Adwaa Mohammed Abdulmajeed¹, Duaa Abdul Rida Musa², Ola Abdul Hussain², Emad Kadum Njim³, Royal Madan^4,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076622 - 09 April 2026

Abstract A data-driven optimization framework that integrates machine learning surrogate models, finite element analysis (FEA), and a multi-objective optimization algorithm is used in this study for developing thermoplastic elastomer (TPE) parts for aerospace applications. By using FEA simulations and experiments, a database of input design parameters (e.g., geometry and structural shape modifier) is generated. Afterwards, we train surrogate models (e.g., Gaussian Process Regression, neural networks) to approximate mappings from design space to performance space. Finally, we propose Pareto-optimal TPE designs using the surrogate embedded in a multi-objective optimization loop (such as NSGA-II or gradient-based methods). The… More >

Oskar Kapuśniak¹, Adam Piórkowski^2,*, Julia Lasek³, Karolina Nurzyńska⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076062 - 09 April 2026

Abstract The efficacy of Active Shape Models (ASM) for automated ventricular segmentation was evaluated to address the computational demands of manual segmentation and the interpretability limitations of deep learning. A statistical shape model was constructed using a limited cohort of 19 Coronary Computed Tomography Angiography (CCTA) scans derived from patients with diverse cardiac abnormalities. Principal Component Analysis (PCA) was employed to encapsulate morphological variability, and strict point correspondence was enforced to maintain topological consistency. Validation was conducted via leave-one-out cross-validation, benchmarking automated segmentations against expert-delineated ground truths using the Dice Similarity Coefficient (DSC) and Hausdorff Distance More >

Chinmai Bhat^1,2, Mayur Jiyalal Prajapati², Yulius Shan Romario³, Wojciech Macek⁴, Maziar Ramezani⁵, Cho-Pei Jiang^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075197 - 09 April 2026

Abstract This study aims to develop a digital twin framework for fabricating automotive components through additive manufacturing (AM) technology. The framework comprises topology optimization (TO), finite element analysis (FEA), and fabrication analysis using Simufact Additive, which ensures the first-time-right fabrication of the component. Using TO-FEA, the component is designed with reduced overall weight without compromising the structural and functional performance. After the successful design of the component, it is analyzed for fabrication feasibility before undergoing the actual fabrication process. In the present study, an automotive flange fork is designed and fabricated through AM laser powder-bed fusion… More >

Hoyoon Lee¹, Jeonghoon Jee¹, Hoseon Kim², Cheol Oh^1,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076980 - 12 March 2026

Abstract Analyzing the driving behavior of autonomous vehicles (AV) in mixed traffic conditions at urban intersections has become increasingly important for improving intersection design, providing infrastructure-based guidance information, and developing capability-enhanced AV perception systems. This study investigated the contributing factors affecting AV driving behavior using the Waymo Open Dataset. Binarized autonomous driving stability metrics, derived via a kernel density estimation, served as the target variables for a random forest classification model. The model’s input variables included 15 factors divided into four types: intersection-related, surrounding object-related, road infrastructure-related, and time-of-day-related types. The random forest classification model was… More >

Si-Woo Kim, Yong Soo Kim^*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073492 - 12 January 2026

Abstract Predictive maintenance (PdM) is vital for ensuring the reliability, safety, and cost efficiency of heavy-duty vehicle fleets. However, real-world sensor data are often highly imbalanced, noisy, and temporally irregular, posing significant challenges to model robustness and deployment. Using multivariate time-series data from Scania trucks, this study proposes a novel PdM framework that integrates efficient feature summarization with cost-sensitive hierarchical classification. First, the proposed last_k_summary method transforms recent operational records into compact statistical and trend-based descriptors while preserving missingness, allowing LightGBM to leverage its inherent split rules without ad-hoc imputation. Then, a two-stage LightGBM framework is developed… More >

Xiaopan Zou^1,2, Meiyang Sun³, Xin Jiang¹, Jingze Yu², Xiaomeng Li^4,*, Bingyu Nie^1,*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.066662 - 30 December 2025

Abstract Objective: Triple-negative breast cancer (TNBC) is highly aggressive and lacks an effective targeted therapy. This study aimed to elucidate the functions and possible mechanisms of action of zinc finger miz-type containing 2 (ZMIZ2) and minichromosome maintenance complex component 3 (MCM3) in TNBC progression. Methods: The relationship between ZMIZ2 expression and clinical characteristics of TNBC was investigated. In vitro and in vivo experiments were performed to investigate the role of ZMIZ2 dysregulation in TNBC cell malignant behaviors. The regulatory relationship between ZMIZ2 and MCM3 was also explored. Transcriptome sequencing was performed to elucidate possible mechanisms underlying the ZMIZ2/MCM3… More >

Junxiang Li^1,2, Zhipeng Dong², Ben Han³, Jianqiao Chen³, Xinxin Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070816 - 10 November 2025

Abstract Owing to their global search capabilities and gradient-free operation, metaheuristic algorithms are widely applied to a wide range of optimization problems. However, their computational demands become prohibitive when tackling high-dimensional optimization challenges. To effectively address these challenges, this study introduces cooperative metaheuristics integrating dynamic dimension reduction (DR). Building upon particle swarm optimization (PSO) and differential evolution (DE), the proposed cooperative methods C-PSO and C-DE are developed. In the proposed methods, the modified principal components analysis (PCA) is utilized to reduce the dimension of design variables, thereby decreasing computational costs. The dynamic DR strategy implements periodic… More >

Zhao Li¹, Hongyu Xu^1,*, Shuai Zhang², Jintao Cui¹, Xiaofeng Liu¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068763 - 10 November 2025

Abstract The moving morphable component (MMC) topology optimization method, as a typical explicit topology optimization method, has been widely concerned. In the MMC topology optimization framework, the surrogate material model is mainly used for finite element analysis at present, and the effectiveness of the surrogate material model has been fully confirmed. However, there are some accuracy problems when dealing with boundary elements using the surrogate material model, which will affect the topology optimization results. In this study, a boundary element reconstruction (BER) model is proposed based on the surrogate material model under the MMC topology optimization… More >

Du-Yi Wang¹, Shih-Chen Shi^1,*, Dieter Rahmadiawan^1,2

Journal of Polymer Materials, Vol.42, No.4, pp. 1075-1095, 2025, DOI:10.32604/jpm.2025.072263 - 26 December 2025

Abstract Polymethyl methacrylate (PMMA) is widely used in diverse applications such as protective components (e.g., automotive lamp covers and structural casings), optical devices, and biomedical products, owing to its lightweight nature and impact resistance. However, its surface hardness and wear resistance remain insufficient under prolonged exposure to abrasive environments. In this study, a multi-filler strategy with nano-silica (SiO₂), brominated lignin (Br-Lignin), and cellulose nanocrystals (CNCs) was employed to enhance PMMA tribological properties. SiO₂ provided localized reinforcement, Br-Lignin established stable network structures, and CNCs improved compactness, enabling strong synergistic effects. As a result, the composites achieved up to More >

Ruikang Ma^1,2, Ziying Huang¹, Zexiu Zhang³, Ruohui Lu⁴, Menghan Li¹, Zhiyi Luo³, Mengni Li⁵, Pengyue Zhang³, Xiaohong Lin³, Guozhuang Zhang^1,*, Linlin Dong^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.11, pp. 3721-3748, 2025, DOI:10.32604/phyton.2025.072062 - 01 December 2025

Abstract Dendrobium officinale (DO) is a well-recognized medicinal and edible plant with a long history of application in traditional medicinal practices across China and Southeast Asia. Recent studies have demonstrated that DO is abundant in diverse bioactive compounds, including polysaccharides (DOP), flavonoids, alkaloids, and bibenzyls thought to exert a range of pharmacological effects, such as anti-tumor and immunomodulatory effects. However, our comprehensive understanding of two key aspects—pharmacological functions and biosynthetic mechanisms—of DO’s major constituents remains limited, especially when considered within the clinical contexts of traditional use. To address this gap, this study reviews DO’s historical applications, clinical effects, and… More > Graphic Abstract