Consumer-generated narratives contain rich experiential knowledge for product design, yet their perceptual and weakly structured nature limits direct translation into actionable engineering insight. This challenge is particularly evident in EV design, where user insight plays a critical role but remains weakly grounded in formal engineering representations. To address this gap, this study proposes a Structured consumer semantic insight small language model (SCSI-SLM). First, Structured Semantic Encoding (SSE) maps weakly structured user narratives into a multi-level engineering semantic space, transforming perceptual expressions into comparable and reusable semantic tokens. Second, Product-User Dynamic Mapping (PUDM) decouples product-centric performance assessment from user-centric preference representation, enabling importance-performance analysis and preference heterogeneity to be modelled from the same corpus. Third, an Engineering Design Knowledge Graph (EDKG) integrates user profiles, product attributes, perceptual features, and textual evidence into an interpretable reasoning space. Building on this representation, a hybrid retrieval-reasoning engine combines semantic similarity search with graph-constrained inference to generate evidence-backed and engineer-readable insight chains. A case study in the EV domain demonstrates that the proposed framework can generate traceable design insights from large-scale consumer feedback, suggesting structured language modelling as a viable paradigm for data-driven engineering design.

This paper critically examines two major global, transnational perspectives on diplomacy, the “Ambassador as Representative Theory” and the “Ambassador as Functional Need Theory.” Historically, diplomacy has always been interpreted through the Representative Theory, in which the ambassador is primarily assumed to act as a formal representation of the sending state's will and interests for government-to-government exchanges. This classical, elite-centric position underpins both state sovereignty and international order, but has come to increasingly inadequately address citizenship for the 21st century, with a notable "gap between theory and reality" occurring, marginalising citizens rather than meeting their practical cross-border needs, increasingly in developing countries and in post-conflict settings. In contrast, Functional Need Theory, influenced by functionalism, maintains that ambassadors are still relevant because they meet the essential needs of societies for practical service and cross-border solutions. Thus, it recasts the ambassador as a “provider of services” and a problem-solver through such roles as protecting citizens, providing consular support, enabling trade, supporting diasporas, and public diplomacy. Such functionalists see diplomacy as a service-oriented, multi-actor endeavour, dependent on efficacy and trust in the public domain rather than solely state authority. In this article, the authors argue that modern diplomacy can neither be accounted for nor acted from the perspective of either theory alone. Instead, it calls for a hybrid that intentionally enmeshes both representation and functionality in an integrated citizen-centric ‘foreign policy’ system. In the context of diplomatic services and particularly in fragile states, functional diplomacy is necessary to build public trust, secure foreign policy legitimacy, and reinforce national cohesion. Diplomats have a dual responsibility: loyalty to the constitutional state and duty for the citizens as rights-holders, with professionalism measured by both political access and effective public service delivery.

We extend the representation frame formalism, previously introduced to account for key cosmological observations in the Einstein static universe, to non-relativistic quantum mechanics. In this framework, each inertial observer is associated with a flat representation referential Robs, defined as the tangent space to the spatial manifold at the observer’s position, in which all measurements are represented. The Euclidean structure of Robs allows quantum systems to be described using the standard Schrödinger formalism, avoiding the technical ambiguities that arise when quantising directly on curved manifolds. We derive the relation between the Hamiltonian governing quantum dynamics in Robs and its counterpart defined on the physical manifold U, and show that curvature effects enter as observer-dependent modifications of effective potentials. Although the resulting quantum description depends on the observer’s representation frame, we show that this does not lead to contradictions between observers: consistency of measurement outcomes follows from the standard structure of quantum correlations established by physical interactions. We illustrate the formalism with explicit applications, including the hydrogen atom in an Einstein static universe and quantum systems in the vicinity of a black hole, highlighting how spacetime curvature manifests itself in the observer’s quantum description.

The aim of this study was to develop suitable measures and strategies through a trans- and interdisciplinary discourse to improve pandemic management in (acute) inpatient care facilities (clinics, nursing homes) and thereby reduce the burden on nursing staff.Two scenario-based workshops were conducted-one focused on acute care and the other on long-term care. Following a collaborative, transdisciplinary approach, the workshops integrated perspectives from nursing practice, research, and relevant institutions. Using foresight methods such as the Futures Wheel and Ideation Canvas, participants co-developed potential solutions for optimizing pandemic preparedness and response. A total of 38 participants took part in the two-day workshops (acute care: 18; long-term care: 20).Participants developed outcomes at both structural and organizational levels, identifying short- and long-term effective strategies and interventions. Key findings addressed areas such as communication, internal organization, interface management, and the involvement of nursing practitioners. Examples include the (political) empowerment and increased participation of nurses, the development of digital and interdisciplinary structures, including skill-grade mix teams. Across both care settings, participants emphasized the need for formal nursing representation and clearly defined professional roles and responsibilities.Effective crisis management in inpatient care settings requires a systemic approach that integrates actions at the micro, meso, and macro levels. Coordinated collaboration between policy, science, and practice is essential to strengthen system resilience and sustainably improve working conditions in healthcare.

This paper proposes a unified analysis of reduplication as the lexical spell-out of a relational part–whole/inclusion predicate (⊆) in morphosyntax. Adopting the framework of Manzini and colleagues, we argue that reduplicative morphology—across diverse languages and domains—encodes a subset relation, whereby an event, individual, or property is interpreted as included in a larger set or continuum of similar instances. We bring evidence from a range of typologically diverse languages (Tagalog, Bikol, Malay, Fulfulde, Italian, and sign languages) to show that reduplication correlates with non-maximality: plural number (members of a set), distributivity (individuals/events taken one by one), iterative aspect (sub-events in a larger event), and evaluative attenuation or intensification (a degree as part of a scale). The analysis is developed in a formal syntactic representation where reduplication is triggered by an elementary inclusion operator (⊆) at the X or XP level. We show that a single semantic primitive (⊆) can account for the varied meanings of reduplication in nominal, verbal, and adjectival domains. We discuss the implications of this unified approach, suggesting that reduplication is not a mere iconic or phonological process, but rather the surface reflex of a fundamental grammatical operation of inclusion.

This research undertakes a systematic literature review to explore the integration and application of ontologies within Business Intelligence (BI) components across a variety of domains. Ontologies, as formal representations of knowledge, have emerged as a key enabler in enhancing the functionality and intelligence of BI systems, particularly in the era of big data and digital transformation. The objective of this study is to analyze how ontologies are designed, implemented, and utilized to improve data integration, semantic interoperability, and system adaptability. The review draws upon data sources from Scopus, IEEE Explore, Science Direct, and Google Scholar, ensuring a rigorous and comprehensive coverage of relevant literature. Following a structured selection process based on inclusion and exclusion criteria, 27 peer-reviewed articles published between 2011 and 2024 were identified as meeting the quality and relevance standards for this study. The selected studies reveal that ontology-driven BI components offer several advantages, including the unification of heterogeneous data sources, improved semantic clarity, and enhanced reasoning capabilities for decision support. Moreover, ontologies contribute significantly to the flexibility and scalability of BI systems, facilitating the development of context-aware and domain-specific analytical tools. Despite these advantages, the review also highlights persistent challenges, such as difficulties in managing large-scale ontologies, real-time processing limitations, and organizational resistance to adoption due to complexity and integration costs. By synthesizing the existing body of knowledge, this review not only consolidates the current understanding of ontology-driven BI but also provides a conceptual framework for future research. It emphasizes the need for innovative approaches that address identified limitations and align ontology development with dynamic organizational requirements. The findings serve as a valuable resource for both researchers and practitioners, offering strategic insights into the design and deployment of advanced BI solutions. Ultimately, this study contributes to the evolving discourse on intelligent decision-making systems by bridging theoretical perspectives with real-world applications.

Revised proofs of Kenneth Arrow's impossibility theorem, one of the most influential theorems in economics, political science, and philosophy, have been presented in prose form, incorporating novel ideas such as decisive sets and pivotal voters. This study develops another approach to proving the theorem. Using a proof calculus in formal logic, we construct a proof with a full mathematical representation. While previous proofs emphasize intuitive accessibility, this one focuses on meticulous derivation and reveals the global structure of the social welfare function central to the theorem. The primary aim is to contribute methodologically to research on the theorem by demonstrating the effectiveness of systematically applying techniques from formal logic to its proof. Additionally, it accommodates a broader range of preference relations than those typically considered rational in standard economic models, allowing for the integration of diverse human behavior patterns into a single theoretical framework. The interdisciplinary relevance of the theorem is also discussed, including its relation to epistemology and philosophy.

Anthropogenic activities interact through multifactorial processes that generate harmful factors, hindering wastewater management (WWM) and causing environmental degradation, particularly in rapidly urbanizing coastal regions. Understanding how these processes operate is essential for identifying effective interventions under data-limited conditions. This study introduces a process-based and context-driven approach whose main contribution lies in the construction of semantic pathways that represent how indirect anthropogenic drivers give rise to direct harmful factors affecting WWM and the environmental state. Semantic networks are used as a formal representation tool to model these pathways, where nodes represent factors and directed arcs represent causal relationships. Harmful semantic pathways are evaluated within a multidisciplinary decision-making framework supported by multi-criteria decision-making (MCDM) methods that account for environmental, social, economic, and sustainability criteria. The approach is applied to a coastal tourist municipality on the Mexican Pacific coast, where rapid urban expansion and insufficient basic services have severely constrained wastewater management and contributed to environmental damage. Results show that constructing and analyzing semantic pathways enables decision-makers to identify critical harmful factors and prioritize viable pro-environmental actions. The resulting priorities (range 0–1) highlight the restoration of wastewater treatment plants and improved urban planning as the most effective interventions, followed by mangrove reforestation and changes in agricultural practices. The proposed approach supports transparent, context-sensitive decision-making and is transferable to coastal tourist municipalities facing similar wastewater management challenges.

This study aims to identify and analyze mathematical concepts embedded in the processing of Gudeg Yogyakarta through an ethnomathematical approach. The research employed a qualitative method with an educational ethnographic design. The research subjects consisted of traditional gudeg producers, besek craftsmen, and home-based gudeg industry practitioners in the Yogyakarta region, selected purposively. Data were collected through participant observation, in-depth interviews, and visual documentation to capture cultural practices and the underlying mathematical meanings. Data analysis was conducted descriptively and analytically through the stages of data reduction, data display, and conclusion drawing by transforming cultural activities into formal mathematical representations. The results indicate that the gudeg processing process contains various mathematical concepts, including three-dimensional and plane geometry in raw materials and packaging, fractions and ratios in ingredient measurements, sequences and series in the canning process, and algorithmic logic in deep cooking techniques. These findings confirm that Gudeg Yogyakarta has significant potential as a meaningful, contextual mathematics learning resource grounded in local wisdom.

ABSTRACT This article argues that despite constituting a majority of the workforce, women workers in tea plantations of Assam have remained largely excluded from trade union leadership and formal political representation. Drawing on subaltern perspectives and feminist labor theory, it examines how plantation patriarchy, labor hierarchies and male-dominated union bureaucracies particularly within the Assam Chah Mazdoor Sangha (ACMS) have historically constrained women’s political voice. Based in-depth interviews, archival sources and participant observation conducted in two tea estates in Jorhat district, the study shows that trade unionism after independence did little to disrupt these gendered exclusions. At the same time, it reveals how women cultivate alternative forms of leadership through everyday practices and informal collective spaces such as Mothers’ Clubs and Self-Help Groups. These arenas, initially framed around welfare, have become sites where women negotiate authority, articulate grievances and develop political capabilities. The article contends that such informal forms of leadership constitute a subaltern political process that reshapes labor politics from below, even as formal union structures remain resistant to gender inclusion. By foregrounding women’s everyday political practices, the study contributes to labor history debates on agency, representation and the gendered transformation of leadership in postcolonial tea plantations .

Translating unstructured user feedback into Business Process Model and Notation (BPMN) is challenging due to informal language, contextual ambiguity, and the lack of explicit structural cues. We present FB2BPMN, an end-to-end pipeline that combines natural language processing (NLP), large language models (LLMs), and fuzzy string matching to automatically generate BPMN elements from raw feedback. The pipeline comprises four stages: sentence structuring, fact extraction, role-activity mapping, and fuzzy-based semantic alignment. We evaluate FB2BPMN on 125 annotated feedback instances sampled from academic journal management systems. Using expert-authored BPMN as reference, FB2BPMN attains precision 0.97, recall 0.88, and F1 0.91 on element identification and accuracy 0.85 on process flow construction, outperforming a rule-based baseline. Results indicate strong structural and semantic correspondence, showing that FB2BPMN effectively bridges informal feedback and formal process representations.

Abstract In formal epistemology, a variety of probability-based coherence measures have been proposed that provide a quantitative formal representation of the coherence of a set of information pieces. While research has long focused on whether coherence measures are truth-conducive, the truth-conduciveness of coherence measures has so far been evaluated in static settings only: Coherence provides assessments about the truth of incoming information, but does not actively guide decisions to believe or discard pieces of information. In this paper, we propose to assess the truth-conduciveness of coherence measures with respect to their ability to lead agents to select true information and form correct beliefs in a dynamic iterative setting. At every time step, an agent receives a number of noisy signals about the actual truth values of a finite set of atomic propositional variables. The agent uses a coherence measure to decide which signals to trust and which to discard. By repeatedly picking signals that maximise the coherence of the propositions they currently believe to be true, the agent tries to select truthful signals and learn the correct truth-value assignment for the atomic variables. The contribution of this paper is three-fold. First, we propose a computational model to assess the truth-tracking abilities of different coherence measures. Second, using computational simulations, we compare a number of widely discussed coherence measures from the novel standpoint of our iterated data-collection setting: We show that, when signals are not too noisy, agents who employ the Glass-Olsson relative overlap measure outperform agents employing all other tested measures, and that all measures become progressively worse at leading agents towards the truth as signals degrade. Finally, we discuss how coherence affects the emergence of different dynamics and attitudes in belief revision.

Large language models (LLMs) have shown remarkable progress in general reasoning and understanding, but their ability to perform formal logical reasoning remains under-explored. In this paper, we introduce DLReasonSuite, a novel benchmark designed to rigorously evaluate LLMs on reasoning tasks grounded in Description Logic (DL). DL-ReasonSuite comprises 4740 tasks spanning seven distinct task types and organized into three reasoning tracks: (1) DLCore, covering fundamental ontology reasoning tasks (consistency checking, subsumption, and instance checking); (2) DLQuery, focusing on answering entailment-aware SPARQL queries; and (3) DLBridge, bridging natural language and formal logic (bidirectional NL ↔ OWL translation and tool-augmented entailment resolution). We detail the methodology for designing and implementing this benchmark, including task construction, automatic evaluation metrics and validation using reliable OWL reasoners. Then, we present an empirical evaluation of five leading reasoning LLMs as stateofart models: Kimi k1.5, LlamaNemotron Ultra, DeepSeekR1, Phi4 Reasoning Plus, and Phi4 Reasoning on the full suite of tasks. Our results reveal significant variability in LLM performance on formal reasoning was observed. While the best model, Phi4 Reasoning Plus, achieves an overall accuracy of 85% and excels especially in tool-augmented tasks, other models struggle notably with complex query reasoning for DL and precise OWL translation. We analyze the strengths and weaknesses of each model across different DL metrics and task categories, providing insights into current limitations of LLM reasoning such as handling SPARQL queries and maintaining logical consistency and the benefits of neuro-symbolic techniques. DL-ReasonSuite is a comprehensive framework for assessing and advancing LLMs’ Description Logic reasoning capabilities aiming to bridge the gap between natural language understanding and formal knowledge representation.

Digital product passports outline information about a product’s lifecycle, circularity, and sustainability-related data. Sustainability data contains claims about carbon footprint, recycled material composition, ethical sourcing of production materials, etc. Also, upcoming regulatory directives require companies to disclose this type of information. However, current sustainability reporting practices face challenges, such as greenwashing, where companies make incorrect claims that are difficult to verify. There is also a challenge of disclosing sensitive production information when other stakeholders, such as consumers or other economic operators, wish to verify sustainability claims independently. Zero-knowledge proofs (ZKPs) provide a cryptographic system for verifying statements without revealing sensitive information. The goal of this research paper is to explore ZKP cryptography, trust models, and implementation concepts for extending DPP capability in privacy-aware reporting and verification of sustainability claims in products. To achieve this goal, first, formal representations of sustainability claims are provided. Then, a data matrix and trust model for generating proofs are developed. An interaction sequence is provided to show different components for various proof generation and verification scenarios for sustainability claims. Lastly, the paper provides a circuit template for the proof generation of an example claim and a credential structure for their input data validation. The proposed approach is assessed using a scenario-based evaluation to check the performance metrics for data credential verification and proof generation for verifying material composition in a product.

We consider pseudo Nambu-Goldstone bosons arising from Dirac fermions transforming in real representations of a confining gauge group as dark matter candidates. We consider a special case of two Dirac fermions and couple the resulting dark sector to the Standard Model using a vector mediator. Within this construction, we develop a consistent low energy effective theory, with special attention to Wess-Zumino-Witten term given the topologically non-trivial coset space. We furthermore include the heavier spin-0 flavour singlet state and the spin-1 vector meson multiplet, by using the Hidden Local Symmetry Lagrangian for the latter. Although we concentrate on special case of two flavours, our results are generic and can be applied to a wider variety of theories featuring real representations. We apply our formalism and comment on the effect of the flavour singlet for dark matter phenomenology. Finally, we also comment on generalisation of our formalism for higher representations and provide potential consequences of discrete symmetry breaking.

Introduction The rapid advancement and adoption of CRISPR-Cas technologies in crop improvement has significantly outpaced existing regulatory frameworks, leading to inconsistencies in the global oversight of gene-edited organisms. As governments and international bodies struggle to reconcile scientific innovation with policy governance, a pressing need has emerged for methodologies that can translate biological edits into regulatory-compliant representations across jurisdictions. Traditional approaches often compartmentalize genomic and legal domains, lacking the formalism to bridge biological intent and compliance precision. These methods are typically static, unable to adapt to jurisdictional policy drift or incorporate real-time exemption logic, thereby undermining both regulatory interpretability and technical fidelity. Methods To address this gap, I propose a unified computational framework built around the novel GeneRegAlignNet model and the Constraint-Aware Policy Induction (CAPI) strategy. This framework embeds regulatory semantics directly into the learning architecture, enabling the alignment of gene-editing features with heterogeneous policy descriptors in a shared latent space. GeneRegAlignNet employs symbolic gating, contrastive manifold learning, and exemption-aware vectorization to predict alignment likelihoods between edits and legal categories with high precision. CAPI extends this model with a risk-calibrated policy optimization pipeline that accounts for policy evolution, regulatory variance, and jurisdictional priorities. Results and Discussion Empirical validation demonstrates improved performance in regulatory alignment accuracy and resilience to policy drift across a diverse set of gene-editing scenarios. By tightly integrating formal representations of molecular edits with dynamic, multi-jurisdictional policy inference, our framework offers a scalable and interpretable path forward in enhancing regulatory precision and global harmonization in the oversight of CRISPR-Cas-edited crops.

Airport digital transformation is commonly approached through technological integration and data-driven optimization, yet such perspectives provide limited insight into system-level reasoning and governance. This paper introduces the cognitive airport paradigm (CAP) as a mathematically grounded framework that models the airport as a domain-specific cognitive digital twin within a complex aviation ecosystem. Methodologically, the study follows a conceptual–analytical and design-science research approach, combining system analysis, conceptual modeling, ontology engineering, and formal mathematical representation of cognitive transitions and governance constraints. CAP represents airport cognition as an explicit state space characterized by cognitive maturity, governance integrity, and semantic stability. Analytical reasoning, adaptive learning, and orchestration mechanisms are formalized through instrument dominance profiles and cognitive performance functionals, enabling analytical comparison of airport configurations and identification of cognitive regimes. The results include (i) a formalization of airports as cognitive digital twins with measurable cognitive and governance properties; (ii) quantitative indices such as the cognitive readiness index, governance integrity index, and ethical alignment coefficient supporting structured evaluation of airport cognitive maturity; and (iii) illustrative expert-based parameterizations and a geometric interpretation in a cognitive simplex demonstrating that governance-oriented orchestration stabilizes airport cognition under increasing system complexity. Airport development is interpreted as continuous cognitive evolution rather than discrete stages of digitalization. The paper further proposes a cognitive roadmap for guiding airport evolution through structured cognitive rebalancing. The framework contributes to the theoretical foundations of cognitive digital twins and is transferable to other safety-critical and institutionally governed socio-technical systems.

ABSTRACT The rapid adoption of deep learning (DL) in Earth Observation (EO) has raised concerns about model sharing and reuse. Existing DL models (DLM) in EO are not well described in a unified and interoperable approach. To address this issue, a comprehensive and interoperable descriptive framework is needed to operationalize the FAIR (Findable, Accessible, Interoperable, Reusable) principles for EO DLM. The paper proposes a formal representation for EO DLM. Key considerations for formalizing the model are analyzed, including model lifecycle, model provenance, model inference, model constraints, and model quality. The conceptual and content models are then proposed, followed by an implementation schema. Use cases demonstrate the applicability of the approach for sharing and reusing DLM in Spatial Data Infrastructures (SDIs). The results help transform the traditional SDI into an AI‐ready SDI.

This study stems from the challenge of understanding the concept of SPLDV, which is generally still procedural and lacks meaning, as well as the diversity of students' initial abilities that influence their thinking processes and problem-solving strategies. The research method used was qualitative descriptive within the framework of Didactical Design Research (DDR) at SMP Muhammadiyah 5 Surakarta with three ninth-grade students representing high, medium, and low initial abilities as subjects. Data were collected through SPLDV concept comprehension tests and in-depth interviews. The results showed that the application of RME-based HLT helped students understand concepts gradually, starting from real contexts, situational models, mathematical models, to formal representations. Students with high initial abilities were able to fulfill all of Polya's problem-solving indicators, including the evaluation stage. Students with medium initial abilities were able to understand the problem and develop a plan but were not consistent in evaluating the results. Meanwhile, students with low initial abilities were only able to identify basic information without being able to model or implement a solution strategy. Overall, RME-based HLT proved to be effective in facilitating differences in students' initial abilities and improving their understanding of SPLDV concepts through a structured and contextual learning path.

Designing effective human-robot interaction (HRI) for multi-Autonomous Guided Vehicle (AGV) systems in manufacturing remains a significant challenge. While existing modeling tools offer formal representations for system behavior or task flow, they lack support for explicitly modeling multimodal, bidirectional communication between humans and distributed autonomous agents. In this paper, we introduce HASIGN: Human-Autonomous System Interaction Graphical Notation in order to make the design space of human-multi-AGV interaction explicit and tractable. Developed through a Research through Design approach, HASIGN integrates agent roles, interaction modalities, and temporal intent communication into a unified and practical representation. We apply HASIGN to five diverse industrial case studies drawn from ongoing research and development projects. These cases demonstrate the notation's flexibility and domain suitability, while uncovering unexplored areas in the interaction design space. Rather than aiming to replace existing modeling tools, HASIGN complements them by focusing on human-centered communication in autonomous systems. This paper contributes a visual design tool for practitioners and researchers, and lays the foundation for further evaluation, standardization, and adoption in the context of human-centered autonomous intelligent systems (HCAIS).