TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396
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Article SYSTEM DESIGN AND PROTOTYPE MANUFACTURING FOR THE RECOVERY OF LUBRICANT POWDER USED IN WIRE DRAWING PROCESS(Kahramanmaraş Sütçü İmam Üniversitesi, 2023) EREN, Orhan; GERÇEKÇİOĞLU, Eyüp; BENLİCE, Esra; YILMAZ, Erkan; DURAN, AliRecycling processes have gained great importance for both environmental and economic sustainability and_x000D_ development. A prototype system was developed using physical separations including size sieving and magnetic_x000D_ separation for the recycling of solid die soaps used as lubricants in industrial wire drawing processes. The chemical_x000D_ composition of the waste obtained after the wire drawing process was elucidated by using X-ray fluorescence (XRF)_x000D_ analysis and extraction methods. The results showed that there was 67% reusable soap in the waste, and most of the_x000D_ remaining waste was made up of metals. Parameters such as particle size, sieve pore diameters, shaking time and_x000D_ magnetic field strength were optimized and an industrial scale prototype recycling system was designed. Finally, a_x000D_ prototype recovery system was established. Scanning electron microscopy (SEM), light microscopy,_x000D_ thermogravimetric/differential thermal analyzes (TGA/DTA), X-ray fluorescence spectroscopy (XRF) and Fourier_x000D_ transform infrared spectroscopy (FTIR) were used for the characterization. 88% of the soap in the waste was_x000D_ recovered, and the soap obtained was successfully used in wire drawing process without causing any deformation in_x000D_ the wire. These findings clearly demonstrate that offered system design engineered solution has a great potential to_x000D_ become a way out point for the waste recycling gain in the recovery and reuse of lubricant powder.Article Overcoming the Obstacles of Peace Education through Wellbeing Practices(Adıyaman Üniversitesi, 2021) Bengü, Elif; Bilgin, Gülistan GurselA growing body of literature reports structural, cultural, social, and political barriers making_x000D_ it challenging and stressful to integrate peace education in teacher education and in-service_x000D_ teacher education programs. To support peace educators in achieving what they stand for, this_x000D_ study proposes integrating wellbeing practices and approaches into the curricula. Drawing_x000D_ from the fields of peace education, educational leadership and policy studies and higher_x000D_ education, this study examines wellbeing as a potentially promising scholarly field to support_x000D_ peace education scholarship. For happiness and life satisfaction, wellbeing links a person's_x000D_ physical, mental, emotional and social health factors not just to internal factors such as_x000D_ optimism, resilience and self-esteem but also external factors such as income, satisfaction at_x000D_ work and social networks. In order to explore the ways wellbeing can contribute to peace_x000D_ education, we first expand on peace education as a controversial and challenging practice_x000D_ especially for practitioners in the field. Next, we discuss wellbeing practices as they relate to_x000D_ educational settings. Finally, we discuss that peace educators can be supported by wellbeing_x000D_ practices to overcome the degrading and demotivating effects of their practices.Article Movie Recommendation Systems Based on Collaborative Filtering: A Case Study on Netflix(Erciyes Üniversitesi, 2021) Sütçü, Muhammed; Erdem, Oğuzkan; Kaya, EcemUser ratings on items like movies, songs, and shopping products are used_x000D_ by Recommendation Systems (RS) to predict user preferences for items that have_x000D_ not been rated. RS has been utilized to give suggestions to users in various domains_x000D_ and one of the applications of RS is movie recommendation. In this domain, three_x000D_ general algorithms are applied; Collaborative Filtering that provides prediction_x000D_ based on similarities among users, Content-Based Filtering that is fed from the_x000D_ relation between item-user pairs and Hybrid Filtering one which combines these_x000D_ two algorithms. In this paper, we discuss which methods are more efficient in movie_x000D_ recommendation in the framework of Collaborative Filtering. In our analysis, we use_x000D_ Netflix Prize dataset and compare well-known Collaborative Filtering methods_x000D_ which are Singular Value Decomposition, Singular Value Decomposition++, KNearest Neighbour and Co-Clustering. The error of each method is calculated by_x000D_ using Root Mean Square Error (RMSE). Finally, we conclude that K-Nearest_x000D_ Neighbour method is more successful in our dataset.Article Life Cycle Assessment of the Neutralization Process in a Textile WWTP(Erciyes Üniversitesi, 2020) Şener Fidan, Fatma; Kızılkaya Aydoğan, Emel; Uzal, NiğmetAlthough industrial wastewater treatment plants (WWTP) have become_x000D_ an important part of textile facilities in reducing environmental pollution_x000D_ problems, they also produce sludge and various emissions such as high chemical_x000D_ oxygen demand, color and conductivity which have serious negative impacts on_x000D_ the environment. One of the processes with enormous chemical consumption in_x000D_ industrial WWTP of textile facilities is the neutralization process, which aims to_x000D_ adjust the pH of the wastewater. Neutralization processes needed to be optimized_x000D_ in order to determine its overall environmental impacts and then identify the most_x000D_ environmentally appropriate options. The aim of this study is to compare the_x000D_ environmental impacts of carbon dioxide and sulfuric acid, which are two_x000D_ alternative chemicals used in the neutralization process of textile facilities, using_x000D_ Life Cycle Assessment (LCA) approach. The environmental impacts resulting from_x000D_ the use of these two chemicals proposed according to the Reference document on_x000D_ Best Available Techniques (BREF) Document for Textile Industry were revealed by_x000D_ the CML-IA method and the gate-to-gate method. According to the results, using_x000D_ carbon dioxide instead of sulfuric acid, the best improvement was in the abiotic_x000D_ depletion category with 92%, while the least improvement was in the_x000D_ eutrophication potential with 39%. No improvement was observed in the global_x000D_ warming potential and human toxicity impacts.Article Hydroponic Agriculture with Machine Learning and Deep Learning Methods(Gazi Mühendislik, 2023) Bulut,Nurten; Hacıbeyoğlu, MehmetIn the face of the rapidly increasing population of our world today, researchers have turned to studies that use existing resources more effectively and efficiently in addition to searching for new resources in order to meet the rapidly decreasing needs such as raw materials and nutrients. The use of hydroponic agriculture, which is one of the alternative methods that can be used to meet the need for nutrients, which is one of the greatest needs of humanity, has become more popular day by day. The use of nutrient solution water instead of soil, the fact that it is not affected by weather conditions, that it can be applied indoors and that it can be vertically oriented are the characteristics that make hydroponic agriculture different from other agricultural methods. In addition, the lack of soil in this agricultural method brings with it the need for more observation and supervision. The aim of this study is to show that the observation and surveillance needs necessary to increase yield in hydroponic agriculture can be achieved using machine learning and deep learning methods. For this purpose, it has been observed that the efficiency of hydroponic agriculture has been increased in experimental studies conducted using five machine learning and deep learning methods. The deep learning method has achieved better results with 99.7% success compared to other methods.Article G7 Countries Unemployment Rate Predictions Using Seasonal Arima Garch Coupled Models(2021) MUĞALOĞLU Erhan; KILIÇ Edanur; Kılıç, Edanur; Mugaloglu, ErhanDespite the unemployment data have been recently released as seasonally adjusted, seasonality may still exist in moving average (MA) or auto-regressive (AR) terms. This can be detected by searching for a regular pattern in auto-correlation function (ACF) and partial ACF (PACF) diagrams. Therefore, models that aim to forecast unemployment rates should consider their seasonal properties so as to obtain better mean equation estimations. Univariate models mostly employ integrated ARMA (ARIMA) or generalized auto regressive conditional heteroscedastic (GARCH) models or any combination of them. Once the mean equations are structured better, GARCH estimations of variance equation is expected to perform better accuracy in forecasts. This study first examines the ACF's and PACF's of seasonally adjusted unemployment rate data in G-7 countries for 1995-2019 period. Then it compares the 4-quarter and 8-quarter ahead forecast performance of the seasonal ARIMA (SARIMA) coupled volatility models of GARCH in mean, absolute value GARCH, GJR-GARCH, exponential GARCH and asymmetric GARCH models. The performance of these models is also compared to SARIMA and MA filtered volatility models. The results show that seasonality should be re-examined even in seasonally adjusted unemployment data, since SARIMA models outperform ARIMA models in terms of out of sample forecast errors. Besides SARIMA-GARCH models provide better out of sample prediction accuracy.