78146 research outputs found
Sort by
A case study integrating scenario thinking with dialysis policymaking in Thailand
No full textBackground: Healthcare policymaking often struggles to account for complex future uncertainties, particularly for policies affecting healthcare infrastructure or stakeholder behaviour. Scenario thinking is a structured approach for exploring multiple plausible futures. It has, however, been underutilised in healthcare policymaking. We applied scenario thinking to the 2024 dialysis policy in Thailand to guide policymaking under future uncertainty. Methods: Our study is composed of three parts: scenario development, policy analysis, and evaluation of scenario thinking in the policy process. We developed future scenarios using the intuitive logics method, which systematically identifies and explores key uncertainties, and the critical scenario method to evaluate stakeholder responses. Policy options were evaluated for their robustness across future scenarios. We assessed the overall approach according to fitness-for-purpose, influence, and efficiency indicators from established evaluation frameworks for health technology assessment. Results: Four scenarios are presented along the dimensions of kidney transplant accessibility and skilled workforce availability. Approval of patients prior to dialysis initiation and a quality monitoring system can strengthen the dialysis programme’s adaptability to future change, while considerable uncertainty exists regarding performance of service provider payment mechanisms. Our evaluation suggests that scenario thinking is an efficient method to characterise future uncertainty within available resources for policymaking. Conclusions: This study demonstrates how scenario thinking can systematically evaluate healthcare policies under future uncertainty, providing a framework for more robust policies. Further research is needed to facilitate its use, building on the adaptations developed in this study to bridge scenario thinking with established processes and standards for healthcare policy
High-performance curved sections in 3D printed continuous carbon fibre reinforced thermoplastic composites using aligned fibre deposition
Get PDFThis paper presents high-performance curved sections in 3D printed continuous carbon fibre reinforced thermoplastic composites using an aligned fibre deposition (AFD) method on a 6-axis robotic arm. 3D printed curved composite beams using both AFD and conventional methods are mechanically tested under four-point bending and scanned by X-ray computed microtomography (μCT) to characterise the fibre distribution and develop image-based finite element models. Compared to conventional printing method using a commercial nozzle, the proposed AFD method significantly improves the fibre alignment and reduces void content in the printed composites, and the curved beam strength is calculated as 204.2 N and 224.5 N for the cases of 7 mm and 15 mm radius of curvature, achieving an improvement of 34.0 % and 45.3 %, respectively. The modelling produces excellently matched stiffness with the experimental measurement, providing useful insights into the stress and strain distributions. The combination of experimental data and modelling results shows that the alignment of fibres in the curved composite beams plays a key role in improving mechanical performance and determining the final failure mode
Hybrid machine teaching with human oversight for classification of seismograms
No full textWhile Artificial Intelligence (AI) is gaining popularity, two main problems limit its adoption by geoscientists for analysing and classifying large volumes of seismic recordings to monitor seismic activity associated with landslides and natural disasters. Firstly, AI often operates as a black box, whose inner workings, geoscientists do not understand and therefore are reluctant to trust its outcomes. Secondly, classification relies on learning from large labelled datasets, which are not available in practice because of the sheer volume of seismograms needing labels and the limited confidence of geoscientists in labelling low magnitude seismograms amidst anthropogenic noise. To reduce the workload on geoscientists, and accommodate the need for human agency and oversight as one of the main trustworthy AI principles, a hybrid machine teaching framework is proposed where the human teacher (domain expert) provides a few representative labels for each class based on which the machine teacher (a Siamese deep learning network) selects and labels training examples for the learner (a deep learning-based multi-classifier). The training is performed in stages, incrementally expanding training set based on provided labelled samples at each stage, resembling the way humans learn, starting with more obvious examples and progressing towards more complex ones. Leveraging the publicly available Résif Seismological Data Portal of the Super-Sauze Landslide in the Southern French Alps, characterised by low-amplitude signals, generally highly attenuated at short distances, we show that the proposed hybrid machine teaching framework effectively classifies low-magnitude earthquakes, quakes, rockfall and anthropogenic noise with F1 scores of 0.86, 0.61, 0.87 and 0.70, respectively
Influence of deformation path on microstructure evolution during multi-step deformation of a high strength steel : experiments and FE analysis
No full textThis study aims to investigate the effect of deformation path on deformation and microstructure evolution during the multi-step deformation of a high strength steel. The deformation paths were illustrated by flat and concave surface anvils. Multi-step deformation experiments were conducted on high strength steel specimens using thermomechanical simulator, Gleeble 3800 equipped with MaxStrain module, a special purpose attachment. All tests were performed at a strain rate and temperature of 0.01 s⁻¹ and 1150 °C respectively, considering two deformation paths. A total true strain of 0.84 was imparted over four steps, with approximately 0.21 strain applied per step. The results were used to analyze the influence of varying deformation paths on microstructure evolution and hardness distribution. A finite element (FE) model was developed using Forge NxT 3.2 FE code to simulate the multi-step deformation process. The strain distribution obtained via FE model was correlated to the average grain size and hardness distribution after the multi-step deformation experiments for validation. This validated FE model was capable of predicting strain distribution, dynamic recrystallization (DRX) volume fraction, and grain size evolution during the multi-step deformation process. A comparative study of results obtained from two deformation paths was conducted to determine the optimal deformation path, aiming homogeneous strain and grain size distribution. The Coefficient of Variation (CoV) was used to assess the heterogeneity of the hardness distribution. The results suggest that concave anvils promote a higher and more uniform strain distribution, leading to a homogeneous distribution of grain size and hardness. An increased and more uniform strain distribution promotes complete dynamic recrystallization (DRX) with finer grain size leading to homogeneous hardness distribution
Single photon event-driven 3D imaging
No full textEvent-based imaging is at the forefront of high-speed sensing applications due to the low latency of asynchronous detection and low data volume. Conventionally, events used in this form of sensing correspond to changes in intensity on the microsecond scale, this inherently makes the approach incompatible with scenarios where single photon detection is required such as single photon Light Detection and Ranging (LiDAR), and low-light-level imaging. Here we propose a new imaging modality which is driven instead by events generated from the detection of individual photons. We use a form of single-pixel imaging in which information from a 3-Dimensional scene is encoded entirely in the time-of-arrival of the photons such that each detected photon can be used to update an estimation of all transverse positions simultaneously. The image reconstruction is performed by a Spiking Convolutional Neural Network (SCNN) which has a natural complementarity with single photon detection that allows the scheme to run fully asynchronously and be driven by the detection of each individual photon. Our approach for processing LiDAR information asynchronously, driven by each detected photon, has the potential for minimal latency 3D imaging and sensing even in weak light conditions with applications in high speed target detection and robotics. [Abstract copyright: © 2025. The Author(s).
The effects of cannabidiol (CBD) in transport water on the behaviour of ornamental fishes
Get PDFThe ornamental fish supply chain has multiple transportation phases which can induce stress in fishes. Previous studies have considered methods of improving welfare during transport, by adding water conditioners based on natural compounds known to have anxiolytic effects. Cannabidiol (CBD) has recently emerged as a compound of interest with beneficial immunomodulatory, anti-inflammatory, and anxiolytic effects in mammals. In the first part of the present study, we identified whether addition of CBD to the transport water of ornamental fishes (at nominal concentrations of 3.9, 7.8 or 15.6mg/l) had an effect on group behaviour post-transport. Variatus platys were transported for 30min in bags containing one of five treatment groups (control, solvent control, 3.9, 7.8 or 15.6mg/l CBD). They were then videoed as a group (15min) immediately after introduction post-transport into an empty tank, with further videoing performed 30min and 2h after release. Behaviours analysed included biting, chasing, erratic movements and time spent immobile. The lowest concentration found to affect a range of behaviours was 7.8mg/l (the middle concentration). Based on these findings, this concentration was used in a follow-on study to identify whether the use of CBD during transport affected individual behaviour and physiology post-transport. Fish were transported in the same way, and then fish were placed individually into open field arenas immediately after transport and videoed for 15min. Behaviours analysed in the open field arenas included distance travelled, mean speed, time spent immobile, and time spent in the central zone. Water cortisol and skin mucus quantity were also analysed. CBD significantly affected behaviour post-transport, with those fish exposed to CBD exhibiting significantly reduced stress-related behaviours than those in the control and solvent control groups at both the group and individual level. No effects on mucus or water cortisol were seen. These findings highlight the potential for using CBD within commercial water conditioners to reduce the effects of transport stress for ornamental fishes
Modulated parameter-less predictive voltage control for dual-active-bridge converters
Get PDFConventional model-based predictive voltage control in dual-active-bridge (DAB) converters suffers from voltage regulation limitations because the limited phase-shift angle candidate set impacts dynamic response and the parametric sensitivity impacts robustness. To address the above limitations, this letter proposes a modulated parameter-less predictive voltage control (MPL-PVC), where a parameter-less modeling architecture replaces the conventional mathematical models, eliminating dependence on precise circuit parameters through real-time parameter-less model updating, and a modulated optimal phase-shift angle calculation replaces conventional phase-shift angle candidate set, enhancing voltage regulation capacity while decoupling control performance from parametric variations. Experimental validation on a DAB prototype confirms the effectiveness of proposed MPL-PVC in enhancing dynamic performance and eliminating parametric effect
Competition and the value of innovation
Get PDFWe examine how competition affects the economic returns firms derive from their patented innovations. Using a stock market-based measure that reflects the discounted cash flows of newly granted patents, we document a robust negative relationship between competition intensity and the economic value of patents. To establish causality, we implement a quasi-experimental design and consider horizontal M&A as events that are likely to reduce competition for non-merging industry peers. Leveraging the random timing of peers’ patent grant dates within a narrow window around M&A announcements, we show that patents granted immediately after such events on average experience a 2.8% increase in value. This effect is stronger for deals that are more likely to be anti-competitive, but is absent for non-horizontal mergers that are unlikely to alter competition intensity. Overall, we offer new insights into the competition-innovation relationship by showing that competition weakens the economic returns that incentivize firm innovation
Innovative design of aerostatic bearings with enhanced dynamic stability inspired by the Laval nozzle principle
No full textMicrovibrations caused by airflow self-excitation within pressurized air films significantly degrade the dynamic stability of aerostatic bearings. However, effectively controlling supersonic flow velocity, which is critical for suppressing the turbulent airflows that cause this self-excitation, remains a significant challenge in the current designs of aerostatic bearings. To address this gap, a novel aerostatic restrictor inspired by the Laval nozzle principle is proposed to enhance the dynamic stability of bearings by decelerating supersonic pressurized airflows. Computational fluid dynamics (CFD) simulations are conducted to elucidate the underlying mechanism by which the proposed restrictor improves performance (i.e., by suppressing turbulent airflows by mitigating adverse pressure gradients). On the basis of the CFD simulation results, the key geometrical parameters of the newly designed restrictor are identified. The effectiveness of the proposed restrictor is evaluated through experimental testing, with the results indicating that it achieves improved dynamic stability and reduced vibration amplitude compared with a conventional aerostatic restrictor design. This work is expected to advance the theory of restrictor design by enhancing the dynamic stability of aerostatic bearings
Microbially induced carbonate precipitation for soil improvement : insights from a meter-scale radial grouting trial
Get PDFDespite the growing interest in microbially induced carbonate precipitation (MICP) for geotechnical applications, reports on meter-scale MICP trials for soil improvement remain limited, and controlling and predicting cementation efficiency on a large-scale is even more scarce. This study presented a meter-scale improvement of a poorly-graded sand (initial dry density: 1581 kg/m3, porosity: 40%) through MICP in a cylindrical cell (diameter: 1 m; thickness: 15 cm) using a radial flow injection strategy, which involves injecting fluids radially from a single well located at the center while maintaining a constant hydraulic head at the outer boundary. Nine cycles of a two-phase MICP treatment were applied: Phase 1- injection of 0.7 pore volumes (PVs) of bacterial solution and 1-L water pulse; Phase 2- injection of 1.4 PVs of 0.5 M cementing solution in two stages (i) 0.7 PV injection two hours after the bacteria were injected, and (ii) a further 0.7 PV injection the following morning after an overnight static reaction period. We observed non-uniform CaCO3 precipitations along the distance from the central well and over the depth, which was induced by the decreasing flux towards the outer boundary under the radial flow pattern, along with influences from layered soil packing and hydraulically induced flow channels. CaCO3 precipitation with distance from the central well follows a symmetric Gaussian-type distribution, with sufficient cementation to retrieve full-length cores occurring near the midpoint between the central well and the outer boundary. The unconfined compressive strengths of the full-length cores were in the range of 1.2-6.8 MPa with CaCO3 contents of 0.08-0.17. Our study suggests that cementation level under radial flow conditions is controllable on a large scale and highly dependent on the injection volume of both bacteria and rinsing water pulse. The study provides a solid baseline for predicting and controlling CaCO3 distribution in large-scale MICP soil improvement using a two-phase radial injection approach
Access Repository Dashboard
Do you manage University of Strathclyde Institutional Repository? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!