An important operational task for thermal turbines during run-up and run-down is to keep the stresses in the structural elements at a right level. This applies not only to their instantaneous values, but also to the impact of them on the engine lifetime. The turbine shaft is a particularly important element. The distribution of stresses depends on geometric characteristics of the shaft and its specific locations. This means a groove manufactured for fixing the rotor blades. The extreme stresses in this place occur during the start-up and the shaft heating to normal operating temperature. The process needs optimisation. Optimization tasks are multidisciplinary issues and can be carried out using different methods. In recent years, particular attention in optimisation has been paid to the use of artificial intelligence methods. Among them, a special role is assigned to genetic algorithms. The paper presents a genetic algorithm method to optimise the steam turbine shaft heating process during its start-up phase. The presented optimization task of this algorithm is to carry out the process of the shaft heating as soon as possible at the conditions of not exceeding the stresses at critical locations at any heating phase.

Rat robots have great potential in rescue and search tasks because of their excellent motion ability. However, most of the current rat-robot systems relay on human guidance due to variable voluntary motor behaviour of rats, which limits their application. In this study, we developed a real-time system to detect a rat robot’s transient motion states, as the prerequisite for further study of automatic navigation. We built the detection model by using a wearable inertial sensor to capture acceleration and angular velocity data during the control of a rat robot. Various machine learning algorithms, including Decision Trees, Random Forests, Logistic Regression, and SupportVector Machines,were employed to performthe classification of motion states. This detection system was tested in manual navigation experiments, with detection accuracy achieving 96.70%. The sequence of transient motion states could be further used as a promising reference for offline behaviour analysis.

Three coins were found during a small archaeological excavation at the medieval castle in the Lower Silesian village of Stare Kolnie (Popielów Commune, Opole County, Opolskie Voivodeship): a Silesian kwartnik from the beginning of the 14th century, a Wrocław heller from the years 1417-20 and a counterfeit Polish half-groschen struck after 1410. The kwartnik belongs to the oldest artifacts found at the castle, whereas the fifteenth-century coins were found in the layer related to the final demolition of the castle during the fighing in 1443.