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ULTRA ‐Metrics: Delphi-derived framework for assessing ultrasound competency
Get PDFObjectives: Ultrasound competency is critical in modern healthcare, yet no standardized framework currently supports ultrasound skill monitoring across diverse clinical settings and user types. Existing frameworks often lack generalizability, overemphasize exam counts, and fail to assess key skills such as interpretation, limiting ultrasound's safe and effective integration into clinical practice. The objective of this study is to develop a consensus‐based, universal framework for monitoring ultrasound competency across clinical applications and disciplines. Methods: A modified Delphi process was conducted with an international panel of Point‐of‐Care ultrasound experts. Panelists independently evaluated framework elements categorized by competency domains (experience, skills, autonomy), skill domains (indication, acquisition, interpretation, clinical integration), metrics (eg, exam counts, entrustability, interpretation accuracy, etc.), answer sets (score‐based inputs used by assessors), and score criteria (requirements for each score). Consensus thresholds were defined as strong consensus at >84%, and weak consensus at 68–84%. Two Delphi rounds were completed. Results: Nineteen experts participated across 2 Delphi rounds. Strong consensus was reached to include 3 competency domains (experience, skills, autonomy) and 4 skill domains (indication, acquisition, interpretation, and clinical integration). Optional components, including the use of acquisition skill trees and varied answer set granularity, were favored by some participants to allow ultrasound programs to tailor the framework to specific examinations, assessment scenarios, and job roles. Conclusion: The resulting modular framework provides a flexible, consensus‐based approach to ultrasound competency assessment, enabling cross‐program comparisons and evaluation of training methods. Validation across diverse settings is needed to support its use in global competency standards and ultrasound education expansion
Numerical investigation of combustor inlet temperature effects on emissions and performance in non-premixed ammonia-fueled micro-gas turbines
No full textAmmonia combustion is emerging as a carbon-free energy solution, offering the potential to lower greenhouse gas emissions. Its role as a green hydrogen carrier makes it suitable for use in hard-to-abate sectors as a renewable fuel. However, its combustion produces high NOx emissions due to the nitrogen content in the fuel molecule, especially under lean conditions at elevated air temperatures, as in gas turbine combustors.
In this study, a detailed CFD-based numerical investigation of an optimized 20-kW staged rich–lean ammonia-fueled, non-premixed combustion chamber was carried out to investigate the effects of combustor inlet temperature (CIT) on emissions, combustion efficiency, and overall performance. The CIT was increased from 500 K to 1050 K at a
of 0.12. This temperature rise resulted in a 430 K increase in combustor outlet temperature (COT), from 860 K to 1300 K, and a 50 % rise in NOx emissions from 308 to 627 ppm. The higher COT, combined with reductions in unburned NH3 and H2 by 90 % (1094 to 101 ppm) and 27 % (0.03 to 0.02 Vol.%), respectively, contributed to a 5 % improvement in combustion chamber efficiency based on output power.
This CFD-based study also integrates the effects of CIT and different
(0.12–1.2) to comprehensively assess the factors influencing combustion efficiency and emissions. The findings provide simulation-driven insights to optimize performance, reduce emissions, and guide the design of future ammonia-fueled micro gas turbines
Hierarchical triples versus globular clusters: binary black hole merger eccentricity distributions compete and evolve with redshift
Get PDFThe formation mechanisms of merging binary black holes (BBHs) observed by the LIGO-Virgo-KAGRA collaboration remain uncertain. Detectable eccentricity provides a powerful diagnostic for distinguishing between different formation channels, but resolving their eccentricity distributions requires the detection of a large number of eccentric mergers. Future gravitational wave detectors such as the Einstein Telescope and Cosmic Explorer will detect tens of thousands of BBH mergers out to redshifts z ≥ 10, making it critical to understand the redshift-dependent evolution of eccentricity distributions. We simulate this evolution for two key channels: dynamical assembly in globular clusters (GCs), which leads to rapid, eccentric mergers; and hierarchical triples in the field, where three-body dynamics can induce eccentricity in the inner binary. When considering all BBH mergers, the GC channel dominates overall, consistent with previous studies. However, when focusing on mergers with detectable eccentricity in next-generation detectors, we find that hierarchical triples dominate the eccentric merger rate at 0 ≤ z ≤ 4, with GC mergers becoming competitive at higher redshifts. Across all model variations, eccentric mergers in the local Universe (z ≲ 1) have significant contributions from field triples, challenging the common view that such systems primarily form in dense environments. We show that, regardless of cluster and stellar evolution uncertainties, hierarchical triples contribute at least 30 per cent of eccentric mergers across a large range of redshifts
Teacher-led robot intervention in early primary school classrooms improves pupil and teacher outcomes.
Get PDFProgramming is often taught through robots in early primary education to support young children’s computational thinking (CT), but many teachers lack the confidence and training to use them effectively. This paper presents a school-based robot intervention for children aged 4 - 7 (n = 430) and their classroom teachers (n = 17), delivered under three conditions: Intervention (robot intervention only), Intervention+ (intervention plus teacher education), and Control (no intervention). The two intervention groups assessed whether teacher education, in addition to classroom robot experience, influenced pupils’ prediction and debugging, transferable skills (programming transfer and picture sequencing), and teachers’ beliefs (enjoyment, relevance, self-efficacy, anxiety). The intervention improved children’s prediction and debugging scores significantly, but only Intervention+ significantly outperformed Control for both prediction and debugging. Performance on the programming transfer and picture sequencing tasks improved across all groups. Teachers in both intervention groups reported improved relevance beliefs, though only Intervention+ showed a significant difference from Control. Self-efficacy also improved significantly in Intervention+ only. These findings offer practical guidance for embedding programming with robots in primary education and underscore the importance of teacher education for significant impact
Exploring customer incivility in the service sector: a systematic review and roadmap for future research
No full textCustomer incivility (CI) increasingly shapes service work, from frontline hospitality staff to digital agents in retail and banking. This study applies the PRISMA protocol to review 112 empirical articles published between 2009 and 2025. Using the Theory, Context, Characteristics, and Methods (TCCM) framework, we synthesise key findings and highlight the dominant themes in CI research. Our analysis identifies under-explored areas, including digital CI dynamics and cultural influences, and proposes a framework to guide future inquiry. We set out a research agenda across five themes: theory development, cultural comparisons, digitalisation, intersectionality, and intervention design. Conceptually, the review advances the theorisation of CI by clarifying boundaries and neglecting dynamics. Methodologically, it demonstrates the value of TCCM for structuring evidence and systematic analysis. Practically, it translates insights into strategies such as de‑escalation training, platform‑level moderation, and organisational policies that reduce reliance on individual resilience
Designing physically separated bimetallic catalysts through cooperative redox enhancement (CORE)
Get PDFLiquid-phase heterogeneous catalysis underpins numerous chemical manufacturing processes, ranging from essential products to renewable energy sources, such as hydrogen. Despite the differences in reactor setups and the driving forces between thermos- and electro-catalysis, it is commonly overlooked that the two disciplines are fundamentally governed by the same underlying fundamentals. In this tutorial review, we explore the similarities between electro- and thermocatalysis and introduce how electrochemical methodologies can be applied to characterize thermocatalysis to gain both fundamental and experimental insights. Here, we discuss the recent discovery of Cooperative Redox Enhancement (CORE), a phenomenon whereby selectivity differences for two electrochemical half reactions on two physically separated but electrochemically connected dissimilar metal catalyst particles lead to acceleration of the overall catalytic rate. This approach suggests a new paradigm for the design of heterogeneous catalysis
Interventions to reduce empathy-based stress and enhance compassionate care in mental health wards: a systematic review
Get PDFMental health wards are an important healthcare context with the potential to positively impact patient trajectories. Compassionate care in these wards is important, and can be impacted by staff levels of empathy-based stress (compassion fatigue, burnout and secondary trauma). It is important to consider the evidence-base for mental health ward interventions to improve compassionate care for patients and to reduce empathy-based stress for staff. A systematic review was conducted of robust evaluations of mental health ward interventions designed to improve compassionate care and/or reduce staff empathy-based stress, with the aim of synthesising interventional evidence on these interventions' effectiveness, implementation and acceptability. Programme theory papers, outcome evaluations (RCTs and non-RCTs), economic evaluations and process evaluations were included. A meta-integration of intervention content, effectiveness and influence of contextual factors on implementation and acceptability was performed. 18 eligible study reports of 11 interventions were identified. Interventions were multi-level, and aimed to increase staff resources rather than decrease staff demands. Staff training interventions were most evaluated, with mixed evidence for effectiveness. Other approaches included changes to ward approach, environment, use of participatory action research methods and peer-review programmes. There was no clear evidence for a particular intervention type. Two interventions showed evidence of iatrogenic harm. Equity harms and economic effects were not well-evaluated. Mechanisms of change were under-theorised and lacked clear logic models. Patient and public involvement was sporadic. Current interventions are being offered without a clear evidence-base or guiding model, and risk harming staff. Multi-level interventions using clearer logic models which tackle both job demands and resources are recommended. A model of implementation factors which may help interventions to succeed is proposed. More high-quality controlled intervention studies, considering contextual and process factors, and incorporating co-production, are needed, especially given the risk of iatrogenic harm. [Abstract copyright: © 2025. The Author(s).
Contested identities in Europe: Historical insights into the construction of citizenship education from the bottom up
Get PDFCitizens need to be educated. However, both understandings of citizenship and the education required to disseminate them are inherently contested. After presenting an analytical framework that allows for a conceptually grounded analysis of such contestation, this introduction draws on the papers included in the special issue to identify three axes that have structured the debate around citizenship education in modern Europe: the roles of the state, of place, and of conflict versus consensus. In conclusion, we present the papers included in this special issue and consider their value in both showcasing the diversity of understandings of citizenship in contemporary Europe, as well as the issues that this diversity raises for education research
Towards comprehensive interactive change understanding in remote sensing: A large-scale dataset and dual-granularity enhanced VLM
No full textRemote sensing change understanding (RSCU) is essential for analyzing remote sensing images and understanding how human activities affect the environment. However, existing datasets lack deep understanding and interactions in the diverse change captioning, counting, and localization tasks. To tackle these gaps, we construct ChangeIMTI, a new large-scale interactive multi-task instruction dataset that encompasses four complementary tasks including change captioning, binary change classification, change counting, and change localization. Building upon this new dataset, we further design a novel vision-guided vision-language model (ChangeVG) with dual-granularity awareness for bi-temporal remote sensing images (i.e., two remote sensing images of the same area at different times). The introduced visionguided module is a dual-branch architecture that synergistically combines fine-grained spatial feature extraction with high-level semantic summarization. These enriched representations further serve as the auxiliary prompts to guide large vision-language models (VLMs) (e.g., Qwen2.5-VL-7B) during instruction tuning, thereby facilitating the hierarchical cross-modal learning. We extensively conduct experiments across four tasks to demonstrate the superiority of our approach. Remarkably, on the change captioning task, our method outperforms the strongest method Semantic-CC by 1.39 points on the comprehensive S∗m metric, which integrates the semantic similarity and descriptive accuracy to provide an overall evaluation of change caption. Moreover, we also perform a series of ablation studies to examine the critical components of our method. The source code and associated data for this work are publicly available at Github