The aim of this article is to provide an overview of other alternative directions of coal supply to Poland following the February 2022 embargo on coal imports from Russia. Due to the dominant role of steam coal in imports to Poland, the authors focused on this type of coal. Analysis of the share of Russian steam coal imported into Poland in domestic consumption and production suggests that this commodity has played a relatively important role in the Polish market. In 2010–2021, between 4.8 and 12.9 million tonnes were imported annually from Russia to Poland, accounting for 8–25% of domestic steam-coal consumption. In 2018–2021, steam coal imported into Poland accounted for 22–29% of the volume of coal shipped by Russia to all EU -27 countries. In order to fill the gap left by Russian coal, this article considers alternative routes of coal supply to Poland, namely from Australia, Indonesia, Colombia, South Africa and the US, and presents the qualitative characteristics of the coal offered by these alternative routes of coal supply and traded on the international market. Between 2010 and 2021, steam-coal-price offers from these countries followed a consistent trend, with the difference between the minimum and maximum offer ranging from USD 5–32/tonne. As the steam coal supply of each of the analyzed routes of supply is fraught with some risk, the authors have also identified in the article those directions that may present some difficulties. It was found that coal offerings from Australia, South Africa, Indonesia and Colombia have low sulphur content (less than 1%), while coals from Australia and South Africa have relatively high ash content (from 12% to nearly 25%). Towards the end, the article also addresses issues related to the transport of coal to Poland and its dispatching within the country. As the analyzed alternative directions of coal imports involve importing this commodity by sea, the authors also analyzed the reloading capacity of Polish seaports and the rail transport fleet.

The MEMS inclinometer integrates a tri-axis accelerometer and a tri-axis gyroscope to solve the perceived dynamic inclinations through a complex data fusion algorithm, which has been widely used in the fields of industrial, aerospace, and monitoring. In order to ensure the validity of the measurement results of MEMS inclinometers, it is necessary to determine their dynamic performance parameters. This study proposes a conical motion-based MEMS inclinometer dynamic testing method, and the motion includes the classical conical motion, the attitude conical motion, and the dual-frequency conical motion. Both the frequency response and drift angle of MEMS inclinometers can be determined. Experimental results show that the conical motions can accelerate the angle drift of MEMS inclinometers, which makes them suitable for dynamic testing ofMEMSinclinometers. Additionally, the tilt sensitivity deviation of theMEMS inclinometer by the proposed method and the turntable-based method is less than 0.26 dB.We further provide the research for angle drift and provide discussion.

Distribution centres are the important elements of modern supply chains. A distribution centre stores and ships products. In this paper, we investigate the model of the dimensioning of shelf space on the rack with vertical and horizontal product categorisation in a distribution centre, where the objective is to maximise the total product movement/profit from all shelves of the rack which is being managed by a packer who needs to complete orders selecting the products from the shelves and picking them to the container. We apply two newly developed heuristics to this problem and compare the results to the optimal solution found by the CPLEX solver. There are 8 steering parameters that allow for reducing the search space implemented in heuristics. Among them are parameters that decrease the number of products on the shelves, the category with a range for assigning most space for the most profitable products within the category; two versions of steering parameters for the number of generated product allocations, the step parameters for the intensity of solution diversification, and the movement/profit below which the solutions are not generated. The computational results are presented and indicate that higher-quality solutions can be obtained using the new heuristics. In 10 from 15 tests, both heuristics can find optimal solutions without exploring the whole solution space. For the rest test sets, the solutions received by heuristics are not less than 92.58%.

Structural health monitoring (SHM) of bridges is constantly upgraded by researchers and bridge engineers as it directly deals with bridge performance and its safety over a certain time period. This article addresses some issues in the traditional SHM systems and the reason for moving towards an automated monitoring system. In order to automate the bridge assessment and monitoring process, a mechanism for the linkage of Digital Twins (DT) and Machine Learning (ML), namely the Support Vector Machine (SVM) algorithm, is discussed in detail. The basis of this mechanism lies in the collection of data from the real bridge using sensors and is providing the basis for the establishment and calibration of the digital twin. Then, data analysis and decision-making processes are to be carried out through regression-based ML algorithms. So, in this study, both ML brain and a DT model are merged to support the decision-making of the bridge management system and predict or even prevent further damage or collapse of the bridge. In this way, the SHM system cannot only be automated but calibrated from time to time to ensure the safety of the bridge against the associated damages.