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Supporting people with musculoskeletal conditions from underserved communities in the United Kingdom to engage with physical activity: A realist synthesis and Q-methodology study
Get PDFIn the United Kingdom, 20 million individuals suffer from a musculoskeletal condition, for which physical activity (PA) is a core treatment. Minority ethnic communities experience a disproportionate impact, experiencing higher levels of pain and engaging in less PA. Research has identified various factors that affect their participation in PA, including lack of access to support, limited knowledge of resources, language barriers, fear of racial harassment while exercising, and insufficient communication/information from healthcare professionals. This project adopted a realist perspective, aiming to understand "what works, in which circumstances, and for whom?" The project had 4 steps: (1) defining review scope with stakeholders; (2) searching and extracting literature, creation of initial program theories; (3) refining/validating initial program theories with stakeholders; and (4) adopting Q-methodology to highlight priority areas. 17 papers were included. Three program theories were identified: (1) Lack of initial access to health service support; (2) Nature of musculoskeletal self-management support-the importance of the therapeutic relationship and value of shared conversations; and (3) Accessible long-term support for PA engagement. The Q-sort exercise highlighted priority areas: (1) complex booking procedures and inadequate translation services, (2) time constraints impact effective patient-centered care, (3) dismissive attitudes/mismatched expectations impact shared decision making, (4) rebuilding trust to strengthen therapeutic relationships, (5) cultural relevance in developing therapeutic relationships, and (6) clinician recommended PA opportunities increase knowledge of PA. Our findings shed light on inequities across the UK's musculoskeletal pathways, specifically in relation to PA engagement. This points toward priority areas for future research and interventions
Findings: Survey of Airlander in South Yorkshire
Get PDFThe study explored public perceptions of Airlander in the Doncaster area, focusing on:1.Its economic and environmental impacts.2.Public awareness and alignment with the UKRI Future Flight initiative.Broad Awareness: 75% of respondents had heard of Airlander, primarily through digital media. The aircraft is recognised for its sustainability and novel design, although some link it to older airship technology.Economic and Local Impacts: Over 90% believe the production facility will generate “good jobs”. Most respondents expressed optimism about Doncaster's role in sustainable aviation. Concerns about road traffic and wildlife impacts were noted but did not dominate opinions.Sustainability: Airlander’s low emissions and innovative use of materials resonated with respondents. Sustainability attributes like renewable energy use and recyclable materials were highly valued. Local manufacturing was seen as crucial for economic revival.Future Transport Potential: Respondents favoured Airlander for freight, remote surveying, and domestic leisure travel. Passengers' top concerns, especially among younger groups in the sample (mainly aged 40-80), were environmental impact and cost. Attributes like journey time and comfort were rated moderately important, while onboard entertainment was less critical.Emotional Responses: 85% expressed positive feelings about the project, including optimism, excitement, and relaxation. Prior knowledge of Airlander correlated with stronger positive responses and reduced scepticism.Challenges and Considerations: Public expectations about fares (£69 average estimate for a Doncaster-London trip) were benchmarked by some against rail. Though minor, concerns about wildlife and infrastructure impacts suggest areas for stakeholder communication. Generational differences in cost sensitivity and environmental priorities highlight the need for targeted messaging.In conclusion, the survey reflects strong public support for Airlander as a sustainable and economically beneficial innovation. Clear communication on environmental and economic benefits will be critical for broader acceptance. Future research should address the identified sample bias and investigate nuanced societal trends
Minimum distance and minimum time optimal path planning with bioinspired machine learning algorithms for faulty unmanned air vehicles
No full textUnmanned air vehicles operate in highly dynamic and unknown environments where they can encounter unexpected and unseen failures. In the presence of emergencies, autonomous unmanned air vehicles should be able to land at a minimum distance or minimum time. Impaired unmanned air vehicles define actuator failures and this impairment changes their unstable and uncertain dynamics; henceforth, path planning algorithms must be adaptive and model-free. In addition, path planning optimization problems must consider the unavoidable actuator saturations, kinematic and dynamic constraints for successful real-time applications. Therefore, this paper develops 3D path planning algorithms for quadrotors with parametric uncertainties and various constraints. In this respect, this paper constructs a multi-dimensional particle swarm optimization and a multi-dimensional genetic algorithm to plan paths for translational, rotational, and Euler angles and generates the corresponding control signals. The algorithms are assessed and compared both in the simulation and experimental environments. Results show that the multi-dimensional genetic algorithm produces shorter minimum distance and minimum time paths under the constraints. The real-time experiments prove that the quadrotor exactly follows the produced path utilizing the available maximum rotor speeds
High resolution data visualization and machine learning prediction of free chlorine residual in a green building water system
No full textPeople spend most of their time indoors and are exposed to numerous contaminants in the built environment. Water management plans implemented in buildings are designed to manage the risks of preventable diseases caused by drinking water contaminants such as opportunistic pathogens (e.g., Legionella spp.), metals, and disinfection by-products (DBPs). However, specialized training required to implement water management plans and heterogeneity in building characteristics limit their widespread adoption. Implementation of machine learning and artificial intelligence (ML/AI) models in building water settings presents an opportunity for faster, more widespread use of data-driven water quality management approaches. We demonstrate the utility of Random Forest and Long Short-Term Memory (LSTM) ML models for predicting a key public health parameter, free chlorine residual, as a function of data collected from building water quality sensors (ORP, pH, conductivity, and temperature) as well as WiFi signals as a proxy for building occupancy and water usage in a “green” Leadership in Energy and Environmental Design (LEED) commercial and institutional building. The models successfully predicted free chlorine residual declines below 0.2 ppm, a common minimum reference level for public health protection in drinking water distribution systems. The predictions were valid up to 5 min in advance, and in some cases reasonably accurate up to 24 h in advance, presenting opportunities for proactive water quality management as part of a sense-analyze-decide framework. An online data dashboard for visualizing water quality in the building is presented, with the potential to link these approaches for real-time water quality management
Hallucinations of an Interior
No full textThe work of Deleuze and Guattari is often presented in terms of spatial ontology as it explores relationality between fragments that constitute the real. As such, it is readily accepted as a theoretical framework for architects and geographers who use this approach to develop their consecutive fields. Similarly to these disciplines, the work of the duo is permeated with imagery that help in representing concepts they are exploring in a soft and non-abrasive way. While recognising the capacity of imagery in representing spatial concepts they curiously avoid architectural drawings but venture into a different kind of illustrations always describing them or presenting their own way of producing adequate diagrams. In one instance, the imagery that they described in a crucial moment of focussing on a key concept for the pair showcases what could be understood as a metaphysical reading of the world – one that likely stands at the border between the virtual and the actual. These are the renderings of Robert Gie. A reading of the comparison between his renderings and the architectural drawing convention which is lacking in the texts of Deleuze and Guattari, can help reconcile the concepts in the texts which discuss desire and problematic ideas of interiority
Advances in Teaching and Learning for Cyber Security Education
No full textThis book showcases latest trends and innovations for how we teach and approach cyber security education. Cyber security underpins the technological advances of the 21st century and is a fundamental requirement in today’s society. Therefore, how we teach and educate on topics of cyber security and how we overcome challenges in this space require a collective effort between academia, industry and government.The variety of works in this book include AI and LLMs for cyber security, digital forensics and how teaching cases can be generated at scale, events and initiatives to inspire the younger generations to pursue cyber pathways, assessment methods that provoke and develop adversarial cyber security mindsets and innovative approaches for teaching cyber management concepts. As a rapidly growing area of education, there are many fascinating examples of innovative teaching and assessment taking place; however, as a community we can do more to share best practice and enhance collaboration across the education sector. CSE Connect is a community group that aims to promote sharing and collaboration in cyber security education so that we can upskill and innovate the community together.The chapters of this book were presented at the 4th Annual Advances in Teaching and Learning for Cyber Security Education conference, hosted by CSE Connect at the University of the West of England, Bristol, the UK, on July 2, 2024. The book is of interest to educators, students and practitioners in cyber security, both for those looking to upskill in cyber security education, as well as those aspiring to work within the cyber security sector
Exploring the contemporary moon under water through illustration: Nostalgia and the power of the image
Get PDFThis article uses images from the visual essays produced for Plume of Feathers, an audio-visual project, to examine the notion of {\textquoteleft}reflective nostalgia{\textquoteright} as an attitude towards meaning-making within image creation, in particular illustration that can counter certain political uses of images that present a restorative–nostalgic world view. The project at the core of the article is concerned with the decline of public houses and their social function in the UK. However, the image of the pub is embroiled within the visual rhetoric related to the UK{\textquoteright}s (2016) Brexit referendum. This article explores the ways in which the illustrated image can provide a different view of the pub that reveals the conceptual construction of the notion of {\textquoteleft}pub{\textquoteright} and offers a critical alternative. The constructed nature of the illustrated image is then explored for its potential to visualize the past differently, following Svetlana Boym{\textquoteright}s proposal of reflective nostalgia in The Future of Nostalgia (2001) in order to address problems in the present. The article proposes that reflective nostalgia{\textquoteright}s utility as a critical tool lies in its consideration of the key role played by the surface of the image, through the material signifiers of age. As illustrators, embracing these nostalgic triggers when making images allows the viewer to reconnect to the past with critical distance, thereby returning politics to the surface of the image, something that Fredric Jameson saw in Postmodernism, Or the Cultural Logic of Late Capitalism (1991) as recuperated and rendered politically neutral. Illustration is therefore cast as a meaning-making practice that shapes the world it operates within, with the article suggesting that by making nostalgic images, illustrators can exercise their agency as agitators
Infection concern on public transport
Get PDFPrevious research suggests that some people are still hesitant to use public transport due to concerns about infections. For example, the 'Our Changing Travel survey' (Department for Transport, November 2023), indicated that almost a fifth of respondents (19%) had avoided public transport due to concerns about flu, coughs, colds and COVID-19. To inform the future development of interventions that improve transport for the user, DfT commissioned qualitative research exploring people’s concerns about such infections in relation to their public transport use. The objectives were:■ to understand people’s infection concerns when using public transport;■ to understand the behaviour changes arising from these concerns, and what underpins them;■ to understand the impacts that these changes have on people’s lives (for example, financial, social, or mental health impacts); and■ to explore how passengers with different levels of infection concern could collectively benefit from an improved user experience, and therefore either enable or increase their public transport use.In-depth interviews were conducted with 24 members of the public in early 2024. The level of infection concern varied substantially across participants. Some had very few concerns and did not raise these in the interview until prompted, with other factors affecting their use of public transport more than infection concern. Others were very concerned about infection, generally sharing these concerns in the interviews without prompting. Multiple causes of these concerns were identified, with the cleanliness of public transport and the amount of personal space available to users generally being most important to participants
Deep reinforcement learning and fuzzy logic controller codesign for energy management of hydrogen fuel cell powered electric vehicles
Get PDFHydrogen-based electric vehicles such as Fuel Cell Electric Vehicles (FCHEVs) play an important role in producing zero carbon emissions and in reducing the pressure from the fuel economy crisis, simultaneously. This paper aims to address the energy management design for various performance metrics, such as power tracking and system accuracy, fuel cell lifetime, battery lifetime, and reduction of transient and peak current on Polymer Electrolyte Membrane Fuel Cell (PEMFC) and Li-ion batteries. The proposed algorithm includes a combination of reinforcement learning algorithms in low-level control loops and high-level supervisory control based on fuzzy logic load sharing, which is implemented in the system under consideration. More specifically, this research paper establishes a power system model with three DC-DC converters, which includes a hierarchical energy management framework employed in a two-layer control strategy. Three loop control strategies for hybrid electric vehicles based on reinforcement learning are designed in the low-level layer control strategy. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) is used with a network. Three DRL controllers are designed using the hierarchical energy optimization control architecture. The comparative results between the two strategies, Deep Reinforcement Learning and Fuzzy logic supervisory control (DRL-F) and Super-Twisting algorithm and Fuzzy logic supervisory control (STW-F) under the EUDC driving cycle indicate that the proposed model DRL-F can ensure the Root Mean Square Error (RMSE) reduction for 21.05% compared to the STW-F and the Mean Error reduction for 8.31% compared to the STW-F method. The results demonstrate a more robust, accurate and precise system alongside uncertainties and disturbances in the Energy Management System (EMS) of FCHEV based on an advanced learning method
Classical structural identifiability methodology applied to low-dimensional dynamic systems in receptor theory
Get PDFMathematical modelling has become a key tool in pharmacological analysis, towards understanding dynamics of cell signalling and quantifying ligand-receptor interactions. Ordinary differential equation (ODE) models in receptor theory may be used to parameterise such interactions using timecourse data, but attention needs to be paid to the theoretical identifiability of the parameters of interest. Identifiability analysis is an often overlooked step in many bio-modelling works. In this paper we introduce structural identifiability analysis (SIA) to the field of receptor theory by applying three classical SIA methods (transfer function, Taylor Series and similarity transformation) to ligand-receptor binding models of biological importance (single ligand and Motulsky-Mahan competition binding at monomers, and a recently presented model of a single ligand binding at receptor dimers). New results are obtained which indicate the identifiable parameters for a single timecourse for Motulsky-Mahan binding and dimerised receptor binding. Importantly, we further consider combinations of experiments which may be performed to overcome issues of non-identifiability, to ensure the practical applicability of the work. The three SIA methods are demonstrated through a tutorial-style approach, using detailed calculations, which show the methods to be tractable for the low-dimensional ODE models