Search Results (1,630)

This article explores the structural paradox of the byeong-gut (Korean shamanic healing ritual): why it adheres to the rigid and canonical format of the jaesu-gut (shamanic blessing ritual) instead of adopting a specialized clinical procedure. Critiquing the instrumental trap of previous scholarship that reduces shamanic healing to psychological comfort or social liberation, this study proposes a relational displacement model by integrating Roy Rappaport’s theory of ritual invariance with the relational ontologies of Bruno Latour and Tim Ingold. The article demonstrates that shamanic healing operates through a dual mechanism. First, at the non-discursive (material) level, the ritual functions as an ontological technology that objectifies and displaces individual suffering onto external surrogates. Second, at the discursive (linguistic) level, a meticulous analysis of the manse-baji (invocation chant) illustrates how the patient’s fragmented life is re-assembled into a meshwork of human and non-human agencies. Ultimately, this article argues that the byeong-gut transcends mere functional curing; it serves as a sophisticated knowledge system that re-maps the isolated ego onto a relational cosmology, transforming the Geertzian bafflement of suffering into an intelligible event within a shared and sacred cosmic order. Full article

Zero-shot remote sensing is attractive for scene classification because new regions, sensors, and label taxonomies often appear before sufficient annotated data are available for supervised adaptation. We present OATS-RS, an inference-centric framework that keeps a remote sensing vision–language model (VLM) backbone frozen and improves zero-shot decisions through ontology-aware prompt construction, hierarchical and contrastive scoring, adaptive multi-view aggregation, unlabeled transductive refinement, ambiguity-aware local re-ranking, and selective prediction. The method targets the common remote sensing regime in which neighboring classes such as annual crop, permanent crop, forest, pasture, herbaceous vegetation, river, and sea or lake overlap strongly in red–green–blue (RGB) appearance, meaning that they require more than a single class-name prompt. On the supplied final EuroSAT RGB evaluation with a GeoRSCLIP Contrastive Language–Image Pre-training (CLIP)-family Vision Transformer Base with 32 × 32-pixel patches (ViT-B-32) backbone, the complete pipeline obtains top-1 accuracy of 0.522, balanced accuracy of 0.522, macro-averaged F1 score (macro-F1) of 0.535, and top-3 accuracy of 0.887. The strongest classes are industrial area, residential area, river, highway, and pasture, whereas the weakest classes remain herbaceous vegetation and several fine-grained vegetation categories. Selective prediction increases accepted-example accuracy to 0.538 at 0.934 coverage, but the expected calibration error (ECE) remains high at 0.384. These results support a qualified conclusion: ontology-guided zero-shot inference can already recover useful semantic shortlists for structured remote-sensing scenes, but fine-grained natural-class disambiguation, calibrated confidence, multi-dataset transfer, component-level ablations, and measured runtime remain essential before dependable deployment claims can be made. Full article

In addition to its role in energy storage, adipose tissue contributes substantially to energy metabolism, endocrine regulation, and inflammatory processes. Sujiang pigs, a hybrid breed approved by the National Livestock and Poultry Genetic Resources Committee of China as a new national breed in 2013, possess a genetic predisposition for substantial fat deposition, making them an ideal model for investigating the mechanisms underlying adipose tissue accumulation. In this study, back fat (BF; subcutaneous adipose tissue), greater omentum (GOM; visceral adipose tissue), and mesenteric adipose tissue (MAD; visceral adipose tissue) were collected from three 6-month-old male Sujiang pigs for RNA-seq analysis. Comparative analyses identified 3005 differentially expressed genes (DEGs) between BF and GOM, 975 DEGs between BF and MAD, and 892 DEGs between GOM and MAD. To validate the reliability of the sequencing data, five DEGs were randomly selected for RT-qPCR verification. The DEGs were further subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. By integrating protein–protein interaction (PPI) networks with bioinformatics analyses, we identified candidate genes potentially associated with lipid metabolism (e.g., WNT9A, WNT5A, and PDGFRA) and inflammatory responses in adipose tissue (e.g., CSF1R, C1QB, and CD4). These findings indicate potential molecular differences between porcine visceral and subcutaneous adipose tissues and may serve as a reference for further studies on the molecular regulation of adipose tissue metabolism. Full article

(1) Background: Cybersecurity has grown in scale and complexity, increasing the need for shared conceptual frameworks that enable consistent, interoperable, and machine-readable representations of security knowledge. Ontologies address this need by structuring core cybersecurity concepts, yet existing efforts vary widely in purpose and methodological rigour. Prior developments tend to follow either an instrumental path—prioritizing usability and rapid adoption—or a formal path, emphasising logical precision and reasoning capabilities. This divergence has resulted in a fragmented landscape lacking analytical synthesis. (2) Methods: To clarify current practices and uncover research opportunities, we conducted a systematic literature review of 93 cybersecurity ontologies published over the past decade. Following PRISMA guidelines, we analysed their conceptual coverage, development methods, validation strategies, and alignment with the NIST Cybersecurity Framework (CSF) 2.0. (3) Results: Despite heterogeneity in scope, the ontologies consistently model core entities such as Asset, Threat, Vulnerability, Attack, and Countermeasure. However, conceptual coverage remains uneven: most contributions focus on the Identify and Detect functions of the NIST CSF, while Respond and Recover are largely underrepresented. This reveals a prevailing emphasis on preventive security rather than resilience and highlights gaps in empirical validation and industrial deployment. (4) Conclusions: The field shows strong conceptual maturation but limited methodological consistency and operational impact. Advancing cybersecurity ontologies will require integrating pragmatic and formal modelling traditions, incorporating emerging techniques such as knowledge graphs and LLM-assisted ontology learning, and expanding coverage toward post-incident response and recovery. These steps are essential for developing a unified, explainable, and adaptive cybersecurity knowledge base capable of supporting real-world security operations. Full article

The volumetric mass transfer coefficient ($k_{L}a$) governs the design, operation, and scale-up of aerobic bioprocesses, yet its dependence on reactor geometry, impeller design, operating conditions, and fluid properties limits prediction by empirical correlations. Machine learning (ML) improves accuracy but faces two barriers in bioprocess practice: selecting the best model among many candidates requires expertise, and small, highly multicollinear data make models chosen based on test error alone prone to overfitting. Using a browser-based, no-code platform, we trained 14 regression algorithms under an identical pipeline on a published $k_{L}a$ dataset, and introduced a composite objective, the generalization-penalized error (GPE), which is the test RMSE plus the absolute train–test RMSE gap. Minimizing GPE rather than test RMSE expanded the top statistically equivalent group to include not only boosting ensembles but also simpler, interpretable models, indicating that black-box models hold no clear advantage once train–test consistency is assessed. Sensitivity analysis showed that tree models produce discontinuous responses, whereas algebraic learning via elastic net (ALVEN) yields smooth surfaces. Shapley additive explanations (SHAP) and an ontology graph, interpreted by a retrieval-augmented language-model agent, identified rotational speed and gas flow rate as dominant, reproducing the established mass transfer mechanism. The framework offers a reproducible, interpretable, expertise-light route to bioprocess model selection. Full article

Metallurgical lifting equipment operates under prolonged heavy-load, high-impact, and complex working conditions. The resulting failure reports contain rich field knowledge applicable to fault diagnosis and predictive maintenance. Nevertheless, reliably extracting traceable, structured knowledge from procedural and implicit maintenance records remains a significant challenge. To address this, the paper proposes a generative information extraction method for large language models (LLMs) that integrates ontology schema with inference chain constraints, targeting knowledge extraction and knowledge graph construction from failure reports of metallurgical lifting equipment, named generative constrained information extraction for operations and maintenance (GCIE-OM). A domain ontology schema is first constructed, defining seven entity types and nine relation types to establish explicit knowledge boundaries for structured LLM generation. An inference chain-assisted structured parsing method, termed IC-ASP, is then designed to guide the model through a sequential extraction pipeline comprising scene identification, scope of entity boundary, inference of relation type, evidence traceability with localization, and triple output. This stepwise process strengthens the model’s capacity to comprehend equipment hierarchies, fault evolution chains, and maintenance action logic. Building on this, ChatGLM or LLaMA serves as the backbone model and is adapted to the target domain via LoRA fine-tuning. Entity alignment and character-level source localization mechanisms are further introduced to establish precise mappings between generated outputs and their textual evidence in the source documents. The extracted results are ultimately converted into standardized knowledge triples and stored in a Neo4j graph database. Based on this, a prototype system for generative information extraction is designed and implemented to demonstrate the practical effectiveness and adaptability of the proposed method. Experimental results show that the proposed method outperforms baseline methods across entity recognition, relation extraction, and structured output quality, providing robust knowledge support for fault tracing and predictive maintenance of metallurgical lifting equipment. Full article

Contemporary Blue Planetary Health frameworks frequently approach marine degradation primarily as a technical management problem while insufficiently addressing the relational, ethical, and political–economic conditions driving ocean collapse. The framework proposes that dominant marine governance paradigms continue to reproduce anthropocentric and extractivist assumptions that reduce oceans to economic assets rather than recognizing them as living multispecies relational systems. In response, the study develops the Blue Stratified Relational Responsibility Framework (BSRRF), an interdisciplinary model integrating multispecies ethics, marine psychophysiology, environmental humanities, political ecology, Indigenous relational ontologies, and ocean governance. The framework advances three central claims: marine sustainability requires relational rather than purely instrumental governance; humans possess asymmetrical ecological responsibility due to their technological and institutional power; and meaningful Blue Planetary Health transformation requires simultaneous shifts in moral imagination, affective perception, governance systems, and political economy. The study further critiques dominant Blue Economy paradigms for reproducing extractivist and colonial dynamics under narratives of sustainability and innovation. Ultimately, the framework argues that although the ocean crisis manifests ecologically, its underlying drivers are simultaneously epistemological, political, economic, and civilizational. Consequently, advancing Blue Planetary Health requires integrated transformations in education, governance, public policy, and multispecies ethical responsibility. Full article

Rising complexity in cyber-physical systems development exposes challenges in the consistent and reusable specification of graphical domain-specific languages (DSLs). Despite the benefits of model-based systems engineering (MBSE), the absence of a standardized, life-cycle-wide specification process results in semantic inconsistencies, tool dependence, and limited interoperability. While our previous work has addressed individual stages of DSL definition, a comprehensive, standards-based process integrating these stages remains missing. Building on these foundations, this paper introduces a unified language specification process for graphical DSLs grounded in established standards—the Meta-Object Facility (MOF), Unified Modeling Language (UML), Web Ontology Language (OWL), and Resource Description Framework (RDF). The process integrates three core artifacts: a tool-independent ontology capturing domain semantics, a MOF-conforming metamodel unifying abstract syntax, semantics, and concrete syntax, and a UML-profile-based implementation. To support and exemplify this process, a prototypical toolchain is introduced that enables automated transformations between these artifacts, thereby facilitating the consistent propagation of semantics from ontology to implementation. The applicability of the proposed process is demonstrated through both a top-down automotive case and a bottom-up cybersecurity DSL, illustrating its cross-domain generalizability. By explicitly structuring and connecting ontology, metamodel, and implementation, this work contributes a semantically consistent, machine-interpretable, and tool-independent specification process for graphical DSLs in MBSE. Full article

Intellectual property reasoning depends on the interaction of factual context, doctrinal tests, exceptions, evidentiary uncertainty, and regulatory objectives. These features make patent, copyright, and trademark analysis difficult to support through text-level processing or isolated rule encoding. This article proposes a bounded DIKWP-guided semantic modeling framework for representing selected intellectual property reasoning patterns as queryable semantic structures. The framework is conceptual and design-oriented; it is specified at the design level through a formal graph characterization of DIKWP, a modular ontology fragment, rule schemas, SPARQL-style queries, and worked examples from patent, copyright, and trademark reasoning. Methodologically, the study uses a qualitative legal-informatics design approach. The three IP domains are selected because they represent complementary reasoning patterns: claim-element correspondence and equivalence screening in patent law, expression and exception analysis in copyright law, and factor-based confusion assessment in trademark law. The examples are used to derive semantic entities, relations, rule-linked structures, uncertainty annotations, explanation paths, and human-review triggers. DIKWP is treated not as a complete legal ontology or autonomous adjudicator, but as a network-structured meta-architecture for coordinating data, information, knowledge, wisdom, and purpose in reviewable legal decision support. The article illustrates how selected IP reasoning patterns can be represented in forms that remain traceable to legal sources and open to human review. It does not claim empirical validation, jurisdiction-specific doctrinal completeness, or autonomous legal decision-making. Its contribution is to specify how semantic legal representation can be made more operational, auditable, and institutionally constrained in the intellectual property domain. Full article

The growing complexity of modern systems has pushed safety science beyond tradition-al analysis methods. In a world where the unknown matters as much as the known, knowledge graphs emerge as a powerful means for representing, connecting, and extending knowledge. However, the intersection between safety science and knowledge graphs remains largely unexplored. Which communities of researchers are leveraging knowledge graphs for safety? Is there any common pattern in how they are being used? This paper addresses these questions by presenting a systematic review of the literature on the use of knowledge graphs in the context of safety. Based on 173 eligible documents, we propose a classification framework structured around three dimensions: the originality of knowledge characterization, the originality of knowledge extraction, and the maturity of safety analysis. The framework identifies three archetypes of knowledge graph users: Assemblers, who rely on existing models and tools; Alchemists, who adapt available knowledge structures or extraction procedures; and Shapers, who develop novel ontologies, extraction methods, or both. The obtained results show how the latter represents the largest group among the reviewed studies, suggesting a tension between analytical maturity and the need for customized solutions. More broadly, the classification framework presented in this review may support researchers from both the safety and the artificial intelligence communities in fostering a shared path for the scientific development of these disciplines. Full article

Ensuring legally compliant safety and health documentation remains a significant challenge in construction projects because practitioners often lack expertise in identifying and applying relevant statutory provisions. This study proposes a deep learning-based legislation recommendation system to reduce inconsistencies in statutory citation and improve the legal traceability of safety documentation. The system integrates domain-specific ontologies and context-aware language models to recommend appropriate legal provisions based on user-inputted risk factors and keywords. For empirical validation, the system was applied to the Design for Safety (DfS) report, a representative safety document prepared during the design phase of construction projects. A training dataset comprising 1355 DfS reports and 356 safety legislation articles was used, with semantic relationships enhanced through ontology-based vocabulary expansion and Word2Vec embeddings. KoELECTRA, a Korean pre-trained language model, achieved the best performance, with top-1 accuracy of 58.1%, F1-score of 56.6%, and top-3 accuracy of 71.8%. A web-based application was also developed to support legal referencing during document preparation. The findings demonstrate the system’s potential to assist practitioners in identifying relevant legislation, enhance regulatory compliance, and improve the consistency and quality of construction safety documentation. Full article

Digital twins have become a central paradigm for cultural heritage documentation, monitoring, and preventive preservation. Yet, when cultural heritage systems promise prediction, simulation, intervention planning, and decision support, a more explicit account is needed of the computational commitments behind such claims. This position paper proposes the notion of the heritage world model as a conceptual and architectural abstraction that uses the semantic digital twin as its representational layer and extends it toward prediction, memory, uncertainty-aware reasoning, and intervention evaluation. We define a heritage world model as a structured, temporally updated, semantically grounded, and action-aware model of a heritage asset and its preservation environment, capable of integrating observations, estimating latent risk states, predicting plausible future trajectories, and evaluating interventions under uncertainty. The paper does not present a validated deployed system. Rather, it clarifies the architectural conditions under which a decision-support digital twin infrastructure could support the kind of world-model-like preservation system proposed here. It further argues that such a model becomes operationally meaningful only when it includes a human-supervised controller layer that maps semantic state, predicted risk trajectories, uncertainty, memory, and institutional constraints into preservation-relevant actions, alerts, monitoring adaptations, or requests for expert review. Sensor data, remote sensing, computational models, risk assessments, policies, and conservation actions are interpreted as possible observational, dynamic, and intervention layers of a heritage world model. The paper reviews adjacent work in heritage digital twins, semantic and reactive ontologies, risk-aware preservation, agentic AI, and modern AI world models, and proposes a research agenda for moving toward predictive, memory-bearing, and intervention-aware preservation intelligence. Full article

This article examines the impact of the integration of artificial intelligence (AI) on human formation from the perspective of Christian theological anthropology. Although recent scholarship highlights the advantages of AI for personalised learning and educational efficiency t frequently neglects the ontological and spiritual dimensions of human development. This study argues that the widespread use of AI in education risks externalising interior processes such as reflection, discernment, and memory. In contrast, the Christian theological tradition—as articulated by Augustine of Hippo (Confessions), Dumitru Stăniloae (Orthodox Dogmatic Theology), John Zizioulas (Being as Communion), and Christos Yannaras (The Freedom of Morality)—conceives of education as an inner transformation rooted in communion and participation in divine life. Drawing on interdisciplinary dialogue among theology, the philosophy of technology, and AI studies, this article introduces the Integrative Theological Formation Model (ITFM), comprising three dimensions: functional, reflexive, and contemplative–relational. The model seeks to integrate technology into education while safeguarding interiority and the spiritual dimension of the person. The article concludes that, while AI can support educational processes, it cannot generate communion, interiority, or ontological transformation. Full article

Smart city planning involves a wide range of knowledge domains. However, general intelligent Question Answering systems often fall short when applied to this domain, and the relevant studies are not yet sufficient. To this end, this paper constructs an intelligent QA system that combines a large language model with a domain-specific knowledge graph. Capable of understanding questions accurately and generating professional answers, this system is designed to provide efficient knowledge services for smart city planning by following four steps. First, based on four authoritative planning guidelines, a domain-specific knowledge graph with a four-layer framework is constructed using Neo4j Community Edition 5.26.24. The framework includes top-level goals, knowledge modules, standard terminology and community scenarios. Subsequently, natural language questions are classified and matched with the templates before being converted into structured queries. Finally, the system performs Cypher query language queries and invokes ChatGLM4 to generate professional answers. The knowledge graph contains 100 entity nodes and 44 relations, and its ontology layer defines 28 entity types and 12 relation types. Therefore, the domain knowledge is structured and visualized, and planning professionals can intuitively retrieve diverse planning elements. In addition to its intelligent knowledge query function, this system assists planning professionals in preparing planning schemes and verifying compliance, reducing the time spent on reviewing regulations and comparing clauses, improving the efficiency of scheme preparation, and facilitating the refined implementation of urban renewal projects. It has high application value in smart city planning practices. Its construction approach can also serve as a reference for intelligent knowledge services in other fields. Full article

Legume–rhizobium symbiosis represents a crucial biological nitrogen fixation system. The AP2/ERF transcription factor ERN1 plays a vital role in nodulation of model legumes; however, its function in peanut (Arachis hypogaea), a typical crack-entry infection legume, remains unclear. To explore this, we performed transcriptome sequencing of peanut roots at 3 days post-inoculation (dpi) with rhizobium. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that differentially expressed genes (DEGs) were mainly enriched in DNA-binding transcription factor activity, plant–pathogen interaction, and plant hormone signal transduction pathways. The most strongly up-regulated gene was AhERN1, which was highly expressed in peanut roots and nodules. Subcellular localization indicated that AhERN1 was a nuclear-localized protein, and yeast transcriptional activation assays confirmed that AhERN1 functions as a transcriptional activator relying on its C-terminal domain. Furthermore, hairy root overexpression of AhERN1 significantly increased the number of peanut nodules. Collectively, these results reveal that AhERN1 acts as a positive regulator to promote rhizobium-induced nodule development in peanut, providing new insights into the regulatory mechanism of nodulation in dalbergoid legumes. Full article