21827 research outputs found
Sort by
Understanding Lived Experience-Driven Co-production in Health and Social Services: The Sowing and Growing Model
Full text linkHighlights • Person-centred care is often illusive in traditionally hierarchical systems. • Co-production equalizes professional and client power by centering lived experience. • These values mobilize professional and client advocacy driving 5 phases of growth. • A co-production mindset germinates, sprouts, blossoms, and propagates to new sites. • A community garden fosters sustainability, helping individual sites weather threats
The role of climate and migration concerns in shaping personal economic insecurity in European societies
Full text linkThis study investigates how perceptions of societal threats, specifically climate change and migration, influence subjective economic insecurity across eleven European countries (Austria, Belgium, Czechia, Finland, France, Hungary, Iceland, Poland, Portugal, Slovenia, and the United Kingdom). The selection of countries was based on data availability within the European Social Survey (ESS) ERIC CRONOS-3 Wave 2 survey design. Nonetheless, the sample reflects significant institutional diversity across welfare regimes, labour market structures, and political discourse environments: Nordic social-democratic regimes; continental corporatist systems; post-socialist transitions; and liberal-leaning contexts. We formulate a composite measure of economic insecurity that encompasses concerns about maintaining living standards, managing financial shocks, and future economic prospects. Perceived climate and migration threats are operationalised as separate multi-item indices. Results reveal that both threat domains independently increase economic insecurity, controlling for income, age, education, and gender. Interaction models demonstrate systematic variation across socioeconomic strata, with lower-income and less-educated groups exhibiting heightened sensitivity to threat perceptions. Cross-national analysis shows substantial variation, suggesting that institutional contexts—particularly welfare state architectures and political discourse environments—moderate how macro-level concerns translate into personal economic assessments. These findings advance affective political economy by demonstrating spillover mechanisms linking societal threats to economic self-evaluations. Policy implications are twofold: targeted social protection should bolster resilience among vulnerable groups, while broader efforts should reframe climate and migration as collective challenges focused on adaptation rather than threats. This includes strengthening trust in institutions and ensuring universal, predictable guarantees that restore a sense of control and shared efficacy
Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation
Full text linkMedical image segmentation is increasingly reliant on deep learning techniques, yet the promising performance often come with high annotation costs. This paper introduces Weak-Mamba-UNet, an innovative weakly-supervised learning (WSL) framework that leverages the capabilities of Convolutional Neural Network (CNN), Vision Transformer (ViT), and the cutting-edge Visual Mamba (VMamba) architecture for medical image segmentation, especially when dealing with scribble-based annotations. The proposed WSL strategy incorporates three distinct architecture but same symmetrical encoder-decoder networks: a CNN-based U-Net for detailed local feature extraction, a Swin Transformer-based Swin-UNet for comprehensive global context understanding, and a VMamba-based Mamba-UNet for efficient long-range dependency modeling. The key concept of this framework is a collaborative and cross-supervisory mechanism that employs pseudo labels to facilitate iterative learning and refinement across the networks. The effectiveness of Weak-Mamba-UNet is validated on two publicly available datasets with processed scribble annotations, where it surpasses the performance of a similar WSL framework utilizing only U-Net or Swin-UNet, as well as other baseline methods. This paper highlights the potential of Mamba for medical image segmentation in scenarios with sparse or imprecise annotations. The source code, dataset, and all baseline methods are made publicly accessible https://github.com/ziyangwang007/Mamba-UNet
TaIlored ManagEment of Sleep (TIMES) for people with dementia and mild cognitive impairment in primary care in England: protocol for a feasibility cluster-randomised controlled trial
No full textBACKGROUND: People living with dementia (PLWD) and mild cognitive impairment (MCI), and their family carers, often experience sleep disturbance which can impair daily living and care. There are limited options for effective long-term pharmacological management of sleep disturbance, yet recent advances in non-pharmacological approaches offer promising alternatives. TIMES is a novel, complex intervention, which aims to improve wellbeing for PLWD/MCI and their carers in primary care, by developing whole-person, tailored care plans that optimise management of sleep disturbance in context. METHODS: Two-arm cluster-randomised (1:1), single-blinded, feasibility trial in 10 general practice sites in England, recruiting 64 patient-carer dyad participants (32 intervention + 32 treatment as usual). Co-primary objectives are to assess the feasibility and acceptability of conducting a subsequent definitive cluster-randomised controlled trial (cRCT) of the TIMES intervention. Secondary objectives include assessing the ability to collect data to address putative primary and secondary outcomes of a definitive cRCT. We will collect participant demographics at screening, and the following outcome measures at baseline, 9 and 15 week follow-ups: Sleep Disorders Inventory (SDI); Epworth Sleepiness Scale (ESS); Activities of Daily Living assessed with the Disability Assessment for Dementia (ADL DAD); Dementia Quality of Life Measure (DEMQOL); EQ-5D 5 level (EQ-5D-5L); ICEpop Capability measure for older people aged ≥ 65 (ICECAP-O); Neuropsychiatric Inventory Questionnaire (NPI-Q); Client Service Receipt Inventory (CSRI); Telephone Montreal Cognitive Assessment (T-MoCA); patient medical records review; patient serious adverse events (SAEs). We will conduct Process Evaluation interviews and Discrete Choice Experiments to inform refinement of the intervention content and delivery. DISCUSSION: Our findings will inform the refinement and delivery of a subsequent definitive cRCT that tests the clinical and cost-effectiveness of the TIMES intervention compared with usual care. TRIAL REGISTRATION: This study received approval from the Health Research Authority (HRA) and London-Harrow Research Ethics Committee (Reference: 24/LO/0123), and is sponsored by the University of Exeter (Reference: 2021-22-38). TRIAL REGISTRATION: ISRCTN, ISRCTN54051676, registered 20 March 2024, https://www.isrctn.com/ISRCTN54051676
Hierarchical dynamics-aware deep learning for electricity consumption prediction in discrete manufacturing enterprises
No full textPredicting the overall electricity consumption of manufacturing enterprises is an important step in achieving energy demand-side management and industrial energy system stability. Due to the diversity of consuming units and complexity of dynamic consuming distribution in discrete manufacturing enterprises (DME), their electricity consumption exhibits hierarchical dynamics. However, current temporal modeling and analysis methods cannot overcome the coupled variation caused by hierarchical dynamics, which makes accurate electricity consumption prediction (ECP) in DME still a bottleneck problem. In this paper, a hierarchical dynamics sensitive prediction network (HDSPN) is proposed to realize the ECP in DME. First, the basic structure and distribution of electricity consumption in DME are demonstrated to analyze the hierarchical dynamics caused by discrete production characteristics. Second, the HDSPN is designed to perform frequency recognition and two-dimensional reorganization on one-dimensional temporal data to extract the inter- and intra-period dynamic information of electricity consumption. Through the integration training using data from the overall enterprise and its internal multiple consuming units, HDSPN can sensitively capture the hierarchical dynamics about the electricity consumption of DME. Finally, verified on a typical DME, Dongfang Electric Machinery Co., Ltd., the root mean square error (RMSE) of HDSPN reaches the optimal 0.020194, 0.056044, 0.114503 respectively in very short-term, short-term and long-term prediction compared with the mainstream models, thereby meeting the multi-timescale requirements of demand-side management
Policing and care in mental health crisis response: boundary work and the politics of safety and authority
No full textDemands for alternatives to police responses to mental health crises have driven significant transformations in the frontlines of emergency care. This ethnographic study (2022-2025) analyzes how the boundaries between policing and behavioral health have been negotiated, contested, and reconstructed during the implementation of a large-scale crisis response initiative in California, USA. Taking an ethnographic approach, we demonstrate how boundary work among law enforcement, behavioral health professionals, and organizational leaders unfolds through intertwined dynamics of competition, collaboration, and reconfiguration. Our findings highlight how boundary work is embedded within broader socio-political contexts shaped by advocacy for racial justice, critiques of police violence, and demands for systemic reform. Specifically, we reveal how the frontline enactment of crisis response is characterized by ongoing negotiations around authority, legitimacy, safety, and care, reflecting and reshaping political and ethical debates on criminalization and police reform. This paper contributes to boundary work theory by illustrating how professional and institutional boundaries are dynamic sites of dialectical engagement that both respond to and actively shape contemporary struggles around race, violence, mental health, and justice
IMI-global trends in myopia management attitudes and strategies in clinical practice – A nine-year review
Full text linkPurpose Surveys in 2015, 2019, and 2022 identified a high level of eye care practitioner activity and concern about pediatric myopia, reflected by an uptake of appropriate control techniques. This research provided updated information, examining global trends from 2015 to 2024. Methods A self-administered, internet-based questionnaire was distributed in 18 languages to eye care practitioners globally. The questions examined awareness of increasing myopia prevalence, perceived efficacy, prescribing of available strategies and barriers to adoption. Responses were compared with data from previous surveys. Results A total of 2,993 practitioners responded in 2024. From 2015 to 2024, practitioner concern had increased in all continents besides Australasia (all p < 0.05), being consistently highest in Asia (8.4 ± 1.8 to 8.6 ± 1.9, respectively). Practitioner activity level had increased markedly in every continent (all p < 0.001), with the greatest change in North America (4.7 ± 3.0 to 7.1 ± 2.6, respectively). Perceived efficacy of soft contact lenses approved for myopia control more than doubled since 2015 (24.4 ± 25.0 % to 52.2 ± 24.0 %, p < 0.001). Combination therapy and orthokeratology were perceived to be the most efficacious interventions, yet single vision spectacles were the most prescribed option. However, the frequency of prescribing single vision spectacles had decreased since 2015 (by −11.1 %, p < 0.001). Globally, cost to the patient remained practitioners’ primary reason for not prescribing myopia interventions. Conclusions More practitioners are prescribing appropriate control methods to children with lower degrees of myopia than identified previously. However, consistent hindrances need addressing, namely increased affordability and accessibility of effective control options
Advancing Theoretical Integration of Distrust: A Multilevel Examination of Its Theoretical Foundations, Dynamics, and Mechanisms
Full text linkThis paper addresses persistent gaps in distrust scholarship by systematically reviewing studies published from 1998 to 2024. We refine distrust as a construct distinct from trust, mistrust, and suspicion, shaped by unique cognitive, emotional, and behavioral mechanisms. Substantial evidence supports that distrust is not merely the absence of trust but an independent phenomenon. Our review synthesizes research on how distrust emerges, escalates, and spills over across market settings. We develop a comprehensive model illustrating key themes and propositions at individual, dyadic, organizational, and systemic levels. This analysis reveals the complex antecedents of distrust, its varied influences on decision‐making and market interactions, and the measurement challenges arising from conflating distrust with low trust. By offering a unified framework, this review promotes the theoretical integration of distrust and offers practical guidance for mitigating its impact
AI legitimacy in energy: A model to improve corporate narratives on sustainability and responsibility
Full text linkCONTEXT: The integration of artificial intelligence (AI) in the energy sector is pivotal for achieving Sustainable Development Goal 7 (SDG7). Within the European Union, the regulatory landscape, particularly the proposed AI Act, influences how organisations navigate responsible AI (RAI) adoption while addressing societal expectations, creating a critical need to examine how they communicate their commitment to RAI and sustainability. OBJECTIVE: This study uncovers how narratives employed in the public communications of EU energy stakeholders legitimise corporate efforts and signal alignment with RAI principles. METHOD: A grey literature search of website pages, whitepapers, and reports was conducted. Thematic analysis, using inductive and deductive coding, was employed to identify emerging themes and evaluate how organisations frame their initiatives in response to regulatory and societal pressures. RESULT: Analysis of 28 reports reveals that EU energy stakeholders predominantly frame AI as an inevitable technological advancement while lacking concrete strategies for RAI implementation. Communications focus on aspirational commitments rather than measurable actions. To address these gaps, this study develops the Responsible AI (RAI) Communication Model. This framework guides stakeholders in structuring their communication around three core pillars: (1) aligning AI initiatives with measurable sustainability goals and governance, (2) developing trustworthy and accountable narratives backed by concrete evidence, and (3) establishing organisational legitimacy through active stakeholder engagement. CONCLUSION: By adopting this model, energy stakeholders can move beyond rhetorical narratives towards sharing demonstrable practices. This fosters greater trust, ensures effective communication of priorities like transparency and accountability, and promotes regulatory alignment
High-gain U-band discrete Raman amplifier for multi-band optical transmission systems
No full textWe experimentally demonstrate a set of U-band discrete Raman amplifiers using backward incoherent pumping in 1 km HNLF, achieving up to 22.3 dB net gain, and 4.2-5.8 dB NF. Three different types of Raman gain fiber have been investigated, including 1 km HNLF, 0.51 km HNLDSF, 8 km and 7.6 km IDF. Using HNLF achieved the highest gain and the lowest NF, while using 8 km IDF yielded up to 16.2 dB net gain and a minimum of 6.3 dB NF due to its low Raman gain coefficient and higher fiber loss. Using 0.51 km HNLDSF gave up to 21 dB net gain, but at a cost of over 8.1 dB noise figure. These amplifiers were incorporated in a C + L + U-band coherent transmission system using 516 × 24.5GBd DP-64/256QAM channels over 50 km SSMF. We achieved a maximum decoded data rate of 123.5Tb/s across C + L + U bands, with 25.6Tb/s specifically in the U-band (1625-1650 nm)
Data sources:
Access Repository Dashboard
Do you manage Aston Publications Explorer? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!