11237 research outputs found
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‘I’m registered blind, but I can’t play blind golf’: exploring the experiences of golfers with a visual field impairment
No full textGolfers with vision impairment have access to numerous competitive participation opportunities, but their eligibility is dependent on successful classification. Currently, a person’s visual field (VF) is not considered during the classification process in vision impaired (VI) golf, with golfers classified on their visual acuity alone. There is a growing body of qualitative research focused on athletes’ classification experiences, but to date there is a lack of research on golfers with a VF impairment (GVFI). Ten GVFI were recruited for an online semi-structured interview through a video conferencing platform. Data were analysed using reflexive thematic analysis, underpinned by a relativist-constructionist philosophical position. Five themes were generated: 1) perception of impairment, 2) playing golf through a limited field of vision, 3) the golfer–guide relationship, 4) classification experiences, and 5) benefits of playing golf. Results demonstrate that GVFI may utilise medicalised language to legitimise their relatively non-visible impairment(s). Furthermore, a social relational understanding of disability is used to show that whilst impairment-effects on performance are evident in GVFI, the classification system and social structures within it ultimately disable and marginalise GVFI. Results suggest that guides are often selected out of convenience instead of performance factors, and socialising with other VI golfers empowers GVFI. This study advances understanding of the qualitative experiences of GVFI and informs future work into the development of an evidence-based classification system specific to VI golf.</p
A digital twin framework for predicting and simulating type 2 diabetes onset using retrospective lifestyle data
No full textIntroduction: Type 2 Diabetes Mellitus (T2DM) is a rising global health concern, heavily influenced by modifiable lifestyle and psychosocial factors. However, most predictive tools focus on biomedical markers and rely on real-time data from wearables or electronic health records, limiting their scalability in resource-constrained settings. This study presents a novel digital twin (DT) framework that uses retrospective lifestyle, behavioral, and psychosocial data to forecast T2DM onset and simulate the estimated effects of preventive interventions. Methods: Data were drawn from 19,774 participants in the UK Biobank cohort, followed for up to 17 years. A penalized Cox proportional hazards model was employed to estimate individual time-to-event risk trajectories based on 90 candidate predictors. Predictors were selected through univariate screening, multicollinearity assessment, and variance filtering, yielding a final model with 14 significant variables. Causal inference techniques, including directed acyclic graphs (DAGs) and counterfactual simulations, were used to explore intervention effects on disease progression. Results: The model demonstrated strong predictive performance (C-index = 0.90, SD = 0.004). Psychosocial stressors such as loneliness, insomnia, and poor mental health emerged as strong independent predictors and were associated with estimated increases in absolute T2DM risk of approximately 35 percentage points individually and nearly 78 percentage points when combined, under the modeled assumptions. These effects were partly reinforced through diet, with high intake of processed meat, salt, and sugary cereals acting as risk amplifiers within the modeled causal pathways. Cheese intake was protective overall, but its estimated benefit was attenuated under psychosocial stress, where reduced consumption produced a small, directionally harmful mediation effect. Counterfactual simulations suggested that improvements in psychosocial conditions could reduce estimated T2DM risk by approximately 11.6 percentage points within the modeled cohort, with protective dietary patterns such as cheese consumption re-emerging as psychosocial stress was alleviated. The model also revealed pronounced ethnic disparities, with South Asian, African, and Caribbean participants exhibiting significantly higher estimated risk than White counterparts within this cohort. These findings highlight the potential of integrated, stress-informed prevention strategies that address both psychosocial and dietary pathways. Conclusion: This study introduces a transparent, simulation-enabled DT framework for estimating T2DM risk and exploring behavioral intervention scenarios without reliance on real-time data streams. It enables interpretable, personalized prevention planning and supports exploration of scalable deployment in public health, particularly in underserved or low-infrastructure environments. The integration of psychosocial and lifestyle data represents an important step toward more equitable and behaviorally informed digital health solutions.</p
Disposable printed CamBlobs charts for measuring contrast sensitivity in patients with glaucoma
No full textPurpose: Contrast sensitivity (CS) is lowered in glaucoma. We aimed to assess whether a simple inexpensive CamBlobs CS test is just as effective as an established Pelli–Robson CS chart in assessing CS in people with glaucoma.Methods: This study included two-groups of participants (27 no ocular disease and 23 glaucoma patients) who underwent CS testing using CamBlobs chart and a Pelli–Robson chart in each eye. Agreement and reliability between CamBlobs and Pelli–Robson charts were examined using Bland–Altman plots and receiver operating characteristic (ROC) curves to assess the diagnostic test accuracy. Visual field data were also obtained and compared with CS data from both charts.Results: A total of 54 eyes of control and 44 eyes of glaucoma patients were included. There were significant differences in CS between the control and glaucoma groups measured by the CamBlobs chart (p Conclusions: CamBlobs charts are comparable to Pelli–Robson CS charts for assessing CS in people with glaucoma. CamBlobs offer a simple, portable method for CS testing beyond conventional clinical settings.</p
Archives and Algorithms: The Plancondor.org Experience and the Human Rights Potential of New Technologies
No full textThis article explores the transformative role of new technologies in documenting human rights violations. While much has been written about the promise of new tools in amplifying truth seeking and memorialization, this article examines how technologies are reshaping three key stages of documentation: data collection, analysis and visualization. Grounded in the authors’ experience building the plancondor.org platform, the article offers a reflective account of the potential and the challenges that these technologies pose. It analyses how optical character recognition, natural language processing and metadata categorization can enhance access to archives, while raising concerns about algorithmic opacity, outsourcing and representational politics. Innovations such as blockchain-backed preservation initiatives for data integrity and safeguarding and interactive visual platforms are examined, arguing that technological decisions are never neutral but reflect power relations and ethical trade-offs. Ultimately, we call for a victim-centred, transparent and contextually grounded use of technology, offering a framework that can inform future projects and research.</p
Type 2 Diabetes Prediction Without Labs: A Systems-Level Neural Framework for Risk and Behavioral Network Reorganization
No full textBackground: Prediction models for Type 2 Diabetes Mellitus (T2DM) often rely on biochemical markers such as glycated hemoglobin, fasting glucose, or lipid profiles. While clinically informative, these indicators typically reflect established dysglycemia, limiting their value for early prevention. In contrast, psychosocial stress, sleep disturbance, tobacco use, and dietary quality represent
modifiable, non-clinical factors that can be observed long before metabolic abnormalities are clinically detectable. Yet most studies examine these factors in isolation or as additive lifestyle scores, overlooking how their interdependencies reorganize in the preclinical phase. A systems level approach is therefore needed to capture how disruptions in behavioral coherence signal
emerging vulnerability.
Methods: This study develops a dual-analytic framework that integrates Cox proportional hazards models with artificial neural network (ANN) coherence analysis. Using longitudinal data from the UK Biobank (n = 15,774; follow-up up to 17 years), we identified non-clinical predictors of incident T2DM and examined how behavioral networks reorganize across health states. Predictors were screened through multivariate survival analysis and mapped into ANN derived influence matrices to quantify stability, direction, and systemic coherence of relationships among diet, sleep, psychosocial states, and demographics.
Results: Eighteen significant predictors of T2DM onset were identified. Elevated risk was linked to loneliness, psychiatric consultation, emotional distress, insomnia, irregular sleep, tobacco use, and high intake of processed meat, beef, and refined grains. Protective effects were observed for 7–8 hours of sleep, oat and muesli consumption, and fermented dairy. ANN analyses revealed a pronounced breakdown of behavioral coherence in T2DM: foods that stabilized mood in healthy individuals became associated with distress, age and BMI lost their anchoring roles, and emotional states emerged as dominant but erratic drivers of diet. These reversals and destabilizations were consistent across model iterations, suggesting robust signatures of preclinical vulnerability.
Conclusion: T2DM risk is better conceptualized as systemic reorganization within behavioral networks rather than the additive effects of isolated factors. By combining survival models with ANN-derived coherence mapping, this study demonstrates that early prediction is possible from modifiable, everyday behaviors without laboratory measures. The framework highlights leverage
points for psychologically informed, personalized prevention strategies.</p
Assessing the social factors affecting solar energy transition in the mining sector
No full textThis study examines the social factors shaping solar energy transitioning in Zambia’s mining sector, a key contributor to the national resource economy. It focuses on how policies, incentive systems, communication channels, training and skills development, and stakeholder engagement and partnership influence the sector’s readiness to adopt solar energy and support more reliable operations. A quantitative design was used, with a 5-point Likert-scale questionnaire administered to 192 respondents from mining companies, regulatory bodies, energy suppliers and local authorities. Data were analysed using descriptive statistics, reliability tests and hierarchical regression. The results show that stakeholder engagement and partnership and incentive systems are the strongest predictors of transition intentions, underscoring their role in improving energy security and supporting sustainable production. Policies, communication and training had weaker effects, indicating the need for stronger institutional coordination and targeted capacity building. Overall, the findings illustrate how social conditions shape renewable energy adoption and contribute to more resilient mining activities.</p
Deep probabilistic surrogate modelling for uncertainty quantification in mangrove hydro-morphodynamics
No full textIntroduction: Mangrove ecosystems are increasingly recognised as essential nature-based solutions for enhancing coastal resilience against sea-level rise and climate-induced extreme events. However, achieving robust uncertainty quantification for hydro-morphodynamic models of mangrove systems remains a critical challenge due to the complexity of physical processes and the high computational cost of solving Navier–Stokes partial differential equations. Conventional uncertainty quantification approaches, including Gaussian Process surrogates and physics-informed neural networks, are limited by their inability to adequately capture non-Gaussian behaviour, high-dimensional interactions, or to scale efficiently to large-scale coastal systems. Methods: To address these limitations, we propose an efficient and scalable probabilistic framework based on Deep Gaussian Processes, which hierarchically stack multiple Gaussian Process layers to represent complex, multi-scale, and non-Gaussian dependencies in hydro-morphodynamic dynamics. The framework is applied to a high-resolution numerical model of mangrove systems and trained using a variational inference approach to enable efficient surrogate modelling and uncertainty propagation. Results: The proposed Deep Gaussian Process model reduces computational cost by more than three orders of magnitude (approximately 1.4 minutes compared to over five days for the full numerical solver), while achieving substantially improved predictive accuracy relative to standard Gaussian Process models. Specifically, a fivefold reduction in error is observed, with an RMSE of 0.0095 m compared to 0.0465 m for conventional Gaussian Processes. The framework enables reliable propagation of uncertainty across complex, nonlinear system dynamics. Discussion: These results demonstrate the potential of Deep Gaussian Processes to provide accurate and computationally efficient uncertainty quantification for hydro-morphodynamic modelling of mangrove ecosystems. The proposed approach supports evidence-based planning for climate adaptation and ecosystem-based coastal resilience, offering a practical pathway for integrating advanced uncertainty quantification into operational decision-making for sustainable coastal management.</p
Using virtual reality to enable emotional engagement around knife crime in young people
No full textThe carrying and use of knives by young people represents significant public and political concerns in the United Kingdom and educational interventions have the potential to promote behavioural change in relation to knife crime behaviours. Here, we first describe the creation of a novel virtual reality immersive film – RELOADED – designed to educate young people about the dangers of carrying knives. Co-created with secondary-school students, RELOADED explores how one victim’s story can enable stronger emotional connections and better understandings about knife crime in young people. Next, we conducted a preliminary qualitative assessment of engagement with RELOADED among students (n = 16) and teachers and professionals (n = 6) in a school environment, with a view to understanding whether RELOADED can be deployed to improve attitudes toward knife crime in young people. Thematic analysis of interview data identified five inter-connected themes relating to engagement with the environment, the role of the individual, the importance of support networks, the inevitability of knife crime, and the significance of a true story. Our findings underscore the potential of VR and immersive storytelling in fostering understanding among young people concerning complex social issues, such as knife crime.</p
Transformative times for key account management: Disruptive events, digitisation, and sustainability. Special issue editorial
No full textKey Account Management (KAM) is recognised as a powerful relationship marketing tool for establishing and maintaining long-term business relationships with important customers (Brehmer & Rehme, 2009; Homburg, Workman Jr, & Jensen, 2002; Ivens & Pardo, 2007). KAM involves carrying out additional activities for key accounts that are not necessarily performed for ‘average’ customers, such as providing customised products and services (Tzempelikos & Gounaris, 2015; Workman Jr, Homburg, & Jensen, 2003)...</p
Effect of landscape diversity on temporal stability of Normalized Difference Vegetation Index across spatial scales
No full textUnderstanding how landscape diversity (i.e., land-cover heterogeneity) influences ecosystem stability across spatial scales is critical for predicting ecosystem responses to environmental change and for designing effective landscape-level conservation strategies. This study aims to quantify the scale dependence (ranging from 0.0625 km2 to 2500 km2) of landscape diversity effects on multiple dimensions of ecosystem temporal stability, resistance, and resilience in response to climatic events using satellite-derived Normalized Difference Vegetation Index (NDVI) time series from 2000 to 2020 across four major vegetation types (meadows, shrubs, wetlands, and coniferous forests) on the Qinghai-Xizang Plateau. We also calculated temporal stability of growing season temperature and precipitation at matching spatial scales. We found that both landscape diversity and temporal stability of NDVI increased with spatial scales, whereas resistance and resilience showed no consistent scale dependence. The effects of landscape diversity on temporal stability of NDVI varied significantly across spatial scales in all four vegetation types. In alpine wetlands and shrubs, higher landscape diversity was associated with lower precipitation stability, which in turn was linked to reduced temporal stability of NDVI; however, this indirect relationship was reversed in meadows. Our findings demonstrate that precipitation stability modulates the effect of landscape diversity on NDVI temporal stability across spatial scales. This work not only extends diversity-stability theory by incorporating scale-dependent mechanisms and climatic mediators, but also provides novel guidance for landscape-scale conservation and ecosystem management under changing environmental conditions.</p
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