Samuel Acheme^*, Glory Nosawaru Edegbe

Journal of Cyber Security, Vol.8, pp. 1-31, 2026, DOI:10.32604/jcs.2026.073923 - 07 January 2026

Abstract Federated learning (FL) enables collaborative model training across decentralized datasets, thus maintaining the privacy of training data. However, FL remains vulnerable to malicious actors, posing significant risks in privacy-sensitive domains like healthcare. Previous machine learning trust frameworks, while promising, often rely on resource-intensive blockchain ledgers, introducing computational overhead and metadata leakage risks. To address these limitations, this study presents a novel Decentralized Identity (DID) framework for mutual authentication that establishes verifiable trust among participants in FL without dependence on centralized authorities or high-cost blockchain ledgers. The proposed system leverages Decentralized Identifiers (DIDs) and Verifiable Credentials… More >

Manaliben Amin^*

Journal on Artificial Intelligence, Vol.8, pp. 1-18, 2026, DOI:10.32604/jai.2026.073895 - 07 January 2026

Abstract Artificial intelligence (AI) is steadily making its way into pharmaceutical validation, where it promises faster documentation, smarter testing strategies, and better handling of deviations. These gains are attractive, but in a regulated environment speed is never enough. Regulators want assurance that every system is reliable, that decisions are explainable, and that human accountability remains central. This paper sets out a Human-in-the-Loop (HITL) AI approach for Computer System Validation (CSV) and Computer Software Assurance (CSA). It relies on explainable AI (XAI) tools but keeps structured human review in place, so automation can be used without creating… More >

Benyu Wang^*, Ke Chen, Bingjian Wang^#,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.070137 - 08 January 2026

Abstract To examine stress redistribution phenomena in bridges subjected to varying operational conditions, this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail steel arch bridge. An initial statistical comparison of sensor data distributions reveals clear temporal variations in stress redistribution patterns. XGBoost (eXtreme Gradient Boosting), a gradient-boosting machine learning (ML) algorithm, was employed not only for predictive modeling but also to uncover the underlying mechanisms of stress evolution. Unlike traditional numerical models that rely on extensive assumptions and idealizations, XGBoost effectively captures nonlinear and time-varying relationships between stress… More >

Shenghui Su, Yu Zeng, Jiaxin Chen, Xieping Dong^*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.070558 - 30 December 2025

Abstract Background: Recent studies have shown glycerolipid metabolism played an essential role in multiple tumors, however, its function in osteosarcoma is unclear. This study aimed to explore the role of glycerolipid metabolism in osteosarcoma. Methods: We conducted bioinformatics analysis using data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and single-cell RNA sequencing. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to identify the Glycerolipid metabolism-related genes associated with the clinical outcome of osteosarcoma. Tumor-associated macrophages (TAMs) and their interactions with immune cells were examined through single-cell analysis and co-culture experiments.… More >

Kai Gui^1,#, Tianyi Yang^1,#, Chengying Xiong¹, Yue Wang¹, Zhiqiang He¹, Wuxian Li^2,3,*, Min Tang^1,*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.070143 - 30 December 2025

Abstract Objectives: The mechanism by which specific tumor subsets in colorectal cancer (CRC) use alternative metabolic pathways, particularly those modulated by hypoxia and fructose, to alter the tumor microenvironment (TME) remains unclear. This study aimed to identify these malignant subpopulations and characterize their intercellular signaling networks and spatial organization through an integrative multi-omics approach. Methods: Leveraging bulk datasets, single-cell RNA sequencing, and integrative spatial transcriptomics, we developed a prognostic model based on hypoxia-and fructose metabolism-related genes (HFGs) to delineate tumor cell subpopulations and their intercellular signaling networks. Results: We identified a specific subset of stanniocalcin-2 positive (STC2+)… More > Graphic Abstract

Binjiang Hu^1,*, Yihua Zhu², Liang Tu^1,2, Zun Ma³, Xian Meng³, Kewei Xu³

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069777 - 27 December 2025

Abstract This paper proposes an equivalent modeling method for photovoltaic (PV) power stations via a particle swarm optimization (PSO) K-means clustering (KMC) algorithm with passive filter parameter clustering to address the complexities, simulation time cost and convergence problems of detailed PV power station models. First, the amplitude–frequency curves of different filter parameters are analyzed. Based on the results, a grouping parameter set for characterizing the external filter characteristics is established. These parameters are further defined as clustering parameters. A single PV inverter model is then established as a prerequisite foundation. The proposed equivalent method combines the… More >

Bindi Saurabh Thakkar¹, Pradeep Kumar Karsh^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-22, 2026, DOI:10.32604/cmc.2025.072839 - 09 December 2025

Abstract This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies. Hybrid composites, widely used in aerospace, automotive, and structural applications, often face variability in material properties, geometric configurations, and manufacturing processes, leading to uncertainty in their dynamic response. To address this, three surrogate-based machine learning approaches like radial basis function (RBF), multivariate adaptive regression splines (MARS), and polynomial neural networks (PNN) are integrated with a finite element framework to efficiently capture the stochastic behavior of these plates. The research focuses on predicting the first three natural frequencies… More >

Meenakshi Aggarwal¹, Vikas Khullar^2,*, Nitin Goyal³

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-31, 2026, DOI:10.32604/cmc.2025.072673 - 09 December 2025

Abstract The Internet of Vehicles, or IoV, is expected to lessen pollution, ease traffic, and increase road safety. IoV entities’ interconnectedness, however, raises the possibility of cyberattacks, which can have detrimental effects. IoV systems typically send massive volumes of raw data to central servers, which may raise privacy issues. Additionally, model training on IoV devices with limited resources normally leads to slower training times and reduced service quality. We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning (TinyML) approach, which operates on IoV edge devices without sharing sensitive raw data. Specifically, we focus on… More >

Xiaoyi Duan, Tianqi Zou, Chenyang Wang, Yu Gu, Xiuying Li^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-24, 2026, DOI:10.32604/cmc.2025.070683 - 09 December 2025

Abstract Person recognition in photo collections is a critical yet challenging task in computer vision. Previous studies have used social relationships within photo collections to address this issue. However, these methods often fail when performing single-person-in-photos recognition in photo collections, as they cannot rely on social connections for recognition. In this work, we discard social relationships and instead measure the relationships between photos to solve this problem. We designed a new model that includes a multi-parameter attention network for adaptively fusing visual features and a unified formula for measuring photo intimacy. This model effectively recognizes individuals More >

Charlotte Olivia Namagembe, Mohamad Ibrahim, Md Arafatur Rahman^*, Prashant Pillai

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.070316 - 09 December 2025

Abstract The rapid proliferation of commercial unmanned aerial vehicles (UAVs) has revolutionized fields such as precision agriculture and disaster response. However, their heavy reliance on GPS navigation leaves them highly vulnerable to spoofing attacks, with potentially severe consequences. To mitigate this threat, we present a machine learning-driven framework for real-time GPS spoofing detection, designed with a balance of detection accuracy and computational efficiency. Our work is distinguished by the creation of a comprehensive dataset of 10,000 instances that integrates both simulated and real-world data, enabling robust and generalizable model development. A comprehensive evaluation of multiple classification More >