This paper presents the results of research on the use of machine learning algorithms and electrical tomography in detecting humidity inside the walls of old buildings and structures. The object of research was a historical building in Wrocław, Poland, built in the first decade of the 19th century. Using the prototype of an electric tomograph of our own design, a number of voltage measurements were made on selected parts of the building. Many algorithmic methods have been preliminarily analyzed. Ultimately, the three models based on machine learning were selected: linear regression with SVM (support vector machine) learner, linear regression with least squares learner, and a multilayer perceptron neural network. The classical Gauss–Newton model was also used in the comparison. Both the experiments based on real measurements and simulation data showed a higher efficiency of machine learning methods than the Gauss–Newton method. The tomographic methods surpassed the point methods in measuring the dampness in the walls because they show a spatial image of the interior and not separate points of the examined cross-section. Research has shown that the selection of a machine learning model has a large impact on the quality of the results. Machine learning has a greater potential to create correct tomographic reconstructions than traditional mathematical methods. In this research, linear regression models performed slightly worse than neural networks.

Purpose:The objective of this study involves the determination of data-driven solutions needed to increase the usability of e-commerce systems and its profitability. Design/Methodology/Approach: In the research implementation process, logic generalization and induction to identify and analyze the most beneficial data science tools in e-commerce. deign of the study is to generalize existing approaches of data science usage in e-commerce, to develop practical recommendations to ensure the competitive advantages of e-commerce market participants and to estimate the cost of technical tools needed to launch the data science project in e-commerce. Findings: The results clearly demonstrate that in 2020 businesses that have e-commerce system were financially successful and in next 3 years online sales will increase rapidly. The simple analytics will not cover the demand of online business and it is needed to implement advanced data-driven decisions now. Practical Implications: The present research provides generalized knowledge on how to launch a data science project in e-commerce and how to choose the best programming and visualization app to ensure the profitability of a project. The scientific paper gives an instruction on the marketing contribution analysis, which is the tool of key importance for online marketplaces. Originality/Value: The main research value drawn from the study is to launch the datadriven models in e-commerce company it is needed to observe the real business need and available data, find the best programming and visualization tools. It was defined that the most beneficial data science solutions are demand forecasting, estimation of the marketing contribution, customers clustering, recommendation system and customers' attitude analysis. The main business need for each e-commerce company is to estimate the contribution of all marketing channels and advertisement formats separately. This issue may be easily handled with a regression modelling, which helps to understand a set of factors influencing sales.

This article diagnoses the use of instruments supporting entrepreneurship by the Podkarpackie Province communes. The main research problem was formulated as follows: Do the instruments of supporting entrepreneurship used by self-government affect the development of economic initiatives in the area of the surveyed communes? We analyzed it in two areas. The first one focuses on the present state, analyzing the quality and directions of actions taken by commune authorities in supporting economic initiatives as well as their results. The second one attempts at pointing the solutions conducive to enterprise development and instruments ensuring their stimulation. The results of the conducted analyses allowed us to assess the effectiveness of the instruments supporting entrepreneurship used by local government units. The main conclusion derived from the research is that the use of fiscal instruments does not constitute the strongest factor in determining the location of economic activity. The use of tax forms of support dependant on the economic situation turns out to be much less important than the use of solutions such as improvement of infrastructure conditions, selection of areas for investment, lease of commune facilities for economic activities, creation of capital back-up comprised of loan funds, as well as implementation of organizational changes aiming at better efficiency of the office. ARTICLE INFO

The article aims to identify the relationship between energy efficiency and particular indicators of energy losses in Europe. The results of the bibliographic analysis showed a knowledge gap in energy losses in Europe regarding the new challenges of energy security. For the analysis, annual panel data from 32 European countries were collected from 1990 to 2019. The authors used the Jarque–Bera test to assess the normality of the residuals, utilized the Breush–Pagan test for heteroskedasticity check, and applied regression analysis to determine the relationship between energy efficiency and energy loss rates in Europe. To assess the effects of energy losses, the authors performed OLS modeling using the stats model’s package in Python. According to the modeling results, an increase in distribution losses (% of available energy from all sources) by 1% in Europe leads to an increase in energy consumption by 17.16% under other constant conditions. There is significant heterogeneity between European countries concerning energy efficiency and energy loss coefficients. Such a situation requires the development of new strategies and mechanisms to reduce energy losses, considering the challenges of energy security in Europe in turbulent times. Further research can be devoted to clustering European countries according to the main groups of energy losses: in the extraction, distribution, storage, and transformation of energy.

The aim of this paper was to examine whether the COVID-19 epidemic has slowed the fulfilment of one of the core tasks of the energy sector “Ensure Access to Affordable, Reliable, Sustainable and Modern Energy for All” (SDG7) taking into account corporate social responsibility. Four research questions and hypotheses were posed, relating to the perspectives of local authorities, the activities of large energy companies, the impact of the epidemic on the implementation of the SDG7 and, in addition, to the understanding of CSR principles from the point of view of ordinary entrepreneurs. A qualitative descriptive analysis based on two reliable databases and a survey procedure (Question 4) was used to answer the research questions posed. The goal was achieved by positively confirming three hypotheses and testing one negatively, relating to COVID-19’s slowing role in SDG7 implementation. The analysis showed that the 2020–2021 epidemic in Poland has led to more initiatives in this area, contrary to expectations. However, they were linked to the simultaneous implementation of other SDGs, which distorted their importance for achieving Goal 7. In summary, although energy companies were more active than expected during the epidemic, they had a low contribution to SDG 7. This also applies to local authorities. An analysis of the knowledge about CSR in a group of entrepreneurs from the Lublin district (case study) confirmed the opinion appearing in the literature about the lack of understanding of the concept and the need for its application.