Torrington Hospital

Computed tomography-based fractional flow reserve computation (CT-FFR) is widely used in clinical practice, based on its efficacy demonstrated in many studies. However, major assumptions remain with the outflow boundary conditions (BCs) representing coronary microvasculature, especially in hyperaemia. We here propose a novel method to estimate patient-specific microvascular response to hyperaemia for CT-FFR calculations, based on patients' routinely available demographic data. A statistical model to predict microvascular flow response (MFR) from routinely collected patient demographic parameters was derived using PET-based perfusion data of 101 patients with coronary artery disease. CT-FFR computations were then conducted with patient-specific anatomical models and outflow BCs derived from various MFR models including the proposed approach. The FFR values were calculated for an independent test cohort of 10 patients who had undergone CT coronary angiography, CT perfusion imaging and invasive FFR measurement. Computed FFR values were compared against invasive FFR and other CT-FFR algorithms. A multivariate regression model predicting patient-specific MFR was derived as a function of sex, diabetes and smoking status of the patient. FFR values computed using our model agreed well with the invasive FFR (0.76 ± 0.09 vs. 0.75 ± 0.10, P = 0.217). The FFRs predicted with our model were also comparable to those calculated using outflow BC tuned with patient-specific perfusion data (FFR: 0.74 ± 0.10, P = 0.233 vs. invasive FFR) and showed marked improvement over the conventional approach (FFR: 0.68 ± 0.11, P = 0.004 vs. invasive FFR). Diagnostic accuracy vs. invasive FFR were 100, 91 and 82% for CT-FFR with CTP-based MFR, demography-based MFR, and conventional approach, respectively. The proposed demography-based MFR model significantly improves FFR computation accuracy compared with a typical conventional model that assumes constant, healthy and population average MFR. Although its diagnostic accuracy is slightly lower than that of CT-FFR calibrated with patient-specific perfusion imaging data (91 vs. 100%), the demography-based model offers a substantial practical advantage by not requiring additional non-standard data acquisition, such as perfusion imaging. Consequently, it shows strong potential as a practical enhancement to conventional CT-FFR algorithms.

The selective extraction of neodymium (Nd) from Nd and mixed Nd/Fe aqueous feeds is studied here, utilizing the 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ionic liquid [ C 4 m i m ] [ T f 2 N ] with the trioctylphosphine oxide (TOPO) extractant. 100% selective extraction of Nd was achieved in batch equilibrium studies - which was faster for the Nd compared to the Nd/Fe feed. The extraction was intensified by using small channels with 0.5 mm internal diameter. High extraction efficiencies of Nd were achieved, equal to 80% (Nd feed) and 65% (Nd/Fe feed), in residence times of ∼ 37 s. A maximum mass transfer coefficient of 0.1 s − 1 was obtained in 7.5 s for both feeds.

Maritime transportation is the cornerstone of the global economy, making understanding vessel activities and their impacts vital. This paper presents a graph-based approach for classifying navigational statuses of vessel trajectories, employing a hierarchical multi-classification deep learning framework using Automatic Identification System (AIS) data. AIS messages are first segmented into continuous tracks, then divided into fixed-size mini-trajectories called a ‘trajectlet’. Labels are assigned using domain knowledge and rule-based methods. Each trajectlet is modelled as a directed acyclic graph, capturing sequential and spatio-temporal movement dynamics. A Graph Neural Network (GNN) first classifies the AIS messages within each trajectlet as ‘stationary’ or ‘underway’. These node-level predictions are then pooled and combined with global features of each trajectlet, and then passed through a multi-layer perceptron to classify the entire trajectlet into one of four navigational statuses. The framework is validated on AIS data from vessels in UK waters, achieving 98% accuracy and F-score, and a 99% AUC–ROC, demonstrating strong predictive performance. This method enhances understanding of vessel operations and supports applications in emissions modelling, economic forecasting, autonomous navigation, and maritime situational awareness. • Models AIS messages as graphs with spatio-temporal and sequential links. • Classifies vessel status using feature-level attention in a GNN framework. • Combines node and graph-level outputs in a hierarchical multi-level classification. • Achieves 98% accuracy and F1-score, 99% AUC–ROC on UK AIS data. • Supports autonomy, port monitoring, emissions and economic modelling.

• Identifies five governance archetypes in transport systems. • Proposes a framework linking archetypes to governance configurations. • Highlights the interplay of values, causal attributions, and rationales. • Offers conceptual tools to facilitate problem structuring in complex mobility contexts. This study examines expert perceptions on achieving equitable, smart, and sustainable urban mobility and transport development. Through qualitative interview analysis, we identify five archetypes with distinct core values, causal attributions, and rationales: the Institutional Architect, Behaviour & Culture Challenger, Equity Champion, Resource Optimiser, and Inclusive Mobiliser. We propose a framework to consider these archetypes relationally along the dimensions of ‘orientation’ (system | people) and ‘approach’ (incremental | transformative). We discuss the implications of our findings, highlighting their potential in diverse, pluralistic decision-making environments characteristic of mobility and transport planning. Our study contributes a framework and conceptual repertoire of archetypes to support constructive decision-making in complex contexts.

The flurry of news worldwide surrounding the launch of DeepSeek’s artificial intelligence (AI) tool, operating at ∼5% of OpenAI’s GPT models, has drawn significant attention to global competitiveness in the AI field. Greater efficiency in software design to lower costs is of clear benefit to AI customers. It is also, however, critical to acknowledge the urgent need to sustain the otherwise continuous growth of compute power needed for AI systems and to reduce the energy consumption of AI systems, both of which are generally growing exponentially. Hardware has a critical role to play here. Thus, in addition to software progress, fundamental innovations across the hardware stack—from functional materials and novel devices to efficient system architectures and new computing paradigms—are pressing needs. Considering the ongoing climate crisis, we argue for the urgent need for accelerated and connected government- and industry-funded research and development in the hardware domain. We believe that the route to sustainable energy efficiency of compute systems requires closely linked efforts between academia and industry, starting with the most fundamental level of the AI system stack.

Psychological distress, smoking and alcohol use are interconnected. This study explores how distress and smoking independently, and in combination, are associated with alcohol consumption, past attempts and motivation to restrict alcohol use among people in Great Britain. Pooled cross-sectional data of people (aged≥16) in Great Britain (N=87326) collected monthly from April 2020 to June 2023 in the nationally representative Smoking and Alcohol Toolkit Study. Multinomial and binary logistic regressions assessed how past 30-day distress (Kessler Psychological Distress Scale) and smoking were associated with alcohol use risk level (AUDIT-C), past-year attempts to restrict alcohol use, and motivation to restrict alcohol use in three months, adjusting for sex, age, socioeconomic and geographic characteristics.Findings.The interaction between distress and smoking on alcohol use risk level was significant. At all distress levels, odds of using alcohol at increasing or high risk were higher among participants who smoked in the past or currently. Moderate or serious distress was associated with lower odds of using alcohol at increasing risk among participants who did not smoke. Higher distress was positively associated with past-year attempts to restrict alcohol use, while smoking in those reporting low-to-moderate distress was negatively associated with past-year attempts. Higher distress was positively associated with motivation to restrict alcohol use in three months. Smoking currently or in the past was independently associated with increased odds of using alcohol at increasing- or high-risk levels. Among those not smoking, higher distress was associated with reduced odds of using alcohol at increasing risk.

Abstract. Ground penetrating radar reveals subsurface geometry and ice stratigraphy that contains information about past and present dynamics of the cryosphere. Synthetic Aperture Radar (SAR) is a processing technique based on averaging the received radar echoes along multiple locations as the radar moves relative to the target. Due to this averaging, directional features are lost. A Doppler frequency shift accounts for the distance rate from the radar to the target. We introduce an alternative representation of SAR images that preserves directional information encoded in its Doppler spectrum. With this technique, called Red-Green-Blue Doppler Decomposition (RGB-DD), the Doppler spectrum of a SAR image is split into three equalised bands, each band representing a primary direction of arrival. A primary colour is assigned to each band to allow joint representation in a single RGB image. We apply our representation framework to several datasets acquired with the British Antarctic Survey (BAS) airborne ice-sounding radar over three Antarctic ice streams. Compared to the standard SAR method that is based solely on the averaged intensity level, this method facilitates the enhanced interpretation of englacial features such as ice stratigraphy, crevasses, tephra layers, and along-flow transitions in strain-rate. The technique may be extended to other sensors and applications.

The columnar grains in additively manufactured alloys increase the tendency to form solidification cracks and cause anisotropy. Studying the effect of process parameters on microstructure development helps to guide the manufacturing of the equiaxed grain microstructure. First, the effect of process conditions on the melt pool dimensions using in situ synchrotron X‐ray imaging and thermal profile and solidification condition using finite element simulation and calculation of thermodynamics phase diagrams of CrMnFeCoNi high‐entropy alloy fabricated by directed energy deposition is studied. Increasing the laser power reduces the thermal gradient to solidification rate ratio, pushing the solidification closer to the columnar‐equiaxed transition. Nevertheless, the simulations still indicate the columnar microstructure for all scan conditions in contrast to the experimental observation that shows single‐wall samples built at 200 W consisted of dominantly equiaxed grains, whereas columnar grains are dominant in samples built at 100 W. It is believed that in addition to the effect of thermal gradient and solidification rate, the chemical segregation (Mn and Ni) during solidification may promote dendrite detachment, hence assisting the transition to equiaxed grains. The multitrack deposition results in more solid beneath a new melt pool, increasing the thermal gradient that promotes more columnar grains in comparison to single tracks.

Novel approaches to materials design, fabrication processes and device architectures have accelerated next-generation electronics component production, pushing device dimensions down to the nano- and atomic-scale. For device metrology methods to keep up with these developments, they should not only measure the relevant electrical parameters at these length-scales, but ideally do so during active operation of the device. Here, we demonstrate such a capability using the full functionality of an advanced scanning microwave/scanning capacitance/kelvin probe atomic force microscope to inspect the charge transport and performance of an atomically thin buried phosphorus wire device during electrical operation. By interrogation of the contact potential, carrier density and transport properties, we demonstrate the capability to distinguish between the different material components and device imperfections, and assess their contributions to the overall electric characteristics of the device in operando. Our experimental methodology will facilitate rapid feedback for the fabrication of patterned nanoscale dopant device components in silicon, now important for the emerging field of silicon quantum information technology. More generally, the versatile setup, with its advanced inspection capabilities, delivers a comprehensive method to determine the performance of nanoscale devices while they function, in a broad range of material systems.

<p><strong>Keywords: Ikaite; Glendonite; Palaeoclimate; Powder X-Ray Diffraction; Clumped isotope thermometry</strong></p><p>Marine sedimentary ikaite is the parent mineral to glendonites, stellate pseudomorphs found throughout the geological record. Glendonites are a controversy in palaeoclimatic studies as there is an ongoing debate as to whether their presence in sedimentary successions may be used as cold-climate indicators. Glendonites are typically found associated with glacial sediments, and for a long time ikaite was believed to only nucleate and grow at temperatures < 7 °C. However, with the successful laboratory synthesis of ikaite at higher temperatures, the climatic significance of glendonites was brought into question. This study uses a combination of physical and inorganic chemistry techniques to demonstrate the variable stability of natural marine sedimentary ikaites over short (minutes to hours) and longer (weeks to months) timescales, both between ikaite samples, and in a given ikaite. We examine the nucleation of calcite from the destabilized ikaite, observing that this process is much more complex than previous studies suggest. We demonstrate that over much longer (e.g. months or more) timescales, natural marine sedimentary ikaites are not stable above 5 °C, and thus glendonite presence in sedimentary successions may be considered cold climate indicators.</p>