Welcome to Academic Digital Repository
OpenAccess@KU is an academic, open-access repository. It aims to collect, preserve and make Kastamonu University’s scientific output available online, without any or the least financial, legal or technical restrictions, in order to increase the impact and the visibility of the institution and its authors. It was established in 2010 to support the dissemination of knowledge produced by the University members to the wider community both locally and globally.
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Recent Submissions
The Interplay of Post-Traumatic Stress Symptoms, Psychological Resilience, and Mother-Infant Attachment in Predicting Postpartum Depression After Earthquakes
(2026) Şimsek-Çetinkaya, Ş.; Şimsek, F.
This study investigates the complex predictors of postpartum depression (PPD) among women affected by the 2023 Kahramanmaraş earthquakes, focusing on the roles of post-traumatic stress symptoms (PTSS), psychological resilience, and mother-infant attachment. A cross-sectional study of 270 postpartum women utilized measures of PTSS (Impact of Event Scale-Revised [IES-R]), PPD (Edinburgh Postnatal Depression Scale [EPDS]), psychological resilience (Brief Psychological Resilience Scale [BPRS]), and mother-infant attachment (Mother-Infant Attachment Scale [MIAS]). Initial analyses showed that disaster-related exposure (e.g., following news and losing relatives) was significantly associated with PTSS severity. Hierarchical regression analyses demonstrated that PTSS was a significant positive predictor of PPD. Crucially, psychological resilience was found not only to be a direct negative predictor of PPD but also a significant moderator, weakening the positive relationship between PTSS and PPD. Furthermore, mother-infant attachment was a significant independent negative predictor of PPD, contributing to the variance beyond trauma and resilience. The findings confirm that in a post-disaster context, PTSS is a major risk factor for PPD, while psychological resilience serves a critical buffering function, and a strong mother-infant bond offers unique protection. These results underscore the necessity for integrated interventions that address trauma, foster resilience, and support the mother-infant relationship to mitigate PPD in vulnerable populations.
Independent boards win: How independent board members drive financial success in hospitality and tourism firms
(2026.01.01) Köseoglu, M.A.; Arici, H.E.; Campos, L.
This study examines the influence of board composition and Environmental, Social, and Governance (ESG) practices on financial performance within the hospitality and tourism industry, leveraging advanced machine learning techniques. Data spanning 2015-2024 from the Refinitiv database is analyzed through boosting algorithms, SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDPs) to uncover nonlinear interactions and relative predictor importance. The findings emphasize that Independent Board Members Score (IBMS) consistently drives financial performance across metrics, while Non-Executive Board Members Score (NEBMS) provides complementary support, particularly in interaction with IBMS. ESG variables, including Workforce Score and Product Responsibility Score, emerge as critical predictors, underscoring the importance of sustainability and governance practices. The results validate agency and resource dependence theories, incorporating ESG dimensions and demonstrating the utility of machine learning for governance-performance analyses. It contributes to the theoretical and practical understanding of governance mechanisms, providing a robust framework for achieving competitive financial outcomes in dynamic and sustainability-focused industries.
Digging SiC Semiconductor Efficiency for Trapping Main Group Metals in Cell Batteries: Application of Computational Chemistry by Mastering the Density Functional Theory Study
(2025.01.01) Mollaamin, F.; Monajjemi, M.
In this research article, a silicon carbide (SiC) nanocluster has been designed and characterized as an anode electrode for lithium (Li), sodium (Na), potassium (K), beryllium (Be), magnesium (Mg), boron (B), aluminum (Al) and gallium (Ga)-ion batteries through the formation of SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC nanoclusters. A vast study on energy-saving by SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC complexes was probed using computational approaches accompanying density state analysis of charge density differences (CDDs), total density of states (TDOS) and molecular electrostatic potential (ESP) for hybrid clusters of SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC. The functionalization of Li, Na, K, Be, Mg, B, Al and Ga metal/metalloid elements can raise the negative charge distribution of carbon elements as electron acceptors in SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC nanoclusters. Higher Si/C content can increase battery capacity through SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC nanoclusters for energy storage processes and to improve the rate performance by enhancing electrical conductivity.
Bibliometric analysis and recent trends on women research in tourism: a comprehensive review
(2025.01.01) Tanrisever, C.; Koc, D.E.; Arici, H.E.; Köseoglu, M.A.
This study presents a bibliometric analysis of women research in tourism, drawing on 696 Scopus-indexed articles published up to August 2022. Using VOSviewer and Biblioshiny, we apply co-citation, bibliographic coupling, and keyword co-occurrence analyses to map the field's intellectual structure and thematic evolution. Findings reveal five clusters: women's empowerment; women tourists' experiences; motivations of women tourists; gendered perspectives; and women's sexual behaviour in tourism. The study highlights publication peaks and emerging trends such as gender inequality, empowerment, and evolving travel behaviours. It also offers a future research agenda with guiding questions to address key gaps and advance scholarship in gender and tourism studies.
Artificial Intelligence-Assisted Lung Ultrasound for Pneumothorax: Diagnostic Accuracy Compared with CT in Emergency and Critical Care
(2025.01.01) Dal, I.; Akyol, K.
Simple Summary Pneumothorax is a life-threatening condition that requires rapid and accurate diagnosis, especially in emergency and critical care settings. Although lung ultrasound (LUS) offers a fast and radiation-free diagnostic option, its accuracy can vary depending on the operator's experience. This study evaluated the potential of artificial intelligence (AI) to assist clinicians by automatically detecting pneumothorax on LUS images and videos. Using transformer-based deep learning models, we compared the diagnostic performance of Vision Transformer (ViT), DINOv2, and Video Vision Transformer (ViViT) architectures. When tested on data from different patients, the DINOv2 model achieved 90% accuracy, demonstrating reliable generalization without overfitting. Furthermore, when video sequences were analyzed, both Random Forest and eXtreme Gradient Boosting classifiers trained on ViViT-derived features achieved 90% accuracy, showing that AI can effectively interpret dynamic pleural motion. These results indicate that transformer-based AI can enhance pneumothorax diagnosis by improving consistency and reducing operator dependence, supporting broader use of lung ultrasound in emergency and point-of-care environments.Abstract Background: Pneumothorax (PTX) requires rapid recognition in emergency and critical care. Lung ultrasound (LUS) offers a fast, radiation-free alternative to computed tomography (CT), but its accuracy is limited by operator dependence. Artificial intelligence (AI) may standardize interpretation and improve performance. Methods: This retrospective single-center study included 46 patients (23 with CT-confirmed PTX and 23 controls). Sixty B-mode and M-mode frames per patient were extracted using a Clarius C3 HD3 wireless device, yielding 2760 images. CT served as the diagnostic reference. Experimental studies were conducted within the framework of three scenarios. Transformer-based models, Vision Transformer (ViT) and DINOv2, were trained and tested under two scenarios: random frame split and patient-level split. Also, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) classifiers were trained on the feature maps extracted by using Video Vision Transformer (ViViT) for ultrasound video sequences in Scenario 3. Model performance was evaluated using accuracy, sensitivity, specificity, F1-score, and area under the ROC curve (AUC). Results: Both transformers achieved high diagnostic accuracy, with B-mode images outperforming M-mode inputs in the first two scenarios. In Scenario 1, ViT reached 99.1% accuracy, while DINOv2 achieved 97.3%. In Scenario 2, which avoided data leakage, DINOv2 performed best in the B-mode region (90% accuracy, 80% sensitivity, 100% specificity, F1-score 88.9%). ROC analysis confirmed strong discriminative ability, with AUC values of 0.973 for DINOv2 and 0.964 for ViT on B-mode images. Also, both RF and XGBoost classifiers trained on the ViViT feature maps reached 90% accuracy on the video sequences. Conclusions: AI-assisted LUS substantially improves PTX detection, with transformers-particularly DINOv2-achieving near-expert accuracy. Larger multicenter datasets are required for validation and clinical integration.