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Children's perceptions of physical literacy: exploring meaning, value, and capabilities for lifelong physical activity
No full textIntroduction
The concept of physical literacy has been defined differently across the world. To create a consensus statement and definition of physical literacy for England, it was felt important to incorporate the views and opinions of children and young people who are often the focus of interventions to increase physical activity and physical literacy. The aim of this qualitative study was to understand what physical literacy means to children by exploring their perceptions of meaningful physical activity and what they think will be needed to continue to be active for life.
Methods
Through a series of directed tasks and thematic analysis, several important considerations are discussed. These included the pertinence of social relationships-whether it was to share experiences, support and encourage friends, or learn from your family.
Results and Discussion
Children discussed how physical activity positively affects their emotions and the importance of enjoyment in continuing to engage in movement for the rest of their lives. In addition, there was an awareness of the benefits for mental and physical health, which indicated the prominence of knowing these benefits in engaging. The findings offer some important contributions from children to better understand what physical literacy means in England
A Human Intention and Motion Prediction Framework for Applications in Human-Centric Digital Twins
No full textIn manufacturing settings where humans and machines collaborate, understanding and predicting human intention is crucial for enabling the seamless execution of tasks. This knowledge is the basis for creating an intelligent, symbiotic, and collaborative environment. However, current foundation models often fall short in directly anticipating complex tasks and producing contextually appropriate motion. This paper proposes a modular framework that investigates strategies for structuring task knowledge and engineering context-rich prompts to guide Vision–Language Models in understanding and predicting human intention in semi-structured environments. Our evaluation, conducted across three use cases of varying complexity, reveals a critical tradeoff between prediction accuracy and latency. We demonstrate that a Rolling Context Window strategy, which uses a history of frames and the previously predicted state, achieves a strong balance of performance and efficiency. This approach significantly outperforms single-image inputs and computationally expensive in-context learning methods. Furthermore, incorporating egocentric video views yields a substantial 10.7% performance increase in complex tasks. For short-term motion forecasting, we show that the accuracy of joint position estimates is enhanced by using historical pose, gaze data, and in-context examples
Data-driven optimisation of residential air-to-water heat pump performance using IoT and machine learning
No full textResidential heating accounts for about 27 % of the UK’s energy consumption. While residential heat pumps (RHPs) are central to the transition toward sustainable energy, optimising their real-world performance requires robust experimental monitoring and predictive modelling. This study presents a data-driven approach for evaluating and optimising the performance of residential air-to-water heat pumps (A2WHPs) using real-time data and machine learning (ML). A full-scale experimental setup was deployed in a UK-based end-terrace building, incorporating IoT-enabled sensors to capture 275 days of operational data that was processed into a 6,600-hour dataset. Key thermal, electrical, and environmental parameters were measured at high temporal resolution and used to develop predictive models for the system’s coefficient of performance (COP). Several ML models, including Random Forest, Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM), were evaluated using rigorous preprocessing, principal component analysis, and GridSearchCV hyperparameter tuning. LSTM, XGBoost, and ANN achieved the highest prediction accuracy with low error values across MAE, MSE, RMSE, CVRMSE, and NMBE. Diagnostic plots and residual analysis further confirmed the generalisability of the models and their sensitivity to non-linear operational behaviours. The findings demonstrate that integrating ML with real-world data can provide a robust predictive framework for operational diagnostics, performance evaluation, and efficiency improvement in residential heat pumps. This approach supports scalable, data-driven energy management and contributes to decarbonising the built environment
Creative endings: visual cultures, generative AI & machinic intuitions
No full textThe widespread use of Generative Artificial Intelligence (GenAI), a form of AI that produces images, videos and other digital artefacts, poses a potentially decisive challenge to the ideal that artistic creativity is the sole preserve of humans. This challenge is personified in the post-human, apparently intuitive level of creative thinking that we encounter in Ava (Alicia Vikander), the central character in Ex Machina (Dir. Alex Garland, 2014). An AI seemingly on the verge of attaining Artificial General Intelligence (AGI), Ava substantiates her creative impulses through a series of drawings. In keeping with Vilém Flusser’s theory of ‘technical’ images, her drawings can be understood to be the output of an artificially intelligent apparatus that, through computational means, simulates human innovation. Ava’s drawings, however, are not machinic anomalies nor odd contrivances that demonstrate a computational prowess. They are, on the contrary, exemplary paradigms of how we produce images today. Ava’s creativity does not, this article will propose, present either a threat or a challenge to the ideal of human ingenuity. Rather, she demonstrates the increasingly mechanistic conditioning of human creativity and, in turn, illuminates the uncanniness of image production in our algorithmic age
Benchmarking supplier performance: How scorecard comparisons and supply risk influence termination decisions
No full textThis study examines how supplier termination decisions are influenced by scorecard performance, peer supplier performance, and component supply risk in multisourcing supply chains. While supplier selection has been extensively studied, supplier termination remains underexplored, particularly in dynamic industries like electronics manufacturing. Drawing from agency theory, we examine how relative performance evaluation (RPE) mechanisms and supply risk shape termination decisions. Using proprietary data from a major electronics firm covering 78 suppliers across 15 components over 42 months, we provide novel evidence that firms employ comparative evaluation rather than absolute performance thresholds. Supplier scorecard improvements significantly increase survival probability, while strong peer performance decreases survival probability for other suppliers, demonstrating RPE effectiveness. However, component supply risk systematically moderates these relationships through distinct mechanisms: supplier scarcity weakens both own performance and peer performance effects on termination decisions. In contrast, component specialization selectively reduces peer performance effects but does not moderate own performance effects. These patterns reflect situations where supply constraints limit performance-based termination effectiveness. The study contributes by extending agency theory's RPE to inter-firm settings and demonstrating how different supply risk dimensions selectively moderate different performance evaluation types. These insights guide supply chain managers in understanding when performance-based termination criteria are most effective
Parents’ perspectives of discharge information and support for their newborn baby during COVID-19: a cross-sectional survey
No full textBackground
There was no scientific knowledge available about parenting in a pandemic at the start of this study. The study was necessary to ascertain parents’ experiences, sources of information and support.
Methods
A cross-sectional online survey, recruiting via social media during July - August 2020, in collaboration with two local Maternity and Neonatal Voices Partnership groups in three rural English counties. Participants were parents of newborn babies who had been discharged from a maternity unit or had a home birth. Responses were analysed using descriptive statistical, thematic and content analysis.
Results
Participants (N=371) were predominantly mothers (n=369, 99.4%), aged between 25-34 (n=252, 67.8%), fit and healthy (n=314, 85%), white British (n=351, 94,5%) on maternity leave (n=252, 67.9%) and for half of the participants this was their first baby (n=186, 50.1%). Three sub-themes included: lack of information (antenatally and postnatally), lack of professional support and social support (which linked to the impact of ‘no partner’ restrictions). Lack of support for breastfeeding or feeding problems impacted mothers’ experiences. Parents relied on information from online sources and social media due to the lack of specific professional advice about the impact of the Coronavirus pandemic for their baby. A challenge for mothers was the lack of support for breastfeeding or feeding problems
Conclusion
Parents navigated their postnatal journey without the anticipated support from professionals or their normal social support networks, relying on information from online sources and social media due to a lack of pandemic specific information from professionals. Reduced postnatal services negatively affected the information and support received by new parents
Domain-Adaptive Graph Attention Semi-Supervised Network for Temperature-Resilient SHM of Composite Plates
No full textThis study introduces GAT-CAMDA, a novel framework for the structural health monitoring (SHM) of composite materials under temperature-induced variability, leveraging the powerful feature extraction capabilities of Graph Attention Networks (GATs) and advanced domain adaptation (DA) techniques. By combining Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL) losses with a domain-discriminative adversarial model, the framework achieves scalable alignment of feature distributions across temperature domains, ensuring robust damage detection. A simple yet at the same time efficient data augmentation process extrapolates damage behaviour across unmeasured temperature conditions, addressing the scarcity of damaged-state observations. Hyperparameter optimisation via Optuna not only identifies the optimal settings to enhance model performance, achieving a classification accuracy of 95.83% on a benchmark dataset, but also illustrates hyperparameter significance for explainability. Additionally, the GAT architecture’s attention demonstrates the importance of various sensors, enhancing transparency and reliability in damage detection. The dual use of Optuna serves to refine model accuracy and elucidate parameter impacts, while GAT-CAMDA represents a significant advancement in SHM, enabling precise, interpretable, and scalable diagnostics across complex operational environments
Aspects of body image as moderators and mediators in the relationship between minority stress and depression among diverse LGBTQIA+ identities
No full textLGBTQIA+ (Lesbian, Gay, Bisexual, Transgender, Queer/Questioning, Intersex, Asexual, plus) individuals are at higher risk of adverse mental health outcomes than cisgender heterosexual (cishet) individuals due to experiences of minority stress. In the current study, we compared levels of appearance anxiety, depressive symptoms, body appreciation and self-esteem among LGBTQIA+ and cishet individuals. Further, among LGBTQIA+ individuals, we tested a hybrid theoretical model to examine the protective effects of body appreciation and self-esteem in the relationships between minority stress, appearance anxiety, and depression. A total of 581 participants (aged 16–65) completed demographic and psychometric measures, including the Minority Stress Measure, Body Appreciation Scale-2, Rosenberg Self-Esteem Scale, Physical Appearance State and Trait Anxiety Scale, and Beck Depression Inventory, via an online survey. Results confirmed that LGBTQIA+ individuals had poorer mental health outcomes than cishet participants, characterised by higher levels of depressive symptoms and appearance anxiety, and lower body appreciation and self-esteem. Further, our hybrid model showed that LGBTQIA+ individuals with lower body appreciation and self-esteem were particularly vulnerable to appearance anxiety and depression related to minority stress, whilst body appreciation reduced the impact of minority stress on depression. These findings may inform potential directions for interventions targeted towards LGBTQIA+ populations
Sustainable Insulation Technologies for Low-Carbon Buildings: From Past to Present
No full textBuilding facade insulation technologies have evolved from primitive thermal barriers to high-performance, multifunctional systems that enhance energy efficiency and indoor comfort. Historical insulation methods, such as thick masonry walls and timber-based construction, have gradually been replaced by advanced materials and innovative facade designs. Studies indicate that a significant proportion of a building’s heat loss occurs through its external walls and windows, highlighting the need for effective insulation strategies. The development of double-skin facades (D-SFSs), adaptive facades (AFs), and green facades has enabled substantial reductions in heating and cooling energy demands. Materials such as vacuum insulation panels (VIPs), aerogels, and phase change materials (PCMs) have demonstrated superior thermal resistance, contributing to improved thermal regulation and reduced carbon emissions. Green facades offer additional benefits by lowering surface temperatures and mitigating urban heat island effects, while D-SF configurations can reduce cooling loads by over 20% in warm climates. Despite these advancements, challenges remain regarding the initial investment costs, durability, and material sustainability. The future of facade insulation technologies is expected to focus on bio-based and recyclable insulation materials, enhanced thermal performance, and climate-responsive facade designs. This study provides a comprehensive review of historical and modern facade insulation technologies, examining their impact on energy efficiency, sustainability, and future trends in architectural design