Recep Tayyip Erdoğan Üniversitesi Kurumsal Akademik Arşivi
DSpace@RTEÜ, Recep Tayyip Erdoğan 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.
Güncel Gönderiler
Validity and reliability of the Turkish version of the food neophobia scale in adolescents
(Turkish Association of Nervous and Mental Health, 2025) Yazıcı, Merve; Kıvrak, Mehmet; Puşuroğlu, Meltem; Hocaoğlu, Çiçek
Objective: Food neophobia is an aversion or reluctance to eat new or unfamiliar foods. The aim of this study was to establish the Turkish validity and reliability of the Food Neophobia Scale, which is used to measure the fear of trying new foods, in the adolescent age group. Method: The study was conducted with high school students in the province of Rize during the 2023-2024 academic year. The sample of the study was comprised of 466 students in 13-18 age range. Data were collected using a questionnaire containing demographic characteristics and the Food Neophobia Scale. To assess the validity and reliability of the scale, Exploratory Factor Analysis, Confirmatory Factor Analysis, and Cronbach’s alpha internal consistency coefficient were used. Results: The mean age of the students included in the study was 15.4±1.1 years 50.6% were male. The items in the scale used in the study were found to have sufficient correlation and the dataset was suitable for factor analysis (KMO=0.747; Bartlett’s sphericity test, p<0.001). The Confirmatory Factor Analysis revealed that the scale had a good model fit (χ2=3.78, p<0.001). The Cronbach α internal consistency coefficient for the scale integrity was 0.71. Conclusion: The Turkish Food Neophobia Scale is a valid and reliable measurement tool. The scale can determine food neophobia in adolescents aged 13-18.
Seismic resilience of existing RC dual-system buildings during the 2023 Kahramanmaraş earthquakes: a case study
(Taylor and Francis Ltd., 2025) Tonyalı, Zeliha; Kıral, Adnan; Ergün, Mustafa; Garcia, Reyes
This study investigates the seismic resilience of an existing reinforced concrete (RC) dual-system during the 2023 Kahramanmaraş earthquakes. The shear wall-frame dual building was designed according to the Turkish guidelines (TEC 2007) and experienced negligible damage during the earthquakes, whereas all neighboring buildings collapsed. The case study building is modeled in SAP2000® adopting a lumped plasticity approach. The 3D building model was subsequently subjected to pushover and nonlinear time-history analyses (NTHAs) using real ground motions recorded during the first and strongest mainshock of the Kahramanmaraş earthquakes ((Formula presented.) = 7.7 Pazarcik earthquake). The results from the NTHAs indicate that the maximum inter-story drift (IDR) ratios on all floors of the case study building remained below the Immediate Occupancy performance level (IDR = 1.0%). Moreover, the dual-system building designed with TEC 2007 survived the earthquakes without damage, even when the design earthquake scenarios exceeded those considered in the new and more stringent TBEC 2018 (i.e. a 475 return period). The limited damage experienced by the RC dual-system building can be largely attributed to its high wall index (WI = 1.5% and 1.84%) and high average lateral stiffness index (H/T ≥45.3), which are above the minimum values (ρ = 0.6% and H/T ≥45) suggested in previous research. This study also highlights the critical importance of site-specific ground motion selection, particularly in the context of Hatay province, where the seismic demands exhibited significant variability and intensity. This study contributes to a better understanding of the resilience of RC shear wall-frame buildings in seismic zones.
Power plant ash as a backfill material for subsidence mitigation and mining district recovery: a review and case study in Zonguldak, Turkiye
(Taylor and Francis Ltd., 2025) Bilen, Mehmet; Toroğlu, İhsan; Yılmaz, Erol
This study investigates the use of fly ash (FA) as a sustainable backfilling material in underground coal mining in Zonguldak, Türkiye, aiming to reduce land subsidence, enhance void stability, and support ecological recovery. Laboratory analyses assessed FA’s physical, chemical, and hydraulic properties, while Darcy and channel-flow tests simulated drainage behaviour. Findings show FA is mainly fine-grained (94% < 0.5 mm) with hydraulic conductivity of 0.02–0.03 cm/s and high SiO₂, Al₂O₃, Fe₂O₃ contents (> 70%), indicating strong pozzolanic potential. Strength tests confirmed FA–cement blends effective, highlighting FA’s role in sustainable mining and circular economy.
Modeling the distribution of the endemic Turkish moss species Cinclidotus bistratosus Kürschner & Lüb.-Nestle (Pottiaceae) under various climate change scenarios
(Frontiers Media SA, 2025) Abay, Gökhan; Gül, Serkan
The extant literature on the subject is inconclusive, with only a paucity of studies addressing variations in the distribution patterns of moss species, particularly those with restricted distributions, in the framework of climate change. Consequently, we constructed simulated current and predicted prospective potential distribution models of Cinclidotus bistratosus, a narrow-range endemic moss species belonging to Türkiye, using the CMCC-ESM2, HadGem3-GC31-LL, and MIROC6 climate models. The purpose of this paper is to examine the distinct habitat requirements of the endemic moss, the key environmental factors that influence its distribution, and the distribution changes of the species under climate change over a substantial spatial-temporal scale (between the periods 2021-2100). Precipitation of driest, hottest and coldest quarters has been identified as a key factor influencing C. bistratosus distribution models. The findings of this study indicate that the highest probability of habitat suitability for C. bistratosus is currently in the coastal regions of western and southern Türkiye. However, future projections indicate a substantial decline in suitable habitats and a potential expansion towards northern regions of the country. In the scenario of prospective climate warming, the appropriate habitat of C. bistratosus may shift towards northern and high-altitude regions under the SSP5-8.5 climate scenario. However, the species will not entirely withdrawal from the Mediterranean distribution range, and its possible distribution will be restricted in Türkiye. The present study provides significant information and support for understanding the effects of climate change on the distribution of C. bistratosus, as well as its future distribution and conservation strategies.
Efficient detection of piezoelectric material defects in smart structures using nonlinear vibration and neural networks validation
(Techno-Press, 2025) Mohammad, Suleiman Ibrahim; Vasudevan, Asokan; Alkasassbeh, Abdelmajeed; Ahmad, Omar Asad; Alfugara, Akif; Kahla, Nabil Ben; Yaylacı, Murat
This paper presents a new technique for detecting defects in piezoelectric materials located inside smart structures, mainly the troubleshooting of functionally graded piezoelectric (FGP) porous plates excited by an electric field. This research accounts for the Von-Karman nonlinearity to study the influence of mechanical and electrical loadings on the dynamic behavior of world material. Maxwell’s equations are used to describe the coupling of electric fields with the piezoelectric properties of the plate, and porosity is closely examined for its impact on the defect detection process. Hamilton’s principle is used to obtain the equations of motion, which can replicate the system’s non-linear dynamics. The harmonic differential quadrature method (HDQM) is used to achieve numerical results, while equations governing the response of the system under different boundary conditions can be discretized appropriately. Deep learning models known as deep neural networks (DNN) are used to validate the mathematical model and extract more information from this complex, large dataset, which is obtained from these simulations. The DNN model provides a robust framework for detecting defects based on learning the complex relationships between different related features of the system, and its efficient classification aids in the detection and classification of defects. The study also explores the parameter selection and optimization in the DNN algorithm, so as to balance the model accuracy and computational efficiency. The results are important as they provide a cost-effective, accurate technique to detect defects in piezoelectric materials and help in smart structure health monitoring, which is a rapidly growing field. The experimental setup detailed in this research can form the basis for future developments of structural diagnostics and the implementation of smart materials in engineering applications.
