Organic solar cells are cheaper and easier to produce than inorganic solar cells. In addition, organic solar cells have a wide range of applications due to their flexible structure. However, since the efficiency of organic solar cells is lower than that of inorganic solar cells, studies are carried out to increase efficiency. Maximum power point monitoring studies in solar systems provide improvement in the efficiency of these systems. DC-DC converters are needed for the application of maximum power point tracking algorithms to solar systems. Due to the high output voltages of organic panels, specially designed converters should be used to apply maximum power point algorithms to these panel types. Flyback converters are preferred in high voltage applications because their output circuits are isolated from the input circuits. Considering the advantages of organic panels and high output voltages, a flyback converter design was carried out for organic panels produced by R2R (roll to roll) method in this study. In this way, with the smaller, more powerful, and portable converter, compact devices such as mobile phones and smart watches will be able to be charged with portable, foldable, lightweight organic panels in their environment or in open air environments.

Cysts are common in healthcare and can be associated with various diseases. They can develop in different body parts and contain fluid, semi-solid, or air. Brain cysts are masses that form in the brain, and surgical methods may be used to treat them. The importance of deep learning in medical imaging is steadily growing. This study attempted to detect brain cysts using various architectures, including Random Forest, Unet, AlexNet, and LeNet. The Random Forest algorithm was found to be more successful than the other algorithms. This algorithm is crucial for classification and regression problems as it trains a series of decision trees and consolidates their predictions to create a robust and powerful model. Magnetic resonance imaging was used to detect cysts. The accuracy rates for cyst detection were 79.52%, 89.99%, 90.41%, and 97.26%, respectively. Several models were employed for this purpose, including LeNet, AlexNet, Unet, and the Random Forest algorithm. The accuracy rates for cyst detection were 79.52%, 89.99%, 90.41%, and 97.26%, respectively.

The aim of this study is to analyze the employees of the low cost carriers (LCC) and Full Service Carriers (FSC) personal traits, organizational indentification and Turnover. The research data were collected by the Personality Traits Scale and Turnover Scale. The sample consisted of 175 working in the LCC business model and 175 working in the FSC business model. The models obtained by the path analysis for the research variables were statistically tested and fit indices were investigated for each model. It was determined that the Organizational Identification (OI) has a full mediating effect on the relationship between the Emotinonal Stability (ES) and the Turnover (TO) in the LCC business model. In the same way, employees working in the full service airline business model; In the effect of ES variable on TO variable, OI variable was determined to have full mediating effect. When structural equation model is applied to the data obtained from all partipicipants, it is determined that the OI variable has a partial mediating effect on the effect of the Conscientiousness (C) on the TO. It can be thought that the research findings will contribute to airline human resources professionals and researchers in strategic planning.

The aim of this study was to determine the effects of employee’s emotional intelligence and work-related strain on anxiety and depression. In order to collect data, face-to-face questionnaires were administered to 210 healthcare workers who agreed to fill in the information required.The survey was conducted with the use of an easy sampling method. According to the findings: (1) the anxiety / depression (HAD) variable was positively affected by the work-related strain variable; (2) the HAD variable was negatively affected by emotional intelligence; (3)when HAD (a dependent variable) and two other variables (independent ones) were subjected to a stepwise multiple regression analysis, 45 % of the variance in the HAD variable was explained by the emotional intelligence and work-relatedstrain variables. The research was limited to 210 people working in health institutions operating in Istanbul region.