Antonio Pizzi^*

Journal of Renewable Materials, Vol.14, No.7, 2026, DOI:10.32604/jrm.2026.02026-0056 - 28 July 2026

Abstract Cross Polarization Magic Angle Spinning Carbon 13 Nuclear Magnetic Resonance (CP-MAS ¹³C NMR) analysis of commercial procyanidin (pine bark) and delphinidin (pecan nut membranes) condensed flavonoid tannin extracts reacted with hexamethylenetetramine (hexamine) have shown that due to the highly reactive imino-amino methylene species formed before its degradation reaches the generation of formaldehyde, benzylamine bridges between the flavonoid units are formed. This study shows that tannin-based biobenzoxazines are also generated at the same time, with both benzylamines and benzoxazines bridges being in more marked proportions, the more reactive the tannin reacted with hexamethylenetetramine is. This is not More > Graphic Abstract

Yiyan Zhang^1,*, Yi Xin², Qin Li²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082841 - 27 July 2026

Abstract Medical data has specificity compared to other fields of data, and the description of medical data characteristics is still in a qualitative stage. This study included 293 sub-datasets of 138 independent datasets. First, data preprocessing was performed using methods such as incomplete data removal, inconsistent data normalization, and data integration. Then, the characteristics of 293 research datasets were quantified using 26 indicators in three categories: simple indicators, statistical indicators, and informational indicators. Furthermore, statistical analysis was performed on the above-mentioned quantitative characteristics, and stepwise regression and decision tree methods were used for modeling learning. The… More >

Teng Long¹, Leyu Wang^2,*, James D. Lee³, Cing-Dao Kan²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.080866 - 27 July 2026

Abstract Titanium alloy Ti-6Al-4V has been widely applied in many industries, for example, aerospace, marine, automotive, and biomedical engineering systems, where accurate characterization of plastic deformation is important for evaluating material performance and potential failure under severe loading conditions. This material shows nonlinear plasticity and tension–compression asymmetry, which makes the strain hardening characterization important for computational failure analysis and crashworthiness-related simulations. However, conventional strain hardening models and parameter identification methods often rely on linear or extrapolation-based assumptions and are sensitive to initial guesses due to the non-convex nature of the optimization problem. In this study, a More >

Laura Schaltz^1,2, Simon Lassnig^1,2, Karola Schlote^1,2, Christian Schwerk³, Horst Schroten³, Nicole de Buhr^1,2,*

BIOCELL, Vol.50, No.8, 2026, DOI:10.32604/biocell.2026.079046 - 27 July 2026

Abstract Objectives: Streptococcus suis (S. suis) is a worldwide occurring pathogen in pigs and humans that can cross the blood-cerebrospinal fluid barrier (BCSFB) to cause meningitis, while host neutrophils counter infection through mechanisms including the release of neutrophil extracellular traps (NETs). NET-formation involves the release of nuclear DNA with antimicrobial components, which can bind and kill bacteria. We aimed to characterize the host-pathogen interaction between S. suis and neutrophils within the CSF compartment, focusing on NET-formation. Methods: A 3D cell culture model of the porcine BCSFB was used by cultivating a porcine choroid plexus epithelial cell line (PCP-R) on filter… More >

Amit Pimpalkar^1,*, Kapil N. Vhatkar², Rachna K. Somkunwar³, Shweta Koparde⁴, Dalia H. Elkamchouchi⁵, Ateeq Ur Rehman^6,*, Pooja Verma⁷, Salil Bharany⁸

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084384 - 23 July 2026

Abstract Plants are fundamental to global food security; however, plant diseases significantly reduce agricultural productivity, making early and accurate detection essential. Traditional inspection approaches rely heavily on manual observation, which is labor-intensive, subjective, difficult to scale, and susceptible to human error. In contrast, artificial intelligence (AI) combined with computer vision (CV) offers an effective solution for early-stage disease detection, minimizing yield losses while overcoming the limitations of manual monitoring systems. In this study, a novel deep learning architecture, the Swin Transformer with Harmonic Densely Connected Network (STHarDNet), is proposed. The framework integrates a Swin Transformer (ST)… More >

Xiangyi Le¹, Deyu Lin^1,2,*, Yufei Zhao², Wang Miao³, Yong Liang Guan²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083306 - 23 July 2026

Abstract The emergence of Unmanned Aerial Vehicle (UAV)-enabled Wireless Energy Transfer (WET) and Simultaneous Wireless Information and Power Transfer (SWIPT) technology provide a promising solution to overcome the energy sustainability limitations of traditional harvesting-reliant sensor networks. However, in large-scale Battery-free SWIPT-enabled Sensor Networks (BSSN) characterized by sparse node distribution and heterogeneous energy consumption and harvesting rates, employing a single UAV for energy replenishment often suffers from insufficient operation continuity and low charging efficiency. To overcome these challenges, a Multi-UAV Collaborative Energy Charging for BSSN Based on Multi-Agent Deep Deterministic Policy Gradient (MCEC-MADDPG) is proposed in this… More >

Maurice Kyla Octaviano, Jin-Taek Seong^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082842 - 23 July 2026

Abstract Integrating high-frequency sequential signals with low-frequency contextual descriptors into a unified deep encoder is a recurring challenge in computational modelling, exemplified by cross-sectional stock ranking where price dynamics must be jointly modelled with quarterly accounting fundamentals. Existing approaches use late concatenation, where the contextual signal influences only the final prediction head and cannot shape upstream feature extraction. We propose Feature-wise Linear Modulation (FiLM) as an intermediate conditioning mechanism: fundamentals generate per-channel scaling (gamma) and shifting (beta) parameters that affinely transform the encoder’s intermediate representations before aggregation. The same price sequence thus yields different temporal features… More >

Hongzhen Liu¹, Liang Xie¹, Zhiqiang Ru^2,*, Yuan Wan¹, Zhe Zhang¹, Xi Fang^1,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082755 - 23 July 2026

Abstract Federated learning is a privacy-preserving machine learning framework that facilitates model training directly on decentralized data that, due to privacy concerns or transmission costs, cannot be centralized on a server for traditional model training. To prevent adversaries from reconstructing the original data via parameters transmitted during the process, homomorphic encryption is a commonly adopted method. However, it introduces significant communication and computation costs and risks total security failure if any secret key is compromised. This paper proposes a privacy-preserving aggregation mechanism that enables each client to independently generate partial keys for encryption while allowing decryption… More >

Thossapon Kaewrakmuk, Jakkree Srinonchat^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081437 - 23 July 2026

Abstract Robotic systems require reliable tactile perception to evaluate object stiffness during physical interaction. This study proposes a lightweight dual-branch architecture, named Hybrid-CNN-ResVgg, designed to improve hardness recognition using data from a low-cost piezoresistive tactile sensor. The model combines a one-dimensional convolutional neural network (1D-CNN) based on a ResNet8-Lite architecture for learning temporal signal patterns and a two-dimensional convolutional neural network (2D-CNN) based on a VGG6-Lite architecture for learning spatial representations derived from Gramian Angular Difference Fields (GADF). A cross-architecture fusion mechanism is introduced to integrate temporal and spatial features while reducing redundant representation learning. Experiments… More >

Hwa-Young Jeong^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080596 - 23 July 2026

Abstract The rapid growth of electric vehicle (EV) charging infrastructures has introduced new challenges in monitoring abnormal load behaviors under strict latency and resource constraints. Conventional anomaly detection approaches either rely on centralized processing or incur excessive false alarms, limiting their practical applicability in large-scale deployments. This paper proposes a hierarchical edge-fog anomaly detection framework that integrates lightweight edge-level filtering with a fog-level Temporal Convolutional Network (TCN) detector. The edge component suppresses non-informative patterns, while the fog layer performs temporal modeling on selectively forwarded data. This design enables controllable reduction of fog-level processing load. Under corrected… More >