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Topographic effects on reflected acoustic waves from the OSIRIS-REx reentry observed from stratospheric balloons

During long-distance sound propagation in planetary atmospheres, acoustic waves may reflect off the air/surface interface one or more times. For low sound frequencies and flat interfaces, the incident and reflected wave tend to be nearly identical. However, this may not be the case when the downgoing acoustic wave encounters topography. Here, we describe a set of direct and reflected acoustic signals recorded on free-flying balloons during the hypersonic entry of the OSIRIS-REx sample return capsule (SRC). In two of the three cases presented here, an impulsive reflected arrival similar in form to the direct sonic boom of the SRC is observed, followed by a diffuse coda. In contrast, one of the floating stations lacked an impulsive reflection entirely, with only the coda present. We use full waveform modeling to show how reflection in the presence of complex topography can explain coda seen in all three examples as well as the lack of impulsive arrival on the third. Our results indicate that the complex signals often observed in long range acoustic propagation could be due, in part, to interactions with topography during transmission.

Lees, Jonathan M. [University of North Carolina, C

Characterization of Flashback and Flame-Holding in a Jet-in-Crossflow Mixing Configuration With Methane–Hydrogen Fuel Blends

An approach that combines experimental and numerical analyses has been implemented to characterize the fundamentals of flashback events and flame-holding phenomena during high-hydrogen combustion in a jet-in-crossflow (JICF) configuration. Such flame dynamics are visualized experimentally using nanosecond (ns)-based hydroxyl planar laser-induced fluorescence (OH-PLIF) and chemiluminescence diagnostics techniques. The JICF burner has an optically accessible premixing tube allowing the optical diagnostics. The testing was conducted for varied premixer velocities (V) and equivalence ratio (ϕ) for 90–100% (H 2 , by mole) H 2 /CH 4 reactant mixtures at atmospheric temperature and pressure conditions. Two distinct flashback events were identified – conventional rich flashback and lean flashback, recorded while increasing ϕ and decreasing ϕ, respectively. The cause of lean flashback was attributed to lower momentum flux ratio which bends the jet sharply, closer toward the injection plane. The mean OH-PLIF images characterized the flame-holding behavior, where the flame was found to be stabilized on the leeward side only or on both windward and leeward sides as a lifted flame near the fuel port. A large eddy simulation (LES) with detailed chemistry combustion modeling approach was implemented along with an OH* submechanism, and it showed qualitative agreement with the integrated line-of-sight chemiluminescence results as well as the planar OH-PLIF measurements.

chemiluminescence diagnostic

Fast, Nondestructive and Precise Biomass Measurements Are Possible Using Lidar-Based Convex Hull and Voxelization Algorithms

Light detection and ranging (lidar) scanning tools are available that can make rapid digital estimations of biomass. Voxelization and convex hull are two algorithms used to calculate the volume of the scanned plant canopy, which is correlated with biomass, often the primary trait of interest. Voxelization splits the scans into regular-sized cubes, or voxels, whereas the convex hull algorithm creates a polygon mesh around the outermost points of the point cloud and calculates the volume within that mesh. In this study, digital estimates of biomass were correlated against hand-harvested biomass for field-grown corn, broom corn, and energy sorghum. Voxelization (r = 0.92) and convex hull (r = 0.95) both correlated well with plant dry biomass. Lidar data were also collected in a large breeding trial with nearly 900 genotypes of energy sorghum. In contrast to the manual harvest studies, digital biomass estimations correlated poorly with yield collected from a forage harvester for both voxel count (r = 0.32) and convex hull volume (r = 0.39). However, further analysis showed that the coefficient of variation (CV, a measure of variability) for harvester-based estimates of biomass was greater than the CV of the voxel and convex-hull-based biomass estimates, indicating that poor correlation was due to harvester imprecision, not digital estimations. Overall, results indicate that the lidar-based digital biomass estimates presented here are comparable or more precise than current approaches.

Environmental Sciences & Ecology

Nature of the diffuse emission sources in the H i supershell in the galaxy IC 1613

ABSTRACT We present a study of the nearby low-metallicity dwarf galaxy IC 1613, focusing on the search for massive stars and related feedback processes, as well as for faint supernova remnants (SNR) in late stages of evolution. We obtained the deepest images of IC 1613 in the narrow-band H α, He ii and [S ii] emission lines and new long-slit spectroscopy observations using several facilities (6-m BTA, 2.5m SAI MSU, and 150RTT telescopes), in combination with the multiwavelength archival data from MUSE/VLT, VLA, XMM–Newton, and Swift/XRT. Our deep narrow-band photometry identifies several faint shells in the galaxy, and we further investigate their physical characteristics with the new long-slit spectroscopy observations and the archival multiwavelength data. Based on energy balance calculations and assumptions about their possible nature, we propose that one of the shells is a possible remnant of a supernova explosion. We study five out of eight Wolf–Rayet (WR) star candidates previously published for this galaxy using the He ii emission line mapping, MUSE/VLT archival spectra, and new long-slit spectra. Our analysis discards the considered WR candidates and finds no new ones. We found P Cyg profiles in H α line in two stars, which we classify as Luminous Blue Variable (LBV) star candidates. Overall, the galaxy IC 1613 may have a lower rate of WR star formation than previously suggested.

Astronomy & Astrophysics

Eddy Covariance Theory: A Review

Eddy covariance (EC), the gold standard for measuring ecosystem scale gas and heat exchanges, has transformed our understanding of the breathing of the biosphere, and thus global change biology. Despite numerous methodological improvements and insights gained from the technique, the community faces persistent challenges that have been present since the first EC measurements. Here, we review the theoretical developments underpinning EC. We present theoretical developments in four important areas that have relevance to EC measurements of the net ecosystem exchanges (NEE) of gases and heat from a single tower: (i) measuring the total vertical flux density, (ii) flux attenuation, (iii) coordinate rotations, and (iv) energy balance closure. Persistent problems with EC measurements, such as the inability to close the energy budget, led us to identify two priorities for revisiting the theory underlying: (i) sensible heat flux calculations, and (ii) constraining the mean vertical wind velocity. We present a framework for improved calculation of sensible heat flux derived from first principles of fluid mechanics and thermodynamics that considers coupled heat and mass transfer so that conservation of both is obeyed. These refinements are motivated by the need for unbiased measurements of energy and mass transfer between the land surface and atmosphere for ecosystem research and to validate satellite observations and land surface models.

ecosystem fluxes

Fast Active-Set Thresholding Method for Nonnegative Least Squares

Nonnegative Least Squares (NNLS) is a fundamental constrained optimization problem encountered in many applications such as image deblurring, signal processing, nonnegative matrix factorization, magnetic microscopy, and hyperspectral imaging. Active-set based methods are a common class of algorithms for solving NNLS which identify the optimal variable set of the NNLS solution. They do so by iteratively solving a series of unconstrained least squares problems, identifying which variables violate the nonnegativity constraints, and then swapping variables in/out of consideration until the optimal set of variables is found. Several variations improving upon this method exist in the literature. In this work, we propose an active-set swap heuristic which further improves upon existing active-set based methods for NNLS. Our optimizations are based upon adding multiple variables to the passive set within a threshold of the smallest gradient value and removing variables within a similar threshold of the closest boundary constraint. We leverage these optimizations to yield a Fast Active-Set Thresholding NNLS (FAST-NNLS) algorithm which significantly outperforms the existing state-of-the-art NNLS algorithms for a wide range of problems. Rigorous convergence guarantees are proven for the proposed method. We demonstrate the effectiveness of our proposed method on multiple synthetic datasets and two realworld text analysis applications. In doing so, we present the most comprehensive NNLS solver comparison in the literature to date.

Cobb, Benjamin [Georgia Institute of Technology]

Proactive Wildfire Management: A Remote Sensing and Multimodal CNN-MLP Architecture for Ignition Risk Forecasting

As the frequency and intensity of wildfires increase, with fire seasons now starting earlier and ending later than they have over the past decades, current monitoring systems, such as lookout towers and satellites, are hindered by cloud cover, low-resolution imagery, and static data gaps that fail to track vegetation moisture levels fast enough to catch rapid pre-ignition changes. This report proposes a Machine Learning-enabled Wildfire Ignition Prediction framework that combines satellite monitoring with dynamic and high-resolution remote sensing from Unmanned Aerial Vehicle (UAV) swarms. The method would use multispectral and thermal data from the Landsat program to create a baseline for vegetation health, calculating a two-band Enhanced Vegetation Index (EVI2) and the moisture content of the vegetation. These inputs will later be fused with microscale UAV weather data, including thermal hotspots found through thick canopies, hyperspectral chemical signatures of pre-visual combustion, and local weather streams. The multispectral satellite, multispectral Light Detection and Ranging (LiDAR), and thermal data would then be processed through a Convolutional Neural Network (CNN), alongside a Multilayer Perceptron (MLP) for the micro-weather telemetry. The outputs of these networks would be fused into a single feature representation and passed through a final prediction network to generate real-time ignition risk scores and hotspot alerts. Model performance would be assessed using standard classification metrics, including a Receiver Operating Characteristic - Area Under the Curve (ROC AUC) and F1 score. This system would allow first responders to identify high-risk zones and intervene before ignition occurs, improving emergency response time compared to current approaches.

machine learning

Multi-sensor Observations of a High-velocity Fireball over the South Atlantic on 2026 April 1

A high-velocity fireball was detected over the South Atlantic (41.9°S, 54.7°W) on 2026 April 1 at 02:13:14 UTC by U.S. Government (USG) sensors, with peak brightness at 90.5 km altitude. The event was well-observed from geostationary orbit by two civilian lightning imagers with near-orthogonal geometry, the Geostationary Operational Environmental Satellite-East Geostationary Lightning Mapper (GLM) and the Exploitation of Meteorological Satellites Meteosat Third Generation Imager 1 Lightning Imager, and it produced low-frequency acoustic signatures. The GLM measured a total radiated energy of 2.4 × 10 10 J, corresponding to a calculated impact energy of 0.086 kt TNT equivalent. Stereoscopic triangulation of the imager tracks yields an independent pre-atmospheric velocity of ~57 km s −1 , some 18% below the USG-reported value. Because orbital provenance is acutely sensitive to the entry velocity, whose reported uncertainty could be substantial, this discrepancy is notable. We document the multi-sensor record and identify the analysis required to assess provenance, which a subsequent study will present.

Silber, Elizabeth Allaryce [Sandia National Lab. (

An enzyme-based approach for highly efficient self-replication of DNA origami dimers

Self-replication and exponential growth are essential to all living things, the driving force for Darwinian evolution, and potentially useful in nanotechnology for large-scale production of nanoscopic materials. An artificial (nonliving) self-replication system has been shown to exhibit exponential growth and selection using DNA monomer origami tiles templated on a dimer seed. That system purposefully avoided the use of enzymes to get a hint of how self-replication might have evolved in a prebiotic world by using CNV K and UV light to crosslink complementary DNA single strands. For further investigations into competition and extinction and for potential applications involving biocompatibility, we wanted to investigate enzymatic ligation to replace the chemical photo crosslinking step. Here, we present a system which uses thermotolerant T4 DNA ligase and no UV. This system has several additional advantages including a much faster cycling time, yielding 2,000,000 amplifications in 12 h. We also introduce competition to study the possibility of Darwinian-like evolution. Two pairs of DNA origami tiles compete for the same connection strands and show different growth rates under different connection strand concentrations. This system has the potential to combine with other enzymes, such as RNA polymerase to support feedback, allowing us to fine-tune replication dynamics and achieve sophisticated, life-like behaviors. The highly efficient self-replication and exponential growth of DNA origami dimers demonstrated in this work not only enhances our understanding of Darwinian evolution in nature but also opens the door to applications ranging from synthetic biology to smart materials.

Science & Technology - Other Topics

Design, Build, and Testing of the Roman Space Telescope’s Wide Field Instrument Optical Stimulus System (SORC)

The Stimulus Of Ray Cones (SORC) is an optical stimulus system developed to verify, characterize, and calibrate the Roman Space Telescope’s (RST) Wide Field Instrument (WFI) under simulated operational conditions. SORC provides several critical test modes, the primary being point-source mode, which projects an image anywhere on the Focal Plane Assembly (FPA) detectors. This mode provides precise position knowledge of the FPA within WFI. Additional modes enable capturing WFI pupil alignment, evaluation of WFI’s selectable optical elements, and verification of focal plane fiber operation for higher-level system testing. The light source system incorporates a suite of narrowband and broadband fiber fed sources spanning the visible to near-infrared range, with options for pulsed or continuous wave illumination. SORC was designed and built at NASA’s Goddard Spaceflight Center (GSFC). Initial system-level testing occurred under ambient conditions in GSFC’s Spacecraft Systems Development and Integration Facility (SSDIF), followed by vacuum testing at operational temperatures (SORC at 214-222 K) in the Space Environment Simulator (SES). The system was then shipped to BAE Systems in Boulder, CO for post-shipment ambient and cryogenic testing before integration with WFI for two thermal vacuum test campaigns at cryogenic temperatures. This presentation will focus on design, development, and performance of SORC. Details of WFI verification and calibration using SORC, along with test results, have been published previously and will be referenced only as needed to describe SORC. The SORC ground test capability is critical to ensuring WFI meets stringent optical performance requirements, directly supporting the mission’s science objectives.

Stimulus of Ray Cones

Control Room of the Future Testbed Workshop – After-Action Report

The U.S. Department of Energy’s Office of Electricity is supporting a one-year, multi-laboratory effort to define the needs and requirements for a Control Room of the Future testbed, or CROFT. The effort responds to increasing grid complexity driven by large new loads, dynamic generation resources, and the growing adoption of advanced technologies and tools, including artificial intelligence (AI) and machine learning (ML). To support safe, secure, and effective grid modernization, CROFT will focus on how emerging technologies and tools can be rigorously evaluated in realistic operational settings, with attention to human-machine interaction, cognitive load, and workforce readiness. The project team includes Argonne National Laboratory, Idaho National Laboratory, National Laboratory of the Rockies, and Pacific Northwest National Laboratory. As part of the scoping effort, the team conducted two industry-focused workshops: one at DTECH on February 5, 2026, informed by prior industry interviews, and a second on May 4, 2026, adjacent to IEEE T&D. These engagements brought together utilities, vendors, consultants, national laboratories, academia, and government stakeholders to identify and prioritize use cases, barriers, validation needs, data-sharing constraints, and near- and longer-term requirements. This feedback will directly inform CROFT’s architecture and research focus areas, ensuring the testbed is grounded in real-world operational needs and designed to evaluate emerging technologies and tools in realistic control-room environments.

artificial intelligence

Trust-Based Detection and Mitigation of Cyber Attacks in Distributed Cooperative Control of Islanded AC Microgrids

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.

Computer Science

Vesicular Stomatitis Virus Transmission Dynamics Within Its Endemic Range in Chiapas, Mexico

Vesicular stomatitis virus (VSV), comprising vesicular stomatitis New Jersey virus (VSNJV) and vesicular stomatitis Indiana virus (VSIV), emerges from its focus of endemic transmission in Southern Mexico to cause sporadic livestock epizootics in the Western United States. A dearth of information on the role of potential arthropod vectors in the endemic region hampers efforts to identify factors that enable endemicity and predict outbreaks. In a two-year, longitudinal study at five cattle ranches in Chiapas, Mexico, insect taxa implicated as VSV vectors (blackflies, sandflies, biting midges, and mosquitoes) were collected and screened for VSV RNA, livestock vesicular stomatitis (VS) cases were monitored, and serum samples were screened for neutralizing antibodies. VS cases were reported during the rainy (n = 20) and post-rainy (n = 2) seasons. Seroprevalence against VSNJV in adult cattle was very high (75–100% per ranch) compared with VSIV (0.6%, all ranches). All four potential vector taxa were sampled, and VSNJV RNA was detected in each of them (11% VSNJV-positive of 874 total pools), while VSIV RNA was only detected in four pools of mosquitoes. Our findings indicate that VSNJV is the dominant serotype across our sampling sites with a variety of potential insect vectors involved in its transmission throughout the year. Although no livestock cases were reported in Chiapas during the dry season, VSNJV was detected in insects during this period, suggesting that mechanisms other than transmission from livestock support VSV endemicity.

Virology

Three-dimensional nucleation and growth of deformation twins in magnesium

At two-thirds the weight of aluminum, magnesium alloys have the potential to reduce the fuel consumption of transportation vehicles. These advancements depend on our ability to optimize the desirable versus undesirable effects of deformation twins, which are three-dimensional (3D) microstructural domains that form under mechanical stresses. Previously only characterized through surface or thin-film measurements, we here present 3D in situ characterization of deformation twinning inside an embedded grain over mesoscopic fields of view using dark-field x-ray microscopy supported by crystal plasticity finite element analysis. The results revealed the role of triple junctions on twin nucleation and the sequence and irregularity of twin growth and showed that twin-grain junctions, twin-twin junctions, and twin boundaries were the sites of localized dislocation accumulation.

Lee, Sangwon [Univ. of Michigan, Ann Arbor, MI (Un

Coordinated Natural Gas and Electric Planning: Case Studies of Current Approaches and Practices

This paper examines how natural gas and electric utilities across eight U.S. states and two Canadian provinces are beginning to coordinate historically separate planning processes in response to growing system interdependencies, policy mandates, aging infrastructure, and changing customer energy choices. Electricity planning has long relied on robust integrated resource planning frameworks that weigh numerous objectives, risks, and costs. Natural gas planning, on the other hand, is typically less transparent and more narrowly focused on safety and system integrity. As economic, reliability, and policy drivers place new and shared demands on both systems, jurisdictions and utilities are experimenting with approaches such as coordinated forecasting, non pipeline alternatives, and combined planning pilots. A few have issued regulatory or statutory directives for greater data sharing and methodological alignment. Case studies from British Columbia, California, Colorado, Illinois, Massachusetts, Minnesota, New York, Québec, Rhode Island, and Washington illustrate a wide range of approaches to rethinking siloed planning. The paper identifies several common themes across the jurisdictions examined. It provides observations on the methods, processes, and organizational steps that may be needed in the future to address the challenges being faced by states and utilities. Lastly, it identifies some initial steps that states and utilities can consider if they would like to pursue more integrated, cost-effective, policy-aligned energy system planning.

03 NATURAL GAS

Developing affordable and efficient heating devices for enhanced live cell imaging in confocal microscopy

Temperature control is crucial for live cell imaging, particularly in studies involving plant responses to high ambient temperatures and thermal stress. This study presents the design, development, and testing of two cost-effective heating devices tailored for confocal microscopy applications: an aluminum heat plate and a wireless mini-heater. The aluminum heat plate, engineered to integrate seamlessly with the standard 160 mm × 110 mm microscope stage, supports temperatures up to 36°C, suitable for studies in the range of non-stressful warm temperatures (e.g., 25-27°C forArabidopsis thaliana) and moderate heat stress (e.g., 30-36°C forA. thaliana). We also developed a wireless mini-heater that offers rapid, precise heating directly at the sample slide, with a temperature increase rate over 30 times faster than the heat plate. The wireless heater effectively maintained target temperatures up to 50°C, ideal for investigating severe heat stress and heat shock responses in plants. Both devices performed well in controlled studies, including the real-time analysis of heat shock protein accumulation and stress granule formation inA. thaliana. Our designs are effective and affordable, with total construction costs lower than $300. This accessibility makes them particularly valuable for small laboratories with limited funding. Future improvements could include enhanced heat uniformity, humidity control to mitigate evaporation, and more robust thermal management to minimize focus drift during extended imaging sessions. These modifications would further solidify the utility of our heating devices in live cell imaging, offering researchers reliable, budget-friendly tools for exploring plant thermal biology.

Plant Sciences

Uranus Orbiter and Probe: Mission Challenges and Concept Updates Since the Origins, Worlds, and Life Decadal Survey

Origins, Worlds, and Life: Planetary Science and Astrobiology in the Next Decade identified a Uranus Orbiter and Probe as the highest-priority strategic mission for the decade 2023–2032, as it enables broad cross-disciplinary science in the largely unexplored Uranian system. The mission architecture evaluated by the Decadal Survey was a singular proof of concept demonstrating that a moderately instrumented mission could deliver Decadal-priority science with a reduced cost and risk posture by leveraging existing technologies to the maximum extent possible. With revised assumptions since the Decadal, we have explored a large trade space including launch vehicles, propulsion options, cruise trajectories, available power sources, viable concept of operations, and science data return for later launch dates without a Jupiter gravity assist. The most repeatable trajectory solutions employ either a commercially derived solar electric propulsion (SEP) transfer stage or the availability of a more capable launch vehicle under development, such as the SpaceX Starship. Orbit insertion has been moved farther from Uranus to acknowledge the remaining uncertainty in Uranian ring structure. A streamlined, SEP-adaptable, orbiter design was developed using two Next Gen Radioisotope Thermoelectric Generators, and the probe design was matured, reducing the entry gravitational acceleration, and assuming the largest Decadal-recommended payload to provide margin for future instrument selections. With this updated design, we also constructed a detailed concept of operations for three representative science cases, returning 13–15 Gbit of science data and spacecraft telemetry per ∼34 day orbit.

Amy A Simon

Improving the Performance of NEML2 with Modern Graph Compilation Backends

NEML2 vectorizes constitutive-model evaluation for large-scale multiphysics simulation, using PyTorch as its tensor backend so that a batch of material-point updates runs on CPU or GPU through a single implementation. In the two prior reports in this series it was a C++-native library, deployed through TorchScript tracing and just-in-time (JIT) compilation; it has since been rewritten from the ground up into a Python-native library deployed through Ahead-of-Time Inductor (AOTInductor), a modern PyTorch graph-compilation backend. The rewrite is driven by a persistent tension, not a language preference: NEML2 composes constitutive models at runtime from a registry of small, independently-authored pieces, and that flexibility is difficult to reconcile with the compile-time knowledge an efficient GPU kernel needs. This report documents the rewrite and the investment that accompanied it: the AOTInductor export pipeline that turns a Python-authored model into a portable, Python-free compiled artifact loadable from pure C++; the eager and compiled runtimes and the new implicit solver layer built on them; a head-to-head benchmark of legacy JIT against AOTInductor; the physics-model catalog and its worked examples; the developer tooling; and the corresponding overhaul of MOOSE’s NEML2 integration that lets MOOSE consume it. A central objective is to examine whether modern PyTorch graph-compilation backends are effective for MOOSE GPU integration. The benchmark answers directly: AOTInductor outperforms legacy JIT on every GPU scenario measured, by 1.0–4.5×. Modern graph-compilation backends are effective for MOOSE GPU integration, and AOTInductor specifically – not compilation in the abstract – is why.

Hu, Gary (Tianchen) [Argonne National Laboratory (