Mayuri Shukla¹, Soraya Boonmag², Parichart Boontem¹, Piyarat Govitrapong^1,*

BIOCELL, Vol.50, No.1, 2026, DOI:10.32604/biocell.2025.072557 - 23 January 2026

Abstract Ischemic stroke is one of the major causes of long-term disability and mortality worldwide. It results from an interruption in the cerebral blood flow, triggering a cascade of detrimental events like oxidative stress, mitochondrial dysfunction, neuroinflammation, excitotoxicity, and apoptosis, causing neuronal injury and cellular death. Melatonin, a pleiotropic indoleamine produced by the pineal gland, has multifaceted neuroprotective effects on stroke pathophysiology. Interestingly, the serum melatonin levels are associated with peroxidation and antioxidant status, along with mortality score in patients with severe middle cerebral artery infarction. Melatonin exhibits strong antioxidant, anti-inflammatory, and anti-apoptotic properties and preserves 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 >

Shaojie Min¹, Yaxiao Luo¹, Kebing Liu¹, Qingyuan Gong², Yang Chen^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.073175 - 09 December 2025

Abstract User identity linkage (UIL) across online social networks seeks to match accounts belonging to the same real-world individual. This cross-platform mapping enables accurate user modeling but also raises serious privacy risks. Over the past decade, the research community has developed a wide range of UIL methods, from structural embeddings to multimodal fusion architectures. However, corresponding adversarial and defensive approaches remain fragmented and comparatively understudied. In this survey, we provide a unified overview of both mapping and anti-mapping methods for UIL. We categorize representative mapping models by learning paradigm and data modality, and systematically compare them… More >

Dong Hyun Lee¹, Na Kyung Lee², Young Seo Lee^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.069976 - 09 December 2025

Abstract There has been an increasing emphasis on performing deep neural network (DNN) inference locally on edge devices due to challenges such as network congestion and security concerns. However, as DRAM process technology continues to scale down, the bit-flip errors in the memory of edge devices become more frequent, thereby leading to substantial DNN inference accuracy loss. Though several techniques have been proposed to alleviate the accuracy loss in edge environments, they require complex computations and additional parity bits for error correction, thus resulting in significant performance and storage overheads. In this paper, we propose FeatherGuard,… More >

Fang Liu^*, Xianghui Meng, Jiachen Li, Sibo Guo

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069243 - 09 December 2025

Abstract With the popularization of smart devices, Location-Based Services (LBS) greatly facilitates users’ life, but at the same time brings the risk of users’ location privacy leakage. Existing location privacy protection methods are deficient, failing to reasonably allocate the privacy budget for non-outlier location points and ignoring the critical location information that may be contained in the outlier points, leading to decreased data availability and privacy exposure problems. To address these problems, this paper proposes a Mix Location Privacy Preservation Method Based on Differential Privacy with Clustering (MLDP). The method first utilizes the DBSCAN clustering algorithm… More >

Songsong Zhang¹, Huazhong Jin^1,2,*, Zhiwei Ye^1,2, Jia Yang^1,2, Jixin Zhang^1,2, Dongfang Wu^1,2, Xiao Zheng^1,2, Dingfeng Song¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068044 - 10 November 2025

Abstract Multi-label feature selection (MFS) is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels. However, traditional centralized methods face significant challenges in privacy-sensitive and distributed settings, often neglecting label dependencies and suffering from low computational efficiency. To address these issues, we introduce a novel framework, Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization (DHBCPSO-MSR). Leveraging the federated learning paradigm, Fed-MFSDHBCPSO allows clients to perform local feature selection (FS) using DHBCPSO-MSR. Locally selected feature subsets are encrypted with differential privacy (DP) and transmitted… More >

Mintao Xu^1,#, Zichao Jin^1,#, Yangyang Tian¹, Jingcheng Zhang^1,*, Huiqin Ma¹, Yujin Jing¹, Jiangxing Wu², Jing Zhai²

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 4059-4078, 2025, DOI:10.32604/phyton.2025.074422 - 29 December 2025

Abstract The frequent outbreaks of crop diseases pose a serious threat to global agricultural production and food security. Data-driven forecasting models have emerged as an effective approach to support early warning and management, yet the lack of user-friendly tools for model development remains a major bottleneck. This study presents the Multi-Scenario Crop Disease Forecasting Modeling System (MSDFS), an open-source platform that enables end-to-end model construction-from multi-source data ingestion and feature engineering to training, evaluation, and deployment-across four representative scenarios: static point-based, static grid-based, dynamic point-based, and dynamic grid-based. Unlike conventional frameworks, MSDFS emphasizes modeling flexibility, allowing… More >

Virna Margarita Martín Giménez¹, SebastiáN GarcíA MenéNdez^2,3, Luiz Gustavo A. Chuffa⁴, Vinicius Augusto SimãO⁴, Russel J. Reiter⁵, Ramaswamy Sharma⁶, Walter Balduini⁷, Carla Gentile⁸, Walter Manucha^2,3,*

BIOCELL, Vol.49, No.12, pp. 2245-2282, 2025, DOI:10.32604/biocell.2025.071830 - 24 December 2025

Abstract Neurodegenerative diseases (NDs) such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS) are characterized by progressive neuronal loss, which is closely linked to mitochondrial dysfunction. These pathologies involve a complex interplay of genetics, protein misfolding, and cellular stress, culminating in impaired energy metabolism, an increase in reactive oxygen species (ROS), and defective mitochondrial quality control. The accumulation of damaged mitochondria and dysregulation of pathways such as the Integrated Stress Response (ISR) are central to the pathogenesis of these conditions. This review explores the critical relationship between mitochondrial stress… More >

Kunjabihari Swain¹, Murthy Cherukuri^1,*, Indu Sekhar Samanta², Bhargav Appasani^3,*, Nicu Bizon^4,5, Mihai Oproescu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1993-2015, 2025, DOI:10.32604/cmes.2025.067121 - 26 November 2025

Abstract Transmission line faults pose a significant threat to power system resilience, underscoring the need for accurate and rapid fault identification to facilitate proper resource monitoring, economic loss prevention, and blackout avoidance. Extreme learning machine (ELM) offers a compelling solution for rapid classification, achieving network training in a single epoch. Leveraging the Internet of Things (IoT) and the virtual instrumentation capabilities of LabVIEW, ELM can enable the swift and precise identification of transmission line faults. This paper presents a regularized radial basis function (RBF) ELM-based fault detection and classification system for transmission lines, utilizing a LabVIEW More >

Huaiqin Liu¹, Meng Li¹, Jianwen Shao², Weishen Zhang¹, Qifan Yang¹, Yutong Li¹, Tian Su^1,3,*, Xuefeng Mei⁴

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1563-1588, 2025, DOI:10.32604/sdhm.2025.073009 - 17 November 2025

Abstract Rock collapse is a significant geological disaster that poses a serious threat to life and property in mountainous regions worldwide. Investigating the response of protective structures to rockfall impacts can provide valuable references for the design and placement of such structures. In this study, RocPro3D and ABAQUS were employed to comprehensively analyze rockfall movement trajectories and the structural response upon impact. The results indicate that when the impact velocity of rockfall at the protective structure reaches 20–30 m/sec, the corresponding bounce height ranges from 5 to 8 m, and most rockfall accumulates at the slope More > Graphic Abstract