The question of electromobility is greatly discussed theme of the present especially in connection with the reduction of greenhouse gas emissions. In order to fulfill decarbonization targets, incentives of many countries lead to the support of electromobility. In this paper we ask to which extend are Visegrád Group countries prepared for the widespread utilization of electric cars and define a new coefficient K called the infrastructural country electromobility coefficient. Its computing is covered by appropriate analysis and calculations done previously. Several indices that keep particular information about the state of preparation for electromobility are defined and debated here, as well. Their product forms the coefficient K. Obtained results include outcomes and discussion regarding the level of infrastructural electromobility preparedness for the chosen states, among which we extra focus on the position of Slovakia compared to the European Union average and European electromobility leaders. Based on the data obtained, we found out that the stage of preparation of Slovakia for electromobility among Visegrad Group countries is rather good, although it is far behind the European Union leaders. We realized that there was a rapid growth of electromobility infrastructure in Slovak Republic in the last five years as its infrastructural country electromobility coefficient grew 334 times.
The current great expansion of automation and robotics affects a multiplicity of various fields. A prominent example is industry, where the different manufacturing processes and technologies embrace a certain level of automation and robotics. Thus, the use of robotics and automation implementation is part of a rapidly rising trend in industry. The presented paper deals with the manufacturing segment in the context of automation. The main subject is data analysis, with our own subsequent model building and final realization of the prediction corresponding to the machinery and electrical machinery sector as a highly relevant automation driver through the use of mathematical modeling. The design of the model is accompanied by optimization of the particular weights. Determination of the most suitable model is preceded by creating and testing a number of models to decide upon the final one. The construction of the mathematical model pursues the aim of making predictions relating to the machinery and electrical machinery sector for the specific national economy as the concluding investigation step. We apply a polynomial approximation as the research method. The software selected for our purposes is Matlab.
The main aim of this paper was to analyze the actual state of buying and selling cut-flowers in a chosen company. New controlling system was suggested for this company on the basis of collected and consequently separated, compiled and organized data. This system should provide the information on the anticipated development of purchase in the future. The motivation for creating the new system was given by the actual problems appearing due to not very clear and transparent way of flowers buying decision making. Especially considering the decisions about the amount and sorts of cut-flowers booked in advance. The new controlling system was designed in order to improve the current state of warehouse supply chain management. We believe that it would be useful in the process of planning, deciding, controlling and evaluation of orders exactness.
The inducted paper discusses economic effect resulting from industrial activities realized within national economy of the chosen country. The country of selection represents Poland. Economic impact is scrutinized through reflexing on gross domestic product. Industrial segment is deputized over various indicators whose scope strives to include different views on the industry field. The main point of this paper is to identify the exact relationship between dependent variable (gross domestic product) and a group of independent variables (picked industrial representatives). Such determination offers thereafter the possibility to estimate dependent variable’s value and its next forecast. What is more, the eventual sorting of involved industrial indicators is facilitated according to their importance. The multiple regression analysis is utilized as the method of investigation. Findings answer the stated questions and aims with a suggestion of an appropriate equation.
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