İstanbul Kent Üniversitesi Araştırma ve Akademik Performans Sistemi
DSpace@Kent, İstanbul Kent Üniversitesi’nin bilimsel araştırma ve akademik performansını izleme, analiz etme ve raporlama süreçlerini tek çatı altında buluşturan bütünleşik bilgi sistemidir.
Güncel Gönderiler
Öğe Türü:Öğe, A bibliometric review of reverse innovation: Disciplinary evolution and emerging research frontiers(Marmara Üniversitesi, 2026) Karabulut, Ahu Tuğba; Sayan, İlknur; Pehlivanoğlu, M. Çağrı; Akdal, SeremReverse innovation (RI) is a strategic concept that inverts the traditional flow of innovation by enabling innovations to emerge from developing markets and spread to developed economies. Over the past decade, RI has attracted significant academic attention, particularly in domains such as healthcare, sustainability, and various sectors. RI is not only concerned with technology transfer from one country to another but is also connected to current global challenges. The purpose of this study is to conduct a comprehensive bibliometric analysis of RI by examining publications indexed in the Web of Science database between 2010 and 2024. Two hundred thirty-two publications containing the term ‘RI’ in the title, abstract, keywords, or author keywords were analyzed using VOSviewer software. This article examines studies in the field of RI by exploring various elements, including publication years, indexing databases, citation metrics, most-cited articles, research fields, related concepts, authors, journals, institutions, and countries of origin for the conducted studies. It also examines the relationship between RI and the United Nations Sustainable Development Goals (SDGs). RI is related to SDG 9 (industry, innovation, and infrastructure), SDG 3 (good health and well-being), sustainability, reverse logistics, green supply chains, and the circular economy. This research presents a comprehensive overview of the current state of RI and provides guidance for both academics and industry leaders.Öğe Türü:Öğe, Resistance profiles of uropathogenic E. coli: ESBL and colistin in focus(Journal of Infection in Developing Countries, 2026) Uçak, Şafak Ceren; Aktaş, Ahmet; Akgün Karapınar, D. Bahar; Nakipoğlu, Yaşar; Öngen, BetigülIntroduction: Escherichia coli is among the most common causes of urinary tract infections (UTIs), and the rise in extended-spectrum beta lactamase (ESBL)-producing isolates is a growing concern because of increasingly limited treatment options. This study aimed to investigate the resistance profiles of ESBL-positive and -negative uropathogenic E. coli isolates to antibiotics used in treatment and colistin. Methodology: Urine samples sent to the Central Laboratory of Istanbul University, Istanbul Faculty of Medicine, for routine examination between September 2023 and January 2024 were included in the study. The presence of ESBL and susceptibility to antibiotics other than colistin were determined by the Vitek2 (bioMérieux, France) automated system, and colistin susceptibility was determined by the reference broth microdilution method. Results: Of the 80 patients with E. coli, 80% were female, and 20% were male; 32.5% were children, and 67.5% were adults. All E. coli isolates were susceptible to nitrofurantoin, ertapenem, and meropenem. 40% of the strains were ESBL-positive. In respect of multidrug-resistant bacteria, among ESBL-positive and-negative isolates, 12.5% and 2.1% were resistant to six, 28.1% and 8.3% to five, 34.4% and 6.25% to four, and 18.75% and 16.7% to three different antibiotic groups, respectively. Of 80 E. coli strains, 92.5% of which were sensitive to colistin, the MIC50 value was 0.5 mg/L, and the MIC90 value was 2 mg/L. Conclusions: Although the colistin resistance rate and minimum inhibitory concentration (MIC) values are not high, it is important to monitor resistance when treating problematic infections with multiple resistance.Öğe Türü:Öğe, Mandibular angle bone appositions in bruxism: A deep learning-based detection and staging study(Springer Nature, 2026) Yüce, Fatma; Öziç, Muhammet ÜsameThis study aims to automatically detect, classify, and stage bone apposition changes in the mandibular angle region associated with bruxism using panoramic radiographs. The deep learning-based YOLO11x architecture was implemented to identify and categorize these structural stages. A total of 800 panoramic radiographs were annotated as stage 0–3 by a specialist dentomaxillofacial radiologist. Bruxism status was determined from clinical records, while bone appositions were staged radiographically. Each radiograph was divided along the midline and the left side mirrored, yielding 1600 half-jaw images used to train the YOLO11x model. Performance was evaluated using confusion matrices, precision, recall, F1-score, and mean average precision (mAP). The deep learning model achieved an overall mAP@50 of 0.864 on the validation set evalu ated during training and 0.834 on the independent test set, with the highest discriminative performance observed during training in stage 3 (0.901 mAP@50) and during testing in stage 0 (0.909 mAP@50). Confusion matrices confirmed high proficiency in anatomical localization and bounding-box precision. Classification errors were limited to adjacent stages, primarily due to morphological similarities in transition zones. Deep learning approaches have demonstrated acceptable performance in detecting bone changes associated with bruxism in panoramic radiographs, thereby establishing a basis for automatic staging. The findings reveal that the model has the potential to serve as an auxiliary tool to support radiographic assessment and staging of mandibular gonial bone appositions in clinically diagnosed/probable bruxism cases, despite the challenges encountered, particularly in transitional stages.Öğe Türü:Öğe, Öğe Türü:Öğe, Strategic management of university-industry partnerships in emerging tech hubs: A comparative analysis of techno park management strategies in Istanbul and Astana(Red & River Publications, 2026) Gökmen, Ahmet MünirThis study compares the governance, policy support, and performance outcomes of two leading technoparks in emerging economies—ITU ARI Teknokent (Türkiye) and Astana Hub (Kazakhstan)—over 2015–2024. Using official technopark reports, ministry indicators, and intellectual-property statistics, we compile a panel of annual indicators (active firms, patent activity, R&D expenditure, investment, exports) and apply descriptive analysis, correlation matrices, ordinary least squares regressions, and compound annual growth rates (CAGR). We find complementary strengths: ITU ARI exhibits depth in research-intensive firm creation and employment, while Astana Hub shows rapid scale up and international market orientation. Export performance is positively associated with R&D expenditure in the Astana Hub sample, while new-firm formation is more tightly linked to job creation at ITU ARI. Governance and policy design implications for technoparks in middle income contexts are discussed.
