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Swallowing prehabilitation for people with head and neck cancer: a pilot cluster-randomised feasibility trial of the SIP SMART intervention
No full textObjectives To assess the feasibility of delivering the swallowing prehabilitation intervention known as Swallowing Intervention Package: Self-Monitoring, Assessment and Rehabilitation Training (SIP SMART) within the National Health Service (NHS) head and neck cancer care pathway.
Design Two-arm cluster-randomised pilot trial: SIP SMART2 trial.
Setting and participants Adults newly diagnosed with stage II–IV head and neck cancer receiving curative treatment within a multidisciplinary team who agree to participate.
Interventions Six hospitals were randomised. Trained clinicians at the intervention sites delivered the manualised SIP SMART intervention, while standard care was provided at care as usual (CAU) sites. The intervention included two 45-minute consultations incorporating an X-ray swallow assessment, tailored exercises/advice and specific behaviour change strategies while CAU involved a single consultation of information giving and provision of a generic exercise sheet.
Outcomes Study outcomes related to feasibility of the cluster-randomised design, recruitment of both sites and patients and completeness of clinical and health economic data collected at baseline, 4 weeks, 12 weeks and 24 weeks after treatment.
Results 12 hospitals expressed interest and six were randomised (50%) and provided data to the point of study completion. Patient recruitment across all sites (n=76) reached the target, although two sites fell short of their individual targets. The proportion of people with HNC recruited versus those eligible for each arm was 39% (95% CI 29 to 49) for SIP SMART group and 55% (95% CI 43 to 66) for CAU. The end point data at 24 weeks were completed for 50% (95% CI 33 to 67) for SIP SMART and 78% (95% CI 62 to 89) for CAU. Adherence to the intervention was above 50% at all time points. No harms related to the intervention were reported.
Conclusions It is feasible to deliver the SIP SMART intervention embedded within the NHS cancer care pathway using a cluster-randomised design. A future trial will be optimised for efficiency in set-up and follow-up data collection based on these findings and learnings from the accompanying process evaluation study
When water is valued, sustainability follows: reducing leakage and promoting water reuse
No full textAccording to the UN, the failure to fully value water in all its different uses lies at the heart of its political neglect and mismanagement. Yet, determining the 'true' value of water as a natural resource is highly complex and widely recognised as an impossible task. Using London as a case study, we revisit a key infrastructure decision made approximately two decades ago by the capital’s water/wastewater service provider. In response to a regulatory request to increase water supply, the company ultimately prioritised the development of a desalination plant over large-scale mains replacement to address systemic leakage, on the basis of its cost-effectiveness; a decision that has since proven to be highly problematic. This analysis re-examines that decision and compares the natural capital value of water to its replacement cost as means for improving how water was valued in the process. Findings demonstrate that when adopting a whole water cycle perspective and incorporating the natural capital value of water into the decision-making, more sustainable outcomes emerge. Assigning such value to the water abstracted from nature, water companies and utilities can make better-informed investment decisions and promote sustainable alternatives that can ultimately decouple urban water consumption from the depletion and pollution of water resources
Exploring UK medical schools' perception and intent for teaching on antimicrobial resistance and stewardship
No full textBackground
Antimicrobial resistance (AMR) is a key global public health threat. Developing healthcare worker awareness and knowledge in AMR and antimicrobial stewardship (AMS) are UK national action plan targets. Competency frameworks have been developed to support improved undergraduate teaching, but their uptake is unclear.
Methods
A targeted survey was sent to all UK medical schools to determine curriculum learning objectives (LOs) on AMR/AMS and distribution of teaching opportunities across courses. Replies were mapped to the six McMaster competency framework domains to identify areas of strength and potential gaps in undergraduate teaching models.
Results
Replies were received from 50/52 (96.2%) medical schools. AMR/AMS teaching through dedicated teaching sessions was offered by 32/50 (64.0%), whereas 11/50 (22.0%) had fully integrated courses and 7/50 (14.0%) were unable to identify any LOs. Data on dedicated teaching provision (mean 7.7 h/course, IQR 3.0–10.5) were provided by 32/50 (64.0%). Evidence for mapping to the McMaster competencies was highly variable, ranging from 1/50 (2.0%) to 35/50 (70.0%) across the six key domains. Where identified, mapping to knowledge-based competencies was observed to be significantly greater than to practical-based competencies (OR 3.2, 95% CI 2.5–4.1, P < 0.0001). Ten of 50 (20.0%) highlighted learning opportunities in clinical years were opportunistic, variable, indirect or incidental.
Conclusions
Current approaches to UK undergraduate AMR/AMS medical education appear limited, with greater focus on knowledge-based competencies and less focus on practical skills. Reliance on opportunistic teaching in clinical years presents challenges for programme assurance and impact assessment. Minimizing unwanted variability in medical school teaching and learning in AMR/AMS should be pursued, and inclusion in the UK Medical Licensing Assessment content map may provide such an opportunity
Digital twinning for onboard health monitoring of solar panels for space exploration
No full textSpacecraft solar panels are among the most failure-prone subsystems, requiring onboard health monitoring to ensure reliable operation in harsh space environments. This work demonstrates and validates the FREEDOM digital twin for onboard near real-time health management of spacecraft solar arrays under limited computational resources. The twin is trained offline through a two-stage data-assimilation process, and operates online to predict degradation via inverse Bayesian optimization. Validation performed on a solar panel integrating AZUR SPACE 3C44 cells under incipient damages across a ten-year geostationary-orbit mission scenario yielded a mean damage-prediction error of 5.2 % with a computational time of 62 s
Mechanical treatment for enhancing the pozzolanic reactivity of recycled powder from waste wind turbine blades
No full textThe pozzolanic reactivity of a recycled glass fibre reinforced polymer (GFRP)-derived supplementary cementitious material (SCM) was enhanced through mechanical treatment involving ball milling (120 rpm for 60–360 min). The nature of the amorphous phase was characterised using quantitative X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectrometry, which indicated the material to be significantly short-range disordered (>95 wt%). The pozzolanic reactivity was evaluated by determination of the bound water content in parallel with twin-phase amorphous quantification using XRD. The quantity of residual glass was evaluated using an arbitrary phase populated with a calibrated peak list following the PONKCS (partial or no known crystal structure) method, standardised to a 50 wt% corundum spike. The amorphous hydrate content was determined with a spike phase. The strength development of mortar with this new SCM was then investigated. All experimental tests were repeated at least three times to ensure statistical significance. The results indicated a matching trend correlating reactivity, strength, and fineness. The compressive strength at both 7 and 14 days increased by approximately 50 % when the milling duration was extended from 60 min to 360 min. The increase in reactivity of the milled GFRP was attributed to a combination of an increase in specific surface and the alteration to Si-O-Si sites at the reactive surface which enhanced the solubility characteristics
Object-centric neuro-argumentative learning
No full textOver the last decade, as we rely more on deep learning technologies to make critical decisions, concerns regarding their safety, reliability and interpretability have emerged. We introduce a novel Neural Argumentative Learning (NAL) architecture that integrates Assumption-Based Argumentation (ABA) with Object-Centric (OC) deep learning for im- age analysis. Our OC-NAL architecture consists of neural and symbolic components. The former segments and encodes images into facts, while the latter applies ABA learning to develop ABA frameworks enabling image classification. Experiments on synthetic data show that the OC-NAL architecture can be competitive with a state-of-the-art alternative. The code can be found at https://github.com/AbdulRJacob/Neuro-AL
Testing the BIO-WELL scale in situ: measuring human wellbeing responses to biodiversity within forests
No full textThe benefits of nature for human health and wellbeing are well documented. However, nature is not homogenous, and there remains a gap in our understanding of the role biodiversity (the diversity within species, between species, and of ecosystems) plays specifically. BIO-WELL, a psychometric scale, asks people to consider themselves in a forest (ex situ), measuring human wellbeing across five domains for 17 biodiversity metric and attribute stem questions. Here, we adapt and validate BIO-WELL for use in situ with 510 participants in British forests during spring and summer. We found good internal consistency, and exploratory and confirmatory factor analyses reaffirmed 1-factor structures for most stem questions (construct validity); variability in model fit statistics for some of the biodiversity stem questions indicates uncertainty in how they were conceived by participants. We found strong concurrent validity, meaning the scale is suitable and reliable for use in situ. Perceived variety of sounds, smells, and colours were positively associated with BIO-WELL scores. People who felt visiting the outdoors was an important of their life also scored higher. Participants reported higher BIO-WELL scores in relation to the diversity of, and interactions between, species in spring compared to summer, which is perhaps attributable to seasonal differences in ecological processes. There was no difference in BIO-WELL scores between people who reported sensory impairments. The scale can be deployed to generate empirical evidence to support policy and practice decision-making for planning and managing natural environments for both biodiversity conservation and human wellbeing
Natural carbonation of concrete: a data-driven analysis
No full textThis study presents a data-driven analysis of long-term natural carbonation in concrete, using a newly compiled database comprising 1079 mixes and 8194 carbonation depth measurements over 65 years, representing one of the largest natural carbonation datasets assembled to date. Four different tree-based machine learning models (CatBoost, XGBoost, Random Forest and Decision Tree) were evaluated for predicting carbonation rate (k), with CatBoost emerging as the most effective. Partial dependence and SHAP analyses were used to quantify the relative importance of 13 features influencing k, including binder composition, mix proportion, curing and exposure conditions. The water-to-calcium oxide (w/CaO) ratio emerges as the most important feature, encapsulating the effects of porosity, carbonatable content, and supplementary cementitious material (SCM) type and content. Higher SCM replacement levels increase carbonation, while aggregate-related features show comparatively less pronounced effects. Carbonation environment is a more important feature than curing condition in predicting k. By combining machine learning with interpretable SHAP analysis, the model independently recognised trends consistent with literature findings. This study offers a comprehensive dataset and robustly selected features to facilitate future machine learning-based predictions of concrete carbonation
Hybrid digital twin for reliable sensing frameworks in aerospace structural health monitoring
No full textModern aerospace SHM systems depend on dense sensor networks to safeguard structural integrity in flight, but their reliability is threatened by sensor faults that can mislead safety-critical diagnostics. This work proposes HyRelM, a hybrid physics- and data-driven sensing digital twin designed to ensure reliable information flow to the SHM logic. The twin performs automated classification of structural and sensing states, reconstructs corrupted measurements, and quantifies sensor reliability in real time, enabling accurate fault isolation and data correction. Demonstrated on a composite wing panel with fiber-cut damage, HyRelM successfully distinguished structural and sensing anomalies, updated corrupted measurements, and restored sensor reliability, confirming the potential of sensing digital twins to deliver resilient SHM for safer aircraft