature T_d, and relative dielectric permittivity ε_r. A database of 214 records from 34 publications was compiled, including 204 records for model development and 10 records for independent literature validation. The correlation analysis involved 38 variables, including 35 candidate input descriptors and three target properties. After Pearson correlation-based redundancy filtering, 30 nonredundant input descriptors were retained for model development. To… More >

Yusra Mansoor¹, Huma Jamshed^1,*, Mohammed Khouj², Muhammad I. Masud^2,*, Urooj Waheed¹, Abdul Wahid Memon³, Najeeb Ur Rehman Malik^4,*, Touqeer Ahmed Jumani⁵

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085975 - 15 September 2026

Abstract The increasing demand for sustainable and energy-aware agricultural practices due to climate change, limited natural resources, and increasing food requirements has accelerated the adoption of Internet of Things (IoT) and machine learning (ML) technologies in smart farming. This study proposes a solar-powered IoT and ML-based framework for energy-aware crop suitability prediction in precision agriculture. The system employs low-power IoT sensors to continuously monitor important environmental and soil parameters, including temperature, humidity, soil moisture, soil temperature, heat index, nitrogen (N), phosphorus (P), and potassium (K) levels. To reduce dependence on conventional energy sources, the framework is… More >

Wasnaa Kadhim Jawad^*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084636 - 15 September 2026

Abstract The accuracy of the electrode properties is important in the lithium-ion battery manufacturing process because the thickness variation is a direct influence on the compaction and structural uniformity, transport behavior and overall manufacturing quality. Of the different types of monitoring, ultrasonic frequency-domain relies on a non-destructive pathway for quality evaluation in a process-aware manner and is a promising approach; but, interpretable predictive modeling has been limited at the electrode level. In this study, an open-access database of ultrasonic frequency-domain data of lithium-ion battery electrodes under coating and calendering conditions was used to develop an artificial… More >

Mohammadreza Nehzati^*

Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 155-177, 2026, DOI:10.32604/jimh.2026.082983 - 14 September 2026

Abstract Background: Exponential growth of diverse clinical data presents challenges for real-time predictive analytics in healthcare. Federated learning offers a paradigm for multi-institutional model training without centralized data sharing, but large-scale deployment across diverse healthcare settings with real-world electronic health record (EHR) integration challenges remains limited. Methods: We implemented Phase 1 of a federated learning network deploying federated histogram-based XGBoost across 47 U.S. healthcare institutions from January to June 2023 as a quality improvement initiative. The system processes clinical data locally, transmitting only gradient and Hessian histograms with differential privacy (ε = 1.0, δ = 10⁻⁵). Primary… More >

Ziyou Bai^1,2,#, Xiaoyan Song^3,#, Jiayin Sun^1,2,#, Wen Xiao^1,2,*, Xiangui Meng^1,2,*, Wei Dong^1,2,*

Oncology Research, Vol.34, No.10, 2026, DOI:10.32604/or.2026.086230 - 14 September 2026

Abstract Bladder cancer (BC) is a prevalent malignancy characterized by a high recurrence rate and the necessity for long-term surveillance demands, creating a need for accurate yet practical tools for early detection and monitoring. While current standards including cystoscopy, urinary cytology, and imaging remain indispensable, their clinical utility is constrained by invasiveness, suboptimal sensitivity for selected lesions or low-grade lesions, inter-observer variability, and cumulative costs. Currently, biomarker research has expanded from single protein assays to multi-analyte strategies encompassing DNA, RNA, proteins, extracellular vesicle-associated cargo, and metabolomics signatures. This review synthesizes recent advances in diagnostic, surveillance, prognostic, More >

Zhuwei Tao, Yanfeng He^*, Hui Xu^*, Shengda Zhang, Zetao Sun, Ying Wu, Peng Li, Xing Shi

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.8, 2026, DOI:10.32604/fdmp.2026.086018 - 04 September 2026

Abstract This study elucidates the mechanisms governing sand production during multi-thermal fluid huff-and-puff in unconsolidated sandstone heavy oil reservoirs and develops a temperature-pressure coupled prediction model for accurately quantifying sand production. Orthogonal laboratory experiments were conducted on reservoir samples from a representative case (Block X, Oilfield L), to compare the mechanical response and sand production behavior under multi-thermal fluid and conventional steam stimulation. The relative importance of the governing parameters was quantified using analysis of variance (ANOVA), and an exponential prediction model incorporating the coupled effects of temperature and pressure was established. The results reveal that… More >

Jiaxi Shen, Zhiyun Wang^*

Frontiers in Heat and Mass Transfer, Vol.24, No.4, 2026, DOI:10.32604/fhmt.2026.081961 - 31 August 2026

Abstract This study focuses on the modeling and performance prediction of the condensation heat transfer process within an ACC system of a 100 MW unit. A one-dimensional physical model was established, and solutions were obtained using an iterative numerical method based on the first law of thermodynamics. The core of this work involves precise modifications to the heat transfer coefficient. Firstly, the Shah correlation for horizontal tube condensation was improved by incorporating the air mass fraction and the Jakob number to quantify the impact of non-condensable gases. Secondly, to assess the influence of the pipe installation More >

Mohammed Almajed¹, Mahbub Hassan², Turjoy Das Turjo³, Md Ashequl Islam^4,*, Md Ehtesamul Haque¹, M. M. Hafizur Rahman⁵

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.084024 - 30 August 2026

Abstract Accurate per-kilometer energy consumption prediction is essential for effective electric bus (EB) fleet management in urban transit networks. Existing studies report mean absolute percentage errors of 5%–8% and rarely cross-validate explainability attributions or quantify prediction uncertainty. This study presents a physics-informed, interpretable machine learning framework for EB energy consumption prediction under Bangkok-like tropical urban conditions. Due to the institutional inaccessibility of operational telemetry, a parametric dataset of 4000 trip-level scenarios was constructed across 29 input features anchored to published primary sources. Four algorithms were benchmarked under Bayesian hyperparameter optimization: Extreme Gradient Boosting (XGBoost), Light Gradient… More >