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Education represents the basic pillar of preparing individuals for integration into the labor market, but also is a crucial component of ensuring sustainable development. The purpose of this research was to identify the type of influences existing between education and the labor market in EU member countries in the context of different levels of investment in the educational system. Cluster analysis and the ordinary least squared method were used to identify the type of influences between the indicators characterizing the level of education and the labor market between 2000 and 2021. The empirical results showed that there was a significant negative correlation of the educational dropout rate with the level of employee compensation, number of hours worked by each employee, and their labor productivity, in the countries with the poorest level of educational investment. In the countries with significant investments in education, getting a graduate diploma and participating in vocational training programs led to a better compensation of employees and a higher employee productivity while the financial aid given by the state for pupils and students reduced the number of worked hours, brought down unemployment amongst people with primary and secondary education and, last but not least, increased the employment rate for higher education graduates. An average level of educational investment led to negative influences between early-stages and employees’ payment level and real labor productivity, while becoming involved in educational activities and participating in vocational training programs increased their rates of remuneration and real productivity. A significant impact of higher education graduates on both increasing unemployment rates and falling employment rates was noticed as has been identified in other studies.

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Unfortunately, accidents caused by bad weather have regularly made headlines throughout history. Some of the more catastrophic events to recently make news include a plane crash, ship collision, railway derailment, and several vehicle accidents. The public’s attention has been directed to the severe issue of safety and security under extreme weather conditions, and many studies have been conducted to highlight the susceptibility of transportation services to environmental factors. An automated method of determining the weather’s state has gained importance with the development of new technologies and the rise of a new industry: intelligent transportation. Humans are well-suited for determining the temperature from a single photograph. Nevertheless, this is a more challenging problem for a fully autonomous system. The objective of this research is developing a good weather classifier that uses only a single image as input. To resolve quality-of-life challenges, we propose a modified deep-learning method to classify the weather condition. The proposed model is based on the Yolov5 model, which has been hyperparameter tuned with the Learning-without-Forgetting (LwF) approach. We took 1499 images from the Roboflow data repository and divided them into training, validation, and testing sets (70%, 20%, and 10%, respectively). The proposed model has gained 99.19% accuracy. The results demonstrated that the proposed model gained a much higher accuracy level in comparison with existing approaches. In the future, this proposed model may be implemented in real-time.