In this study, the factors influencing the cognitive load of computer programmers during the perception of different code tasks were investigated. The eye movement features of computer programmers were used to provide a significant relationship between the perceptual processes of the sample codes and cognitive load. Thanks to the relationship, the influence of various personal characteristics of programmers on cognitive load was examined. Various personal parameters such as programming experience, age, native language, and programming frequency were used in the study. The study was performed on the Eye Movements in Programming (EMIP) dataset containing 216 programmers with different characteristics. Eye movement information recorded during two different code comprehension tasks was decomposed into sub-information, such as pupil movement speed and diameter change. Rapid changes in eye movement signals were adaptively detected using the z-score peak detection algorithm. Regarding the cognitive load calculations, canonical correlation analysis was used to build a statistically significant and efficient mathematical model connecting the extracted eye movement features and the different parameters of the programmers, and the results were statistically significant. As a result of the analysis, the factors affecting the cognitive load of computer programmers for the related database were converted into percentages, and it was seen that linguistic distance is an essential factor in the cognitive load of programmers and the effect of gender on cognitive load is quite limited.

In this study, the effect of the COVID‐19 pandemic on electrical energy storage technologies was investigated. The results of the crises and opportunities created by this unpredictable epidemic in the future processes were evaluated according to energy and especially electrical energy storage areas. Home quarantines used to reduce the spread of the epidemic significantly increased the electrical energy needs of home users. In addition, great changes have occurred in public transportation habits during the COVID‐19 pandemic, and it has been observed that approximately 20% of individuals may prefer electric micromobility devices working with lithium batteries due to the risk of infection in closed areas such as public buses or metros. Although the demand for main energy sources has decreased significantly during the COVID‐19 pandemic, it is important that the percentage of energy produced from renewable energy sources increases by about 1%. With the increasing use of renewable energy sources and the increasing importance of the concept of sustainability in all areas of our lives, energy storage systems compatible with renewable energy technologies will become more and more important. At the beginning of these technologies, hydroelectric energy storage systems with closed‐loop pumps are preferred for large‐scale applications.

In most aspects, the COVID-19 pandemic has been the most impactful disease in the last few decades. Although two years have passed since the start of the COVID-19 pandemic, it is clear that the pandemic is far from being brought under full control. This pandemic, which was kept under control in part by the discovery of vaccines, has had a negative impact on people’s daily lives as well as global economies. The transmission and death rate of this disease, which was detected in Wuhan, China in December 2019, has differed between countries. The reason for these different rates according to countries has been investigated while developing a mathematical model in this comprehensive study. The effect of 16 different socioeconomic indicators of countries on the parameters of the COVID-19 pandemic was analyzed in this study, which was performed on 35 countries, the majority of which are OECD countries. The main objective of this research is to minimize potential negative effects by improving or taking these indicators into account in future similar outbreaks. In this study, canonical correlation analysis was used to obtain the appropriate mathematical model and the performance of the obtained model has been tested statistically. According to the mathematical model obtained, the age factor comes to the fore as the most important indicator in the COVID-19 pandemic and the next important indicator is the health expenditures of the countries. The influence of 16 different socioeconomic indicators on the COVID-19 pandemic parameters are presented as a percentage with details.