Search Results (93)

The automotive industry is undergoing a significant transition, where the development of Battery Electric Vehicles (BEV) and the increasing use of intelligent vehicle functions are transforming vehicles into advanced Cyber-Physical Systems. For heavy-duty OEMs, this transition challenges a Product Development (PD) heritage inherent in an ecosystem of established processes, IT systems, and organization structures. This study primarily comprises semi-structured interviews, conducted at a heavy-duty OEM, and a focused literature search. The study contributes by the following: (i) identifying key PD challenges in the ICE–BEV transition, (ii) outlining obstacles in adopting Model-Based Systems Engineering (MBSE) for managing architectural complexity, and (iii) synthesizing recommendations for architecture-driven collaboration. Interview findings, highlighted intertwined challenges such as fragmented architecture descriptions across physical and software domains, weak continuity between early-phase system context and detailed design, and collaboration constrained by inconsistent terminologies, strained communication channels, and manual reconciliation of architectural information through documents and disconnected tools. These factors hinder function-component traceability and concurrent development across domains. While MBSE is often recommended to address such issues, practical obstacles are noted, including trade-offs between modeling effort and fidelity, limited support for early spatial layout integration, difficulties in bridging physical and software architectures, and the limited integration of document-based practices preferred in early conceptual phases. Based on these insights, the study recommends architecture-driven collaboration anchored in a federated vehicle-architecture description, supported by a distributed systems-engineering function. A layered development approach combining document artifacts with progressively rigorous MBSE is advised for early-phase agility, later-stage traceability, and structured information flow. Full article

Background/Objectives: Large Language Models (LLMs) may help translate complex Magnetic Resonance Imaging (MRI) fistula reports into accessible, patient-friendly summaries. This study evaluated the clinical utility, safety, and patient acceptability of Generative Pre-trained Transformer (GPT-4o) in generating such reports. Methods: A three-phase study was conducted at a single centre. Phase I involved prompt engineering and pilot testing of GPT-4o outputs for feasibility. Phase II assessed 250 consecutive MRI fistula reports from September 2024 to November 2024, each reviewed by a multi-disciplinary panel to determine hallucinations and thematic content. Phase III randomised patients to review either a simple or complex fistula case, each containing an original report and an Artificial Intelligence (AI)-generated summary (order randomised, origin blinded), and rate readability, trustworthiness, usefulness and comprehension. Results: Sixteen patients participated in Phase I pilot testing. In Phase II, hallucinations occurred in 11% of outputs, with unverified recommendations also identified. In Phase III, 61 patients (mean age 48, 41% female) evaluated paired original and AI-generated summaries. AI summaries scored significantly higher for readability, comprehension, and usefulness than original reports (all p < 0.001), with equivalent trust ratings. Mean Flesch-Kincaid scores were markedly higher for AI-generated summaries (66 vs. 26; p < 0.001). Clinicians highlighted improved anatomical structuring and accessible language, but emphasised risks of inaccuracies. A revised template incorporating Multi-Disciplinary Team (MDT)-focused action points and a lay summary section was co-developed. Conclusions: LLMs can enhance the readability and patient understanding of complex MRI reports but remain limited by hallucinations and inconsistent terminology. Safe implementation requires structured oversight, domain-specific refinement, and clinician validation. Future development should prioritise standardised reporting templates incorporating clinician-approved lay summaries. Full article

The United Nations Convention on the Rights of Persons with Disabilities (UNCRPD) is the first international treaty to provide a basis for standards for the rights of persons with disabilities. It also represents the first human rights convention formally ratified by the European Union. In 2008, the UNCRPD was ratified by the majority of EU and EEA member states. Article 30 of the Convention specifically addresses the right to participate in cultural life, recreation, leisure, and sport is referenced and addressed in the UNCRPD States Parties reports submitted by all EU and EEA countries, as well as the United Kingdom. Research Question: How is sport represented in the State Party reports submitted under the UNCRPD? Methods: Data were collected from the UN Treaty Body Database. When multiple States Parties reports were available for a country, both reports were included for analysis. Results: Thematic analysis of 31 UNCRPD States Parties reports from EU, EEA, and UK countries revealed six key themes: General Factors, Sport in Article 30, Types of Support, Entities, Assistive Technologies, and Assistive Technologies in Sport. Sport was mentioned in all reports, with 90.3% referencing recreational sport and 83.9% elite-level sport. Funding and programmes were the most frequently cited supports for inclusive sport. Nearly half of the countries reported dedicated entities overseeing disability sport. Assistive technology was widely referenced across multiple UNCRPD articles, but only 16.1% of countries discussed its use specifically in sport. Countries differ significantly in their implementation of the UNCRPD in the context of sports. While some nations are advancing toward full inclusion, where disability does not affect an individual’s ability to participate in sports, others remain in the early stages of addressing participation in sport. These countries often rely on targeted programs specifically designed to facilitate the participation of persons with disabilities. Discussion: The analysis reveals significant disparities in how countries report and implement sport-related provisions under the UNCRPD. Ambiguities in categorizing elite versus recreational sport, underreporting of existing programs and entities, and limited references to strategic frameworks like the Kazan Action Plan highlight inconsistencies in reporting. Assistive technology (AT), while widely acknowledged across UNCRPD articles, is rarely linked to sport, despite its critical role in facilitating access and participation. These gaps suggest a need for clearer guidelines and more comprehensive reporting to ensure inclusive and equitable sport opportunities for persons with disabilities. Conclusions: There are notable disparities among countries’ reports in terms of mentioning participation for people with disability in sport, with some mentioning greater emphasis in integration and accessibility than others. To advance the UNCRPD rights through sport, clearer guidelines, standardized terminology, and more comprehensive reporting practices are essential. Full article

Traditional geoportals depend primarily on keyword-based search, which often fails to retrieve relevant datasets when metadata are heterogeneous, incomplete, or inconsistent with user terminology. This limitation reduces the efficiency of data discovery and selection, particularly in domains where metadata quality varies widely. This study aims to address this challenge by developing a semantic collaborative filtering recommendation system designed to enhance dataset discovery in geoportals through the analysis of implicit user interactions. The system captures users’ search queries, viewed datasets, downloads, and applied filters to infer feedback and organize it into a user–item matrix. Because interaction data are typically sparse, semantic user clustering is applied to mitigate this limitation by grouping users with semantically related interests through hierarchical relationships represented in the Simple Knowledge Organization System (SKOS). However, as users often need complementary datasets to complete specific tasks, association rule mining is employed to identify co-occurrence patterns in search histories and enhance task-related result diversity. The final recommendation scores are then computed by factorizing the user–item matrix with Alternating Least Squares (ALS), using cosine similarity on the latent user vectors to identify nearest neighbors, and applying a standard user-based neighborhood prediction model to rank unseen datasets. The system is implemented within an existing ontology-based geoportal as a standalone, configurable component, requiring only access to user interaction logs and dataset identifiers. Evaluation using precision, recall, and Precision@5 demonstrates that increasing user interactions improves recommendation performance by strengthening behavioral evidence used for ranking. The findings indicate that integrating semantic relationships and behavioral patterns can strengthen dataset discovery in geoportals and complement conventional metadata-based search mechanisms. Full article

Overcoming conceptual and institutional barriers demands interdisciplinary collaboration, improved governance, and stronger stakeholder engagement to promote sustainable urban planning and enhance ecosystem resilience. In the transition toward resilient cities, the concept of ecosystem services serves as a critical interface between science, planning, and governance, fostering stakeholder engagement and translating the complex ecosystem functions into indicators for urban planning. This study aims to assess existing knowledge on Urban Ecosystem Services (UESs) and their implications for urban green infrastructure planning across Central and Eastern Europe. A comprehensive, qualitative and quantitative review of the peer-reviewed literature retrieved from Web of Science and SCOPUS, was conducted for 11 former socialist countries that joined the European Union after 2004. The results reveal major barriers to UES integration, including inconsistent terminology, institutional inertia, fragmented governance, and limited stakeholder participation. Although research interest in UESs is increasing, research remains geographically concentrated in a few cities, mainly capitals, thereby constraining the understanding of spatial patterns and drivers of UES supply and demand across the region. Moreover, production services and ecological processes sustaining urban systems are largely underexplored. The study concludes that advancing UES research and practice requires a holistic, multi-scale, and standardized approach that identifies key stressors and context-specific impacts. Overcoming conceptual and institutional barriers demands interdisciplinary collaboration, improved governance, and enhanced stakeholder engagement to promote sustainable urban planning and enhance ecosystem resilience. Full article

Integrating heterogeneous and multilingual geoscience texts into coherent knowledge graphs is challenged by semantic inconsistencies from terminology variations, diverse expressions, and data heterogeneity, hindering the construction of reliable mineral exploration knowledge systems. We propose a semantic-aware fusion framework that enables consistent and sustainable integration of mineral exploration knowledge. Built on a standardized geological knowledge schema defining core entities and their interrelations, the framework incorporates an incremental update paradigm via a schema-guided fusion mechanism that detects and resolves semantic conflicts while preserving provenance for traceable evolution. Evaluated on textual sources, the framework achieves an overall triple extraction F1-score of 0.82. Notably, for the critical task of entity extraction, it attains an F1-score of 0.88, outperforming BERT-BiLSTM and BERT-BiLSTM-CRF baselines by up to 11 points. Precision for key metallogenic elements exceeds 0.90. It identifies 1432 conflicts during fusion and generates a refined knowledge graph of 18,204 high-quality de-duplicated triples, retaining 87.3% of inputs. The resulting graph supports downstream applications, including case analysis, visualization, question answering, and mineral prospectivity prediction. Unlike conventional aggregation approaches, this work treats knowledge fusion as a semantically guided dynamic process, enhancing consistency, transparency, and adaptability. It provides a practical pathway toward intelligent and sustainable geoscience knowledge infrastructures. Full article

Energy efficiency in buildings is a vital subject within sustainable construction and climate change mitigation, yet comprehensive bibliometric analyses mapping the complete evolution of this domain remain limited. This study provides a comprehensive four-decade analysis (1981–2025) of building energy efficiency research using data from the Web of Science database, employing VOSviewer (1.6.20), Bibliometrix (4.3.0), and custom Python (3.12.3) scripts with automated terminology normalization through TF-IDF vectorization (n-grams 2–3) and cosine similarity algorithms (threshold = 0.75). Two critical methodological innovations distinguish this investigation: first, Pruned Exact Linear Time changepoint detection statistically validated 2011 as the field’s statistically validated transition point (Mann–Whitney U test, p < 0.000001, effect size = 2.48), replacing arbitrary decade-based periodization; second, computational keyword harmonization enabled precise thematic evolution mapping across inconsistent terminology. The analysis reveals marked increase in research post-2011, with median annual output increasing from 15 articles (1981–2011) to 840.5 articles (2012–2024), and China emerging as the preeminent research center with 2978 publications. Thematic evolution analysis demonstrates fundamental transformation from seven specialized research themes (i.e., behavior, heat-transfer, simulation, impact, performance, consumption, optimization) in the foundational period to dramatic consolidation into two dominant themes (i.e., performance and simulation) in the contemporary period, reflecting maturation from fragmented, component-focused investigations toward holistic, integrated frameworks. International collaboration network analysis identifies four distinct geographic clusters with China, United States, United Kingdom, and Italy serving as central hubs. These findings provide actionable intelligence for researchers, policymakers, and industry stakeholders, while the computationally enhanced framework offers a replicable methodology for bibliometric analysis in other rapidly evolving interdisciplinary domains. Full article

Wood and wood-based products are increasingly recognized for their renewability and carbon storage capacity, supporting sustainable development and circular economy goals in the EU. This paper provides a comprehensive review of 42 material flow analysis (MFA) studies on wood resources conducted in the European Union and Switzerland between 2000 and 2024, introducing a five-level data risk classification. It examines how MFA is applied, including system boundaries, data sources, unit consistency, flow representation, and uncertainty handling. Results show that while volume-based units and Sankey diagrams are widely used, there is substantial variation in terminology, data quality, and methodology. The building stage is frequently excluded, limiting the completeness of wood flow assessments. Key challenges include restricted data access, inconsistent spatial and temporal scales, and varying levels of data processing risk. The study recommends harmonized units and terminology, open-access databases, standardization in visualization practices, and ultimately a wood-specific MFA framework to improve data quality, comparability, and policy relevance. Full article

Modular monolithic architecture (MMA) has recently emerged as a hybrid architecture that is positioned between traditional monoliths and microservices. It combines operational simplicity with modularity and maintainability. Although industry adoption of the architecture is growing, academic research on MMA remains fragmented and lacks systematic synthesis. This paper presents the first systematic literature review (SLR) of MMA in cloud environments. The review follows Kitchenham’s guidelines; we searched six major digital libraries for peer-reviewed studies published between 2020 and May 2025. From 369 retrieved records, we included 15 primary studies through a structured review protocol. Our synthesis highlights the problem of inconsistent terminology usage in the literature. It also identifies the architectural scope of MMA, and specifies the adoption drivers such as simplified deployment, maintainability, and reduced orchestration overhead. We also analyse implementation practices—including Domain-Driven Design (DDD), modular boundaries, and containerised deployment—and highlight comparative evidence showing MMA’s suitability when microservices introduce excessive complexity or costs. Key research gaps include the absence of consensus on a clear comprehensive definition, limited empirical benchmarking, and insufficient tools support. Thus, this study establishes a conceptual foundation for future research and provides practitioners with structured insights to inform architectural decisions in cloud-native environments. Full article

This paper surveys existing ontologies for visualization, which formally define and organize knowledge about visualization concepts, techniques, and tools. Although visualization is a mature field, the rapid growth of data complexity makes semantically rich frameworks increasingly essential for building intelligent and automated visualization systems. Current ontologies remain fragmented, heterogeneous, and inconsistent in terminology and modeling strategies, limiting their coverage and adoption. We present a systematic analysis of representative ontologies, highlighting shared themes and, most importantly, the gaps that hinder unification. These gaps provide the foundations for developing a comprehensive, generic ontology of visualization, aimed at unifying core concepts and supporting reuse across research and practice. Full article

Hardware systems are foundational to critical infrastructure, embedded devices, and consumer products, making robust security assurance essential. However, existing hardware security standards remain fragmented, inconsistent in scope, and difficult to integrate, creating gaps in protection and inefficiencies in assurance planning. This paper proposes a unified, standard-aligned, and threat-validated taxonomy of Security Objective Domains (SODs) for hardware security assurance. The taxonomy was inductively derived from 1287 requirements across ten internationally recognized standards using AI-assisted clustering and expert validation, resulting in 22 domains structured by the Boundary-Driven System of Interest model. Each domain was then validated against 167 documented hardware-related threats from CWE/CVE databases, regulatory advisories, and incident reports. This threat-informed mapping enables quantitative analysis of assurance coverage, prioritization of high-risk areas, and identification of cross-domain dependencies. The framework harmonizes terminology, reduces redundancy, and addresses assurance gaps, offering a scalable basis for sector-specific profiles, automated compliance tooling, and evidence-driven risk management. Looking forward, the taxonomy can be extended with sector-specific standards, expanded threat datasets, and integration of weighted severity metrics such as CVSS to further enhance risk-based assurance. Full article

The transition to decarbonized and distributed energy systems has increased interest in flexibility markets as a key tool to manage variability and coordinate distributed energy resources. However, the fast growth and conceptual fragmentation of this field hinder the building of coherent models and scalable solutions. This paper presents a scoping review of 243 peer-reviewed articles published between 2013 and 2025, applying the TEAM Framework and Business Model Canvas. Through a structured data matrix of 35 variables, we analyze how flexibility is defined and modelled, the coordination mechanisms applied, and how business dimensions are integrated. The results reveal major inconsistencies in terminology, actor roles, price formation, and interoperability modelling. We identify critical gaps in cost modelling and business model integration, especially in low-TRL studies. This review provides a comprehensive and cross-cutting synthesis of existing approaches, offering a reference framework for future research, policy design, and market implementation of distributed flexibility mechanisms. Full article

Dermoscopy is an essential, non-invasive diagnostic tool that has transformed the evaluation of pigmented skin lesions and is nowadays also increasingly recognized for its utility in general dermatology. Originally developed for the early detection of melanoma, dermoscopy now aids in diagnosing a wide range of non-neoplastic skin disorders—including inflammatory, infectious, and infiltrative conditions—by revealing morphological features invisible to the naked eye. Among these, facial dermatoses represent a diagnostically challenging group of disorders with overlapping clinical presentations. This review provides a comprehensive overview of the latest literature on dermoscopy in general dermatology, with a specific focus on facial dermatoses. Relevant information for this article was obtained through a comprehensive PubMed search using disease names along with the terms ‘dermoscopy’ and ‘dermatoscopy’. Despite its growing relevance, this field remains underexplored, largely due to the lack of standardized dermoscopic criteria and inconsistent terminology, which pose challenges to broader clinical implementation. Nonetheless, current evidence highlights the promising role played by dermoscopy as an adjunctive diagnostic method, particularly when used by experienced clinicians in combination with detailed patient history and clinical examination. Dermoscopy of facial dermatoses has the potential to significantly improve diagnostic precision in everyday practice. With continued research, greater standardization, and wider clinician training, dermoscopy is well-positioned to become as integral to the diagnosis of inflammatory and infectious dermatoses as it is to skin cancer detection. Full article

Background/Objectives: Clinical documentation of injuries is essential for forensic evaluation, especially in criminal cases. However, vague descriptions and inconsistent terminology often reduce the forensic utility of these records. This study examines the terminological quality and forensic interpretability of clinical injury documentation with the aim of improving medico-legal assessments. Methods: A corpus of 1000 Hungarian medical diagnostic reports of injuries was analyzed using descriptive statistics and manual terminological review. Results: Significant gaps in morphological detail and frequent terminological inconsistencies were found. Terms describing incised and chop wounds were often used interchangeably or inaccurately, impairing forensic interpretation of injury mechanisms, instruments, severity, and anatomical location. These variations also challenge bodily harm classification and injury event reconstruction. Conclusions: The findings highlight the need for harmonizing clinical and forensic terminology. Improved consistency in injury documentation will enhance its clarity, reliability, and forensic value, facilitating better interdisciplinary collaboration and legal outcomes. Full article

Clinical notes often contain unstructured text filled with abbreviations, non-standard terminology, and inconsistent phrasing, which pose significant challenges for automated medical information extraction. Named Entity Recognition (NER) plays a crucial role in structuring this data by identifying and categorizing key clinical entities such as symptoms, medications, and diagnoses. However, traditional and even transformer-based NER models often struggle with ambiguity and fail to produce clinically interpretable outputs. In this study, we present a hybrid two-stage framework that enhances medical NER by integrating a fine-tuned BERT model for initial entity extraction with a Dictionary-Infused Retrieval-Augmented Generation (DiRAG) module for terminology normalization. Our approach addresses two critical limitations in current clinical NER systems: lack of contextual clarity and inconsistent standardization of medical terms. The DiRAG module combines semantic retrieval from a UMLS-based vector database with lexical matching and prompt-based generation using a large language model, ensuring precise and explainable normalization of ambiguous entities. The fine-tuned BERT model achieved an F1 score of 0.708 on the MACCROBAT dataset, outperforming several domain-specific baselines, including BioBERT and ClinicalBERT. The integration of the DiRAG module further improved the interpretability and clinical relevance of the extracted entities. Through qualitative case studies, we demonstrate that our framework not only enhances clarity but also mitigates common issues such as abbreviation ambiguity and terminology inconsistency. Full article