The University of Finance and Administration (Czech: Vysoká škola finanční a správní o.p.s., VŠFS) is a private business school in the Czech Republic. It was founded by the Bank Academy and Czech Coal Group in 1999 and was one of the first private business schools in the country. It has had full university status from the accreditation committee of the Czech government since 2009.
Sustainable development of territorial units and the standard of living of inhabitants have recently become the subject of economic analysis and scientific research, as well as public debate. The research aimed to assess the position of European Union (EU) countries and changes in terms of sustainable development and the standard of living of inhabitants. In this paper, we applied a hybrid multi-criteria decision-making procedure for assessing the level of sustainable development and standard of living of residents in EU countries. This procedure is based on the modified positional technique for order of preference by similarity to the ideal solution (MP-TOPSIS). The research utilized data from the Eurostat database from 2015–2021. The standard of living of inhabitants of EU countries was assessed, as well as the level of their sustainable development. The results of the research made it possible to determine the relationship between the standard of living of the population and the level of sustainable development in EU countries. The conducted research revealed that the level of sustainable development of EU countries and the standard of living of inhabitants was strongly differentiated but increasing throughout analysed period.
This paper examines mainstream platform moderation as it encounters contemporary pagan religious practice in Europe and reads what it finds as a symptom of a wider condition: the algorithm functions less as a neutral tool than as a productive instrument of an extractive political economy whose characteristic operation is the appropriation, classification, and revenue-conditioned filtering of human expression. Minority religious traditions are structurally exposed within it: too small to constitute an accommodated market, too polysemic for classifiers trained on majority devotional and Anglo-American extremism corpora, and too fragmented to extract policy concessions. Empirically, the paper draws on netnography of pagan online communities across Europe, informal conversations with 97 practitioners across nine European jurisdictions, and a corpus of 247 takedown notices and appeal exchanges (2019–2025). Three recurrent rationale-clusters—devotional content classified as occult, as extremism-adjacent, and as unsafe activity—are read as predictable outputs of the system’s cost structure rather than as ordinary classifier errors. Two concepts are proposed. Algorithmic sacredness names the transfer of gate-keeping functions previously held by ecclesiastical, state, and editorial actors, routed through Bourdieu’s meta-capital as extended to platforms. Algorithmic pluralism names a programmatic direction toward infrastructures whose governance is not capital’s.
Emotional resilience is a key psychological competence enabling individuals to adapt positively to stress, overcome adversity, and maintain wellbeing in both personal and professional contexts. In dynamic, high-stakes environments such as security forces, it plays a critical role in professional performance, decision-making, and mental stability. This study analyses expert feedback from 224 personnel with operational experience to identify educational and training elements that enhance readiness, adaptability, and the capacity to cope with demanding situations. Rather than directly measuring emotional resilience, the research focuses on training-related factors perceived as contributing to its development. Using qualitative content analysis supported by statistical testing, several weaknesses within the current education and training system were identified. Based on the respondents' insights, the study proposes a revised training framework with a stronger emphasis on practical training, teamwork, international cooperation, and the involvement of external experts. The findings highlight the importance of structured educational interventions and continuous training programmes in fostering emotional resilience and strengthening professional competence within security forces.
The increasing number of connected vehicles and intelligent transportation systems has created a significant need for efficient offloading and resource management in vehicular networks. These networks face challenges such as high mobility, variable network conditions, and diverse resource types. Traditional centralized methods cannot handle these issues effectively. Scalable and decentralized solutions are necessary to reduce latency, energy use, and computational overhead while maintaining reliable task execution. This research introduces a Decentralized PDE-Guided Microservice Offloading and Resource Allocation Framework. It uses a Multi-Agent Deep Reinforcement Learning (DRL) approach. The system is modeled as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP). This allows vehicles and fog nodes to make localized decisions. We model vehicle density as Partial Differential Equations (PDEs) and Directed Acyclic Graphs (DAGs) to represent microservices at a fine-grained level. The framework applies Multi-Agent Proximal Policy Optimization (MAPPO) to optimize task splitting, offloading, and routing. Simulation results show the framework’s strong performance. Compared to baseline methods, it achieves up to 21.14% lower task completion time, 23.73% energy savings, and 17.75% reduced offloading latency.
In flying ad hoc networks (FANETs), high node mobility, dynamic topology, and limited resources, such as energy and bandwidth, lead to unstable links and short-lived routes. In such an environment, although Q-learning-based routing methods are adaptable, they face serious challenges in practice due to large state space, high computational load, and slow convergence. To address these issues, this paper proposes a two-level Q-learning-based geographic routing protocol called TLQ-Geo for FANETs. This protocol integrates hierarchical decision-making with adaptive reinforcement learning. TLQ-Geo divides the routing process into two layers: the guided region selection (GRS) layer and the Q-learning-based routing (QRL) layer. The GRS layer determines a bounded search corridor between the source and the destination using a chain of intelligent decision points (IDPs), while the QRL layer performs distributed path optimization within this virtual corridor via Q-learning. By restricting the state space to the region guided by IDPs, TLQ-Geo significantly reduces convergence time and computational overhead. In addition, a dynamic inter-layer feedback mechanism periodically evaluates the performance of each IDP chain and adaptively reconfigures it under topology variations. Extensive simulations demonstrate that when the node density varies, TLQ-Geo achieves higher network lifespan (approximately 4.51
