Experiments were performed that (i) document the effect of the steady spanwise buffer layer blowing on the mean characteristics of the turbulent boundary layer for a range of momentum thickness Reynolds numbers from 4760 to 10 386, and (ii) document the effect of the buffer layer blowing on the unsteady characteristics and coherent vorticity in a boundary layer designed to provide sufficiently high spatial resolution. The spanwise buffer layer blowing of the order of $u_{\tau }$ is produced by a surface array of pulsating direct current (pulsed-DC) plasma actuators. This was found to substantially reduce the wall shear stress that was directly measured with a floating element coupled with a force sensor. The direct wall shear measurements agreed with values derived using the Clauser method to within $\pm 0.85$ %. The degree to which the buffer layer blowing affected $\tau _w$ was found to primarily depend on the inner variable spanwise spacing between the pulsed-DC actuator electrodes, i.e. ‘blowing sites’. Utilizing pairs of $[u,v]$ and $[u,w]$ hot-wire sensors, the latter experiments correlated significant reductions in the $\omega _y$ and $\omega _x$ vorticity components that resulted from the buffer layer blowing and translated into lower Reynolds stresses and turbulence production. The time scale to which these observed changes in the boundary layer characteristics would return to the baseline condition was subsequently documented. This revealed a recovery length of $x^+ \approx 86\,000$ that translated to a streamwise fetch of $x \approx 66\delta$. Finally, a comparison with the recent work by Cheng et al. (2021, J. Fluid Mech. vol. 918, A24) and Wei & Zhou (2024 in TSFP13, June 25–28, 2024) that followed our experimental approach to achieve comparable wall shear stress (drag) reductions has led to a new scaling based on the baseline boundary layer $\textit{Re}_{\tau }$ and buffer layer blowing velocity.

Attention-deficit hyperactivity disorder (ADHD) is one of the most common mental disorders in adolescents, and a full syndrome diagnosis requires a combination of persistent symptoms. In a multicentre cross-sectional study from Italy using a non-clinical sample from a secondary school comprising 440 adolescents, published in this issue of BJPsych Open, Gostoli et al examined whether unhealthy lifestyle habits are linked to both clinical manifestation of ADHD and subclinical symptomatology. In line with the literature, the authors demonstrate an association between clinical ADHD diagnosis, unhealthy lifestyle behaviours and psychosocial impairments. Modifiable, adverse lifestyle behaviours are also prevalent in subclinical ADHD manifestations. This observation may be important for child and adolescent psychiatry when considering targeted health promotion approaches that delay or prevent progression from subclinical to clinical ADHD. In this article, we discuss from a clinical perspective the putative relevance of addressing subclinical ADHD symptoms in the context of the existing literature.

Stable water isotope records of six firn cores retrieved from two adjacent plateaus on the northern Antarctic Peninsula between 2014 and 2016 are presented and investigated for their connections with firn-core glacio-chemical data, meteorological records and modelling results. Average annual accumulation rates of 2500 kg m−2 a−1 largely reduce the modification of isotopic signals in the snowpack by post-depositional processes, allowing excellent signal preservation in space and time. Comparison of firn-core and ECHAM6-wiso modelled δ18O and d-excess records reveals a large agreement on annual and sub-annual scales, suggesting firn-core stable water isotopes to be representative of specific synoptic situations. The six firn cores exhibit highly similar isotopic patterns in the overlapping period (2013), which seem to be related to temporal changes in moisture sources rather than local near-surface air temperatures. Backward trajectories calculated with the HYSPLIT model suggest that prominent δ18O minima in 2013 associated with elevated sea salt concentrations are related to long-range moisture transport dominated by westerly winds during positive SAM phases. In contrast, a broad δ18O maximum in the same year accompanied by increased concentrations of black carbon and mineral dust corresponds to the advection of more locally derived moisture with northerly flow components (South America) when the SAM is negative.

Political debates are structured by underlying conflict dimensions, such as left-right and economic and cultural ideology, which form the basis for voter choice and party competition. However, we know little about how voters arrive at perceptions of parties' positions on these dimensions. We examine how the emphasis parties place on the different issues that make up a higher-level ideological dimension affects perceptions of their position on that dimension. Using two population-based survey experiments, we present respondents with either short or long statements that communicate the same issue stances. We then test whether the length of statements affects positional perceptions on the higher-level dimension. The empirical results show support for our hypotheses and imply that political parties – and the context in which they compete – can affect their perceived position even if underlying issue stances remain stable.

In this work, we focus on a multi-hole pressure-probe-based flow measurement system for wind tunnel measurements that provides real-time feedback to a robot probe-manipulator, rendering the system autonomous. The system relies on a novel, computationally efficient flow analysis technique that translates the probe's point measurements of velocity and pressure into an updatable mean flow map that is accompanied by an uncertainty metric. The latter provides guidance to the manipulator when planning the optimal probe path. The probe is then guided by the robot in the flow domain until an available time budget has been exhausted, or until the uncertainty metric falls below a prescribed target threshold in the entire flow domain. We assess the capabilities of our new measurement system using computational fluid dynamics data, for which the ground truth is available in the form of a mean flow field. An application in a real wind tunnel setting is provided as well.

Hyperbolic random graphs (HRGs) and geometric inhomogeneous random graphs (GIRGs) are two similar generative network models that were designed to resemble complex real-world networks. In particular, they have a power-law degree distribution with controllable exponent $\beta$ and high clustering that can be controlled via the temperature $T$.

We present the first implementation of an efficient GIRG generator running in expected linear time. Besides varying temperatures, it also supports underlying geometries of higher dimensions. It is capable of generating graphs with ten million edges in under a second on commodity hardware. The algorithm can be adapted to HRGs. Our resulting implementation is the fastest sequential HRG generator, despite the fact that we support non-zero temperatures. Though non-zero temperatures are crucial for many applications, most existing generators are restricted to $T = 0$. We also support parallelization, although this is not the focus of this paper. Moreover, we note that our generators draw from the correct probability distribution, that is, they involve no approximation.

Besides the generators themselves, we also provide an efficient algorithm to determine the non-trivial dependency between the average degree of the resulting graph and the input parameters of the GIRG model. This makes it possible to specify the desired expected average degree as input.

Moreover, we investigate the differences between HRGs and GIRGs, shedding new light on the nature of the relation between the two models. Although HRGs represent, in a certain sense, a special case of the GIRG model, we find that a straightforward inclusion does not hold in practice. However, the difference is negligible for most use cases.

We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our approach involves constructing and training two neural networks: an autoencoder that finds a proper latent space representation (LSR) of plasma state by compressing the multi-modal diagnostic measurements and a forward model using multi-layer perception (MLP) that projects a set of plasma control parameters to its corresponding LSR. By combining the forward model and the decoder network from autoencoder, this new data-driven surrogate model is able to predict a consistent set of diagnostic measurements based on a few plasma control parameters. In order to ensure that the crucial detachment physics is correctly captured, highly efficient 1D UEDGE model is used to generate training and validation data in this study. The benchmark between the data-driven surrogate model and UEDGE simulations shows that our surrogate model is capable of providing accurate detachment prediction (usually within a few per cent relative error margin) but with at least four orders of magnitude speed-up, indicating that performance-wise, it has the potential to facilitate integrated tokamak design and plasma control. Comparing with the widely used two-point model and/or two-point model formatting, the new data-driven model features additional detachment front prediction and can be easily extended to incorporate richer physics. This study demonstrates that the complicated divertor and scrape-off-layer plasma state has a low-dimensional representation in latent space. Understanding plasma dynamics in latent space and utilising this knowledge could open a new path for plasma control in magnetic fusion energy research.

This article is a clinical guide which discusses the “state-of-the-art” usage of the classic monoamine oxidase inhibitor (MAOI) antidepressants (phenelzine, tranylcypromine, and isocarboxazid) in modern psychiatric practice. The guide is for all clinicians, including those who may not be experienced MAOI prescribers. It discusses indications, drug-drug interactions, side-effect management, and the safety of various augmentation strategies. There is a clear and broad consensus (more than 70 international expert endorsers), based on 6 decades of experience, for the recommendations herein exposited. They are based on empirical evidence and expert opinion—this guide is presented as a new specialist-consensus standard. The guide provides practical clinical advice, and is the basis for the rational use of these drugs, particularly because it improves and updates knowledge, and corrects the various misconceptions that have hitherto been prominent in the literature, partly due to insufficient knowledge of pharmacology. The guide suggests that MAOIs should always be considered in cases of treatment-resistant depression (including those melancholic in nature), and prior to electroconvulsive therapy—while taking into account of patient preference. In selected cases, they may be considered earlier in the treatment algorithm than has previously been customary, and should not be regarded as drugs of last resort; they may prove decisively effective when many other treatments have failed. The guide clarifies key points on the concomitant use of incorrectly proscribed drugs such as methylphenidate and some tricyclic antidepressants. It also illustrates the straightforward “bridging” methods that may be used to transition simply and safely from other antidepressants to MAOIs.

OBJECTIVES/GOALS: The Fulkerson Home Food Inventory (HFI) is widely used to assess the home food environment, a key target of behavioral weight loss trials. However, no standardized report is available. We created publicly available procedures to automate and standardize HFI reporting, yielding a personalized report to enhance this measures clinical utility. METHODS/STUDY POPULATION: Parents in the TEENS adolescent behavioral weight loss trial complete the HFI at 0-, 2-, 4-, 8-, and 12m and receive personalized reports at each timepoint. In REDCap, participants identify foods available in their home. HFI syntax is applied to calculate the obesogenic home food availability score. Categories of foods found are identified, with specific guidance provided to enhance their home food environment. Prior to automation, procedures were time intensive and error prone. To address this, HFI data are exported into Excel by a PowerShell (v7.2) command-line script using Python (v3.10) with the REDCap API. Results are calculated with F# (v6.0) using Microsoft Excel Interop API and inserted into a report template with F# using the Microsoft Publisher Interop API. This process is repeated at each timepoint. RESULTS/ANTICIPATED RESULTS: The new automated procedures significantly reduce time to generate reports and enhance accuracy. Procedures yield a 2-page individualized report that includes the obesogenic home food environment score and identifies categories of healthy items found (e.g., fruits, vegetables, whole grains) as well as areas of improvement (e.g., high-fat dairy products, processed meats). Specific items found in each category are identified. The report identifies food found in the home (e.g., chicken nuggets) with suggested healthier substitutions (e.g., lean chicken breast). This syntax and commands will be made publicly available for use in the scientific and clinical community. DISCUSSION/SIGNIFICANCE: These publicly available procedures optimize, automate, and standardize reporting for the HFI. Procedures improve efficiency within large-scale clinical trials and yield a personalized report to enhance the clinical utility of this measure and empower participants to make informed decisions about their health behaviors.

The success and longevity of coalition governments depends on the ability to keep conflicts between coalition members at bay. The risk of such conflicts is often assessed by drawing on proxy measures, such as the ideological heterogeneity among government parties. This article presents a new approach to measuring the atmosphere between government parties. The ‘coalition mood’ is a time-varying measure that draws on applause patterns between coalition partners during legislative debates. The article exemplifies the measurement approach based on automated analyses of over 105,000 plenary debates in Germany and Austria. The article then assesses the measure's face, concurrent and predictive validity. It finds the measure well aligned with qualitative evidence, shows that the coalition mood is correlated with poll ratings of the government parties and helps to predict the duration of legislative processes. The conclusion highlights future applications of the coalition mood for research on coalition politics and public policy.