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At least 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Large-scale survey of prion protein genetic variability in scrapie disease-free goats from the United States

Scrapie is a slowly progressive neurodegenerative disease of small ruminants caused by an accumulation of an abnormal isoform of prion protein in the central nervous system. Polymorphisms of the prion protein gene (PRNP) strongly modulate scrapie resistance and incubation period in goats. The aim of this study was to identify PRNP genetic variability in goats across the United States. Blood from a total of 6,029 apparent scrapie disease-free goats from 654 operations and 19 breeds were analyzed. Sequencing of PRNP revealed 26 genotypes with different rates based on eight codons. The GG127, RR154, and QQ222 genotypes were predominant and showed a remarkably high rate across all goats. The QK222 and NS146 genotypes, known to be protective against scrapie, were found in 0.6% [with 95% CI = (0.3, 1.2)] and 22.0% [95% CI = (19.1, 25.2)] of goats, respectively. The QK222 genotype was found in 23.1% of Oberhasli goats tested, with 95%CI = (3.9, 68.7)] and 22.0% of Toggenburg goats tested with 95%CI = (9.7, 42.5)], while NS146 was found in 65.5% of Savannah goats tested, with 95%CI = (30.8, 89.9), 36.7% of Boer goats tested, with 95%CI = (33.1, 40.4), 36.3% of Nubian goats tested, with 95%CI = (27.0, 46.7)], and 35.6% of LaMancha goats tested, with 95%CI = (22.8, 50.8%). The MM142 and IM142 genotypes were found more frequently in goats on dairy operations, while the HR143, NS146, and ND146 genotypes were found more frequently in goats on meat operations. Goats in the east region had a higher percentage of goats with RH154, RQ211, and QK222 genotypes than goats in the west region. The results of this study showed high genetic variability of PRNP among the U.S. goat population, with differences by location and breed, and may serve as a rationale for development of goat breeding programs at the national level to mitigate the risk of scrapie.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes

A promising approach for scalable Gaussian processes (GPs) is the Karhunen-Loève (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator. Such decomposed kernels have the potential to be very fast, and do not depend on the selection of a reduced set of inducing points. However KL decompositions lead to high dimensionality, and variable selection thus becomes paramount. This paper reports a new method of forward variable selection, enabled by the ordered nature of the basis functions in the KL expansion of the Bayesian Smoothing Spline ANOVA kernel (BSS-ANOVA), coupled with fast Gibbs sampling in a fully Bayesian approach. It quickly and effectively limits the number of terms, yielding a method with competitive accuracies, training and inference times for tabular datasets of low feature set dimensionality. Theoretical computational complexities are O ( N P 2 ) in training and O ( P ) per point in inference, where N is the number of instances and P the number of expansion terms. The inference speed and accuracy makes the method especially useful for dynamic systems identification, by modeling the dynamics in the tangent space as a static problem, then integrating the learned dynamics using a high-order scheme. The methods are demonstrated on two dynamic datasets: a ‘Susceptible, Infected, Recovered’ (SIR) toy problem, along with the experimental ‘Cascaded Tanks’ benchmark dataset. Comparisons on the static prediction of time derivatives are made with a random forest (RF), a residual neural network (ResNet), and the Orthogonal Additive Kernel (OAK) inducing points scalable GP, while for the timeseries prediction comparisons are made with LSTM and GRU recurrent neural networks (RNNs) along with the SINDy package.

Hayes, Kyle↗
DOE OSTI2021

Stochastic simulations of temporally and spatially variable Fe cycling within a floodplain aquifer

This data package contains model input and output files for local and global sensitivity analysis (SA) of a reactive transport model simulating redox cycling within a floodplain aquifer. The reaction network is implemented in CrunchFlow and focuses on spatially and temporally heterogeneous Fe cycling, though it contains several other species and redox pathways (37 aqueous species and 7 minerals in total). The package contains data from 600 Monte Carlo simulations (for global SA) and 104 one-at-a-time simulations (for local SA). It includes all input files required to run the CrunchFlow model, as well as limited model output – .csv files containing time series of primary species concentrations at 21 grid cells within the domain. These time series files contain the following species: H+, CO32-, SO42-, Cl-, Ca2+, Mg2+, Na+, K+, Fe(II), Fe(III), HS-, CH3COO-, O2(aq), S(aq), and Br-.These data were used to demonstrate the application of a novel form of global SA, distance-based generalized sensitivity analysis (DGSA) to a reactive transport model. The 2D model simulates the export of reduced species from pockets of fine-grained, organic-rich sediments embedded in a coarse sand aquifer. Stochastic simulations jointly varied 17 key model input parameters across several orders of magnitude, including the spatial variability of each parameter. DGSA on the model results reveal that the amplitude and variability of exported Fe(II) are most sensitive to the interaction between sand permeability and individual reaction rates. By contrast, the propagation of reducing conditions downgradient from the fine-grained lenses depends most heavily on the rate of dissolved organic carbon and sulfate release from the fines.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”

The package contains the data layers used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”. The study aims to use multi-source remote sensing and GIS datasets to investigate the spatial heterogeneity and identify spatial zones with similar environmental characteristics and understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. We employed unsupervised hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, then determined the main driving factors using Random Forest regression and SHapley Additive exPlanations (SHAP). Spatial data layers include soil respiration, kernel Normalized Difference Vegetation Index (kNDVI) computed from Harmonized Landsat 8 and Sentinel-2 time series, climate variables from the Daymet dataset, land cover, biodiversity, topographical metrics, soil property, and tidal elevation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability: Supporting Data and Code

This repository contains R code and associated datasets for reproducing the analysis described in the manuscript titled “Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability” (DOI: 10.1029/2024JG008604). The provided scripts facilitate a comprehensive analysis of snow depth variability influenced by microtopography and vegetation distribution in a subarctic watershed. Included datasets are high-resolution spatial maps of snow depth, terrain elevation, vegetation height, and distance from shrubs taller than 1 meter, all formatted as text files (.txt). These data are fully describe in doi:10.15485/2316038. Users can adapt the provided R scripts to accommodate different data formats or larger spatial domains, noting that some output files may require modification due to their size.The code includes implementations for boosted regression tree analysis adapted from methods outlined in Elith et al. (2008). Users interested in understanding or modeling landscape-scale snow distribution patterns, particularly in Arctic or subarctic ecosystems, will find this package useful. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Timeseries Photos of a Variably Inundated Stream: Umtanum Creek, Washington, United States

This dataset is associated with a broader study using game camera timeseries photos collected to evaluate stream variable inundation via changes in width (i.e. wet fraction). Four game cameras were deployed along Umtanum Creek (Washington, United States) to track changes in stream inundation over time. Drone imagery was collected at the same location on October 18, 2024 which was used to construct a digital elevation model (DEM) of the streambed topography. The associated paper and data can be found at https://doi.org/10.1016/j.envsoft.2025.106715 (Bao et al., 2025a)) and https://doi.org/10.15485/2589885 (Bao et al., 2025b), respectively. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to this readme, this data package also includes a file-level metadata (FLMD) files that describes each file and a data dictionaries (DD) that describe all column/row headers and variable definitions. This dataset is comprised of (1) file-level metadata; (2) data dictionary; (3) readme; (4) field metadata; (5) field protocol; and (5) folders containing game camera photos. Game camera photos are organized into folders for each camera (CDL, CUL, CDR, CUR; see readme for information on camera naming) by the month photos were collected. All files are .csv, .jpg, or .pdf.

AI image segmentation↗
DOE OSTI2024

Compound Flooding in Halmstad: Common Causes, Interannual Variability and the Effects of Climate Change

Compound flooding was investigated in Halmstad on the Swedish west coast. The highest sea level ever recorded in Sweden was recorded by the Halmstad tide gauge. Moreover, through the city runs Nissan, one of the largest hydropower-producing rivers in southern Sweden. Together these features make the city an interesting location to study compound flooding. Co-variability between different hazards are studied using a unique set of downscaled climate scenario projections produced using state-of-the-art regional atmosphere, ocean and hydrological models. River discharge and sea level annual maxima are found to be correlated with peak strength on interannual time scales. This co-variability is found to derive from a mutual dependence that these hazards have on the North Atlantic Oscillation (NAO). Precipitation also shares this dependence in winter months and acts as a driver, building up the fluvial river hazard, while potentially adding additional pluvial hazard. Trends are also assessed, and it was found that future changes in compound flooding will most likely be driven primarily by mean sea level rise and secondarily by increases in mean river discharge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Validation of Deterministic Broadband Ground Motion and Variability from Dynamic Rupture Simulations of Buried Thrust Earthquakes

According to the article, we numerically model broadband ground motion (up to 5–7.5 Hz⁠) from blind‐thrust scenario earthquakes matching the fault geometry of the 1994 Mw 6.7 Northridge earthquake. Several realizations are modeled (by varying the hypocenter location in the dynamic rupture simulation) in a 1D‐layered velocity profile. In addition, we include Q(f )⁠, nonlinear effects from Drucker–Prager plasticity, and superimpose small‐scale medium complexity in both a 1D‐layered and 3D velocity model within the subsequent wave propagation. We investigate characteristics of the ground motion and its variability up to 50 km from the fault by comparing them with ground‐motion prediction equations (GMPEs), simple proxy metrics, as well as strong ground motion records from the Northridge event. We find that median ground motion closely follows the trend predicted by GMPEs and that the intraevent standard deviation, although varying with hypocenter location, lies near that of GMPE models. Plasticity affects ground‐motion amplitudes in regions near the source, reducing intraevent variability above ∼0.5 Hz⁠. Heterogeneity in the velocity structure on both the regional and small scales is needed for the simulated data to match two proxy metrics: the period‐to‐period correlation of spectral acceleration (SA) and the ratio of maximum‐to‐median SA. Although small‐scale heterogeneity has a negligible effect on median SA for this style of rupture, it serves to significantly increase the cumulative absolute velocity, better agreeing with observations. When compared with strong‐motion data, we find that long‐wavelength velocity structure within our deterministic simulations reduces bias at both short and long periods. Finally, synthetic ground motion at both footwall and hanging‐wall sites has no clear dependence on the distance to rupture (at both short and long periods); directivity is likely overpowering any hanging‐wall effect.

Geochemistry & Geophysics↗
DOE OSTI2024

Impact of the Earthquake Rupture on Ground-Motion Variability of the 24 August 2016 M w 6.2 Amatrice, Italy, Earthquake

Here, the devastating 24 August 2016 M w 6.2 earthquake that struck Amatrice, Italy, marked the beginning of a prolonged seismic sequence dominated by three subsequent M w ≥6.0 events in the central Apennines region. The earthquake destroyed Amatrice’s historic center, claiming the lives of 299 individuals and causing widespread damage in the neighboring villages. The severity of the ground shaking, with a recorded maximum acceleration of 850 cm/s 2 on the east–west component at the Amatrice station, was far greater than the predicted acceleration based on the Italian ground-motion model (GMM). As pointed out by several investigations, the observed ground-motion amplitude and its spatial variability during the earthquake can be linked to specific rupture characteristics, including slip distribution and rupture directivity effects revealed by the observed data (Tinti et al., 2016; Pischiutta et al., 2021). In this study, we conducted physics-based 3D numerical simulations of ground motion for the Amatrice earthquake for frequencies up to 3 Hz. We employed a series of kinematic rupture models and a well-constrained local 3D velocity model incorporating surface topography. The kinematic rupture realizations were generated using multiscale hybrid and fully stochastic models, following the technique proposed by Graves and Pitarka (2016). We focused on assessing the sensitivity of near-fault ground-motion amplitudes to earthquake rupture characteristics, in particular, the spatial slip pattern. To evaluate the quality of our simulations, we employed goodness-of-fit measurements performed in comparisons of simulated and recorded ground motions. The simulated ground motions compare well with the recorded data and predictions from GMMs for Italy, ITA18 (Lanzano et al., 2019). However, we found that the simulated interevent ground-motion variability (randomness in the source process) of peak ground velocity, σ (PGV) is higher than the constant σ (PGV) predicted by conventional GMMs. Our simulations using several rupture scenarios demonstrate that the near-fault ground-motion amplification pattern is directly related to the slip distribution pattern.

3D Ground Motions Simulations↗
DOE OSTI2018

Is Corn Stover Harvest Predictable Using Farm Operation, Technology, and Management Variables?

Core Ideas Corn stover removal was not common in 2010. Crop sequences were similar for stover and non‐stover harvest farms. State‐level variation occurred in the relative size of farms removing corn stover. Soil erosion control measures were not frequently adopted by either farm group. Crop residue management, provision of animal feed or bedding, and increased income are potential reasons for harvesting corn ( Zea mays L.) stover. Reasons for not doing so include the need for crop residue to restore or increase soil organic matter, protect against wind and water erosion, and cycle plant nutrients. Bioenergy market development may increase the number of producers harvesting corn stover. Can farming practice data predict the likelihood for harvesting corn stover at a national scale? Farm operation, technology, and management variables from the 2010 Agricultural Resource Management Survey (ARMS) of U.S. corn growers were compared between operations that removed corn stover and those that did not. Nationwide, stover was removed from approximately 6.3% of all corn operations, indicating stover harvest was not a common practice in 2010. Factors increasing the likelihood for stover harvest included producing feed corn, managing crop residues for pest control, and farmland ownership. Technology and conservation practice adoption rates were similar in both groups. Excessive stover removal can increase soil degradation. Both groups had erosion control adoption rates of ≤10%, which may provide an additional disincentive to harvest stover. Overall, the evaluated variables were similar between producers that did and did not harvest stover. This assessment provides a 2010 national baseline that can be used for future evaluations as bioenergy and bioproduct markets develop.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Long-Term Variability of Soil Moisture in the Southern Sierra: Measurement and Prediction

Using 6 yr (Water Year [WY] 2009–WY 2014) of hourly in situ measurements from a spatially distributed water-balance cluster, we quantified the long-term accuracy of an algorithm used to predict spatial patterns of depth-integrated soil-water storage within the rain–snow transition zone of the southern Sierra Nevada. The algorithm—the multivariate, non-parametric regression-tree estimator Random Forest—was used to predict soil-water storage based on a combination of attributes at each instrument cluster (soil texture, topographic wetness index, elevation, northness, and canopy cover). Out-of-bag R 2 (similar to cross-validation for Random Forest) was used to quantify the accuracy of the estimator for unobserved data. Accuracy was consistently high during the wet-up, snow-cover, and early recession periods of average and wet years. The accuracy declined at the end of a 3-yr dry period, and the relative rank of the independent variables in the model shifted. Soil texture was the highest-ranked independent variable across all years, followed by elevation and northness. Topographic wetness increased in importance during dry periods. Northness exhibited high importance during the wet-up and early recession periods of most water years. As a result, during dry years, the importance of elevation declined. In dry years, notable differences in soil-water storage at each depth include lower-than-average storage in the deeper regolith at the beginning of the water year and lower storage in near-surface layers during the winter resulting from transient snow cover.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Full event particle-level unfolding with variable-length latent variational diffusion

The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of dimensions. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic t\bar{t} t t ‾ production at the Large Hadron Collider.

Shmakov, Alexander↗
DOE OSTI2017

Collaborative Research: Quantifying the Uncertainties of Aerosol Indirect Effects and Impacts on Decadal-Scale Climate Variability in NCAR CAM5 and CESM1

The goal of this proposed project is to assess the climatic importance and sensitivity of aerosol indirect effect (AIE) to cloud and aerosol processes and feedbacks, which include organic aerosol hygroscopicity, cloud condensation nuclei (CCN) activation kinetics, Giant CCN, cloud-scale entrainment, ice nucleation in mixed-phase and cirrus clouds, and treatment of subgrid variability of vertical velocity. A key objective was to link aerosol, cloud microphysics and dynamics feedbacks in CAM5 with a suite of internally consistent and integrated parameterizations that provide the appropriate degrees of freedom to capture the various aspects of the aerosol indirect effect. The proposal integrated new parameterization elements into the cloud microphysics, moist turbulence and aerosol modules used by the NCAR Community Atmospheric Model version 5 (CAM5). The CAM5 model was then used to systematically quantify the uncertainties of aerosol indirect effects through a series of sensitivity tests with present-day and preindustrial aerosol emissions. New parameterization elements were developed as a result of these efforts, and new diagnostic tools & methodologies were also developed to quantify the impacts of aerosols on clouds and climate within fully coupled models. Observations were used to constrain key uncertainties in the aerosol-cloud links. Advanced sensitivity tools were developed and implements to probe the drivers of cloud microphysical variability with unprecedented temporal and spatial scale. All these results have been published in top and high impact journals (or are in the final stages of publication). This proposal has also supported a number of outstanding graduate students.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Characterization of the March 2017 tank 10 surface sample (combination of HTF-10-17-30 AND HTF-10-17-31) and variable depth sample (combination of HTF-10-17-32 and HTF-10-17-33)

Two surface samples (HTF-10-17-30 and HTF-10-17-31) and two variable depth samples (HTF-10-17-32 and HTF-10-17-33) were collected from SRS Tank 10 during March 2017 and submitted to SRNL for characterization. At SRNL, the two surface samples were combined in one container, the two variable depth samples (VDSs) were combined in another container, and then the two composite samples were each characterized by a series of physical, ionic, radiological, and elemental analysis methods. The surface sample composite was characterized primarily for Tank Farm corrosion control purposes, while the VDS composite was characterized primarily for Tank Closure Cesium Removal (TCCR) purposes.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Variable Renewable Energy in Long-Term Planning Models: A Multi-Model Perspective

Long-term capacity expansion models of the U.S. electricity sector have long been used to inform electric sector stakeholders and decision makers. With the recent surge in variable renewable energy (VRE) generators - primarily wind and solar photovoltaics - the need to appropriately represent VRE generators in these long-term models has increased. VRE generators are especially difficult to represent for a variety of reasons, including their variability, uncertainty, and spatial diversity. To assess current best practices, share methods and data, and identify future research needs for VRE representation in capacity expansion models, four capacity expansion modeling teams from the Electric Power Research Institute, the U.S. Energy Information Administration, the U.S. Environmental Protection Agency, and the National Renewable Energy Laboratory conducted two workshops of VRE modeling for national-scale capacity expansion models. The workshops covered a wide range of VRE topics, including transmission and VRE resource data, VRE capacity value, dispatch and operational modeling, distributed generation, and temporal and spatial resolution. The objectives of the workshops were both to better understand these topics and to improve the representation of VRE across the suite of models. Given these goals, each team incorporated model updates and performed additional analyses between the first and second workshops. This report summarizes the analyses and model 'experiments' that were conducted as part of these workshops as well as the various methods for treating VRE among the four modeling teams. The report also reviews the findings and learnings from the two workshops. We emphasize the areas where there is still need for additional research and development on analysis tools to incorporate VRE into long-term planning and decision-making.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Incorporation of Spatial Stochastic Variability into Paracousti-UQ

Waves propagating through natural materials such as ocean water encounter spatial variations in material properties that cannot easily be predicted or known in advance. Deterministic wave simulation algorithms must assume that all properties throughout the model space are precisely known. However, a stochastic wave simulation tool can parameterize the material as a stochastic medium with a certain probability distribution and correlation length. This report documents the addition of spatial stochastic variability into Paracousti-UQ, Sandia Geophysics Department's 3-D full waveform acoustic algorithm within stochastic media. The ability of the code to replicate Monte Carlo solutions in 1-D spatially variable media is also evaluated.

58 GEOSCIENCES↗
DOE OSTI2019

Introduction to Mixed Variable Optimization (MVO) [Slides]

Mixed Variable Optimization (MVO) is an optimization method that uses discrete (categorical) and continuous variables in its optimization process. This method of optimization is applicable to highly nonlinear problems where the information used in the optimization is of poor quality of has minor inaccuracies in the data sets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

PV Variability at the DeSoto Next Generation Solar Energy Center (Cooperative Research and Development Final Report, CRADA Number CRD-11-425)

In order to support the continued integration of utility-scale solar photovoltaic sites into the Florida transmission system and to further enhance our understanding of the effects of cloud-induced output variability on the site and transmission operations, participant elects to enter a Cooperative Research and Development Agreement (CRADA) with the National Renewable Energy Lab (NREL). Under this agreement, NREL will work with participant to instrument their PV plant in DeSoto county FL and obtain solar radiation and power output data. The Florida Power & Light's (FPL) DeSoto Next Generation Energy Center is the largest installed PV plant in the country (25 MW). Our goal is that this site be instrumented and used for research into PV variability and forecasting.

14 SOLAR ENERGY↗