Search Results (1,545)

This research examines the contemporary policy framework through which the European Union seeks to counter-disinformation, situating this agenda within the broader challenges posed by ongoing democratic backsliding. It focuses on the EU media regulatory architecture and addresses the institutional and normative capacity of candidate countries such as Serbia to achieve alignment. The applied theoretical framework conceptualizes the disinformation within the contemporary processes of democratic backsliding and the growth of autocratization. The analysis further explores the regulatory challenges that arise in the context of current autocratization processes. The study is guided by two central research questions: (1) What are the main challenges associated with defining and implementing counter-disinformation policies within the EU and Serbia? (2) How do differences in political systems shape the respective approaches to countering disinformation? The research design combines theoretical source analysis with descriptive comparative methods. The paper maps the principal obstacles that hinder Serbia’s alignment with the EU’s evolving media regulatory framework. Also, the findings highlight several contextual patterns that characterize the approaches to disinformation across EU member states and Serbia. Full article

Behavioral steering—through nudges, defaults, incentives, and informational feedback—has become a dominant approach in promoting sustainable consumption. Drawing on a selective, problem-oriented engagement with the related literature, this study notes that a substantial body of studies has reported challenges associated with such interventions, particularly with regard to durability, cross-domain spillovers, and context sensitivity. Rather than providing an exhaustive empirical synthesis, this study uses these findings diagnostically to identify underlying conceptual tensions in prevailing policy approaches. It argues that one contributing source of these limitations lies in an implicit and narrow conception of consumer agency, which frames individuals primarily as reactive decision-makers rather than as agents whose habits, dispositions, and practical judgment develop over time through participation in social practices. Integrating insights from virtue ethics and social practice theory, this study develops a normative framework that emphasizes the cultivation of stable orientations, competencies, and dispositions. It further explores how this framework can inform the design and evaluation of a series of policies and institutions, such as learning-oriented interventions, participatory programs, and practice-enabling infrastructures. By offering a normative diagnosis of problems emerging in empirical sustainability research, the study outlines promising directions for more resilient and ethically grounded sustainability governance. Full article

This study proposes a new formalized approach to the stabilization of linear transformations in artificial neural networks, based on discrete algebraic properties. In contrast to existing stability methods that rely on spectral norms, regularization techniques, or empirical heuristics, this work introduces the concept of algebraic stabilization—stability that arises from the structural properties of the matrices defining linear operators. The central object of investigation is the class of integer-valued matrices for which exponentiation to a form of the type ${\mathrm{W}}^{\mathrm{k}}=\mathrm{I}+\mathsf{\mu }\mathrm{D}$ is possible, where $\mathrm{D}\in {\mathbb{Z}}^{\mathrm{n}\times \mathrm{n}},\mathsf{\mu }\in {\mathbb{Z}}_{>1}$. A well-known problem in group algebra is considered that guarantees the existence of such an exponent under the condition that $\mathsf{\mu }$ is coprime with the determinant of $\mathrm{W}$. Within this framework, modular arithmetic, reduction modulo $\mathsf{\mu }$, and the group structure of ${\mathrm{G}\mathrm{L}}_{\mathrm{n}}\left({\mathbb{Z}}_{\mathsf{\mu }}\right)$ are employed, thereby linking the proposed method to the theory of finite groups and linear automata. The advantages of the approach are discussed, including formal control over the iterative behavior of transformations, compatibility with quantized and finitely representable networks, the possibility of embedding stabilizing conditions directly into the network architecture, and the potential to improve model interpretability and reliability. At the same time, limitations are identified, particularly those related to constructive implementation, the selection of suitable hyperparameters, and generalization to broader classes of transformations. Full article

The regulatory framework governing EU electricity markets is highly complex, fragmented across multiple normative acts and sensitive to citation accuracy and contextual completeness. While Large Language Models (LLMs) offer promising capabilities for regulatory question answering (QA), their tendency to hallucinate legal references and omit critical conditions makes them unreliable for compliance-sensitive domains. This paper presents the design of a domain-specific Retrieval-Augmented Generation (RAG) system for EU electricity market regulations, explicitly engineered to deliver source-grounded, traceable and low-hallucination answers. The answering component is based on Google’s gemini-2.5-flash model. The Open AI’s gpt-4o-mini model is responsible for both relevant document selection before building the RAG prompt and playing the judge LLM role for Retrieval Augmented Generation Assessment (RAGAS) evaluation. We build a legal corpus comprising multiple core EU regulatory acts related to REMIT and market operation and propose a regulatory QA architecture that integrates: (i) three chunking strategies (article-based, structure-aware, sliding window), (ii) two embedding models and (iii) a novel LLM-based document selection agent that restricts retrieval to the most relevant normative acts before vector search, improving contextual focus and retrieval precision. Using a fixed benchmark of regulatory questions and a reproducible evaluation protocol, we quantitatively assess system performance with RAGAS metrics and classical information-retrieval measures. While all configurations achieve strong faithfulness (up to 0.96), answer relevancy varies substantially with embedding and chunking choices. The findings confirm that retrieval engineering, particularly embedding selection, chunking strategy and pre-retrieval document filtering, has a high impact for building reliable regulatory AI systems. The sliding window strategy combined with bge-small-en-v1.5 delivered the strongest rank-sensitive retrieval performance, achieving the highest Precision@10 and NDCG@10. In contrast, article-level chunking with the same model yielded a modest improvement in Recall@10, indicating a clear trade-off between recall and precision-oriented ranking quality in legal corpora. Full article

This study aims to provide an empirical, decision-oriented comparison of four widely used sustainability assessment frameworks applied to agri-food systems (LCA, MESMIS, SAFA, and RISE). Using a Scopus-based search (2000–2025), we compiled a purposively balanced corpus of 89 empirical applications and coded each study with a standard rubric spanning normative, systemic, and procedural dimensions. Dimension indices were constructed using polychoric PCA (first component) and rescaled to 0–1, and a global index was computed as the mean of the three dimensions. Within the constructed corpus, MESMIS shows the highest mean global index (0.54), followed by LCA (0.46), SAFA (0.45), and RISE (0.32). LCA leads the normative dimension (0.56), while MESMIS leads the systemic (0.64) and procedural (0.53) dimensions; SAFA presents a balanced profile and explicitly incorporates governance considerations. Findings are interpreted as descriptive patterns within the constructed corpus rather than population-level estimates of inherent method superiority. We conclude that framework choice should be driven by evaluation purpose and information conditions and that hybrid approaches can combine complementary strengths across methods. Full article

This paper develops a reliability-based Decision Support System (DSS) for logistics networks, grounded in the Eurocode EN 1990 and Recommendations for Maritime Works ROM 0.0 framework. The DSS defines logistics-specific limit states (i.e., operational failure thresholds for the overall network) and computes annual exceedance probabilities through a multi-hazard fault-tree model. Its contribution is conceptual and regulatory: it transfers structural reliability principles to system-level assessment, generating auditable, norm-referenced indicators aligned with the EU Critical Entities Resilience Directive (CER) and the Network and Information Security Directive (NIS2). A central result is the Criticality Flip: Systemic vulnerability does not decline monotonically with hub density. Instead, risk shifts non-linearly between gateways and inland integrators, yielding a narrow operating range where the reliability margin (β) is maximized and annual limit-state exceedance is minimized. Beyond this range, additional hubs may provide limited—or even adverse—reliability improvement. The system operates as a compliance audit tool rather than a simulation engine: it evaluates whether a given network configuration meets declared reliability thresholds under multi-hazard scenarios, using standardized input formats and static topology. To support strategic decision-making, the DSS provides normalized and reproducible compliance indicators—such as annual limit-state exceedance probabilities and the associated reliability margin ($\mathsf{\beta }$) referenced to declared thresholds—supporting cross-network benchmarking under CER and NIS2 constraints within an engineering reliability framework. Full article

Amid the exponential growth of streaming data and rising demands for real-time decision-making, Online Convex Optimization (OCO) has emerged as a foundational framework for sequential data processing in dynamic environments. This survey presents a systematic review of recent evolutionary and adaptive OCO strategies, offering a detailed taxonomy that classifies algorithms according to their constraint-handling mechanisms and environmental feedback. The analysis first examines Constrained OCO, elucidating the trade-offs between computational efficiency and theoretical guarantees across projection-based methods, projection-free Frank–Wolfe variants, and general convex optimization approaches. It then explores the Unconstrained OCO landscape, emphasizing the shift from parameter-dependent methods to fully adaptive, parameter-free algorithms capable of handling unknown comparator norms and gradient scales. Furthermore, the study synthesizes state-of-the-art applications in power systems, network communication, and quantitative finance, bridging theoretical OCO models with robust engineering solutions. The paper concludes by outlining critical open challenges and future research directions, such as the integration of OCO with deep learning, non-convex optimization, and robustness against adversarial corruptions in data-intensive scenarios. Full article

Intergenerational wealth transfer represents a central mechanism through which genealogical bonds, family continuity, and economic stability are maintained across generations. This article examines inheritance law and financial regulation from a genealogical perspective, focusing on the role of family property as both a legal institution and a socio-economic structure rooted in kinship and lineage. By integrating approaches from genealogy, legal studies, and financial analysis, the study explores how inheritance frameworks shape intergenerational relations, preserve family identity, and influence patterns of economic inequality. The article analyzes inheritance law as a key instrument through which genealogical continuity is institutionalized, highlighting the ways in which legal norms regulate the transmission of assets, rights, and obligations within families. Particular attention is given to the interaction between financial regulation and family-based wealth, demonstrating how legal structures affect long-term economic sustainability and social cohesion. The study adopts a qualitative and theoretical methodology, supported by comparative references to selected legal traditions, in order to illustrate how inheritance systems reflect broader cultural, historical, and genealogical values. By situating inheritance and wealth transfer within the broader framework of genealogical relations, this article contributes to interdisciplinary discussions on family, law, and the economy. It argues that inheritance law should be understood not merely as a financial or legal mechanism, but as a genealogical process that shapes intergenerational bonds, social structures, and economic outcomes over time. Full article

Community detection in graphs can be viewed as the estimation of a partition map that remains stable under admissible perturbations of graph topology and node attributes. While modern graph neural networks (GNNs) achieve strong empirical accuracy, they often exhibit severe assignment drift under minor perturbations, leading to illusory community structures. In this work, we propose DISPEL-GNN, a stability-aware graph learning framework that integrates spectral operator regularization, Bayesian uncertainty modeling, and risk-aware dynamic attention for perturbation-bounded community detection. The model explicitly constrains graph operators through uniform spectral norm bounds, high-frequency energy suppression, and commutator alignment while dynamically modulating message passing based on node-level spectral risk and epistemic uncertainty. We further formalize instability via assignment of drift functional and establish perturbation bounds linking drift to operator norms and spectral gaps, complemented by a PAC-Bayesian generalization guarantee. Extensive experiments on real-world benchmarks including Cora, Citeseer, Pubmed, Cora-Full, and DBLP demonstrate that DISPEL-GNN consistently reduces assignment drift by 18–35% under feature noise and edge perturbations while improving clustering quality with up to +3.0 NMI and +0.04 ARI compared to strong baselines such as GAT and Bayesian GNNs. The normalized mutual information (NMI), adjusted Rand index (ARI), and PAC-Bayesian (PAC) constraints serve as evaluative and theoretical instruments in this study. Additional studies on synthetic graphs with controlled spectral gaps confirm that the proposed method maintains stable community assignments in low-gap regimes where classical spectral and GNN-based methods degrade sharply. These results establish DISPEL-GNN as a mathematically grounded and practically effective framework for robust and interpretable community detection. A metric-wise dominance analysis shows that DISPEL-GNN achieves metric-wise dominance across most accuracy and robustness criteria, with minor tradeoffs in modularity on selected datasets. These results indicate that explicitly modeling stability and uncertainty provides a principled pathway toward reliable and interpretable community detection in noisy graph environments. Full article

Plastic packaging represents a critical focus in the European Union’s transition to a circular economy owing to its resource-intensive production and substantial greenhouse gas emissions. This article examines Poland’s implementation of plastic packaging regulations within the evolving European Union regulatory framework, alongside complementary policy instruments. It employs legal-normative analysis of European Union and Polish legislation, documentary review of national strategic frameworks, and statistical assessment of packaging generation and recycling performance. Poland has introduced substantial legislative measures, including carrier-bag fees, charges on single-use plastic products, recycled-content mandates for polyethylene terephthalate bottles, and a deposit-return system launched in October 2025. Moreover, national voluntary agreements created by non-governmental organisations and industry stakeholders to improve collection and sorting have been active on the Polish market. Nevertheless, performance indicators reveal significant gaps between regulatory ambitions and operational outcomes. To diagnose these implementation gaps and prioritise the most critical interventions, the article applies a governance-oriented MoSCoW analysis. The article concludes that while the deposit-return system constitutes an essential intervention, achieving European Union circular economy objectives requires comprehensive policy integration encompassing upstream prevention, eco-design standards, extended producer responsibility mechanisms, and coherent strategic planning. An effective regulatory system, sound management practices, and improved information sharing among stakeholders are crucial for promoting eco-innovation and advancing circularity, reuse, and waste reduction. Full article

In this article we highlight and discuss how norm-challenging pedagogy in music education can be encouraged and executed from three different angles. We primarily focus on activities such as democratic learning situations for pupils and teachers, to be explored as safe and brave spaces. With a starting point in norm-critical pedagogy, we explore the possibility of using norm-challenging pedagogy as, through and in music educational settings. Norm-challenging pedagogy as music education can challenge dominant ways of assimilating, processing, and expressing knowledge, whereas norm-challenging pedagogy through music education concerns how traditional views on, for example, gender, race, or disability identities can be challenged through music activities. Norm-challenging pedagogy in music education critically reflects on who has the right to learn and express themselves musically and in what ways, related to gender, race or disability. The article is based on a phenomenological view of aesthetic experience and music education as a life of equal value, where de Beauvoir’s concepts of freedom, facticity, and ambiguity constitute crucial analytical concepts. The author’s own experiences of ambiguous norm-challenging situations as, through and in music education will be used and discussed in relation to the philosophical framework. The results of the exploration will be critically reflected upon in relation to organisational, collegial, didactic and relational aspects of music education. Full article

Desire, a transformative force, is one of contemporary serial narratives’ most intricate and multifaceted dimensions. Far from being reducible to a mere representation of sexual attraction, desire in television seriality operates as a prism through which to explore issues of intimacy, identity, and power. This paper seeks to analyze how desire is staged and problematised within a set of emblematic series that have significantly shaped contemporary cultural imagination. Grey’s Anatomy explores the entanglement of desire with professional life, emotional fragility, and collective trauma, constructing narratives where eros intersects with affective labour and the negotiation of identity within high-pressure contexts. Sex and the City proposes a very different model, placing female desire at the centre as a space of autonomy, experimentation, and confrontation with the normative frameworks of late capitalist society. By contrast, The Handmaid’s Tale reimagines desire within a dystopian theocracy, assigning it an overtly political function: here, erotic impulses and affective attachments become acts of resistance against systemic repression and biopolitical control. More recently, Sex Education embodies a cultural shift, presenting desire through a plural and inclusive lens that embraces diversity of sexual orientation, gender identity, and relational practices. These case studies, when viewed collectively, reveal how television series rework cultural codes of sexuality and intimacy, producing new imaginaries of the body, pleasure, and identity. In this perspective, serial narratives emerge as key cultural laboratories, reproducing and challenging dominant ideologies of desire while offering audiences opportunities for recognition, critique, and affective engagement beyond the screen. Full article

Organizations increasingly deploy artificial intelligence (AI) in human resource (HR) decision processes to improve efficiency and strategic execution, yet ethical failures persist when principles remain decoupled from everyday workflow enactment. This paper addresses AI-ethics in HR practice by advancing a behavior-first premise: AI-ethics becomes durable organizational practice only when ethical intent is translated into observable routines and cues that employees can interpret as legitimate and consistently enforced. We introduce the Socially Aware Framework for Ethical AI (SAFE-AI), which integrates normative ethical reasoning (consequentialist and deontological logics), social information processing, and socially informed heuristics as a practical translation layer for HR workflows. SAFE-AI specifies three stages of implementation—moving in (initiation), moving through (navigation), and moving out (culmination)—to guide scoping and constraints, feedback-driven interpretation management, and institutionalized accountability. Because enactment depends on the organizational cue environment, leadership behaviors (ethical intent-setting, resourcing, sensegiving transparency, and enforceable accountability) function as necessary conditions for sustained implementation beyond HR-local governance. We conclude with implications for practice and a testable agenda for research focused on implementation fidelity, cue-consistency mechanisms, and boundary conditions across organizational contexts. Full article

A mix of internal and external factors affect farmers’ recycling practices with regarding pesticide packaging waste. However, most of the research that has been done so far has concentrated on the individual effects of these elements rather than providing a clear explanation of the intricate mechanisms by which a variety of internal and environmental factors work together to drive recycling behavior. This study builds an integrated “internal–external” factor analysis framework based on Lewin’s Behavior Model, integrating Organizational Support Theory and the Theory of Planned Behavior to close this gap. We examine the many configurational pathways influencing farmers’ willingness to recycle pesticide packaging debris using fuzzy-set Qualitative Comparative Analysis (fsQCA). The results showed that there are four different configurational approaches that lead to a high readiness to recycle: the information–norm interaction-driven path, the capability–belief-driven path, the norm–emotion endogenous-driven path, and the psychology-driven dominant path. Farmers’ attitudes toward recycling were found to be a key component in all four routes, indicating that it is essential to attaining high recycling willingness. The findings of this study offered policy insights for encouraging recycling behavior and assisted in identifying the intricate causal processes of multi-factor synergy impacting farmers’ propensity to recycle pesticide packaging trash. Full article

Background/Objectives: Photobiomodulation (PBM) therapy has shown potential in managing orofacial neuropathic pain (ONP); however, inconsistent PBM dosimetry and methodological variability limit its clinical application. This World Association for Photobiomodulation Therapy (WALT) Position Paper aims to critically appraise current evidence and provide recommendations for Clinical Practice Guidelines (CPG) and Expert Consensus Opinion (ECO) where appropriate. Methods: Evidence evaluation was guided by the HANNA (Holistic Analysis & Novel Normative Actions) Framework, a structured multi-step methodology integrating systematic review, quality appraisal, and expert consensus. A systematic review was conducted in accordance with PRISMA 2020 guidelines. Methodological quality was assessed using validated tools: AMSTAR 2 for systematic reviews, RoB2 for randomized controlled trials (RCTs), and ROBINS-I for non-randomized studies (NRCTs). The AGREE II Reporting Checklist was applied to ensure transparency and rigor in the development of WALT recommendations. The Somerfield Criteria were used to rate the level of evidence (LoE) for each included ONP condition, where deemed appropriate. Results: WALT CPG were established for primary burning mouth syndrome (BMS), supported by robust evidence (LoE I) from 204 patients across six “Low RoB” RCTs and NRCTs, and 557 patients included in a “High-Confidence” systematic review and meta-analysis of “low RoB” RCTs. WALT ECO were developed for idiopathic trigeminal neuralgia (TN) and post-herpetic neuralgia (PHN), both supported by LoE II. Insufficient evidence precluded formal recommendations for post-traumatic trigeminal neuralgia, glossopharyngeal neuralgia, and occipital neuralgia. Conclusions: This Position Paper introduces the HANNA Framework, for the first time, as a robust and transparent methodology for developing WALT recommendations by delivering evidence-based CPG for PBM in the management of neuropathic pain associated with primary BMS, along with ECO for both TN and PHN. These recommendations support PBM as a safe and effective therapeutic approach, and provide a structured roadmap for future research and periodic guidelines updates. Full article