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
    Comparative Analysis of Modulation Shapes on Laser Diode Performance with a High-Efficiency LLC Resonant Converter Driver
    (Wiley, 2026-02-06) Yigit, Hayri; Rifat boynuegri, Ali; Tekgun, Burak; Rifat Boynuegri, Ali
    High-power laser diodes (LDs) are key components in laser-based wireless power transfer (WPT) systems, where end-to-end efficiency is one of the most critical performance metrics. This study investigates the driving performance of an LD powered by a high-efficiency LLC resonant converter under three distinct excitation waveforms-sinusoidal, triangular, and rectified-sine-using a MATLAB/SIMULINK model and an experimental setup designed to reproduce real-world operating conditions. Each waveform is synthesized through frequency modulation of a full-bridge inverter stage and filtered at the output. The analysis examines the impact of modulation shape on output current ripple, converter efficiency, and overall LD efficiency. Experimental validation confirms the simulation trends, underscoring the trade-offs between waveform smoothness, implementation complexity, and efficiency. Beyond confirming that constant-current operation yields the highest LD efficiency, this study explicitly quantifies how low-frequency current ripple induced by different modulation waveforms propagates through the LLC resonant converter, alters RMS current stress, and translates into measurable efficiency degradation at both the driver and LD levels. By experimentally correlating waveform symmetry, ripple magnitude, and loss mechanisms, the work establishes practical design boundaries for waveform-modulated laser drivers in WPT systems.
  • 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
    Toward the Design of New Α-Carboline Derivatives Against Anaplastic Lymphoma Kinase (Alk): A Comprehensive in Silico Approach
    (Wiley-VCH Verlag GmbH, 2025-11) Sari, Ceyhun; Akcok, Ismail
    After the first description of anaplastic lymphoma kinase (ALK) in an anaplastic large cell lymphoma cell line as a nucleophosmin (NPM) fusion partner, ALK and its various fusion partners have been implicated in numerous cancers such as non-small cell lung cancer (NSCLC), anaplastic large cell lymphoma (ALCL), neuroblastoma, and rhabdomyosarcoma. In the last decade, several compounds targeting ALK have been developed and approved by the Food and Drug Administration (FDA). Despite the advances of generations of ALK inhibitors, a recent study highlighted that around half of the ALK-positive NSCLC patients will go through disease progression in response to first-line alectinib, which is a second-generation ALK inhibitor. In this study, we aimed to propose a novel alpha-carboline compound targeting the ALK tyrosine kinase domain to be used against various types of cancer in which ALK fusion proteins may be involved. In this regard, we designed more than 200 alpha-carboline derivatives and investigated their binding properties against ALK tyrosine kinase by using in silico protocols consisting of molecular docking studies, molecular dynamics simulations, MM/PBSA binding free energy calculation, and essential dynamics analysis. Considering the obtained results, we developed two promising candidates, compounds 208 & 209 with -9.05 and -9.80 binding energies, respectively, which demonstrated improved binding profiles over the course of a 300 ns simulation.
  • 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.
  • Conference Object
  • Conference Object
  • Article
    An Extension of Lucas's Theorem
    (indian Nat Sci Acad, 2025-10-31) Cinkir, Zubeyir; Ozturkalan, Aysegul
    We give elementary proofs of some congruence criteria to compute binomial coefficients modulo a prime number. These criteria are analogues to the symmetry property of binomial coefficients. We give extended version of Lucas's Theorem by using those criteria. We give applications of these criteria by describing a method to derive identities and congruences involving sums of binomial coefficients.
  • 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
    Interaction of Inula Viscosa (L.) Aiton with IBA1 via Rosmarinic Acid and Rutin: Insights from Computational Models and Biological Effects
    (Wiley-VCH verlag GmbH, 2025-10-29) Aktas Pepe, Nihan; Acar, Busra; Ceylan Ekiz, Yagmur; Senol, Ayse Merve; Semiz, Gurkan; Sen, Alaattin; Celik Turgut, Gurbet
    Inula viscosa (L.) Aiton is a traditional medicinal plant extensively utilized in Mediterranean nations for the treatment of rheumatic pain, inflammatory disorders, diabetes, anemia, and cancer. This study further explored its anti-inflammatory mechanisms through the highest components, chlorogenic acid, rosmarinic acid, and rutin, on the expression of the ionized calcium-binding adapter molecule 1 (Iba1) on monocyte-derived macrophage-like cells. Iba1 is known to contribute pathogenesis of diverse inflammatory diseases. HPLC analysis identified 13 major phenolic compounds, with rosmarinic acid, chlorogenic acid, and rutin as major components. The aqueous extract of the plant and its major components exhibited dose-dependent antiproliferative activity on pTHP-1, RAW264.7, and PCS-201-012 cells. Immunofluorescence staining revealed a significant reduction in Iba1 protein expression, which is associated with inflammation, at the high dose of I. viscosa and rutin. Molecular docking studies indicated that rosmarinic acid and rutin had the strongest predicted interactions with Iba1, with docking scores of -12.403 and -12.301 kcal/mol and MM/GBSA binding energies of -64.47 and -84.20 kcal/mol, respectively. I. visoca and its major components were observed to significantly suppress iNOS activity in LPS-stimulated cells; these findings were also supported by RT-PCR results. Treatment with the high dose of I. viscosa resulted in 9.45% necrotic cells and caused cell cycle arrest in the S phase (59.2 +/- 5.23%). This suggests that it may potentially reduce the proliferation of activated macrophages. In the fibroblast migration assays, the relative wound closure rate was found to be significant 27.06 +/- 18.09% at the low dose of I. viscosa and 31.59 +/- 22.42% at the high dose of I. viscosa. Although the relatively low wound closure rate limits tissue repair, it may benefit chronic wounds and fibrosis by suppressing excessive cell proliferation and inflammation. These results suggest that I. viscosa is a promising natural source of bioactive compounds with potential applications in anti-inflammatory drug development.