Rayleigh-Taylor Instability in a Relativistic Shock

We present a new two-dimensional (2D) map of total Galactic extinction, AV, across the entire dust half-layer from the Sun to extragalactic space for Galactic latitudes ∣b∣ > 13°, as well as a three-dimensional (3D) map of AV within 2 kpc of the Sun. These maps are based on AV and distance estimates derived from a data set, which utilizes Gaia Data Release 3 parallaxes and multi-band photometry for nearly 100 million dwarf stars. We apply our own corrections to account for significant systematics in this data set. Our 2D map achieves an angular resolution of 6

1, while the 3D map offers a transverse resolution of 3.56 pc—corresponding to variable angular resolution depending on distance—and a radial resolution of 50 pc. In constructing these maps, we pay particular attention to the solar neighborhood (within 200 pc) and to high Galactic latitudes. The 3D map predicts AV from the Sun to any extended object within the Galactic dust layer with an accuracy of σ(AV) = 0.1 mag. The 2D map provides AV estimates for the entire dust half-layer up to extragalactic distances with an accuracy of σ(AV) = 0.07 mag. We provide AV estimates from our maps for various classes of extended celestial objects with angular size primarily in the range of 2′–40′, including 19,809 galaxies and quasars, 170 Galactic globular clusters, 458 open clusters, and several hundred molecular clouds from two lists. We also present extinction values for 8293 Type Ia supernovae. Comparison of our extinction estimates with those from previous maps and literature sources reveals systematic differences, indicating large-scale spatial variations in the extinction law and suggesting that earlier 2D reddening maps based on infrared dust emission tend to underestimate low extinction values.

We explore the use of tutorials to assist upper-division electricity & magnetism (E&M) students with problem solving. Specifically, we test two versions of tutorials; one that implements a problem-solving framework, and another with the framework removed. Data examined in this paper includes student exam problems, an end-of-semester survey, interviews, and problem-solving exercises administered using a think-aloud protocol. Preliminary results when considering exam scores alongside student comments offer some evidence that tutorials structured on a problem-solving framework may improve conceptual parts of problem solving for students. Student problem-solving processes are also examined, and we find that these upper-division students struggle with practices of checking their reasoning, an area for future attention.

This paper introduces a neural network-based calibration for low-force sensors that operate in the sub-newton regime (0−1N) for wearable applications. The proposed calibration utilizes a fully-connected neural network to digitally reduce the sensor nonlinearity. The neural network is trained using data from a custom low-force measurement system with a novel compliant mechanism. Detailed study explores the tradeoffs between the neural network size and activation function with calibration accuracy. Measurement results demonstrate 4X improvement in the force sensor linearity, achieving errors less than 0.005 N. The proposed calibration is well-suited for wearable applications requiring precise low-force measurements.

Over the past decade, an interdisciplinary team of scientists conducted a series of at-sea measurements designed to further our understanding of acoustics in complex ocean environments. Most of these efforts focused on bottom-interacting acoustics in areas characterized by fine-grained sediments. Geographically, the primary experimental sites and data analysis took place in an area of the Western Atlantic Ocean approximately 60 miles south of Martha's Vineyard, MA, known as the New England Mudpatch, and extending south to include the shelf break and upper slope characterized by larger-grained sediments. This introductory paper provides a summary of the various experimental techniques and analysis approaches detailed in the collection of 23 papers that make up this special issue focused on Assessing Sediment Heterogeneity on Continental Shelves and Slopes.

The Small Body Dynamics Tool (SBDynT) is software written for the community of solar system small body researchers to perform dynamical classification, characterization, and investigation. SBDynT provides advanced simulation analysis capabilities that make it straightforward to determine mean-motion resonance occupation, proper orbital elements, and a variety of stability indicators. These calculations can be performed for small bodies that are known, newly discovered, or simulated; observational uncertainties can be incorporated through the use of dynamical clones. In this paper, we describe the methods for producing proper orbital elements and stability indicators, which serve as essential tools for characterizing dynamical stability and long-term evolution. Through extensive validation, we demonstrate that this code offers a robust open-source framework for investigating the dynamics of solar system small bodies with high accuracy. We also aim for computational efficiency allowing SBDynT to provide dynamical information for the several-fold increases in small bodies expected in the Legacy Survey of Space and Time era.

Machine learning is revolutionizing protein design by enabling the rapid generation of sequences with precise structural and functional properties. Controlling protein conformational states remains a major challenge, particularly for enzymes regulated by complex structural switches. Here, using high-resolution structural data and probabilistic sequence-structure models, a machine learning-driven framework for conformationally biased protein design is presented titled Conformation-Specific Design or CSDesign. This approach generates sequences predicted to favor a desired conformation while disfavoring alternative states. As a proof-of-concept, this approach is applied to extracellular signal-regulated kinase 2 (ERK2), generating variants predicted to favor the active or inactive state. Experimental validation of relative kinase activity in a controlled assay confirmed that an active-biased variant, CSD104, exhibits robust kinase activity without native upstream phosphorylation, while an inactive-biased variant, CSD101, remains inactivated. Structural analysis suggests that engineered interactions stabilize active-like features in place of phosphorylation. These results demonstrate machine learning control of protein conformational ensembles, with potential to design enzymes and other conformationally regulated proteins without relying on phosphomimetic mutations or extensive experimental screening.