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Effect of Few-Layered Graphene on the Corrosion Behaviour of the Al–Cu Matrix Composites
No full textIn the transportation, maritime and aviation industries, aluminum alloys — particularly those in the 2xxx series (Al–Cu type) — are frequently used because they offer an ideal combination of properties, including toughness, a high strength-to-weight ratio and fatigue resistance. Graphene, a two-dimensional material with a single-atom thickness composed of carbon atoms arranged in a hexagonal lattice, attracts interest due to its remarkable properties and is commonly utilized as a reinforcement in composite materials. Few-layered graphene (FLG) reinforced Al–4 wt.% Cu matrix composites were prepared via mechanical alloying (MA, 500 rpm, ball-to-powder ratio 7 : 1), uniaxial pressing (300 MPa), and conventional sintering (59°C, 3 hours, argon gas flow). The present work investigates corrosion behaviors of FLG (0.25 and 0.5 wt.%) reinforced Al–4 wt.% Cu composites with different MA durations. Open-circuit potential (OCP), potentiodynamic polarization, and electrochemical impedance spectroscopy (EIS) measurements were carried out in a 3.5% NaCl solution to determine the corrosion behavior. Following the corrosion test, X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) analysis were performed on the specimen that exhibited the optimum results. The data obtained before and after the test were compared to investigate the structural changes that occurred as a result of the corrosion test. The analysis demonstrated that the sample reinforced with 0.5 wt.% FLG and subjected to 7 hours of MA had the highest corrosion resistance
Exploring the Potential of a High School English Preparatory Coursebook Package in Developing Genre Awareness
No full textThe present study analyzes an English coursebook package used in English preparatory programs prior to high-school education in Türkiye in terms of written genres. The research follows a qualitative approach and employs content analysis to examine writing activities in the Progress coursebook package. The books are analyzed based on the types of genres included and how strictly the writing tasks follow the genre teaching stages. Findings reveal a somewhat diverse representation of genres but indicate a need for broader inclusion of personal, heuristic, and informative types. While the stages of genre teaching are followed in some tasks, many fail to include one or more stages of modeling, negotiation, or construction. Additionally, the accompanying workbook is found to be insufficient in revising previously studied genres. These results underscore a need for broader inclusion of certain genres, improved application of genre teaching stages, and more compatible tasks in the workbook to revise genres.</p
Kentsel Dönüşümün Yaşam Kalitesi Üzerindeki Etkileri: Mekân Kalitesi, Mahalle Memnuniyeti ve Konut İlişkisine Dair Literatür Temelli Bir Okuma
No full textStochastic SO(3) Lie Method for Correlation Flow
No full textIt is very important to create mathematical models for real world problems and to propose new solution methods. Today, symmetry groups and algebras are very popular in mathematical physics as well as in many fields from engineering to economics to solve mathematical models. This paper introduces a novel methodological framework based on the SO(3) Lie method to estimate time-dependent correlation matrices (correlation flows) among three variables that have chaotic, entropy, and fractal characteristics, from 11 April 2011 to 31 December 2024 for daily data; from 10 April 2011 to 29 December 2024 for weekly data; and from April 2011 to December 2024 for monthly data. So, it develops the stochastic SO(2) Lie method into the SO(3) Lie method that aims to obtain the correlation flow for three variables with chaotic, entropy, and fractal structure. The results were obtained at three stages. Firstly, we applied entropy (Shannon, Rényi, Tsallis, Higuchi) measures, Kolmogorov–Sinai complexity, Hurst exponents, rescaled range tests, and Lyapunov exponent methods. The results of the Lyapunov exponents (Wolf, Rosenstein’s Method, Kantz’s Method) and entropy methods, and KSC found evidence of chaos, entropy, and complexity. Secondly, the stochastic differential equations which depend on S2 (SO(3) Lie group) and Lie algebra to obtain the correlation flows are explained. The resulting equation was numerically solved. The correlation flows were obtained by using the defined covariance flow transformation. Finally, we ran the robustness check. Accordingly, our robustness check results showed the SO(3) Lie method produced more effective results than the standard and Spearman correlation and covariance matrix. And, this method found lower RMSE and MAPE values, greater stability, and better forecast accuracy. For daily data, the Lie method found RMSE = 0.63, MAE = 0.43, and MAPE = 5.04, RMSE = 0.78, MAE = 0.56, and MAPE = 70.28 for weekly data, and RMSE = 0.081, MAE = 0.06, and MAPE = 7.39 for monthly data. These findings indicate that the SO(3) framework provides greater robustness, lower errors, and improved forecasting performance, as well as higher sensitivity to nonlinear transitions compared to standard correlation measures. By embedding time-dependent correlation matrix into a Lie group framework inspired by physics, this paper highlights the deep structural parallels between financial markets and complex physical systems
INVESTIGATION OF THE EFFECT OF CAPPADOCIA VOLCANIC TUFF AS BASE MATERIAL IN GEOPOLYMER COMPOSITES ON MECHANICAL AND DURABILITY PROPERTIES
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Transforming GRACE mascon TWS-L3 data into vertical displacements using a regression approach: a case study of the Türkiye region
No full textBereavement and Resilience in Earthquake Affected Individuals: Serial Mediation through Psychological Distress and Hope
No full textNatural disasters have existed as long as the world itself. Earthquakes are among the most common and destructive natural disasters. In earthquake-prone countries like Türkiye, which experienced the February 6 earthquake centered in Kahramanmaraş, conducting studies to mitigate the negative psychosocial effects following such disasters is of great social importance. This study investigated the effect of bereavement on psychological resilience, as well as the mediating roles of psychological distress and hope in earthquake-affected individuals. A total of 387 participants (318 females, 82.2%; 69 males, 17.8%) with an average age of 25.6 years completed scales assessing bereavement, psychological distress, hope and resilience. Structural equation modeling revealed that psychological distress and hope fully mediated the relationship between bereavement and resilience. Specifically, bereavement increased psychological distress and reduced hope, which in turn negatively affected resilience. These findings highlight the critical role of addressing distress and fostering hope to support resilience in earthquake-affected individuals
Utilization of machine learning algorithms in estimation of syngas fractions and exergy values for gasification of biomass-lignite mixtures in fixed and fluidized bed gasifiers
No full textEarth's environmental challenges, such as climate change and pollution, require urgent emission reductions. A thermochemical method that transforms carbon-rich substances into syngas, biomass gasification produces clean hydrogen as a sustainable energy carrier. This process ensures high carbon conversion efficiency while minimizing greenhouse gas emissions. This study examines the gasification of nine biomass-lignite blends using fluidized-bed and fixed-bed gasifiers. A wide range of biomass samples blended with lignite enabled the analysis of different sample characteristics and their impact on the gasification technique. ASPEN Plus® simulations assess the effects of biomass-to-lignite ratio, equivalence ratio (ER), steam to biomass ratio (SBR), and reactor temperature on syngas fraction and system efficiency. Machine learning models gaussian process regression (GPR), random forest (RF), support vector machine (SVM), and decision tree (DT) predict syngas and product gas exergy values, providing a data-driven optimization approach. For hazelnut shell validation, R2 values were 0.98 for the fixed-bed model and 0.96 for the fluidized-bed model. The Random Forest algorithm demonstrated the highest accuracy (R2 = 0.93), outperforming other models. The study also analysed the amount of data required and demonstrated robust models capable of learning with limited data. Since a significant portion of the machine learning process involves dataset creation, the ability to learn from small datasets is crucial. This highlights the significance of data-efficient learning in machine learning applications. Findings contribute to advancing biomass gasification for cleaner hydrogen production
