DSpace Arşivi :: Ana Sayfa

İstanbul Gelişim Üniversitesi Kurumsal Açık Erişim Arşivi

DSpace@Gelişim, İstanbul Gelişim Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve yayınların etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.

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Can Machines Detect Ultra-Processed Foods? A Head-To-Head Evaluation of Large Language Models Using NOVA Classification
(Oxford University Press, 2026) Bayram, Hatice Merve; Arslan, Sedat; Öztürkcan, Arda
This cross-sectional study compared three large language models (LLMs) (Grok 4.1, Gemini 3, and ChatGPT 5.2) in classifying ultra-processed foods (UPF) using best-selling products from leading supermarket chains covering 53.2% of the national market. Of 3,001 products, 2,920 with complete ingredient information were included; two trained dietitians assigned NOVA groups as the reference standard. In the reference classification, 74.3% of products were UPF. Under the baseline prompt, all models underestimated UPF prevalence compared with the reference standard (p < .001). ChatGPT 5.2 yielded the highest binary UPF detection performance (accuracy: 69.01%; sensitivity: 59.01%; specificity: 98.00%; F1: 73.88%). Prompt sensitivity analyses revealed that a minimal prompt substantially outperformed the detailed baseline for most models (Gemini 3 F1: 94.20%; ChatGPT 5.2 F1: 92.62%), though Grok 4.1’s gain reflected a specificity trade-off (sensitivity: 97.79%; specificity: 21.09%). Low inter-run agreement (κ: 0.01–0.27) indicated sensitivity to model updates, supporting prompt calibration and human oversight. These findings suggest that off-the-shelf LLMs require prompt calibration and human oversight before UPF surveillance workflows.
Restructuring Through Organizational Resilience During the Crisis Period: The Effects of Organizational Learning Capabilities on Product Innovation in Exporting Firms
(Ebru Bağcı, 2026) Çelik, Derya; Özer Koçak, Aybige
Nowadays, competition, which is increasing under the influence of globalization, has caused enterprises to expand beyond their borders in order to survive. Increased global uncertainty and unexpected disruptive events foster the emergence of innovation while making organizations vulnerable. It is thought that enhanced “organizational learning capabilities” of enterprises in crisis environments, especially those caused by unexpected disruptive events such as pandemics, wars, or major economic crises, will positively impact their product innovation potential through the mediating and moderating effects of resilience capacity. The purpose of the current research was to make a contribution to the literature in the field by clarifying how the impact of “organizational learning capabilities” on “product innovation” varies according to “organizational resilience” during crisis periods, particularly in exporting firms that are greatly affected by volatile conditions, and to guide the development of these firms. To test the proposed conceptual model, a field research was done on employees of exporting enterprises operating under the Istanbul Textile and Apparel Exporters' Association (ITKIB) in Istanbul province. Factor analysis, validity, reliability, and correlation analyses were done on the data using SPSS, whereas mediation and moderation analyses were performed in SPSS Process Macro V.4. The empirical findings obtained from the field research reveal that “organizational learning” significantly and positively impacts “product innovation”, “organizational learning” positively impacts “organizational resilience”, “organizational resilience” significantly and positively correlates with “product innovation”, and “organizational resilience” does not moderate the impact of “organizational learning” on “product innovation”, but has a full mediating effect.
Design of an Undergraduate Course on Storigami for Child Development
(John Wiley and Sons Ltd, 2026) Tanju Aslışen, Ebru Hasibe; Bafralı, Görsev
This paper is a part of a larger study that discusses and explains the design, implementation and evaluation process of a 14week face-to-face elective course on Storigami for the child development departments. In this study, the design process of the course is explained as the first phase of the project. The creation of the course is based on a student-centred approach to promote conceptual understanding of the undergraduate students (Hansen, E. J. 2011). Idea-based learning: A course design process to promote conceptual understanding. Routledge.). The planning and the content of the course are constructed upon the needs of the 21st century higher education and professional life. An integrated course design process and model (Fink, L. D. (2013). Creating significant learning experiences: An integrated approach to designing college courses. John Wiley & Sons.) was employed. There are three phases to this course design process, namely: initial design phase, intermediate design and final design phase. At the end of the study, a course and syllabus designed in line with this model involving course structure, course content, weekly readings, tasks, assignments and assessment for a 14 week semester.
User-Centred Smart Building Technologies for Climate-Resilient Urban Development: Evidence from Istanbul
(Frontiers Media SA, 2026) Agboola, Oluwagbemiga Paul; Baby, Ann
Promoting sustainable urban development requires strengthening climate resilience while encouraging environmentally responsible lifestyles in cities. In densely urbanised and climatically diverse contexts such as Istanbul, Smart Environmental Control Technologies (SECTs) offer significant potential to reduce energy demand, enhance thermal comfort, and support low-carbon urban living. This study examines the role of SECTs in advancing climate-resilient urban development by analysing occupants’ perceptions, satisfaction, and behavioural responses in residential and commercial buildings. The study aims to: (i) assess user satisfaction with SECTs; (ii) evaluate their contribution to energy efficiency in buildings (EEB); (iii) analyse their role in climate resilience and emission reduction (CRER); and (iv) propose design-oriented recommendations for optimising Smart Energy Building Design (SEBD). A mixed-methods approach was adopted, combining semi-structured interviews with 20 key informants and a quantitative survey of 237 building occupants. Quantitative data were analysed using SPSS (version 24), applying descriptive statistics, ANOVA, regression analysis, Pearson correlation, the Relative Importance Index (RII), and thermal comfort modelling based on Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD). Qualitative data were examined through thematic analysis to support triangulation. The results reveal high levels of occupant awareness and acceptance of smart technologies, with automated HVAC systems, IoT-enabled sensors, and smart thermostats identified as the most influential solutions for thermal comfort management (RII > 0.85). Regression analysis indicates that SECT adoption significantly predicts user satisfaction and energy efficiency performance (β = 0.64, p < 0.001), while correlation analysis confirms a strong association between objective comfort indices and subjective perceptions (r = 0.68, p < 0.01). PMV/ PPD modelling shows that smart systems maintained generally acceptable comfort conditions, with mean PPD values ranging from 8.4% in residential units to 18.9% in shared offices, and peak dissatisfaction exceeding 22% during highoccupancy periods. Buildings equipped with adaptive SECTs achieved average peak energy demand reductions of approximately 15–20% compared to conventional systems. Overall, the findings provide evidence-based guidance for designing user-centred, energy-efficient, and climate-resilient smart buildings, reinforcing the strategic role of SEBD in sustainable urban development.
The Impact of Electric Charging Unit Conversion on the Performance of Fuel Stations Located in Urban Areas: A Sustainable Approach
(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Yetimoğlu, Merve; Karaşahin, Mustafa; Yıldırım, Mehmet Sinan
The rapid increase in electric vehicle (EV) ownership necessitates the adaptation of fuel stations to charging infrastructure and the re-evaluation of capacity planning. In the literature, demand forecasting and installation costs are mostly examined; however, station-scale queue analyses supported by field data remain limited. This study aims to examine the integration of EV charging in fuel stations through simulation-based capacity analyses, taking current conditions into account. In this context, a scenario in which one and two dual-hose pumps at a fuel station located on the Turkey–Istanbul E-5 highway side-road is converted into a charging unit has been evaluated using a discrete-event microsimulation model. The analyses were conducted using a discrete event-based microsimulation model. The simulation inputs were derived from field observations and survey data, including the hourly arrival rates of internal combustion engine vehicles (ICEVs), the dwell times at the station, and the charging durations of EVs. In the study, station capacity and service performance were evaluated under scenarios representing EV shares of 5%, 10%, and 20% within the country’s passenger vehicle fleet. Within the scope of the study, the hourly arrival rates and dwell times of ICEVs were determined through field measurements, while EV charging durations were assessed by jointly analyzing field observations and survey data. Simulation results showed that the average number of waiting vehicles increases as the EV share rises; for example, in the 10% EV share scenario, 15.6 (±0.84) EVs were observed waiting within the station, while 34.06 (±1.23) EVs were identified in the 20% scenario. These queues constrict internal circulation within the station, limiting the maneuverability of ICEVs and causing delays in overall service operations. Furthermore, when two dual-hose pumps are replaced with charging units, noticeable increases in waiting times emerge, particularly during the evening peak period. Specifically, 5.88% of ICEVs experienced queuing between 17:00–18:00, rising to 12.33% between 18:00–19:00. In conclusion, this study provides a practical and robust model for short- and medium-term capacity planning and offers data-driven, actionable insights for decision-makers during the transition of fuel stations to EV charging infrastructure.