The accessibility of FinTech services is increasing, and their convenience is making them more popular than traditional banks, particularly among Generation Z. The objective of this research is to identify and compare the factors influencing the conscious use of FinTech services among Generation Z members, who are the most active participants in this field of financial technology. The questionnaire based purposive sample consisted of Generation Z students who demonstrated adequate financial literacy and utilized FinTech, and who were learning in a university environment in Hungary and Romania. A sample of 600 respondents was selected for analysis after cleaning the data online. The methodological approach entailed the utilization of covariance-based structural equation modeling (CB-SEM). The results indicate that social influence (β = 0.18), consumer attitude (β = 0.53) and facilitating conditions intention (β = 0.11) all have a significant effect on the behavior intention, explaining 49% of the variance. In the context of performance expectation, the effect of facilitating conditions intention is not significant (p = 0.491). The motivation of Generation Z towards fintech solutions is evident in their preference for speed and ease of use. However, in order to reinforce consumer expectations and transfer the necessary experience and attitudes, it may be beneficial for service providers to adopt a partially different strategy in different countries. Generation Z can thus serve as a crucial reference point for the even more discerning expectations of subsequent generations. The findings may inform the formulation of strategies for fintech service providers to better understand customer behavior.

In the evolving landscape of the 21st century, universities are at the forefront of re-imagining their infrastructural identity. This conceptual paper delves into the transformative shifts witnessed within university infrastructure, focusing on the harmonisation of tangible physical assets and the expanding world of digital evolution. As brick-and-mortar structures remain pivotal, integrating digital platforms rapidly redefines the academic landscape, optimising learning and administrative experiences. The modern learning paradigm, enriched by this symbiotic relationship, offers dynamic, flexible, and comprehensive educational encounters, thereby transcending traditional spatial and temporal constraints. Therefore, this paper accentuates the broader implications of this infrastructural metamorphosis, particularly its significant role in driving economic development. The synergistic effects of physical and digital infrastructures enhance academic excellence and position universities as key players in addressing and navigating global challenges, setting forth a resilient and forward-looking educational blueprint for the future. In conclusion, integrating physical and digital infrastructures within universities heralds a transformative era, shaping a holistic, adaptable, and enriched academic environment poised to meet 21st-century challenges. This study illuminates the symbiotic relationship between tangible university assets and digital innovations, offering insights into their collective impact on modern education and broader economic trajectories.

This article describes a classification tool to cluster SARAL/AltiKa waveforms. The tool was made using Python scripts. Radar altimetry systems (e.g., SARAL/AltiKa) measures the distance from the satellite centre to a target surface by calculating the satellite-to-surface round-trip time of a radar pulse. An altimeter waveform represents the energy reflected by the earth’s surface to the satellite antenna with respect to time. The tool clusters the altimetric waveforms data into desired groups. For the clustering, we used evolutionary minimize indexing function (EMIF) with k-means cluster mechanism. The idea was to develop a simple interface which takes the altimetry waveforms data from a folder as inputs and provides single value (using EMIF algorithm) for each waveform. These values are further used for clustering. This is a simple light weighted tool and user can easily interact with it.

In this study, the entropy weight method, the α convergence model, the absolute β convergence model and the conditional β convergence model are used to evaluate the 31 provinces’ innovative potential in China from 2011 to 2022. It is found that the innovative potential in nationwide China and in various regions are all increasing year by year, and the innovative potential in the eastern region is obviously better than that in the central region and western region. No matter considering the influence of external factors or not, the gap of innovative potential among provinces in different regions will gradually expand over time, with the largest gap among provinces in the eastern region, followed by the central region and the smallest in the western region. The conclusion of this study is instructive to enhance the innovative potential of China and promote the balanced development of regional innovative potential in China.