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The determination of the temperature in and above the slab in subduction zones, using models where the top of the slab is precisely known, is important to test hypotheses regarding the causes of arc volcanism and intermediate-depth seismicity. While 2D and 3D models can predict the thermal structure with high precision for fixed slab geometries, a number of regions are characterized by relatively large geometrical changes over time. Examples include the flat slab segments in South America that evolved from more steeply dipping geometries to the present day flat slab geometry. We devise, implement, and test a numerical approach to model the thermal evolution of a subduction zone with prescribed changes in slab geometry over time. Our numerical model approximates the subduction zone geometry by employing time dependent deformation of a Bézier spline that is used as the slab interface in a finite element discretization of the Stokes and heat equations. We implement the numerical model using the FEniCS open source finite element suite and describe the means by which we compute approximations of the subduction zone velocity, temperature, and pressure fields. We compute and compare the 3D time evolving numerical model with its 2D analogy at cross-sections for slabs that evolve to the present-day structure of a flat segment of the subducting Nazca plate. 

Favorable relaxation processes, high-field spectral properties, and biological compatibility have made spin-7/2 Gd3+-based spin labels an increasingly popular choice for protein structure studies using high-field electron paramagnetic resonance. However, high-field relaxation and decoherence in ensembles of half-integer high-spin systems, such as Gd3+, remain poorly understood. We report spin–lattice (T1) and phase memory (TM) relaxation times at 8.6 T (240 GHz), and we present the first comprehensive model of high-field, high-spin decoherence accounting for both the electron spin concentration and temperature. The model includes four principal mechanisms driving decoherence: energy-conserving electron spin flip-flops, direct “T1” spin–lattice relaxation-driven electron spin flip processes, indirect T1-driven flips of nearby electron spins, and nuclear spin flip-flops. Mechanistic insight into decoherence can inform the design of experiments making use of Gd3+ as spin probes or relaxivity agents and can be used to measure local average interspin distances as long as 17 nm. 

This study applied Transmodal Analysis (TMA), a newly developed quantitative ethnographic approach, to examine whether and how virtual patient simulations can aid in educating undergraduate nursing students with competencies that exemplify practice-ready nurses. Multimodal transcripts capturing patient interactions, exam actions, and documentation were obtained from two students who used Elsevier’s Shadow Health® Digital Clinical Experiences (DCE) in Fall 2022 and Spring 2023. Patient scenarios were situated in three content areas (Gerontology, Mental Health, and Community Health) and two assignment types (focused exam and contact tracing). In each scenario, similar patterns of engagement were observed for both students as they completed learning activities such as collecting patient data and establishing a caring relationship. These activities—guided by the instructional design of DCE—indicated how students practiced recognizing and analyzing cues, subjective assessment, diagnosing and prioritizing hypotheses, generating solutions, evaluating outcomes, therapeutic communication, and care coordination and management in relation to each patient’s needs and conditions. A statistical difference was observed between competencies practiced while completing focused exam and contact tracing assignments. This study provides evidence for using simulations to facilitate competency-based education in nursing. Additionally, it provides motivation for using Transmodal Analysis combined with Ordered Network Analysis (T/ONA) to advance quantitative ethnography research in health care and health professions education. 

Abstract One of the most conspicuous signals of climate change in high‐latitude tundra is the expansion of ice wedge thermokarst pools. These small but abundant water features form rapidly in depressions caused by the melting of ice wedges (i.e., meter‐scale bodies of ice embedded within the top of the permafrost). Pool expansion impacts subsequent thaw rates through a series of complex positive and negative feedbacks which play out over timescales of decades and may accelerate carbon release from the underlying sediments. Although many local observations of ice wedge thermokarst pool expansion have been documented, analyses at continental to pan‐Arctic scales have been rare, hindering efforts to project how strongly this process may impact the global carbon cycle. Here we present one of the most geographically extensive and temporally dense records yet compiled of recent pool expansion, in which changes to pool area from 2008 to 2020 were quantified through satellite‐image analysis at 27 survey areas (measuring 10–35 km²each, or 400 km²in total) dispersed throughout the circumpolar tundra. The results revealed instances of rapid expansion at 44% (15%) of survey areas. Considered alone, the extent of departures from historical mean air temperatures did not account for between site variation in rates of change to pool area. Pool growth was most clearly associated with upland (i.e., hilly) terrain and elevated silt content at soil depths greater than one meter. These findings suggest that, at short time scales, pedologic and geomorphologic conditions may exert greater control on pool dynamics in the warming Arctic than spatial variability in the rate of air temperature increases. 

The field of geology is poised to make a fundamental transition in the quality, character, and types of science that are possible for practitioners. Geologists are developing data systems—consistent with their workflow—to digitally collect, store, and share data. Separately, geologists and cognitive scientists have been working together to develop tools that can characterize the level of uncertainty of both data and models. The transformational change comes from the simultaneous combination of these two approaches: digital data systems designed to capture and convey scientific uncertainty. This approach promotes better data collection practice, improves reproducibility, and increases trust in community-based digital data. We applied these methods—attending to uncertainty and its incorporation into digital repositories—to the Sage Hen Flat pluton in eastern California, USA, where two published maps provide different interpretations. Incorporating uncertainty into our workflow, from field data collection to publication, allows us to move beyond binary choices (e.g., is this data/model right or wrong?) to a more nuanced view (e.g., what is my level of uncertainty about the data/model?) that is shareable with the larger community. 

In acquiring morphology, the language learner faces the challenge of identifying both the form of morphemes and their location within words. For example, individuals acquiring Chamorro (Austronesian) must learn an agreement morpheme with the form -um- that is infixed before the first vowel of the stem (1a). This challenge is more difficult when a morpheme has multiple forms and/or locations: in some varieties of Chamorro, the same agreement morpheme appears as mu- prefixed on verbs beginning with a nasal/liquid consonant (1b). The learner could potentially overcome the acquisition challenge by employing strong inductive biases. This hypothesis is consistent with the typological finding that, across languages, morphemes occupy a restricted set of prosodically-defined locations (Yu, 2007) and there are strong correlations between morpheme form and position (Anderson, 1972). We conducted a series of artificial morphology experiments, modeled after the Chamorro pattern, that provide converging evidence for such inductive biases (Pierrehumbert & Nair, 1995; Staroverov & Finley, 2021).