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.
Communities in DSpace
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Recent Submissions
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Psychological impact of disaster relief operations: a study following consecutive earthquakes in Turkey
(Cambridge University Press, 2024-05) İşeri, Ali; Baltacı, Recep; 135066
Objective: This cross-sectional study investigates the immediate psychological effects of disaster relief operations on team members following 2 consecutive major earthquakes in Turkey.
Methods: A total of 170 participants, including professional firefighters, search and rescue (SAR) workers, and volunteers, were surveyed approximately 1 month after the conclusion of active SAR operations. The study utilizes the DSM-V criteria and the Posttraumatic Stress Disorder Checklist (PCL-5) to assess symptoms of post-traumatic stress disorder (PTSD) among participants.
Results: The findings reveal a point prevalence of 35.3% for probable PTSD, highlighting the substantial psychological impact on disaster relief teams. Factors such as age, residency in affected areas, and active SAR involvement significantly influenced probable PTSD rates. Interestingly, actively engaged SAR members had lower probable PTSD rates, possibly due to their training. Those who directly witnessed the earthquakes had higher scores, highlighting the impact of firsthand exposure. Additionally, individuals aged 50 and above displayed a higher mean total severity score compared to younger participants.
Conclusions: This research contributes to understanding the mental well-being of disaster relief professionals. The study’s findings underscore the importance of timely mental health support and training for these responders, emphasizing the need for preparedness in disaster relief teams.
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Türk itfaiyecilerde bir mesleki psikolojik hastalık olarak travma sonrası stres
(Serpil Aytaç, 2024-06) Baltacı, Recep; İşeri, Ali; 135066
Bu çalışmada, Türkiye'deki profesyonel itfaiyeciler arasında Travma Sonrası Stres Bozukluğu (TSSB) yaygınlığı araştırılmıştır. Bu amaçla DSM (Diagnostic and Statistical Manual of Mental Disorders) IV kriterlerine dayanan PTDS (the Posttraumatic Diagnostic Scale)’nin Türkçe tercümesi ve demografik, mesleki ve psikososyal risklere yönelik bazı ek sorular kullanılmıştır. Antalya, Adana, Konya ve Ankara Büyükşehir Belediye İtfaiyelerinde çalışan 273 itfaiyeci gönüllü olarak çalışmaya katılmıştır. Bulgular, çalışmaya katılan itfaiyecilerde TSSB yaygınlığının %16,5 (%95 GA: %12,1-%20,9) olduğunu göstermiştir. Bu oran, diğer ülkelerdeki itfaiyecilere kıyasla düşük, ancak genel popülasyondan önemli ölçüde yüksektir. Çalışma, TSSB'nin itfaiyeciler için bir meslek hastalığı olabileceğini düşündürmektedir. Toplam şiddet skoru metriği üzerinden yapılan analizlerde, yaş, medeni durum, eğitim, gelir, sigara ve alkol kullanımı, deneyim gibi faktörlerin TSSB ile ilişkisi ortaya konamamıştır. Ancak, çalışılan il, psikolojik tedavi geçmişi, mesleğin istemli seçimi, mobbing uygulamalarına maruz kaldığını düşünmek ve müdahale edilen olay sayısı gibi psikososyal risk faktörleri TSSB ile anlamlı bir şekilde ilişkili bulunmuştur.
Publication
Mathematical model to upcycle end-of-roll leftover fabrics in apparel manufacturing
(Sage, 2024-06) İşeri, Ali; Kılılaslan, Recep; 135066
This study addresses the problem of end-of-roll leftover fabrics originating after the production of baby/child apparel. The ineffective management of these leftovers results in excess inventory, occupies storage space, and imposes economic and environmental loads. To address this challenge, a novel mathematical modeling approach is proposed. The model maximizes the upcycling of leftovers by incorporating these into the manufacturing of garments while adhering to marketing, production, and ordering constraints. This model also introduces the feasibility of ordering new fabrics with a penalty, as defined by the decision makers, to increase utilization. The model was tested using actual end-of-roll leftover data. The upcycling utilization of leftovers was calculated to be between 57% and 87%. Notably, at an upcycling rate of 58%, 96% of the utilized fabrics were sourced from leftovers. The case study results validate the model efficacy and provide insights into leftover-fabric management.
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Fashioning the self in Jean Rhys’s voyage in the dark and good morning, midnight
(Çankaya Üniversitesi, 2024-04) Koç, Nesrin; 13778
Jean Rhys held a deep passion for fashion and stylish attire. Her perspective on fashion, as an instrument of adopting “a second skin” finds expression in her focus on fashioning the self, a recurring motif in Rhys’s oeuvre. The physical difficulty Rhys’s female characters, whose lives bear strong similarities to her own, have in obtaining fashionable clothes represents the broader struggles they go through as the objects of the patriarchal and colonial gaze, in their voyages through the physical and metaphorical darkness of urban spaces like Paris and London in the early 1900s. Focusing on two of these women, Anna of Voyage in the Dark and Sasha in Good Morning, Midnight, for whom fashionable clothing appears to be the only way of navigating the modern society which marginalizes them, this study explores Rhys’s multilayered portrayal of fashion as a reflection of the near impossibility of attaining a cohesive sense of self, mirroring the characters’ struggles in fashioning their inner and outer selves.
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Utilizing large language models for EFL essay grading: an examination of reliability and validity in rubric-based assessments
(Wiley, 2024-05) Yavuz, Fatih; Çelik, Özgür; Yavaş Çelik, Gamze; 131069
This study investigates the validity and reliability of generative large language models (LLMs), specifically ChatGPT and Google's Bard, in grading student essays in higher education based on an analytical grading rubric. A total of 15 experienced English as a foreign language (EFL) instructors and two LLMs were asked to evaluate three student essays of varying quality. The grading scale comprised five domains: grammar, content, organization, style & expression and mechanics. The results revealed that fine-tuned ChatGPT model demonstrated a very high level of reliability with an intraclass correlation (ICC) score of 0.972, Default ChatGPT model exhibited an ICC score of 0.947 and Bard showed a substantial level of reliability with an ICC score of 0.919. Additionally, a significant overlap was observed in certain domains when comparing the grades assigned by LLMs and human raters. In conclusion, the findings suggest that while LLMs demonstrated a notable consistency and potential for grading competency, further fine-tuning and adjustment are needed for a more nuanced understanding of non-objective essay criteria. The study not only offers insights into the potential use of LLMs in grading student essays but also highlights the need for continued development and research.
