WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 13Role of Pretty Nanoflowers as Novel Versatile Analytical Tools for Sensing in Biomedical and Bioanalytical Applications(Wiley, 2024-02) Dadi, Seyma; Ocsoy, IsmailIn recent years, an encouraging breakthrough in the synthesis of immobilized enzymes in flower-shaped called "organic-inorganic hybrid nanoflowers (hNFs)" with greatly enhanced catalytic activity and stability were reported. Although, these hNFs were discovered by accident, the enzymes exhibited highly enhanced catalytic activities and stabilities in the hNFs compared with the free and conventionally immobilized enzymes. Herein, we rationally utilized the catalytic activity of the hNFs for analytical applications. In this comprehensive review, we covered the design and use of the hNFs as novel versatile sensors for electrochemical, colorimetric/optical and immunosensors-based detection strategies in analytical perspective. Formation of nanoflowers and their biosensor function in biomedical and bioanalytical applications. imageArticle AI-Enhanced PV Power Forecasting Using Cloud Thickness and Motion in Kayseri, Türkiye(Wiley, 2025-01) Yavuz, Levent; Onen, Ahmet; Awad, Ahmed; Ahshan, Razzaqul; Al-Badi, AbdullahThe incorporation of renewable energy in photovoltaic (PV) systems has made significant progress. The inherent intermittency nature of PV generation, nevertheless poses an obstacle to accurate energy forecasting. Historical PV production plus meteorological data such as temperature, humidity, and atmospheric pressure are largely utilized in present methods of forecasting. However, cloud thickness and dynamics-integrated system, has not been investigated and tested in real-world examples yet.This research seeks to fill this gap in research through the development of a new AI-based PV forecasting model that incorporates cloud thickness, cloud motion, and solar position into the forecasting model. Cloud properties and their impact on solar radiation are computed through a deep learning-based panel-shadowing model. For cloud movement forecasting, a gated recurrent unit (GRU) is used, while multiple convolutional neural networks (CNNs) are used for estimating cloud thickness. These outcomes are then integrated with measurements from environmental sensors to improve the accuracy of the predictions.The system was implemented and tested at Abdullah G & uuml;l University and exhibited a remarkable improvement in forecasting accuracy compared to current models. The results prove that cloud motion and thickness improve the accuracy of PV predictions, which is important for energy market stability and power grid operations.