This paper presents the development of a piece of induction hardening equipment based on the foundations of the design, starting from zero. It was intended for steels in general, and was tested on unalloyed low- and medium-carbon steels, whereas the results for EN 1C60 steel are shown in this study. The EN 1C60 steel showed average results, and was chosen as a representative of a wider group of engineering steels. The main objective of this work was to develop a flexible system for mild steel hardening that can be used for various hardening depths and steel types. The system design’s priorities were the use of standard electronic components to avoid supply chain disruptions and to achieve high energy efficiency. The construction of the prototype in full detail is also presented. The optimal process parameters are listed, as well as the procedure of their obtaining by using the appropriate simulation method. The key parameters were adjusted in consecutive steps. This study resulted in high matching between the model predictions and experimental results. The basic goal of this research was achieved, with the system having a minimum energy efficiency of 75.3%, a most frequent energy efficiency of 90% and a maximum energy efficiency of 95.1%.

Students engage in remote learning within a diverse computer environment. While virtual machines can address the challenges posed by heterogeneity, there remain unresolved issues, particularly related to the complexity of software management. An imperative is to discover an automated solution that facilitates the creation of consistent software environments for educational purposes. This paper introduces ontology engineering principles as a means to tackle the complexities associated with software management. A suitable ontology is developed using OWL syntax, integrating knowledge pertaining to the required software within a specific academic domain. The practical applicability of this knowledge is enabled through the implementation of dedicated SPARQL queries within a Python program. The effectiveness of the automated solution in achieving identical software environments is verified through testing, conducted in both controlled laboratory settings and by students themselves, thus simulating authentic teaching scenarios. The solution not only adheres to the principles of reusability but can also be adapted or integrated into existing ontologies. Furthermore, it presents an opportunity to create automated and self-adjusting virtual machines, offering significant potential for educational and other domains.

High-frequency wood drying is the modern method used in raw wood drying so that treated wood can be used further in various processes. Such systems are used because of the economy, energy efficiency, obtaining of good mechanical properties of the wood after treatment, as well as reducing time consumption. Therefore, it is extremely important to understand each component of such systems and processes. The mentioned systems are implemented using high-frequency generators based on vacuum tubes (VT). Their development and, in particular, optimization are by far more complex than the transistor systems; therefore, the development is now compelled to rely on computer modelling and simulation. In this research, a high-frequency (HF) generator of 20 kW output power and 1.5–15 MHz adjustable frequency based on VT was produced and then, with the corresponding model for VT itself and the rest of the developed circuit, was followed by computer simulation and real-system measurement. The model parameters were adjusted, which provided additional system optimization. An extra match of the results from the simulation and measurement was obtained; thus, the optimization was performed faster and more precisely. In addition, an easier and quicker way of adjusting parameters of the PID controller using a developed software-based control system was attained. The problems of cooling the VT anode under high DC voltage, as well as temperature measurement in the HF electric field, have been solved.

Laboratory exercises are an important part of the teaching process at many faculties. However, despite the fact that today the implementation of laboratory exercises is carried out on modern equipment, the full potential of that equipment is often not used, that is, often some of the possibilities of modern equipment are not fully used in student practice. In the paper, it will be shown on two cases from practice how the student user experience can be expanded by using exactly those „forgotten“ options. In the first case, a solution based on the full use of Standard Commands for Programmable Instruments (SCPI), which are implemented on most modern measuring devices, will be presented. The second case will consider the application of open source hardware and software supported by LoRa connectivity, which enables the expansion of the spectrum of exercises performed by students. It will be shown that both solutions can be implemented without a high level of complexity and large financial efforts for the higher education institution itself.

Reliable transition of laboratory-developed gas-sensing nanomaterials into environmental monitoring systems remains limited by the lack of integrated testing platforms capable of reproducing realistic operating conditions while providing automated, high-quality sensor characterization. In this work, we present a comprehensive framework for environmental gas sensor development centered on a custom-designed gas sensor chamber (GSC) and its connection to standalone measuring stations (sMSs) intended for smart city air quality monitoring. The GSC was developed as a dedicated platform for controlled evaluation of sensing nanomaterials through automated regulation of temperature, relative humidity, gas concentration, and pressure, combined with impedance measurements over a broad frequency range (10 Hz–100 kHz). The system integrates dedicated chamber hardware, environmental control loops, an LCR/impedance-based measurement unit, and in-house software for instrument control, data acquisition, synchronization of environmental and impedance data, and post-processing with visualization and correlation analysis. The functionality of the platform was demonstrated using graphene oxide-based nanocomposites deposited on interdigital electrodes and tested under variable parameters of temperature, humidity, and carbon monoxide concentration. The chamber enabled stable and reproducible extraction of frequency-dependent sensor responses and identification of optimal operating conditions for selected materials. Building on these results, the same sensing concept was translated into an energy-autonomous sMS architecture based on modular interdigital sensor elements, impedance-based indirect gas quantification, remote communication, and photovoltaic power support. By integrating material evaluation, environmental simulation, automated data handling, and field-oriented station design within a single workflow, the presented approach bridges the gap between laboratory gas sensor research and practical deployment in distributed smart city monitoring networks.

This paper describes an example of applying the machine learning to predict the SO2 blower vibrations. The SVR (Support Vector Regression) algorithm was used to form the model. The paper includes an overview of machine learning and mathematical fundamentals of the SVM model. Based on a large amount of data, obtained from the monitoring system, a mathematical model was constructed. After testing, the measures of model accuracy such as the MSE (Mean Squared Error) and R2 were provided.