Results 181 to 190 of about 8,875 (240)

Digital Twin Integration in Project Life Cycle Management—A Review

open access: yesEngineering Reports, Volume 8, Issue 3, March 2026.
This study explores the integration of digital twin (DT) technology within project life cycle management (PLM), focusing on its transformative impact on industries like aerospace, automotive, healthcare, and construction. By creating real‐time virtual models synchronized with physical assets, DTs enable predictive maintenance, operational optimization,
Md. Injamamul Haque Protyai   +2 more
wiley   +1 more source

HADA: A Hybrid Anomaly Detection Approach Using Unsupervised Machine Learning

open access: yesEngineering Reports, Volume 8, Issue 3, March 2026.
Overview of HADA, an unsupervised fraud detection pipeline that preprocesses and scales transaction data, applies PCA for dimensionality reduction, scores anomalies using Isolation Forest, selects anomalous transactions via thresholding, and clusters the selected anomalies using Agglomerative Hierarchical Clustering (AHC) to produce interpretable ...
Francis Thiong'o   +3 more
wiley   +1 more source

Seismic Enhancement of Masonry‐Infilled Substandard Reinforced Concrete Frames Using Lightweight Steel Exoskeleton

open access: yesEarthquake Engineering &Structural Dynamics, Volume 55, Issue 3, Page 603-623, March 2026.
ABSTRACT A significant portion of existing reinforced concrete (RC) structures in seismically active regions was constructed prior to the adoption of modern seismic design standards, leaving them highly susceptible to earthquake‐induced damage. The vulnerabilities of these structures, often exacerbated by material degradation, have been starkly ...
İsmail Ozan Demirel   +3 more
wiley   +1 more source

Performance Monitoring of Photovoltaic Modules Using Machine‐Learning‐Based Solutions: A Survey of Current Trends

open access: yesEnergy Science &Engineering, Volume 14, Issue 3, Page 1663-1682, March 2026.
The graphical abstract presents the concept of applying machine‐learning algorithms to assess the performance of photovoltaic modules. Data from solar panels are fed to surrogates of intelligent models, to assess the following performance metrics: identifying faults, quantifying energy production and trend degradation over time. The combination of data
Nangamso Nathaniel Nyangiwe   +3 more
wiley   +1 more source

A CNN‐Based Deep Learning Architecture for Discriminating Botanical Adulteration and Complexities Among Commercial Apiaceae Medicinal Species

open access: yesFood Science &Nutrition, Volume 14, Issue 3, March 2026.
This study presents a deep learning framework for the automated authentication of 15 medicinal Apiaceae species using digital images of their mericarps. The pipeline integrates an augmentation module—incorporating rescaling, rotation, and flipping—to improve model generalization, followed by feature extraction using pre‐trained DenseNet121 as the core ...
Elyas Aryakia, Ersam Aryakia
wiley   +1 more source

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