We observe the relationship and causality between cryptocurrencies on one, and commodities, currencies, equity indexes and web search results on the other side. We use prices of Bitcoin and Ethereum for cryptocurrencies, prices of crude oil and gold for commodities, Euro-US Dollar, Euro-Swiss Franc exchange rates for currencies, Dow Jones Industrial Average for market index and Google Trends® data as a measure of worldwide web search results for cryptocurrencies of interest. We find that Bitcoin and web search results correlation went from highly positive to low negative during the COVID-19 period. The results of the study show that the price of Bitcoin and Ethereum can be modelled using different combinations of commodities, currencies, indexes and web search results, with web search results and Dow Jones Industrial Average exhibiting best predictive power both concurrently and one day in advance. Our best performing models were able to explain more than 95% and 90% of Bitcoin and Ethereum price variability respectively. We also find strong evidence of web search traffic impacting both Bitcoin and Ethereum prices at all tested lags, as well as some evidence of gold impact on Bitcoin and EUR/CHF impact on Ethereum.

The use of renewable energy sources is imposed as an imperative for the competitiveness and stable energy policy of companies. The basic production equipment in the "Kreka" coal mine in Tuzla has electrical plants with a total peak load of 12 MW, and the costs for electrical energy exceed €3.5 million annually. The paper analyzes the suitability of infrastructure facilities and devastated areas of the Kreka Coal Mine in Tuzla for the construction of photovoltaic power plants and the effects of such plants. The results of the analysis show a significant resource for the installation of photovoltaic power plants with a capacity of 230 MW. The production of electricity from these photovoltaic power plants enables a reduction of emissions into the air by 190 t/year of particle emissions, by 316.2 •103 t/year of CO2 emissions, by 58.15 t/year of SO2 emissions and by 251.9 t/year of emissions NOx. With the subsidized purchase of electricity, it is possible to return the investment within 6 years.

Optical character recognition represents the mechanical or electronic conversion of handwritten, typed or printed images into coded text. Optical character recognition is widely used as a form of data entry from records that have been printed, and it can include invoices, bank statements, passports and many more. In the research, Optical character recognition reads data from the Re-Captcha dataset of images, converts them into strings, and these strings are used for testing, training and calculating prediction accuracy. The methodologies used are Convolutional neural network and Recurrent neural network. The convolutional neural network consist of neurons that receive data and group them according to similarity. A recurrent neural network cycle can be created between the connections of nodes, allowing the output from nodes to influence the subsequent input to other nodes. For data were used Re-Captcha images, and for the prediction of characters from images was used TensorFlow with Keras. The best results that are produced can be compared between first and last result, where the loss for first result was 20.63 and value loss was 16.45, while last result has loss of 0.56 and value loss of 2.96

There is a growing technological development in intelligent teaching systems. This field has become interesting to many researchers. In this paper, we present an intelligent tutoring system for teaching mathematics that helps students un-derstand the basics of linear programming using Linear Program Solver and Service for Solving Linear Programming Problems, through which students will be able to solve economic problems. It comes down to determining the minimum or maximum value of a linear function, which is called the objective function, according to pre-set limiting conditions expressed by linear equations and inequalities. The goal function and the limiting conditions represent a mathematical model of the observed problem. Working as a professor of mathematics in high school, I felt the need for one such work and dealing with the study of linear programming as an integral part of mathematics. There are a number of papers in this regard, but exclusively related to traditional ways of working, as stated in the introductory part of the paper. The center of work as well as the final part deals with the study of linear programming using programs that deal with this topic.

The paper points out the leading role of the HR department in crisis situations, with special emphasis on the crisis caused by the COVID-19 pandemic, which affected the whole world in early 2020. Regardless of the intensity and form in which they occur, crises threaten the functioning and survival of organizations. The HR department is an important factor in the successful functioning of organizations and, in a period of crisis, this department takes a leading role in the process of crisis management and subsequent recovery. During COVID-19 pandemic, the HR department of every organization had to confront new challenges in order to protect the health of employees, while ensuring the normal functioning of organizations. In order to find out how this department dealt with crisis that occurred, empirical research with a specially designed questionnaire was conducted and filled in by 108 respondents from Serbia, Bosnia and Herzegovina, and Croatia in 2020. The research results showed that HR department has taken a leading role in preserving the health and safety of employees, as well as in the process of adapting organizations to function in crisis conditions. According to results, the key activities carried out by the HR department in organizations are work from home (wherever the nature of the work allowed), flexible working hours, reorganization of the working space to achieve the required physical distance between employees, improved hygiene measures, etc. In addition, the research results showed that, during the global COVID-19 pandemic, the HR departments took an active role in providing psychological assistance to employees to adapt to new conditions and ways of working.

: The objective of this study was to test the inhibitory effect of five newly synthesized arylmethylene-bis(3-hydroxy- 5,5 -dimethylcyclohex-2-en-1- one) derivatives. The structural characterization and stereochemistry of synthesized compounds were deduced from analyses of experimental FT- IR, 1 H, 13 C NMR spectra and theoretical methodology of DFT study based on the global chemical reactivity indices calculated using the 6- 31G** level of theory. the stability of the newly synthesized compounds, the reactivity descriptors obtained at B3LYP level ( E gap , dipole moment, μ , η , ω ) were computed. The docking study and the selected quantum chemical descriptors computed for compounds 1 −5 exhibit a good agreement. The strongest inhibitors showed 25 to 30 % inhi bition of tyrosinase activity. Results were supported by docking studies of the binding of the strongest inhibitors to the enzyme. The results suggest that tetraketones of this type, due to their tyrosinas e inhibitory effect, represent potential agents in the treatment of various types of melanomas and skin hyperpigmentation. 189.42 3´), 190.75 - C -1´). Anal. Calcd. mass fractions of elements, w / %, for C 23 H 26 Br 2 O 4 ( M r = 524.02) are: C = 52.49, H = 4.98; found: C = 52.75, H = 5.02.

Th is article presents the results of testing the social-entrepreneurial intention model on a student sample at the University of Bihać. Th e classical model of the Th eory of planned behavior was used as a theoretical framework. Regression analysis determined that signifi cant direct predictors of social entrepreneurial intention are personal attitude towards social entrepreneurship ( β =0.212; p=0.007) and perceived behavioral control ( β =0.644; p=0.000), while subjective norms were not confi rmed as a statistically signifi cant direct predictor. Th e model explains 54.4% of the variance of social entrepreneurial intention. 53.8% of respondents have an entrepreneur in their close family. Students who have an entrepreneur in their immediate family achieve statistically signifi cantly higher values in perceived behavioral control, but also statistically signifi cantly lower values in personal attitude, compared to students who do not have a close person who is an entrepreneur. Th at is, students who have a person in their close family who is an entrepreneur, compared to students who do not have such a person, may feel more capable of starting a social entrepreneurial venture, but they may also have a lower degree of desirability to become social entrepreneurs. Due to the lack of quantitative studies in the fi eld of social entrepreneurship, which is still in the phase of building theoretical models, we believe that the results of testing the model of social entrepreneurial intention, presented in this article, will contribute to a better understanding of the application of the theory of planned behavior in the fi eld of social entrepreneurship.