23343 research outputs found
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Spine Motion Segment Analogues: 3D Printing, Multiscale Modelling and Testing to Produce More Biofidelic Examples
Full text linkComputed tomography and magnetic resonance imaging are two powerful modalities which can be used in the clinical setting to produce data for the creation of patient-specific finite element analysis (FEA) models and physical analogues—for instance, by using additive manufacturing (AM)—that mimic the properties of soft and hard tissues, both morphologically and mechanically. However, there remains a gap between creating a perfect biofidelic physical analogue and its computational counterpart. This gap exists because, firstly, in silico models are often too complex to realise, and secondly, real-life conditions are challenging to emulate both computationally and mechanically, as they involve multiscale situations that are inherently heterogeneous and patient specific. In this study, we applied a multi-scale approach to design and model porcine vertebral specimens. Our results identified critical design factors that affect the quality and accuracy of the models, specifically highlighting that scanning resolution/fidelity and the thresholding technique have a directly proportional impact on model accuracy. A small shift up and down the greyscale level by 20 units can affect the behaviour of the AM sample by as much as [−15% +47%]. Working up the levels for manufacturing, testing and modelling (i) cylindrical cores to (ii) whole vertebrae and then (iii) a whole spine motion segment, we observed that the fidelity of predictions reduces, and errors increase as the structure becomes more complicated and intricate (3.6%, 7.5% and 15%, respectively). We are confident that further material-level developments will provide solutions for the more intricate parts of spinal motion segments, such as the intervertebral discs and facets, which in their natural form are highly sophisticated structures. To the best of our knowledge, this is the first time a holistic multiscale approach has been implemented to produce AM biofidelic analogues of skeletal parts. Our data showed good agreement between the physical and in silico models, confirming that it is possible to model real-time objects and situations both physically and in silico. This ultimately will enable the development of accurate, patient-specific physical models for use in biomechanical testing and medicolegal applications
Mitigating Transition Shock in New Nurses: The Role of Effective Preceptorship in General Practice
No full textTransition shock occurs when pre-registration nurses learn what is expected of them in their newly registered roles. Sarah Butler reflects on how preceptorship programmes in primary care could mitigate the effects on the workforce
Proceedings of The 6th International Conference on Education Development and Studies 2025 : Empowering Learning Through Technology
No full textThis book compiles research and insights gleaned from the proceedings of The 6th International Conference on Education Development and Studies 2025 (ICEDS 2025). With a specific focus on digital transformation in education, the book covers a broad spectrum of topics, ranging from e-learning and virtual innovations to learning and teaching methodologies, assessment in the digital context, technology integration in education, and innovative pedagogies tailored for the digital classroom. It caters not only to educators but also to researchers, policymakers, and other key stakeholders in the education landscape, equipping them with invaluable insights and fresh perspectives on prevalent trends and challenges within the discipline
Evaluating the effectiveness and acceptability of free door-to-door transport to increase the uptake of breast screening appointments in Yorkshire: a cluster randomised GP feasibility trial (DOORSTEP protocol)
Full text linkIntroduction Breast screening uptake remains low in parts of the UK, partly due to barriers including limited transport access. Offering free transport to screening appointments may help address this and improve uptake. This general practitioner (GP) cluster-randomised feasibility trial will assess whether offering free door-to-door transport alongside routine screening invitations increases attendance.Methods and analysis Eight general practices in Yorkshire will be randomised to either the intervention (routine invitation plus information about booking free door-to-door transport) or control (routine invitation only) group. Around 8000 women due for routine breast screening will be included. Primary feasibility outcomes include GP recruitment and randomisation, intervention fidelity, proportion of women from the 10% most deprived areas, acceptability and data transfer processes. Secondary outcomes include understanding travel behaviour, cost-effectiveness and screening uptake. Data will be collected from routine National Health Service (NHS) screening records, data linkage with NHS England, travel surveys and qualitative interviews exploring experiences and acceptability. Patient and public involvement is embedded throughout with members contributing to advisory and oversight roles.Ethics and dissemination The trial has received ethical approval from the London–Harrow Research Ethics Committee, Section 251 approval from the Confidentiality Advisory Group and other relevant regulatory bodies. The University of Hull is the study sponsor. Results will be disseminated through peer-reviewed journal publications, conference presentations and plain English summaries for participants and the public. Findings will inform the feasibility and design of a potential larger trial to improve breast screening uptake via transport support
Impact of Qatar's Foreign Investment Laws on Attracting Foreign Direct Investment: A Critical Assessment
No full textThis thesis examines Qatar’s foreign investment laws and their impact on attracting Foreign Direct Investments (FDIs) to the State of Qatar, with particular attention to emerging technologies and regulatory frameworks. Against the backdrop of Qatar’s National Vision 2030 and its economic diversification agenda, this study critically assesses how Qatar’s investment policies either encourage or dissuade entrepreneurs.This study employs a qualitative doctrinal research approach based entirely on desk-based analysis of primary and secondary sources. The research methodology incorporates a systematic examination of legal texts, policy documents, regulatory frameworks, and published materials to analyse Qatar's investment climate. This approach enables rigorous examination of regulatory challenges, operational barriers, and strategic opportunities across education, healthcare, tourism, technology, finance, and energy sectors through comprehensive document analysis. Furthermore, the methodology integrates analysis of diverse primary sources, including legislation, government reports, and official publications, alongside secondary academic and industry literature to ensure comprehensive coverage, while employing comparative doctrinal analysis frameworks to contextualise Qatar’s regulatory position against Singapore and the UAE as benchmark investment destinations.The study traces the historical evolution of Qatar’s foreign investment laws and benchmarks them against international best practices. It identifies several challenges in Qatar’s current legal and regulatory frameworks, including ownership restrictions in certain strategic sectors, bureaucratic bottlenecks or inefficiencies, geopolitical uncertainties, and inconsistent dispute resolution mechanisms. Clearly, these factors have contributed to the apparent volatility in the inflow patterns of FDI into Qatar when compared with other regional competitors like the UAE and Saudi Arabia.Furthermore, this thesis explores how emerging technologies such as Fintech, Blockchain, and Artificial Intelligence are reshaping the investment landscape and regulatory approaches in Qatar. The study thus analyses how technological advancements can enhance regulatory transparency and investors’ confidence while noting current gaps in legal frameworks for data protection and intellectual property rights.The findings of this study reveal that despite Qatar’s establishment of investment-friendly entities like the Qatar Financial Centre and Qatar Free Trade Zones Authority, significant regulatory and implementation barriers persist. Based on these findings and the underlying analysis of the challenges, this study proposes evidence-based strategies to enhance Qatar’s investment legal framework, addressing identified shortcomings while preserving national interests. These evidence-based recommendations aim to strengthen Qatar’s position as a competitive investment destination, supporting its economic diversification goals and sustainable development beyond the hydrocarbon sector
Green social prescribing delivered by community organisations supports wellbeing among ethnically diverse communities in England
Full text linkSocial prescribing connects people to community activities and services to meet practical, social and emotional needs affecting their health and wellbeing. Green social prescribing (GSP) focuses on prescribed activities that are nature-based using green and/or blue spaces. GSP has the potential to address health inequalities faced by migrants and refugees. We aimed to understand how GSP activities impacted the health and wellbeing of individuals in ethnically diverse communities within Hull and York (England) working alongside community organisations (n = 6) to deliver the research. This study utilised a mixed methods design; A pre-post intervention study design (n = 103) and face-to-face, semi-structured interviews (n = 20). Participants were recruited from community organisations (n = 6). Our study identified that a high proportion of people participating in GSP activity were able to sustain or improve their overall wellbeing. After 6-months of engagement in GSP activity, slight improvements were observed in scores for all ONS4 questions however only the anxiety construct demonstrated a statistically significant change (Mean change: -0.7 (95% CI -1.4 to 0.01). Qualitative data indicated community organisations are seen as trusted providers who can support access and engagement with green space. The study provided evidence that GSP can help to maintain and improve wellbeing within underserved communities, even at times of social and economic stress. Community organisations have a strong reach to those suffering the most health inequalities and can play a significant role in supporting health and wellbeing when empowered and provided with the appropriate resources
Actuator Fault-Tolerant Control of Anthropomorphic Manipulator Using Adaptive Backstepping and Neural Estimation of LuGre Friction Torque
Full text linkThis paper presents a fault-tolerant control (FTC) strategy for a six-degree-of-freedom (DoF) anthropomorphic manipulator operating under actuator faults and complex friction dynamics. The proposed framework integrates a backstepping control methodology with LuGre friction modeling and a feedforward neural network (FFNN) for friction estimation. Actuator faults are considered in the form of multiplicative efficiency losses and additive disturbances. An adaptive control law is developed to estimate and compensate for both friction and actuator faults in real time. The stability of the closed-loop system is guaranteed through Lyapunov theory. The simulation results validate the effectiveness and robustness of the proposed approach in ensuring precise trajectory tracking despite faults and friction uncertainties
Modelling the evolution and influence of dust in cosmological simulations that include the cold phase of the interstellar medium
Full text linkWhile marginal in mass terms, dust grains play an outsized role in both the physics and observation of the interstellar medium (ISM). However, explicit modelling of this ISM constituent remains uncommon in large cosmological simulations. In this work, we present a model for the life-cycle of dust in the ISM that couples to the forthcoming COLIBRE galaxy formation model, which explicitly simulates the cold ISM. We follow six distinct grain types: three chemical species, including carbon and two silicate grains, with two size bins each. Our dust model accounts for seeding of grains from stellar ejecta, self-consistent element-by-element metal yields and growth by accretion, grain size transfer (shattering and coagulation) and destruction of dust by thermal sputtering in the ISM. We detail the calibration of this model, particularly the use of a clumping factor, to account for unresolved gas clouds in which dust readily evolves. We present a fiducial run in a 253 cMpc3 cosmological volume that displays good agreement with observations of the cosmic evolution of dust density, as well as the z = 0 galaxy dust mass function and dust scaling relations. We highlight known tensions between observational data sets of the dust-to-gas ratio as a function of metallicity depending on which metallicity calibrator is used; our model favours higher normalization metallicity calibrators, which agree with the observations within 0.1 dex for stellar masses > 109 M⊙. We compare the grain size distribution to observations of local galaxies, and find that our simulation suggests a higher concentration of small grains, associated with more diffuse ISM and the warm-neutral medium, which both play a key role in boosting H2 content. Putting these results and modelling approaches in context, we set the stage for upcoming insights into the dusty ISM of galaxies using the COLIBRE simulations
Artificial intelligence and Earth observation for agricultural applications
No full textModern agriculture faces an increasing pressure from a combination of environmental, demographic, and economic challenges. Climate change is already affecting planting cycles and making traditional agricultural methods more unpredictable. At the same time, a growing global population is pushing the demand for higher food production. Projections from climate models suggest that crop yields could decline, depending on the extent of global warming. Meanwhile, many traditional agricultural practices remain unsustainable, damaging the soil, straining water supplies, and releasing large amounts of greenhouse gases, all of which threaten long-term food security. Within this context, the use of technologies such as Earth observation (EO) and artificial intelligence (AI) offers opportunities for making agriculture more efficient and sustainable. EO technology, such as satellite remote sensing, provides vital information about environmental conditions over large areas. The use of EO technology allows continuous monitoring of agricultural land at different resolutions, providing farmers and researchers with greater insight into the current or past condition of the land. In addition to EO, AI, especially machine and deep learning models, is being used together with EO data to analyse large and complex datasets to discover patterns and make predictions. Both technologies help deliver better performance for tasks such as identifying crop types, predicting yields and estimating biophysical or biochemical parameters. This chapter explores how the integration of EO and AI can enhance sustainable, resilient, and productive agricultural practices. It highlights different applications, for example, identifying crop types, assessing key biophysical and biochemical parameters, and predicting yields. As challenges to agriculture become increasingly severe, realizing the potential of these technologies could be critical to securing future food systems and doing less damage to natural systems in the process
