159370 research outputs found
Sort by
Risk Disclosure as a Resilience Tool in Central Governments: Assessing Current Practices in the UK and the Netherlands.
Get PDFBeyond the Spotlight:Co-Designing AI for Theatre Audience Communication
Get PDFTheatres and concert halls play a crucial role within the performing arts, where managerial and administrative staff are essential to bringing live performances to audiences. Existing AI research has focused on artistic creation, but less attention has been paid to the purposeful design of AI systems that support organisational practices. This paper addresses this gap by identifying the needs, challenges and opportunities for AI integration into everyday workflows, forming the basis for design principles to guide the architecturing, training, and deployment of AI systems that empower staff, rather than replace them. This is explored through a co-design workshop with theatre marketing and communication professionals. Through reflections of the themes explored in the workshop and by following the guiding principles, this paper presents examples of implementation of AI systems that could be adopted, offering concrete directions for developing AI that benefits the cultural sector
An app-based behavioral support intervention promoting physical activity (APPROACH) in patients diagnosed with breast, prostate, or colorectal cancer: protocol for a randomized controlled trial
Get PDFBackground:
Strong evidence highlights that sufficient physical activity (PA) has multiple benefits for people living with and beyond cancer. However, many are not meeting PA recommendations. APPROACH is a trial of a theory-driven, app-based behavioral support intervention to promote brisk walking after breast, prostate, or colorectal cancer.
Objective:
The aim of this trial is to evaluate the efficacy and cost-effectiveness of the intervention.
Methods:
APPROACH is a multicenter, phase III, 2-armed, individually randomized controlled trial (N=472). We will recruit patients with localized breast, prostate, or colorectal cancer from hospitals in Yorkshire and surrounding areas in the North of England, United Kingdom, and randomize them 1:1 between the intervention and control arm (usual care). The intervention consists of an app designed for the general population to encourage brisk walking (NHS Active 10), supplemented with habit-based behavioral support, including 2 brief telephone or video calls, a leaflet, website, and walking planners. The primary endpoint is the difference between trial arms in the changes from baseline in activPAL-assessed average minutes of brisk walking (≥100 steps per minute) after 3 months.
Demographic and medical characteristics will be collected through self-report and hospital records. Secondary outcomes (assessed at 0, 3, and 6 months) will be the other activPAL-assessed outcomes (brisk walking at 6 months, total steps, light PA, standing time, and sitting times, weekly metabolic equivalent of task), self-reported PA, and self-reported BMI and waist circumference. Patient-reported outcome measures of quality of life, fatigue, sleep, anxiety, depression, self-efficacy, habit strength for walking, and social support will also be collected. Interviews will explore experiences of receiving the intervention. We will use health economic modeling to estimate the cost-effectiveness of the intervention over a lifetime horizon.
Results:
The study was funded in June 2019. Trial recruitment commenced in November 2023 and is planned to be completed in 2025. As of December 2025, a total of 473 participants have been randomized. The publication of the main results is expected in autumn 2027 after all follow-up data collection and analysis are complete.
Conclusions:
Overall findings will determine the clinical and cost-effectiveness of the intervention for patients diagnosed with breast, prostate, or colorectal cancer. If successful, APPROACH provides a potential model of supportive care to increase PA among people living with and beyond cancer.
Trial Registration:
ISRCTN Registry ISRCTN14149329; https://www.isrctn.com/ISRCTN14149329
International Registered Report Identifier (IRRID):
DERR1-10.2196/7709
Exploring the bidirectional temporal association between daily knee pain and physical activity in people with knee osteoarthritis: an exploratory smartwatch study
No full textObjective
Effective physical activity interventions for knee osteoarthritis (OA) require an understanding of the relationship between physical activity and pain. Using daily concurrent activity and pain measurements, we explored day-to-day changes and the bidirectional temporal association at individual and population level.
Design
This is a secondary analysis of step count and pain collected for 90 days using smartwatches in 26 people with knee OA. People reported pain twice daily on a Numerical Rating Scale (NRS 0-10). We used regression (individual level) and generalised linear mixed models (population) to explore same-day associations, as well as whether step count on one day predicted pain the next day, and vice versa.
Results
We analysed 1,473 daily pain and step count measurements, recorded over a median 58 days. There were considerable day-to-day changes in individuals’ median step count (range 423-7142) and pain (range 0-9). At individual level, associations varied in the strength and direction. At population level, a higher step count was associated with higher pain on the same day (0.04 NRS/1000 step increase, 95%CI 0.01-0.06) and following day (0.05/1000 step increase, 0.03-0.07).
Conclusions
There was a modest association at population level between step count assessed on one day and pain assessed on the same and following day. However, there was variation in the strength and direction of associations when examined at the individual level. This exploratory analysis shows how smartwatches allow daily data collection that enables detailed exploration of complex time-varying relationships in OA
Continuous-wave all-optical single-photon transistor based on a Rydberg-atom ensemble
Get PDFContinuous-wave (cw) architectures provide a promising route to interface disparate quantum systems by relaxing the need for precise synchronization. While essential cw components, including microwave single-photon transistors and microwave–optical converters, have been explored, an all-optical cw single-photon transistor has remained a missing piece. We propose a high-efficiency, high-gain implementation using Rydberg atoms, in which a control photon disrupts the transmission of a continuous probe beam via the van der Waals interaction. This device completes the set of components required for cw processing of quantum signals and paves the way for all-optical processing at the quantum level
Modeling the light response of an optically readout GEM based TPC for the CYGNO experiment
Get PDFThe use of gaseous Time Projection Chambers enables the detection and the detailed study of rare events due to particles interactions with the atoms of the gas with energy releases as low as a few keV. Due to this capability, these instruments are being developed for applications in the field of astroparticle physics, such as the study of dark matter and neutrinos. To acquire events occurring in the sensitive volume with a high granularity, the Cygno collaboration is developing a solution where the light generated during the avalanche processes occurring in a multiplication stage based on Gas Electron Multiplier (GEM) is read out by optical sensors with very high sensitivity and spatial resolution. To achieve a high light output, gas gain values of the order of 105-106
are needed. In this working condition, a dependence of the detector response on the spatial density of the charge collected in the GEM holes has been observed, indicating a gain-reduction effect likely caused by space-charge buildup within the multiplication channels. This paper presents data collected with a prototype featuring a sensitive volume of about two liters, together with a model developed by the collaboration to describe and predict the gain dependence on charge density. A comparison with experimental data shows that the model reproduces, with a percent-level precision, the gain behaviour over nearly one order of magnitude
Dark cities: illicit finance, anomic urbanism, and social harm
Get PDFIllicit capital flows linked to wealthy individuals, corporations and organized criminal networks are firmly integrated within London’s formal financial system and its real estate assets. This article argues that this distinct urban economy, which combines licit and illicit monies, is facilitated by anomic institutional, and political economic, settings. These settings have produced a ‘dark city’ that has generated a series of shadow harms. These harms include the defunding of social institutions, care, and infrastructural decline, such as large losses of public housing. These outcomes can be linked to the morally ambiguous spaces of those institutions that enable illicit financial flows who operate in the areas of finance, law, corporate life, and real estate. Despite the harms of a dark city condition, mirrored in similar cities globally, these stakeholders remain privileged in an urban political economy that continues to validate the aggressive pursuit of enrichment. Using institutional anomie theory in tandem with cutting-edge intelligence on illicit capital flows, the article develops an analysis of the privileging of capital management systems and the continued sidelining of related social harms in the dark city context
A novel dataset for gait activity recognition in real-world environments
Get PDFFalls are a prominent issue in society and the second leading cause of unintentional death globally. Traditional gait analysis is a process that can aid in identifying factors that increase a person’s risk of falling through determining their gait parameters in a controlled environment. Advances in wearable sensor technology and analytical methods such as deep learning can enable remote gait analysis, increasing the quality of the collected data, standardizing the process between centers, and automating aspects of the analysis. Real-world gait analysis requires two problems to be solved: high-accuracy Human Activity Recognition (HAR) and high-accuracy terrain classification. High accuracy HAR has been achieved through the application of powerful novel classification techniques to various HAR datasets; however, terrain classification cannot be approached in this way due to a lack of suitable datasets. In this study, we present the Context-Aware Human Activity Recognition (CAHAR) dataset: the first activity- and terrain-labeled dataset that targets a full range of indoor and outdoor terrains, along with the common gait activities associated with them. Data were captured using Inertial Measurement Units (IMUs), Force-Sensing Resistor (FSR) insoles, color sensors, and LiDARs from 20 healthy participants. With this dataset, researchers can develop new classification models that are capable of both HAR and terrain identification to progress the capabilities of wearable sensors towards remote gait analysis
Using the candidacy framework to explore access to NHS healthcare for street sex workers in Sheffield: an ethnography and art-based research project
Get PDFBackground: Street sex workers (SSWs) experience some of the highest levels of health inequality in the UK, yet face persistent barriers to accessing NHS healthcare. These barriers are shaped by structural disadvantage, stigma, and the complex realities of their lives. Despite significant health needs, engagement with services remains low, and existing models of care often fail to accommodate the lived experiences of this population.
Aims: This study explores how SSWs access, experience, and navigate NHS healthcare. It aims to understand the barriers and enablers of access, identify areas for improvement, and offer recommendations to inform the development of more inclusive service provision.
Methods: An ethnographic approach was undertaken within a South Yorkshire charitable organisation. Data collection involved participant observation and an arts-based scrapbook intended to facilitate trauma-informed, flexible engagement. Thematic analysis was used to analyse the data, organised around a dynamic, processual approach using the candidacy framework.
Findings: Barriers to care were present across all stages of healthcare engagement, including minimisation of health needs, administrative exclusion, lack of continuity, and stigma from professionals. Participants frequently described systems as inaccessible. Key enablers included supportive organisational staff and consistent, trusted relationships with specific providers.
Areas for Improvement and Recommendations: Findings highlight the need to simplify registration processes, provide in-person options, and reduce reliance on digital communication. Greater continuity of care and gender-sensitive, trauma-informed approaches were consistently requested. Services should not be evaluated solely by uptake but by how well they accommodate marginalised users. Healthcare settings that prioritise safety, trust, and consistency were shown to improve engagement. SWs spoke of the work of accessing care, which for many was too hard to gain.
Conclusions: SSWs are not disengaged from healthcare but are routinely excluded by systems that fail to meet their needs. Service redesign must begin from the realities of those who are most marginalised, through co-production, to reduce health inequity and build meaningful access
Data sources:
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇