FBU GCRIS Standard Database (Fenerbahçe University)
Not a member yet
861 research outputs found
Sort by
Evaluation of Barriers Toward Data-Driven Supply Chain Sustainability Via Single-Valued Pythagorean Piprecia
No full textSustainable supply chain management (SSCM) is a holistic approach that encompasses economic, social, and environmental dimensions, enabling firms to enhance their long-term competitiveness by meeting legal requirements and strengthening brand equity. The effective implementation of this approach necessitates a strong emphasis on data-driven decision-making. Accordingly, we aimed to identify the key barriers hindering the implementation of data-driven sustainable supply chain practices and to explore potential strategies to overcome these challenges. In the initial phase of the study, a comprehensive literature review was conducted to identify the major barriers to implementing data-driven sustainable supply chains. Subsequently, the relative importance of these barriers was assessed with input from top and mid-level managers working in manufacturing sector enterprises. The identified barriers were then prioritized using the Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA) method based on Pythagorean fuzzy numbers. Finally, solution proposals were developed to address the most critical barriers. The study revealed that organizational barriers constitute the most prominent category, representing 29.86% of the total identified obstacles. Closely following are technical barriers, which account for 26.41% and reflect the difficulties associated with implementing and integrating digital technologies. Internal and external environmental barriers are similarly substantial, comprising 25.87% of the total. In comparison, economic barriers make up the smallest share, with a relative weight of 17.86%. The number of researchers analyzing the importance weights of barriers in the context of SSCM 4.0 remains limited. The utilization of a more contemporary and robust method compared to previously applied techniques for determining these weights enhances the originality of this study
Denta-Hybridonet: A Hybrid CNN-Transformer Architecture for Automated Detection of Developmental Dental Anomalies in Pediatric Panoramic Radiographs
No full textAccurate identification of developmental dental anomalies (DDAs) in children is clinically important; however, interpreting panoramic radiographs can still vary across readers because of mixed dentition, anatomical overlap, and variable image quality. This variability may delay recognition and complicate early interventional planning. In this study, we curated a pediatric panoramic dataset of 2,001 radiographs (ages 6-14 years) spanning five categories: Dilaceration, Ectopy, Hypodontia, Taurodontism, and Healthy. All images were independently labeled by three experienced pediatric dentists. To avoid patient-level leakage, the dataset was divided into training, validation, and held-out test sets using a patient-wise split. We propose Denta-HybridoNet, a hybrid convolution-transformer architecture designed to capture both fine-grained tooth morphology and broader, arch-wide contextual patterns. Its InceptionNeXt-gMLP block supports multi-scale local representation learning, which helps the model focus on subtle morphological cues, whereas the Swin-gMLP block provides efficient global context modeling across the dental arch. In addition, a gated multilayer perceptron (gMLP) module refines the feature transformation through context-dependent modulation, strengthening diagnostically relevant signals while reducing the influence of irrelevant variation and radiographic noise. To ensure a fair comparison, we benchmarked Denta-HybridoNet against 22 recent convolutional and transformer-based models under the same training protocol and evaluation conditions. On the held-out test set, the proposed method achieved 91.15% accuracy and 91.20% F1 score, representing the best overall performance among the compared architectures. Ablation studies quantified the contributions of hybrid design and gMLP, and Grad-CAM analyses supported interpretability by highlighting clinically meaningful regions.TUESEB under the "2023-C1-YZ" call [33934]; TUESEB; Deanship of Research and Graduate Studies at King Khalid University [RGP2/749/46]This work was supported by a grant from TUESEB under the "2023-C1-YZ" call (Project No: 33934). The authors thank TUESEB for its financial support and scientific contributions. The authors also extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through a Large Group Research grant (RGP2/749/46). Experimental computations were carried out using the computing resources of Igdir University's Artificial Intelligence and Big Data Application and Research Center
Machine Learning Model for Predicting Multidrug Resistance in Clinical Klebsiella pneumoniae Isolates
No full textBackground/Objectives: Klebsiella pneumoniae is an opportunistic pathogen increasingly resistant to carbapenems and broad-spectrum antibiotics, complicating timely infection management. In critical cases like septic shock, where initiating effective antibiotics within 3 h improves survival, culture-based resistance testing is often too slow. This study evaluates machine learning (ML) algorithms for faster antimicrobial resistance prediction than conventional methods. Methods: In this retrospective study, antibiogram results of 607 Klebsiella pneumoniae isolates collected between 2017 and 2024 were combined with demographic and clinical information of the patients from whom the isolates were obtained. Four different ML algorithms, namely Decision Tree (DT), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN) and Random Forest (RF), were applied to classify the resistance status for 22 antibiotics. Model performances were evaluated using accuracy, precision, recall, F-score, AUC and feature importance metrics. Results: The RF model showed the highest overall performance in accurately predicting resistance to 22 antibiotics, achieving an average AUC value of 0.96. In particular, it predicted resistance to treatment-critical antibiotics such as Ertapenem (100%), Imipenem (93%) and Meropenem (95%) with high accuracy. Conclusions: ML models, especially RF, offer a powerful tool for rapid antibiotic resistance prediction, supporting accurate empirical treatment decisions and antimicrobial stewardship
Nurses on the Frontline of Disaster: A Qualitative Metasynthesis of Post-Earthquake Care Experiences
No full textTaylan, Seçil/0000-0002-7243-0734; Eroglu, Nermin/0000-0003-4780-3049Background Earthquakes generate urgent and complex care needs, positioning nurses as key responders. Exploring their post-earthquake care experiences is vital for strengthening disaster preparedness and nursing practice.Aim This study aimed to synthesise qualitative evidence on nurses' experiences of providing care in the aftermath of earthquakes.Study Design This research was conducted through a systematic review and meta-synthesis of qualitative studies. The data were analysed using thematic analysis. The qualitative systematic review was conducted using Sandelowski and Barroso's four-step meta-synthesis methodology. This metasynthesis study, conducted in accordance with the PRISMA statement, is registered in PROSPERO. The following electronic databases and platforms were used for the literature review: MEDLINE, Academic Search Ultimate, CINAHL Complete, Complementary Index, Supplemental Index, Directory of Open Access Journals and WOS.Findings Based on 27 included studies, the research identified four main themes and 13 sub-themes related to nurses' post-earthquake care experiences. These themes were: (1) early post-earthquake nursing challenges, (2) conflict experiences, (3) valued experiences and methods of coping with challenges and (4) impact of rescue experiences.Conclusions This research highlighted nurses' post-earthquake care experiences, emphasising how they overcame challenges, redefined their profession and emerged stronger. It also identified the limitations and facilitators within the post-earthquake care environment, providing valuable insights for enhancing care settings during future earthquakes.Relevance to Clinical Practice This study identified limitations and facilitators in the post-earthquake care environment of clinical settings, providing valuable insights to improve care environments during future earthquakes
Positioning School Readiness as Ecological Fit: The School Readiness Ecological Approach (SERA) for Occupational Therapy and Education
No full textTraditional perspectives on school readiness have emphasized child-level competencies such as cognitive, language, and behavioral skills. While important, the narrow focus overlooks the ecological systems - families, teachers, and communities - that shape children's adaptation to school. The participation-oriented perspective of occupational therapy has also been largely absent from the discourse. The School Readiness Ecological Approach (SERA) reframes school readiness as a matter of ecological fit rather than merely a set of children's skills. Based on Bronfenbrenner's ecological systems theory and the Person - Environment - Occupation model, SERA conceptualizes readiness as the outcome of dynamic exchanges between children, families, educators, schools, and policy environments. SERA addresses key gaps by: (1) shifting focus from isolated child attributes to participation in real-life contexts; (2) integrating occupational therapy's holistic perspective into readiness; and (3) providing a multi-level framework to guide research, practice, and policy. SERA emphasizes four domains - child, family, educational environment, and community - as interconnected contributors to school readiness trajectories. As a conceptual bridge across education, health, and social systems, SERA will broaden theoretical scope, foster interdisciplinary collaboration, and promote inclusive, and sustainable strategies. Reconceptualizing school readiness as ecological fit will help move beyond deficit-based views and support more equitable and effective school transitions
The Role of High-Fidelity Simulators in Vascular Surgery Training: A Systematic Review
No full textJaved, Maryam/0009-0002-7654-3354BackgroundIn vascular surgery, endovascular procedures demonstrated lower morbidity and shorter hospital stays. However, learning these skills is highly challenging. A new method that offers secure, controlled settings for skill development is simulation-based training.AimThis systematic review aims to examine the different high-fidelity simulation programs and their effectiveness in enhancing endovascular skills among vascular surgery trainees.MethodsA comprehensive literature search was conducted across PubMed, SCOPUS, Cochrane CENTRAL, and Web of Science using PRISMA guidelines. We included all studies of all designs involving high-fidelity simulation in vascular surgery training. The eligibility criteria focused on studies assessing simulation interventions comparing pre- and post-course outcomes. Data extraction was done manually using Excel spreadsheets by two independent reviewers, and quality assessment was performed using a 19-point scale. The studies were also evaluated using Kirkpatrick's adapted hierarchy based on their educational impact.ResultsA total of 35 studies met the inclusion criteria, covering a range of simulation modalities, including virtual reality, augmented reality, and physical simulators. The findings demonstrated significant improvements in procedural skills, operation time, operative errors, and participant's confidence across all training levels. However, there were great variations in study methods and design, as well as a lack of a specific framework for skill assessment.ConclusionFor endovascular training, high-fidelity simulation is a useful tool for skill development. However, Standardized training techniques and additional research are needed to assess long-term skill retention
1,2,4-Triazole Conjugates as HEGFR Inhibitors: Synthesis, Anticancer Evaluation, and in Silico Studies
No full textAgrawal, Mohit/0000-0003-0200-7882; Cakmak, Ummuhan/0000-0001-8719-2436A series of novel 1,2,4-triazole-acetamide derivatives was synthesized and evaluated for anticancer and hEGFR inhibitory activity. The compounds were obtained via multistep synthesis and characterized by spectroscopic methods. Cytotoxicity was tested against PC-3, MCF-7, A549, and K562 cell lines. Compounds 18, 19, and especially 24 showed notable antiproliferative effects, with compound 24 exhibiting higher selectivity and potency than gefitinib. It also induced apoptosis and inhibited migration in A549 and PC-3 cells, while selectively promoting invasion in PC-3, suggesting EMT-related behavior. In vitro kinase assays revealed compound 20 as the most potent hEGFR inhibitor (IC50 = 43.8 +/- 1.3 nM). Molecular docking and 200 ns molecular dynamics simulations confirmed its stable interaction with EGFR, particularly involving Cys797. These findings highlight compounds 20 and 24 as promising candidates for further development as EGFR-targeted anticancer agents
Early Detection of Lower Adherence to Long-Term E-Diary Recording: A Checkpoint to Target Early Educational Intervention in Seasonal Allergic Rhinitis
No full textBackground: Digital symptom monitoring via e-Diary apps can support the diagnosis and management of chronic diseases with trigger-induced exacerbations such as pollen allergies. Attrition is a major challenge for continuous e-Diary usage with an unsupervised approach. Objective: To investigate adherence to e-Diary reporting, its early determinants and predictors in a blended care setting among pollen allergic patients with heterogeneous cultural backgrounds. Methods: The @IT.2020 observational multicenter study recruited patients with diagnosed seasonal allergic rhinitis from seven Southern European/Mediterranean countries. Baseline characteristics were investigated through questionnaires, skin prick tests and serum specific IgE measurements. The study doctors asked patients to record their allergy symptoms via e-Diary (AllergyMonitor, TPS) daily during the clinically relevant season of pollination and increased mould concentrations. Results: Among 815 patients (467 adults, 348 children), the average prescribed e-Diary recording period was 106 (SD 47.1) days, with an average completion rate of 75.2% (SD 21.2%). Children (>= 10 years) filled 73.8% (95% CI 68.1-79.4) of prescribed days without parental support. We identified a stable 'higher' and a more variable 'lower' adherence cluster. Adherence was weakly associated with disease severity, but not with age, gender, country, education or digital literacy. Short-term (first 3 weeks) adherence was strongly associated with long-term adherence (partial R-2 = 0.387, p < 0.001), with 87.6% of lower adherent patients remaining poorly adherent beyond 3 weeks. Conclusion: In a blended care setting, adherence to e-Diary compilation among pollen allergic patients is high, irrespective of age and cultural background. Early identification of lower adherence is possible and might inform early interventions to improve patient adherence.Euroimmun Medizinische Labordiagnostika [118583]S.A. was supported by the EAACI Fellowship Award of the European Academy of Allergology and Clinical Immunology. P.M.M. is funded by the Deutsche Forschungsgesellschaft (DFG) (MA47/2-1). This investigator-initiated observational study has been supported by an unrestricted grant from Euroimmun Medizinische Labordiagnostika AG, Grant/Award Number: 118583. The Informatics Platform AllergyCARD and the app AllergyMonitor have been kindly provided by TPS Software Production
Advances and Strategies in Biosensor-Based Diagnostics for Parasitic Infections: A Comprehensive Scoping Review
No full textParasitic diseases are among the most widespread infections worldwide, causing millions of deaths and illnesses each year. So rapid and accurate diagnosis is essential, requiring highly sensitive and specific tests. Biosensors can provide significant advantages over traditional diagnostic methods because of their specificity, sensitivity, speed, simplicity, ease of use, repeatability, and capacity for early-stage disease detection. Recent advances in modern diagnostic tools for detecting parasitic infections use nanomaterials such as gold nanoparticles, carbon nanofibers, and carbon nanotubes. These developments have significantly lowered detection limits to the picogram and femtogram levels. This review will cover recent advancements in biosensor-based diagnostic techniques in parasitology
Mapping the Evolution of Stigmatization in Mental Disorders: A Bibliometric Analysis From 1974 to 2024
No full textBackgroundThis bibliometric study scrutinizes the thematic evolution of research on stigma and discrimination in mental disorders, covering a span of five decades. It reflects on the shifting paradigms within the stigma-focused mental health research community from 1974 to 2024.MethodsA comprehensive bibliometric analysis was employed using the Bibliometrix R package and VOSviewer software, analyzing 1,892 articles from databases like Scopus, Web of Science, PubMed Central, and APA PsycInfo. Adherence to PRIBA guidelines ensured a holistic representation of the evolving research narrative.ResultsThe analysis outlined three distinct periods: the Genesis Period (1974 - 2007), focusing on foundational concepts of mental disorders and stigma; the Growth Period (2008 - 2015), which experienced a broadening into themes of discrimination and diagnostic refinement; and the Rapid Growth Period (2016 - 2024), characterized by a surge in research on child mental disorders and the impacts of posttraumatic stress disorder. Network analyses highlighted significant journals, key authors, and international collaborations that have shaped this field.ConclusionsThe study maps a significant transformation in stigma-focused mental health research themes over fifty years, highlighting the growing complexity and the need for ongoing research into stigma and discrimination. It calls for interdisciplinary approaches to tackle these enduring challenges effectively.Sabanci UniversityOpen access funding provided by the Scientific and Technological Research Council of Turkiye (TUBITAK)