The University of Žilina was established on October 1, 1953 as the College of Railways in Prague. In 1959, the institution changed its name to the University of Transport and moved to Žilina. As a result of the increasing role of communications within the curriculum and research orientation of the university, the name was amended to the University of Transport and Communications in 1980.It was renamed again to the University of Žilina pursuant to the law passed by the Slovak National Council on November 20, 1996.The University of Žilina is the only university located in the northwest region of the Slovak Republic. It provides education at all three levels of higher education both in full-time and part-time forms (Bachelor's degree, Engineer/Master's degree and Doctoral degree). All the university's faculties provide a supplementary course of pedagogical studies for students and graduates.Over the last 57 years, more than 52,000 students have graduated from the university; 1662 of them have been awarded the Ph.D. degree.The university has established contacts with many universities abroad. Professors and research workers at the university participate in international educational and research projects. These include the European Union projects TEMPUS, COPERNICUS, COST, LLP/ERASMUS, Leonardo da Vinci, than CEEPUS, National Scholarship Programme, DAAD. The academic staff are involved in cooperation within the EU's 6th and 7th Framework programmes.
Poka-Yoke is one of the fundamental Lean tools used to prevent or detect errors. However, the existing theoretical framework and classification models are neither sufficiently systematised nor confirmed by empirical research. This study therefore provides the first comprehensive evaluation of three classification models based on: function (I), principle (II), and device type (III). The first two models are the most commonly used in the relevant literature, while the third was developed by the author and improved through the research conducted for a clearer understanding and more practical application. The models were empirically tested using two criteria: classification accuracy assessment and ease of application assessment. The research included 21 examples of PY solutions from literature and industrial practice, evaluated by 30 experts of various profiles. Statistical analysis of the data confirmed the existence of significant differences between the models. Classification models III was found to be the most accurate and simplest to use, thus confirming its practical value in modern industrial practice. This study contributes to the development of a theoretical and practical framework for the PY method, offering empirically based recommendations for standardisation and wider implementation. These recommendations create the conditions for more effective error management, thereby increasing process reliability and ensuring compliance with Industry 4.0 requirements.
The study explored how leadership behaviors shape psychological safety and employee voice in Slovak workplaces, with a particular focus on relational dynamics. Drawing on attachment theory, the study examined the barriers and enablers to speaking up and how leaders influence employees’ perceptions of workplace safety. A qualitative design was employed using semi-structured interviews with 11 employees from diverse industries in Slovakia. Data were analyzed using reflexive thematic analysis, identifying key patterns related to leadership behavior, communication practices, and workplace culture. Seven overarching themes and 17 subthemes emerged, forming a typology of workplace climates: (A) psychologically safe, (B) psychologically unsafe, and (C) emotionally disengaged. Leadership behaviors, such as emotional availability, relational consistency, and accessibility, strongly influenced psychological safety. Key barriers included fear of negative consequences, hierarchical distance, lack of feedback, and unclear roles. Participants identified five leadership competency domains (social, emotional, communication, cognitive, and organizational) as essential for cultivating safety and voice in the workplace. The findings conceptualize psychological safety as a relationally constructed phenomenon co-created through everyday leader-employee interactions. Leadership with attachment-related qualities, defined by emotional support and trust, enables employees to engage, speak up, and contribute authentically. The study contributes a culturally grounded interpretive typology and a dual competency model (relational and functional) for leadership development. Psychological safety should be viewed as an integral component of occupational safety management. Leadership development and organizational interventions may benefit from targeting both relational and functional dimensions to cultivate safe and healthy workplaces.
In recent years, the prediction of corporate bankruptcy has become an increasingly important topic in financial and economic research, particularly in the manufacturing sector of Central and Eastern Europe. Accurate early-warning models are essential for mitigating financial risks and ensuring business sustainability. This study investigates the comparative performance of classical statistical and machine learning (ML) models for predicting corporate bankruptcy across manufacturing firms in the Visegrad Group (V4) countries, addressing the problem of financial distress forecasting in transitional economies. The purpose of the research is to evaluate whether pooled regional models perform as effectively as country-specific models and to examine the influence of national data characteristics, such as sample size and heterogeneity, on predictive accuracy. A balanced dataset of firm-level financial indicators from Slovakia, the Czech Republic, Hungary, and Poland was employed, and three classification techniques, namely logistic regression (LR), artificial neural networks (ANN), and decision trees (DT), were applied to develop predictive models for individual countries as well as for the combined V4 region. Model performance was assessed using multiple classification metrics including accuracy, F1 score, AUC (area under the receiver operating characteristic curve), precision, and recall, with careful attention to handling class imbalance. The results indicate consistently high discriminatory power across all models, with AUC values ranging from 0.929 to 0.991, classification accuracy between 94.9% and 98.3%, and F1 scores from 0.972 to 0.991. Artificial neural networks slightly outperformed logistic regression and decision trees, particularly in countries with larger samples, while pooled models demonstrated performance comparable to country-specific models, highlighting the generalizability of predictive models across V4 economies. The findings carry practical implications for policymakers, creditors, and business managers, supporting the development of scalable early-warning systems, enhancing risk assessment practices, and informing strategic decision-making in dynamic economic environments. Overall, the study contributes both to the theoretical understanding of model performance in bankruptcy prediction and to applied knowledge for regional economic foresight and business intelligence.
This study presents the development of novel hybrid coatings composed of plasma electrolytic oxidation (PEO) and poly(lactic acid) (PLA) on biodegradable ZK60 magnesium alloy. PEO coatings were fabricated using phosphate-based electrolytes under optimized parameters (0.4 A/cm2, 50 Hz, 50% duty cycle) and treatment durations of up to 2 min, resulting in porous ceramic layers approximately 70 & micro;m thick, primarily composed of MgO and Mg3(PO4)2. Subsequent dip-coating with PLA (5 g in 55 mL dichloromethane) effectively sealed the PEO pores, forming a uniform polymer layer approximately 23 & micro;m thick. Corrosion tests demonstrated that the hybrid PEO + PLA coating substantially improved corrosion resistance. The sample treated with a 2-minute PEO process followed by PLA coating exhibited a polarization resistance (Rp) approximately 246 times higher than that of untreated ZK60 after 168 h, and it maintained significant protection after 13 weeks of immersion (Rp approximate to 6.1 & times; 104 Omega & sdot;cm2). Molecular dynamics simulations supported the strong adsorption of PLA on the MgO surface, correlating with improved sealing performance. Overall, this work demonstrates that PEO/PLA duplex coatings markedly enhance the corrosion resistance and mechanical stability of biodegradable ZK60 magnesium alloy, making them highly promising for use in bioabsorbable medical implants.
Developments of artificial intelligence (AI) have significantly impacted cybersecurity and helped innovate various types of cyberattacks, including phishing. Traditional phishing attacks have evolved into a new phenomenon called Phishing 2.0, which uses AI to generate persuasive and personalised content, making it harder to differentiate from legitimate messages. This article focuses on the ability of humans to detect AI-generated content, which is a key component of Phishing 2.0. Through a 12-question survey that included examples of different types of media (human portraits, emails, SMS messages, and fictitious company logos), we analysed respondents’ ability to detect AI-generated content versus human-generated content. The survey was conducted among selected persons involved in critical infrastructure security, transport security, crisis management, and security management. The survey results show that although respondents were able to correctly detect AI-generated content in the vast majority of cases, success rates varied across media types. This paper highlights the need to improve users’ ability to detect AI-generated content, which would reduce the risk of phishing 2.0 attacks being successful.
