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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Strategies for Determining the Cascade Rate in MHD Turbulence: Isotropy, Anisotropy, and Spacecraft Sampling

Exact laws for evaluating cascade rates, tracing back to the Kolmogorov "4/5" law, have been extended to many systems of interest including magnetohydrodynamics (MHD), and compressible flows of the magnetofluid and ordinary fluid types. It is understood that implementations may be limited by the quantity of available data and by the lack of turbulence symmetry. Assessment of the accuracy and feasibility of such third-order (or Yaglom) relations is most effectively accomplished by examining the von Kármán–Howarth equation in increment form, a framework from which the third-order laws are derived as asymptotic approximations. Using this approach, we examine the context of third-order laws for incompressible MHD in some detail. The simplest versions rely on the assumption of isotropy and the presence of a well-defined inertial range, while related procedures generalize the same idea to arbitrary rotational symmetries. Conditions for obtaining correct and accurate values of the dissipation rate from these laws based on several sampling and fitting strategies are investigated using results from simulations. The questions we address are of particular relevance to sampling of solar wind turbulence by one or more spacecraft.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Large-scale Structure of the Heliospheric Current Sheets within the Heliosheath Inferred from Voyager 2 Observations

In this paper, we conducted an analysis of the heliospheric current sheets (HCSs) in the heliosheath (HS), utilizing observations by Voyager 2 between 2008 and 2018. Employing rigorous criteria, we identified a total of 34 HCSs that indicate significant changes in magnetic polarity. These occurrences were more prevalent during solar maximum periods when the HCS expanded to higher latitudes, coinciding with the spacecraft positioned at an average latitude of –31° from the solar equator. We determined certain features of the large-scale structures of the identified HCSs. Most importantly, employing two distinct methods indicates that the thickness of the HCSs within the HS ranges from ~0.003 to ~0.4 au with an average thickness of ~0.03 to ~0.1 au, depending on methods of event selection and fitting techniques. This thickness surpasses that known near 1 au or other heliospheric distances. It is also notably thicker than the typical proton inertial length, implying unfavorable conditions for magnetic reconnection. Additionally, our analysis reveals a frequent tilt of HCS planes relative to the solar equatorial plane by a varying angle up to several tens of degrees, likely implying a common occurrence of a warped structure of the HCS within the HS. Longitudinally, the HCS planes closely align with the Parker spiral field direction expected in the HS. Finally, for a large fraction of the identified HCS events, the HCS planes are likely characterized by a rotational discontinuity. These findings are valid within the limits of the 1 hr resolution data used in this study.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

BD+44°493: Chemo-dynamical Analysis and Constraints on Companion Planetary Masses from WIYN/NEID Spectroscopy

In this work, we present high-resolution (R ~ 100,000), high signal-to-noise ratio (S/N ~ 800) spectroscopic observations for the well-known, bright, extremely metal-poor, carbon-enhanced star BD+44°493. We determined chemical abundances and upper limits for 17 elements from WIYN/NEID data, complemented with 11 abundances redetermined from Subaru and Hubble data, using the new, more accurate, stellar atmospheric parameters calculated in this work. Our analysis suggests that BD+44°493 is a low-mass (0.83 M ⊙ ), old (12.1–13.2 Gyr) second-generation star likely formed from a gas cloud enriched by a single metal-free 20.5 M ⊙ Population III star in the early Universe. With a disk-like orbit, BD+44°493 does not appear to be associated with any major merger event in the early history of the Milky Way. From the precision radial-velocity NEID measurements (median absolute deviation = 16 m s –1 ), we were able to constrain companion planetary masses around BD+44°493 and rule out the presence of planets as small as $m\sin i=2$ M J out to periods of 100 days. This study opens a new avenue of exploration for the intersection between stellar archaeology and exoplanet science using NEID.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Prompt Searches for Very-high-energy γ -Ray Counterparts to IceCube Astrophysical Neutrino Alerts

The search for sources of high-energy astrophysical neutrinos can be significantly advanced through a multimessenger approach, which seeks to detect the γ-rays that accompany neutrinos as they are produced at their sources. Multimessenger observations have so far provided the first evidence for a neutrino source, illustrated by the joint detection of the flaring blazar TXS 0506+056 in high-energy (E > 1 GeV) and very-high-energy (VHE; E > 100 GeV) γ-rays in coincidence with the high-energy neutrino IceCube-170922A, identified by IceCube. Imaging atmospheric Cherenkov telescopes (IACTs), namely FACT, H.E.S.S., MAGIC, and VERITAS, continue to conduct extensive neutrino target-of-opportunity follow-up programs. These programs have two components: follow-up observations of single astrophysical neutrino candidate events (such as IceCube-170922A), and observation of known γ-ray sources after the identification of a cluster of neutrino events by IceCube. Here we present a comprehensive analysis of follow-up observations of high-energy neutrino events observed by the four IACTs between 2017 September (after the IceCube-170922A event) and 2021 January. Our study found no associations between γ-ray sources and the observed neutrino events. We provide a detailed overview of each neutrino event and its potential counterparts. Furthermore, a joint analysis of all IACT data is included, yielding combined upper limits on the VHE γ-ray flux.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

CMIP5 model selection for ISMIP6 ice sheet model forcing: Greenland and Antarctica

Abstract. The ice sheet model intercomparison project for CMIP6 (ISMIP6) effort brings together the ice sheet and climate modeling communities to gain understanding of the ice sheet contribution to sea level rise. ISMIP6 conducts stand-alone ice sheet experiments that use space- and time-varying forcing derived from atmosphere–ocean coupled global climate models (AOGCMs) to reflect plausible trajectories for climate projections. The goal of this study is to recommend a subset of CMIP5 AOGCMs (three core and three targeted) to produce forcing for ISMIP6 stand-alone ice sheet simulations, based on (i) their representation of current climate near Antarctica and Greenland relative to observations and (ii) their ability to sample a diversity of projected atmosphere and ocean changes over the 21st century. The selection is performed separately for Greenland and Antarctica. Model evaluation over the historical period focuses on variables used to generate ice sheet forcing. For stage (i), we combine metrics of atmosphere and surface ocean state (annual- and seasonal-mean variables over large spatial domains) with metrics of time-mean subsurface ocean temperature biases averaged over sectors of the continental shelf. For stage (ii), we maximize the diversity of climate projections among the best-performing models. Model selection is also constrained by technical limitations, such as availability of required data from RCP2.6 and RCP8.5 projections. The selected top three CMIP5 climate models are CCSM4, MIROC-ESM-CHEM, and NorESM1-M for Antarctica and HadGEM2-ES, MIROC5, and NorESM1-M for Greenland. This model selection was designed specifically for ISMIP6 but can be adapted for other applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

RTN-117: Image Calibration and Instrument Signal Removal for the First Year of the LSST

The NSF-DOE Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) requires calibration products that provide uniform, stable, and accurate photometric and astrometric performance across the 3.2 gigapixel focal plane and throughout the 10-year survey. This paper details the algorithms and workflows used to produce instrument calibrations and remove instrumental artifacts for Data Preview 2 (DP2)---the first end-to-end processing demonstration using on-sky data with the LSST Camera (LSSTCam). We describe the verification, acceptance, and certification framework used to assess calibration quality and quantify residual systematics. We show baseline metrics on calibrated science images to evaluate the robustness of the calibration and instrument signature removal (ISR) data processing pipelines for DP2. Finally, we summarize the known limitations observed in DP2 production and outline expected algorithmic improvements for the first public LSST data release (Data Release~1, DR1).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Analysis of twelve genomes of the bacterium Kerstersia gyiorum from brown-throated sloths ( Bradypus variegatus ), the first from a non-human host

Kerstersia gyiorum is a Gram-negative bacterium found in various animals, including humans, where it has been associated with various infections. Knowledge of the basic biology of K. gyiorum is essential to understand the evolutionary strategies of niche adaptation and how this organism contributes to infectious diseases; however, genomic data about K. gyiorum is very limited, especially from non-human hosts. In this work, we sequenced 12 K. gyiorum genomes isolated from healthy free-living brown-throated sloths (Bradypus variegatus) in the Parque Estadual das Fontes do Ipiranga (São Paulo, Brazil), and compared them with genomes from isolates of human origin, in order to gain insights into genomic diversity, phylogeny, and host specialization of this species. Phylogenetic analysis revealed that these K. gyiorum strains are structured according to host. Despite the fact that sloth isolates were sampled from a single geographic location, the intra-sloth K. gyiorum diversity was divided into three clusters, with differences of more than 1,000 single nucleotide polymorphisms between them, suggesting the circulation of various K. gyiorum lineages in sloths. Genes involved in mobilome and defense mechanisms against mobile genetic elements were the main source of gene content variation between isolates from different hosts. Sloth-specific K. gyiorum genome features include an IncN2 plasmid, a phage sequence, and a CRISPR-Cas system. The broad diversity of defense elements in K. gyiorum (14 systems) may prevent further mobile element flow and explain the low amount of mobile genetic elements in K. gyiorum genomes. Gene content variation may be important for the adaptation of K. gyiorum to different host niches. This study furthers our understanding of diversity, host adaptation, and evolution of K. gyiorum, by presenting and analyzing the first genomes of non-human isolates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Introducing the GeoRePORT Resource Size Tool: Reporting on Geothermal Resource Size Estimations Using the Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT): Preprint

The Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT) was developed with funding from the U.S. Department of Energy Geothermal Technologies Office to assist in identifying and pursuing long-term investment strategies through the development of a resource reporting protocol. The assessment protocols used in GeoRePORT allow for comparison of project attributes across locations and geological settings to understand the feasibility of geothermal development. This work introduces the Resource Size Tool, a new feature within the GeoRePORT package that compiles two independent methods for estimating geothermal resource size in terms of energy capacity in MW. Energy production potential for twenty-three case studies was estimated with the Resource Size Tool in order to 1) generate a reasonable range of resource size estimates for a particular geothermal field; 2) illustrate the advantages and limitations of each methodology (such as data input requirements, estimate accuracy and precision, and the appropriate circumstances of use); and 3) test the ability of the resource size tool to provide useful and accurate information for geothermal stakeholders. The tool employs two methods widely used in the geothermal industry: (1) USGS Volumetric and (2) Power Density. Results from our case studies show general overlap between these two methods in terms of resource size estimates; however, they also reveal key differences between the two approaches that should be considered when using such estimates to drive development. First, the two methods rely on different input parameters and therefore one method may be more appropriate and/or accurate for a given project than the other. Second, the Power Density method was found to generate wider ranges of resource size predictions, more consistently aligning with actual power production of the field but with larger scales of error; whereas the USGS Volumetric method predicts narrower ranges but tends to overestimate when compared to current MW production. Future work will refine variables used in the methods with input data from other sections of GeoRePORT and modify uncertainty levels based on the particular datasets used for a given project.

geological↗
DOE OSTI2025

A Dynamic Hierarchical Attention Framework for Multimodal Malware Detection

The increasing use of Android in the worldwide mobile ecosystem has come along with a significant increase in advanced malware, highlighting the critical necessity for efficient, scalable, and adaptable detection systems. Despite recent advancements in machine learning improving malware detection, the majority of current solutions are limited to one, two, or three data modalities, hence neglecting the comprehensive behavioral spectrum of contemporary multi-vector threats. This thesis presents the first comprehensive multimodal framework for Android malware detection, which combines textual, time-series (temporal), graph-based (structural), and visual information using an innovative hierarchical attention mechanism and Dynamic Fusion Controller (DFC). Our methodology consistently classifies and processes modalities as either sequential or structural, facilitating content-adaptive weighting and resilient cross-modal representation learning. We advance the implementation of cutting-edge time series techniques, such as MiniRocket, for malware detection, hence creating new opportunities for temporal analysis in cybersecurity. Comprehensive experimental assessment shows that our framework performs exceptionally well, with 99.46% classification accuracy and 97.15% detection accuracy, significantly outperforming existing approaches through effective multimodal integration and hierarchical attention mechanisms.

Nazmin, Tamanna↗
DOE OSTI2026

Millimeter-wave Detections of Symbiotic Stars in SPT and ACT Data

We present the results of a joint targeted search of candidate symbiotic stars at millimeter wavelengths using the South Pole Telescope (SPT) and the Atacama Cosmology Telescope (ACT). Candidates are selected from the New Online Database of Symbiotic Variables, restricting to objects that are within either the SPT-3G or ACT~DR6 footprint, covering most of the southern hemisphere and up to a declination of $+20^\circ$. Forced photometry on the 828 candidate symbiotic star locations in SPT and ACT data results in 31 unique objects detected with more than a $3σ$ significance using two frequency bands: 18 confirmed and 13 suspected symbiotic stars. We provide the SPT and ACT 95/98, 150, and 220~GHz light curves, along with optical and infrared light curves from 2016--2026, as well as spectral energy distributions, physical parameters from the literature, and brief summaries regarding the nature of each individual object. Using Herschel SPIRE data from 2013, we place upper limits on millimeter flux for CN Cha near the beginning of the optical rise in its 2012/2013 nova, which suggests a strong variability and lag at millimeter wavelengths and results in a rare observance of a Galactic millimeter slow transient. In addition, we provide coadded thumbnails and light curves for the remaining 797 candidate symbiotic stars that did not pass our detection thresholds. Millimeter-wave emission from symbiotic stars is primarily a combination of free-free emission of the ionization region and optically thick blackbody emission of the cooler dust components of the system. When combined with contemporaneous multi-wavelength observations, millimeter-wave observations can be used to test binary models of symbiotic stars and provide insight on the geometry and physical properties of these systems.

Tandoi, C. [Illinois U., Urbana, Astron. Dept.] (O↗
DOE OSTI2022

PolyODENet

Kinetics of a reaction network that follows mass-action rate laws can be described with a system of ordinary differential equations (ODEs) with polynomial right-hand side. However, it is challenging to derive such kinetic differential equations from transient kinetic data without knowing the reaction network, especially when the data are incomplete due to experimental limitations. We introduce a program, PolyODENet, toward this goal. Based on the machine-learning method Neural ODE, PolyODENet defines a generative model and predicts concentrations at arbitrary time. As such, it is possible to include unmeasurable intermediate species in the kinetic equations. Importantly, we have implemented various measures to apply physical constraints and chemical knowledge in the training to regularize the solution space.

Wu, Qin [Brookhaven National Lab. (BNL), Upton, NY↗
DOE OSTI2023

Snow-corrected vegetation indices for improved gross primary productivity assessment in North American evergreen forests

North American evergreen forests cover large areas and influence the global carbon cycle. Satellite remote sensing has been used to track the phenology of ecosystem photosynthesis of these forests by detecting variation in vegetation optical properties associated with physiological and structural features, and most of these methods have been closely tied to vegetation greenness. However, in evergreens, the application of satellite data to monitor photosynthetic phenology is often limited by the lack of sensitivity of greenness-based indices. In this study, we identified 47 evergreen forest flux sites in North America that had MODIS observation overlapping with the flux tower records. We then calculated four vegetation indices using MODIS MAIAC data (MCD19A1), including NDVI, CCI, NIRv, and kNDVI, for the 47 flux sites and evaluated relationships between gross primary productivity (GPP) and vegetation indices across the North American evergreen forests. Our results showed that snow had substantial effects on the performance of all vegetation indices in tracking GPP phenology, particularly in the early spring when rapid changes occurred to both GPP and snow cover. Furthermore, different vegetation indices were affected differently, indicating contradictory and confounding effects of snow on these indices. After correcting for the snow effects, both CCI and NIRv performed well in tracking GPP phenology, albeit for different reasons. CCI is sensitive to seasonal changes in the relative levels of chlorophyll and carotenoid pigments, which are closely tied to GPP phenology in evergreens. NIRv is sensitive to the absorbed photosynthetically active radiation and to the contribution of deciduous components to the overall optical properties. We also found that correlations between GPP and vegetation indices varied among ecoregions and climate classes. In general, regions with pronounced seasonal GPP patterns had stronger correlations between GPP and greenness-based indices than regions with weaker seasonal GPP patterns. These biome differences were less pronounced for CCI. The snow artifacts and complementary vegetation index effects reported here should be considered in any large-scale studies of GPP using reflectance-based indices from optical satellites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Stellar flares from blended and neighbouring stars in Kepler short cadence observations

ABSTRACT We present the results of a search for stellar flares from stars neighbouring the target sources in the Kepler short cadence data. These flares have been discarded as contaminants in previous surveys and therefore provide an unexplored resource of flare events, in particular high-energy events from faint stars. We have measured M dwarf flare energies up to 1.5 × 1035 erg, pushing the limit for flare energies measured using Kepler data. We have used our sample to study the flaring activity of wide binaries, finding that the lower mass counterpart in a wide binary flares more often at a given energy. Of the 4430 flares detected in our original search, 298 came from a neighbouring star, a rate of 6.7 ± 0.4 per cent for the Kepler short cadence light curves. We have used our sample to estimate a 5.8 ± 0.1 per cent rate of false positive flare events in studies using Transiting Exoplanet Survey Satellite short cadence data.

Jackman, James A. G.↗
DOE OSTI2021

Computationally Efficient Multiscale Neural Networks Applied to Fluid Flow in Complex 3D Porous Media

Abstract The permeability of complex porous materials is of interest to many engineering disciplines. This quantity can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive. In particular, the simulation convergence time scales poorly as the simulation domains become less porous or more heterogeneous. Semi-analytical models that rely on averaged structural properties (i.e., porosity and tortuosity) have been proposed, but these features only partly summarize the domain, resulting in limited applicability. On the other hand, data-driven machine learning approaches have shown great promise for building more general models by virtue of accounting for the spatial arrangement of the domains’ solid boundaries. However, prior approaches building on the convolutional neural network (ConvNet) literature concerning 2D image recognition problems do not scale well to the large 3D domains required to obtain a representative elementary volume (REV). As such, most prior work focused on homogeneous samples, where a small REV entails that the global nature of fluid flow could be mostly neglected, and accordingly, the memory bottleneck of addressing 3D domains with ConvNets was side-stepped. Therefore, important geometries such as fractures and vuggy domains could not be modeled properly. In this work, we address this limitation with a general multiscale deep learning model that is able to learn from porous media simulation data. By using a coupled set of neural networks that view the domain on different scales, we enable the evaluation of large ( $$>512^3$$ > 512 3 ) images in approximately one second on a single graphics processing unit. This model architecture opens up the possibility of modeling domain sizes that would not be feasible using traditional direct simulation tools on a desktop computer. We validate our method with a laminar fluid flow case using vuggy samples and fractures. As a result of viewing the entire domain at once, our model is able to perform accurate prediction on domains exhibiting a large degree of heterogeneity. We expect the methodology to be applicable to many other transport problems where complex geometries play a central role.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Data‐Driven Insights into Rare Earth Mineralization: Machine Learning Applications Using Functional Material Synthesis Data

Understanding rare‐earth element (REE) mineralization mechanisms is essential for developing efficient separation strategies. Although the geochemical pathways that generate REE deposits are qualitatively known, quantitative links between specific conditions and mineralization outcomes remain limited. Herein, the repurpose laboratory REE hydrothermal synthesis data—originally collected for functional‐materials fabrication—as a surrogate for studying mineralization with data‐driven methods. The compiled 1,200+ hydrothermal reaction records and trained three machine‐learning models—K‐nearest neighbors (KNN), random forest (RF), and extreme gradient boosting (XGB)—to predict product elements and phases from precursors, additives, reaction conditions, and engineered features. Validation shows XGB achieves the highest accuracy. Feature importance indicates thermodynamic properties of cations and anions dominate model decisions. Correlations reveal positive relationships among precursor concentration, reaction time, pH, and temperature, consistent with classical crystallization behavior. XGB‐based regressors are built to predict crystallization temperature and pH from precursor/product attributes. Performance is strongest when similar training examples exist, while accuracy declines for underrepresented reactions, notably REE carbonates and heavy‐REE systems. Overall, the study shows that functional‐materials datasets can illuminate REE mineralization and provide priors for exploration and processing. Expanding datasets with less‐studied chemistries and conditions will improve generality and support deposit discovery and more efficient REE recovery.

feature importance analysis↗
DOE OSTI2026

Fuel Fabrication Specification Impact Analysis for NBSR LEU Conversion

As part of a national initiative to enhance nuclear security and reduce proliferation risks, significant efforts have been undertaken by the National Nuclear Security Administration Material Management and Minimization Office of Reactor Conversion Program to convert U.S. high performance research reactors (USHPRRs) from the use of highly enriched uranium (HEU) to low-enriched uranium (LEU), including the National Bureau of Standards Reactor (NBSR). The current plan is to procure LEU fuel assemblies from commercial fabricators according to fuel specifications tailored for each USHPRR. The analysis conducted at Brookhaven National Laboratory was part of an effort to identify the sources of uncertainty in the fuel specifications that may impact the performance of the NBSR core after its conversion and, in particular, to assess the range of acceptable tolerance limits from the perspective of core safety and reactor performance. Using the stochastic neutronics code MCNP 6.2, the variations in important NBSR neutronics characteristics were analyzed as a function of the specification parameters independently and in combination. The important NBSR specification parameters analyzed were the fuel isotopic composition, the amount of impurity content in cladding, the fuel plate thickness, and the fuel element 235U mass loading. The range of variation of each specification parameter was based on the technical specification limit or available as-fabricated assay data and uncertainties. The NBSR neutronics characteristics selected for analysis were the reactor reactivity characteristics at equilibrium and the equilibrium fuel cycle length. Results show that with variations in the fabrication parameters of the as-fabricated U-10Mo fuel within the specification limitations, the excess reactivity of the NBSR LEU core remains well below the 15% Δk/k technical specification limit, and the shutdown margin is always significantly greater than the required 0.68% Δk/k. This ensures that the NBSR can be operated safely and reliably shut down for all analyzed cases within the specified fabrication limits after the LEU conversion. In the prototypic case, the fuel cycle length was 1.5 days longer than the targeted 38.5 days. In a credible worst-case scenario, where all low-reactivity parameters were combined, the fuel cycle length was reduced to 35.5 days, which is still considered manageable for reactor operations. Variations in cycle length are primarily driven by changes in 235U loading, with other parameters having secondary effects.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

BrainXcan identifies brain features associated with behavioral and psychiatric traits using large-scale genetic and imaging data

Advances in brain MRI have enabled many discoveries in neuroscience. Case-control comparisons of brain MRI features have highlighted potential causes of psychiatric and behavioral disorders. However, due to the cost and difficulty of collecting MRI data, most studies have small sample sizes, limiting their reliability. Furthermore, reverse causality complicates interpretation because many observed brain differences are the result rather than the cause of the disease. Here we propose a method (BrainXcan) that leverages the power of large-scale genomewide association studies (GWAS) and reference brain MRI data to discover new mechanisms of disease etiology and validate existing ones. BrainXcan tests the association with genetic predictors of brain MRI-derived features and complex traits to pinpoint relevant brain-wide and region-specific features. Requiring only genetic data, BrainXcan allows us to test a host of hypotheses on mental illness, across many MRI modalities, using public data resources. For example, our method shows that reduced axonal density across the brain is associated with schizophrenia risk, consistent with the disconnectivity hypothesis. We also find that the hippocampus volume is associated with schizophrenia risk, highlighting the potential of our approach. Taken together, our results show the promise of BrainXcan to provide insights into the biology of GWAS traits.

Association study↗