The influence of the Casimir effect on the binding potential for 3D wetting

We provide comprehensive details of how a previously overlooked entropic, or low temperature Casimir contribution, WC , to the total binding potential for 3D short-ranged wetting may be determined from a microscopic Landau-Ginzburg-Wilson Hamiltonian. The entropic contribution comes from the many microscopic configurations corresponding to a given interfacial one, which arise from bulk-like fluctuations about the mean-field (MF) constrained profile, and adds to the usual MF con- tribution WM F . We determine the functional dependence of WC on the interface (and wall) shape using a boundary integral method which can be cast as a diagrammatic expansion with each diagram corresponding to successively higher-order exponentially decaying contributions. The decay of WC is qualitatively different for first-order and critical wetting with the change in form occurring at the MF tricritical point. Including the Casimir contribution to the binding potential preserves the global surface phase diagram but changes, radically, predictions for fluctuation effects at first-order and tricritical wetting, even when capillary-wave fluctuations are not considered

Cardiac myosin-binding protein C in ST-elevation myocardial Infarction

Background and Aims: Cardiac myosin-binding protein C (cMyC) is a novel biomarker of myocardial injury, rising and falling more rapidly than cardiac troponins in myocardial infarction (MI), potentially enabling earlier diagnosis. Its performance has not been assessed in reperfused acute ST-segment elevation myocardial infarction (STEMI), against gold-standard biochemical (high-sensitivity cardiac troponin I, hs-cTnI) or imaging (cardiovascular magnetic resonance, CMR) biomarkers. This study tested the hypotheses that: i) cMyC correlates with acute and final MI size by late gadolinium enhancement (LGE) CMR and ii) cMyC is related to the presence of acute microvascular obstruction (MVO) by CMR. Methods: Blood samples were obtained at 6±2 hourly intervals for 24 hours (hrs) for measurement of hs-cTnI and cMyC concentrations in patients with reperfused acute STEMI. Patients underwent 3T LGE-CMR at ~ 3-5 days (n=69) and ~ 4 months (n=65) after reperfusion. Results: Acute cMyC at all timepoints significantly correlated with acute and final MI size on LGE-CMR, most strongly at 6-hrs post reperfusion (r=0.7, p<0.001). cMyC at 6-, 12-, 18- and 24-hrs demonstrated significant discriminatory power in identifying patients with acute MVO, with the 6-hr level having the highest discriminative power. Hs-cTnI correlated more strongly with acute and final MI size compared to cMyC and had significantly higher discriminatory ability in identifying MVO at 12-, 18- and 24-hrs. Conclusions: cMyC is a quantitative biochemical biomarker of myocardial injury in reperfused STEMI. Further studies, using optimised high-sensitivity assays, are warranted to evaluate its potential as a novel biomarker after acute MI

Spectral characterisation of non-premixed H₂/CO₂ jet flames

The impact of CO2 addition on the appearance and spectral characteristics of H2-fuelled flames was examined in two types of non-premixed jet flames, namely constant heat release rate and constant fuel jet exit velocity. The CO2 volumetric fraction in the fuel stream was varied between 0 and 60%. Flame images and spectra for wavelengths 270–850 nm were acquired using a digital colour camera and a spectrograph, respectively. For both flame types, CO2 addition introduced broadband CO2* chemiluminescence with prominent intensities in the blue range, owing to the chemical participation of CO2 in combustion. It also suppressed OH* chemiluminescence and emissions from water molecules because of the thermal and chemical impact of CO2. Consequently, CO2 addition led to a flame colour change from slightly red-dominated for pure H2 flames to strongly blue-dominated, enhancing flame visibility by up to 4 times. The intensity of CO2* chemiluminescence reached a maximum for 40–50% CO2 volumetric fraction for both flame types. Spectral intensities were evaluated for three selected wavelength bands, namely OH (305–315 nm), blue (400–540 nm), and IR (720–850 nm). The intensity ratios of three band combinations, namely blue/OH, blue/IR and OH/IR, demonstrated independence of the flame type and monotonic correlations with the CO2 volumetric fraction, highlighting their potential application for industrial real-time monitoring of H2/CO2 blending ratio in non-premixed H2/CO2 flames. Flame height, evaluated from flame images, increased with CO2 addition for laminar jet flames but decreased with increasing CO2 addition for turbulent jet flames, regardless of flame type

Screening tools to identify a neurogenic cause for pelvic organ dysfunction: a scoping review

Background Pelvic organ dysfunction can be an early sign of neurological disease, potentially preceding recognition of the underlying neurogenic mechanism. Screening tools are widely used in healthcare to aid early detection. Many tools exist for assessing pelvic organ symptoms, but it is unclear if any are designed to identify whether neurological disease is causing pelvic organ symptoms, nor whether there may be a potential neurogenic basis for such symptoms in the absence of prior neurological diagnosis. Objective To identify assessment and diagnostic tools used to evaluate pelvic organ symptoms in neurological disease and determine their intended purposes, including whether any are designed to evaluate likelihood of neurogenic basis. Methods A scoping review was conducted following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) scoping review guidance. Searches of MEDLINE, CINAHL, and Embase databases included terms such as “assessment and screening tools,” “neurological conditions,” and “pelvic organ dysfunction.” Results From 1600 papers screened, 513 were included. Across these, 212 different tools were identified, covering a wide variety of uses. However, none were specifically developed to screen for a neurogenic cause of pelvic organ symptoms in patients with or without a prior neurological diagnosis. Conclusions There are currently no tools designed to establish neurogenic mechanisms underlying pelvic organ symptoms. For undiagnosed individuals, this type of tool would trigger prompt neurology review, potentially improving prognosis. Developing a screening tool focused on detecting neurogenic origins could support earlier recognition and management of many neurological conditions associated with pelvic organ dysfunction

Understanding preferences for low-carbon technologies, retrofit and ownership models for domestic heating in the UK

Preprint versio

TraN variants mediate conjugation species specificity of IncA/C, IncH and Acinetobacter baumannii plasmids

IncA/C and IncH plasmids commonly carry antimicrobial resistance genes, notably blaNDM-1. Although these plasmids disseminate among Gram-negative pathogens via conjugation, the mechanisms underlying mating pair stabilisation (MPS) and conjugation species specificity remain poorly understood. In IncF plasmids, MPS is mediated by interactions between outer membrane proteins (OMP) encoded by the plasmids in the donor (TraN) and by the chromosome in the recipient. Using the Plascad database, we extracted 1,436 TraN sequences from 1517 plasmids: 62.5% (898/1,436), mainly in IncF plasmids, are 550–660aa (we renamed TraN short, TraNS); 15% (216/1,436), in IncA/C plasmids, are 880–950aa (TraN medium, TraNM); and 11% (160/1,436), in IncH plasmids, are 1,050–1,070aa (TraN long, TraNL). One TraN, found in six plasmids from Acinetobacter baumannii (891aa), was designated TraN V-shaped (TraNV). Like TraNS, TraNM and TraNL contain a base and one distal tip domain essential for conjugation, whereas TraNV has a base and two distinct tip domains forming a V-shaped structure. TraNM, TraNL and TraNV determine conjugation species specificity, with TraNL cooperating with OmpA. Tip swapping reverses conjugation specificity, revealing how TraNM and TraNL diversity influence plasmid host range and AMR dissemination. Our new data reveal the molecular basis of plasmid host specificity and broaden our understanding of how conjugation drives the dissemination of antimicrobial resistance genes among clinically relevant bacteria

Hybrid frequency-phase-shift modulation with natural synchronous rectification for CLLC converters

Synchronous rectification (SR) is an effective method to improve the efficiency of the CLLC resonant converter. However, existing SR methods typically rely on calculating specific SR angles based on pulse frequency modulation (PFM) or phase shift modulation (PSM), which do not overcome the intrinsic efficiency drawbacks of these modulations. To address this, this paper proposes a hybrid modulation that integrates switching frequency and phase shift to achieve natural SR and modulation optimization simultaneously. Rather than SR angle calculation, the proposed modulation combines the two control variables to synchronize the operation of the primary side switches and secondary side rectifiers (diodes or SR switches). This alignment enables natural SR by gating the secondary switches concurrently with the primary switches. Furthermore, the proposed modulation reduces the RMS value of inductor current and extends the ZVS range compared to PSM. In contrast to PFM, the proposed method operates at a lower switching frequency, thereby reducing switching losses. The modulation is achieved by a closed-loop control for switching frequency and a precomputed fitting curve for phase shift. A series of experimental results have verified the effectiveness of the proposed modulation

Graph neural networks with hybrid local-global attention for effective prediction of mechanical response in structures

Graph Neural Networks (GNNs) are emerging as a transformative approach for predicting mechanical response in structures by naturally encoding unstructured finite element meshes as graphs. While traditional finite element analysis (FEA) provides trusted solutions for mechanics problems, it encounters significant computational and scalability bottlenecks. Existing machine learning approaches using regular grids fail to capture the irregular nature of real-world meshes, whereas standard GNNs suffer from over-smoothing and limited long-range information propagation that compromise accuracy. To overcome these limitations, we present a Graph Transformer methodology that implements an encoder-processor-decoder framework augmented with a frequency-controlled hybrid local-global attention mechanism, which systematically bridges local mesh connectivity with global information awareness across the computational domain. Our approach integrates seamlessly with real-world FEA workflows through an automated FEM-to-GNN pipeline that converts FE simulations to graph representations and generates training datasets directly from commercial solvers, enabling rapid deployment across various engineering applications without manual preprocessing bottlenecks. We validate our approach on open holes Carbon Fibre Reinforced Polymer (CFRP) laminate plates under various loading conditions and geometric configurations, and extend validation to nonlinear woven composites exhibiting plasticity and progressive damage under shear-dominated loadings, employing systematic hyperparameter optimisation with Tree-structured Parzen Estimator (TPE) sampling and mesh convergence studies. The linear case demonstrates excellent predictive accuracy (R2 = 0.98, RMSE = 0.00028) with 70× speedup over equivalent-accuracy FEA and 58 − 64% reduction in training and validation loss compared to standard message-passing GNN architectures without attention mechanisms, while the nonlinear case achieves R2 = 0.97 (RMSE = 0.0013) with 660× speedup

Comparative evaluation of dielectric liquids for single-phase immersion cooling of electronics

Rising heat fluxes in wide-bandgap power electronics and data center hardware have renewed interest in single-phase immersion cooling (SPIC) as a simple, scalable alternative to two-phase indirect and immersion cooling systems. However, the lack of standardized, property-aware liquid coolant benchmarks has complicated engineering decisions. This study offers a comprehensive evaluation of the thermal and electrical performance of various dielectric liquids for SPIC of electronic devices. An analysis framework was developed using a figure of merit (FOM) based on natural convection correlations, allowing for property sensitivity and weight-factor assessments across different operating liquid temperatures. A buoyancy-driven SPIC experimental platform, featuring a commercial Infineon 10 mm × 12 mm footprint super junction (SJ) silicon MOSFET, was built to measure the cooling and electrical insulation performance of 10 commercial dielectric fluids and 5 candidate chemistries not yet available commercially. Results reveal that high fluid thermal conductivity and density strongly improve cooling performance, while dynamic viscosity primarily limits it. Experimental data categorize the tested fluids into three tiers of cooling effectiveness: four commercial fluids in tier 3 with lowest junction-to-coolant thermal resistances of 7.0-7.8 K/W (3267 - 3745 mm2 ∙ K/W), seven in tier 2 with thermal resistances of 5.9-6.4 K/W (2816 - 3076 mm2∙ K/W), and four candidate fluids in tier 1 achieving the lowest resistances of 4.9-5.4 K/W (2320 - 2564 mm2∙ K/W). Electrical insulation tests show most fluids maintain leakage currents below 5 nA at 550 V. These findings offer clear guidelines for dielectric fluid selection, supporting SPIC deployment in data centers, battery thermal management systems, and power electronics applications