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
Solar thermal systems and AI: Past, present, and future
(Springer, 2025) Cüce, Pınar Mert; Alvur, Emre; Cüce, Erdem; Soudagar, Manzoore Elahi M.; Bouabidi, Abdallah; Guo, Shaopeng; Mostafa, Noha A.
This research explores the role of artificial intelligence (AI) in enhancing the efficiency and reliability of solar thermal, photovoltaic (PV), and hybrid energy systems. As the transition from fossil fuels becomes increasingly crucial due to their contribution to global warming and resource depletion, optimising solar energy systems through AI-driven technologies has become imperative. The study examines solar thermal and PV applications for their ability to generate electricity, heat buildings, and support industrial processes, demonstrating how AI techniques, such as artificial neural networks and machine learning models, enhance system performance and enable real-time monitoring. Additionally, hybrid energy systems, which integrate renewable and non-renewable sources like wind, solar, diesel, and fuel cells, are extensively analysed. AI applications, including support vector machines and genetic algorithms, play a key role in improving the efficiency of these systems by forecasting energy production, optimising storage, and minimising system losses. The research concludes with a SWOT analysis, identifying the strengths, weaknesses, opportunities, and threats of AI integration in energy systems while providing strategic recommendations for future research, policy development, and technological innovation. By leveraging AI in solar and hybrid energy solutions, this study offers a comprehensive framework for enhancing sustainability, reducing emissions, and ensuring a stable energy supply.
Editorial: combination therapies for mash: a step forward or more complexity?
(Wiley, 2025) Zhou, Xiao-Dong; Yılmaz, Yusuf; Noureddin, Mazen; Luu, Hung N.; Zheng, Ming-Hua
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Forecasting future realized variance paths with depth-weighted ridge and conformal diagnostics
(American Institute of Mathematical Sciences, 2025) Sözen, Çağlar; Kabakcı, Fikriye
We studied the problem of forecasting full future realized–variance (FRV) paths yt,1:H over H = 30 trading days. We proposed a depth–weighted ridge (DW–ridge) estimator that (i) enforces the natural monotonicity of cumulative variance via a pool–adjacent violators post–projection and (ii) adapts to market regimes through observation weights derived from a Wasserstein–based curve depth. At the daily frequency, we took squared returns as a practical realized–variance proxy, so that the FRV path is the cumulative sum of next–day squares. Empirically, we used daily data for two liquid U.S. exchange-traded funds (ETFs; XLE and SLV) and two major cryptocurrencies (BTC–USD and ETH–USD) from January 1, 2020, to December 31, 2024, under a 60%/20%/20% train–calibration–test split. On the ETF benchmarks, DW–ridge improved all–horizon pathwise root mean squared error (RMSE) by about 3.1% (XLE) and 2.8% (SLV) relative to a monotone ridge baseline, with statistically significant short–horizon (H1–3/H1–5) mean squared error (MSE) gains under a moving–block bootstrap. On BTC–USD and ETH–USD, all–horizon RMSE reductions were around 6.0% and 6.5%, respectively. A block–conformal diagnostic based on depth–derived nonconformity scores attained near–nominal or conservative coverage on test blocks, so sharper forecasts were not obtained at the expense of reliability. Overall, depth reweighting provided a simple, fast, and empirically effective enhancement to monotone FRV path forecasting across both sector ETFs and major cryptocurrencies.
Adaptability of space in the future of architecture: function transformation
(Jomard Publishing, 2025) İsmailoğlu, Semiha; Seymen, Gizem
The multifaceted developments in the globalizing world necessitate change and transformation and therefore the need for adaptation, to ensure permanence in architecture. The concept of adaptability in architecture is that the structure is open to change in response to the needs of changing and evolving user groups; allows spatial and functional arrangements to support different uses and functions and adapts to modern technologies without major intervention in existing activities and the environment. However, today, the fact that architectural structures cannot adequately respond to changing needs and that the concept of transformability is not addressed systematically enough in the design processes emerges as an important problem. The purpose of this study is to convey through examples that adaptability will have an indispensable place in the future of architecture. The sample was selected from award-winning buildings whose quality was registered to meet the need for different functional spaces. In conclusion, the concept of adaptability offers solutions to needs arising from positive or negative situations and has a key place in architects' future-oriented designs. By repurposing old and idle buildings, new job areas can be created, contributed to regional development and a platform can be created to ensure social harmony.
Preoperative semi-automatic segmentation in incus defects
(Springer, 2025) Yemiş, Tuğba; Aktepe, Rıza; Uzun, Ali Yavuz; Birinci, Mehmet; Çeliker, Fatma Beyazal; Çeliker, Metin; Güneşer, Yunus; Erdivanlı, Özlem Çelebi
Objectives: Long-term success rate of partial ossicular replacement prosthesis (PORP) is low. Three-dimensional (3D) imaging technology to identify and model ossicular chain defects may improve this rate. This study aims to evaluate the diagnostic accuracy of a 3D modelling method to identify incus defects. Methods: This retrospective study comprised high-resolution computered tomography (HRCT) and intraopeative images of patients who underwent endoscopic tympanoplasty in a 6-years period. Ears with an incus defect were included in the study, while those with an intact ossicular chain served as controls. HRCT images were processed using 3D Slicer software for automatic segmentation and 3D model construction. Intraoperative findings were then compared with 3D imaging results. Cohen’s kappa agreement, sensitivity, specificity, predictive values, and accuracy were reported. Results: A total of 129 ears (52.7% right) from 106 patients (69 males, 60 females) were analysed, with a mean age of 40.28 ± 13.11 years. Intraoperative images showed incus defects in 46 ears (35.7%); whereas 3D model identified incus defect in 44 ears (34.1%). The agreement between intraoperative view and 3D model in detecting incus defects was excellent (Cohen’s kappa = 0.83). 3D model showed incus defect in 40 out of 46 patients with actual defect with a sensitivity of 86.96%, specificity of 95.18%, positive predictive value of 90.91%, negative predictive value of 92.94%, and accuracy of 92.24%. Conclusion: 3D imaging has been shown to provide high diagnostic accuracy for incus defects in well-aerated ears without opacification, which may facilitate surgical planning and the design of personalised prostheses.
