Search Results (1,551)

Children’s skin is uniquely vulnerable, requiring specialised design solutions that transcend traditional aesthetics. This exploratory study investigates the importance of paediatric dermatology in informing functional fashion design through expert medical perspectives. Using a qualitative approach, data were gathered from a purposive cohort of paediatric dermatologists and immunoallergologists and analysed through inductive thematic analysis. Findings identify four core themes: the physiological immaturity of children’s skin (notably the prevalence of atopic dermatitis), clothing’s role as a symptomatic aggravator rather than a primary aetiology, the clinical risks posed by chemical additives in synthetic textile processes, and the therapeutic potential of natural fibres and biofunctional agents. The data also highlights significant diagnostic constraints in paediatric patch testing, emphasising the necessity of proactive material safety. The findings suggest that integrating healthcare expertise into human-centred design may support the development of safer paediatric clothing solutions, ensuring that fashion industry innovation meets the physiological requirements of children. By transitioning from hazardous synthetic processes to biocompatible textiles, such as undyed natural fibres and medicinal plant-derived dyes, the industry can transform apparel from a potential irritant into a secondary protective barrier. This provides initial insights for developing clothing that safeguards the skin barrier and improves the overall wellbeing of vulnerable populations. Full article

In conditions of liquid modernity, marked by accelerated technological change, the virtualization of essential services, and the erosion of stable institutional support, digital participation in later life is less a matter of initial access than of continuously renegotiating engagement within unstable socio-technical environments. While established technology adoption models such as TAM, UTAUT, and STAM have provided robust explanations of cognitive and age-related determinants of adoption, they remain limited in accounting for the relational processes through which technological engagement is learned, stabilized, and sustained over time. This article advances a relational perspective on technology appropriation by foregrounding the role of warm experts—trusted informal supporters who mediate learning, interpretation, and adaptation in everyday contexts. Moving beyond dyadic understandings of assistance, the paper conceptualizes mediation as a distributed ecology of roles embedded within relational networks that both enable and constrain digital inclusion. Building on this perspective, the study proposes the Relational Technology Appropriation Model (RELTAM) as a general multi-level architecture integrating individual determinants, relational mediation processes, and network-level support configurations within a dynamic framework of appropriation. The Relational (Older Adult) Technology Appropriation Model (REL(OA)TAM) is introduced as a context-specific instantiation of this broader framework, calibrated to the distinctive conditions of later life. By incorporating temporal instability and mediation ecologies as structural components, REL(OA)TAM offers a socially grounded account of digital inclusion as an ongoing process of adaptive negotiation within the fluid and uncertain conditions of liquid modernity. Full article

Background: Breakpoints in the pre-examination processes and at organizational interfaces are a significant source of failures in specimen identification and tracking in anatomic pathology. While ISO 15189 emphasizes end-to-end traceability and risk-based quality management, implementing these principles in complex, multi-actor specimen pathways remains challenging. This study explores expert perspectives on specimen process chains, tracking mechanisms, and ISO 15189-oriented quality and risk management in pathology. Methods: We conducted 10 semi-structured expert interviews across three settings. Interviews were audio-recorded, transcribed, pseudonymized, and analyzed using structured qualitative content analysis (Mayring) supported by MAXQDA. A deductive category system derived from the theoretical framework and interview guide comprised six main categories and twelve subcategories. Results: Across 512 coded text segments, participants identified several factors as critical for effective implementation, including: (i) interface management along the specimen pathway, with recurrent vulnerabilities at handovers between operating theater/ward/transport and accessioning; (ii) the central role of barcode-based identification and the need for closed-loop traceability; (iii) the importance of measurable quality indicators and incident learning systems to operationalize risk management; (iv) persistent paper–digital handoffs and heterogeneous IT landscapes that undermine data integrity; (v) the need for clearly assigned responsibilities, training, and SOP governance; and (vi) implementation barriers including resources, change management, and vendor integration, alongside practical enablers such as incremental roll-out and cross-professional governance. Conclusions: Experts converge on a pragmatic ISO 15189-aligned roadmap: prioritize interface risks, standardize identifiers and handover rules, define a minimal KPI set for tracking and misidentification events, and reduce paper–digital handoffs by interoperable IT. Future work should quantify baseline error rates and evaluate the impact of digital tracking interventions on patient safety and turnaround times. Full article

Biocultural landscapes emerge from long-term interactions between human societies and ecological systems, yet integrated assessments of biological and cultural values remain limited, particularly within conservation policy frameworks such as Natura 2000. This study evaluates the biocultural value of 24 semi-natural and human-conditioned habitat types characteristic of the Slovak landscape, with the aim of identifying patterns related to biological value, cultural significance, and dependence on human management. An expert-based questionnaire survey was used to score each habitat for biological and cultural value, and management dependence, and these indicators were combined into an overall biocultural value. Data were analysed using descriptive statistics, visualisation techniques, and hierarchical clustering to explore relationships among habitat types. The results reveal a clear gradient structured primarily by land-use intensity and management regime rather than by ecological classification alone. Semi-natural grasslands and wetlands maintained through long-term, low-intensity management—many of which correspond to Natura 2000 habitat types—exhibit the highest biocultural values. Traditionally managed agricultural habitats form transitional groups, while intensively managed systems show consistently lower biocultural values. The findings indicate that human influence is not inherently incompatible with high biological value; instead, management intensity and continuity are key determinants. These results highlight the importance of integrating biocultural perspectives into conservation planning and Natura 2000 management to support both biological value and cultural landscape values. Full article

In the context of immersive technologies deeply integrated into the cultural tourism industry, immersive cultural tourism has become an important means of heritage revitalization. Immersive experience is both a crucial consumer element and a key indicator for evaluating the attractiveness of cultural tourism landscapes. This study evaluates the post-use experience of the cultural tourism landscape of Xidajie Street in Baoding from the perspective of tourist immersion. Through a literature review, investigation of typical immersive districts, and expert interviews, we extract immersive cultural tourism landscape evaluation criteria based on a depth model of immersion, focusing on three dimensions: narrative, enclosure, and interaction. Subjective perception data from tourists is then collected through a survey, and IPA (Importance–Performance Analysis) is employed to identify the strengths and weaknesses of Xidajie’s cultural tourism landscape. The results show that Xidajie excels in spatial environment shaping and historical preservation, but has room for improvement in cultural narrative extension, contextual immersion, and interactive experiences. Therefore, strategies are proposed to enhance the cultural IP, establish a complete narrative structure, create authentic enveloping environments, and enrich interactive games to build a high-quality online and offline immersive cultural tourism landscape. This aims to promote the renewal of Xidajie and the dynamic transmission of Baoding’s local culture. Full article

Specifying non-functional requirements (NFRs) for rapidly evolving domains such as trustworthy APIs in the AI era is challenging as best practices emerge through practitioner discourse faster than traditional requirements engineering can capture them. We present a systematic methodology for deriving prioritized NFR specifications from multimedia practitioner discourse combining AI-assisted transcript analysis, grounded theory principles, and Theme Coverage Score (TCS) validation. Our five-task approach integrates purposive sampling, automated transcription with speaker diarization, grounded theory coding extracting stakeholder-specific themes with TCS quantification, MoSCoW prioritization using empirically derived thresholds (Must Have ≥85%, Should Have 65–84%, Could Have 45–64%, and Won’t Have <45%), and NFR specification consistent with ISO/IEC 25010:2023 principles of stakeholder perspective, measurable quality criteria, and explicit rationale. Applying this methodology to 22 expert presentations on trustworthy APIs yields Weighted Coverage Score of 0.71 and 30 prioritized NFR specifications across five trustworthiness dimensions. MoSCoW classification produces 11 Must Have requirements (Robustness and Transparency), 9 Should Have, 6 Could Have, and 4 Won’t Have. The analysis reveals systematic disparities where Fairness contributes zero Must Have or Should Have requirements due to insufficient practitioner consensus. Each NFR emphasizes stakeholder perspective, measurable quality criteria, and explicit rationale, enabling systematic verification. The validated methodology with complete replication package enables empirically grounded, prioritized NFR derivation from practitioner discourse in any rapidly evolving domain. Full article

Education for Sustainable Development (ESD) is increasingly positioned as a core task of biology education; however, teachers’ conceptions often oscillate between instrumental approaches and more critical-reflective perspectives. Drawing on the theoretical distinction between instrumental (ESD 1) and critically reflective (ESD 2) ESD frameworks, this study developed and validated a questionnaire with a five-point rating scale ranging from 0 (strongly disagree) to 4 (strongly agree) to assess pre-service biology teachers’ ESD-related beliefs and tested whether the proposed approaches emerge as empirically separable dimensions. We generated an initial item pool, which was reviewed by content experts and piloted (n = 30). In Sample 1 (n = 218 pre-service biology teachers), an exploratory factor analysis with iterative item reduction yielded a nine-item, two-factor solution explaining 52.0% of the variance (KMO = 0.82; Bartlett’s test p < 0.001). The factors reflected action- and behavior-oriented beliefs and critical-reflective beliefs (emphasizing social justice, interdisciplinarity and uncertainty), showing good internal consistency (ω = 0.76/0.86). A confirmatory factor analysis in an independent Sample 2 (n = 104 biology-related students) supported the correlated two-factor model (CFI = 0.968; TLI = 0.955; RMSEA = 0.068; SRMR = 0.068; r = 0.45). By operationalizing a differentiated concept of ESD, the instrument enables quantitative research on distinct belief orientations and can support the design and evaluation of ESD-oriented teacher education programs. Full article

Adaptive reuse has emerged to become a tool for implementing the understanding of sustainability in the domain of architectural conservation, as it encourages the continued usage of old buildings as means of reducing environmental impact, as well as preserving socio-cultural capital while generating economic income. However, in its practice, the decisions regarding granting meanings, interpretation, and preserving memories within adaptation processes are dominated by expert-driven approaches that inadequately incorporate stakeholder values or intangible heritage dimensions. To this end, this study aims to contribute to the current debate by adopting a participatory co-evaluation framework that integrates both authenticity perspectives and sustainability dimensions using Multi-Criteria Decision Analysis (MCDA) for evaluating adaptive reuse alternatives for an abandoned prefabricated wooden heritage building. Stakeholder priorities were drawn through a workshop and transformed into normalized weights using the Simos technique. Four design alternative typologies—namely, Continuity, Cultivation, Differential, and Optimization—were assessed and compared against 20 performance indicators across heritage, social, ecological, and economic criteria using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Indicator-level analyses and sensitivity tests (±10% and ±20% weight variations) were applied to confirm the robustness of rankings. The results from the best-performing alternative demonstrated the trade-offs between heritage authenticity and sustainability objectives, as well as demonstrating how combining participatory methods with quantitative evaluation can support evidence-based decision-making for adaptive reuse. The applied integrated framework helps bridge the gap between heritage theory and practice by combining authenticity, participation, and sustainability in one analytical approach, supporting evidence-based decisions for adaptive reuse. Full article

Sustainable forest operations require operational planning tools that effectively integrate productivity, environmental conservation, and social acceptability, particularly within complex and environmentally sensitive forest systems. In Mediterranean small-scale forestry, harvesting decisions are frequently guided by expert judgment rather than by systematic and transparent planning frameworks. This reliance on subjective decision making can result in heterogeneous management practices and, in some cases, suboptimal operational outcomes. This study aims to validate a GIS-based Analytic Hierarchy Process (GIS–AHP) decision support system for the selection of harvesting and wood systems in the chestnut coppices of central Italy and to assess the robustness of its recommendations when expert judgments are provided by different stakeholder groups. The methodology integrates spatial data and multi-criteria analysis to evaluate the suitability of three extraction systems (forwarder, cable skidder, and cable yarder) and three wood systems (Cut-To-Length, Whole-Tree Harvesting, and Tree-Length) across 162 Forest Management Units (1332.5 ha), using weights elicited from four stakeholder categories (researchers, technicians, forest owners, and workers; n = 144). Results show statistically significant differences in mean suitability values among stakeholder groups for all systems; however, convergence at the operational decision level is high. The cable skidder is recommended over 94%–100% of the area depending on the stakeholder category, with full agreement among all groups in 87.7% of the Forest Management Units. For wood systems, Whole-Tree Harvesting is selected over 96.1% of the analysed area, with agreement in 95.1% of the Forest Management Units. Divergences are therefore limited and attributable to differences in AHP weighting structures. Overall, the findings demonstrate that the GIS–AHP approach provides stable and transferable recommendations despite variability in expert perspectives, supporting its applicability as a transparent and robust decision support tool for operational planning in chestnut coppices and similar Mediterranean forest systems. Full article

Sports have progressively incorporated technological advances, yet while the impact on performance and broadcasting is remarkable, the application of Artificial Intelligence (AI) in sports refereeing appears residual. A closer examination of prior research suggests that this limited development reflects deeper conceptual patterns within the field. While existing research on AI in sports officiating has predominantly conceptualized the field under an accuracy-optimization paradigm (focusing on decision precision, visual attention patterns, referee fatigue, and performance enhancement), there is a systematic lack of theoretical and empirical work that frames officiating as a broader socio-technical ecosystem. In particular, the literature does not provide conceptual models addressing (i) AI-assisted risk prevention and athlete safety as a core officiating function, (ii) human–AI task redistribution in cognitively overloaded and hybrid evaluative environments (e.g., disciplines such as artistic gymnastics or bodybuilding, where technical execution and aesthetic judgment are simultaneously assessed), and (iii) the redefinition of the referee’s role when AI operates as an anticipatory or real-time alert system rather than merely as a post hoc verification tool. Thus, the gap is not only one of application but of knowledge production: the dominant paradigm optimizes decision accuracy, yet it leaves the question of how AI can transform refereeing responsibilities, cognitive load distribution, and safety governance within competitive ecosystems under-theorized. This exploratory study adopts a Human–Computer Interaction (HCI) perspective to contrast existing initiatives with the practical expectations of professional referees. The methodology comprises two pillars: a systematic literature review following PRISMA guidelines and qualitative experimentation involving professional referees using focus groups and affinity diagrams techniques. From an initial total of 1251 records retrieved across five academic databases (2019–2025), 1122 articles were analyzed after applying strict inclusion/exclusion criteria. The findings provide preliminary support for our hypothesis of a significant underutilization gap, showing that research is concentrated on accuracy systems, while high-potential areas identified as critical by experts, such as athlete safety, represent only 0.6% of the analyzed literature. The study contributes a conceptual framework based on five categories established by experts, according to the identified use cases, providing guidance for future AI integration and interdisciplinary research in the sports officiating ecosystem. Based on the results, we point to future applications and lines of research aimed at integrating AI as a tool for sports refereeing. Full article

China’s “Sponge City” initiative, launched in 2014, is a transformative approach to urban stormwater management that aims to deliver multiple benefits through nature-based solutions. Despite its widespread adoption in China, questions remain regarding its long-term viability. Through a new conceptual framework, this study examines the viability of the Sponge City model by analyzing insights from 30 practitioners and domain experts working in Beijing, Shanghai, and Shenzhen. Drawing on semi-structured interviews and field studies, it investigates how participants interpret the concept, integrate climate adaptation strategies, and assess effectiveness across six dimensions: governance, economy, environment, urban form, civic engagement, and human wellbeing. The findings reveal diverse perspectives shaped by local contexts, disciplinary backgrounds, and professional experiences. While participants expressed cautious optimism, they also identified persistent challenges, including funding constraints, fragmented planning processes, and insufficient public engagement. Climate adaptation emerged as a central concern, with mixed views on the initiative’s ability to address extreme weather events. Overall, the study suggests that the Sponge City model holds promise, but its viability depends on continued refinement, interdisciplinary collaboration, and adaptive governance tailored to local needs. This study offers insights to inform future practice and broaden global efforts in stormwater management and urban resilience. Full article

The tourism and hospitality sector in Saudi Arabia is undergoing rapid sustainability transformation under the strategic direction of Vision 2030. This study examines the maturity of Sustainability Actions (SAs), their key drivers, and implementation barriers, comparing the perceptions of industry practitioners and academic experts. Using a qualitative abductive research design based on 20 in-depth semi-structured interviews with industry and academic experts in Saudi Arabia, followed by thematic analysis using a machine learning Qualcoder 3.7 software, the findings reveal both convergence and divergence between the two groups. While both recognize Vision 2030 as the primary catalyst and acknowledge financial costs and knowledge gaps as major barriers, industry experts emphasize operational efficiency and short-term performance outcomes, whereas academics advocate systemic transformation grounded in circular economy principles and long-term socio-ecological regeneration. The results demonstrate that sustainability adoption in Saudi Arabia is shaped not only by market demand but also by a strong government-led institutional framework that accelerates sectoral change. The findings are structured across environmental, social, and economic sustainability dimensions, offering differentiated implications for industry practitioners and academic stakeholders within emerging tourism economies. The study contributes to sustainability and tourism and hospitality literature by offering a comparative multi-perspective analysis and by conceptualizing sustainability transition as a hybrid model combining policy direction, market incentives, and knowledge collaboration. Managerially, the findings highlight the need for regulatory clarity, targeted financial mechanisms, capacity building, and stronger industry–academia integration to institutionalize sustainability practices in emerging tourism economies. Full article

Flood risk maps play a critical role in land-use regulation, infrastructure planning, and community preparedness, which are key components of sustainable and climate-resilient urban development. Their production, however, remains costly, labor-intensive, and time-demanding as it relies on simulation-driven workflows that combine hydrodynamic modeling with expert interpretation and extensive validation. To address this issue from a sustainability perspective, we develop a novel, practical, and near-real-time large language model (LLM)-based framework to support property-level flood risk assessment. This framework, which synthesizes geospatial, hydrological, infrastructural, and historical flood information, extends existing research and explores novel risk estimation methods for use in planning practice. Using Brisbane, Australia, as a case study, we develop Flood-LLM, a multi-agent system that transforms multi-source urban datasets into structured textual representations, models diverse neighborhood conditions, and fine-tunes a reasoning model using expert-assessed risk classifications. The results show that Flood-LLM can reproduce official flood risk labels for creek, river, storm tide, and overland-flow hazards with reasonable accuracy, outperforming classical machine learning, deep learning, and untuned LLM baselines. Visual and quantitative analyses indicate that the framework demonstrates a qualitatively nuanced capability to capture salient spatial patterns present in the official maps, while generating a textual chain-of-thought providing a transparent audit trail for its labeling decisions. These findings suggest that such LLM-based approaches can produce potential complementary tools to expert-reviewed planning classifications and support more sustainable, adaptive flood risk management by enabling timely map production and updates that facilitate informed decision-making in rapidly changing environmental conditions. Full article

Scientific evidence is fundamental to uncovering insights about health, including food and nutritional claims. Substantiating such claims requires robust scientific procedures that often include clinical studies, biochemical analyses, and the examination of multiple forms of data. The growing capabilities of artificial intelligence (AI) and large language models (LLMs) present new opportunities for analyzing food–health relationships and supporting health claim validation. Yet, applying these technologies to the food and nutrition domain raises challenges that differ from those encountered in broader biomedical text mining (TM). In this perspective, we review key issues, including the complexity and heterogeneity of food-related data, the scarcity of food-specific language models and standardized resources, difficulties in interpreting nuanced and often contradictory evidence, and requirements for integrating AI tools into regulatory workflows. We compare modern LLM approaches with traditional TM methods and discuss how each may complement the other. Our position is that, despite their promise, current AI and LLM tools cannot yet reliably handle the subtleties of food–health evidence without substantial domain-specific refinement and human expert oversight. We advocate for hybrid approaches that combine the precision of established TM techniques with the analytical breadth of LLMs, supported by harmonized ontologies, multidimensional evaluation frameworks, and human-in-the-loop validation, particularly in regulatory contexts. We also highlight the importance of public education, transparent communication standards, and coordinated cross-disciplinary efforts to ensure these technologies serve broader goals of food safety, consumer trust, and global health. Full article

In-depth research on marine biodiversity is essential for understanding and protecting marine ecosystems, where semantic segmentation of marine species plays a crucial role. However, segmenting microscopic zooplankton images remains challenging due to highly variable morphologies, complex boundaries, and the scarcity of high-quality pixel-level annotations that require expert knowledge. Existing semi-supervised methods often rely on single-model perspectives, producing unreliable pseudo-labels and limiting performance in such complex scenarios. To address these challenges, this paper proposes a consistency-driven dual-teacher framework tailored for zooplankton segmentation. Two heterogeneous teacher networks are employed: one captures global morphological features, while the other focuses on local fine-grained details, providing complementary and diverse supervision and alleviating overfitting under limited annotations. In addition, a dynamic fusion-based pseudo-label filtering strategy is introduced to adaptively integrate hard and soft labels by jointly considering prediction consistency and confidence scores, thereby enhancing supervision flexibility. Extensive experiments on the Zooplankton-21 Microscopic Segmentation Dataset (ZMS-21), a self-constructed microscopic zooplankton dataset demonstrate that the proposed method consistently outperforms existing semi-supervised segmentation approaches under various annotation ratios, achieving mIoU scores of 64.80%, 69.58%, 70.32%, and 73.92% with 1/16, 1/8, 1/4, and 1/2 labeled data, respectively. Full article