Platforms

In the complex landscape of Industry 5.0, traditional management systems for smart manufacturing struggle to harmonize high-speed production with the rapid integration of AI and digital technologies. Crucially, these legacy frameworks often fail to capture tacit human knowledge or ensure trustworthy AI and trusted sharing of sensitive industrial data. This paper proposes a novel Decision Support Framework (DSF) that addresses these challenges through a multi-layered approach. At its core, the framework utilizes Data Spaces to enable secure, sovereign data sharing, ensuring that organizations maintain control over their assets. To handle the inherent ambiguity of industrial data, the system employs fuzzy logic and DAG-based root-cause-oriented investigation to provide robust recommendations, helping users distinguish descriptive correlations from plausible structural dependencies that require expert validation. Furthermore, the framework integrates eXplainable AI (XAI) services and AI-driven visual analytics, transforming complex algorithmic outputs into transparent, intuitive insights. By synthesizing data sovereignty with interpretable machine intelligence, this framework empowers trusted data sharing and human-centric decision-making, providing an advanced platform for achieving operational excellence within the Industry 5.0 vision. The proposed DSF is validated in three different pilot cases with end-users to be a milk industry, an automotive supplier and a machine manufacturer.

Objective: This paper investigates how Generation Z students perceive the quality, accessibility, and capacity of higher education in the Czech Republic, with relevance for the wider Visegrád (V4) region and for the design of educational platforms. Methodology: The study draws on a questionnaire survey of 819 students and analyses 38 Likert-type statements aggregated into five domains: digitalisation and technology, innovation in teaching, practical orientation and mobility, support and well-being, and capacity constraints and infrastructure. The analysis combines descriptive statistics, reliability and dimensionality checks, Pearson correlation analysis with normality and robustness diagnostics, and subgroup comparisons by gender, level of study, field of study, institution type, and age group. Results: Students report the strongest agreement with digitalisation and technology (M = 3.90) and practical orientation and mobility (M = 3.89), while support and well-being (M = 3.40) and capacity constraints and infrastructure (M = 3.36) remain weaker. Perceived adequacy of the student–teacher ratio is positively associated with perceived teacher availability (r = 0.37) and emotional support (r = 0.34), while teacher availability is positively associated with emotional support (r = 0.44). Subgroup tests indicate limited but meaningful differences, particularly by institution type, field of study, and age group. Conclusion: The findings suggest that Generation Z students value digitally enabled, practice-oriented and innovative educational platforms, but sustainable quality improvement also requires investment in human capacity, teacher availability, and student-facing support systems.

Technological and social development is desirable and even indispensable, which necessarily involves the restriction of new life situations within legal frameworks. European legislation has been visibly struggling with this problem in recent years, but the established/ongoing regulation may be an obstacle to development. Among other things, this includes the issue of regulating platform work. The emergence and spread of platform work has numerous advantages from an economic point of view, but from a legal point of view, the cautious regulation of this relatively new employment construction is not acceptable to the majority dealing with labour law. In our opinion, the relevant EU legislation is fundamentally flawed, as it basically seeks to answer the question of whether a given legal relationship is an employment relationship or not. The current binary classification might not be sufficient. Thus, the present study examines why platform work can be considered special and what are the labour law guarantees that are justified to be extended—at least as a rule—in this regard. To further investigate the practical risks of the current rules, a recent and relevant judgement of the Hungarian Supreme Court is also analysed in order to illustrate the uncertainties in litigation. The ruling demonstrates that traditional employment tests fail to recognise algorithmic control—including GPS surveillance, scheduling penalties, and unilateral remuneration determination—as indicators of subordination, while placing an insurmountable burden of proof on workers. This case empirically confirms the practical difficulties of the current binary classification. Our aim is to examine whether it is necessary to develop a minimum guarantee system that allows for easier transparency, greater legal certainty and a more uniform application of the law, unlike the current regulation.