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.

The next-generation radio astronomy instruments are providing a massive increase in sensitivity and coverage, largely through increasing the number of stations in the array and the frequency span sampled. The two primary problems encountered when processing the resultant avalanche of data are the need for abundant storage and the constraints imposed by I/O, as I/O bandwidths drop significantly on cold storage. An example of this is the data deluge expected from the SKA Telescopes of more than 60 PB per day, all to be stored on the buffer filesystem. While compressing the data is an obvious solution, the impacts on the final data products are hard to predict. In this paper, we chose an error-controlled compressor – MGARD – and applied it to simulated SKA-Mid and real pathfinder visibility data, in noise-free and noise-dominated regimes. As the data have an implicit error level in the system temperature, using an error bound in compression provides a natural metric for compression. MGARD ensures the compression incurred errors adhere to the user-prescribed tolerance. To measure the degradation of images reconstructed using the lossy compressed data, we proposed a list of diagnostic measures, exploring the trade-off between these error bounds and the corresponding compression ratios, as well as the impact on science quality derived from the lossy compressed data products through a series of experiments. We studied the global and local impacts on the output images for continuum and spectral line examples. We found relative error bounds of as much as 10%, which provide compression ratios of about 20, have a limited impact on the continuum imaging as the increased noise is less than the image RMS, whereas a 1% error bound (compression ratio of 8) introduces an increase in noise of about an order of magnitude less than the image RMS. For extremely sensitive observations and for very precious data, we would recommend a $0.1\%$ error bound with compression ratios of about 4. These have noise impacts two orders of magnitude less than the image RMS levels. At these levels, the limits are due to instabilities in the deconvolution methods. We compared the results to the alternative compression tool DYSCO, in both the impacts on the images and in the relative flexibility. MGARD provides better compression for similar error bounds and has a host of potentially powerful additional features.

Synthetic-aperture radar images and mesoscale models show that wind-farm wakes differ from single-turbine wakes. For instance, wind-farm wakes often narrow and do not disperse over long distances, contrasting the broader and more dissipating wakes of individual turbines. In this work, we aim to better understand the mechanisms that govern wind-farm wake behaviour and recovery. Hence we study the wake properties of a $1.6$ GW wind farm operating in conventionally neutral boundary layers with capping-inversion heights $203$, $319$, $507$ and $1001$ m. In shallow boundary layers, we find strong flow decelerations that reduce the Coriolis force magnitude, leading to an anticlockwise wake deflection in the Northern Hemisphere. In deep boundary layers, the vertical turbulent entrainment of momentum adds clockwise-turning flow from aloft into the wake region, leading to a faster recovery rate and a clockwise wake deflection. To estimate the wake properties, we propose a simple function to fit the velocity magnitude profiles along the spanwise direction. In the vertical direction, the wake spreads up to the capping-inversion height, which significantly limits vertical wake development in shallow-boundary-layer cases. In the horizontal direction and for shallow boundary layers, the wake behaves as two distinct mixing layers located at the lateral wake edges, which expand and turn towards their low-velocity side, causing the wake to narrow along the streamwise direction. A detailed analysis of the momentum budget reveals that in deep boundary layers, the wake is predominantly replenished through turbulent vertical entrainment. Conversely, in shallow boundary layers, wakes are mostly replenished by mean flow advection in the spanwise direction.

Background: Recent research has demonstrated that DBS sites in Alzheimer’s (AD) and Parkinson’s (PD) influencing cognition are functionally connected to the subiculum. However, the results are mixed, and it is unclear how or if DBS site-subiculum connectivity can be optimized to improve patient cognition. Methods: We studied how subiculum connectivity influenced cognitive outcomes in both PD (subthalamic nucleus) and AD (fornix) DBS patients (total n = 110). We first confirmed DBS site-subiculum connectivity had opposite cognitive effects in each disease. We next investigated patient factors underlying these opposing effects. Lastly, we related our findings back to clinical practice to guide DBS programming in PD and AD. Results: DBS site-subiculum connectivity correlated with cognitive improvement in AD but decline in PD. This was dependent upon hippocampal atrophy; such that higher subiculum connectivity was beneficial when the hippocampus was atrophic but deleterious when it was intact. Finally, we related our findings back to anatomy with cadaveric dissections and present how DBS stimulation can be optimized to improve patient cognition. Conclusions: DBS site-subiculum connectivity influences cognition but depends on patient factors. Thus, to optimize cognition based on patient factors, DBS electrodes can be programmed to stimulate subregions with higher or lower subiculum connectivity.

In this work we focus on expected flow in porous formations with highly conductive isolated fractures, which are of non-negligible length compared with the scales of interest. Accordingly, the definition of a representative elementary volume (REV) for flow and transport predictions may not be possible. Recently, a non-local kernel-based theory for flow in such formations has been proposed. There, fracture properties like their expected pressure are represented as field quantities. Unlike existing models, where fractures are assumed to be small compared with the scale of interest, a non-local kernel function is used to quantify the expected flow transfer between a point in the fracture domain and a potentially distant point in the matrix continuum. The transfer coefficient implied by the kernel is a function of the fracture characteristics that are in turn captured statistically. So far the model has successfully been applied for statistically homogeneous cases. In the present work we demonstrate the applicability for heterogeneous cases with spatially varying fracture statistics. Moreover, a scaling law is presented that relates the transfer coefficient to the fracture characteristics. Test cases involving discontinuously and continuously varying fracture statistics are presented, and the validity of the scaling law is demonstrated.

Understanding firn densification is essential for interpreting ice core records, predicting ice sheet mass balance, elevation changes and future sea-level rise. Current models of firn densification on the Antarctic ice sheet (AIS), such as the Herron and Langway (1980) model are either simple semi-empirical models that rely on sparse climatic data and surface density observations or complex physics-based models that rely on poorly understood physics. In this work, we introduce a deep learning technique to study firn densification on the AIS. Our model, FirnLearn, evaluated on 225 cores, shows an average root-mean-square error of 31 kg m−3 and explained variance of 91%. We use the model to generate surface density and the depths to the $550\,\mathrm{kg\,m}^{-3}$ and $830\,\mathrm{kg\,m}^{-3}$ density horizons across the AIS to assess spatial variability. Comparisons with the Herron and Langway (1980) model at ten locations with different climate conditions demonstrate that FirnLearn more accurately predicts density profiles in the second stage of densification and complete density profiles without direct surface density observations. This work establishes deep learning as a promising tool for understanding firn processes and advancing towards a universally applicable firn model.

Objectives/Goals: Our objective is to examine patient and community perspectives on hospital actions that signify accountability to healthcare equity; part of our overall goal is to identify equity measure concepts representative of community perspectives and priorities for future hospital accountability programs. Methods/Study Population: We conducted a qualitative thematic analysis of secondary data – 32 focus group transcripts from our hospital’s Community Health Needs Assessment (CHNA). A tri-annual CHNA is required of nonprofit hospitals to maintain tax exemption. Diverse participants were recruited from our hospital’s large catchment area. Coding focused on responses to 6 pertinent questions. We adapted the National Committee for Quality Assurance, “Health Equity Measurement Framework for Medicaid Accountability” which consists of 5 domains (access, clinical, experience, structure, and social) to guide the development of our a priori coding tree and subsequent analysis. Two coders double-coded 25% of transcripts. The multidisciplinary research team, including community partners, met iteratively to extract and refine themes. Results/Anticipated Results: We organized our analysis by our conceptual framework’s 5 measurement domains. The “access” and “experience” domains were the most salient for participants. We defined “access” by four sub-domains: financial access, physical access, communication access, and navigability; and “experience” by two subdomains: inclusivity and accomodation. Beyond discussing concepts within these measurement domains, participants debated the “scope” of the hospital’s role with regard to healthcare equity. While some did not think “it was the hospitals” responsibility to give people access to good jobs or fair pay, education…, “other participants felt that healthcare involves not just addressing peoples’ physical health but. their housing… because how can someone take care of their health when they are homeless?” Discussion/Significance of Impact: When asked about hospital accountability to healthcare equity, “access” and “experiences” of care are the most salient measurement domains for patients and communities. The “scope” of the hospital’s role is debated. Policy and health system leaders can apply these perspectives to equity measurement initiatives.