Purpose – This study aims to explore how the Human Development Index (HDI) is associated with students’ perceived academic, personal and skill-development outcomes related to the integration of generative artificial intelligence, particularly ChatGPT, into higher education. From a knowledge management perspective, the research examines adaptive use of AI tools, structuring of information and support of autonomous learning in countries with varying development. Design/methodology/approach – The study draws on 11, 910 valid responses from the 2024 Global ChatGPT student survey, covering 58 countries. Based on 33 Likert-scale items, three reflective constructs were identified. To explore the relationships between HDI, usage intensity and perceived impacts, the analysis combined descriptive statistics, K-means clustering and a partial least squares structural equation modeling (PLS-SEM) mediation model. Findings – The regression analysis showed a weak but statistically significant negative correlation between HDI and perceived impacts: students from lower-HDI countries tended to view ChatGPT’s impacts more positively. The PLS-SEM results indicated that higher national development is associated with lower perceived academic, developmental and skill-related benefits. This relationship appears both direct and indirect, as students in more developed countries report using ChatGPT less frequently and less creatively for academic purposes. Practical implications – The findings highlight the need for context-sensitive, pedagogically grounded artificial intelligence strategies, particularly in highly developed countries and in the support of students from disadvantaged backgrounds. Originality/value – This study is among the first to examine how national development levels shape perceived ChatGPT impacts in higher education. By combining HDI, cluster analysis and mediation modeling, it offers a novel perspective on digital inequality.

Purpose – The study aimed to examine family-friendly practices of employers in Hungary and compared public and private sector organisations to better understand their approaches to promoting the work–life balance of employees. Design/methodology/approach – A cross-sectional, quantitative survey was carried out between April and June 2023, involving 702 organisations certified as family-friendly workplaces in Hungary, of which 101 managers responded. The data were analysed using descriptive statistics and Pearson’s chi-squared tests to examine associations between sector and the prevalence of family-friendly practices. Findings – The findings indicate sectoral variation in the implementation of family-friendly practices. Private sector organisations more frequently reported flexible working arrangements, whereas public sector employers more often reported traditional measures such as childcare services and child-friendly work environments. Teleworking was available in 87.3% of private organisations, compared with 67.4% of public organisations (p = 0.016). Similarly, part-time work was offered by 87.3% of private organisations and 63.3% of public organisations (p = 0.004). Conversely, on-site childcare (52.2% vs. 21.8%, p = 0.002) and child-friendly workplaces (58.7% vs 23.6%, p < 0.001) were more prevalent in the public sector. Research limitations/implications – The study is geographically limited to Hungary and focuses mainly on certified family-friendly workplaces, which may not represent all organisations uniformly. However, it provides a basis for benchmarking international research on family-friendly workplace policies. In addition, it does not include a qualitative study to provide more in-depth insight or to capture the views of employees, but identifies further research directions in this way. Practical implications – Based on these findings, organisations should consider implementing family-friendly policies that better promote work–life balance. The study recommends that the public sector extend flexible working arrangements and increase family benefits to improve recruitment and retention. Conversely, private sector employers should focus on developing childcare facilities on-site and fostering family-friendly work environments to support their employees’ needs effectively, thereby increasing job satisfaction. Social implications – A better understanding of family-friendly workplace policies can help to raise awareness of the importance of promoting work–life balance. The implementation of these policies has the potential to improve the well-being of employees and contribute to wider societal goals, including gender equality, family stability and the development of a more sustainable society. Originality/value – The study provides a comprehensive analysis of family-friendly workplace policies in the various sectors in Hungary, highlighting sectoral strategies and practical recommendations for better promoting work–life balance and organisational efficiency.

Purpose – The purpose of this study is to identify the key labour-market skills, the challenges graduates face and recommendations for aligning higher education with employer expectations in Hungary, a Central European country. This study underscores the importance of soft skills, such as adaptability, problem-solving and independence, for graduates in the Hungarian labour market. Design/methodology/approach – A cross-sectional survey was conducted among all students of a Hungarian university involved in work placements (n = 1, 556). The research subjects were the organisations where undergraduate students from a Hungarian university completed their practical semester, and a total of 1, 556 student evaluations were processed. The study analysed 15 distinct skills suggested by NACE and identified four factors: agility, professional knowledge, interpersonal skills and generic skills. Findings – The study's findings indicate that agility, encompassing adaptability, problem-solving skills and independence, emerged as the paramount criterion in employers' evaluations. The findings indicate substantial disparities in the perceived significance and assessment of skills across diverse academic disciplines. The research findings show that, in the labour market, soft skills may be more significant than technical expertise. The subsequent development of the skills and competence measurement tool identified in the literature review is proposed, particularly with respect to the hard and soft skills of the field of study. Research limitations/implications – The main limitation of this research is the use of a non-validated measurement instrument developed for internal quality management, which may lack scientific rigour. Additionally, the application of a uniform competency framework across all disciplines prevents the study from capturing the specific skill nuances required by different professional fields. Practical implications – Universities should move beyond simply adding soft skills modules and instead embed cognitive and interpersonal development across all curricula. Key mechanisms for this include structured industry partnerships, work-integrated learning and a shared competency framework that aligns academic content with specific labour-market expectations. Social implications – By addressing the skills mismatch, these findings foster a more efficient transition from education to employment, ultimately enhancing graduate career prospects. Recognising the hierarchical structure of skills, where technical knowledge is the baseline but agility and interpersonal skills drive advancement, helps society better prepare a workforce capable of long-term professional growth. Originality/value – This study underscores the significance of soft skills in the Central European/Hungarian labour market context, offering direct evidence from employer evaluations of student work placements. The implications of this phenomenon extend to the need for educational reform that integrates soft skills training. Furthermore, there is a compelling need for enhanced collaboration between universities and employers. Additionally, policy adjustments are imperative to ensure the successful implementation of these changes. Aligning educational curricula with employer expectations can help mitigate the skills gap and stimulate economic growth in Central Europe.

Capacity sizing and calculating cost savings for residential households in a rapidly evolving energy market, influenced by fluctuating electricity prices and changing government incentives, is a highly complex problem. The key challenges stem from multiple interacting factors, including retail electricity prices, the desired payback period, household size, applicable electricity schemes, and the capacity factor of the photovoltaic (PV) system. The nominal power output of the solar energy system is constrained by both the specifications and the number of installed inverters and PV panels. As solar generation is intermittent and non-dispatchable, it is inherently weather-dependent and often unable to align with the dynamic fluctuations in household electricity consumption. From a financial modelling perspective, the length of the accounting period directly determines the time resolution of the model, influencing both the accuracy of cash flow estimation and investment decision-making. The proposed two-level investment planning model is based on the process network synthesis approach. At the upper level of the process model, solar generation technologies, including inverters and solar panels, are technically and economically assessed. At the lower level, which represents the load consumption side, the periodical energy balances for production, storage, demand, and purchase are considered. In order to accurately evaluate the solar energy system, the model is developed with both a monthly framework and a detailed hourly framework. The time resolution allows the model to account for grid intake, electricity sold, and storage inventory conditions over the defined periods, ultimately providing the optimal sizing for a solar system equipped with battery storage. Case studies are conducted to investigate the effects of household size, extended payback periods, varying retail electricity prices, and grid reliability. These scenarios demonstrate the key parameters that significantly influence the economic feasibility and optimal sizing of the solar energy system, which are discussed in detail in this paper.

Finite control set model predictive control (FCS-MPC) has emerged as a powerful strategy for permanent magnet synchronous motor (PMSM) drives. However, its performance strongly depends on appropriately chosen weighting factors, which directly affect control quality and, in some cases, may even lead to instability. Despite the crucial role of weighting factors, there is no systematic or generally accepted procedure for selecting their values, which limits the robustness and practical applicability of conventional FCS-MPC methods. To overcome this limitation, this paper presents the experimental validation of a sequential direct speed predictive control strategy for PMSM. The individual cost functions are evaluated sequentially, thereby tuning is simplified and weighting factors are reduced. Experimental results show that the original version of sequential direct speed control, as proposed in the literature, exhibits promising dynamic performance but suffers from instability and current ripples under certain conditions. To address these issues, an enhanced version of the sequential direct speed predictive control is proposed in the paper. It effectively suppresses instabilities and enhances the speed dynamic response of the drive. The proposed approach was experimentally validated using the OP 5600 rapid control prototyping platform running RT-LAB software and a 1.1 kW PMSM machine.

This paper investigates the role of institutional quality and democratic governance in enhancing environmental performance across global economies. As countries confront escalating environmental challenges, understanding these factors is crucial for achieving sustainable development. Despite existing literature emphasizing the importance of high-quality institutions and democratic governance in improving environmental performance, empirical evidence remains inconsistent. Our study refines this understanding by exploring how specific dimensions of institutional quality and varying degrees of democracy impact environmental performance. Basically, we use a stochastic directional distance model to assess how democracy and institutional quality directly affect environmental performance in an unbalanced heterogeneous sample (127 economies) between 1996 and 2018. We find that high institutional quality generally improves environmental performance, though its impact varies with different degrees of democracy. The synergy between higher institutional quality and democratic governance enhances environmental performance, though the effects’ magnitude and direction are context-dependent. This paper provides new insights into how institutional quality and democratic governance work together to enhance environmental performance, offering valuable implications for policy development aimed at balancing economic growth with ecological sustainability.

This study examines the alignment between the Sustainable Development Goals (SDGs) and the Environmental, Social, and Governance (ESG) pillars through the lens of the European Sustainability Reporting Standards (ESRS) taxonomy, complemented by the Global Reporting Initiative (GRI). The research introduces a policy-relevant framework that categorizes SDG targets within ESG pillars, offering structured guidance for policymakers and regulatory bodies to harmonize global sustainability goals with corporate reporting practices. By mapping 199 GRI and 201 ESRS accounting entries to the 17 SDGs, the study identifies significant opportunities to address thematic and procedural gaps in existing reporting systems. The findings demonstrate that SDG 8 (“Decent Work and Economic Growth”) exhibits the highest linkage rate to ESRS accounting items, reinforcing its relevance for policy-driven frameworks that integrate economic resilience with social equity. This harmonized approach underscores the role of policy in fostering alignment between corporate ESG strategies and broader sustainability objectives, mitigating greenwashing risks, and advancing standardization across regions and sectors. The study advocates for policy interventions that leverage this framework to enhance transparency, accountability, and long-term decision-making for sustainable development.

This paper introduces a novel method for the enhancement of automated vehicle safety and efficiency during critical manoeuvres. The fundamental of the presented method is the observer design architecture, in which lateral dynamic states of the vehicle are evaluated. The novel observer consists of both model-based and machine-learning-based methods to ensure the selected design performances, such as efficient trajectory tracking and safety evaluation of the autonomous vehicle. In contrast to the already introduced and applied stability index-based methods, the proposed safety evaluation process is able detect stability loss and performance degradation of the autonomous vehicle. In the proposed observer-based safety evaluation method, stability and performance loss detection is based on the comparison of model-based and learning-based state observation. The main novelty of the paper is the design of the reinforcement learning (RL) based observer in a guaranteed structure that results in small observation error even under nonlinear vehicle dynamics. Furthermore, a lateral safety index is defined based on the value of the improvement vector representing the addition to the model-based estimation. By this means, with the proposed safety evaluation method both safety and performance loss hazards can be identified simultaneously.

Microplastic (MP) pollution in river systems has become an increasing environmental concern, particularly in transboundary rivers such as the Danube. This study provides the first detailed assessment of microplastic contamination in the Szigetköz section of the Danube and its major tributary, the Mosoni-Danube. Depth-resolved pumped water samples were collected at three locations (Rajka, Mecsér and Gönyű) and analysed using Fourier Transform Infrared (FTIR) spectroscopy combined with automated spectral evaluation. MP concentrations showed a clear downstream increase, with average values of 83.8 particles/m³ at Rajka, 237.6 particles/m³ in the Mosoni-Danube at Mecsér, and 795.9 particles/m³ at Gönyű. Polyethylene (PE) was the dominant polymer in the tributary (70.6%), whereas both PE and alkyd resins were prevalent at the main Danube sites (Rajka: alkyd 37.7%, PE 31.8%; Gönyű: alkyd 39.9%, PE 37.3%). Particle size distribution also shifted downstream, with a higher proportion of smaller (50–100 μm) particles detected at Gönyű compared to upstream sites. The results suggest that the tributary may represent an important input to the main Danube channel in this section, while differences in polymer composition point to varying source characteristics within the study area. These findings provide an important baseline for future monitoring and support the development of targeted mitigation strategies in this transboundary river system.