Mykhailo Kushch-Zhyrko

Data analysis and predicting play an important role in managing heat-supplying systems. Applying the models of predicting the systems’ parameters is possible for qualitative management, accepting appropriate decisions relating control that will be aimed at increasing energy efficiency and decreasing the amount of the consumed power source, diagnosing and defining non-typical processes in the functioning of the systems. The article deals with comparing two methods of ma-chine learning: random forest (RF) and support vector machine (SVM) for predicting the temperature of the heat-carrying agent in the heating system based on the data of electronic weather-dependent controller. The authors use the following parameters to compare the models: accuracy, source cost and the opportunity to interpret the results and non-obvious interrelations. The time spent for defining the optimal hyperparameters and conducting the SVM model training is deter-mined to exceed significantly the data of the RF parameter despite the close meanings of the root mean square error (RMSE). The change from 15-min data to once-a-minute ones is done to improve the RF model accuracy. RMSE of the RF model on the test data equals 0.41°С. The article studies the importance of the contribution of variables to the prediction accuracy.

Official work in Ukraine: characteristics of legal status and recurring problems

The aim of the article is to investigate the current state of illegal merchandise trafficking in Ukraine, as well as the basic conditions that promote the spread of counterfeiting in this country together with the methods to counteract this phenomenon. The research methodology was chosen based on the purpose and objectives of the study, consequently, a set of methods and approaches to scientific knowledge were combined, of a general theoretical and special scientific nature. Everything indicates that Ukraine is one of the world leaders in the illicit production and distribution of software, intellectual property, etc., and this problem has not been a priority for the authorities for a long time. It is concluded that the basic conditions that promote the spread of counterfeiting in Ukraine are determined on the basis of the analysis of the activities of the police and supervisory authorities in the fight against illegal trafficking of goods. The most efficient methods are proposed to counteract the illegal traffic of goods, within the framework of the underground economy in Ukraine.

The aim of the article is to analyze the characteristics of civil liability of Ukrainian police officers for damage caused under the influence of force majeure. At a methodological level to achieve the objectives of the article, the following scientific methods were combined: general (dialectical, formal-logical, system-structural) and special (historical, comparative-legal, etc.). Essentially, the concept of force majeure and its characteristics are studied, as well as two main theories of force majeure (objective and subjective). In addition, the main differences between force majeure and case are established. The types of force majeure circumstances and their particular properties are studied. By way of conclusion, the fact that police officers are exonerated from civil liability for causing damage under the influence of force majeure is discussed, if they can prove the effect of this influence. For the rest, given that Ukraine's jurisprudence on the exemption from civil liability of police officers due to force majeure is quite limited to formulating a precedent, it is based on the general rule that, in tort, the recognition that the damage was the result of accidental circumstances excludes the occurrence of liability for damages.