University of Žilina

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.

High-strength low-alloy (HSLA) steels are primarily used in the segment of vehicle production. They are characterized by an advantageous combination of mechanical properties, such as high yield strength, tensile strength and ductility. In the production of steel structures, it is possible to use elements with smaller cross-sections. This reduces weight, fuel consumption and emissions. In this case, we can talk about the so-called "green material". An important technology in the production of such lightweight structures is welding. Fusion welding methods significantly heat the base material in the weld area, in the so-called heat-affected zone. High-strength steels are sensitive to short-term exposure to high temperatures. The so-called softening effect occurs, which is localized in a narrow metallurgical area of ​​the welded joint. One of the options for monitoring these structural changes due to welding is dilatometric tests. With their help, we can determine the transformation temperatures of austenite decomposition by controlled heating and cooling. Also the resulting microstructure and hardness of the steel for a given cooling rate. The aim of the paper is to determine, based on the analysis of dilatometry results, a welding in-situ CCT diagram that takes into account real temperature cycles during welding and the cooling rate in the form of the parameter t8/5. The control temperature cycles were designed according to the measured real welding cycles and cycles determined by simulation. The cooling rate was determined by the parameter t8/5 in the range of 2.2 s to 24.0 s. The results show that at the used cooling rates, the resulting microstructure is formed by bainite and martensite. This corresponds to a hardness in the range of 262 HV10 to 346 HV 10 for S960MC steel.

This paper investigates the load-carrying capacity of existing railway box-girder bridges through a comprehensive analytical approach. The reduced stress method is utilized, which determines stress limits for stiffened and unstiffened plates under combined loading conditions. This method offers an alternative to the traditional effective width approach used for plated structural elements. The paper presents three case studies, demonstrating the integration of global analysis using 2D finite element models, linear buckling analysis of plate/shell structure, and the verification of planar elements via the reduced stress method. The proposed procedure enables more realistic estimations of the load capacity of planar elements by accounting for the complex stress distributions within the panels, thereby enhancing the accuracy of structural assessments, especially details.