Aksaray Üniversitesi Kurumsal Akademik Arşivi
DSpace@Aksaray, Aksaray 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 telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
A Comparative Assessment of Large Language Models in Pediatric Dialysis: Reliability, Quality and Readability
(John Wiley and Sons Inc, 2025) Ensari, Esra; Akyol Önder, Esra Nagehan; Ertan, Pelin
This study evaluated the reliability, quality, and readability of ChatGPT (OpenAI, San Francisco, CA), Gemini (Google, Mountain View, CA), and Copilot (Microsoft Corp., Washington, DC) which are among the most widely used large language models (LLMs) today in answering frequently asked questions (FAQs) related to pediatric dialysis. Methods: A total of 45 FAQs were entered into LLM. The Modified DISCERN (mDISCERN) scale assessed reliability; the Global Quality Score (GQS) evaluated quality; and readability was assessed using five metrics: Coleman-Liau Index (CLI), Simple Measure of Gobbledygook (SMOG), Gunning Fog Index (GFI), Flesch Reading Ease (FRE) and Flesch–Kincaid Grade Level (FKGL). Questions were directed to the chat robots twice, on January 25, 2025, and February 1, 2025. Results: All three chatbots displayed high reliability, achieving median mDISCERN scores of 5. Quality scores on the GQS were similarly high, with median scores of 5 across platforms; however, Gemini exhibited greater variability (range 1–5) compared to ChatGPT-4o and Copilot (ranges 3–5). Readability scores revealed that chatbot responses were written at an advanced level. Conclusion: This study found that LLMs responses to dialysis FAQs were reliable and high quality, but difficult to read; improving readability through expert-reviewed content could increase their impact on public health.
Comparison of LSTM and SVM methods through wavelet decomposition in drought forecasting
(Springer Science and Business Media Deutschland GmbH, 2025) Tuğrul, Türker; Hınıs, Mehmet Ali; Oruç, Sertaç
Many researchers are working to prevent, monitor and identify drought, which is one of the most insidious and dangerous natural disasters that negatively affects life. For this purpose, various drought indices are developed and new methods are proposed. One of the most widely used of these indexes is the Standard Precipitation Index (SPI). Since it is not known when the drought will begin, taking preventive measures is a difficult and challenging task. In the last decade, machine learning techniques have been preferred to increase success in predicting droughts. In this study, SPI was used as the drought index and Support Vector Machine (SVM) and Long-Short Term Memory Network (LSTM) methods, which are increasingly reliable among the most preferred machine learning and deep learning methods in drought predictions, were used as the prediction method, and furthermore, to increase the prediction power of these methods, new powerful models have been proposed using Wavelet transform and Variational mode transform. Support Vector Machines with Wavelet decomposition (SVM-W), Long-Short Term Memory Networks with Wavelet decomposition (LSTM-W), and Long Short Term Memory Networks with Variational Mode Decomposition (LSTM-VMD) were used as prediction models for drought analysis and the performances of these models were compared.
Two new moss records for Turkey and Southwest Asia
(Taylor and Francis Ltd., 2025) Ezer, Tülay; Aslan Ergenekon, Züleyha; Uygur, Ahmet; Keskin, Ali; Alataş, Mevlüt; Batan, Nevzat
Hymenoloma mulahaceni and Mnium blyttii found as new to Turkey following a recent bryological excursion to the Bolkar Mountains (Taurus Mountain range) in Turkey. Also, this is the first record of the Mnium blyttii from Southwest Asia. Illustrations, geographic distributions, ecological characteristics, identification keys and comparisons with morphologically similar taxa are given.
Enhancing the Strength of Polylactic Acid Material by Bonding Glass Fiber-Reinforced Polymer Composite Plates With Various Fabric Weights and Orientations
(John Wiley and Sons Inc, 2025) Horasan, Murat; Saraç, İsmail; Benli, Semih
This study investigates the impact of applying bidirectional glass fiber fabric-reinforced polymer (GFRP) composite coatings to the top and bottom surfaces of three-dimensional printed polylactic acid (3D-printed PLA) parts on their mechanical properties. The study uses tensile, three-point bending tests, and finite element method (FEM) analysis to examine how the coatings affect the PLA parts. The objective is to enhance the mechanical properties of PLA parts produced by additive manufacturing (AM) so that they can be used in applications requiring high strength. The study involves bonding bidirectional GFRP composites to the outer surfaces of 3D-printed PLA parts using epoxy adhesive to create sandwich-structured composite materials. Two different types of bidirectional glass fiber fabric (GFF) with low weight (25 g/m2) and high weight (100 g/m2) are used as reinforcement materials, while epoxy serves as the matrix material in the composite coatings. The production process involves creating bidirectional-GFF reinforcement materials in two layers, cut at 0° and 45° orientation angles, and bonding them to PLA specimens with epoxy adhesive. Mechanical tests demonstrate increased tensile and flexural strength of PLA parts coated with bidirectional GFRP composite compared to uncoated PLA material. The finite element analyses that simulated tensile and flexural tests showed consistent computational results with experimental findings.
A study on pre-service English teachers’ perceptions of virtual reality to develop speaking skills
(Routledge, 2025) Çetin Köroğlu, Zeynep
These days, utilising digital tools has gained importance in developing language skills. From this perspective, the current study aims to determine EFL student teachers’ current knowledge level of virtual reality applications and their perceptions towards virtual reality applications usage to develop speaking skills. Forty-one pre-service English language teachers in the 4th grade of an English language teaching undergraduate programme at a state university in Türkiye participated in the study. The study utilised both quantitative and qualitative data. The quantitative data were collected through a 30-item Likert-type scale, and the qualitative data were collected through a written structured interview. The results showed that student teachers have limited knowledge about virtual speaking applications and less experience using these tools to develop speaking skills. However, the results revealed that they are eager and motivated to learn more about these tools. Participants perceive that virtual reality applications will improve speaking skills in English.
