Jia Yan Lim^1,2, Siti Madiha Muhammad Amir³, Roslan Yahya³, Marta Peña Fernández², Tze Chuen Yap^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070707 - 09 December 2025

Abstract Additive Manufacturing, also known as 3D printing, has transformed conventional manufacturing by building objects layer by layer, with material extrusion or fused deposition modeling standing out as particularly popular. However, due to its manufacturing process and thermal nature, internal voids and pores are formed within the thermoplastic materials being fabricated, potentially leading to a decrease in mechanical properties. This paper discussed the effect of printing parameters on the porosity and the mechanical properties of the 3D printed polylactic acid (PLA) through micro-computed tomography (microCT), computational image analysis, and Charpy impact testing. The results for both… More >

Muhammad Adil¹, Nadeem Javaid^1,*, Imran Ahmed², Abrar Ahmed³, Nabil Alrajeh^4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071215 - 10 November 2025

Abstract Heart disease remains a leading cause of mortality worldwide, emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention. However, existing Deep Learning (DL) approaches often face several limitations, including inefficient feature extraction, class imbalance, suboptimal classification performance, and limited interpretability, which collectively hinder their deployment in clinical settings. To address these challenges, we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture. The preprocessing stage involves label encoding and feature scaling. To address the issue of… 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 >

Jinsi Yuan, Haijiang Wang^*, Jiaming Bai^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-2, 2025, DOI:10.32604/icces.2025.011073

Abstract Solid oxide fuel cells (SOFCs) have attracted considerable attention for their high efficiency, environmental advantages, and versatility in fuel sources. Research has shown that optimizing the structure of SOFCs can lead to significant performance improvements. Additive manufacturing (AM) has emerged as a promising technology for geometrical optimization of SOFCs, owing to its capability to create complex and programmable structures. However, fabricating three-dimensional electrode structures with fine, highly resolved features remains a significant challenge. Herein, a vat photopolymerization (VPP) 3D printing process was developed for fabricating the Nickel Oxide-Yttria Stabilized Zirconia (NiO-YSZ) anode structure of SOFC.… More >

Fida Hussain^1,*, Farooq Khan², Javed Ahmad¹, Heqiang Huo³, Tao Jiang³, Iqrar Rana⁴, Sajida Habib⁵, Muhammad Umer Farooq^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.10, pp. 3231-3249, 2025, DOI:10.32604/phyton.2025.069654 - 29 October 2025

Abstract The analysis of combining ability and heterosis is very important in enhancing the yield and oil quality of sunflowers under adverse conditions, and it reveals the potential of the parents and the mechanism of gene action. In this study, twenty-one hybrids were developed by crossing seven cytoplasmic male sterile (CMS) lines with three restorer lines and evaluated for agronomic and quality traits. Highly significant general combining ability (GCA) and specific combining ability (SCA) effects were observed, confirming the role of both additive and non-additive gene actions. Among the tested crosses, A-42 × R-86, A-92 ×… More >

Yifei Wang, Zhao Zhang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.011121

Abstract Additive manufacturing (AM) reveals high anisotropy in mechanical properties due to the thermal accumulation induced microstructures. How to reveal the internal connection between the microstructures and the mechanical properties in additive manufacturing is a challenge. There are many methods to predict the mechanical properties based on the microstructural evolutions in additive manufacturing [1–3]. Here we summarized the main methods for the prediction of the mechanical properties in additive manufacturing, including crystal plasticity finite element method (CPFEM), dislocation dynamics (DD), and molecular dynamics (MD). We systematically examine these primary approaches for mechanical property predictions in AM,… More >

Xiang Gao, Zhao Zhang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.011092

Abstract Microstructures is the key factor determining the properties of the additively manufactured components [1]. It can be highly affected by the temperatures generated during the additive manufacturing process. Phase field method, as established based on Ginzburg-Landau theory is an efficient tool to simulate the microstructural evolutions in additive manufacturing [2]. It can be used to simulate solidification, diffusion, phase transformation and grain growth [3]. Here we compared the new progress on the phase field method in the field of additive manufacturing. Due to the differences between the temperature field and the grain field, how to… More >

Madihan Yusof^1,2,3,*, Muhamad Saiful Sulaiman^1,3, Ros Syazmini Mohd Ghani^1,3,4, Sofiyah Mohd Razali^1,5

Journal of Renewable Materials, Vol.13, No.10, pp. 2059-2075, 2025, DOI:10.32604/jrm.2025.02025-0101 - 22 October 2025

Abstract This study investigates the development of an oil palm trunk (OPT) high-performance flame-retardant composite derived from an inexpensive and sustainable biomass source, processed using sodium chloride (NaCl) as a low-cost flame retardant, polyvinyl alcohol (PVA) as an adhesive, and calcium carbonate (CaCO₃) as an additive. The work aims to address the inherent flammability of OPT and to enhance its mechanical performance, dimensional stability, and fire resistance in an environmentally friendly and cost-effective manner. Results indicate that a 10% NaCl treatment optimally improves the performance of the composite, increasing bending strength (MOR) from 5.95 to 12.61 MPa… More > Graphic Abstract

Sen Yang¹, Jie Zhong¹, Boyu Gan¹, Yi Sun¹, Changming Bu¹, Mingtao Zhang¹, Jiehong Li^1,*, Yang Yu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2943-2967, 2025, DOI:10.32604/cmes.2025.067282 - 30 September 2025

Abstract Foam concrete is widely used in engineering due to its lightweight and high porosity. Its compressive strength, a key performance indicator, is influenced by multiple factors, showing nonlinear variation. As compressive strength tests for foam concrete take a long time, a fast and accurate prediction method is needed. In recent years, machine learning has become a powerful tool for predicting the compressive strength of cement-based materials. However, existing studies often use a limited number of input parameters, and the prediction accuracy of machine learning models under the influence of multiple parameters and nonlinearity remains unclear.… More >

Kaiyang Li¹, Renjing Li¹, Hui Wang², Naiqiang Zhang^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.012643

Abstract Zn is a promising biomedical implant for its good biocompatibility, moderate mechanical strength, and suitable degradation rate. As a novel fabricating method, Additive Manufacturing (AM) could prepare biomedical Zn by raw powder deposition, melting, and molten pool solidification in a layer-by-layer pattern, which favors the customized shape and well-controlled geometry of the final product. Meanwhile, the rapid heating and solidification from AM often induces unique structural changes compared with traditional fabrication techniques, thus subsequently affecting the degradation behavior. Still, setting up the correlations among AM fabrication, structural changes and degradation behavior of Zn remains a… More >