This paper refers to the method of using the deep neural long-short-term memory (LSTM) network for the problem of electrocardiogram (ECG) signal classification. ECG signals contain a lot of subtle information analyzed by doctors to determine the type of heart dysfunction. Due to the large number of signal features that are difficult to identify, raw ECG data is usually not suitable for use in machine learning. The article presents how to transform individual ECG time series into spectral images for which two characteristics are determined, which are instantaneous frequency and spectral entropy. Feature extraction consists of converting the ECG signal into a series of spectral images using short-term Fourier transformation. Then the images were converted using Fourier transform again to two signals, which includes instantaneous frequency and spectral entropy. The data set transformed in this way was used to train the LSTM network. During the experiments, the LSTM networks were trained for both raw and spectrally transformed data. Then, the LSTM networks trained in this way were compared with each other. The obtained results prove that the transformation of input signals into images can be an effective method of improving the quality of classifiers based on deep learning.

This study deals with the optimization of energetic and exergetic parameters of a hybrid-solar dryer to dry basil leaves under determined experimental conditions at three air temperatures (40 °C, 55 °C, and 70 °C) and three bed thickness levels (2, 4, and 6 cm). The optimization of the thermodynamic parameters was performed using the response surface method (RSM) based on the central composite design (CCD) and the desirability function (DF) to maximize the drying rate, exergy efficiency, improvement potential rate and the sustainability index, and to minimize the energy utilization, energy utilization ratio and exergy loss rate. These parameters were calculated on the basis of the first and second laws of thermodynamics as the response variables. Based on the results obtained, it was determined that the optimal conditions for basil drying were at a drying air temperature of 63.8 °C and a bed thickness of 2 cm. At this point, the parameters of the drying rate, energy utilization, energy utilization ratio, exergy efficiency, exergy loss rate, improvement potential rate and sustainability index were obtained with the maximum utility function (D = 0.548) as 0.27, 0.019 (kJ/s), 0.23, 65.75%, 0.016 (kJ/s), 1.10 (kJ/s) and 0.015, respectively.

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.

The natural environment is one of the areas of sustainable development. The implementation of the goals of sustainable development is associated with the creation of conditions supporting ecological behaviours as well as the greening of consumption. It cannot be ignored that the implementation of behavioural marketing concepts contributed to the development of excessive consumerism, whereas the use of marketing innovations by enterprises with high strategic potential and dominant market position often translated into shortening product life cycles. In this context, there arises a research question concerning the role that marketing has to perform nowadays, and the challenges to its concept, resulting from sustainable development. The paper is based on literature studies and the results of an empirical research that was performed on a sample of 140 entities shaping the offer of organic farming products in Poland. The study included both organic farming entities and entities dealing with the distribution of organic farming products in Poland. The goal of the paper is to show the role of marketing as a multi-paradigmatic concept in shaping the development of the market of organic farming products in Poland. The analysis of the research results aimed at identifying the reasons for introducing organic farming products by distributors into their offer, as well as showing the nature of the relationships between producers and distributors of organic farming products in Poland. The analysis of the research results shows that the relationships between producers and distributors of organic farming products in Poland are perceived by both parties as long-term and based on trust. Special attention is also paid to the hierarchy of factors stimulating the development of sales of organic farming products in Poland. Identification of stimulants for the development of the market of organic farming products indicates the great importance of marketing in this area because the main factors of development of this market are of marketing nature or are inherently related to marketing.

One of the three most important components of the effective work of an organization or enterprise in the energy sector is properly organized staff work. Thus, all the risks arising in this process should be given proper consideration. Conventional methods can hardly be of any assistance, which is why this paper proposes the innovative rapidly adaptive model for the evaluation of existing risks and the forecasting of emerging ones due to the COVID-19 pandemic in modern personnel management. The model employs a probability theory and applies it to four major risk groups, which are related to many factors. It also describes the methods of mitigating these risks. This made it possible to improve personnel management efficiency, thus offsetting the adverse consequences of the COVID-19 pandemic.

The following article presents a proprietary real-time localization system using temporal analysis techniques and detection and localization algorithms supported by machine learning mechanisms. It covers both the technological aspects, such as proprietary electronics, and the overall architecture of the system for managing human and fixed assets. Its origins lie in the ever-increasing degree of automation in the management of company processes and the energy optimization associated with reducing the execution time of tasks in an intelligent building supported by in-building navigation. The positioning and tracking of objects in the presented system was realized using ultra-wideband radio tag technology. An exceptional focus has been placed on reducing the energy requirements of the components in order to maximize battery runtime, generate savings in terms of more efficient management of other energy consumers in the building and increase the equipment’s overall lifespan.