Journal of Next-Generation Research 5.0
Not a member yet
7559 research outputs found
Sort by
Financial and Operational Impacts of Regulatory Compliance on the Austrian Securities Industry
Full text linkRegulatory compliance in the Austrian securities sector imposes fixed costs that affect market participants asymmetrically. Larger firms can absorb these burdens more efficiently through economies of scale, whereas smaller providers operate with tighter margins and face higher relative cost ratios. This study models profit as a function of revenue, operational costs, and regulatory burdens, including mandatory contributions to the Austrian Financial Market Authority and the Austrian Economic Chamber, administrative procedures, information technology systems, external consultancy, and expected sanctions. Empirical support is based on survey data from 53 licensed financial service providers and three expert assessments conducted in 2024 and 2025. Smaller firms report significantly higher compliance burdens in proportion to their revenue and indicate that administrative pressure influences their market participation. Many respondents allocate a substantial share of working time to regulatory tasks, indicating considerable operational strain. The imbalance between regulatory design and firm size contributes to market consolidation and a decline in provider diversity. Feedback highlights excessive documentation, procedural duplication, and limited responsiveness from supervisory institutions. International comparisons demonstrate the feasibility of proportional regulatory models and thresholds for small and medium-sized enterprises. The integration of quantitative modelling and empirical evidence provides a structured basis for assessing compliance costs and supports policy reforms focused on proportionality, administrative simplification, and effective supervision
Artificial Intelligence in Economic Relations in the Small and Medium-Sized Enterprise Sector
Full text linkThis article analyses the impact of artificial intelligence on economic relationships in the small and medium-sized enterprise sector, based on empirical research conducted among companies in the Wielkopolska region. The aim of the study was to assess the extent to which implementing AI tools influences inter-organizational collaboration, process automation, operational efficiency, and the level of trust and satisfaction of business partners. The study utilized a diagnostic survey (CAWI) method and analysed 620 companies using digital solutions, including AI systems. The results indicate that AI has become a significant factor in the transformation of economic relationships – particularly in the areas of process automation, streamlined communication, improved financial performance, and increased innovation. Barriers related to limited system interoperability, a lack of digital skills, and regulatory uncertainty were also identified. The research confirms the existence of a statistically significant relationship between the level of AI use and the quality of economic relationships. The conclusions indicate that the development of AI in the SME sector requires not only technological investments but also educational, organizational, and regulatory activities supporting responsible and effective digital transformation
Generic Agnostic AI and Distributed Ledger Enterprise System for Scalable Domain Adaptation
Full text linkThis paper presents a formal system model integrating artificial intelligence and decentralised verification in higher education, aligned with European regulatory frameworks. The architecture comprises two components: EduAI, a multi-agent AI framework structured by institutional roles; and EduDVS, a distributed verification system for regulatory audit, credential authentication, and tamper-evident recordkeeping. Legal instruments such as the GDPR, EU AI Act, EQF, ECTS, and ESG are encoded as structural constraints. The system also supports financial regulations including MiFID II, MiCA, AMLD, DORA, and Swiss counterparts (FMIA, FinSA, FADP). EduAI agents are defined by stakeholder class and governed by a constrained optimisation function ensuring compliance with legal boundaries. EduDVS operates on a DAG-based, permissionless ledger maintained by a federated academic consortium. It supports verifiable academic tokens, programmable stablecoins, and audit-ready interactions. The combined model offers a regulatory-compliant infrastructure with practical use in cross-border academic environments. The framework enables regulatory prototyping, governance simulation, and structured empirical validation
Digital Humanities and Museums: Cultural Practices and Ethical Issues
Full text linkThe humanities and social sciences (HSS) encompass a wide range of disciplines, from psychology to sociology, as well as anthropology, history, and many others. These disciplines analyze social relationships and their dynamics, thereby influencing how societies evolve. They play a crucial role in understanding social issues and contribute to solving certain problems and challenges. In this context, the evolution of information and communication technologies, and more specifically digital technologies and artificial intelligence, has profoundly impacted the HSS. The rise of digital humanities contributes to a significant reconfiguration of human practices and activities, both across society and in artistic and cultural domains, particularly in museum practices. However, AI raises questions about the authenticity and aura of artworks and brings up ethical, technical, and social challenges. This prompts a fundamental question: to what extent do digital humanities contribute to the preservation of cultural specificities while raising new ethical issues? Through this article, we aim to examine this issue by referring to case studies of digital museums in two culturally different countries, highlighting the role of artificial intelligence in facilitating access to and democratizing artistic and cultural practices. We also aim to emphasize the main challenges and issues resulting from this transformation
Federated AI Infrastructure with Verifiable Storage and ESG Integration
Full text linkCentralised AI infrastructure scales but conflicts with latency, auditability, and energy constraints. This paper sets the objective to specify and analyse a Federated AI Infrastructure that aligns with regulatory and ESG commitments while remaining financeable for private operators. The design separates centralised training from decentralised inference and storage across five node classes (μ, S, M, L, XL), coordinated by a verifiable orchestrator and a permissioned DAG implementing asynchronous Byzantine fault tolerance. An incentive model ties a size-neutral availability floor to tiered workload rewards, applying bounded multipliers for service-level attainment, ESG performance, and anti-concentration. Formal optimisation spans investor allocation, congestion-aware routing, and policy instruments, yielding equilibrium conditions for mixed-class participation. Compliance is developed against the Swiss regime, including token-to-fiat conversion through a regulated issuer under FINMA or an equivalent national authority, and aligned with EU frameworks such as the GDPR and ISO 27001. Results indicate a resilient mixed fleet: L and XL nodes concentrate on throughput-intensive inference and ledger validation, while μ to M nodes provide edge inference, storage, and continuous DAG activity. Anti-concentration terms and ESG-adjusted pricing sustain node diversity without material efficiency loss. Implementation depends on trusted metering, on-chain attestations, and posted pricing calibrated to observed queue depths. Limitations include parameter identification, metering fidelity, and jurisdiction-specific licensing
Comparison of Meshless and Finite Element Methods for Elastic–Plastic Assessment of HIPPS Pressure Vessels
Full text linkThis article compares the meshless method and the classical finite element method in the case of elastic-plastic analysis of pressure vessels. The finite element method has proven to be a robust approach over the years and has been continuously developed for both static and dynamic analyses, as well as linear and nonlinear applications. However, the use of the finite element method presents some intrinsic critical issues, such as the generation of the calculation grid or mesh and dependence, leading to the introduction of methods that do not require the explicit generation of a calculation grid, or meshless methods. The purpose of this paper is to review the performance of a meshless resolution compared with the traditional mesh-based approach, in the case of the verification of industrial HIPPS valves, focusing on the precision and quality of the results obtained as well as the operating cost, assessing the method’s operational advantages at an industrial level
The Transforming Clinical Practice: The Role of AI-Powered Medical Assistants in Enhancing Healthcare Efficiency and Decision-Making
No full textIntegrating Artificial Intelligence (AI) into healthcare systems fundamentally transforms clinical workflows by augmenting diagnostics, documentation, and patient engagement. AI-powered medical assistants, driven by Natural Language Processing (NLP) and Machine Learning (ML), facilitate operational efficiency, mitigate clinician burnout, and improve quality and continuity of care. This study critically examines the impact of AI medical assistants on clinical productivity, patient outcomes, and administrative operations. Through a systematic literature review of peer-reviewed studies, case analyses, and empirical evaluations, we identify core use cases where AI contributes measurable gains, such as enhanced documentation accuracy, optimized triage, and reduced clerical workloads. These systems, often integrated with Electronic Health Records (EHRs), enable real-time data capture, automated symptom screening, and tailored treatment suggestions. Despite their benefits, adoption is constrained by algorithmic bias, data governance challenges, and professional resistance. This paper underscores the transformative potential of AI assistants in clinical settings while emphasizing the need for ethical frameworks, interoperability standards, and robust regulatory compliance to ensure safe, equitable, and effective AI deployment
A regulatory-compliant AI and verification system for higher education under ESG-aligned constraints
Full text linkThis paper introduces a formal system model for integrating artificial intelligence and distributed verification intohigher education under European regulatory constraints. The architecture consists of two interlinked components:EduAI, a role-specific, multi-agent artificial intelligence framework for institutional operations; and EduDVS, adecentralised verification infrastructure for regulatory audit, credential authentication, and tamper-evident recordkeeping. The model encodes legal instruments such as the GDPR, EU AI Act, EQF, ECTS, and ESG directivesas structural system constraints, with additional compatibility for financial frameworks including MiFID II, MiCA,AMLD, DORA, and Swiss equivalents (FMIA, FinSA, FADP). EduAI agents are formalised by stakeholder classand governed by a constrained optimisation function ensuring legally admissible outputs. EduDVS operates as aDAG-based, permissionless ledger maintained by a federated educational consortium, supporting verifiable academictokens, programmable stablecoins, and audit-ready interactions. Results are presented as a theoretical framework forcompliant digital infrastructures, with direct applicability to cross-jurisdictional academic ecosystems. The modelprovides a foundation for regulatory prototyping, governance simulation, and controlled empirical validation
Tiered Compliant AI System for Regulated Financial Institutions: ulti Agentic Execution Capable Framework with Built-In DLT Audit Trails for Financial Operations in DACH
Full text linkWe present a compliance-first architecture for AI in regulated finance that treats regulation as an orientation layer rather than a deterministic ruleset. A matrix of regulatory intent and exposure provides a compact classification handle, which a governed policy compiler then maps into concrete prohibitions, obligations and runtime budgets. Prohibitions constrain feasibility and block externalisation, while obligations extend tasks with artefacts that must meet explicit admissibility criteria. Committee activation remains policy-driven and proportionate, preserving efficiency while ensuring supervisory oversight. Evidence, decisions and reason codes are bound to a permissioned DAG with deterministic timestamping, enabling replay, provenance checks and clear attribution of failure. Clause-level legal indexing with effective dates and capability-based agent routing ensure portability across DACH and the wider EU. The result is assurance by construction: compliance is embedded in execution and verifiable by auditors without sacrificing proportionality or transparency
Differentiated Modelling of Emotions by Artificial Intelligence: A Comparative Analysis of GPT, Deepseek and Gemini
Full text linkThis article presents an exploratory study on how three generative artificial intelligence models – ChatGPT (GPT), Deepseek (DS) and Gemini (GEM) – highlight emotions in a stock market simulation context. The aim is to compare the evolution of the emotional profiles produced by these models based on queries representing increasingly emotionally charged situations. These queries are part of a progressive sequence: a semi-structured post-experiment interview (Q1), consideration of simulated stock market performance (Q2), a market configuration perceived as negative (Q3), the introduction of a gender factor (Q4) and the addition of a competitive element linked to a financial reward for students (Q5). The AI responses were analysed using an emotional typology based on nine emotions (fear, happiness, sadness, optimism, disgust, positive surprise, negative surprise, positive anticipation, negative anticipation) associated with their affective valence. The data were then studied according to a dual logic: counting the emotions by AI and by query and evaluating the dominant or ambivalent emotional valence of each response. The results highlight significant differences between the models. GPT adopts an overall pessimistic emotional profile, characterised by a high recurrence of fear and negative anticipation. GEM follows a similar trend, although slightly more nuanced. Conversely, DS exhibits more ambivalent pattern, articulating positive and negative emotions within a more contrasting dynamic. Beyond the inter-model comparison, the study highlights the importance of parallel human reading in the interpretation of emotional productions. It emphasises the need for a critical approach to assessing the consistency, relevance and contextualisation of the affects produced by AI, particularly in simulated environments. This research thus opens perspectives on how AI can potentially be integrated into emotional analysis or mediation systems and calls for interdisciplinary dialogue between communication sciences, affective sciences and artificial intelligence development.