PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1928
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Article Citation - WoS: 10Citation - Scopus: 13The Role of Cumulative Risk and Armed Conflict Exposure in Adolescent Psychological Symptoms in Turkey(Wiley, 2024-04-05) Kara, Buket; Selçuk, BilgeExposure to risk factors and adversity may cause immediate, and sometimes prolonged, psychological symptoms in adolescents. Identifying universal and specific risk factors in a particular context and examining their cumulative effects is crucial for understanding the mechanisms underlying psychological symptoms and informing about strategies for intervention. Using concurrent measures, the current study aimed to examine the role of armed conflict experiences and cumulation of other risk factors (e.g., maternal psychological symptoms, socioeconomic indicators) in predicting adolescent psychological symptoms in an underresearched community. The sample included 161 adolescents (54.7% female) aged 11-14 years (M = 12.36, SD = 1.27) and their mothers living in the east of Turkey. The cumulative risk index was calculated by summing the standardized scores of the corresponding factors. Hierarchical multiple regression analyses were conducted to predict internalizing and externalizing symptoms among adolescents by introducing demographic variables (age, gender) in the first step, armed conflict experiences and cumulative risk in the second step, and their interaction in the final step. Results showed that the levels of internalizing and externalizing symptoms were predicted by gender, armed conflict experience and cumulative risk. Being a girl was associated with higher levels of internalizing symptoms and lower levels of externalizing symptoms. Higher levels of internalizing and externalizing symptoms were predicted by exposure to armed and cumulative risk. After controlling for other factors, the interaction of armed conflict experience and cumulative risk significantly predicted externalizing, but not internalizing symptoms. These findings suggested that cumulative risk was a stronger predictor of psychological symptoms, and further amplified the strength of the association between armed conflict experiences and externalizing symptoms. These findings can be used in the formulation of intervention strategies and policies to promote psychological well-being in adolescents living in armed conflict zones under multiple risks.Article Citation - WoS: 2Citation - Scopus: 1Examining Factors Influencing Turkish Jewish Attitudes Towards the Armenian Genocide(Wiley, 2024-05-25) Kaymak, Özgür; Nefes, Türkay Salim; Gürpınar, DoğanThe most prominent issue influencing Turkish-Armenian relations is the international recognition of the Armenian genocide. However, there is a notable absence of empirical analyses regarding the perceptions of the genocide among the Turkish population. This study aims to fill this scholarly gap by exploring, for the first time, the perspectives of Turkish Jews. It analyses evidence collected from interviews conducted with 14 Turkish Jews, utilising Stanley Cohen's (2001) theoretical framework, which aids in delineating significant factors by a categorisation of types of acceptance and denial. The findings highlight a diversity of responses linked to political attitudes, which can be broadly categorised into Kayades and Avlaremoz mindsets. They also show that Turkish Jews' views on the Holocaust influence how they perceive the Armenian genocide. Additionally, the results indicate that Cohen's approach is useful in explaining non-denying responses. In conclusion, the study argues that Turkish Jews' perspectives appear to be strongly related to their stance towards the Turkish state and the Holocaust.Article Citation - WoS: 5Citation - Scopus: 12Predicting Cash Holdings Using Supervised Machine Learning Algorithms(Springer, 2022-05-18) Özlem, Şirin; Tan, Ömer FarukThis study predicts the cash holdings policy of Turkish firms, given the 20 selected features with machine learning algorithm methods. 211 listed firms in the Borsa Istanbul are analyzed over the period between 2006 and 2019. Multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), decision trees (DT), extreme gradient boosting algorithm (XGBoost) and multi-layer neural networks (MLNN) are used for prediction. Results reveal that MLR, KNN, and SVR provide high root mean square error (RMSE) and low R2 values. Meanwhile, more complex algorithms, such as DT and especially XGBoost, derive higher accuracy with a 0.73 R2 value. Therefore, using advanced machine learning algorithms, we may predict cash holdings considerably.Article Citation - WoS: 51Citation - Scopus: 58An Optimization Model for Carbon Capture & Storage/Utilization Vs. Carbon Trading: a Case Study of Fossil-Fired Power Plants in Turkey(Academic Press Ltd- Elsevier Science Ltd, 2018-06-01) Uctug, Fehmi Görkem; Ağralı, Semra; Türkmen, Burçin AtılganWe consider fossil-fired power plants that operate in an environment where a cap and trade system is in operation. These plants need to choose between carbon capture and storage (CCS), carbon capture and utilization (CCU), or carbon trading in order to obey emissions limits enforced by the government. We develop a mixed-integer programming model that decides on the capacities of carbon capture units, if it is optimal to install them, the transportation network that needs to be built for transporting the carbon captured, and the locations of storage sites, if they are decided to be built. Main restrictions on the system are the minimum and maximum capacities of the different parts of the pipeline network, the amount of carbon that can be sold to companies for utilization, and the capacities on the storage sites. Under these restrictions, the model aims to minimize the net present value of the sum of the costs associated with installation and operation of the carbon capture unit and the transportation of carbon, the storage cost in case of CCS, the cost (or revenue) that results from the emissions trading system, and finally the negative revenue of selling the carbon to other entities for utilization. We implement the model on General Algebraic Modeling System (GAMS) by using data associated with two coal-fired power plants located in different regions of Turkey. We choose enhanced oil recovery (EOR) as the process in which carbon would be utilized. The results show that CCU is preferable to CCS as long as there is sufficient demand in the EOR market. The distance between the location of emission and location of utilization/storage, and the capacity limits on the pipes are an important factor in deciding between carbon capture and carbon trading. At carbon prices over $15/ton, carbon capture becomes preferable to carbon trading. These results show that as far as Turkey is concerned, CCU should be prioritized as a means of reducing nationwide carbon emissions in an environmentally and economically rewarding manner. The model developed in this study is generic, and it can be applied to any industry at any location, as long as the required inputs are available. (C) 2018 Elsevier Ltd. All rights reserved.