We study linear convective instability in a mushy layer formed by solidification of a binary alloy, cooled by either an isothermal perfectly conducting boundary or an imperfectly conducting boundary where the surface temperature depends linearly on the surface heat flux. A companion paper (Hitchen & Wells, J. Fluid Mech., 2025, in press) showed how thermal and salinity conditions impact mush structure. We here quantify the impact on convective instability, described by a Rayleigh number characterising the ratio of buoyancy to dissipative mechanisms. Two limits emerge for a perfectly conducting boundary. When the salinity-dependent freezing-point depression is large versus the temperature difference across the mush, convection penetrates throughout the depth of a high-porosity mush. The other limit, which we will call the Stefan limit, has small freezing-point depression and inhibits convection, which localises at onset to a high-porosity boundary layer near the mush–liquid interface. Scaling arguments characterise variation of the critical Rayleigh number and wavenumber based on the potential energy contained in order-one aspect ratio convective cells over the high-porosity regions. The Stefan number characterises the ratio of latent and sensible heats, and has moderate impact on stability via modification of the background temperature and porosity. For imperfectly conducting boundaries, the changing surface temperature causes stability to decrease over time in the limit of large freezing-point depression, but in the Stefan limit combines with the decreasing porosity to yield non-monotonic variation of the critical Rayleigh number. We discuss the implications for convection in growing sea ice.

We model transient mushy-layer growth for a binary alloy solidifying from a cooled boundary, characterising the impact of liquid composition and thermal growth conditions on the mush porosity and growth rate. We consider cooling from a perfectly conducting isothermal boundary, and from an imperfectly conducting boundary governed by a linearised thermal boundary condition. For an isothermal boundary we characterise different growth regimes depending on a concentration ratio, which can also be viewed as characterising the ratio of composition-dependent freezing point depression versus the temperature difference across the mushy layer. Large concentration ratio leads to high porosity throughout the mushy layer and an asymptotically simplified model for growth with an effective thermal diffusivity accounting for latent heat release from internal solidification. Low concentration ratio leads to low porosity throughout most of the mushy layer, except for a high-porosity boundary layer localised near the mush–liquid interface. We identify scalings for the boundary-layer thickness and mush growth rate. An imperfectly conducting boundary leads to an initial lag in the onset of solidification, followed by an adjustment period, before asymptoting to the perfectly conducting state at large time. We develop asymptotic solutions for large concentration ratio and large effective heat capacity, and characterise the mush structure, growth rate and transition times between the regimes. For low concentration ratio the high porosity zone spans the full mush depth at early times, before localising near the mush–liquid interface at later times. Such variation of porosity has important implications for the properties and biological habitability of mushy sea ice.

This paper assesses the psychometric value of allowing test-takers choice in standardized testing. New theoretical results examine the conditions where allowing choice improves score precision. A hierarchical framework is presented for jointly modeling the accuracy of cognitive responses and item choices. The statistical methodology is disseminated in the ‘cIRT’ R package. An ‘answer two, choose one’ (A2C1) test administration design is introduced to avoid challenges associated with nonignorable missing data. Experimental results suggest that the A2C1 design and payout structure encouraged subjects to choose items consistent with their cognitive trait levels. Substantively, the experimental data suggest that item choices yielded comparable information and discrimination ability as cognitive items. Given there are no clear guidelines for writing more or less discriminating items, one practical implication is that choice can serve as a mechanism to improve score precision.

Restricted latent class models (RLCMs) provide an important framework for supporting diagnostic research in education and psychology. Recent research proposed fully exploratory methods for inferring the latent structure. However, prior research is limited by the use of restrictive monotonicity condition or prior formulations that are unable to incorporate prior information about the latent structure to validate expert knowledge. We develop new methods that relax existing monotonicity restrictions and provide greater insight about the latent structure. Furthermore, existing Bayesian methods only use a probit link function and we provide a new formulation for using the exploratory RLCM with a logit link function that has an additional advantage of being computationally more efficient for larger sample sizes. We present four new Bayesian formulations that employ different link functions (i.e., the logit using the Pòlya—gamma data augmentation versus the probit) and priors for inducing sparsity in the latent structure. We report Monte Carlo simulation studies to demonstrate accurate parameter recovery. Furthermore, we report results from an application to the Last Series of the Standard Progressive Matrices to illustrate our new methods.

Evidence-based insertion and maintenance bundles are effective in reducing the incidence of central line-associated bloodstream infections (CLABSI) in intensive care unit (ICU) settings. We studied the adoption and compliance of CLABSI prevention bundle programs and CLABSI rates in ICUs in a large network of acute care hospitals across Canada.

Most infections with pandemic Vibrio cholerae are thought to result in subclinical disease and are not captured by surveillance. Previous estimates of the ratio of infections to clinical cases have varied widely (2 to 100 infections per case). Understanding cholera epidemiology and immunity relies on the ability to translate between numbers of clinical cases and the underlying number of infections in the population. We estimated the infection incidence during the first months of an outbreak in a cholera-naive population using a Bayesian vibriocidal antibody titer decay model combining measurements from a representative serosurvey and clinical surveillance data. 3,880 suspected cases were reported in Grande Saline, Haiti, between 20 October 2010 and 6 April 2011 (clinical attack rate 18.4%). We found that more than 52.6% (95% Credible Interval (CrI) 49.4-55.7) of the population ≥2 years showed serologic evidence of infection, with a lower infection rate among children aged 2-4 years (35.5%; 95%CrI 24.2-51.6) compared with people ≥5 years (53.1%; 95%CrI 49.4-56.4). This estimated infection rate, nearly three times the clinical attack rate, with underdetection mainly seen in those ≥5 years, has likely impacted subsequent outbreak dynamics. Our findings show how seroincidence estimates improve understanding of links between cholera burden, transmission dynamics and immunity.

Diffuse optical spectroscopy (DOS) techniques characterize scattering media by examining their optical response to laser illumination. Time-domain DOS methods involve illuminating the medium with a laser pulse and using a fast photodetector to measure the time-dependent intensity of light that exits the medium after multiple scattering events. While DOS research traditionally focused on characterizing biological tissues, we demonstrate that time-domain diffuse optical measurements can also be used to characterize snow. We introduce a model that predicts the time-dependent reflectance of a dry snowpack as a function of its density, grain size, and black carbon content. We develop an algorithm that retrieves these properties from measurements at two wavelengths. To validate our approach, we assembled a two-wavelength lidar system to measure the time-dependent reflectance of snow samples with varying properties. Rather than measuring direct surface returns, our system captures photons that enter and exit the snow at different points, separated by a small distance (4–10 cm). We observe clear, linear correlations between our retrievals of density and black carbon concentration, and ground truth. For black carbon concentration the correlation is nearly one-to-one. We also find that our method is capable of distinguishing between small and large grain sizes.

Using a national audit of mayors in the United States, this paper examines responsiveness to Latine lesbian and gay constituents who request that their city issue an LGBTQ pride proclamation. Drawing on theories of intersectionality, descriptive representation, and political institutions, we articulate the conditions under which mayors are responsive to public-facing constituency service requests to issue LGBTQ pride proclamations. We find that mayors are more responsive to requests from lesbian couples than gay couples. In addition, baseline responsiveness to our inquiry was influenced by mayors’ identity characteristics. LGBTQ mayors were more likely to respond than non-LGBTQ mayors, but Latine mayors were less likely to respond than non-Latine mayors. In addition, mayors who represent cities where nondiscrimination ordinances protect LGBT people from discrimination were more responsive than mayors who represent cities where LGBT people are not protected from discrimination. These findings demonstrate how intersectional frameworks can advance audit experiments and that shared descriptive characteristics do not inevitably translate into responsiveness, a common assumption in single-axis studies of representation.

OBJECTIVES/GOALS: We report an automated manufacturing system, and a series of cylindrical multi-layer microfluidic artificial lungs manufactured with the system and tested for fluidic fidelity and function. METHODS/STUDY POPULATION: A Roll-to-Roll (R2R) system to engrave multiple-layer devices was assembled. A 100 um-thick silicone sheet passes through an embedded CO2 laser engraver, which creates patterns of any geometry on the surface. The sheet is plasma-activated to create an irreversible bond, and rerolled into a processed device. Unlike typical applications of R2R, this process is synchronized to achieve consistent radial positioning. This allows the fluidics in the device to be accessed without being unwrapped. The result is a cylindrical core surrounded by many layers of microfluidic channels that can be accessed through the side of the device or through fluidic vias. This core is cut to expose the microfluidic layers, and then installed into a housing which routes the fluids into their respective microfluidic flow paths. RESULTS/ANTICIPATED RESULTS: To demonstrate the capabilities of the R2R manufacturing system, this method was used to manufacture multi-layer microfluidic artificial lungs (µALs). Gas and blood flow channels are engraved in alternating layers and routed orthogonally. The close proximity of gas and blood separated by gas-permeable PDMS permits CO2 and O2 exchange. Three µALs were successfully manufactured. Their flow paths were visualized using dyed water and checked for leaks. Then they were evaluated using water for pressure drop and CO2 gas-exchange. The top performing device had 15 alternating blood and gas layers. Test with whole blood demonstrated oxygenation from venous (70%) saturation levels to arterial (95%) saturation levels at a flow rate of 3 ml/min. DISCUSSION/SIGNIFICANCE: The ability to cost-effectively produce high surface area microfluidic devices would bring many small-scale technologies from the realm of research to clinical and commercial applications. In particular, most microfluidic artificial lungs only have small rated flows due to a lack of manufacturing processes able to create high surface area devices.

OBJECTIVES/GOALS: Empowering the Participant Voice (EPV) is a 6-CTSA Rockefeller-led collaboration to developcustom REDCap infrastructure to collect participant feedback using the validated Research Participant Perception Survey (RPPS), demonstrate its value in use cases, and disseminate it for broad adoption. METHODS/STUDY POPULATION: The EPV team developed data and survey implementation standards, and specifications for the dashboard and multi-lingual RPPS/REDCap project XML file. The VUMC built a custom At-a-Glance Dashboard external module that displays Top Box scores (percent best answer), with conditional formatting to aid analysis, and response/completion rates. Results populate site dashboards, and aggregate to a multi-site dashboard for benchmarking. Results can be filtered by participant/study characteristics. Sites developed individual use cases, leveraging local infrastructure, initiatives and stakeholder input. Infrastructure and guides were designed for dissemination through public websites. RESULTS/ANTICIPATED RESULTS: Five sites sent 23,797surveys via email, patient portal or SMS. 4,133 (19%) participants diverse in age, race, and ethnicity, returned responses. Sites analyzed their data and acted on selected findings, improving recruitment, communication and feeling valued. Aggregate scores for feeling listened to and respected were hight (>90%%); scores for feeling prepared by the consent process were lower (57-77%) and require action. Some groups experiences were better than others. Sites differed significantly in some scores. Dissemination of EPV is underway. Infrastructure and guides are downloadable free of charge, with advice from the EPV team. In 2023, a sixth site began piloting a lower literacy survey version and syncing data to the consortium dashboard. DISCUSSION/SIGNIFICANCE: The EPV RPPS/REDCap infrastructure enabled sites to collect participant feedback, identify actionable findings and benchmark with peers. Stakeholders and collaborators designed and tested local initiatives to increase responses and diversity, address disparities, and discover better practices.

Empowering the Participant Voice (EPV) is an NCATS-funded six-CTSA collaboration to develop, demonstrate, and disseminate a low-cost infrastructure for collecting timely feedback from research participants, fostering trust, and providing data for improving clinical translational research. EPV leverages the validated Research Participant Perception Survey (RPPS) and the popular REDCap electronic data-capture platform. This report describes the development of infrastructure designed to overcome identified institutional barriers to routinely collecting participant feedback using RPPS and demonstration use cases. Sites engaged local stakeholders iteratively, incorporating feedback about anticipated value and potential concerns into project design. The team defined common standards and operations, developed software, and produced a detailed planning and implementation Guide. By May 2023, 2,575 participants diverse in age, race, ethnicity, and sex had responded to approximately 13,850 survey invitations (18.6%); 29% of responses included free-text comments. EPV infrastructure enabled sites to routinely access local and multi-site research participant experience data on an interactive analytics dashboard. The EPV learning collaborative continues to test initiatives to improve survey reach and optimize infrastructure and process. Broad uptake of EPV will expand the evidence base, enable hypothesis generation, and drive research-on-research locally and nationally to enhance the clinical research enterprise.

Automatic dialog systems have become a mainstream part of online customer service. Many such systems are built, maintained, and improved by customer service specialists, rather than dialog systems engineers and computer programmers. As conversations between people and machines become commonplace, it is critical to understand what is working, what is not, and what actions can be taken to reduce the frequency of inappropriate system responses. These analyses and recommendations need to be presented in terms that directly reflect the user experience rather than the internal dialog processing.

This paper introduces and explains the use of Actionable Conversational Quality Indicators (ACQIs), which are used both to recognize parts of dialogs that can be improved and to recommend how to improve them. This combines benefits of previous approaches, some of which have focused on producing dialog quality scoring while others have sought to categorize the types of errors the dialog system is making. We demonstrate the effectiveness of using ACQIs on LivePerson internal dialog systems used in commercial customer service applications and on the publicly available LEGOv2 conversational dataset. We report on the annotation and analysis of conversational datasets showing which ACQIs are important to fix in various situations.

The annotated datasets are then used to build a predictive model which uses a turn-based vector embedding of the message texts and achieves a 79% weighted average f1-measure at the task of finding the correct ACQI for a given conversation. We predict that if such a model worked perfectly, the range of potential improvement actions a bot-builder must consider at each turn could be reduced by an average of 81%.