University of Žilina

Phytomass, a renewable energy source, faces the challenge of ash agglomeration due to its low ash fusion temperature. To address this, chemical modification (adding additives) and co-combustion with other fuels are explored. This study focuses on combustion of phytomass with wood biomass and mechanical modifications to the combustion chamber. The goal is to maintain the chamber temperature below the ash fusion point. Modifications included a modulated burner and water cooling. Combustion of phytomass with wood biomass in the ratio of 60/40, 40/60 and 20/80 had a beneficial effect on increasing the heat source output by 5% and reducing SO X emissions from 61 to 21 mg ⁻³ by adding the wood to hay. The modification of the burner resulted in a reduction of particulate matter from the range 110–140 mg m ⁻³ without cooling to the range 90–110 mg m ⁻³ with cooling depending on the phyto/wood ratio and a reduction in the formation of deposits. By cooling the burner, the temperature at the exit from the combustion chamber was reduced from approx. 560 to 460 °C and at the height above the reaction zone of the flame from 500 to 320 °C. The co-combustion and additional cooling were not such effective, two other modifications were suggested for breaking up or sliding the resulting agglomerates from the surface of the retort. The proposed devices could have potential, as the investment in such a device is more cost-effective than the purchase of a special boiler for burning alternative pellets.

This article explores research into the use of meme images generated by artificial intelligence as a means of media communication in the context of students oriented to work in the cultural and creative industries sector. The aim of the primary descriptive research is to analyse the ability of future cultural and creative professionals in the context of visual literacy to distinguish the source of content, i. e. to identify whether a given image comes from an artificially intelligent system or from a human user. The paper registers the impact of these technological tools on students’ communication skills and creative potential and evaluates the possibilities of their integration into the educational process. Furthermore, this research provides insight into how emerging technologies, like AI, could be integrated into educational processes to support innovative teaching methods. By assessing the benefits and challenges of incorporating AI-generated content in academic environments, the study highlights the importance of preparing students for a future where AI is a key element of media communication. This perspective underscores the role of educational institutions in equipping students with the skills to critically engage with and leverage AI tools, thus fostering a media-savvy and adaptable workforce ready for the demands of the cultural and creative industries. The findings of this research will contribute to understanding the impact of AI on student competencies in media communication, offering a valuable resource for academics and educators seeking to integrate new technologies into their teaching practices.

Human capital is represented by people who are carriers of knowledge, ideas and experience that contribute to increasing the performance and competitiveness of the company and the entire society. Human capital management helps companies achieve business goals through efficiency, which includes the effective use of human capital and the effectiveness of investment in it. The article aims to explain the essence of human capital management, offers new metrics for measuring the effectiveness of human capital, and points out a possible approach to implementing human capital management in a specific small trading company. We used the observation method in obtaining data about the trading company, content analysis in the examination of scientific articles and internal documents of the company, synthesis, descriptive statistics, and mathematical methods in the design of metrics for measuring the value of human capital through Excel statistical software, and deduction in the design of generally applicable rules for all types of companies. IBM SPSS software was used for the statistical evaluation of the questionnaire survey. We obtained the basis for making the article through a questionnaire survey, which was carried out in 350 Slovak trading companies. Using a survey, we found that up to 89% of companies do not have a human capital management concept in place and do not use and do not know metrics to measure human capital effectiveness. It is while companies are aware of the impact of investments in human capital on the overall performance and the size of the company´s profit. Based on it, we created metrics to measure the human capital´s value. Thanks to quantitative analysis, we identified KPIs for small business company—sales, profit. We found that external factors also affect the value of human capital. In the analysed company, the improvement of the working environment helped to increase the value of human capital, which also led to higher customer satisfaction. The article fills a gap in the scientific literature because it deals with the issue of implementing the human capital management concept and metrics for measuring the human capital´s value in the company. The theoretical and practical contribution of our contribution also lies in the use of common accounting documents in the quantification of the value of human capital. Companies can be inspired by the article because it provides an overview of the human capital management implementation in a small company, and designed metrics to measure the value of human capital.

The sustainability of rail transport is a highly debated topic. Historically, there has always been strong political support to maintain even lightly used regional lines in operation. However, the economic situation and increasing demands for rail safety have introduced new challenges, including the fundamental question of whether to continue operating a line or discontinue it. So far, no simple solution has been applied across the board. Nevertheless, there are already some Public Service Obligations (PSOs) planning for the next 10 to 15 years, putting pressure on the infrastructure manager to implement measures that guarantee a specific system travel time on the line. If these expectations are not met, they may consider shifting transport services to alternative modes of transport in the medium term. One such region facing this challenge is the Hradec Králové Region, where a thesis was developed to analyze the regional route Doudleby nad Orlicí - Rokytnice v Orlických horách. The study focuses on the technological requirements for achieving the necessary system travel time, which is also the primary goal of this paper.

This study presents an investigation into the tribological, corrosion, and tribocorrosion properties of AISI 316Ti (austenitic) and AISI 430 (ferritic) stainless steels. The comparative analysis focuses on microstructural characterization, hardness, and a series of tribological, electrochemical, and tribocorrosion tests conducted in 0.9% NaCl using a specialized linear tribometer to reveal the quality of the studied materials in tribocorrosion applications. Friction tests were performed under both dry and corrosive conditions, while tribocorrosion tests were conducted under open circuit potential (OCP) conditions in 0.9% NaCl, with the electrode potential of the test specimen monitored during friction. To evaluate the electrochemical behavior of the materials, potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) were conducted using a 0.9% NaCl solution. The measured corrosion potential (Ecorr) suggests that AISI 430 is thermodynamically more stable than AISI 316Ti; however, AISI 316Ti demonstrated higher polarization resistance (RP) values compared to AISI 430. The findings indicate that material qualities significantly influence the coefficient of friction (CoF). Additionally, a notable antifriction effect of 0.9% NaCl was observed during tribological testing, resulting in a lower CoF compared to dry friction conditions. A cathodic shift in OCP during tribocorrosion testing was also observed in both materials, indicating an increase in corrosion vulnerability when the passive layer is degraded.

Smooth operation of railway stations and yards is vital for the efficient functioning of the whole railway system. Being complex systems, their operation is extremely sensitive to various influences, which makes their management, especially at the operational level, very difficult. Efficient tools to aid the decision-making process of dispatchers of such stations are therefore needed. With an emphasis on increasing the effectiveness of decision support tools, we propose a simulation-based optimization algorithm. This algorithm extracts a dataset from a simulation model and then reduces it to a partial dataset to be able to use specific exact optimization method in operational management. The partial dataset is limited by certain time horizon. The applicability of the proposed algorithm has been verified on two distinct tasks, namely, personnel assignment and service task assignment in a maintenance depot, confirming the usability of the proposed approach.

The rising environmental concerns and the growing demand for renewable materials have surged across various industries. In this context, lignin, being a plentiful natural aromatic compound that possesses advantageous functional groups suitable for utilization in biocomposite systems, has gained notable attention as a promising and sustainable alternative to fossil-derived materials. It can be obtained from lignocellulosic biomass through extraction via various techniques, which may cause variability in its thermal, mechanical, and physical properties. Due to its excellent biocompatibility, eco-friendliness, and low toxicity, lignin has been extensively researched for the development of high-value materials including lignin-based biocomposites. Its aromatic properties also allow it to successfully substitute phenol in the production of phenolic resin adhesives, resulting in decreased formaldehyde emission. This review investigated and evaluated the role of lignin as a green filler in lignin-based lignocellulosic composites, aimed at enhancing their fire retardancy and decreasing formaldehyde emission. In addition, relevant composite properties, such as thermal properties, were investigated in this study. Markedly, technical challenges, including compatibility with other matrix polymers that are influenced by limited reactivity, remain. Some impurities in lignin and various sources of lignin also affect the performance of composites. While lignin utilization can address certain environmental issues, its large-scale use is limited by both process costs and market factors. Therefore, the exact mechanism by which lignin enhances flame retardancy, reduces formaldehyde emissions, and improves the long-term durability of lignocellulosic composites under various environmental conditions remains unclear and requires thorough investigation. Life cycle analysis and techno-economic analysis of lignin-based composites may contribute to understanding the overall influence of systems not only at the laboratory scale but also at a larger industrial scale.

Bioaccumulation of trace elements in aquatic environments can be influenced by local environmental conditions such as temperature fluctuations, pH levels, sediment composition, dissolved organic matter content, and the presence of other chemical substances. We analyzed the differences in trace elements accumulation (S, Cl, K, Ca, Ti, Cr, Mn, Fe, Cu, Zn, Rb, Sr, Mo, Ba, and Pb) between two trophic guilds—scrapers (Ephemeroptera) and predators (Plecoptera)—of freshwater benthic macroinvertebrates collected from mountain streams in Kazakhstan and Slovakia. Trace elements in dried insect bodies were analyzed using an X-ray spectrometer, and physicochemical parameters of stream water were investigated at each sampling site. Our results showed significant differences in Fe, Ti, and Sr levels in predators from Kazakhstan and Cu levels in predators from Slovakia. Despite some trace elements showing higher concentrations in one group over another, the overall differences between regions were more pronounced. Principal component analysis (PCA) revealed that the primary factors influencing trace elements variability were associated with environmental conditions such as temperature, oxygen levels, and total dissolved solids (TDS). PCA components indicated a higher load of trace elements in the warmer, less oxygenated streams, particularly in Kazakhstan. These findings suggest that both biotic (feeding strategies) and abiotic (geographical and environmental conditions) factors significantly influence trace elements dynamics in freshwater ecosystems.

Currently used machine diagnostic systems are based on very modern solutions based on the acquisition and recording of their operating parameters in real time. Increasingly available and high-tech sensor systems mean that the number of recorded parameters is increasing and their quality is improving. These data are mainly used to assess the technical condition of machines and the processes they perform. In mining, these data can also be used to assess and, at a later stage, improve the safety of the underground mining process. Referring to this issue, the paper presents examples of the use of diagnostic systems for powered roof supports and longwall shearers to assess the safety status of the underground hard coal mining process. In the case of the wall support, the focus was on measuring the pressures in the stands of its individual sections. Temporary changes in the values of these pressures constitute a valuable source of information regarding the interaction of the support with the rock mass. In particular, this concerns the identification of the effects of the informational impact of the rock mass on the longwall excavation protected by the support. The research results presented in the paper, especially in the case of very dangerous dynamic impacts, indicate the possibility of both diagnosing the operating condition of the section and identifying symptoms of exposure to such events. This undoubtedly significantly expands the possibilities of using the measured pressures. Diagnostic signals from a longwall shearer are also widely used. The current intensities drawn by its motors while cutting the rock mass, as well as the advance speed and its position in the wall make it possible to analyze these parameters and their changes before, during and after the occurrence of various types of events. These data enable the assessment of the effects of the rock mass on its operational efficiency and safety status. It also enables the identification of symptoms that precede the occurrence of such events. The presented examples indicate the need for a broader and more holistic approach to the use of diagnostic parameters of mining machines. In particular, this concerns the study of the cooperation between the support and the rock mass and its influence on the efficiency and safety of the rock mass mining process. The subject matter addressed relates to very important and current issues, and the developed methodology and obtained results should be applied in practice as soon as possible.

Endometrial cancer (EC) is the most prevalent cancer within the female reproductive system in developed countries. Despite its high incidence, there is currently no established laboratory screening test for EC, making early detection challenging. This study introduces an innovative, minimally invasive, and cost-effective method utilizing three-dimensional fluorescence analysis combined with machine learning algorithms to enhance early EC detection. Intrinsic fluorescence of blood serum samples was measured using a luminescence spectrophotometer, which captured fluorescence spectra as synchronous excitation spectra and visualized them through wavelength contour matrices. The spectral data were processed using machine learning algorithms, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD), along with exploratory techniques such as Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). Fluorescence ratios R300/330 and R360/490, indicative of altered tryptophan metabolism and redox state changes, were identified as fluorescent spectral markers and represent key metabolic biomarkers. These ratios demonstrated high diagnostic efficacy with AUC values of 0.88 and 0.91, respectively. Among the ML algorithms, LR and RF exhibited high sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), showing significant promise for clinical application. After optimization, LR achieved a sensitivity of 0.94, specificity of 0.89, and an impressive AUC value of 0.94. The application of this novel approach in laboratory diagnostics has the potential to significantly enhance early detection and improve prognosis for EC patients.

Economic expediency encourages mobile operators to deploy 5G networks in places with a high concentration of speed-demanding subscribers. In such conditions, sharp fluctuations in the volume of traffic with regulated requirements for the quality of service are inevitable. Note that 5G operates in the millimeter range. Accordingly, the quality of traffic service is affected both by the number of subscribers simultaneously initiating requests from one sector of coverage, and by the appearance of obstacles opaque to radio radiation in the space between the subscriber device and the base station. Effective smoothing of 5G traffic fluctuations, taking into account these disturbing factors, is an urgent task. The goal of this research is to evaluate the service quality parameters in a target area characterized by a specific user density. It takes into account that if the declared QoS requirements for connection speed for users in a network segment deployed within the licensed frequency range for 5G are not met, they can utilize a network segment deployed in the unlicensed high-frequency range for 5G under conditions of free competition. The metric being studied is the probability of session loss in the licensed network segment and the achievable transmission speed in the unlicensed network segment. Based on this, a method for assessing the density of base station deployment in the unlicensed network segment necessary to support the specified user density in the licensed network segment with defined QoS guarantees in terms of bandwidth is formalized. The experiment results showed that the probability of losing sessions with regulated requirements for the quality of service in both network segments, in addition to the base station placement density and subscriber devices, is significantly affected by the minimum data transfer rate, the intensity of obstacles, and the value of the Contention Window.

The naturally pressurized gating system appears to be an appropriate solution for reoxidation reduction, but this type of gating system can result in supercritical melt velocity. The paper is focused on the determination of the unconventional elements effect in the gating system on the melt velocity and melt flow and their influence on the mechanical properties and microstructure. In experimental works was observed the influence of dimensioned gate, foam filters with 10 and 30 ppi density, trident gate and combination of trident gate and vortex element. Melt velocity was observed by simulation software and via velocity measurement by contact method in the mold during casting. Melt flow was analyzed by simulation software and water model experiment. Experimental casts have been made for the purpose of evaluating mechanical properties and microstructure determination. The best results were achieved by 30 ppi foam filter.

As the modern automotive industry is looking for lightweight alternatives to minimize car emissions and fuel consumption, recycled Al-Si alloys play a key role in achieving this due to their lightweight, high specific strength, good castability, and corrosion resistance. In contrast to many other benefits, these alloys have reduced metallurgical micropurity as a result of recycling. The most significant complication of alloys is iron contamination. Higher Fe contents cause β-Fe-intermetallic phases in the form of long and brittle platelets that negatively affect corrosion resistance and fatigue. Neutralizing elements lead to the formation of less harmful α-Fe-rich phases, therefore a positive effect on properties is also expected. For this reason, the study investigates the effect of Mn addition on the corrosion properties achieved by immersion test and potentiodynamic polarization test and fatigue of secondary AlSi7Mg0.6 secondary alloy.

The paper examines the effect of precipitation hardening temperature on selected properties of AlSi5Cu2Mg alloy alloyed by 0.20 wt.% of Zr. The newly developed AlSi5Cu2Mg alloy intended for cylinder head castings is specific due to its limited Ti content, which prevents the use of standard Al-Ti-B type grain refiners. Zr added in the form of AlZr20 master alloy acts as a grain refiner. The grain refinement effect of Zr positively affects the mechanical properties. However, the physical properties defining the lifetime of cylinder head castings are not affected by the presence of Zr-rich phases. For this reason, the research focuses on the proposal of the optimal T6 heat treatment procedure in order to positively influence the physical and mechanical properties of the AlSi5Cu2Mg alloy. For the research, four T6 thermal regimes with graduated aging temperatures by 20°C from 180 to 240°C ± 5°C were selected. The results showed that increasing aging temperature positively affects physical properties, especially thermal conductivity, and mechanical properties of Rm, Rp0.2, and HBW. On the other hand, with increasing aging temperature up to 220°C ± 5°C, a negative decrease in ductility was achieved. Optimum ductility of, especially, AlSi5Cu2Mg alloy with 0.20 wt.% Zr was achieved by the T6-240 thermal regime. Optimal combination of thermal conductivity and mechanical properties of the AlSi5Cu2Mg alloy with 0.20 wt.% Zr was achieved by the T6-240 heat treatment due to the requirements placed on cylinder head castings.

Austenitic nodular cast iron is a versatile material that offers a unique combination of properties, making it suitable for use in a wide range of applications where high strength, ductility, toughness, corrosion resistance and wear resistance are required. This material is commonly used in a variety of applications in the chemical and petrochemical industries, in the automotive and aerospace industries, as well as in marine and offshore applications. For the experiments, one of the most common austenitic nodular cast irons (alloyed with nickel and manganese) was chosen. The aim of this paper is to evaluate the corrosion resistance of this austenitic nodular cast iron and compare it with other (non-austenitic) types of nodular cast iron (SiMo- and SiCu-type). Corrosion resistance was determined by an exposure immersion test and an electrochemical potentiodynamic polarization test. Both tests were performed in a 3.5% NaCl solution (to simulate seawater) at ambient temperature. Experimental results prove that austenitic NiMn-nodular cast iron has a higher corrosion resistance than SiMo- and SiCu-nodular cast iron. Moreover, austenitic nodular cast iron has better plastic properties (higher elongation and absorbed energy) but worse strength and fatigue properties (lower tensile strength, hardness and fatigue limit) than the other types of nodular cast iron.

The following paper presents an innovative approach to determining vehicle precrash velocity when hitting an immovable obstacle facing forward. Precrash velocity is necessary in order to perform a crash reconstruction. It is needed for the time-space analysis of the events, as well as to assesscrash mitigation and to evaluate drivers’ technique and tactics. For this task, the authors are using Gaussian Process Regression (GPR). Such an approach offers a number of advantages over the currently used methods that prove to be outdated when considering modern vehicles. The mathematical model was trained on a database shared by the National Highway Traffic Safety Administration.. This database covers a large number of crash tests of different kind, however authors focus on frontal collisions of the subcompact car class. Due to low accuracy of linear methods used up till now, Authors developed an innovative approach to determine the EES parameter utilizing Gaussian process regression. The newly developed method is an effective and accurate way to determine the vehicle’s velocity and shows promising results, as is demonstrated in this paper.

Article pays attention to the impact of using the trailer on driving performances of a studied vehicle combination.The research was performed via extensive experimental measurements. A new methodology of determining a technical condition of the trailer’ brakes was proposed and verified, which can be also applied during the regular technical inspection. Various load distributions in the trailer caused the centre of gravity’s position of the vehicle was changed, which may increase the risk of skidding or disconnection of the trailer from the towing vehicle. Also, the vehicle’s ability to accelerate and decelerate decreased considerably due to the loading up. There were assessed the braking characteristics of the combination of vehicles as well, depending on the technical condition of the trailer’s brakes. The importance of this article lies in the quantification of selected factors on road safety in relation to driving with a combination of vehicles.

The issue of security currently has a number of challenges. One of the most important is global warming, another is the significant increase in the global population, cyber threats and the accelerating development of SMART technologies in connection with artificial intelligence. More than 71 per cent of the Earth’s surface is covered by seas and oceans. Much of it is very remote from human settlements. Changes in the global environment have led to a shift in the approach in protecting critical infrastructure and their transition to enhancing the resilience of critical entities. Research on the new approach has been successfully conducted at the University of Žilina for a long period of time. Past and current research projects are directed to the area of linking high technologies with social sciences. Security research is always multi-level and multi-disciplinary.