Mudanya University Institutional Academic Archive System
Mudanya University's Dspace system is a platform that digitally stores and opens academic studies. Academic content such as articles, presentations, theses, books, and reports are included here. Dspace@Mudanya provides easy access, making it a valuable resource for researchers and students. It serves as a digital archive for Mudanya University's academic outputs, facilitates access to scientific information and supports its sharing. For more information and assistance, please contact us.
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A novel deep learning approach for accurate and efficient design of LNOI power splitters
(Springer, 2026-04) Gencal, Huriye; Aksoy, Abdullah; Yiğit, Enes; Aydemir, Umut; Demirtaş, Mustafa; 426722
Photonic Integrated Circuits (PICs), owing to their high speed, low power consumption, and compact structure, lie at the core of modern optoelectronic technologies. The design of these circuits requires high accuracy and intensive computational cost. In this study, a novel Deep Neural Network (DNN)-based framework is proposed for designing and predicting the performance of arbitrary-ratio power splitters on the Lithium Niobate on Insulator (LNOI) platform. A dataset constructed using fundamental geometric parameters such as width, height, length, and auxiliary dimensions was processed with the proposed DNN model, yielding high prediction accuracy. The model achieved strong agreement in the training, validation, and testing stages, with R² values of 0.95, 0.97, and 0.97, respectively. The corresponding error metrics were RMSE = 3.08, 2.4, and 2.5, and MAPE = 4.02%, 3%, and 3.1%, respectively. Extensive analyses across various epoch numbers (500–10,000), batch sizes (2–64), and optimizers (Adam, SGD, RMSProp) revealed that the Adam optimizer, with 5,000 epochs and a batch size of 64, achieved the optimal balance between accuracy, convergence speed, and generalization. Furthermore, a detailed analysis of the influence of input parameters on outputs revealed that L1 and W were the most critical factors. The trained model was also validated on an independent dataset from the literature, demonstrating excellent generalization ability with R = 0.991, RMSE = 1.98, and MAPE = 3.42%. To facilitate practical use of the proposed framework, an interactive MATLAB application was developed, enabling both forward prediction of power-splitting ratios from user-defined geometric inputs and inverse design of optimal parameters corresponding to a target output ratio through an integrated DNN–optimization workflow. This tool significantly accelerates device evaluation and design-space exploration, making the methodology readily applicable to real-world photonic design tasks. These results indicate that the proposed approach not only accelerates the design process but also enhances the understanding of input-output relationships, thereby providing a reliable methodology for photonic device optimization.
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Does Pulmonary Function Testing Affect Autonomic Nervous System Activity?
(GALENOS PUBL HOUSE, 2026-03) Yalçın, Gülay; Erdoğmuş Gülcan, Server; Kavlak, Erdoğan; 308202; 392815
Objective: The autonomic nervous system (ANS) regulates vital functions such as heart rate (HR) and respiration. Pulmonary function tests (PFTs), which require forced breathing maneuvers, may influence ANS activity, potentially affecting the accuracy of autonomic measurements. This study aimed to investigate the effects of PFT on ANS activity and to assess the reliability of the test order.
Material and methods: Forty-eight healthy university students (32 women, 16 men; mean age 19±0.92 years) participated. ANS activity was assessed by heart rate variability (HRV) analysis using the Elite HRV Corsense device. HRV was recorded at rest in a seated position (first measurement), was repeated after a 5-minute rest (second measurement), and was recorded again following PFT performed with a Medwelt SP10 spirometer (third measurement).
Results: Comparison of the first and second measurements showed a statistically significant increase only in the root mean square of successive differences (RMSSD) parameter, with no significant changes in other indices. Comparison of the second and third measurements revealed no significant differences in RMSSD or average HR; however, significant changes were observed in the low-frequency (LF) and high-frequency (HF) components and in the LF/HF ratio.
Conclusion: Respiratory maneuvers during PFT may temporarily alter ANS activity, particularly affecting parasympathetic-sympathetic balance. The differences between the first and second measurements emphasize the importance of adequate rest periods before HRV assessment. Measurements taken prior to PFT appear to be more reliable for the accurate evaluation of autonomic function.
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ARTIFICIAL INTELLIGENCE INDUCED UNEMPLOYMENT FEAR SCALE
(ISARC, 2026-02) Ulusoy, Kaan; İşcan, Orhan; Boz, Dursun; 331826
The insurance sector is currently one of the industries that makes extensive use of artificial intelligence technologies (AI). In operational terms, AI is actively used in a wide range of areas, from sales and marketing processes to customer relationship management and after-sales services, from reporting and claims–compensation management to new product development and information technology applications. Within the “Fintech” concept, which has emerged and gained prominence through the convergence of finance and technology, insurance technologies known as “Insurtech” operate by developing digital solutions aimed at making existing models in the insurance sector more profitable, efficient, and effective. Technological initiatives continue their activities in regions such as Silicon Valley, where R&D activities are supported, benefiting from various incentives and industry collaborations. In the Turkish insurance sector, where similar practices are present, national companies are also observed to be adapting to modern technologies and being influenced by digitalization processes. One of the most prominent concerns arising from the rapid development and deep sectoral penetration of AI technologies is the possibility that AI may replace human labor in the future. In this study, fear of unemployment caused by artificial intelligence was taken as the central focus, and the fear of job loss experienced by individuals working in insurance companies operating in Turkey that use artificial intelligence technologies was examined. As a result of the literature review, no previous scale development study was found in the relevant field; therefore, the need to develop a new scale emerged. In the pilot study, the Artificial Intelligence Induced Fear of Unemployment Scale, consisting of 25 items and developed for the first time in the field, was administered to 100 insurance company employees through convenience sampling on a voluntary basis. As a result of the analyses conducted, it was determined that the factor loadings of the sub-dimensions of the scale named “Managerial Approach”, “Technophobia”, “Self-Efficacy”, and “Adaptation” ranged between 0.579 and 0.908. The developed scale has been introduced to the literature as a tool applicable not only to the insurance sector but also to different sectors.
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COMPARISON OF THE EFFECTS OF NERVUS VAGUS STIMULATION, TENS, AND BACKUP STIMULATION DEVICE ON THE AUTONOMIC NERVOUS SYSTEM AND PAIN IN FIBROMYALGIA PATIENTS
(TÜRKİYE FİZYOTERAPİSTLER DERNEĞİ, 2025-09) Yalçın, Gülay; Kayış, Rukiye; Beceni, Esra; Külekçioğlu, Sevinç; 308202; 392833
Purpose: This study aimed to compare the acute effects of vagus nerve stimulation (VNS), transcutaneous electrical nerve stimulation (TENS), and the backup stimulation device (Backup) on pain, sympathetic, and parasympathetic nervous system functions in fibromyalgia patients. Methods: Thirty fibromyalgia patients (aged 20-45) from a hospital in Bursa were randomly assigned to three groups: VNS, TENS, and Backup stimulation device. Each group received a 30-minute session once weekly for five sessions. Pain was assessed using the visual analog scale (VAS), and sympathetic and parasympathetic functions were measured with the Elite heart rate variability device. Parameters included heart rate, root mean square of successive differences, proportion of NN50 divided by total RR intervals, low-frequency/high-frequency (LF/HF) power, and LF/HF ratio. Results: No significant post-intervention changes were found in autonomic parameters across groups (p>0.05). However, all groups showed a significant reduction in VAS scores (p<0.05), indicating effective pain relief. Heart rate significantly decreased only in the Backup group (p<0.05), suggesting a shift toward parasympathetic dominance. Between-group analysis revealed significant differences in VAS scores between the TENS and VNS groups, and the VNS and Backup groups (p<0.05), indicating variability in pain response. Conclusion: TENS, VNS, and Backup stimulation devices effectively reduce pain in fibromyalgia patients. The heart rate reduction in the Backup group suggests a potential effect on autonomic regulation, which may offer a beneficial approach for managing fibromyalgia symptoms. Although autonomic parameters showed no significant changes overall, further research is needed to understand the long-term effects and clinical relevance of these treatments.
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The Effect of Physical Activity and Sleep on Quality of Life and Depression Level in 18- 25 Years Old University Students
(2025-05) Kavlak, Erdoğan; Erdoğmuş Gülcan, Server; Yalçın, Gülay; 392815; 308202
Aim: Physical activity,which involves movements exceeding basal energy levels, affects both physical and psychological health.This descriptive study examines the effects of physical activity and sleep on quality of life and depression in university students. Methods: A total of 141 university students aged 18-25 years were included in the study.Sociodemographic data was collected using a form.Physical activity was assessed with the International Physical Activity Questionnaire short form (IPAQ), sleep quality with the Pittsburgh Sleep Quality Index (PSQI), depression levels with the Beck Depression Inventory, and quality of life with the Quality of Life Scale (SF-36). Results: While there was no statistically significant correlation between PSQI,which was used to evaluate sleep duration and quality, and SF-36 sub-parameters Physical Function (r=-0.127;p=0.133) and Physical Role Difficulty (r= - 0.155;p=0.066);There was a weak negative statistically significant relationship between the sub-parameters of Vitality (r=-0.281*;p=0.001),Social Functioning (r=-0.278*;p=0,001),Pain (r=-0.296*;p=0.000), General Health (r=-0.290*;p=0.000). A statistically significant relationship was found between PSQI and Emotional Role Difficulty (r=-0.300*;p=0.000), Mental Health (r=-0.409*;p=0.000) sub-parameters at a moderate negative level. There was also a statistically significant moderate positive correlation between the total scores of PSQI and Beck Depression Inventory (r=0.483*;p=0.000). Conclusions: Adequate sleep and physical activity improve the quality of life and mood in university students,a critical life stage.Therefore, interventions to assess and improve physical activity levels and sleep quality are necessary. In this population, physical activity levels and sleep quality should be questioned and interventions to improve them are needed.
