Transport and Telecommunication Institute

Financial institutions operate within an increasingly complex digital environment, shaped by rapidly evolving regulatory frameworks, heightened geopolitical uncertainty, and accelerated technological innovation. Digital transformation is impacted by the exogenous factors such as shocks or crisis such as the COVID-19 pandemic, inflation surges, [1] etc., as well as prevailing narratives surrounding technologies like artificial intelligence (AI), blockchain, and digital platforms [2]. However, with ever-evolving technology, in the end it is firms’ strategic decision to adopt ICT and innovation. In line with Romer’s endogenous growth theory (EGT), the technological investment of the banks is a deliberate strategic decision to support the growth [3]. This paper aims to analyze how strategic decisions are made and what is the underlying model or theory. The object of this research is the technology adoption process in European financial institutions; the subject of the research is the key decision-making endogenous factors. The study contributes to unexplored interconnection of Endogenous technological change theory and microeconomic dynamics of the European financial system.

Despite growing interest in predictive maintenance (PdM) within the aviation industry, the success of such initiatives often depends less on algorithmic performance or sensor capabilities and more on internal organizational competencies. This study examines how aviation companies’ internal structures shape the feasibility, scope, and sustainability of PdM projects. It introduces a decision-support framework that aligns the complexity of maintenance strategies with company-specific competencies across three key dimensions: technical (T), economic (E), and IT (I). A fourth dimension—Integration (M)—is added to capture the managerial ability to coordinate cross-functional collaboration effectively. The research classifies aviation companies into seven strategic groups and identifies which PdM strategies are realistically attainable based on current competency profiles. A dynamic project lifecycle model is also proposed, demonstrating how the evolution of these competencies over time influences project sustainability and return on investment. The paper makes two key contributions. First, it provides a practical framework for assessing organizational readiness and selecting PdM methods aligned with internal capabilities, thereby increasing the likelihood of project success. Second, it offers strategic recommendations for the broader aviation ecosystem—including airlines, MROs, OEMs, IT firms, and regulatory bodies—on how to build a more collaborative, standardized, and competency-driven environment for predictive maintenance adoption.

The primary objective of contemporary corporate reporting is to cultivate trust among a diverse range of stakeholders; in this regard, financial transparency is of paramount importance. The issue of financial transparency is a subject that is being discussed in contemporary academic literature and in practice. This is because there is no consensus among academics and practitioners on the essence of transparency and the criteria for measuring it, or on approaches to measuring transparency. This article aims to identify, through an analysis of scientific literature, what methods can be used to assess the transparency of financial statements, and to draw conclusions about the completeness and comprehensiveness of the information they provide. The literature review enabled the authors of this paper to identify six possible methods for assessing financial transparency, five of which were applied by the authors to the assessment of corporate reporting of 50 public IT and IT consulting companies for the year 2023. Thus, the aggregate number of cases under consideration was 250. To identify any potential discrepancies between traditional and sustainable performance indicators, a comparison was made with ESG scores. The findings of the study demonstrate that the assessments obtained through diverse approaches do not permit the drawing of unambiguous conclusions regarding the company's financial situation, nor do they facilitate a precise evaluation of the level of information transparency. Therefore, this fact motivates to further develop a universal tool for assessing and monitoring financial transparency which could be accessible to a wide range of stakeholders and easily manageable in a digital environment.

This research presents and evaluates an automated assignment assessment framework using GPT-5. The approach addresses limitations of manual grading by providing a scalable, consistent, and objective alternative for assessing student work across diverse academic disciplines. Educators can upload student submissions, optionally accompanied by grading rubrics, which the framework uses to produce evaluations based on either the uploaded rubrics or general academic standards. Key features include cross-document consistency checks, bias mitigation to support fair and responsible grading practices, and the generation of detailed formative feedback. The methodology was evaluated using a dataset of 604 authentic student submissions from eight university-level STEM courses—including Data Science Fundamentals, Probability Theory and Mathematical Statistics, Econometrics and other. A comparative analysis between human-assigned grades and model-generated evaluations revealed a strong Pearson correlation coefficient of 0.9569, with the majority of grade discrepancies falling within one grade point. These findings demonstrate the potential of large language models to enable consistent, scalable grading aligned with the principles of responsible AI and established academic standards.

This paper proposes and analyzes a symmetry-based shared-pool architecture for service continuity in UAV-assisted wireless sensor networks with aerial base station placement. Unlike the conventional dedicated architecture, in which individual sensor clusters are structurally associated with specific serving UAVs and replacement units provide cluster-centered redundancy, the proposed architecture treats all available UAVs as a common pool of functionally interchangeable service units that can be dynamically assigned to active clusters. The novelty of the approach lies in formalizing this functional interchangeability as permutation symmetry of the underlying stochastic model. Under homogeneous UAV reliability parameters and homogeneous cluster-demand characteristics, relabeling UAVs or non-tagged clusters does not change the system dynamics. This invariance permits the identity-dependent microscopic state space to be reduced to occupation-number macrostates defined by the number of unavailable UAVs and simultaneously active service demands, while symmetric access in overloaded states determines the tagged-cluster service probability. On this basis, an analytical availability model is developed that combines UAV failure–recovery dynamics, limited recovery capacity, stochastic cluster activity, and demand-driven allocation from a shared aerial resource. The proposed architecture is compared with a dedicated-service baseline under both equal-fleet and target-availability criteria. The results show that shared pooling can convert permutation-based functional symmetry into a practical fleet-efficiency gain: under low or moderate demand overlap and adequate recovery capacity, the same service-continuity target can be achieved with fewer UAVs than in the dedicated architecture, whereas the advantage decreases under strong demand overlap or severe pool depletion. The study therefore establishes a direct link between permutation symmetry, stochastic state-space reduction, shared redundancy, and UAV fleet efficiency in service-oriented aerial sensor networks.