University of Finance and Administration

Digitalization is one of the key phenomena shaping the modern world, and its impact on various sectors of the economy is increasingly important. The development of the tourism sector is inextricably linked to the development of digital technologies. This study investigates the relationship between the level of digitization and the competitiveness of the tourism sector across European Union countries. Drawing on the literature review, the research hypothesis posits a significant association between these two variables. Using the statistics obtained from Eurostat on digitization, the synthetic Hellwig measure was calculated to create a ranking of the level of digitization of EU countries. Then, the Pearson correlation coefficient was calculated; that is, the relationship between the ranking of digitization and the ranking according to the synthetic Travel & Tourism Development Index 2024 (TTDI) was examined. The study shows a positive correlation between the level of digitization and the competitiveness of the tourism sector in European Union countries. Countries with higher levels of digitization tend to perform better in the TTDI rankings. The study contributes to the academic discussion focusing on building competitive and resilient tourist destinations.

As Artificial intelligence (AI) continues to redefine the boundaries of technological progress, industries are undergoing a fundamental transformation marked by automations, standardization, and strategic adaptability.Healthcare, a traditionally complex and data-intensive sector, is increasingly aligning with this shift.The application of AI in big data processing offers the potential to streamline operations, enhance analytical capacity, and reallocate human expertise toward higher-order thinking and innovation.This convergence not only facilitates more agile and responsive healthcare systems but also lays the groundwork for transformative, patientcentered models of care in the evolving digital era.The article discusses the current landscape of healthcare finance management, with a particular focus on the role of big data processing and the integration of Artificial Intelligence (AI).As healthcare institutions, including large hospitals, medical devices manufacturers, and pharmaceutical companies, grapple with increasingly complex financial operations, the need for efficient, data driven solutions have become critical.AI technologies offer significant potential to optimize financial processes such as but not limited -medical service recording, billing, invoicing, revenue cycle reporting, planning and budgeting, cost forecasting.By leveraging AI in the analysis of large-scale healthcare financial data, organizations can enhance accuracy, streamline workflows, and support strategic decision-making.Furthermore, the article explores how emerging AI technologies may continue to reshape financial systems, offering new opportunities for optimization, scalability, and long-term sustainability in the global healthcare sector.

Background: Developing young people’s technical and Information and Communication Technology skills is an SDG 4 target (4.4.), while the use of online educational material is a promoted tool to implement SDGs-related measures. Methodology: This is a case study exploring the complex role of ICT in teaching social sciences at a higher education institution using quantitative and qualitative research methods. Employing Urie Bronfenbrenner’s ecological systems theory, the research investigates how digital tools impact students’ academic performance across three courses with varying levels of ICT integration. The study evaluates students’ digital skills, compares the effectiveness of ICT-based and traditional teaching methods, and analyses how these methods influence the students’ comprehension and performance of the subject matter. Results: The findings revealed that while certain ICT skills and competencies enhance students’ academic performance, students value ICT tools differently depending on the course’s subject matter and their own digital competencies. The research also highlights the importance of teachers’ ability to blend ICT and non-ICT activities effectively to enhance students’ understanding. Conclusions: The study contributes to the ongoing discourse on ICT in education, emphasising the need for a more nuanced, multi-layered approach to understanding the intersection between technology and education, particularly in non-technical fields like social sciences.

Abstract This paper focuses on circumstances and consequences of health payers' configuration in Poland and Czechia since the 1990s. It aims to identify the differences in healthcare financing between the two neighboring countries that had started from a similar position, but eventually incorporated different arrangements of payers. The paper conducts comparative analysis of four distinctive elements: the configuration of contracting and payment methods, the power of the payers, coverage and contributions, and the dominant anchor's search attempts. The results map the evolution of healthcare in both countries with a focus on the payers' role, show the payers' economic position and behavior to providers, citizens and health policy authorities. Polish single‐payer system was identified as cheaper and more effective for administratively based expenditure control, while the Czech multipayer one showed higher institutional stability and a wider coverage paid by public health insurance. The paper brings an empirical assessment of existing theoretical knowledge concerning the options for a payers' configuration, demonstrating that the choice and adjustments thereof are country‐specific and depend on the preferred priorities of health policy.

Flying ad hoc network (FANET) is a new technology, which creates a self-organized wireless network containing unmanned aerial vehicles (UAVs). In FANET, routing protocols deal with important challenges due to limited energy, frequent link failures, high mobility of UAVs, and limited communication range of UAVs. Thus, a suitable path is always essential to transmit data between UAVs. In this paper, a local filtering-based energy-aware routing scheme (LFEAR) is proposed for FANETs. LFEAR improves the template of the route request (RREQ) packet by adding three fields, namely the energy, reliable distance, and movement similarity of the relevant route to create stable and energy-efficient paths. In the routing process, LFEAR presents a local filtering construction technique to avoid the broadcasting storm issue. This filter limits the broadcasting range of RREQs in the network. Accordingly, only UAVs inside this local filtered area can rebroadcast RREQs and other UAVs must eliminate these packets. After ending the route discovery process, the destination begins the route selection phase and extracts information about each discovered route, including the number of hops, route energy, reliable distance, and movement similarity. Then, the destination node calculates a score for each path based on the extracted information, selects the route with the highest score, and sends a route reply (RREP) packet to the source node through this route. Finally, the simulation process of LFEAR is performed using the NS2 simulator, and two simulation scenarios, namely change in network density and change in the speed of UAVs, are defined to evaluate network performance. In the first scenario, LFEAR improves energy consumption, packet delivery rate, network lifespan, and delay by 1.33%, 1.77%, 6.74%, and 1.71%, while its routing overhead is about 16.51% more than EARVRT. In the second scenario, LFEAR optimizes energy consumption and network lifetime by 5.55% and 5.67%, respectively. However, its performance in terms of routing overhead, packet delivery rate, and delay is 23%, 2.29%, and 6.67% weaker than EARVRT, respectively.

Over the past 25 years, few alternative sanctions have received as much attention as electronically monitored house arrest. In the view of relatively dynamic development of electronic surveillance technologies and related ethical and legal issues at stake, this interest continues to this day. In the Czech Republic, electronically monitored house arrest was introduced in 2010. Somewhat oddly, the electronic surveillance system had not been implemented at the time. Yet, legislators and sanctions policy makers placed high hopes in this form of punishment. In particular, it was expected to significantly help combat the relentless hypertrophy of the prison population. But the expectations of sanction policy makers were not met due to the reluctance of the courts to impose house arrest. This had remained unchanged over the years, and opinions had begun to emerge that the state's failure to introduce electronic monitoring was primarily to blame. In 2019, electronic monitoring was eventually implemented, but the number of sentences imposed still did not increase. If the legislature's sanctions policy is not translated into practice, its aims cannot be achieved. For this to happen, it is essential that house arrest becomes more prevalent in the structure of sentences imposed. Increased application rates will not happen spontaneously; certain steps need to be taken to address the reasons for the current state of affairs and to mitigate factors that negatively affect application practice. For this purpose, such causes and negative factors must first be identified. This paper therefore examines the importance of electronic monitoring in terms of the application practice of house arrest in the Czech Republic, and the main reasons for not imposing house arrest. Building on these findings, it offers suggestions that would contribute to more frequent imposition of house arrest in appropriate cases.

The objective of this paper is to use a meso-level empirical analysis to confirm the usefulness of the concepts of the Non-Accelerating Inflation Rate of Unemployment (NAIRU) and the Non-Accelerating Infection Rate of Unemployment (NARRU). The atypically negative values are consistent with the findings in an economy in transition and during a global financial and economic recession. A higher value of NARRU than NAIRU signals that the intensity of the government's counter-pandemic measures is inadequate to match the depth of the COVID-19 pandemic's impact on the labour market. The lower value of the positive gap between unemployment for the NAIRU concept versus that of NARRU implies that the impact of counter-pandemic measures on the labour market was smaller than that required by the pandemic burden on society. The government of the Czech Republic should have implemented a stricter lockdown or ensured a sufficient supply of respirator masks. Localised negative Phillips curve (PC) slopes at the meso level confirm the suitability of applying the NAIRU and NARRU concepts during the period of COVID-19. Ad hoc analysis and regression estimation of PC shifts on a rolling basis and a Break model in the last period analysed (April 2021 to March 2022) found that the PC in the first phase (April to August 2021) and in the fourth phase (February to March 2022) typically had a negative slope in both concepts. In the second phase (September and October 2021) and third phase (November 2021 to January 2022), the analysis located a PC with an atypically positive slope, which is related to fluctuations associated with the pandemic and measures against it.