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Reorientation, the process of regaining one’s bearings after becoming lost, requires identification of a spatial context (context recognition) and recovery of facing direction within that context (heading retrieval). We previously showed that these processes rely on the use of features and geometry, respectively. Here, we examine reorientation behavior in a task that creates contextual ambiguity over a long timescale to demonstrate that male mice learn to combine both featural and geometric cues to recover heading. At the neural level, most CA1 neurons persistently align to geometry, and this alignment predicts heading behavior. However, a small subset of cells remaps coherently in a context-sensitive manner, which serves to predict context. Efficient heading retrieval and context recognition correlate with rate changes reflecting integration of featural and geometric information in the active ensemble. These data illustrate how context recognition and heading retrieval are coded in CA1 and how these processes change with experience. 

Cone penetration tests (CPTs) are a commonly used in situ method to characterize soil. The recorded data are used for various applications, including earthquake-induced liquefaction evaluation. However, data recorded at a given depth in a CPT sounding are influenced by the properties of all the soil that falls within the zone of influence around the cone tip rather than only the soil at that particular depth. This causes data to be blurred or averaged in layered zones, a phenomenon referred to as multiple thin-layer effects. Multiple thin-layer effects can result in the inaccurate characterization of the thickness and stiffness of thin, interbedded layers. Correction procedures have been proposed to adjust CPT tip resistance for multiple thin-layer effects, but many procedures become less effective as layer thickness decreases. To compare or improve these procedures and to develop new ones, it is critical to have pairs of measured tip resistance ( q^m) and true tip resistance ( q^t) data, where q^mis the tip resistance recorded by the CPT in a layered profile, and q^trepresents the tip resistance that would be measured in the profile absent of multiple thin-layer effects. Unfortunately, data sets containing q^mand q^tpairs are extremely rare. Accordingly, this article presents a unique database containing laboratory and numerically generated CPT data from 49 highly interlayered soil profiles. Both q^mand q^tare provided for each profile. An accompanying Jupyter notebook is provided to facilitate the use of the data and prepare them for future statistical learning (or other) applications to support multiple thin-layer correction procedure development. 

This poster looks to apply machine learning in different aspects such as predicting dynamic viscosity of ionic liquids, determining parameters to generate a nonwoven mat through electrospinning, and predictions of extent of damage along with residual strength in fiber reinforced polymer composites. Through the use of machine learning we look to better understand the factors that go into each of these predictions and to eliminate time and costs in each process. 

Multiple stakeholders are interested in measuring undergraduate student success in college across academic fields. Different metrics might appeal to different stakeholders. Some metrics such as the fraction of first-time, full-time students who start in the fall who graduate within six years, the graduation rate, are federally mandated by the U.S. Department of Education, Integrated Postsecondary Education Data System (IPEDS). We argue that this calculation of graduation rate is inherently problematic because it excludes up to 60% of students who transfer into an institution, enroll part-time, or enroll in terms other than the fall. By expanding the starters definition, we propose a graduation rate definition that includes conventionally excluded students and provides information on progression in a specific program. Stickiness is an even more-inclusive alternative, measuring a program’s success in graduating all undergraduates ever enrolled in the program. In this work, programs are grouped into six academic fields: Arts and Humanities, Business, Engineering, Other, Social Sciences, and STM (Science, Technology, and Mathematics. Stickiness is the percentage of students who ever enroll in an academic field that graduate in the same field. We use the Multiple Institution Dataset for Investigating Engineering Longitudinal Development (MIDFIELD) 2023 which contains unit-record data for over 2 million individual students at 19 institutions. For the academic fields studied, Engineering has the highest graduation rate and third highest stickiness. Social Sciences and Business also have higher graduation rates and stickiness than the other fields. We also track the relative fraction of students migrating to and from each academic field. This paper continues our work to derive better metrics for understanding student success. 

Terrestrial LiDAR scans (TLS) offer a rich data source for high-fidelity vegetation characterization, addressing the limitations of traditional fuel sampling methods by capturing spatially explicit distributions that have a significant impact on fire behavior. However, large volumes of complex, high resolution data are difficult to use directly in wildland fire models. In this study, we introduce a novel method that employs a voxelization technique to convert high-resolution TLS data into fine-grained reference voxels, which are subsequently aggregated into lower-fidelity fuel cells for integration into physics-based fire models. This methodology aims to transform the complexity of TLS data into a format amenable for integration into wildland fire models, while retaining essential information about the spatial distribution of vegetation. We evaluate our approach by comparing a range of aggregate geometries in simulated burns to laboratory measurements. The results show insensitivity to fuel cell geometry at fine resolutions (2–8 cm), but we observe deviations in model behavior at the coarsest resolutions considered (16 cm). Our findings highlight the importance of capturing the fine scale spatial continuity present in heterogeneous tree canopies in order to accurately simulate fire behavior in coupled fire-atmosphere models. To the best of our knowledge, this is the first study to examine the use of TLS data to inform fuel inputs to a physics based model at a laboratory scale.