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Large language models for clinical trials in the Global South: opportunities and ethical challenges
Full text linkLarge language models (LLMs) show promise for improving clinical trials in wealthy countries but remain underexplored in low- and middle-income countries (LMICs), where healthcare infrastructure is weaker and resources limited. This article explores opportunities for LLM integration and addresses ethical challenges in LMIC clinical trials through a review of recent literature (2024–2025) on LLMs in healthcare and clinical research, examining adaptation potential for LMICs using thematic analysis to identify ethical issues specific to LMIC contexts, including data control, fairness, and sustainability. LLMs can accelerate protocol development, improve multilingual patient recruitment, streamline regulatory processes, and address data gaps through synthetic records; however, implementation raises concerns about data privacy, community representation, AI transparency, and technological dependence on foreign platforms. While LLMs can enhance clinical trial efficiency and inclusivity in LMICs, successful integration requires locally-adapted models, community-centered ethical oversight, and regional partnerships, with thoughtful implementation potentially democratizing healthcare innovation benefits across Global South populations
What social and environmental considerations are important for socially assistive robotic adoption for pre-frail older adults at home: a scoping review, life cycle assessment and survey
No full textBackground
A complex evaluation of robotic adoption for older adults is necessary, given the pressing need to prevent ill health and disability. Critical thinking that incorporates an understanding of sustainability is needed for the future, if assistive robots are to be widely used. This research was undertaken as part of I’MACTIVE, a project that combined emergent technologies for older adults at risk of becoming frail. The aim was to analyse the sustainability of emerging socially assistive robotic technology for use with community dwelling older people and to assess the likelihood of adoption and acceptability.
Methods
The study includes a scoping review of the social and environmental impacts of socially assistive robotics, a life cycle impact evaluation of a robot and potential metrics for assessing its circularity, and a subsequent survey in two stages of the perspectives of older adults on assistive robotics. The evaluation sought to assess the likelihood of adoption, given priorities and perspectives of older people. Framework analysis was used to combine findings.
Results
The review included eight studies, but none addressed the social and environmental features of the robotic implementation in terms of needs of older people. Theories of adoption and acceptance do not yet include life cycle assessments of technologies. The environmental assessment of the TurtleBot4 robot indicated that its global warming potential is estimated at 68–118 kg CO2-eq. The most important components were the printed circuit boards and batteries, together responsible for over 60% of the impacts. The survey demonstrates that older adults are willing to use socially assistive robotics if robots have an appropriate level of function, cost, environmental impact and legal risk.
Conclusions
Socially assistive robotics may benefit some older people but currently the design process inconsistently factors in the values and choices of older people. Nor does it consider the environmental implications of scaling the deployment of robotics in personal residences. Robots have the potential to help with the caregiving and domestic needs of the growing ageing population but need to be sustainably produced and have tangible functional benefit, identified by older people, to be acceptable
Exploring spatial and temporal patterns across solar cycles: Focusing on active longitudes
Full text linkContext. Active longitudes (ALs) are proposed behavioural patterns on the Sun, whereby certain solar phenomena tend
to appear at preferred longitudes, with these longitudes shifting over time on a scale of a few Carrington Rotations
(CRs). The existence of ALs remains a topic of debate, largely due to our limited understanding of their origin, evolution,
and physical significance.
Aims. This study aims to provide support and further evidence towards the existence of ALs by utilising longer-term
sunspot and solar flare datasets. As part of this effort, an artificial test dataset for control was also constructed in which
the longitudes of sunspots were randomised, allowing a direct comparison with the observational data.
Methods. Kernel density estimation (KDE) was employed to search for longitudinal groupings of sunspot groups and
flares on synoptic maps. Furthermore, we explored larger-scale structures by applying a 2D KDE to the peaks of the
1D KDEs (longitude as a function of CR). Finally, we generate artificial solar cycles by simulating sunspots with
randomised properties, most notably assigning longitudes from a uniform distribution.
Results. Distinguishable features were identified in the 2D KDEs, showing that during certain periods of the solar cycle,
a specific longitude range may exhibit heightened activity, which can later switch off entirely, and a new one can appear
∼180◦
away, consistent with the AL’s flip-flop effect. Although our randomised datasets also exhibited active longitudes
in their 2D KDEs, these differed notably from the observed patterns: inactive longitudes were less pronounced, and
active patches appeared shorter-lived and more numerous. We also identified a parameter for a qualitative comparison:
the number of KDE peaks (in the 1D KDE) per number of CRs in a solar cycle. This indicator shows a markedly
different distribution between the randomised and observed datasets, confirmed by a Cucconi test p-value of 0.0177
Mobile sensors for hydraulic calibration of pipe network models
Full text linkThis paper is the first to explore the potential use of mobile sensors in the hydraulic calibration of water distribution system and sewer system network models. Novel simulation and optimisation functionality is developed to simulate, utilise and analyse the data that would be collected from mobile hydraulic sensors. Comparable functionality is obtained for static sensors to demonstrate the benefits for a mobile sensor approach. Real world case studies are used to show and compare the accuracy of resulting model calibration, with pipe roughness used to independently assess the calibration quality achieved. Mobile sensors achieved substantially lower pipe roughness error values, around 50% lower in the water supply network and around 25% lower in the sewer network. This level of relative predictive performance was demonstrated for 24 hours of data collection from a single mobile sensor, in comparison to nearly 97% nodal coverage of the water supply network and 66% coverage of combined sewer network by static sensors – all sensors sampled at the same frequency. The evidence generated shows the significant potential of mobile sensors, deployed on robotic platforms, to transform the accuracy of water supply and sewer network model calibration. Such improvements are essential to enable, and as part of, digital twin paradigms and to confidently inform proactive management driven from accurate and comprehensive assessment of system performance
The NICE experience of designing and utilising severity weights
Full text linkBackground
In January 2022, NICE changed from “End of Life” (EoL) to “severity” weights, whereby additional value is applied within cost effectiveness analysis to health benefits arising from health technologies deemed to qualify This study examines the relationship between these concepts, how they relate to patient age, and whether the new system is cost-neutral as intended.
Methods
Data was extracted from 555 NICE Technology Appraisal decisions from 2009 to 2024. Absolute (AS) and proportional shortfall (PS) severity indicators were estimated for pre 2022 decisions. Post 2022 decisions were judged against EoL criteria.
We describe the relationship between severity weights, including the constituent AS and PS elements, age and EoL. Comparisons are made between the distribution of AS, PS and overall severity categories using descriptive statistics and significance tests.
Results
AS and PS have different relationships with patient age. In NICE appraisals, AS reduces with age but the relationship is flat between 40 and 60 years. All decisions in the highest AS category (AS > 18 QALYs) have a starting age below 20 years. PS peaks around 60 years. EoL applies almost exclusively where age exceeds 40 years. 91 % of appraisal decisions meeting EoL would receive a severity weight above 1.
There is no difference in the mean severity weight between pre and post 2022 appraisal decisions (1.116 vs 1.125). Mean AS is lower in post 2022 appraisals.
Conclusions
Severity weights do not correlate precisely with EoL. They have been applied as often as expected. The change from EoL to severity weights has been approximately cost-neutral
From symbiosis to scarcity: evaluating disruption associated with decarbonisation to circular waste materials between the UK cement and steel sectors
No full textThe UK cement and steel industries are decarbonising rapidly to meet net-zero targets. This study explores the unintended consequences of these efforts, particularly the potential disruption of industrial symbiosis between sectors. Cement production in the UK increasingly relies on ground granulated blast furnace slag (GGBS), a low carbon supplementary cementitious material (SCM). However, the shift from primary to secondary steelmaking threatens domestic GGBS supply. This research uses material flow analysis, life cycle assessment, and economic modelling to evaluate future GGBS availability, carbon intensities, and supply chain vulnerabilities. Findings indicate that although the steel sector is expected to reduce its environmental impact, this will cause the cement sector to face a potential shortfall in domestic SCMs, increasing reliance on imports through cross-sector decoupling and stagnation of decarbonisation. Addressing these challenges is vital to ensure a sustainable cross-sector supply chain and support future UK and global infrastructure resilience
Who is experiencing faculty/staff-student sexual misconduct in UK higher education?
No full textSexual misconduct perpetrated by staff/faculty in higher education towards students remains underexplored compared to perpetration by peers. The article examines the types of sexual misconduct students in UK higher education experience from staff and differences in experience across groups, drawing on findings from a non-representative survey of students in UK higher education (n=1768). It opens up a methodological discussion around measuring power-based sexual misconduct, introducing an adapted version of the Sexual Experiences Questionnaire that removes references to whether students said no to sexual advances; accounts for how power imbalances affected ability to consent; and incorporates intersectionality. These adaptations aimed to capture the extent to which students experience a sexualised environment – i.e. sexual misconduct – rather than asking whether sexualised behaviours are unwanted. Data is analysed using a hurdle model, analysing factors that increase likelihood of experiencing sexual misconduct as well as factors that increase the amount of sexual harassment experienced. Findings show that while women were the only group to be more likely than men to have experienced sexual misconduct in general, when examining amount of misconduct experienced, women, non-binary students, Asian students, gay/lesbian and bisexual students, first-in-family, and disabled students were all more likely to experience more incidents
Can I trust GenAI to plan my next trip? A multi-method approach to optimizing media mix
Full text linkAlthough tourists can now book trips directly using generative artificial intelligence (GenAI), it remains unclear whether the real-time travel information it provides is comprehensive and sufficiently trustworthy enough to make booking decisions. The present research addresses this gap by integrating media richness, trust transfer, and the value-based adoption model (VAM) to investigate the impact of varying levels of travel information richness (text-only, text-image, and text-image-audio) on the booking behaviors of tourists using GenAI such as ChatGPT. With data from 578 participants, we tested the proposed structural and configurational models using a multi-analytical approach. Our findings revealed that the three media richness levels yield both analogous and distinctive effects on tourist perceptions regarding benefits, costs, trust formation, and intentions in ChatGPT online travel booking. Specifically, the text-image group demonstrated the strongest links from media richness to trust in ChatGPT, perceived benefit to value, and ultimately value to increased booking intention. Our findings from configurational modeling confirm a significant opportunity to harness the power of AI-empowered platforms for online travel booking
Experiencing Ourselves Emotionally
No full textThis chapter sketches an account of one way in which emotional experience incorporates a sense of self. I develop a conception of self-experience as a multi-faceted orientation through which we find things significant in emotional ways. This orientation is experienced as dynamic, open to change, fragile, vulnerable, and to varying degrees conflicted. I add that certain emotional experiences, most of which lack established names, are concerned primarily with tensions and incongruities within the self, rather than with how events and situations are significant in relation to the self. More generally, emotional self-experience should be construed in terms of the dynamic, temporally extended experience of significant, cohesively organized possibilities
Attitudes, intentions and behavior change
No full textAre attitudes or intentions related to behavior change? Does changing attitudes or intentions change behavior? These are important questions for increasing our understanding of the determinants of behavior and how to change behavior. This
review employs four stages of the experimental medicine approach to answer these questions. First, attitudes and intentions have been identified as key determinants of behavior in many theories (identification stage). Second, correlational studies show that attitudes and intentions have small to medium-sized relationships with behavior change, while experimental studies show that medium-sized changes in attitudes and intentions produce small-sized changes in behavior (validation stage). Third, evidence shows that interventions can change attitudes or intentions (engagement
stage). Fourth, changes in attitudes and intentions at least partially mediate intervention effects on behavior change (intervention stage). A systematic program of experimental work is needed to extend understanding of what works for whom, when, and how, and for what behaviors
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