Fırat University

This study presents a machine learning (ML) framework for predicting heat transfer in magnetohydrodynamic (MHD) mixed convection within a complex V-shaped cavity filled with a nano-enhanced phase change material (NEPCM) suspension. Accurate computational fluid dynamics (CFD) simulations are essential for understanding heat transfer mechanisms in such systems, but generating comprehensive data through high-fidelity models remains computationally expensive. To address this challenge, we develop an integrated ML approach that combines synthetic data generation, physics-informed feature engineering, and optimized ensemble boosting. The methodology first augments a limited 34-sample CFD dataset to 2034 samples using Latin Hypercube Sampling with Radial Basis Function interpolation. Next, 14 physics-based features are engineered to encode the underlying physical phenomena. Finally, hyperparameters of three gradient boosting models—eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost)–are optimized via cross-validation. The framework predicts the average Nusselt number ( N u avg ) and average kinetic energy ( K E avg ) from seven geometric and operational inputs. CatBoost achieved optimal performance for N u avg ( R 2 = 0 . 9745 , mean absolute percentage error = 1.27%), while XGBoost excelled for K E avg ( R 2 = 0 . 9920 , mean absolute percentage error = 1.04%). The novelty of this work lies in its ability to generalize across different output variables and significantly reduce computational cost, enabling rapid design optimization and in-depth parametric analysis. This generalizable approach reduces computation time from hours to milliseconds, facilitating efficient design optimization and in-depth parametric studies. • ML framework predicts heat transfer in MHD mixed convection of NEPCM suspension. • Combines physics-informed feature engineering with optimized ensemble boosting. • CatBoost best for Nusselt number (R 2 = 0.9745), XGBoost for kinetic energy (R 2 = 0.9920). • Synthetic data generation expands limited CFD dataset from 34 to 2034 samples. • Reduces simulation time from hours to milliseconds for efficient design exploration.

Bu çalışma, kıraat alanında otorite kabul edilen Zeyd b. Sâbit’e nispet edilen okuyuş vecihlerini, kıraat ve tefsir literatürü çerçevesinde inceleyerek onun kıraat ilmindeki konumunu ve Kur’an âyetlerinin anlaşılmasına sunduğu katkıları ortaya koymayı amaçlamaktadır. Zeyd b. Sâbit’in kıraatini doğrudan Hz. Peygamber’den (s.a.s.) almış olması, İbn Abbâs gibi önde gelen isimlerin kıraat tercihinde genellikle onun okuyuşunu esas alması, kıraat-i aşere imamlarının çoğunun isnadında yer alması onun bu alandaki yetkinliğinin göstergeleri arasında yer almaktadır. Araştırmanın temel hedefi, Zeyd b. Sâbit’e nispet edilen kıraat vecihlerini tespit etmek, bu vecihlerin âyetlerin anlaşılmasına olan katkılarını incelemek ve kıraattefsir ilişkisi bağlamında değerlendirmektir. Bu doğrultuda kıraat ve tefsir kaynakları taranmış, Zeyd b. Sâbit’e atfedilen kıraat örnekleri sistematik olarak sınıflandırılmıştır. Çalışmanın özgün yönü, Zeyd b. Sâbit’in kıraat ilmindeki otoritesini, ona nispet edilen kıraat vecihlerinin anlam merkezli ortaya koymasıdır. Mevcut literatürde Zeyd b. Sâbit’in Kur’an ilimlerindeki rolü ele alınmış olmakla birlikte, onun kıraat vecihlerinin doğrudan âyetlerle ilişkilendirilerek incelenmesi ve bu okuyuşların âyetin anlaşılmasına olan katkılarının ortaya konulması, bu çalışmayı önceki araştırmalardan ayırmaktadır. Bu yönüyle araştırma, yalnızca sahabe dönemindeki kıraat geleneğine dair bir çerçeve sunmakla kalmamakta; aynı zamanda kıraat ile tefsir ilmi arasındaki ilişkiyi de ortaya koymaktadır.

Abstract This study examines the role of socially responsible investment (SRI) in education. Using bibliometric analysis and a systematic literature review, it was found that while SRI has received increasing attention over the years, studies focusing specifically on education are limited. The findings demonstrate that SRI has the potential to enhance students’ ethical awareness and foster a sense of social responsibility. The importance of educational institutions in raising responsible individuals for the future is emphasized, particularly through the development of SRI-focused curricula and projects. Furthermore, interdisciplinary research is required to address the existing gaps in this area.

Conducting energy, exergy and sustainability analyses is a well-known method to increase the efficiency of renewable energy resources and detect losses. In this context, a Hydroelectric Power Plant (HEPP) in Bingol-Turkey was examined as a case study. It has been determined that exergy loss in HEPP occur in penstocks, Francis turbines, generators, transformers and switchyards. The efficiency of the HEPP is determined as 90%, the annual average exergy efficiency of the power plant is 91% and the exergy sustainability index of the power plant is 9.3%. The highest exergy loss in the equipment in the HEPP occurred in Francis turbines. It has been demonstrated that examined HEPP is an environmentally friendly, green and sustainable energy facility.