Yaşar University

About: Yaşar University is a education organization based out in Izmir, Turkey. It is known for research contribution in the topics: Exergy & Job shop scheduling. The organization has 760 authors who have published 1436 publications receiving 20813 citations. The organization is also known as: Yaşar Üniversitesi.

İkili ve Çok Taraflı Anlaşmaların Türkiye’nin İhracatı Üzerindeki Etkisi

Abstract: Whether bilateral and multilateral trade and economic agreements has effect on Turkey’s export to 113 countries is tested between 1996-2006 and within Gravity Model framework. Results show that while bilateral agreements has no statistically significant effect on Turkey’s export, except for Customs Union, multilateral agreements have statistically significant and positive effect on Turkey’s export.

Deliberating in difficult times: lessons from public forums in Turkey in the aftermath of the Gezi protests

Abstract: This study examines the prospects of public deliberation in a semi-authoritarian political context and unfavourable political cultural setting through an in-depth analysis of three public f...

Regression models for sediment transport in tropical rivers.

Abstract: The investigation of sediment transport in tropical rivers is essential for planning effective integrated river basin management to predict the changes in rivers. The characteristics of rivers and sediment in the tropical region are different compared to those of the rivers in Europe and the USA, where the median sediment size tends to be much more refined. The origins of the rivers are mainly tropical forests. Due to the complexity of determining sediment transport, many sediment transport equations were recommended in the literature. However, the accuracy of the prediction results remains low, particularly for the tropical rivers. The majority of the existing equations were developed using multiple non-linear regression (MNLR). Machine learning has recently been the method of choice to increase model prediction accuracy in complex hydrological problems. Compared to the conventional MNLR method, machine learning algorithms have advanced and can produce a useful prediction model. In this research, three machine learning models, namely evolutionary polynomial regression (EPR), multi-gene genetic programming (MGGP) and M5 tree model (M5P), were implemented to model sediment transport for rivers in Malaysia. The formulated variables for the prediction model were originated from the revised equations reported in the relevant literature for Malaysian rivers. Among the three machine learning models, in terms of different statistical measurement criteria, EPR gives the best prediction model, followed by MGGP and M5P. Machine learning is excellent at improving the prediction distribution of high data values but lacks accuracy compared to observations of lower data values. These results indicate that further study needs to be done to improve the machine learning model's accuracy to predict sediment transport.

Salary differences under the salary cap in Major League Soccer

Abstract: Professional soccer is the world’s most popular sport; a number of National Leagues are under the control of National Associations. The economic theory behind soccer is the continuing competition to earn much more than other sports do in the sports market. Since the supply of talent is limited, teams’ demand for certain professionals is so strong that it leads to salary differences between players. Therefore, in this study, attention is given to the determinants of the differences in workers’ salaries in the Major League Soccer labor market using Generalized Least Squares (GLS) estimation on panel data from 2007 to 2016. Birth place is the most influential determinant of a player’s salary, along with a player’s position, a player’s age, whether the player has a national team duty, and the number of games in which the player started in the first eleven. Conversely, moving from one Major League Soccer team to another and the number of games played as a substitute have a negative effect on players’ salaries.