Search Results (1,378)

Tropical maize often exhibits photoperiod sensitivity, which limits its adaptation to temperate regions. Understanding its proteomic dynamics under long-day conditions is therefore crucial for germplasm improvement. This study employed a Tandem Mass Tag (TMT)-based proteomic approach to investigate stage-specific protein expression patterns in the tropical maize inbred line QR273 under long-day conditions (16 h light/8 h dark). Seeds were cultivated in climate chambers, and leaves were collected at the four-leaf (P4) and nine-leaf (P9) stages. A total of 2881 differentially expressed proteins (DEPs) were quantified between the P4 and P9 stages, among which only 7 were upregulated and 2874 were downregulated at the P9 stage. Gene Ontology (GO) enrichment analysis revealed that these DEPs were significantly enriched in processes related to proteolysis, membrane components, and ATP binding. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed the enrichment of DEPs in amino acid biosynthesis, secondary metabolite biosynthesis, and aminoacyl-tRNA biosynthesis pathways. Protein–protein interaction (PPI) network analysis identified 60S ribosomal protein L12, adenosine 5′-phosphosulfate reductase, and RuvB helicase as core hub proteins. Based on functional annotation of representative DEPs, the DEPs were classified into four categories: 9 proteins related to storage material protection, 14 proteins related to protein modification, 12 proteins related to photosynthesis, and 25 proteins with other biological functions. Comparative analysis demonstrated a decrease in storage material protection, protein modification, and photosynthetic capacity at the P9 stage relative to the P4 stage. These findings provide insights into the proteomic dynamics underlying tropical maize development under long-day conditions and offer a theoretical basis for genetic improvement of tropical maize germplasm. Notably, inferences regarding nutrient reallocation based on DEP downregulation are derived solely from proteomic data and require further experimental validation. Full article

This paper critically examines disability-inclusive design theory and practice through the lens of new materialism, tracing a shift from designing for users to designing with and ultimately from disability. It reveals a key paradox: while participatory and disability-led approaches foreground lived experience and plural voices, design outcomes must still function across shared, pluriversal contexts. Individual accounts of disability generate situated, partial knowledge that cannot be straightforwardly generalised, creating persistent tensions between singular experiences and collective design needs. By introducing a relational ontology, the paper reframes design knowledge as emergent from dynamic interactions between people, materials and contexts, destabilising binaries such as designer/user and disabled/non-disabled. The proposed praxeology advances disability leadership, positionality and embedded participation as core to design practice. These insights prompt new research questions around how plural, situated knowledges can inform scalable design decisions, how conflicting lived experiences can be ethically negotiated, and how relational, material perspectives can reshape methodologies for inclusive and socially just design. Full article

Industrial systems increasingly rely on MQTT-based message streaming to enable automated, data-driven production processes at the network edge. While semantic models such as the SSN/SOSA ontology enable machine-interpretable descriptions of observations and actuations, an explicit model of message transport is rarely considered. Consequently, MQTT-based communication remains opaque, particularly regarding information processing, hindering the semantic analysis of application-specific topic structures and the behavior of transport protocols. To close this gap, this work introduces the revised MQTT4SSN ontology as a key contribution, extending existing semantic models with protocol-aware representations of MQTT entities, control packets, and transport-level interactions. MQTT4SSN enables end-to-end semantic traceability, from sensor observations and actuator controls to the underlying message transmission within distributed systems. Building on this contribution, the MQTT2RDF integration framework incorporates MQTT4SSN as its core to capture live MQTT traffic and represent both payload meaning and transport-level provenance within an RDF knowledge graph. This work presents a novel approach for representing edge computing and information processing over MQTT, addressing two key challenges. First, the framework supports semantic interpretation of topic hierarchies and provides configurable mappings between MQTT topics, payload structures, and observation or actuation semantics. This approach facilitates the setup of edge computing systems and enables context-aware subscription management and structured data formatting, thereby improving interoperability between heterogeneous deployments. Second, transport-level provenance analytics provide a semantic basis for query-based detection, classification support, and diagnostic analysis of malformed or incomplete MQTT communication. The approach provides explainable, traceable information processing through transport provenance, which is essential for safety-critical industrial environments. The contributions are validated through an industrial use case from a production environment, demonstrating its applicability for system monitoring, troubleshooting, and semantic analytics of MQTT-based infrastructures. Full article

The article presents a systematic and critical theoretical–methodological review and conceptual synthesis of the region as a fundamental analytical category and the central subject matter of regional geography. The primary objective of the study is to critically re-examine and conceptually redefine the region through an ontological and epistemological analysis of classical and contemporary geographical paradigms. The study is based on a qualitative interpretative methodology that combines analytical–synthetic, historical–genetic, comparative, critical, and conceptual approaches in order to examine the ontological and epistemological foundations of the region within classical and contemporary geographical thought. The region is conceptualized as a complex, multilayered, and dynamic socio-spatial entity whose ontological status has continuously evolved—from the essentialist notion of an objective spatial reality characteristic of classical geographic paradigms toward a relational and constructivist concept shaped by the interaction of social practices, political processes, and identity articulations within contemporary theoretical frameworks. Attention is also given to the epistemological foundations of regional knowledge, linking various modalities of the production and interpretation of scientific knowledge. Furthermore, the paper examines the roles of power, knowledge, identity, and institutionalization in the formation of regions, as well as the significance of centripetal and centrifugal forces in maintaining or destabilizing regional coherence. The research challenges traditional concepts of the region and proposes its redefinition in accordance with contemporary approaches that conceptualize it as an open, fluid, and context-dependent analytical framework. In conclusion, from the perspective of new regional geography, the region is interpreted as an emergent relational configuration whose understanding requires a broad interdisciplinary and critical approach. Full article

Dimensional and transdiagnostic models have become central to contemporary efforts to move psychiatric nosology beyond DSM/ICD categories. This shift reflects persistent limitations of categorical syndromes as final biological targets, including within-diagnosis heterogeneity, cross-diagnostic comorbidity, developmental instability, and incomplete alignment with underlying mechanisms. This article examines a central unresolved problem in this transition: when, if ever, a descriptive or predictive psychiatric dimension can be interpreted as a candidate disease axis. We conducted a conceptual synthesis of major dimensional and transdiagnostic frameworks, including Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP), the general psychopathology factor, cross-disorder genomic models, clinical staging approaches, and data-driven subtyping. The analysis separates three levels of inference that are often conflated in psychiatric research: descriptive structure, predictive utility, and disease-level biological validity. The synthesis identifies a recurrent inferential error in which reproducible factors, clusters, or classifiers are prematurely treated as evidence of disease architecture. Such constructs may describe real covariance patterns or improve prognostic prediction without establishing biological validity. We propose an eight-domain hierarchical framework for promotion to candidate disease-axis status, organized into four core gatekeepers—replication across cohorts, ascertainment, and methods, developmental coherence, incremental prognostic value beyond diagnosis and nonspecific severity, and discriminability from nonspecific severity—and four supporting/disciplining domains: cross-level convergence, mechanistic constraint, clinical leverage, and explicit falsifiability/boundary conditions. On this basis, middle-level transdiagnostic spectra and selected cross-disorder genomic liabilities appear more defensible as candidate disease axes than highly global or weakly specified constructs. Psychiatry was justified in turning toward dimensional models, but dimensionality alone does not confer biological validity. The key task is not to choose between categories and dimensions, but to define the evidential thresholds under which dimensional constructs warrant ontological promotion. Full article

The Porous Line is a drawing inquiry that uses materials and processes to engage in a dialogue with a suburban ecosystem. I follow the physicist David Bohm’s proposal to use dialogue as a mode of engagement where habitual modes of thought are suspended, a form of non-judgmental curiosity. I reflect on how immersing a large roll of French imported paper in my everyday environs reveals the porousness between nature and culture. The binary separation of nature and culture has undergone significant criticism as we deal with the climate crisis. As a foundational medium within western art and thought, how can drawing communicate this growing ontological shift? The essay engages in dialogue with Yolngu art from Yirrkala as a guide on what an ecological art practice entails. Their commitment to work with what ‘country’ provides has resulted in innovative and thoughtful new works. In response to propositions seen in Yolngu artworks, this essay engages with place, materiality, and relationality through the process of merging line and ground, the fundamentals of drawing, physically and conceptually. I reflect on the challenges that need to be addressed within western ontologies to develop an ecological approach in drawing. 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

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 battery cell production, a cornerstone of the net-zero vision, is a multifaceted process chain involving diverse processes, spanning from batch to continuous to single-unit steps. The quality of the battery cell as the final product is affected by various product and process parameters along this process chain. In the era of Industry 4.0, data-driven approaches have emerged as a promising solution to navigate these complexities and derive effective quality management practices. A key prerequisite for the successful implementation is the availability of accurate data. A tracking and tracing system in battery cell production provides the foundation to acquire such data. It supports the development of a digital twin of the product, enabling real-time monitoring of key performance indicators, in-line quality control, resource optimization, and compliance fulfillment, among others. This article presents an implementation methodology and discusses the key aspects to consider for upscaling such a system focusing on data management, including relevant parameters, data acquisition, and storage, as well as data structuring and mapping. It highlights the advantages of using ontology-based data descriptions, enabling semantically mapped production environments. Lastly, this article explores potential use cases facilitated by a traceability system, emphasizing its potential to realize intelligent, data-driven production. 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

Moroccan ICH is a rich domain that remains difficult to structure formally due to the heterogeneity of textual descriptions and the diversity of documented cultural practices. This article proposes a semi-automatic and adaptable methodological framework for constructing an ontology-driven knowledge graph from unstructured texts, applied to Moroccan ICH. The approach begins by classifying documents into the five predefined UNESCO categories using lexical, semantic, and hybrid methods, followed by intra-category semantic clustering to identify thematic substructures that inform ontological modeling. The results show that hybrid approaches achieve the best performance in document classification, while clustering requires an adaptive strategy for each category. Building on these stages, ICHOnto was generated as a CIDOC CRM-aligned ontology enriched with UNESCO categories, expert-validated subcategories, and entities and relations extracted from the texts. The resulting resource was evaluated through logical consistency, SHACL compliance, and functional assessment using Competency Questions. The evaluation confirms that ICHOnto provides a coherent, exploitable, and interoperable semantic resource for representing, organizing, and querying Moroccan ICH. Its modular structure also supports adaptation to other heritage corpora or domains based on unstructured textual data. Full article

This article aims to propose a unifying conceptualization for the leadership domain utilizing ontology engineering to address conceptual ambiguity and information overlap. The approach involves identifying and defining structural, common, and underlying elements to organize leadership information. The methodology is based on a literature review guided by principles of ontology engineering. It focuses on collecting, analyzing, disambiguating, and synthesizing information to represent general elements in the leadership domain as categories, subcategories, and instances, including their relationships. The result is a unifying conceptualization comprising defined categories, subcategories, general instances, and relationships. This structure creates a taxonomic framework to organize the leadership’s domain information, classifying approaches, theories, models, styles, variables, characteristics, and organizational results. Expert validation confirms their suitability to represent the leadership domain in a general manner. Researchers can use it to understand and compare information, identify conceptual similarities and differences, and support knowledge dissemination within the leadership domain, reducing ambiguity and overlap. Full article

The FAIR principles have become a central framework for research data management and digital infrastructures, yet their implementation remains challenging within the long-tail of research. This paper examines how FAIR principles can be operationalized in practice through a case study on the FAIRification of the INFRA-ART Spectral Library, a specialized heritage science data service hosting multi-analytical spectral datasets related to art and archaeological materials. The FAIRification process was approached as an iterative and incremental workflow structured around three interconnected dimensions: technical interoperability, semantic alignment, and governance-oriented stewardship practices. Implementation activities included machine-actionable metadata exposure, semantic enrichment through ontology mappings and controlled vocabularies, interoperability-oriented infrastructure development, and the adoption of TRUST-aligned governance mechanisms. The results demonstrate substantial improvements in metadata quality, discoverability, interoperability, and repository transparency. At the same time, the FAIRification process highlighted persistent challenges related to fragmented semantic resources, evolving interoperability requirements, limited stewardship capacity, and dependence on project-based funding and institutional support. The study argues that effective FAIRification in long-tail data services depends on context-sensitive and incremental implementation approaches rather than rigid compliance models. Full article

Mental health interventions involve complex and evolving situations that require careful reasoning and transparency. This paper presents a knowledge-based system designed to simulate and analyze intervention strategies for student depression in a controlled and explainable setting. The system combines reinforcement learning with formal ontological modeling. A simulation environment, grounded in large-scale integrated student mental health datasets containing questionnaire-derived indicators, represents the evolution of students’ psychological states under a set of clinically informed intervention actions. The proposed framework is evaluated using a composite dataset constructed by integrating multiple publicly available student mental health datasets from Kaggle and Figshare, incorporating integrated student mental health datasets containing questionnaire-derived indicator measures such as depression, anxiety, and lifestyle indicators. A learning agent explores alternative intervention strategies through interaction with this environment. All states, actions, and outcomes are formalized within an OWL ontology, making the decision structure explicit. By embedding learned policies into a structured knowledge representation, the system allows intervention dynamics to be inspected, queried, and analyzed independently of the underlying learning mechanism. Reinforcement learning is used to generate and refine candidate strategies, while ontology provides a stable and interpretable model of the decision space. Experimental results show that the approach can identify coherent intervention strategies within the simulation environment while preserving transparency. The study demonstrates how adaptive learning and symbolic knowledge representation can be integrated within a single knowledge-based system, offering a structured and explainable approach to sequential decision analysis in sensitive domains. Full article

Developing an agricultural ontology will facilitate the advancement of agriculture in transferring information between fields and natural language processing (NLP). Grammatical and contextual comprehension of the domain data is required to construct a domain-specific ontology. Although there are datasets available for agriculture, there is a lack of standardized and large-scale annotated datasets developed specifically for the purpose of ontology development and relationship extraction. Thus, because of the unavailability of a structured and annotated domain-specific dataset, a standard methodology with a combination of both grammatical and contextual analysis is required for effective data processing. Though there are many approaches to lay the foundations for the agriculture domain-specific ontologies, in this paper, pretrained DeBerta with regular expressions and the Graph Attention Network (GAT) method with regular expressions for term with domain relations extraction are proposed. From the acquired entities and connections between the entities, an ontology graph is constructed. The proposed work is evaluated using performance measures and compared with existing work. It was found that the proposed Ontology Construction for Agriculture Domain (OCAD) method performs better than other methods. The proposed OCAD framework achieves a precision of 99.64%, a recall of 99.26%, and an F1 score of 99.5%, demonstrating strong performance within a domain-specific setting over existing methods. Full article