WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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  • Article
    Non-Contact Acoustic Screening for Sleep Apnea: A Subject-Aware Deep Learning Approach
    (Springer Science and Business Media Deutschland GmbH, 2026-02-11) Aygün Çakıroğlu, M.; Kizilkaya Aydoǧan, E.; Bolatturk, Ö.F.; Aydoğan, S.; Ismailoǧullari, S.; Delice, Y.
    Purpose: To explore the feasibility of using camera-derived, non-contact audio synchronized with PSG for clinically relevant sleep-apnea classification, and to benchmark compact deep models under a subject-aware design using a previously unstudied, real-world dataset. Methods: Thirty-two adults underwent simultaneous polysomnography (PSG) and camera-based non-contact audio recording. The synchronized audio segments were used to train and compare three compact deep-learning architectures (convolutional, attention-augmented, and transformer-based) under a subject-aware evaluation design that prevented identity leakage. Model performance and calibration were assessed at both segment and subject levels using standard statistical tests. Results: Subject-level evaluation was based on a very small, imbalanced test set of six subjects (one positive). Within this limited yet previously unstudied local dataset, the CNN_trans model achieved an apparent perfect ranking performance (AUC = 1.00; 95% CI 0.00–1.00), though this likely reflects the small, imbalanced test cohort, with recall = 1.00 and precision = 0.55. The wide confidence interval reflects substantial statistical uncertainty, and DeLong comparisons showed no significant AUC difference between CNN_trans and CNN_att (ΔAUC = − 0.042; p = 0.43). Conclusion: PSG-synchronized, non-contact audio supports accurate and well-calibrated sleep-apnea classification with compact deep models. This subject-aware evaluation suggests that contactless acoustic monitoring may have potential clinical relevance, motivating larger, multi-site validation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026.
  • Article
    Noninvasive Condition Monitoring for Eccentricity Fault Detection in Large Hydro Generators
    (TÜBİTAK Scientific & Technological Research Council Turkey, 2026-01-16) Lemeski, Atena Tazikeh; Tekgun, Didem; Keysan, Ozan; Leblebicioglu, Kemal; Gol, Murat; Leblebicioglu, Mehmet Kemal
    Eccentricity faults in electric machines remain a critical concern, as they generate uneven magnetic forces that increase vibration and noise, ultimately raising the risk of premature motor failure. This study proposes a method for the early detection of dynamic eccentricity (DE) faults in hydropower plants through an advanced optimization-based parameter identification technique integrated with finite element analysis (FEA). Finite element modeling (FEM) is first used to analyze an existing salient-pole synchronous generator (SPSG) from a hydroelectric power plant in T & uuml;rkiye. The effects of DE faults on the SPSG's magnetic equivalent circuit parameters are then examined under various fault severities. A comprehensive hydropower plant model-including the synchronous generator, governor, and excitation system-is developed in MATLAB/Simulink, with all input parameters obtained from real plant data and equivalent circuit variations extracted from FEA. After completing the modeling stage, including fault scenarios, MATLAB and Simulink are employed together to estimate key magnetic equivalent circuit parameters using a modified particle swarm optimization (MPSO) algorithm, achieving highly accurate parameter estimation. Since the hydropower system allows measurement of the three-phase output currents, parameter estimation is performed based on current variations under different fault conditions. The simulation results verify the method's ability to detect faults with high accuracy; thus, this integrated and noninvasive approach provides a robust framework for ensuring the operational reliability and longevity of large hydro generators.
  • Article
    Deep-Learning Detection of Open-Apex Teeth on Panoramic Radiographs Using YOLO Models
    (Springer, 2025-12-23) Edik, Merve; Celebi, Fatma; Cukurluoglu, Aykagan
    ObjectivesThe use of deep learning in detecting teeth with open apices can prevent the need for additional radiographs for patients. The presented study aims to detect open-apex teeth using You Only Look Once (YOLO)-based deep learning models and compare these models.MethodsA total of 966 panoramic radiographs were included in the study. Open-apex teeth in panoramic radiographs were labeled. During the labeling process, they were divided into 6 classes in the maxilla and mandible, namely incisors, premolars, and molars. AI models YOLOv3, YOLOv4, and YOLOv5 were used. To evaluate the performance of the three detection models, both overall and separately for each class in the test dataset, precision, recall, average precision (mAP), and F1 score were calculated.ResultsYOLOv4 achieved the highest overall performance with a mean average precision (mAP) of 87.84% at IoU (Intersection over Union) 0.5 (mAP@0.5), followed by YOLOv5 with 85.6%, and YOLOv3 with 84.46%. Regarding recall, YOLOv4 also led with 90%, while both YOLOv3 and YOLOv5 reached 89%. Moreover, the F1 score was the highest for YOLOv4 (0.87), followed by YOLOv3 (0.86) and YOLOv5 (0.85).ConclusionsIn this study, YOLOv3, YOLOv4, and YOLOv5 were evaluated for the detection of open-apex teeth, and their mAP, recall, and F1 scores exceeded 84%. Deep learning-based systems can provide faster and more accurate results in the detection of open-apex teeth. This may help reduce the need for additional radiographs from patients and aid dentists by saving time.
  • Article
    Modeling and Simulation of Dynamic Energy Management Systems for Smart Buildings
    (TÜBİTAK, 2025-11-25) Ozel, O.; Rıfat Boynueğrİ, A.; Yigit, H.; Tekgun, B.; Boynuegri, Ali Rifat
    This study presents a dynamic energy management system tailored for smart residential buildings, integrating thermal and electrical models to achieve both natural gas and electricity bill cost reduction. By harnessing wind and solar energy sources, the system aims to meet the diverse energy needs of modern homes. Through load shifting and thermal storage strategies, known as power-to-heat (P2H) approaches, the system ensures efficient renewable energy utilization while maintaining resident comfort. Validation of the proposed system was conducted using real-world data from the Yıldız Technical University Smart Home Laboratory, demonstrating its practical applicability and effectiveness. Results indicate significant reductions in both natural gas and electricity consumption, leading to substantial cost savings. Specifically, the proposed system reduced natural gas consumption by 3.79% and electricity consumption by 35.62%, highlighting its potential to enhance energy efficiency and sustainability in residential settings. © This work is licensed under a Creative Commons Attribution 4.0 International License.
  • Conference Object
    Bioinformatics Analysis of Antifungal Mechanisms in Serratia Fonticola: Protein-Protein Interaction with Botrytis Cinerea BAG1 and Genome-Encoded Enzyme Reportoire
    (Wiley, 2025) Bozkurt, E. B.; Baysal, O.; Marzec-Grzadziel, A.; Silme, R. S.; Can, A.; Belen, I. N.; Korkut, A.
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  • Article
    Sustainable Stabilization of Peat Soil with Hybrid Geopolymer Jet Grout Columns
    (Springer Int Publ A.G., 2025-10-15) Yalcin, Hakan; Erol, Aykut; Kaya, Zulkuf; Cadir, Cenk Cuma; Uncuoglu, Erdal; Akin, Muge K.
    Peat soils present severe challenges in geotechnical engineering due to their low shear strength, high water content, and aggressive chemical environments such as sulfate exposure. While cement-based jet grouting (JG) is widely used, it entails high carbon emissions and energy consumption. Hybrid geopolymer jet grout columns (HGJGCs) are presented in this work as a viable and sustainable alternative. Unlike conventional geopolymer studies that rely on pre-cured molds later exposed to aggressive environments, this research simulates realistic field conditions by injecting fresh geopolymer directly into sulfate-rich peat, where early-age durability and strength are critical. To address early strength limitations commonly seen in aggressive situations, a tiny amount of cement was added to the fly ash/GGBFS-based combination. Crucially, there is no need for high heat because the mechanism cures at room temperature. Physical model testing, laboratory-scale jet grouting, and performance comparisons with conventional JGCs were all carried out. Results show that HGJGCs increased the bearing capacity of peat by 5.5 times, improved compressive strength (5.3-5.7 MPa), and reduced settlement more effectively than JGCs. Additionally, CO2 emissions were reduced by 25.14% due to lower binder-related emissions and energy demand. This work shows that hybrid geopolymer systems are a viable, low-carbon substitute for peat stabilization because they can function well in real-world, chemically demanding situations.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    A Comprehensive Review on the Extraction and Recovery of Lithium from Primary and Secondary Sources: Advances Toward Battery-Grade Materials
    (Wiley, 2025-10-20) Top, Soner; Kursunoglu, Sait; Altiner, Mahmut
    Lithium-ion battery (LIB) technologies have become indispensable to modern energy systems, driving global demand for high-purity lithium compounds. This review focuses on lithium recovery and purification strategies for battery-grade lithium carbonate (Li2CO3) and lithium hydroxide (LiOH), addressing both primary sources (brines and minerals) and secondary sources (waste materials). Industrially established processes, such as evaporation-based brine treatment and conventional metallurgical methods, are discussed alongside emerging techniques, including membrane separation, solvent extraction, and CO2-assisted precipitation. Particular attention is given to lithium precipitation mechanisms, the behaviour of co-existing ions during extraction, and the specific quality requirements for cathode material synthesis. By evaluating process scalability, environmental impact, and product purity, this review provides a comprehensive understanding of current practices and future directions. Additionally, it highlights the growing importance of lithium in the context of accelerating electric vehicle (EV) adoption, underscoring the bright and expanding future of the lithium industry.
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    An Innovative Probiotic, Ln-AGA58, Demonstrates Immunosuppressive Effects in Treating Multiple Sclerosis
    (Wiley, 2025) Ozen, M. B.; Bozkurt, E. B.; Ortakci, F.; Sen, A.; Acar, O. Ozgun