Safwan Al-sayed¹, Xi Wang¹, Yijiang Peng^1,*, Esraa Hyarat², Ahmad Ali AlZubi³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.074068 - 12 January 2026

Abstract In modern construction, Lightweight Aggregate Concrete (LWAC) has been recognized as a vital material of concern because of its unique properties, such as reduced density and improved thermal insulation. Despite the extensive knowledge regarding its macroscopic properties, there is a wide knowledge gap in understanding the influence of microscale parameters like aggregate porosity and volume ratio on the mechanical response of LWAC. This study aims to bridge this knowledge gap, spurred by the need to enhance the predictability and applicability of LWAC in various construction environments. With the help of advanced numerical methods, including the… More >

Danping Niu¹, Yuan Ping^1,*, Chun Guo², Xiaojun Wang³, Bin Hao⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073630 - 12 January 2026

Abstract With the increasing complexity of malware attack techniques, traditional detection methods face significant challenges, such as privacy preservation, data heterogeneity, and lacking category information. To address these issues, we propose Federated Dynamic Prototype Learning (FedDPL) for malware classification by integrating Federated Learning with a specifically designed K-means. Under the Federated Learning framework, model training occurs locally without data sharing, effectively protecting user data privacy and preventing the leakage of sensitive information. Furthermore, to tackle the challenges of data heterogeneity and the lack of category information, FedDPL introduces a dynamic prototype learning mechanism, which adaptively adjusts the More >

Shuai Zhao, Zhiwei Zhang^*, Junkai Wang, Ye Yuan, Guoren Wang

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073315 - 12 January 2026

Abstract With the development of sharded blockchains, high cross-shard rates and load imbalance have emerged as major challenges. Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates. Account partitioning based on historical transaction graphs is effective in reducing cross-shard rates but suffers from load imbalance and limited adaptability to dynamic workloads. Meanwhile, because of the coupling between consensus and execution, a target shard must receive both the partitioned transactions and the partitioned accounts before initiating consensus and execution. However, we observe that transaction partitioning and subsequent consensus do not require… More >

José Arturo Ramírez-Fernández¹, Henevith G. Méndez-Figueroa¹, Sebastián Ossandón^2,*, Ricardo Galván-Martínez³, Miguel Ángel Hernández-Pérez³, Ricardo Orozco-Cruz³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072707 - 12 January 2026

Abstract In this study, artificial neural networks (ANNs) were implemented to determine design parameters for an impressed current cathodic protection (ICCP) prototype. An ASTM A36 steel plate was tested in 3.5% NaCl solution, seawater, and NS4 using electrochemical impedance spectroscopy (EIS) to monitor the evolution of the substrate surface, which affects the current required to reach the protection potential (Eprot). Experimental data were collected as training datasets and analyzed using statistical methods, including box plots and correlation matrices. Subsequently, ANNs were applied to predict the current demand at different exposure times, enabling the estimation of electrochemical More >

Md Minhazul Islam^1,2, Yunfei Yin^1,2,*, Md Tanvir Islam^1,2, Zheng Yuan^1,2, Argho Dey^1,2

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072550 - 12 January 2026

Abstract Automatic segmentation of landslides from remote sensing imagery is challenging because traditional machine learning and early CNN-based models often fail to generalize across heterogeneous landscapes, where segmentation maps contain sparse and fragmented landslide regions under diverse geographical conditions. To address these issues, we propose a lightweight dual-stream siamese deep learning framework that integrates optical and topographical data fusion with an adaptive decoder, guided multimodal fusion, and deep supervision. The framework is built upon the synergistic combination of cross-attention, gated fusion, and sub-pixel upsampling within a unified dual-stream architecture specifically optimized for landslide segmentation, enabling efficient… More >

Zixuan Guo¹, Houbin Wang², Sameer Kumar^1,*, Yuanfang Chen³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072064 - 12 January 2026

Abstract With the rapid development of digital culture, a large number of cultural texts are presented in the form of digital and network. These texts have significant characteristics such as sparsity, real-time and non-standard expression, which bring serious challenges to traditional classification methods. In order to cope with the above problems, this paper proposes a new ASSC (ALBERT, SVD, Self-Attention and Cross-Entropy)-TextRCNN digital cultural text classification model. Based on the framework of TextRCNN, the Albert pre-training language model is introduced to improve the depth and accuracy of semantic embedding. Combined with the dual attention mechanism, the… More >

Haoxuanye Ji^*, Zhiliang Chen, Pengfei Jiang, Ziyue Wang, Ting Yu, Wei Zhang

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071880 - 12 January 2026

Abstract Foreign body classification on coal conveyor belts is a critical component of intelligent coal mining systems. Previous approaches have primarily utilized convolutional neural networks (CNNs) to effectively integrate spatial and semantic information. However, the performance of CNN-based methods remains limited in classification accuracy, primarily due to insufficient exploration of local image characteristics. Unlike CNNs, Vision Transformer (ViT) captures discriminative features by modeling relationships between local image patches. However, such methods typically require a large number of training samples to perform effectively. In the context of foreign body classification on coal conveyor belts, the limited availability… More >

Musheng Chen^1,2, Qiang Wen¹, Xiaohong Qiu^1,2, Junhua Wu^1,*, Wenqing Fu¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071207 - 12 January 2026

Abstract In multi-modal emotion recognition, excessive reliance on historical context often impedes the detection of emotional shifts, while modality heterogeneity and unimodal noise limit recognition performance. Existing methods struggle to dynamically adjust cross-modal complementary strength to optimize fusion quality and lack effective mechanisms to model the dynamic evolution of emotions. To address these issues, we propose a multi-level dynamic gating and emotion transfer framework for multi-modal emotion recognition. A dynamic gating mechanism is applied across unimodal encoding, cross-modal alignment, and emotion transfer modeling, substantially improving noise robustness and feature alignment. First, we construct a unimodal encoder More >

Keiko Yanagihara^1,*, Masato Yoshida², Kensaku Awaji², Tamami Yamakawa¹, Sena Kato¹, Miki Tamura¹, Koji Nagata³

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.073891 - 30 December 2025

Abstract Background: Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors have transformed the management of hormone receptor–positive/HER2–negative (HR+/HER2–) advanced breast cancer, yet evidence for elderly or poor-performance patients remains limited. This study examined real-world outcomes of palbociclib plus endocrine therapy in Asian patients, with additional subgroup analyses by age and performance status. Methods: We retrospectively analyzed 46 consecutive Asian patients with recurrent or de novo HR+/HER2− breast cancer treated with first-line palbociclib plus ET between April 2021 and March 2025. The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall response rate (ORR), disease control rate (DCR), and safety.… More >

Jing Cai^1,#, Haoyun Zhu^1,#, Weiling Guo¹, Ting Huang¹, Pangzhou Chen^1,2, Wen Zhou¹, Ziyun Guan^1,3,*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.069902 - 30 December 2025

Abstract Background: Therapeutic responses of breast cancer vary among patients and lead to drug resistance and recurrence due to the heterogeneity. Current preclinical models, however, are inadequate for predicting individual patient responses towards different drugs. This study aimed to investigate the patient-derived breast cancer culture models for drug sensitivity evaluations. Methods: Tumor and adjacent tissues from female breast cancer patients were collected during surgery. Patient-derived breast cancer cells were cultured using the conditional reprogramming technique to establish 2D models. The obtained patient-derived conditional reprogramming breast cancer (CRBC) cells were subsequently embedded in alginate-gelatin methacryloyl hydrogel microspheres… More >