5,636 research outputs found

    Success Factors Facilitating Care During Escalation (the SUFFICE study)

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    Ede, J., Watkinson, P., Endacott, R., (2021) Protocol for a mixed methods exploratory study of success factors to escalation of care: the SUFFICE study. medRxiv 2021.11.01.21264875. Ede J, Petrinic T, Westgate V, Darbyshire J, Endacott R, Watkinson PJ. (2021) Human factors in escalating acute ward care: a qualitative evidence synthesis. BMJ Open Qual 10. Bedford, J. P., Ede, J. and Watkinson, P. J. (2021) ‘Triggers for new-onset atrial fibrillation in critically ill patients’, Intensive and Critical Care Nursing. Elsevier Ltd, 67, p. 103114. doi: 10.1016/j.iccn.2021.103114. Ede, J. et al. (2023) ‘Patient and public involvement and engagement (PPIE) in research: The Golden Thread’, Nursing in critical care, (April), pp. 16–19. doi: 10.1111/nicc.12921. Ede, J., Hutton, R., Watkinson, P., Kent, B. and Endacott, R. (2023) ‘Improving escalation of deteriorating patients through cognitive task analysis: Understanding differences between work-as-prescribed and work-as-done’, International Journal of Nursing Studies.BACKGROUND: In the United Kingdom, there continues to be preventable National Health Service (NHS) patient deaths. Contributory factors include inadequate recognition of deterioration, poor monitoring, or delayed escalation to a higher level of care. Strategies to improve care escalation, such as vital sign scoring systems and specialist teams who manage deterioration events, have shown variable impact on patient mortality. The need for greater care improvements has consistently been identified in NHS care reviews as well as patient stories. Furthermore, current research informing escalation improvements predominantly comes from examining failure to rescue events, neglecting what can be learned from rescue or successful escalation. AIM: The focus of this study was to address this knowledge gap by examining rescue and escalation events, and from this, to develop a Framework of Escalation Success Factors that can underpin a multi-faceted intervention to improve outcomes for deteriorating patients. METHODS: Escalation success factors, hospital and patient data were collected in a mixed methods, multi-site exploratory sequential study. Firstly, 151 ward care escalation events were observed to generate a theoretical understanding of the process. To identify escalation success factors, 390 care records were also reviewed from unwell ward patients in whom an Intensive Care Unit admission was avoided and compared to the records for patients who became unwell on the ward, admitted to an Intensive Care Unit, and died. Finally, thirty Applied Cognitive Task Analysis interviews were conducted with clinical experts (defined as greater than four years’ experience) including Ward Nurses (n= 7), Outreach Nurses (n= 5), Nurse Managers (n=5), Physiotherapists (n=4), Sepsis Nurses (n=3), Advanced Nurse Practitioners and Educators (n=2), Advance Clinical Practitioners (n=2), Nurse Consultant (n=1) and Doctor (n=1) to examine process of escalation in a Functional Resonance Analysis Model. RESULTS: In Phase 1, over half (n= 77, 51%) of the 151 escalation events observed were not initiated through an early warning score but other clinical concerns. The data demonstrated four escalation communication phenotypes (Informative, Outcome Focused, General Concern and Spontaneous Interaction) utilised by staff in different clinical contexts for different escalation purposes. In Phase 2, the 390 ward patient care record reviews (Survivors n=340, Non-survivors admitted to ICU n=50) identified that care and quality of escalation in the Non-survivor’s group was better overall than those that survived. Reviews also identified success factors present within deterioration events including Visibility, Monitoring, Adaptability, and Adjustments, not dissimilar to characteristics of high reliability organisations. Finally, Phase 3 interview data were dynamically modelled in a Functional Resonance Analysis Method. This illustrated differences in the number of escalation tasks contained in the early warning scoring system (n=8) compared to how escalation is successfully completed by clinical staff (n=24). Interview participants identified that 28% (9/32) of these tasks were cognitively difficult, also indicating how they overcome system complexity and challenges to successfully escalate. Interactions between escalation tasks were also examined, including Interdependence (how one affects another), Criticality (how many downstream tasks are initiated), Preconditions (what system factors need to be present), and Variability (factors which affect output reliability). This approach developed a system-focused understanding of escalation and signposted to process improvements. CONCLUSION: This research uniquely contributes to international evidence by presenting new elements to escalation of care processes. This includes indicating how frequently early warning scores trigger an escalation, the different ways in which escalation is communicated, that patient outcomes may inaccurately portray the quality of care delivered and examining the interaction between escalation tasks can identify areas of improvement. This is the first study to develop a preliminary Framework of Escalation Success Factors, which will be refined and used to underpin evidenced based care improvements. A key recommendation would be for organisations to use, when tested, the Framework of Escalation Success Factors to make system refinements that will promote successful escalation of care. PPI: This study has had Patient and Public Involvement and Engagement (PPIE) through a SUFFICE PPI Advisory Group

    An Optimized, Easy-to-use, Open-source GPU Solver for Large-scale Inverse Homogenization Problems

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    We propose a high-performance GPU solver for inverse homogenization problems to design high-resolution 3D microstructures. Central to our solver is a favorable combination of data structures and algorithms, making full use of the parallel computation power of today's GPUs through a software-level design space exploration. This solver is demonstrated to optimize homogenized stiffness tensors, such as bulk modulus, shear modulus, and Poisson's ratio, under the constraint of bounded material volume. Practical high-resolution examples with 512^3(134.2 million) finite elements run in less than 32 seconds per iteration with a peak memory of 21 GB. Besides, our GPU implementation is equipped with an easy-to-use framework with less than 20 lines of code to support various objective functions defined by the homogenized stiffness tensors. Our open-source high-performance implementation is publicly accessible at https://github.com/lavenklau/homo3d

    Digital support for alcohol moderation and smoking cessation in cancer survivors

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    Guiding Reinforcement Learning with Shared Control Templates

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    Purposeful interaction with objects usually requires certain constraints to be respected, e.g. keeping a bottle upright to avoid spilling. In reinforcement learning, such constraints are typically encoded in the reward function. As a consequence, constraints can only be learned by violating them. This often precludes learning on the physical robot, as it may take many trials to learn the constraints, and the necessity to violate them during the trial-and-error learning may be unsafe. We have serendipitously discovered that constraint representations for shared control – in particular Shared Control Templates (SCTs) – are ideally suited for guiding RL. Representing constraints explicitly (rather than implicitly in the reward function) also simplifies the design of the reward function. We evaluate the advantages of the approach (faster learning without constraint violations, even with sparse reward functions) in a simulated pouring task. Furthermore, we demonstrate that these advantages enable the real robot to learn this task in only 65 episodes taking 16 minutes

    Pore-scale Ostwald ripening of gas bubbles in the presence of oil and water in porous media

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    Hypothesis Ostwald ripening of gas bubbles is a spontaneous mass transfer process that can impact the storage volume of trapped gas in the subsurface. In homogeneous porous media with identical pores, bubbles evolve toward an equilibrium state of equal pressure and volume. How the presence of two liquids impacts ripening of a bubble population is less known. We hypothesize that the equilibrium bubble sizes depend on the surrounding liquid configuration and oil/water capillary pressure. Method and numerical experiments We investigate ripening of nitrogen bubbles in homogeneous porous media containing decane and water using a level set method that alternately simulates capillary-controlled displacement and mass transfer between bubbles to eradicate chemical-potential differences. We explore impacts of initial fluid distribution and oil/water capillary pressure on the bubble evolution. Findings Ripening in three-phase scenarios in porous media stabilizes gas bubbles to sizes that depend on their surrounding liquids. Bubbles in oil decrease in size while bubbles in water increase in size with increasing oil/water capillary pressure. Bubbles in oil reach local equilibrium before the three-phase system stabilizes globally. A potential implication for field-scale gas storage is that the trapped gas fractions in oil and water vary with depth in the oil/water transition zone.publishedVersio

    Numerical simulation of surfactant flooding with relative permeability estimation using inversion method

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    Surfactant flooding attracts significant interest in the hydrocarbon industry, with a definite promise to improve oil recovery from depleting oil reserves. In this thesis, surfactant flooding is the primary area of focus as it has significant potential for integration with other chemical enhanced oil recovery techniques, including polymer, nanofluid, alkali, and foam. This combined approach has the potential to reduce interfacial tension to ultralow levels, decrease adsorption, and offer other benefits. However, due to the various mechanism, surfactant flooding poses a more complex model for simulators by encountering numerical issues (e.g., the appearance of spurious oscillations, erratic pulses, and numerical instabilities), rendering the methods ineffective. To address these challenges, the analytical modelling technique of surfactant flooding was studied, leading to the development of a novel inversion method in the MATLAB programming environment. Numerical accuracy issues were discovered in 1D models that used typical cell sizes found in well-scale models, leading to pulses in the oil bank and a dip in water saturation, particularly for low levels of adsorption, highlighting the need for more refined models. Based on these findings, we examined the surfactant flooding technique in 2D models to recover viscous oil in short reservoir aspect ratios. Instabilities such as viscous fingering and gravity tongue were observed on the flood fronts, and the magnitude of the viscous fingers was influenced by vertical dispersion, resulting in errors in computed mobility values at the fronts. Interestingly, introducing heterogeneity only minimally affected the spreading of the front and did not significantly impact viscous fingering or numerical artifacts. To optimize the nonlinearity of flow behaviour and degree of mobility control at the fronts, a homogenous model was considered to develop the inversion method. In summary, the developed inversion method accurately estimated the two-phase relative permeability curves, which were validated using fractional flow theory. The precision of the inverted curves was further improved using the optimization algorithm, demonstrating the method's ability to predict outcomes closer to the observed values for 2D models with instabilities. The obtained results are of significant value for core flood analysis, interpretation, matching, and upscaling, providing insights into the potential of surfactant flooding for enhanced oil recovery. Additionally, the use of the developed MATLAB Scripts promotes open innovation and reproducibility, contributing to the benchmarking, analytical, and numerical method development exercises for tutorials aimed at improving the overall understanding of surfactant flooding

    Using Computational Fluid Dynamics for Predicting Hydraulic Performance of Arced Labyrinth Weirs

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    Our world is dynamic and as hydrologic research continues, the magnitude of flood estimates used in hydraulic design for reservoirs has increased. Consequently, many existing spillways are now undersized and unable to meet discharge requirements. These spillways often have a fixed footprint, so nonlinear weirs (e.g., labyrinth weirs) are often a viable solution. For reservoir applications, arcing labyrinth weirs in plan view increases hydraulic efficiency because of better cycle orientation to the approaching flow from the reservoir. This study supplements available physical arced labyrinth weir hydraulic data by observing flow characteristics of two numerical models (α=16°; θ=10° and α=20°; θ=30°). Both numerical models were developed using two commercially available CFD software. The purpose of the CFD analysis was to assess the appropriateness of default settings in a CFD model and to better understand CFD as a design tool for arced labyrinth weir rating curve development. Results determined that default settings are not always appropriate for a rating curve. For arced labyrinth weirs, CFD can be a useful tool for implementing site-specific conditions; however, CFD models should be calibrated to reliable laboratory or field data. This study’s data may be used, with sound engineering judgement, to aid in the design of arced labyrinth weirs

    Data-driven exact model order reduction for computational multiscale methods to predict high-cycle fatigue-damage in short-fiber reinforced plastics

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    Motiviert durch die Entwicklung energieeffizienterer Maschinen und Transportmittel hat der Leichtbau in den letzten Jahren enorm an Wichtigkeit gewonnen. Eine wichtige Klasse der Leichtbaumaterialien sind die faserverstärkten Kunststoffe. In der vorliegenden Arbeit liegt der Fokus auf der Entwicklung und Bereitstellung von Materialmodellen zur Vorhersage des Ermüdungsverhaltens kurzglasfaserverstärkter Thermoplaste. Diese Materialien unterscheiden sich dabei durch ihre Aufschmelzbarkeit und ihrer damit einhergehenden besseren Recyclebarkeit von thermosetbasierten Materialien. Außerdem erlauben die Kurzglasfasern im Gegensatz zu Langfasern eine einfache und zeiteffiziente Herstellung komplexer Komponenten. Ermüdung ist ein wichtiger Versagensmechanismus in solchen Komponenten, insbesondere für Bauteile z.B. in Fahrzeugen, die vibrationsartigen Belastungen ausgesetzt sind. Durch die inherente Anisotropie des Materials sind die experimentelle Charakterisierung und Vorhersage dieses Versagensmechanismus jedoch äußerst zeitintensiv und stellen somit eine wesentliche Herausforderung im Entwicklungsprozess und für die breitere Anwendung solcher Bauteile dar. Daher ist die Entwicklung komplementärer simulativer Methoden von großem Interesse. Im Rahmen dieser Arbeit werden Methoden zur Vorhersage der Ermüdungsschädigung kurzglasfaserverstärkter Werkstoffe im Rahmen einer Multiskalenmethode entwickelt. Die in der Arbeit betrachteten Multiskalenmodelle bieten die Möglichkeit, allein anhand der experimentellen Charakterisierungen der Materialparameter der Konstituenten, d.h. Faser und Matrix, komplexe anisotrope Effekte des Verbundmaterials vorherzusagen. Der experimentelle Aufwand kann dadurch enorm reduziert werden. Dazu werden zunächst Materialmodelle für die Konstituenten des Komposits entwickelt. Mithilfe FFT-basierter rechnergestützter Homogenisierung wird daraus das Materialverhalten des Komposits für verschiedene Mikrostrukturen und Lastfälle vorhergesagt. Die vorberechneten Lastfälle auf Mikrostrukturebene werden mit datengetriebenen Methoden auf die Makroskala übertragen. Das ermöglicht eine effiziente Berechnung von Bauteilen in wenigen Stunden, wohingegen eine entsprechende Berechnung mit geometrischer Auflösung aller einzelnen Fasern der Mikrostruktur auf heutigen Computern viele Jahre dauern würden. Für die Matrix werden unterschiedliche Schädigungsmodelle untersucht. Ihre Vor- und Nachteile werden analysiert. Die Mikrostruktursimulationen geben einen Einblick in den Einfluss verschiedener statistischer Parameter wie Faserlängen und Faservolumengehalt auf das Kompositverhalten. Ein neues Modellordnungsreduktionsverfahren wird entwickelt und zur Simulation des Ermüdungsschädigungsverhaltens auf Bauteilebene angewandt. Weiter werden Modellerweiterungen zur Berücksichtigung des R-Wert-Verhältnisses und viskoelastischer Effekte in der Evolution der Ermüdungsschädigung entwickelt und mit experimentellen Ergebnissen validiert. Das entstandene Simulationsframework erlaubt nach Vorrechnungen auf einer geringen Menge von Mikrostrukturen und Lastfällen eine effiziente Makrosimulation eines Bauteils vorzunehmen. Dabei können Effekte wie Viskoelastizität und R-Wert-Abhängigkeit je nach gewünschter Modellierungstiefe berücksichtigt oder vernachlässigt werden, um immer das effizientste Modell, das alle relevanten Effekte abbildet, nutzen zu können

    Designing LMPA-Based Smart Materials for Soft Robotics Applications

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    This doctoral research, Designing LMPA (Low Melting Point Alloy) Based Smart Materials for Soft Robotics Applications, includes the following topics: (1) Introduction; (2) Robust Bicontinuous Metal-Elastomer Foam Composites with Highly Tunable Mechanical Stiffness; (3) Actively Morphing Drone Wing Design Enabled by Smart Materials for Green Unmanned Aerial Vehicles; (4) Dynamically Tunable Friction via Subsurface Stiffness Modulation; (5) LMPA Wool Sponge Based Smart Materials with Tunable Electrical Conductivity and Tunable Mechanical Stiffness for Soft Robotics; and (6) Contributions and Future Work.Soft robots are developed to interact safely with environments. Smart composites with tunable properties have found use in many soft robotics applications including robotic manipulators, locomotors, and haptics. The purpose of this work is to develop new smart materials with tunable properties (most importantly, mechanical stiffness) upon external stimuli, and integrate these novel smart materials in relevant soft robots. Stiffness tunable composites developed in previous studies have many drawbacks. For example, there is not enough stiffness change, or they are not robust enough. Here, we explore soft robotic mechanisms integrating stiffness tunable materials and innovate smart materials as needed to develop better versions of such soft robotic mechanisms. First, we develop a bicontinuous metal-elastomer foam composites with highly tunable mechanical stiffness. Second, we design and fabricate an actively morphing drone wing enabled by this smart composite, which is used as smart joints in the drone wing. Third, we explore composite pad-like structures with dynamically tunable friction achieved via subsurface stiffness modulation (SSM). We demonstrate that when these composite structures are properly integrated into soft crawling robots, the differences in friction of the two ends of these robots through SSM can be used to generate translational locomotion for untethered crawling robots. Also, we further develop a new class of smart composite based on LMPA wool sponge with tunable electrical conductivity and tunable stiffness for soft robotics applications. The implications of these studies on novel smart materials design are also discussed
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