Articles in Press

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Summary:
This study aims to explore the opportunities, challenges, principles, and approaches in climate change education through a systematic analysis of 10 scientific articles published in the last five years in Scopus-indexed journals. The findings indicate that the main opportunities for climate change education include student engagement as agents of change, the strategic role of teachers, supportive learning environments, curriculum integration, science education, and supportive government policies. However, significant challenges remain, such as improving teacher competency, material complexity, limited curriculum tools, collaboration between stakeholders, and gaps between student knowledge and skills. In addition, six main principles of climate change education implementation were found, including improving teacher competency, curriculum adjustment, cross-sector collaboration, student environmental awareness, providing knowledge related to climate change, and connecting learning with contextual issues. Student-based learning approaches, such as inquiry-based learning, problem-based learning, and project-based learning, have proven effective in delivering complex and contextual materials.

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Summary:
Drought is a complex natural phenomenon resulting from prolonged periods of below-average precipitation. Its gradual development over large areas makes it challenging to determine its onset, duration, and overall impact accurately. In this study, an artificial neural network (ANN) method-based model was developed to predict the standard precipitation index (SPI), an index commonly used to determine drought severity. SPI12 which reflects a meteorological drought indicator to monitor precipitation anomalies over 12-month accumulation periods were estimated based on artificial neural network (ANN) method using monthly precipitation data recorded in the 1980-2015 period for Kayseri. The North Atlantic Oscillation Index (NAOI), Mediterranean Oscillation Index (MOI), and Arctic Oscillation Index (AOI), which represent large-scale global cycles, were used as input variables in the models. A multilayer perceptron-type ANN model with a single hidden layer was chosen. The model training used 70% of the data and a scaled conjugate gradient backpropagation algorithm. The remaining 30% of the data were used for model testing and control. The activation functions of the ANN model and the number of neurons in the hidden layer were determined using the trial-and-error method. The performances of the models were evaluated using the mean Nash-Sutcliffe coefficient of efficiency (NSE), root square error (RMSE), and coefficient of determination (R2) of agreement between the estimated and observed SPI12 values. This study demonstrated that drought conditions can be successfully predicted at 3, 6, and 12 months in advance using indices that reflect large-scale global climate anomalies.

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Summary:
. Flow and thrust calculations at supersonic speeds require intensive formulas and general assumptions, thus prolonging the preliminary design process. Today, the use of computer programs for aerodynamic design and analysis is inevitable in the development of aircraft. However, in this process, traditional calculation methods are mostly used. By performing the relevant calculations with a source code, time-consuming problems such as data extraction from tables, interpolation or calculation with formulas can be avoided. In this study, a computational tool was created in Python using the formulas contained in compressible flow theory. For the calculation, ISA (International Standard Atmosphere) and altitude values are first obtained from the user to determine the atmospheric conditions. These conditions are known to directly affect static conditions. Then, the data input of one of the selected parameters is made and the remaining parameters according to these input values are presented on the result screen together with the other outputs. These outputs were compared with NASA data and their accuracy was analysed. Two different configurations were created to examine the dependence of compressible flow calculations on atmospheric conditions. In the first one, constant ISA and different altitude values were analysed, while in the other one, constant altitude and different temperature deviations were evaluated. These evaluations revealed the sensitivity of the calculation results to atmospheric variables. The findings provide critical data on how to design and analyse aircraft under different operational conditions. This study, for the first time, provides calculations based on atmospheric variables, enabling them to efficiently obtain the values of parameters that depend on these data. This approach is an innovative contribution to the existing literature, expanding the body of knowledge on the analysis of compressible flows.