As an alternative approach to X-ray crystallography and single-particle cryo-electron microscopy, single-molecule electron diffraction has a better signal-to-noise ratio and the potential to increase the resolution of protein models. This technology requires collection of numerous diffraction patterns, which can lead to congestion of data collection pipelines. However, only a minority of the diffraction data are useful for structure determination because the chances of hitting a protein of interest with a narrow electron beam may be small. This necessitates novel concepts for quick and accurate data selection. For this purpose, a set of machine learning algorithms for diffraction data classification has been implemented and tested. The proposed pre-processing and analysis workflow efficiently distinguished between amorphous ice and carbon support, providing proof of the principle of machine learning based identification of positions of interest. While limited in its current context, this approach exploits inherent characteristics of narrow electron beam diffraction patterns and can be extended for protein data classification and feature extraction.

As a result of studies examining individual differences in temporal perception and the relationship of these differences with sustainable behavior, it has been observed that long-term perception predicts participation in pro-environmental attitudes and sustainable consumption behaviors. In this study, the relationship between the differences in future perceptions and sustainable consumption behaviors was examined in terms of individuals residing in the Turkish Republic of Northern Cyprus (TRNC). In order to test this relationship, “Sustainable Consumption Behaviors” scale and CFC (Consideration of Future Consequences) scale were used. Since the normality assumption of the data could not be achieved, it was decided to use nonparametric tests. As a result of the analysis of the data, collected from 319 participants who were accessed online, it was found a statistically significant but a weak relationship between "Perception Tendency of the Future" and "Sustainable Consumption Behaviors". While no statistically significant relationship was found between the "Short-Term Thinking Tendency" subscale and "Sustainable Consumption Behaviors", it was found that the "Long-Term Thinking Tendency" subscale had a statistically significant relationship with "Sustainable Consumption Behaviors". In line with this finding, it was determined that those who tend to think long-term do their consumption behavior by taking sustainability into account compared to the group who thinks short-term. In addition, it has been determined that there is a noticeable difference between those who do sports and those who never do, and in this context, those who do sports differ significantly in terms of environmental awareness, unnecessary purchasing and sustainable consumption behaviors compared to those who do not do sports. It has been determined that the importance given to healthy eating is significantly related to long-term thinking, similarly, as the importance given to healthy nutrition increases, consumption becomes more environmentally friendly, and as the importance given to healthy nutrition decreases, consumption behavior that takes into account sustainability is abandoned.

Those who work in a learning organization are more satisfied with their lives. Individuals in an organization who experience job satisfaction serve organizational goals more strongly. Therefore, it is possible to say that learning organizations make a positive contribution to the job satisfaction of employees. In this study, it is aimed to test the relationship between organizational learning and job satisfaction. In this context, by using the scales developed by previous researchers, it was investigated whether there is a relationship between organizational learning and job satisfaction, and if there is a relationship, to what extent organizational learning, which is an independent variable, predicts job satisfaction, which is the dependent variable. As a result of the correlation analysis applied to the data, a statistically significant relationship was found between the "Organizational Learning" variable and the "Job Satisfaction" variable. The strength of the relationship between these two variables is moderate. In addition, a statistically significant relationship was found between a total of 7 factors belonging to the organizational learning variable and job satisfaction at medium strength. As a result of the simple linear regression analysis conducted to examine the extent to which organizational learning predicts job satisfaction, it was seen that the variance of 38.6% in job satisfaction varied depending on organizational learning. The findings of this research are consistent with previous research that emphasizes that job satisfaction is an important predictor of employees' individual performance.