Search Results (675)

The global infrastructure industry is faced with increasing system complexity and requirements driven by the Sustainable Development Goals, technological advancements, and the shift from Industry 4.0 to human-centric 5.0 principles. Coupled with persistent infrastructure investment deficits, these pressures necessitate improved methods for efficient requirements management and validation. While digital twins promise transformative real-time decision-making, reliance on static unstructured data formats inhibits progress. This paper presents a novel framework that integrates Building Information Modelling (BIM) and Model-Based Systems Engineering (MBSE), using Linked Data principles to preserve semantic meaning during information exchange between physical abstractions and requirements. The proposed approach automates a step of compliance validation against regulatory standards explored through a case study, utilising requirements from a high-speed railway station fire safety system and a modified duplex apartment digital model. The workflow (i) digitises static documents into machine-readable MBSE formats, (ii) integrates structured data into dynamic digital models, and (iii) creates foundations for data exchange to enable compliance validation. These findings highlight the framework’s ability to enhance traceability, bridge static and dynamic data gaps, and provide decision-making support in digital twin environments. This study advances the application of Linked Data in infrastructure, enabling broader integration of ontologies required for dynamic decision-making trade-offs. Full article

Background: Artificial intelligence (AI) is increasingly applied in the diagnosis of pediatric rare diseases, enhancing the speed, accuracy, and accessibility of genetic interpretation. These advances support the ongoing shift toward personalized medicine in clinical genetics. Objective: This review examines current applications of AI in pediatric rare disease diagnostics, with a particular focus on real-world data integration and implications for individualized care. Methods: A narrative review was conducted covering AI tools for variant prioritization, phenotype–genotype correlations, large language models (LLMs), and ethical considerations. The literature was identified through PubMed, Scopus, and Web of Science up to July 2025, with priority given to studies published in the last seven years. Results: AI platforms provide support for genomic interpretation, particularly within structured diagnostic workflows. Tools integrating Human Phenotype Ontology (HPO)-based inputs and LLMs facilitate phenotype matching and enable reverse phenotyping. The use of real-world data enhances the applicability of AI in complex and heterogeneous clinical scenarios. However, major challenges persist, including data standardization, model interpretability, workflow integration, and algorithmic bias. Conclusions: AI has the potential to advance earlier and more personalized diagnostics for children with rare diseases. Achieving this requires multidisciplinary collaboration and careful attention to clinical, technical, and ethical considerations. Full article

The aging population in Europe and other developed regions is accelerating the demand for adaptable domestic environments that support independent living and care at home. In this context, ontologies offer a promising approach to represent and manage knowledge about built environments, smart technologies, and user needs—especially within Ambient Assisted Living (AAL) systems. This paper presents a systematic literature review examining the role of ontologies in the reconfiguration of domestic living spaces, with a focus on their application in design processes and decision support systems. Following the PRISMA methodology, 14 relevant works published between 2000 and 2025 were identified and analyzed. The review explores key aspects such as ontology conceptualization, reuse, engineering methodologies, integration with CAD systems, and validation practices. The results show that research on this topic is fragmented yet growing, with the first contribution dated 2005 and peaks in 2016, 2018, and 2024. Most works (11) were conference papers, with Europe leading the contributions, particularly Italy. Half of the reviewed ontologies were developed “from scratch”, while the rest relied on conceptualizations such as BIM. Ontology reuse was inconsistent: only 50% of works reused existing models (e.g., SAREF, SOSA, BOT, ifcOWL), and few adopted Ontology Design Patterns. While 11 works followed ontology engineering methodologies—mostly custom or established methods such as Methontology or NeOn—stakeholder collaboration was reported in less than 36% of cases. Validation practices were weak: only six studies presented use cases or demonstrators. Integration with CAD systems remains at a prototypical stage, primarily through semantic enrichment and SWRL-based reasoning layers. Remaining gaps include poor ontology accessibility (few provide URLs or W3IDs), limited FAIR compliance, and scarce modeling of end-user needs, despite their relevance for AAL solutions. The review highlights opportunities for collaborative, human-centered ontology development aligned with architectural and medical standards to enable scalable, interoperable, and user-driven reconfiguration of domestic environments. Full article

Information system design and implementation require generally accepted norms, principles, and standards. Lately, the challenge for achieving a high degree of general acceptance has increased with the presence of formal governance structures. Compliance with norms for information system development depends on the shared recognition of regulations and standards. The research problem in this study concerns standards and their role in the development of a pathology laboratory information system. In this paper, in the theoretical background section, the authors present regulations, standards, and disease classification, which are necessary for planning the pathology laboratory information system. Next, in the template design project section, the authors focus on development of a new, ontology-based, and standard-oriented approach for elaboration of a standardized template of the pathological assessment of histopathology material. Authors use the World Health Organization (WHO) ICD-11 classification to elaborate on that template, which permits the precise coding of diagnoses and medical procedures. The main findings concern the proposed ontology-based document template, which can further be used in the Laboratory Information Management System (LIMS), and as such can be considered a pattern for the development of other LIMS documents. In conclusion, the authors emphasized the standardized method application for designing and implementing medical documents. This original contribution concerns the assessment template design based on existing ontologies ICD-10 and ICD-11. Full article

Aspect-based sentiment analysis (ABSA) plays a pivotal role in understanding the nuances of sentiment expressed in text, particularly in the context of diverse languages and cultures. This paper presents a novel deep convolutional neural network (CNN)-based model tailored for aspect and polarity classification in Hausa movie reviews, as Hausa is an underrepresented language with limited resources and presence in sentiment analysis research. One of the primary implications of this work is the creation of a comprehensive Hausa ABSA dataset, which addresses a significant gap in the availability of resources for sentiment analysis in underrepresented languages. This dataset fosters a more inclusive sentiment analysis landscape and advances research in languages with limited resources. The collected dataset was first preprocessed using Sci-Kit Learn to perform TF-IDF transformation for extracting feature word vector weights. Aspect-level feature ontology words within the analyzed text were derived, and the sentiment of the reviewed texts was manually annotated. The proposed model combines convolutional neural networks (CNNs) with an attention mechanism to aid aspect word prediction. The model utilizes sentences from the corpus and feature words as vector inputs to enhance prediction accuracy. The proposed model leverages the advantages of the convolutional and attention layers to extract contextual information and sentiment polarities from Hausa movie reviews. The performance demonstrates the applicability of such models to underrepresented languages. With 91% accuracy on aspect term extraction and 92% on sentiment polarity classification, the model excels in aspect identification and sentiment analysis, offering insights into specific aspects of interest and their associated sentiments. The proposed model outperformed traditional machine models in both aspect word and polarity prediction. Through the creation of the Hausa ABSA dataset and the development of an effective model, this study makes significant advances in ABSA research. It has wide-ranging implications for the sentiment analysis field in the context of underrepresented languages. Full article

Traditional Case-Based Reasoning (CBR) methods face significant methodological challenges, including limited information resources in case databases, methodologically inadequate similarity calculation approaches, and a lack of standardized case revision mechanisms. These limitations lead to suboptimal case matching and insufficient solution adaptation, highlighting critical gaps in the development of CBR methodologies. This paper proposes a novel CBR framework enhanced by generative AI, aiming to improve and innovate existing methods in three key stages of traditional CBR, thereby enhancing the accuracy of retrieval and the scientific nature of corrections. First, we develop an ontology model for comprehensive case representation, systematically capturing scenario characteristics, risk typologies, and strategy frameworks through structured knowledge representation. Second, we introduce an advanced similarity calculation method grounded in triangle theory, incorporating three computational dimensions: attribute similarity measurement, requirement similarity assessment, and capability similarity evaluation. This multi-dimensional approach provides more accurate and robust similarity quantification compared to existing methods. Third, we design a generative AI-based case revision mechanism that systematically adjusts solution strategies based on case differences, considering interdependence relationships and mutual influence patterns among risk factors to generate optimized solutions. The methodological framework addresses fundamental limitations in existing CBR approaches through systematic improvements in case representation, similarity computation, and solution adaptation processes. Experimental validation using actual case data demonstrates the effectiveness and scientific validity of the proposed methodological framework, with applications in risk assessment and emergency response scenarios. The results show significant improvements in case-matching accuracy and solution quality compared to traditional CBR approaches. This method provides a robust methodological foundation for CBR-based decision-making systems and offers practical value for risk management applications. Full article

This paper explores the application of Large Language Models (LLMs) for ontology development, focusing specifically on cloud service ontologies. We demonstrate how LLMs can streamline the ontology development process by following a modified Ontology Development 101 methodology using Perplexity AI. Our case study shows that LLMs can effectively assist in defining scope, identifying existing ontologies, generating class hierarchies, creating properties, and populating instances. The resulting cloud service ontology integrates concepts from multiple standards and existing ontologies. While LLMs cannot fully automate ontology creation, they significantly reduce development time and complexity, serving as valuable assistants in the ontology engineering process. Full article

A wide range of microorganisms, including endophytes, frequently interact with forest trees. The role of endophytes in industrial conifers has not been fully investigated. The Yezo spruce Picea jezoensis is widely used for logging in Russia and Japan. In this work, the endophytic communities of bacteria and fungi in healthy needles, branches, and fresh wood of P. jezoensis from Primorsky Territory were analyzed using metagenomic analysis. The results indicate that the diversity of endophytic communities in P. jezoensis is predominantly influenced by the specific tree parts (for both bacteria and fungi) and by different tree specimens (for fungi). The most abundant bacterial classes were Alphaproteobacteria, Gammaproteobacteria and Actinobacteria. Functional analysis of KEGG orthologs (KOs) in endophytic bacterial community using PICRUSt2 and the PLaBAse PGPT ontology revealed that 59.5% of the 8653 KOs were associated with plant growth-promoting traits (PGPTs), mainly, colonization, stress protection, bio-fertilization, bio-remediation, vitamin production, and competition. Metagenomic analysis identified a high abundance of the genera Pseudomonas and Methylobacterium-Methylorubrum in P. jezoensis, which are known for their potential growth-promoting activity in other coniferous species. The dominant fungal classes in P. jezoensis were Dothideomycetes, Sordariomycetes, and Eurotiomycetes. Notably, the genus Penicillium showed a pronounced increase in relative abundance within the fresh wood and needles of Yezo spruce, while Aspergillus displayed elevated abundance specifically in the fresh wood. It is known that some of these fungi exhibit antagonistic activity against phytopathogenic fungi. Thus, our study describes endophytic communities of the Yezo spruce and provides a basis for the production of biologicals with potential applications in forestry and agriculture. Full article

Background: Upper tract urothelial carcinoma (UTUC) is a rare and aggressive malignancy with limited prognostic tools to predict disease progression. Due to its low incidence, the molecular pathogenesis of UTUC remains poorly understood, and few studies have explored transcriptomic profiling in this setting. Identifying gene expression biomarkers associated with progression may help improve risk stratification and guide postoperative management. Methods: In this study, we applied a machine learning approach to gene expression data from radical nephroureterectomy (RNU) specimens of 17 consecutive patients with pT2 or pT3 UTUC treated at our institution. RNA was extracted from formalin-fixed paraffin-embedded tissues and sequenced using the Ion AmpliSeq™ Transcriptome Human Gene Expression Kit on an Illumina HiSeq 2500 platform. Differential gene expression was assessed using DESeq2, and results were visualized with volcano plots. Predictive power was evaluated through logistic regression and receiver operating characteristic (ROC) analysis. Gene Ontology enrichment analysis was used to explore biological pathways. Results: A total of 76 genes were differentially expressed between progressive and non-progressive patients. A random forest classifier identified ten key genes with prognostic potential. Validation with logistic regression yielded an area under the ROC curve (AUC) of 0.88, indicating high discriminative ability. These genes were associated with immune regulation, cell cycle control, and tumor progression. Conclusions: This pilot study demonstrates the potential of integrating machine learning with transcriptomic analysis to identify prognostic biomarkers in UTUC. Further validation in larger, independent cohorts is needed to confirm these findings and support their clinical application. Full article

This study introduces the Real-To-Fictional Ontology (RTFO), a structured framework designed to analyze the dynamic relationship between reality and fiction in literary works, with a focus on preparatory materials and their influence on narrative construction. While traditional Italian philology and genetic criticism have distinct theoretical and editorial approaches to avant-text, this ontology addresses their limitations by integrating fine-grained textual analysis with contextual biographical avant-text to enhance character interpretation. Modeled in OWL2, RTFO harmonizes established frameworks such as LRMoo and CIDOC-CRM, enabling systematic representation of narrative elements. The ontology is applied to the case study of Cesare Pavese’s La luna e i falò, with a particular focus on the biographical avant-text of Pinolo Scaglione, the real-life friend who inspired key aspects of the novel. The fragmented and unstable nature of avant-text is addressed through a factoid-based model, which captures character-related traits, states and events as interconnected entities. SWRL rules are employed to infer implicit connections, such as direct influences between real-life contexts and fictional constructs. Application of the ontology to case studies demonstrates its effectiveness in tracing the evolution of characters from preparatory drafts to final texts, revealing how biographical and contextual factors shape narrative choices. Full article

Flammulina filiformis, one of the most delicious and commercially important mushrooms, demonstrates remarkable adaptability to diverse agricultural wastes. However, it is unclear how different substrates affect the degradation of lignocellulosic biomass and the production of lignocellulolytic enzymes in F. filiformis. In this study, label-free comparative proteomic analysis of F. filiformis cultivated on sugarcane bagasse, cotton seed shells, corn cobs, and glucose substrates was conducted to identify degradation mechanism across various substrates. Label-free quantitative proteomics identified 1104 proteins. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of protein expression differences were predominantly enriched in energy metabolism and carbohydrate metabolic pathways. Detailed characterization of carbohydrate-active enzymes among the identified proteins revealed glucanase (GH7, A0A067NSK0) as the key enzyme. F. filiformis secreted higher levels of cellulases and hemicellulases on sugarcane bagasse substrate. In the cotton seed shells substrate, multiple cellulases functioned collaboratively, while in the corn cobs substrate, glucanase predominated among the cellulases. These findings reveal the enzymatic strategies and metabolic flexibility of F. filiformis in lignocellulose utilization, providing novel insights for metabolic engineering applications in biotechnology. The study establishes a theoretical foundation for optimizing biomass conversion and developing innovative substrates using targeted enzyme systems. Full article

Catalysis research is complex and interdisciplinary, involving diverse physical effects and challenging data practices. Research data often captures only selected aspects, such as specific reactants and products, limiting its utility for machine learning and the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) workflows. To improve this, semantic structuring through ontologies is essential. This work extends the established ontologies by refining logical relations and integrating semantic tools such as the Web Ontology Language or the Shape Constraint Language. It incorporates application programming interfaces from chemical databases, such as the Kyoto Encyclopedia of Genes and Genomes and the National Institute of Health’s PubChem database, and builds upon established ontologies. A key innovation lies in automatically decomposing chemical substances through database entries and chemical identifier representations to identify functional groups, enabling more generalized reaction classification. Using new semantic functionality, functional groups are flexibly addressed, improving the classification of reactions such as saponification and ester cleavage with simultaneous oxidation. A graphical interface (GUI) supports user interaction with the knowledge graph, enabling ontological reasoning and querying. This approach demonstrates improved specificity of the newly established ontology over its predecessors and offers a more user-friendly interface for engaging with structured chemical knowledge. Future work will focus on expanding ontology coverage to support a wider range of reactions in catalysis research. Full article

In this position paper, we argue that the conventional understanding of ‘information’ (as generally conceived in science, in a digital fashion) is overly simplistic and not consistently applicable to living systems, which are open systems that cannot be reduced to any kind of ‘portion’ (building block) ascribed to the category of quantity. Instead, it is a matter of relationships and qualities in an indivisible analogical (and ontological) relationship between any presumed ‘software’ and ‘hardware’ (information/matter, psyche/soma). Furthermore, in biological systems, contrary to Shannon’s definition, which is well-suited to telecommunications and informatics, any kind of ‘information’ is the opposite of internal entropy, as it depends directly on order: it is associated with distinction and differentiation, rather than flattening and homogenisation. Moreover, the high degree of structural compartmentalisation of living matter prevents its energetics from being thermodynamically described by using a macroscopic, bulk state function. This requires the Second Principle of Thermodynamics to be redefined in order to make it applicable to living systems. For these reasons, any static, bit-related concept of ‘information’ is inadequate, as it fails to consider the system’s evolution, it being, in essence, the organized coupling to its own environment. From the perspective of quantum field theory (QFT), where many vacuum levels, symmetry breaking, dissipation, coherence and phase transitions can be described, a consistent picture emerges that portrays any living system as a relational process that exists as a flux of context-dependent meanings. This epistemological shift is also associated with a transition away from the ‘particle view’ (first quantisation) characteristic of quantum mechanics (QM) towards the ‘field view’ possible only in QFT (second quantisation). This crucial transition must take place in life sciences, particularly regarding the methodological approaches. Foremost because biological systems cannot be conceived as ‘objects’, but rather as non-confinable processes and relationships. Full article

This paper introduces a hierarchical object-centric descriptor framework called TOSD (Triplet Object-Centric Semantic Descriptor). The goal of this method is to overcome the limitations of existing pixel-based and global feature embedding approaches. To this end, the framework adopts a hierarchical representation that is explicitly designed for multi-level reasoning. TOSD combines shape, color, and topological information without depending on predefined class labels. The shape descriptor captures the geometric configuration of each object. The color descriptor focuses on internal appearance by extracting normalized color features. The topology descriptor models the spatial and semantic relationships between objects in a scene. These components are integrated at both object and scene levels to produce compact and consistent embeddings. The resulting representation covers three levels of abstraction: low-level pixel details, mid-level object features, and high-level semantic structure. This hierarchical organization makes it possible to represent both local cues and global context in a unified form. We evaluate the proposed method on multiple vision tasks. The results show that TOSD performs competitively compared to baseline methods, while maintaining robustness in challenging cases such as occlusion and viewpoint changes. The framework is applicable to visual odometry, SLAM, object tracking, global localization, scene clustering, and image retrieval. In addition, this work extends our previous research on the Semantic Modeling Framework, which represents environments using layered structures of places, objects, and their ontological relations. Full article

Aberrant DNA methylation has been shown to be a fingerprint characteristic in human colorectal tumors. In this study, we hypothesize that investigating global DNA methylation could offer potential candidates for clinical application in CRC. The epigenome-wide association analysis was conducted in both the tumor area (N = 27) and the adjacent tumor-free (NAT) area (N = 15). We found 78,935 differentially methylated CpG sites (DMCs) (FDR < 0.05), 42,888 hypomethylated and 36,047 hypermethylation showing overall hypomethylation. Gene ontology and KEGG analysis of differentially methylated genes showed significant enrichment in developmental genes, as well as in genes involved in metabolic processes and the cell cycle, such as the TFGβ and cAMP signaling pathways. Through filtered analysis, we identified TNXB as the most epigenetically dysregulated gene, hypomethylated and downregulated in CRC (both with p < 0.001) and associated with poor overall survival. In the functional analysis, TNXB was epigenetically regulated in a dose-dependent manner, suggesting a potential role in CRC. The epigenetic dysregulation and functional role of TNXB in CRC could have clinical implications, serving as indicators of malignant potential, with adverse effects associated with disease origin and progression in CRC. Full article