Search Results (264)

This study outlines the conceptual framework and stakeholder-driven requirements for the Just Climate Urban Resilience Service (Just-CURS), an urban Digital Twin platform developed as part of CLIMATE-ADAPT4EOSC EU-Horizon project. Just-CURS is designed to establish a new standard for cross-disciplinary research, promoting a more coherent and effective integration of climate and social data to inform both scientific understanding and policy-making in urban climate adaptation. The approach introduces innovative elements by advancing a more holistic assessment of urban climate risk, in contrast to existing methods that consider environmental and climatic factors separately from socio-economic factors. The Just-CURS framework integrates high-resolution climate modelling with participatory, stakeholder-driven data, embedding the FAIR principles and interoperability standards consistent with the European Open Science Cloud (EOSC) to deliver a unified set of indicators and services for assessing urban climate resilience in extreme heat. This comprehensive integration represents a significant advance over traditional methodologies, enabling continuous monitoring, exploration of “what-if” adaptation scenarios, and evidence-based urban planning to make cities smart. Ultimately, Just-CURS distinguishes itself as a replicable and operational tool that not only enhances local resilience in the Municipality of Aigaleo, Greece, but also contributes to the broader transition towards interoperable, data-driven climate adaptation services across European cities. Full article

Advancing Earth System Sciences (ESS) depends on our ability to integrate highly diverse data from a range of disciplines, including geographical information sciences, palaeontology, marine science, biodiversity research, atmospheric sciences and biology. A major scientific obstacle to this integration is semantic heterogeneity: different disciplines use different methods and terms to describe data, resulting in fragmented vocabularies. We address this challenge by providing the ESS terminology collection, which is a source of state-of-the-art terminologies for this scientific community. This collection already contains 50 curated terminologies. New terminologies can be suggested by scientists at any time. The ESS collection enables the consistent annotation and semantic alignment of heterogeneous ESS research data. It enhances the FAIR data principles—particularly findability, accessibility, interoperability, and reusability—thereby facilitating interdisciplinary research and data reuse. This collection enables the ESS community to assess the quality and completeness of ontology metadata and the extent to which their terms comprehensively represent the scientific content of a field. Full article

Artificial intelligence (AI), explainable AI (XAI), digital twins, and intelligent decision-support systems are increasingly proposed for water management, yet existing reviews usually emphasize algorithms or architectures rather than the evidence chain from prediction to governed decisions and measured outcomes. This PRISMA 2020- and PRISMA-S-informed systematic review critically synthesized 48 records published from 1 January 2017 to 5 August 2026 across hydrology, water distribution, water quality, wastewater treatment, irrigation, and basin management. The corpus comprised 23 empirical, technical, or hybrid application records and 25 secondary or conceptual records. Deployment was coded with a conservative maturity rubric (M0–M4, with M1a for offline benchmark or simulated validation and M1b for retrospective real-world validation), and sustainability evidence was coded from S0 (absent) to S4 (prospectively measured). Forty-three records remained at M0–M2, five reached M3 operational decision support, and no audited M4 closed-loop implementation was identified within the reviewed corpus. Nine records used explicit XAI, but practitioner usefulness was rarely tested. Sustainability evidence was S0 in 6 records, S1 in 18, S2 in 17, S3 in 5, and S4 in 2. The review’s principal contribution is the proposed TRACE-Water framework, which connects traceable data, robust validation, actionable explanation, controlled decision loops, and evaluated sustainability in a water-specific evidence chain. TRACE-Water complements, rather than replaces, FAIR principles, AI risk frameworks, model documentation, and digital-twin governance; it also requires future empirical validation. Full article

As science increasingly embraces the Open Science paradigm, attention is shifting from open access to the implementation of the FAIR (Findable, Accessible, Interoperable, and Reusable) principles for research data. Within this evolving landscape, libraries are expected to play a key role in supporting FAIRification and sustainable research data management. This study maps the scholarly landscape of FAIR data through a bibliometric analysis of 2790 journal articles and conference papers published between 2000 and 2025 and indexed in Scopus, using the Bibliometrix R package (version 5.5.0). The findings reveal a rapidly expanding research field, with Europe emerging as the leading contributor, largely driven by European Union Open Science policies. Thematic analysis identifies metadata, interoperability, data management, and Open Science as central research directions, while the bibliometric visibility of library-related FAIR research remains comparatively limited. The study highlights the importance of interdisciplinary collaboration and the development of new competencies to strengthen the role of libraries within the FAIR data ecosystem. Full article

Background: Interferential current therapy (ICT) and spray-and-stretch are two low-cost, non-invasive modalities that coexist on the treatment menus of outpatient musculoskeletal clinics, yet no published study has directly compared them, and no guidance document provides principled criteria for choosing between them. Objective: To characterise the mechanistic basis, clinical evidence, safety profile, and practical implementation of each modality across the full range of musculoskeletal disorders, and to propose a hypothesis-generating framework for modality positioning in the absence of direct comparative trials. Methods: A narrative review of literature retrieved from PubMed/MEDLINE, Embase, Scopus, the Cochrane Library, PEDro, and Google Scholar from database inception to 1 August 2026, using the search logic (musculoskeletal condition terms) AND (ICT terms OR spray-and-stretch terms), supplemented by hand-searching. The combined search retrieved 3241 records across six databases; 46 primary studies were included after two-reviewer sequential screening. Risk of bias was assessed using the PEDro scale (RCTs) and the Downs and Black checklist (observational studies). No quantitative synthesis was performed. Results: For ICT, multiple randomised trials and meta-analyses across at least four anatomical regions show that stand-alone ICT produces a small and inconsistent analgesic effect versus sham, whereas ICT as a co-intervention yields modest short-term improvements in pain and disability in chronic non-specific low back pain and knee osteoarthritis. For spray-and-stretch, a small number of predominantly fair-quality RCTs, almost all limited to the upper trapezius, show a real but brief effect of the combined procedure; the independent contribution of the vapocoolant has not been isolated in any dismantling study. Conclusions: The two modalities address mechanistically distinct clinical problems and are not directly comparable on the current evidence. We propose a hypothesis-generating positioning framework—unvalidated and intended to guide future comparative trials rather than to serve as a clinical decision rule—in which ICT is considered for deep, regional, multi-session analgesic adjunction, and spray-and-stretch for single-session restoration of range of motion around a discrete taut band. Direct comparative trial data are needed to test this framework. Full article

The rapid expansion of Arctic cruise tourism has intensified debates regarding sustainability, carrying capacity, and infrastructure pressure in vulnerable destinations. In response, the Icelandic government introduced an infrastructure fee on cruise ship passengers. This study examines how policymakers designed and justified the fee as a governance instrument, and what evidence would be required to establish that it functions as one. Using Iceland as a case study, the research combines qualitative document analysis of legislative and stakeholder materials with descriptive statistical data on cruise passenger flows. Policymakers framed the fee around infrastructure financing, the user-pays principle, fair contribution, and sustainable destination stewardship. However, the legislative process revealed stakeholder tensions concerning international competitiveness, regulatory predictability, and regional economic implications. Contextual data indicate a post-pandemic increase in passenger volumes relative to ship calls, highlighting the relevance of passenger-based indicators for understanding visitor intensity alongside vessel arrivals. The Icelandic case sits within a broader international trend, also visible in destinations such as Venice, Alaska, and Norway, in which authorities design and justify tourism taxation as a governance tool rather than solely as a revenue mechanism. The statutory framework does not earmark fee revenue for destination management, and an amendment adopted in 2025 reduced the rate with effect from 2026. The evidence examined establishes the fee’s governance objectives but does not demonstrate its destination-level effects. Full article

University geospatial hubs support research dissemination, teaching, public data access, and community engagement, but their value depends on whether resources can be found, accessed, interpreted, integrated, and reused. This study evaluates 747 publicly listed items in North Carolina Central University’s CoDE Open Data Hub using FAIR principles as an interpretive framework and the Federal Geographic Data Committee Content Standard for Digital Geospatial Metadata (FGDC CSDGM) as the domain-specific measurement basis. Nineteen criteria were scored on explicit criterion-specific 0–2 rules using live ArcGIS item properties, formal metadata XML, and available layer properties. Discoverability was evaluated for a proportionally stratified sample of 153 items through exact-title, keyword, location, item-type, category-browsing, and click-depth tests. The equal-criterion benchmark averaged 28.57 of 38 (75.19%); equal FAIR-dimension weighting produced a mean of 73.26%, and 95.85% of items retained the same descriptive performance band. Independent rescoring of 50 stratified items produced 76.74% exact criterion-level agreement, a linear weighted Cohen’s kappa of 0.587, and an absolute-agreement ICC of 0.715 for total scores. FAIR-aligned scores were highest for Findability (87.25%) and Accessibility (85.96%), followed by Reusability (67.26%) and Interoperability (52.59%). Exact-title and item-type searches retrieved all sampled items, while keyword search was successful for 88.24%. The results distinguish public availability from technical reuse readiness and identify spatial reference, attribute definitions, lineage, limitations, category structure, and navigation depth as priorities. The performance bands are study-specific descriptive summaries rather than FGDC compliance levels or FAIR certification. The study provides an evidence-preserving, type-aware method for applying FAIR principles alongside geospatial metadata standards in heterogeneous ArcGIS Hub catalogs. Full article

Research data are often prepared in spreadsheets, but the meanings of their columns and values must still be translated into reusable metadata schemas. Even with a graphical editor, this translation requires repeated decisions about field names, types, properties, and hierarchy. Large language models (LLMs) can assist with these decisions, but workflows that return a complete JSON Schema make it difficult to inspect individual changes in a visual editor. We present an interactive visual LLM workflow for constructing metadata schemas from spreadsheets through structured editor operations. The workflow derives a schema draft from workbook information and represents both LLM suggestions and manual edits in the same operation model. Users can inspect, accept, or reject individual field-level operations in a graphical user interface (GUI) without reading or editing JSON Schema. The same mechanism supports updating an existing schema from a revised workbook while retaining user-approved changes. Experiments on six real-world datasets show that the operation-based workflow achieves type correctness and target-update accuracy above 97% across the evaluated models. Using the same LLM and datasets, it leaves fewer fields requiring correction than direct complete-schema generation or MetaConfigurator. A user study further shows that LLM assistance reduces the mean schema-construction time by 69.9%. The workflow gives researchers and data managers a practical way to create and maintain machine-readable metadata schemas for sharing spreadsheet data in accordance with the findable, accessible, interoperable, and reusable (FAIR) principles. Full article

Social inequalities often persist despite widespread endorsement of universal norms such as fairness and equality. One explanation is that actors interpret these principles differently in practice, generating divergent forms of action within the same institutional setting. This study examines this process in the context of gender equity in STEM education by analysing how teachers organise beliefs, awareness, pedagogical practices, responsibility, and the perceived value of gender equality when reasoning about pedagogical fairness. Using an exploratory case study with a theory-guided, person-centred typological classification approach, questionnaire data from teachers in a Portuguese school cluster (Grades 1–12) were analysed through quantitative classification and interpretive analysis of open-ended responses. Rather than differing only in degree of commitment to equality, teachers’ reported reasoning was organised into three theory-guided orientations: Procedural Impartiality (fairness as identical treatment), Compensatory Intervention (fairness as corrective action), and Reflexive Professional Responsibility (fairness as situated professional judgement). The resulting profiles showed differentiated configurations across the five analytical dimensions, particularly in the relative organisation of awareness, pedagogical practices, responsibility, and the perceived value of gender equality. Descriptive subgroup distributions also suggested tentative variation across disciplinary field, sex, and teaching experience, although these patterns should not be interpreted as systematic or generalisable associations. The findings indicate that teachers’ reported reasoning can be organised into distinct theory-guided orientations and suggest that shared commitments to gender equality may coexist with different professional interpretations of fairness and legitimate pedagogical intervention. The study therefore proposes a framework for examining how equity is interpreted as a professional category in educational contexts, while recognising that further research is needed to examine how such orientations relate to observed classroom practices and student outcomes. Full article

Rapid urbanization, increasing demand variability, and digitalisation of urban infrastructure require intelligent decision-support systems that can improve sustainability, resilience, and operational efficiency in smart city operations. Artificial intelligence (AI) and data-driven optimization offer strong potential for adaptive urban services, including dynamic pricing, demand response, resource allocation, and energy-aware management. However, many reinforcement learning applications still focus mainly on short-term performance while giving limited attention to transparency, fairness, stability, and accountability. This study proposes a trustworthy reinforcement learning framework for AI-driven urban decision-making, using sustainable dynamic pricing and resource optimization as mechanisms for adaptive and responsible decision-making. A custom reinforcement learning environment was developed using historical e-commerce transactional data as a methodological proxy to simulate interactions among demand, resource or inventory availability, service categories, price elasticity, and changing market conditions. Three reinforcement learning algorithms, namely Deep Q-Network, Proximal Policy Optimization, and Advantage Actor–Critic, were evaluated under comparable experimental conditions. Performance was assessed using profitability, decision stability, fairness-oriented pricing behavior, decision consistency, and interpretability. To improve transparency, trajectory-based policy audits and SHapley Additive exPlanations were applied to identify the main factors influencing pricing decisions. The results show that the Deep Q-Network agent achieved the most balanced performance, increasing total profit by 12.58% while recording no unethical price increases under low-demand conditions. Explainability analysis showed that stock or resource levels, demand shifts, and price elasticity were the strongest positive drivers of pricing actions, whereas inventory hoarding and unfavorable price increases reduced decision quality. The findings indicate that reinforcement learning can support sustainable and resilient urban decision-making when optimization objectives are combined with trustworthy AI principles. The proposed framework provides a practical basis for accountable AI-based decision-support systems in smart city operations, including demand-responsive services, resource optimization, sustainable dynamic pricing, and energy-aware management. Full article

To deepen our understanding of human–ecosystem interactions, researchers need to be able to retrieve and analyze historical dark data such as plant and animal phenology, but there are often barriers to doing so. Despite the development of online digitization and other tools, including library search engines, digital collections, machine translation, OCR (optical character recognition), HTR (handwritten text recognition), and generative AI technologies, and the establishment of standards and frameworks (e.g., FAIR Principles and the International Image Interoperability Framework), barriers to converting analog records to digital records (“digital barriers”) and to translating local languages to an international common language (“language barriers”) still remain. We present a case study example of the use of historical dark data in phenology in Japan and the digital and language barriers encountered. We then briefly summarize factors and challenges hindering use of this data and describe the benefits of further removal of these barriers. Full article

This study examines the role of the ethics of responsibility at individual, societal, and institutional levels and explores its potential to address the ethical challenges of the contemporary world. This ethical framework requires individuals and institutions to consider not only their own interests but also the long-term societal and environmental consequences of their decisions. The study explores how this approach provides guidance in the contexts of technological innovations, environmental crises, and globalization. Technological developments are presented as a domain that both expands the scope of ethics of responsibility and introduces new ethical challenges. It is emphasized that artificial intelligence (AI) systems may give rise to issues such as bias, data privacy violations, and the spread of misinformation through digital platforms. In this context, the necessity of designing “fair AI” and ensuring that digital platforms operate in alignment with ethical principles is emphasized. From the perspective of environmental sustainability, it is stated that while individual efforts, such as recycling and energy conservation, are important, institutions must focus on large-scale initiatives, such as carbon-neutral targets and circular economy models. The article also addresses the criticisms and challenges associated with implementing this framework. The subjective nature of ethical principles and the conflict between diverse cultural values make the universal adoption of this approach difficult. Additionally, the prioritization of individual interests within the capitalist system and the inadequacy of global cooperation are seen as major obstacles to the practical application of ethics of responsibility. This situation underscores the importance of both individual awareness and institutional policies. In conclusion, this framework is presented as an important guide capable of addressing the complex issues of the contemporary world. It is argued that the broader application of this approach is essential in areas such as technological advancements, environmental crises, and global inequalities. Fulfilling the ethical responsibilities of individuals, institutions, and the international community is critical for creating a more equitable and sustainable world. Full article

The convergence of optical burst switching (OBS) and elastic optical networks (EON) offers a promising pathway for 6G IoT backhaul. However, existing solutions treat OBS and EON separately and rely on heuristic resource allocation that fails to meet stringent QoS demands. This paper proposes a fully AI-driven converged OBS/EON architecture integrating a hybrid switching fabric, a multi-agent deep reinforcement learning (DRL) orchestrator, and a federated learning (FL) plane for autonomous, QoS-aware resource provisioning. The control plane implements multi-agent Proximal Policy Optimization (PPO) for joint burst scheduling, routing, modulation selection, and spectrum allocation. The orchestration plane employs q-fair FL for privacy-preserving cross-domain traffic prediction. Mathematical formulations of the optimization problem with spectrum, GSNR, and delay constraints are provided, along with pseudo-algorithms. Simulations over a 14-node NSFNET topology demonstrate a 78% reduction in blocking probability, a 42% improvement in spectral efficiency, sub-millisecond URLLC delays, and a Jain’s fairness index of 0.92, while preserving data privacy. The framework builds upon SDN principles for seamless integration with optical transport infrastructures. Full article

Digital twins (DTs) are dynamic, virtual representations of individual patients that could support predictive diagnostics and personalized therapeutic optimization. Their development depends on patient-level data from real-world data (RWD) sources and electronic medical record (EMR) data, but major barriers in data-stewardship, interoperability, provenance, and governance persist. This review examines critical bottlenecks within the current healthcare ecosystem, with particular attention to fragmented EMRs, limited longitudinal data continuity, and missing, incomplete, or inaccurate information. We address data availability, stewardship, and provenance rather than model construction itself. We also explore ethical imperatives of mitigating representational bias, where over-representation of European ancestry amplifies existing health inequalities. We evaluate blockchain technology as a decentralized trust anchor to ensure provenance, automate consent, and incentivize longitudinal data stewardship. In parallel, we acknowledge that clinically useful DTs also depend on substantial advances in model specification, calibration, and validation, especially for biologically complex diseases. By synthesizing computational, regulatory, and ethical challenges, we provide a roadmap for developing robust, equitable, and interoperable ecosystems for precision medicine. Full article

Background/Objectives: Bedside manners are an important part of patient-centered physical therapy care, yet little is known about how Saudi physical therapists understand and apply them in practice. This study examined therapists’ perceptions of bedside manners and ethics, and explored differences related to gender and work setting. Methods: A cross-sectional mixed-methods study was conducted among 147 licensed Saudi physical therapists. Quantitative data were collected using a 28-item questionnaire, and qualitative data were gathered through semi-structured interviews with 22 participants. Survey responses were summarized using frequencies and percentages, and differences by gender and work setting were reported. Interview data were analyzed using Braun and Clarke’s thematic analysis. Results: Most participants were female (66.7%) and aged 20–30 years (70.7%). Overall, therapists reported very high agreement with bedside-manner principles across most items. Descriptive patterns by gender and work setting in some perceptions (e.g., patient privacy, therapist presence, and attention to minor cases) were noted. Qualitative analysis identified seven themes: building trust, perceived positive contribution to care, body language, differing views on empathy, therapist presence, patient equity, and seeking help from colleagues. Conclusions: Saudi physical therapists strongly supported core bedside manner behaviors, especially communication, privacy, reassurance, and fair treatment of patients. These findings may inform efforts to incorporate bedside manners into physical therapy education and professional development in support of ethical, patient-centered practice. Full article