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Volume 5

V5-Issue-2

Editor:Prof. Dr. Selahaddin Orhan Akansu

INTEGRATING CLIMATE CHANGE EDUCATION: OPPORTUNITIES, CHALLENGES, AND INNOVATIVE APPROACHES

Corresponding Author: Cincin Cintami

Authors:
Cincin Cintami
Indriyani Rachman

DOI Number https://doi.org/10.52924/QWFE3052

Received: 21.01.2025

Accepted: 16.05.2025

Published: 08.06.2025

Abstract(summary) Download Full Text

INTEGRATING CLIMATE CHANGE EDUCATION: OPPORTUNITIES, CHALLENGES, AND INNOVATIVE APPROACHES

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.

Food waste in Educational Media: A systematic literature review

Corresponding Author:

Authors:

DOI Number https://doi.org/10.52924/BUQI5556

Received: 21.01.2025

Accepted: 18.05.2025

Published: 08.06.2025

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Food waste in Educational Media: A systematic literature review

Summary:
Approximately one-third of the food produced for human consumption is wasted each year, which is a major problem worldwide. An estimated 23–48 million tons of food is wasted annually in Indonesia, impacting the economy and the environment. In this regard, education is crucial to raise awareness of the effects of food waste among the younger generation. The purpose of this study was to identify trends in the use of interactive media, such as educational games and digital applications, and evaluate how effective they are in raising students' awareness and understanding of the waste problem in academic settings. This study also systematically reviewed the literature and assessed the impact of interactive media on students' behavior. The results of the analysis indicate that interactive media can help students learn about food waste. This study provides suggestions for better educational practices and suggests the integration of interactive media into formal curricula to create sustainable behavioral changes among students. However, there are still challenges in terms of developing application features and sustaining behavioral changes. This study provides recommendations for better educational practices and proposes the integration of interactive media into formal curricula to create sustainable behavioral changes among students.

Drought Prediction Based on Global Atmospheric Circulation Indices Using Artificial Neural Networks: A Case for City of Kayseri, Türkiye

Corresponding Author: Filiz Dadaşer-Çelik

Authors:
Fadime Kılıç
Ali Ümran Kömüşcü
Filiz Dadaşer-Çelik

DOI Number https://doi.org/10.52924/SNRZ2925

Received: 13.04.2025

Accepted: 11.05.2025

Published: 08.06.2025

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Drought Prediction Based on Global Atmospheric Circulation Indices Using Artificial Neural Networks: A Case for City of Kayseri, Türkiye

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.

Machine Learning Applications in Biogas and Methane Production: A Bibliometric Analysis

Corresponding Author:

Authors:
RIFAT YILDIRIM

DOI Number https://doi.org/10.52924/USCM8798

Received: 26.04.2025

Accepted: 18.05.2025

Published: 08.06.2025

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Machine Learning Applications in Biogas and Methane Production: A Bibliometric Analysis

Summary:
Biogas processes play an important role in the disposal of organic waste. However, these processes are difficult to control because they are highly sensitive and variable. A lot of work has been done to date in order to eliminate this problem. With the development of technology and artificial intelligence, the spread of “Autonomous” systems has become widespread in the control of anaerobic processes as in many other fields. The Anaerobic Digestion Model No. 1 (ADM1) developed by the International Water Association (IWA) has been adopted as the standard model for the AD process since 2002. With the development of this model, Simple Regression Tree (SRT), Probabilistic Neural Networks (PNN), Artificial Neural Networks (ANN), Gradient Boosted Tree (GBT), Linear Regression (LR), Tree Ensemble Regression (TER), Random Forest Regression (RFR), Polynomial Regression (PR), Fuzzy Logic (FL), Adaptive Network-Based Fuzzy İnference System (ANFIS), Different ML algorithms such as Support Vector Machine (SVM), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) Developing Data-Driven Models (DDDV), Deep neural network (DNN) have been used in various studies and tried to perform process optimization, real-time monitoring, disturbance detection and parameter estimation. In this study, with the help of bibliometric analysis, the development of ML models in biogas processes is examined and solutions are presented.

PYTHON BASED INVESTIGATION OF COMPRESSIBLE FLOW CALCULATIONS UNDER VARIABLE ATMOSPHERIC CONDITIONS

Corresponding Author: Prof. Dr. Bilge Albayrak Çeper

Authors:
Makbule Esra SIRÇA
Prof. Dr. Bilge Albayrak Çeper
Prof. Dr. Nafiz Kahraman

DOI Number https://doi.org/10.52924/XYPS8870

Received: 08.05.2025

Accepted: 16.05.2025

Published: 08.06.2025

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PYTHON BASED INVESTIGATION OF COMPRESSIBLE FLOW CALCULATIONS UNDER VARIABLE ATMOSPHERIC CONDITIONS

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.

EES_2025_Volume_5_Issue_2

Corresponding Author:

Authors:

DOI Number https://doi.org/10.52924/QYNT8323

Received: 08.06.2025

Accepted: 08.06.2025

Published: 08.06.2025

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EES_2025_Volume_5_Issue_2

Summary:
Energy, Environment and Storage papers consider the prospects of energy technologies, environment, materials, process control and industrial systems. The Energy, Environment and
Storage will be published 3 times per year.
Contributions describe novel and significant applications to the fields of:
 Hydrogen Fuels
 Hydrogen and Fuel Cell
 Hydrogen Economic
 Biomass
 Solar PV Technology
 Solar Thermal Applications
 Wind Energy
 Materials for Energy
 Drones and Energy Applications
 Nuclear Energy and Applications
 Hydro Power
 Fuel Technologies (CNG, LNG, LPG, Diesel, Gasoline, Ethanol, etc.)
 Numerical Modelling
 Energy Storage and Systems
 Battery Technologies
 Energy Management
 Heat and Mass Transfer
 Aerodynamics
 Aerospace and Energy Applications
 Combustion
 Electric Vehicle Transportation
 Off-grid Energy Systems
 Environment Management
 Air Pollution
 Water and Wastewater Pollution
 Water and Wastewater Management
 Waste Management
 Global Warming and Climate Change
 Environmental Ecosystem
 Environmental System Modelling and Optimization
 Ecological Applications or Conservation

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