https://hdl.handle.net/20.500.12879/74 2026-08-30T03:04:39Z https://hdl.handle.net/20.500.12879/110 The Political Economy of Housing Financialization in Turkey: Links With and Contradictions to the Accumulation Model Erguven, Emre Financialization influenced the Turkish economy and housing industry mostly through financial liberalization moves and soaring capital inflows. It both increased household liabilities and mortgage loans dramatically and offered various facilities for the housing industry. Relevant legal regulations not only helped the Turkish housing industry prosper but also eased its integration into the national and global financial system. In addition, political implications constituted a strong motivation for governments to attach special importance to the housing industry. I examine housing financialization as an integral part of the accumulation model of the Turkish economy and argue that the housing industry lies at the very heart of the contradictions of this model. The large-scale capital inflows both intensified the dependency on foreign resources and increased the role of the domestic demand. This is the main contradiction of the accumulation model; it manifests itself in the interest rate dilemma and is also critical for housing financialization in Turkey because the characteristics of this model are especially valid for the housing industry. Moreover, not only do the contradictions of the accumulation model disrupt the housing industry, but also the characteristics of the housing industry contribute to the disruption of this model. WOS:000505901200001 2020-01-01T00:00:00Z https://hdl.handle.net/20.500.12879/103 Criticality investigations for the fixed bed nuclear reactor using thorium fuel mixed with plutonium or minor actinides Sahin, Suemer; Sahin, Haci Mehmet; Acir, Adem; Al-Kusayer, Tawfik Ahmed Prospective fuels for a new reactor type, the so called fixed bed nuclear reactor (FBNR) are investigated with respect to reactor criticality. These are (1) low enriched uranium (LEU); (2) weapon grade plutonium + ThO2; (3) reactor grade plutonium + ThO2; and (4) minor actinides in the spent fuel of light water reactors (LWRs) + ThO2. Reactor grade plutonium and minor actinides are considered as highly radioactive and radio-toxic nuclear waste products so that one can expect that they will have negative fuel costs. The criticality calculations are conducted with SCALE5.1 using S-8-P-3 approximation in 238 neutron energy groups with 90 groups in thermal energy region. The study has shown that the reactor criticality has lower values with uranium fuel and increases passing to minor actinides, reactor grade plutonium and weapon grade plutonium. Using LEU, an enrichment grade of 9% has resulted with k(eff) = 1.2744. Mixed fuel with weapon grade plutonium made of 20% PuO2 + 80% ThO2 yields k(eff) = 1.2864. Whereas a mixed fuel with reactor grade plutonium made of 35% PuO2 + 65% ThO2 brings it to k(eff) = 1.267. Even the very hazardous nuclear waste of LWRs, namely minor actinides turn out to be high quality nuclear fuel due to the excellent neutron economy of FBNR. A relatively high reactor criticality of k(eff) = 1.2673 is achieved by 50% MAO(2) + 50% ThO2. The hazardous actinide nuclear waste products can be transmuted and utilized as fuel in situ. A further output of the study is the possibility of using thorium as breeding material in combination with these new alternative fuels. (C) 2009 Elsevier Ltd. All rights reserved. Sahin, Sumer/0000-0003-2844-8061; WOS:000269419800004 2009-01-01T00:00:00Z https://hdl.handle.net/20.500.12879/100 FEATURE SELECTION FOR THE PREDICTION OF TROPOSPHERIC OZONE CONCENTRATION USING A WRAPPER METHOD Sakar, C. Okan; Demir, Goksel; Kursun, Olcay; Ozdemir, Huseyin; Altay, Gokmen; Yalcin, Senay High concentrations of ozone (O-3) in the lower troposphere increase global warming, and thus affect climatic conditions and human health. Especially in metropolitan cities like Istanbul, ozone level approximates to security levels that may threaten human health. Therefore, there are many research efforts on building accurate ozone prediction models to develop public warning strategies. The goal of this study is to construct a tropospheric (ground) ozone prediction model and analyze the effectiveness of air pollutant and meteorological variables in ozone prediction using artificial neural networks (ANNs). The air pollutant and meteorological variables used in ANN modeling are taken from monitoring stations located in Istanbul. The effectiveness of each input feature is determined by using backward elimination method which utilizes the constructed ANN model as an evaluation function. The obtained results point out that outdoor temperature (OT) and solar irradiation (Si) are the most important input features of meteorological variables, and total hydrocarbons (THC), nitrogen dioxide (NO2) and nitric oxide (NO) are those of air pollutant variables. The subset of parameters found by backward elimination feature selection method that provides the maximum prediction accuracy is obtained with six input features which are OT, SI, NO2, THC, NO, and sulfur dioxide (SO2) for both validation and test sets. Sakar, C. Okan/0000-0003-0639-4867; Kursun, Olcay/0000-0001-7153-2061; WOS:000208733600001 2011-01-01T00:00:00Z https://hdl.handle.net/20.500.12879/101 Identifying Effective Variables Using Mutual Information and Building Predictive Models of Sulfur Dioxide Concentration with Support Vector Machines Sakar, C. Okan; Kursun, Olcay; Ozdemir, Huseyin; Demir, Goksel; Yalcin, Senay Sulfur dioxide (SO2) is an issue of increasing public concern due to its recognized adverse effects on human health. Therefore, accurate SO2 prediction models are very important tools in developing public warning strategies. The goal of this study is to identify the relevance of meteorological and air pollutant variables using a classical and widely used measure of dependence, Shannon's Mutual Information (MI), and to build an accurate SO, prediction model using the relevant variables as inputs. Specifically, features ranked by MI measure are tested on how much joint predictive power they have of the target using a popular machine learning tool, support vector machines (SVM), and in comparison to multilayer perceptron (MLP), which is the most commonly used machine learning tool in previous studies for the prediction and analysis of air pollutants. It was found that the SVM model gave a higher correlation coefficient (r) and less root mean squared error (RMSE) than MLP for both test and validation sets. The predictive model used 6 input variables for both data sets as the relevant features for maximum SO, concentration prediction at time t+1, which are the average SO,, maximum SO2, outdoor temperature (OT), average nitrogen dioxide (NO2), average ozone (O-3), and average wind speed at time t. The results of this study indicate that MI can be used efficiently in determining the importance of input variables in the prediction of SO2 concentration and SVM is a popular machine learning tool well suited for use in air pollution modeling. Kursun, Olcay/0000-0001-7153-2061; Sakar, C. Okan/0000-0003-0639-4867; WOS:000280989300012 2010-01-01T00:00:00Z