48651 research outputs found
Sort by
Derivation and validation of a short form Nottingham extended activities of daily living (SF-NEADL) scale
Full text linkThe Nottingham Extended Activities of Daily Living (NEADL) assessment is commonly used in research and clinical contexts. However, there are concerns surrounding psychometric properties, and with 22-items, NEADL may be too long for clinical use at scale. We aimed to derive a psychometrically robust short form NEADL. Data were from the Virtual International Stroke Trials Archive, including individual participant data from 3,6,12 months. Six-month data were used to evaluate NEADL reliability and validity. Corrected item-total correlations identified items for inclusion in the short form (SF-NEADL). The resulting SF-NEADL was then assessed at all time-points for reliability, structural and construct validity, including confirmatory factor analysis (CFA). NEADL had high internal consistency, and five items with corrected item-total correlations over 0.7 were selected to create a SF-NEADL. The NEADL and SF-NEADL at 6 months had excellent reliability, and construct validity. SF-NEADL reliability and validity were stable at 3 and 12 months. CFA did not suggest unidimensionality of NEADL or SF-NEADL, but SF-NEADL achieved good fit with a two-item structure. Reliability and validity of our SF-NEADL suggest it is a robust alternative to standard eADL assessments. Its use of fewer and more relevant items makes it suitable for use in busy healthcare settings. Implications for rehabilitation Assessment of ability in extended activities of daily living (eADL) is a fundamental part of research and clinical practice. We derived a short form of the Nottingham eADL scale, containing 5 questions about mobility and kitchen tasks, that captures functional independence in daily life as robustly as the original scale. With 5 items rather than the original 22, the SF-NEADL is easier to administer and less likely to induce participant fatigue and incomplete response, making it suitable for inclusion in a battery of tests as part of a research or clinical protocol
Assessing the effect of climate policy uncertainty on corporate carbon cost leadership strategy: Evidence from China
Full text linkUsing stereodynamical portraits to visualize polarized rotational angular momentum distributions in H2–surface collisions
Full text linkThe magnetic molecular interferometer (MMI) is a molecular beam scattering apparatus, which allows the polarization of the rotational angular momentum (J) of ortho-H2 molecules to be controlled using tunable magnetic fields before they collide with a surface, and their J′ polarization to be determined after the collision. In the current work, quantum population distribution functions, or “stereodynamical portraits,” are used to visualize the rotational angular momentum polarization of ortho-H2 molecules that the MMI creates before the collision with the surface, revealing that the sensitivity of the MMI to stereodynamic effects which depend on the orientation of J with respect to the surface normal can be increased by manipulating the H2 molecules with two perpendicular magnetic fields rather than just a single field. They can also be used to depict the polarization dependence of a H2-surface collision, as shown by the example considered here, where it is found that when H2 molecules undergo diffractive scattering from a Cu(511) surface, different J polarizations are selected to scatter into different diffraction channels, just as different polarizations of J′ are created after scattering. Signals measured with the MMI are necessarily dependent on both the rotational polarization the MMI creates and the dependence of the molecule-surface collision on this, and it is demonstrated that for flux detection measurements it would be possible to analyze the data directly in terms of the polarization moments which characterize these two properties to gain a more immediate insight into the stereodynamics of the collision than is possible using alternative analysis methods
Leveraging convolutional and graph networks for an unsupervised remote sensing labelling tool
Full text linkMachine learning for remote sensing imaging relies on up-to-date and accurate labels for model training and testing. Labelling remote sensing imagery is time and cost intensive, requiring expert analysis. Previous labelling tools rely on pre-labelled data for training in order to label new unseen data. In this work, we define an unsupervised pipeline for finding and labelling geographical areas of similar context and content within Sentinel-2 satellite imagery. Our approach removes limitations of previous methods by utilising segmentation with convolutional and graph neural networks to encode a more robust feature space for image comparison. Unlike previous approaches we segment the image into homogeneous regions of pixels that are grouped based on colour and spatial similarity. Graph neural networks are used to aggregate information about the surrounding segments enabling the feature representation to encode the local neighbourhood whilst preserving its own local information. This reduces outliers in the labelling tool, allows users to label at a granular level, and allows a rotationally invariant semantic relationship at the image level to be formed within the encoding space. Our pipeline achieves high contextual consistency, with similarity scores of SSIM = 0.96 and SAM = 0.21 under context-aware evaluation, demonstrating robust organisation of the feature space for interactive labelling
Attention‐Guided Lightweight CNN‐Transformer Fusion for Real‐Time Traffic Sign Recognition in Adverse Environments: HACTNet
No full textAutonomous driving is also impossible without traffic sign recognition (TSR; also known as traffic sign-on-road), which limits its reliability to domain changes, unfavourable weather, obstruction and hardware capacity. This paper proposes HACTNet, a low-complexity CNN-Transformer hybrid model that pushes the state-of-art in TSR by making a noteworthy set of contributions including (i) efficient convaps to model parts of the image, (ii) transformer encoder to capture the global context and (iii) an attention-based fusion block to dynamically combine the two complementary sets of features. This synergy facilitates strong recognition in presence of blur and occlusion and in varying illumination. In addition to accuracy, HACTNet achieves high robustness (52.8%) against strong PGD adversarial attacks (8/255), but is still efficient (7.9 M parameters and 22.1 FPS) on the NVIDIA Jetson Nano. Moreover, the comparative analysis between the hybrid models (EATFormer, local-ViT) and HACTNet proves that HACTNet has a better accuracy-efficiency ratio. The extraordinary capability to counteract adverse weather conditions, fog, night, rain, snow etc., which is proven by the extensive testing of the real-world ACDC adverse conditions data set, supports the viability of the proposed solutions in the real world. It is plug and play modularity with on-going learning via elastic weight consolidation (3.3% less forgetting) and unsupervised domain adaptation via MMD loss (5.3% better on TT100K with no labels). Moreover, INT8 quantization with quantization-aware training (QAT) incurs little accuracy loss (less than 0.5 percent) and much lower energy (0.27 J/sample) usage, which forms an edge deployment preparedness. Additionally, when adjusting to new traffic signs over time, the model shows compatibility with continuous learning, achieving a low forgetting rate (3.3%), highlighting its practical viability for long-term autonomous deployment. Overall, HACTNet produces a versatile and expandable solution for next-generation intelligent transportation systems by striking a balance between accuracy, robustness and efficiency
Evaluating the Entrepreneurial Ecosystem in Saudi Arabia: Opportunities, Challenges, and the Role of Digital Technology in Shaping Transformational Entrepreneurship within Knowledge-Intensive Businesses
No full textEntrepreneurship is at the heart of Saudi Vision 2030, with knowledge-intensive businesses (KIBs) expected to play a leading role in driving innovation, economic diversification, and broader social transformation. Despite major policy reforms and investment, uncertainty remains over how the entrepreneurial ecosystem (EE) in the Kingdom of Saudia Arabia (KSA) supports or restricts the development of transformational entrepreneurship (TE): the creation of businesses with a mission to be innovative, ethical, and socially impactful. This thesis addresses that gap by examining the opportunities and challenges facing KIBs in the KSA, the influence of EE actors and factors, and the role of digital technology in shaping the adoption of TE.The thesis is underpinned by institutional theory, the triple helix model, and diffusion of innovations theory. A qualitative approach is adopted, drawing on data from 32 semi-structured interviews with entrepreneurs and EE representatives. Thematic analysis reveals four major themes: the distinctive nature of KIB entrepreneurship, the enabling and constraining features of the EE, the impact of digitalisation, and the extent of TE adoption.The findings indicate that Saudi Vision 2030 reforms and the increasing prevalence of support mechanism create unprecedented opportunities for KIBs. However, challenges such as regulatory inconsistency, bureaucracy, cultural resistance to risk-taking, and weak coordination between EE actors constrain the realisation of TE’s full potential.Digital technology acts as a critical enabler, supporting scalability and visibility for KIBs. However, its full potential is constrained by persistent challenges, including shortages of skilled labour, high operational costs, and limited access to finance.Evidence of TE is visible in socially driven, future-oriented businesses, although its adoption remains fragile in the face of institutional and cultural frictions. This thesis makes a valuable contribution by extending the scope of research on EEs, TE, and digital entrepreneurship to an under-researched emerging economy. It shows that entrepreneurship in the KSA is not only shaped by but also reshapes the EE through visible role models and shifting cultural and institutional practices. The findings offer timely insights for policymakers, universities, investors, and entrepreneurs as they seek to realise the ambitions of Saudi Vision 2030
Inverted J–V Hysteresis in Perovskite Solar Cells: Insights from Photovoltaic Quantum Efficiency
Full text linkThe most typical hysteresis in the current density–voltage (J–V) curve of perovskite solar cells (PSCs) shows better performance in the backward (BW) than in the forward (FW) voltage scan (normal hysteresis). The opposite, where the FW scan yields higher photocurrent, is known as inverted hysteresis and is also frequently observed. Here, we examine PSCs exhibiting both normal and inverted hysteresis, depending on scan rate and preconditioning. Spectral changes in the external quantum efficiency (EQE) linked to ionic redistribution reveal that inverted hysteresis arises from blue-range photocurrent losses caused by enhanced recombination at the interfaces due to ionic accumulation. This trend is consistent across PSC architectures, as demonstrated for triple mesoscopic carbon-based (C-PSCs) and planar p-i-n devices. Combined with drift-diffusion simulations, the results show that ionic losses can be bidirectional, and the hysteresis direction depends on how the ionic distribution impacts charge collection efficiency
High temperature interlaminar tensile strength of a SiCf/SiC ceramic matrix composite determined through diametrical compression testing up to 1200°C
No full textThe diametrical compression test method was used in this study to determine the high temperature interlaminar tensile strength of a SiCf/SiC Ceramic Matrix Composite. Two disk geometries are employed (Φ4.5 mm and Φ9 mm) with tests performed up to 1200°C, building upon previous room temperature investigations conducted by the authors [1]. For all tests, disks failed parallel to the loading axis spanning between the upper and lower contact points, ensuring repeatability and reliability even at high temperatures. Digital image correlation was applied to selected tests to measure the full-field strain and observe damage progression to ultimate failure. Weibull distribution was implemented to determine the characteristic strength and distribution, to understand the influence of specimen volume and high temperature oxidation. High temperature results were revealed to have a higher characteristic strength and Weibull modulus owing to the associated oxidation mechanisms, whether the formation of silica rich regions or degradation of the interphase
Association of Covid-19 vaccination uptake with recorded self-harm, neurodevelopmental disorders and mental health conditions during the Covid-19 pandemic: A nationwide e-cohort study in Wales, UK
No full textBackground: Understanding COVID-19 vaccine uptake among individuals who self-harm or with mental health conditions is critical to addressing health inequalities and guiding public health strategies/pandemic preparedness. Evidence on temporal trends and sociodemographic factors shaping vaccine uptake within these populations remains limited.Methods: We linked Wales Immunisation System data to demographic and healthcare records for 2.2 million individuals. Using modified Poisson regressions and growth models, we explored the association between self-harm, neurodevelopmental disorders, mental health conditions, and vaccine uptake from 8 December 2020 to 8 December 2023. Models were adjusted for age, sex, deprivation, ethnicity, and physical comorbidities.Findings: Attention Deficit Hyperactivity Disorder (ADHD), conduct disorder, drug use, and, to a lesser extent, self-harm were associated with lower incidence of vaccination. Conversely, those with autism spectrum disorder, or learning difficulty had slightly higher incidence of vaccination. Individuals with severe mental illness (SMI: schizophrenia, bipolar disorder and other psychotic disorders) exhibited a steeper initial increase and earlier peak in uptake, but their final coverage was lower. Belonging to an ethnic minority group and, to a lesser extent, being male, younger, or leaving in highly deprived areas were also associated with reduced uptake.Interpretation: Disparities in vaccine uptake exist among individuals with self-harm and mental health conditions, driven by intersecting health and social factors. Tailored interventions, effective communication, and trust-building strategies are critical to reducing these inequities. Underserved groups including those with SMI, ADHD, and self-harm, should be prioritised in future vaccination campaigns to improve equity
Data sources:
Access Repository Dashboard
Do you manage Swansea University Cronfa? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!