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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LATİN ALFABEDEN ALGORİTMAYA BİR YOLCULUK: CHANCERY CURSIVE'ÜN BİÇİMSEL VE ÖĞRETİMSEL EVRİMİ
(Bolu Abant İzzet Baysal Üniversitesi, 2026-05) Kipdemir, Serdar; 341649
Bu bildiri, iletişim teknolojilerinin alfabenin maddi formundan algoritmanın soyut diline uzanan tarihsel dönüşümünü, Chancery Cursive yazısının beş yüzyılı aşan serüveni üzerinden incelemeyi amaçlamaktadır. Çalışmanın temel argümanı, bir yazı geleneğinin sürekliliğinin yalnızca estetik değerini korumasına değil, aynı zamanda değişen kültürel ve teknolojik bağlamlarda pedagojik araçlarını yeniden yorumlama ve bu araçlarla birlikte evrilme kapasitesine bağlı olduğudur. Bildiride izlenecek analitik çerçeve, bu dönüşümün üç kritik eşiğine odaklanmaktadır. İlk olarak, Rönesans’ın hümanist alfabe anlayışı içinde Ludovico degli Arrighi’nin La Operina (1522) adlı eseriyle somutlaşan ve yazı bilgisini sistematik biçimde standartlaştıran “kurucu” aşama ele alınacaktır. İkinci olarak, 20. yüzyılın modernleşme ve demokratikleşme sürecinde Edward Johnston ve Alfred Fairbank’ın çalışmalarıyla Chancery Cursive’ün disipline edilerek Formal Italic’e dönüştüğü ve kitlesel eğitim sistemlerine entegre edildiği “standartlaşma” aşaması analiz edilecektir. Son olarak, günümüzün dijital ve ağ-temelli iletişim ortamında, yazı geleneğinin pedagojik ilkelerini koruyarak dijital fontlar, çevrimiçi öğrenme platformları ve etkileşimli uygulamalar aracılığıyla algoritmik araçlarla yeniden üretildiği “yayılım” aşaması değerlendirilecektir. Disiplinlerarası bir perspektif ve tarihsel-analitik yöntemle şekillenen bu inceleme, yazının biçimsel (morfolojik) evrimi ile onun öğretilme biçimleri (pedagojik pratikler) arasındaki diyalektik ilişkiyi merkeze almaktadır. Arrighi’nin ahşap kalıplarından Fairbank’ın ölçülebilir diyagramlarına ve günümüzün yazılım arayüzlerine uzanan bu süreklilik, kültürel bir kodun her teknolojik kırılmada nasıl yeni ifade ve aktarım biçimleri ürettiğini göstermektedir. Sonuç olarak, Chancery Cursive’ün yolculuğunun iletişim tarihindeki dönüşümleri anlamak için güçlü bir mercek sunduğu; “alfabeden algoritmaya” geçişin geleneğin sonu değil, kültürel süreklilik içinde onun yeniden doğuşuna imkân tanıyan bir zemin olduğu ortaya konulmaktadır.
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LETTER AND MEANING: THE CONTINUITY OF CALLIGRAPHIC PHILOSOPHY IN THE ARTS AND CRAFTS MOVEMENT AND ART NOUVEAU
(İNÖNÜ ÜNİVERSİTESİ, 2026-03) Kipdemir, Serdar; 341649
Bu çalışma, "harf ve anlam" olgusu ekseninde, geleneksel kaligrafi felsefesi ile Arts & Crafts ve Art Nouveau akımları arasındaki ontolojik bağı incelemektedir. Arts & Crafts'ın zanaatı yücelten etik duruşu ile Art Nouveau'nun organik forma dayalı estetik özerklik arayışının, geleneksel kaligrafinin temel değerleriyle ontolojik bir süreklilik oluşturduğu savunulmaktadır. William Morris'in endüstriyel üretime karşı geliştirdiği "dürüst zanaat" yaklaşımı, geleneksel kaligrafinin "insan-el-iş" bütünlüğünü yeniden tanımlarken; Alphonse Mucha'nın akıcı çizgiler ve biyomorfik tipografi denemeleri aynı geleneksel estetiğin modern ve seküler bir yorumunu ortaya koymaktadır. Nitel araştırma yöntemiyle gerçekleştirilen çalışmada, hermeneutik yaklaşımla tarihsel ve karşılaştırmalı analiz yapılmış; geleneksel kaligrafi pratikleri ile modern akımların eserleri, el emeğinin metafiziği ve dekoratif ontoloji kavramları çerçevesinde çözümlenmiştir. Sonuç olarak, geleneksel kaligrafinin Arts & Crafts aracılığıyla "zanaatkâr ruhunu", Art Nouveau katkısıyla "biçimsel özerkliğini" kazanması, modern tasarımın felsefi ve estetik temellerinin oluşumunda kurucu rol oynamıştır.
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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.
