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Free, publicly-accessible full text available November 28, 2023
Observations and modeling of symmetric instability in the ocean interior in the Northwestern Equatorial PacificAbstract Symmetric instability is a mechanism that can transfer geostrophic kinetic energy to overturning and dissipation. To date, symmetric instability has only been recognized to occur at the ocean surface or near topographic boundary layers. Analyses of direct microstructure measurements reveal enhanced dissipation caused by symmetric instability in the northwestern equatorial Pacific thermocline, which provides the first observational evidence of subsurface symmetric instability away from boundaries. Enhanced subsurface cross-equatorial exchange provides the negative potential vorticity needed to drive the symmetric instability, which is well reproduced by numerical modeling. These results suggest a new route to energy dissipation for large scale currents, and hence a new ocean turbulent mixing process in the ocean interior. Given the importance of vertical mixing in the evolution of equatorial thermocline, models may need to account for this mechanism to produce more reliable climate projections.Free, publicly-accessible full text available December 1, 2023
Free, publicly-accessible full text available October 9, 2023
Efficient Uncertainty Quantification of Stripline Pulse Response using Singular Value Decomposition and Delay ExtractionFree, publicly-accessible full text available September 1, 2023
Objective Communication Patterns Associated With Team Member Effectiveness in Real-World Virtual Teams
We explore the relationships between objective communication patterns displayed during virtual team meetings and established, qualitative measures of team member effectiveness.
A key component of teamwork is communication. Automated measures of objective communication patterns are becoming more feasible and offer the ability to measure and monitor communication in a scalable, consistent and continuous manner. However, their validity in reflecting meaningful measures of teamwork processes are not well established, especially in real-world settings.
We studied real-world virtual student teams working on semester-long projects. We captured virtual team meetings using the Zoom video conferencing platform throughout the semester and periodic surveys comprising peer ratings of team member effectiveness. Leveraging audio transcripts, we examined relationships between objective measures of speaking time, silence gap duration and vocal turn-taking and peer ratings of team member effectiveness.
Speaking time, speaking turn count, degree centrality and (marginally) speaking turn duration, but not silence gap duration, were positively related to individual-level team member effectiveness. Time in dyadic interactions and interaction count, but not interaction length, were positively related to dyad-level team member effectiveness.
Our study highlights the relevance of objective measures of speaking time and vocal turn-taking to team member effectiveness in virtual project-based teams, supporting the validitymore »
Our approach offers a scalable, easy-to-use method for measuring communication patterns and team member effectiveness in virtual teams and opens the opportunity to study these patterns in a more continuous and dynamic manner.Free, publicly-accessible full text available December 22, 2023
Linear quantile regression is a powerful tool to investigate how predictors may affect a response heterogeneously across different quantile levels. Unfortunately, existing approaches find it extremely difficult to adjust for any dependency between observation units, largely because such methods are not based upon a fully generative model of the data. For analysing spatially indexed data, we address this difficulty by generalizing the joint quantile regression model of Yang and Tokdar (Journal of the American Statistical Association, 2017, 112(519), 1107–1120) and characterizing spatial dependence via a Gaussian or t-copula process on the underlying quantile levels of the observation units. A Bayesian semiparametric approach is introduced to perform inference of model parameters and carry out spatial quantile smoothing. An effective model comparison criteria is provided, particularly for selecting between different model specifications of tail heaviness and tail dependence. Extensive simulation studies and two real applications to particulate matter concentration and wildfire risk are presented to illustrate substantial gains in inference quality, prediction accuracy and uncertainty quantification over existing alternatives.