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BMViewGB: an interactive web based tool for visualising the operations of the balancing mechanism in Great Britain
No full textThe Balancing Mechanism (BM) is the main tool for keeping electricity supply and demand balanced in Great Britain, while respecting network constraints. Between April 2022 and March 2025, its costs were £2–3 billion per year, prompting participants to call for more efficient dispatch and greater transparency. To help meet these needs, the article presents BMViewGB, an open-source, map-based visualisation tool that displays the spatial distribution of BM costs and volumes. The straightforward map layout is designed for users with varied professional backgrounds and can also act as a foundation for further research, such as the development of digital simulators of the Balancing Mechanism
Lubrication to reduce tissue shear loading - effects on comfort perception and tissue damage when wearing a facemask
No full textPatients with respiratory disease often require breathing support delivered via a nasal or facemask. The use of such a mask causes continued loading of the skin which can result in discomfort or skin injury, which may lead to low adherence to treatment. It is hypothesised that lubricating the mask-skin interface will reduce shear stress, thus reducing the load on the skin. The aim of this study was to determine whether the application of a novel lubricant to the mask improved discomfort and/or skin injury measured using three outcomes: subjective comfort, erythema, and the presence of epidermal interleukins.
Ten healthy participants were randomised to wear a lubricated or unlubricated facemask for one hour. Each participant switched over after a one-hour washout period. Subjective comfort was measured using visual analogue scales. Erythema was quantified from facial photographs and interleukins were obtained using tape stripping and ELISA assay analysis. Results show that the subjective comfort significantly improved after one hour with the application of a novel lubricant, compared to no lubrication (p=0.015), however erythema and interleukins were not significantly different. As comfort perception may affect adherence, this novel lubricant may be beneficial in the clinical care of those needing to wear facemasks
Data-driven distributionally robust model predictive control
No full textTo mitigate the detrimental effects of uncertainty and disturbances, two main model predictive control (MPC) frameworks have been developed to explicitly incorporate uncertainty into controller synthesis: robust MPC (RMPC) and stochastic MPC (SMPC). RMPC determines control actions that are optimal under the worst-case realization of uncertainty within a deterministic set, while SMPC assumes or estimates the probability distribution of uncertainty and optimizes a probabilistic objective under probabilistic constraints. Although probabilistic constraints can reduce the conservativeness of RMPC by incorporating distributional information, obtaining the true distribution of uncertainty in real-world systems is often infeasible. Moreover, the high computational burden of SMPC and its sensitivity to distributional discrepancy limit its practical performance.
To address the challenges mentioned above, this thesis considers data-driven distributionally robust MPC (DRMPC) problems. Instead of requiring exact knowledge of the disturbance distribution, DRMPC constructs an ambiguity set using samples of disturbance realizations. This set represents the family of distributions consistent with the observed data, and control actions are determined based on the worst-case distribution within it, achieving robustness to sampling and modeling errors.
Chapters 1–2 motivate the integration of distributional robustness into MPC and present the mathematical foundations of DRMPC.
Chapters 3–5 focus on linear systems. Chapter 3 presents a general DRMPC framework ensuring stability and recursive feasibility. Chapter 4 introduces a Wasserstein-based DRMPC for stochastic linear systems, ensuring tractability and constraint satisfaction. Chapter 5 integrates Wasserstein and moment-based ambiguity sets into the DRMPC framework, ensuring recursive feasibility and stochastic input-to-state practical stability.
Chapters 6–8 extend DRMPC to nonlinear systems. Chapter 6 introduces two linearization-based methods and incorporates an offset-free approach to address model mismatch. Chapter 7 proposes dynamic ambiguity propagation via iterative LQR. Chapter 8 presents a Koopman-based stochastic DRMPC ensuring regulation and constraint satisfaction with finite-sample guarantees.Open Acces
A dust condensation instability in AGN atmospheres: failed winds and the broad line region
No full textActive galactic nuclei (AGN) are important drivers of galactic evolution; however, the underlying physical processes governing their properties remain uncertain. In particular, the specific cause for the generation of the broad-line region is unclear. There is a region where the underlying accretion disc atmosphere becomes cool enough for dust condensation. Using models of the disc’s vertical structure, accounting for dust condensation and irradiation from the central source, we show that their upper atmospheres become extended, dusty, and radiation-pressure-supported. Due to the density–temperature dependence of dust condensation, this extended atmosphere forms as the dust abundance slowly increases with height, resulting in density and temperature scale heights considerably larger than the gas pressure scale height. We show that such an atmospheric structure is linearly unstable. An increase in the gas density raises the dust sublimation temperature, leading to an increased dust abundance, a higher opacity, and hence a net vertical acceleration. Using localised 2D hydrodynamic simulations, we demonstrate the existence of our linear instability. In the non-linear state, the disc atmosphere evolves into “fountains” of dusty material that are vertically launched by radiation pressure before being exposed to radiation from the central source, which sublimates the dust and shuts off the radiative acceleration. These dust-free clumps then evolve ballistically, continuing upward before falling back towards the disc under gravity. This clumpy ionized region has velocity dispersions ≳ 1000 km s−1. This instability and our simulations are representative of the Failed Radiatively Accelerated Dusty Outflow (FRADO) model proposed for the AGN broad-line region
Deriving novel atrial fibrillation phenotypes using a tree-based artificial intelligence-enhanced electrocardiography approach
No full textAtrial fibrillation (AF) is classically categorised by arrhythmia duration, but these subtypes have limitations in capturing mechanistic and prognostic diversity.
A variational autoencoder, trained on >1.1M ECGs, extracted representative features, filtered for an AF cohort of 20,291 unique patients. These features were input into an unsupervised tree-based clustering method to map AF heterogeneity as a tree structure and identify phenogroups. Five phenogroups stratified by future disease risk were identified: (1) higher-risk AF; (2) highest-risk AF with heart failure (HF); (3) average paroxysmal AF; (4) lower-risk paroxysmal AF; and (5) higher-risk paroxysmal AF. The tree trajectory positioned individuals based on shared traits, emphasising explainability. Paroxysmal phenogroups 4 and 5 differed in risk and ventricular structure, with phenogroup 5 exhibiting more adverse features. Mixed AF phenogroup 2 reflected advanced AF with greater HF burden and mortality risk. This AI-ECG framework augments AF subtypes with a risk-based dimension, supporting personalised care
Hepatic metabolism of 11-oxygenated androgens in humans: an integrated in vivo and ex vivo approach
No full textObjective
Excess production of adrenal-derived 11-oxygenated androgens is observed in congenital adrenal hyperplasia, premature adrenarche, and polycystic ovary syndrome. 11-Ketotestosterone is equipotent to testosterone but does not decline with age. To date, the precise hepatic metabolism of 11-oxygenated androgens and subsequent urinary metabolite excretion have not been characterised.
Design
We employed an integrated approach combining an in vivo oral androgen challenge with an ex vivo normothermic machine liver perfusion (NMLP) model to characterise human hepatic 11-oxygenated androgen metabolism.
Methods
Women with polycystic ovary syndrome were randomised to receive 150 mg of either oral dehydroepiandrosterone or 11-ketoandrostenedione (11KA4) for 7 days (n = 10 for each), with collection of 24-hour urine samples for multi-steroid profiling by liquid chromatography-tandem mass spectrometry pre- and post-intervention. We employed human liver tissue explants (n = 3) alongside a whole human liver NMLP model (n = 3) to characterise ex vivo 11-oxygenated androgen metabolism.
Results
In ex vivo studies, the main metabolites identified were 11β-hydroxyandrosterone and 11β-hydroxyetiocholanolone. In vivo priming of the 11-oxygenated pathway with oral 11KA4 resulted in significant increases of urinary 11β-hydroxyandrosterone, 11β-hydroxyetiocholanolone, and 11-ketoetiocholanolone, all known to overlap with glucocorticoid metabolism. In addition, we observed significant increases in the urinary excretion of 11-ketoandrosterone (11KAn).
Conclusions
Using in vivo and ex vivo approaches, we report the predominance of 11β-hydroxy metabolites, highlighting the pivotal role of hepatic 11β-hydroxysteroid dehydrogenase type 1 (HSD11B1) activity in 11-oxygenated androgen metabolism. We identify 11KAn as the only metabolite without overlap with glucocorticoid metabolism, underscoring its specificity and biomarker potential. NMLP represents a novel integrated model to study human hepatic steroid metabolism
Safety of budesonide/glycopyrronium/formoterol fumarate dihydrate delivered by HFO-1234ze versus HFA-134a in chronic obstructive pulmonary disease: a phase 3, multi-site, randomised, double-blind, parallel-group, active-comparator study
No full textBackground Pressurised metered dose inhalers (pMDIs) contain a hydrofluorocarbon propellant, such as
hydrofluoroalkane-134a (HFA-134a), which is known to have global warming potential (GWP). Transitioning pMDIs to propellants with lower GWP will reduce the environmental impact of pMDIs. This study assessed the safety of a near-zero GWP propellant, hydrofluoroolefin-1234ze (HFO-1234ze), compared with HFA-134a when used in the delivery of budesonide/glycopyrronium/formoterol fumarate dihydrate (BGF) in participants with chronic
obstructive pulmonary disease (COPD). The results of this study advance our understanding of the safety of
HFO-1234ze compared with HFA-134a.
Methods This phase 3, double-blind, parallel-group study (ClinicalTrials.gov NCT05573464) across 9 countries (Argentina, Bulgaria, Canada, Germany, Mexico, Poland, Turkey, the United Kingdom, the United States) included participants (aged 40–80 years) with physician-diagnosed COPD using dual or triple inhaled
maintenance therapies, COPD Assessment Test score ≥10, ≥10 pack-years smoking history, and no comorbid
diagnosis of asthma or other clinically significant diseases impacting study outcomes. Participants were
randomised (1:1) to receive either BGF HFO-1234ze or BGF HFA-134a (two inhalations of 160/7⋅2/5⋅0 μg twice
daily) for 12 weeks in the main safety analysis set (or 52 weeks [first 120 participants per treatment]). Safety
endpoints included the incidence of adverse events (AEs), measures of vital signs, clinical laboratory tests, and electrocardiograms.
Findings Participants were recruited between 27 September 2022 and 19 May 2023. A total of 874 participants were screened. Of 558 treated participants (mean [standard deviation] age, 67⋅0 [7⋅4] years; male, 315 [56⋅5%]) in the 12-week safety analysis set, 280 received BGF HFO-1234ze, and 278 received BGF HFA-134a. The AE incidence was balanced between formulations in the 12-week (HFO-1234ze, 124 [44⋅3%]; HFA-134a, 114 [41⋅0%]) and 52-week (HFO-1234ze, 80 [66⋅7%]; HFA-134a, 94 [78⋅3%]) safety analysis sets
Null models for comparing information decomposition across complex systems
No full textA key feature of information theory is its universality, as it can be applied to study a broad variety of complex systems. However, many information-theoretic measures can vary significantly even across systems with similar properties, making normalisation techniques essential for allowing meaningful comparisons across datasets. Inspired by the framework of Partial Information Decomposition (PID), here we introduce Null Models for Information Theory (NuMIT), a null model-based non-linear normalisation procedure which improves upon standard entropy-based normalisation approaches and overcomes their limitations. We provide practical implementations of the technique for systems with different statistics, and showcase the method on synthetic models and on human neuroimaging data. Our results demonstrate that NuMIT provides a robust and reliable tool to characterise complex systems of interest, allowing cross-dataset comparisons and providing a meaningful significance test for PID analyses
The state-space structure around spiral defect chaos in Rayleigh-Bénard convection
No full textThe co-existence of ideal straight rolls (ISRs) and spiral-defect chaos (SDC) as bistable states in Rayleigh-Bénard convection above the onset of the linear instability is well established in extended spatial domains (Γ ≥ 80 where Γ is the aspect ratio of the domain). However, multiple stable states have also been found independently, raising questions about the precise understanding of this observed bistability in extended domains. In this study, we isolate the localised structures of SDC by gradually reducing the spatial domain. By minimising the domain
systematically to Γ = 4π, SDC appears transiently and eventually stabilises into new stable states referred to as elementary states. These elementary states are visibly and statistically similar to the spatially local patterns of SDC, indicative of invariant solutions underpinning the pattern formation in SDC.
To understand the state space structure further, we have examined the edge between ISRs and the elementary states, revealing multiple edge states, and conducted a series of numerical simulations along the unstable manifolds of unstable ISRs. The unstable ISRs near the Busse balloon are connected to stable ISRs and the base state through networks of heteroclinic orbits, forming a basin of attraction for each stable ISR. In contrast, the unstable ISRs further from the Busse balloon contain some unstable manifolds, along which the solution trajectory leads to SDC, suggesting that these unstable ISRs sit on the boundary between stable ISRs and SDC. Finally, we propose a state-space structure around the basic heat conduction state, stable/unstable ISRs, elementary states and transient SDC
Forecasting regional COVID‐19 regional COVID-19 hospitalisation in England using ordinal machine learning method
No full textBackground
The COVID-19 pandemic caused substantial pressure on healthcare, with many systems needing to prepare for and mitigate the consequences of surges in demand caused by multiple overlapping waves of infections. Therefore, public health agencies and health system managers also benefitted from short-term forecasts for respiratory infections that allowed them to manage services. While quantitative forecasts treating hospital admissions as continuous variables existed, many health managers prefer discrete levels of demand, similar to approaches used in weather and flooding. However, effective tools for generating precise sub-national forecasts remained limited.
Methods
We forecast regional COVID-19 hospitalisations in England, using the period from March 2020 to December 2021 for training and evaluating predictions using data from January to December 2022. We transform regional admission counts into an ordinal variable using n-tile and n-uniform methods. We further developed a method based on XGBoost, and used previously for influenza, to enable it to exploit the ordering information in ordinal hospital admission levels. We incorporated different types of data as predictors: epidemiological data including weekly region COVID-19 cases and hospital admissions, weather conditions and mobility data for multiple categories of locations. The impact of different discretisation methods and the number of ordinal levels was also considered.
Results
We found that mobility data brings about a more substantial improvement in predictive performance than relying only on epidemiological data and the inclusion of weather data. When both weather and mobility data are used in addition to epidemiological data, the results are very similar to models with only epidemiological data and mobility data. These results are robust in terms of the number of levels chosen for the forecast target.
Conclusion
Accurate ordinal forecasts of COVID-19 hospitalisations were obtained using XGBoost and mobility data. While uniform ordinal levels showed higher apparent accuracy, we recommend n-tile ordinal levels which contain far richer information