SEARCH · Search NASA

Results for “Bayes”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11

All sources NASA NTRS DOE OSTI

DOE OSTI2026

Precision Measurement of Neutrino Oscillation Parameters with 10 Years of Data from the NOvA Experiment

This Letter reports measurements of muon-neutrino disappearance and electron-neutrino appearance and the corresponding antineutrino processes between the two NOvA detectors in the NuMI neutrino beam. These measurements use a dataset with double the neutrino mode beam exposure that was previously analyzed, along with improved simulation and analysis techniques. A joint fit to these samples in the three-flavor paradigm results in the most precise single-experiment constraint on the atmospheric neutrino mass-splitting, $Δm^2_{32}= 2.431^{+0.036}_{-0.034} (-2.479^{+0.036}_{-0.036}) \times 10^{-3}$~eV$^2$ if the mass ordering is Normal (Inverted). In both orderings, a region close to maximal mixing with $\sin^2θ_{23}=0.55_{+0.06}^{-0.02}$ is preferred. The NOvA data show a mild preference for the Normal mass ordering with a Bayes factor of 2.4 (corresponding to 70% of the posterior probability), indicating that the Normal ordering is 2.4 times more probable than the Inverted ordering. When incorporating a 2D $Δm^2_{32}\textrm{--}\sin^2 2θ_{13}$ constraint based on Daya Bay data, this preference strengthens to a Bayes factor of 6.6 (87\%).

FOS: Physical sciences↗

DOE OSTI2024

Tracking animal movements via collaborative acoustic telemetry networks: Multiscale habitat use, phenology, and management insights

Abstract Estuaries support diverse fish and invertebrate communities, including resident species that rely on estuarine habitats year‐round and transient migratory species. The unique movement patterns of these animals connect habitats within and far beyond the estuary and are integrally linked to fisheries management objectives. With a focus on Chesapeake Bay, this study leveraged data from collaborative acoustic telemetry networks in the northwest Atlantic to assess habitat use and phenology of movements for seven species of fish (cownose rays, dusky sharks, smooth dogfish, alewife, striped bass, common carp, and blue catfish) and one invertebrate (horseshoe crabs). A total of 288 acoustically tagged individuals were detected >3.2 million times (6,743 to 2,095,717 detections per species) on receivers across ~20.5 degrees of latitude spanning the North American Atlantic seaboard from Florida, USA, to New Brunswick, Canada. Common metrics of movement and phenology grouped these species as resident (common carp, blue catfish, horseshoe crabs), primarily resident in estuaries (juvenile striped bass), and coastal migrant (cownose rays, dusky sharks, smooth dogfish, alewife); maximum distance traveled varied by three orders of magnitude among these species. Further analysis of phenology for coastal migrants elucidated the timing and duration of these species' use of Chesapeake Bay. Collectively, movements linked habitats within Chesapeake Bay and connected the estuary to coastal ecosystems both to the north (e.g., alewife) and south (e.g., cownose rays), creating networks of fisheries management jurisdictions that varied in complexity and identified opportunities for enhancement to current management or co‐management of some species. Our results elucidate the importance of estuaries to species with diverse movement behaviors, identify scales and pathways of habitat connectivity via animal movements, and highlight the utility of collaborative acoustic telemetry networks for quantifying movements relevant to both ecological research and fisheries management.

Livernois, Mariah C.↗

DOE OSTI2023

AmeriFlux FLUXNET-1F US-Srr Suisun marsh - Rush Ranch

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Srr Suisun marsh - Rush Ranch. This is the FLUXNET version of the carbon flux data for the site US-Srr Suisun marsh - Rush Ranch produced by applying the standard ONEFlux (1F) software. Site Description - This site is a 4.57 km2 brackish tidal marsh located in the San Francisco Bay National Estuarine Research Reserve (SFBNERR, http://www.nerrs.noaa.gov/reserves/san-francisco-bay.html) in Suisun Bay, CA, USA. Suisun Bay is the most extensive contiguous brackish marsh complex in California. This site is classified as a high marsh, which according to the National Wetland Inventory, is representative of over 58% of estuarine wetlands.

Bergamaschi, Brian↗

DOE OSTI2024

Causes of and Solutions to Wind Speed Bias in NREL's 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf

This report provides the results of a detailed analysis into the causes of high wind speed bias in the 20-year wind resource data set for offshore California the National Renewable Energy Laboratory (NREL) released in 2020, herein called CA20. The data set was developed using the state-of-the-art Weather Research and Forecasting (WRF) model. Notably, no floating lidars were available at the time in offshore California to validate offshore hub-height wind speeds. In late 2020, the Pacific Northwest National Laboratory (PNNL) deployed two floating lidars in the California outer continental shelf (OCS), near the Bureau of Ocean Energy Management (BOEM) call areas of Humboldt and Morro Bay. Using these observations through 2021, NREL found considerable bias in modeled hub-height winds at both locations: up to +2 m/s at Humboldt over a 6-month period, and up to +1 m/s at Morro Bay over a one-year period. Upon the discovery of this bias, the Department of Energy (DOE) and BOEM funded NREL and PNNL to investigate the causes of, impacts of, and solutions to the bias in the CA20 data set. This report summarizes the findings of this research. We first investigated whether different WRF model setups could lead to reduced bias. We found that the choice of planetary boundary layer (PBL) scheme - which controls the vertical turbulent mixing of momentum, heat, and moisture in the lowermost part of the atmosphere - greatly affected hub-height wind speeds in the region. Specifically, switching from the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme used in CA20 (and widely used across a range of operational and research weather models) to the less common Yonsei University (YSU) scheme nearly eliminated the bias at both the Humboldt and Morro Bay lidar locations. The large discrepancy between the MYNN- and YSU-modeled hub-height winds pointed towards the role of atmospheric stability. In general, PBL schemes agree well in conditions of high turbulence and mixing, normally referred to as "unstable" conditions. By contrast, PBL schemes start to diverge in "stable" conditions, where turbulence is low and thermal stratification (i.e., higher temperature air sitting on top of colder air) greatly suppresses vertical mixing. Under such conditions, winds aloft can decouple from surface effects and greatly accelerate, causing high wind speeds at hub-height and frequent low-level jets (LLJs). We determined that these stable conditions are in fact dominant in offshore California. The region is characterized by moderate-to-extreme stable stratification with a LLJ on average around 200 meters above sea-level. To our knowledge, no wind energy area globally has as strongly stable stratification as offshore California. Under these extreme conditions, we determined that the MYNN scheme models higher stability than YSU, resulting in less vertical turbulent mixing than YSU, allowing for the acceleration of hub-height winds, more intense LLJs, and higher-amplitude inertial oscillations. Using surface observations, we found that MYNN overestimates near-surface stability, whereas YSU tends to model stability better. We then considered several short-term case studies to assess additional meteorological drivers of the bias at Humboldt. We found that during synoptic scale northerly flows driven by the North Pacific High and inland thermal low, a coastal warm bias in the MYNN case studies contributes to the modeled wind speed bias by altering the boundary layer thermodynamics via a thermal wind mechanism. Given the strong performance of the YSU-based runs in offshore California, NREL has produced and published an updated version of the CA20 data set with YSU as the PBL scheme. This updated data set is now part of NREL's 2023 National Offshore Wind (NOW-23) data set, which covers all the U.S. offshore waters. The development and final validation of the NOW-23 data set in offshore California is documented in this report.

17 WIND ENERGY↗

DOE OSTI2024

Analysis of Lease Area Delineation Options in the Brookings Wind Energy Area

This study assesses options for delineating offshore wind energy lease areas within the Brookings Wind Energy Area (WEA). The Brookings WEA is one of two areas (Coos Bay and Brookings) offshore the Oregon coast that have been proposed for sale. The Coos Bay WEA is comparable in size to existing offshore wind lease areas in northern California, whereas the Brookings WEA is more than twice as large as the Coos Bay WEA. This study considers how the Brookings WEA can be divided to obtain two lease areas of relatively equal value, which would result in three Oregon lease areas of approximately equal size.

17 WIND ENERGY↗

DOE OSTI2024

Coastal-Urban-Rural Atmospheric Gradient Experiment (CoURAGE) Science Plan

Understanding the mechanisms governing the urban atmospheric environment is critical for informing urban populations regarding the impacts of climate change and associated mitigation and adaptation measures. Earth system (climate and weather) models have not yet been adapted to provide accurate predictions of climate and weather variability within cities, nor do they provide well-tested representations of the impacts of urban systems on the atmospheric environment. These limitations are largely due to limited field data available for testing and development of these models. We will deploy the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s first Mobile Facility (AMF1) to the mid-Atlantic region surrounding the city of Baltimore for the Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE). This deployment will create a four-node regional atmospheric observatory network including Baltimore and its three primary surrounding environments – rural, urban, and bay. CoURAGE investigators will study the interactions among the Earth’s surface, the atmospheric boundary layer, aerosols and atmospheric composition, clouds, radiation, and precipitation at each site, and examine how the spatial gradients across the region interact to create the climate conditions in Baltimore. This study will determine the degree to which Baltimore’s atmospheric environment depends on interactive feedbacks in the atmospheric system and the degree to which conditions in Baltimore depend on the surrounding environment. Some topics of interest include how urban land management exacerbates heat waves, the impact of regional mesoscale winds (nocturnal jet, bay breeze) on urban air pollution and cloud cover, and the impact of the urban heat island and aerosol production on heavy precipitation events. Understanding this integrated coast-urban-rural system quantitatively and with good accuracy and precision is critical to informing climate adaptation and mitigation efforts in the city of Baltimore. The understanding gained should be applicable to many similar coastal, mid-latitude urban centers. Another important objective of CoURAGE is to improve the representation of the climate of coastal cities in Earth systems models (ESMs). CoURAGE investigators will use the observations to test current ESMs, identify weaknesses and work towards improved simulations of this complex environment. The ARM core facility will be deployed in the city of Baltimore, complementing the Baltimore Social-Environmental Collaborative (BSEC), a DOE urban integrated field laboratory (UIFL). Ancillary sites will be deployed to rural Maryland northwest of Baltimore, and to the southern end of Kent Island within Chesapeake Bay. The fourth node will be a long-term atmospheric observatory operated in Beltsville, Maryland by Howard University and the Maryland Department of the Environment. Measurements will be conducted for one year, starting in December of 2024. There will be two intensive operational periods (IOPs), one in summer and one in winter, when the ancillary sites will be enhanced with additional balloon launches, tethered balloon system (TBS) operation, and added atmospheric composition measurements.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Linking large-scale weather patterns to observed and modeled turbine hub-height winds offshore of the US West Coast

The US West Coast holds great potential for wind power generation, although its potential varies due to the complex coastal climate. Characterizing and modeling turbine hub-height winds under different weather conditions are vital for wind resource assessment and management. This study uses a two-stage machine learning algorithm to identify five large-scale meteorological patterns (LSMPs): post-trough, post-ridge, pre-ridge, pre-trough, and California high. The LSMPs are linked to offshore wind patterns, specifically at lidar buoy locations within lease areas for future wind farm development off Humboldt and Morro Bay. While each LSMP is associated with characteristic large-scale atmospheric conditions and corresponding differences in wind direction, diurnal variation, and jet features at the two lidar sites, substantial variability in wind speeds can still occur within each LSMP. Wind speeds at Humboldt increase during the post-trough, pre-ridge, and California-high LSMPs and decrease during the remaining LSMPs. Morro Bay has smaller responses in mean speeds, showing increased wind speed during the post-trough and California-high LSMPs. Besides the LSMPs, local factors, including the land–sea thermal contrast and topography, also modify mean winds and diurnal variation. The High-Resolution Rapid Refresh model analysis does a good job of capturing the mean and variation at Humboldt but produces large biases at Morro Bay, particularly during the pre-ridge and California-high LSMPs. The findings are anticipated to guide the selection of cases for studying the influence of specific large-scale and local factors on California offshore winds and to contribute to refining numerical weather prediction models, thereby enhancing the efficiency and reliability of offshore wind energy production.

17 WIND ENERGY↗

DOE OSTI2021

Cooling methods for medium voltage drive systems

In one aspect, a medium voltage power converter includes a cabinet having: a power cube bay to house a plurality of power cubes, each of the plurality of power cubes adapted within a corresponding enclosure and comprising a low frequency front end stage, a DC link and a high frequency back end stage, the plurality of power cubes to couple to a high speed machine; and a plurality of first barriers adapted to isolate and direct a first flow of cooling air through one of the plurality of power cubes; and a transformer bay having at least one transformer to couple between a utility connection and the plurality of power cubes, the transformer bay including a plurality of cooling fans to cool the at least one transformer.

Ledezma, Enrique↗

DOE OSTI2020

Investigation of Radiological Contamination at Parks in the Gateway National Recreation Area - 20350

In 2005, the National Park Service was notified that elevated radiation levels were detected at Great Kills Park during a baseline radiological mapping effort conducted by the Counter Terrorism Bureau of the New York City Police Department and the United States Department of Energy. Great Kills Park is in the Staten Island Unit of the Gateway National Recreation Area. A records search determined that approximately 280 acres of the park was established over an area in which the New York City Department of Sanitation placed approximately 15.1 million cubic yards of waste fill from 1944 to 1948. Since 2005, a series of response actions and investigations were conducted on behalf of the National Park Service to mitigate risk to human health and the environment and to determine the nature of radiological contamination at Great Kills Park. The findings support that radiological contamination is related to certain components of waste fill, including incinerator residue from both on and offsite incinerators, coal ash from the historical use of coal-fired boilers for heating, and incidental radiological artifacts in the waste fill due to their historical use and subsequent disposal. A majority of the radiological artifacts were determined to contain radium-226 and included radioluminescent markers, radium impregnated switch board buttons, radium paint containers, and brachytherapy devices. The National Park Service has several other Parks in the Gateway National Recreation Area that were established in areas with a similar history to Great Kills Park, and overlie waste fill historically placed during landfilling operations, including Spring Creek Park and Dead Horse Bay. Over the last year, the National Park Service has performed additional radiological investigations at Great Kills Park as part of the ongoing Remedial Investigation under the Comprehensive Environmental Response, Compensation, and Liability Act. In addition, preliminary radiological investigations were performed at Spring Creek Park and Dead Horse Bay Park in the Jamaica Bay Unit of Gateway National Recreation Area. These investigation activities identified gamma radiation levels above the ambient level, several of which were attributable to radiological artifacts similar in nature to those recovered at Great Kills Park. Ongoing investigations at Parks in the Gateway National Recreation Area support an association between radiological contamination and historical landfilling practices that were common prior to the regulation and control of radioactive materials. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Bayesian Framework for Bioburden Density Estimation in Planetary Protection

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Reference Site Conditions for Floating Wind Arrays in the United States

17 WIND ENERGY↗

DOE OSTI2024

Reference Site Condition Datasets for Floating Wind Arrays in the United States

Floating offshore wind farm design is highly site-specific, requiring detailed information about the specific conditions of a project area for realistic design studies. Unfortunately, publicly available site condition data for potential floating offshore wind project sites in the United States is scarce. To support U.S. offshore wind research, we developed reference site condition datasets, including metocean and seabed information, for four potential floating wind project areas in the U.S.: Humboldt Bay, Morro Bay, the Gulf of Maine, and the Gulf of Mexico. These datasets were compiled using publicly available data. Our metocean analysis, covering wind, waves, and surface currents, utilized measurement data from 2000 to 2020. Sources included the National Renewable Energy Laboratory’s National Offshore Wind Dataset for wind data, National Data Buoy Center buoys for wave data, and the High Frequency Radar Network for surface currents. These data were integrated into hourly time series used to compute extreme return periods up to 500 years, monthly statistics, and joint probability clusters for fatigue analysis. Soil conditions were evaluated using the usSEABED database and bathymetry grids were interpolated from the NCEI Digital Elevation Model Global Mosaic. Further information on the datasets and how they were created can be found in: Biglu, M., M. Hall, E. Lozon, S. Housner. 2024. Reference Site Conditions for Floating Wind Arrays in the United States. Golden, CO: National Renewable Energy Laboratory (NREL). NREL/TP-5000-89897. The data are also available at: https://github.com/FloatingArrayDesign/SiteConditions The content of each dataset is as follows: _NOW23_wind.txt: Hourly NOW-23 wind data up to a height of 400 meter. _metocean_1hr.txt: Hourly time series including wind, wave, surface current and temperature data. _Summary.xlsx: Metocean data, including extreme values, joint probability distributions and monthly statistics. _usSEABED_soil.csv: Extract of the usSEABED database for this specific site. _bathymetry_200m.txt (and 500m, 1000m): Gridded seabed depth data.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

MBARI WEC 2021 deployment

This dataset includes data from the Monterey Bay Aquarium Research Institute (MBARI) wave energy converter (WEC) and a nearby located Sofar Spotter buoy. The Monterey Bay Aquarium Research Institute has developed and deployed a small two-body point absorber wave energy device suitable to autonomous underwater vehicle, sensor system, and even aquaculture farm needs. For more information on the MBARI WEC see the research journal attached in the submission.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

DAISY Benchmark Performance Data

This repository contains the underlying data from benchmark experiments for Drifting Acoustic Instrumentation SYstems (DAISYs) in waves and currents described in "Performance of a Drifting Acoustic Instrumentation SYstem (DAISY) for Characterizing Radiated Noise from Marine Energy Converters" (https://link.springer.com/article/10.1007/s40722-024-00358-6). DAISYs consist of a surface expression connected to a hydrophone recording package by a tether. Both elements are instrumented to provide metadata (e.g., position, orientation, and depth). Information about how to build DAISYs is available at https://www.pmec.us/research-projects/daisy. The repository's primary content is three compressed archives (.zip format), each containing multiple MATLAB binary data files (.mat format). A table relating individual data files to figures in the paper, as well as the structure of each file, is included in the repository as a Word document (Data Description MHK-DR.docx). Most of the files contain time series information for a single DAISY deployment (file naming convention: [site]_DAISY_[Drift #].mat) consisting of processed hydrophone data and associated metadata. For a limited number of DAISY deployments, the hydrophone package was replaced with an acoustic Doppler velocimeter (file naming convention: [site]_DAISY_[Drift #]_ADV.mat). Data were collected over several years at three locations: (1) Sequim Bay at Pacific Northwest National Laboratory's Marine & Coastal Research Laboratory (MCRL) in Sequim, WA, the energetic tidal channel in Admiralty Inlet, WA (Admiralty Inlet), and the U.S. Navy's Wave Energy Test Site (WETS) in Kaneohe, HI. Brief descriptions of data files at each location follow. - MCRL - (1) Drift #4 and #16 contrast the performance of a DAISY and a reference hydrophone (icListen HF Reson), respectively, in the quiescent interior of Sequim Bay (September 2020). (2) Drift #152 and #153 are velocity measurements for a drifting acoustic Doppler velocimeter in in the tidally-energetic entrance channel inside a flow shield and exposed to the flow, respectively (January 2018). (3) Two non-standard files are also included: DAISY_data.mat corresponds to a subset of a DAISY drift over an Adaptable Monitoring Package (AMP) and AMP_data.mat corresponds to approximately co-temporal data for a stationary hydrophone on the AMP (February 2019). - Admiralty Inlet - (1) Drift #1-12 correspond to tests with flow shielded DAISYs, unshielded DAISYs, a reference hydrophone, and drifting acoustic Doppler velocimeter with 5, 10, and 15 m tether lengths between surface expression and hydrophone recording package (July 2022). (2) Drift #13-20 correspond to tests of flow shielded DAISYs with three different tether materials (rubber cord, nylon line, and faired nylon line) in lengths of 5, 10, and 15 m (July 2022). - WETS - (1) Drift #30-32 correspond to tests with a heave plate incorporated into the tether (standard configuration for wave sites), rubber cord only, and rubber cord, but with a flow shielded hydrophone (November 2022). (2) Drift #49-58 and Drift #65-68 correspond to measurements around mooring infrastructure at the 60 m berth where time-delay-of-arrival localization was demonstrated for different DAISY arrangements and hydrophone depths (November 2022).

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data

High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as molecular beam mass spectrometry (py-MBMS) analyses are becoming increasingly popular for the rapid analysis of biomass cell wall composition and typically require the use of different data analysis tools depending on the need and application. Here, the authors report the py-MBMS analysis of several types of lignocellulosic biomass to gain an understanding of spectral patterns and variation with associated biomass composition and use machine learning approaches to classify, differentiate, and predict biomass types on the basis of py-MBMS spectra. Py-MBMS spectra were also corrected for instrumental variance using generalized linear modeling (GLM) based on the use of select ions relative abundances as spike-in controls. Machine learning classification algorithms e.g., random forest, k-nearest neighbor, decision tree, Gaussian Naïve Bayes, gradient boosting, and multilayer perceptron classifiers were used. The k-nearest neighbors (k-NN) classifier generally performed the best for classifications using raw spectral data, and the decision tree classifier performed the worst. After normalization of spectra to account for instrumental variance, all the classifiers had comparable and generally acceptable performance for predicting the biomass types, although the k-NN and decision tree classifiers were not as accurate for prediction of specific sample types. Gaussian Naïve Bayes (GNB) and extreme gradient boosting (XGB) classifiers performed better than the k-NN and the decision tree classifiers for the prediction of biomass mixtures. The data analysis workflow reported here could be applied and extended for comparison of biomass samples of varying types, species, phenotypes, and/or genotypes or subjected to different treatments, environments, etc. to further elucidate the sources of spectral variance, patterns, and to infer compositional information based on spectral analysis, particularly for analysis of data without a priori knowledge of the feedstock composition or identity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Addendum to: Combined analysis of neutrino decoherence at reactor experiments

We update our analyses to constrain neutrino decoherence induced by wavepacket separation with RENO and Daya Bay data, now including the final data sets of the two experiments. We find that while the individual bounds from Daya Bay and RENO data improve relative to our original estimates, the combined fits are still dominated by KamLAND data and are only minimally improved.

de Gouvêa, André↗

DOE OSTI2022

Approximating a linear multiplicative objective in watershed management optimization

Implementing management practices in a cost-efficient manner is critical for regional efforts to reduce the amount of pollutants entering the Chesapeake Bay. We study the problem of selecting a subset of practices that minimizes pollutant load—subject to budgetary and environmental constraints—as simulated in a widely used regulatory watershed model. Mimicking the computation of pollutant load in the regulatory model, we formulate this problem as a continuous optimization model with a linear multiplicative objective function and linear constraints. To lay the groundwork for incorporating additional stakeholder requirements in the future, especially those that would require integer variables, we present and study a continuous linear optimization model that approximates the nonlinear model. The linear model, which requires an exponential number of variables, arises naturally as an alternative model for the same underlying physical process. We examine the theoretical behavior of these optimization models and investigate restrictions of the linear model to handle its large number of variables. Through extensive computational tests on real and randomly generated instances, we demonstrate that the linear model and its restrictions provide optimal solutions close to those of the nonlinear model in practice, despite poor approximation properties in the worst case. We conclude that the linear model—together with our approach to handling its large number of variables—provides a viable framework from which to extend the optimization model to better meet the needs of the Chesapeake Bay watershed management stakeholders.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Origin and composition of three heterolithic boulder- and cobble-bearing deposits overlying the Murray and Stimson formations, Gale Crater, Mars

Heterolithic, boulder-containing, pebble-strewn surfaces occur along the lower slopes of Aeolis Mons (“Mt. Sharp”) in Gale crater, Mars. They were observed in HiRISE images acquired from orbit prior to the landing of the Curiosity rover. The rover was used to investigate three of these units named Blackfoot, Brandberg, and Bimbe between sols 1099 and 1410. These unconsolidated units overlie the lower Murray formation that forms the base of Mt. Sharp, and consist of pebbles, cobbles and boulders. Blackfoot also overlies portions of the Stimson formation, which consists of eolian sandstone that is understood to significantly postdate the dominantly lacustrine deposition of the Murray formation. Blackfoot is elliptical in shape (62 x 26 m), while Brandberg is nearly circular (50 x 55 m), and Bimbe is irregular in shape, covering about ten times the area of the other two. The largest boulders are 1.5–2.5 m in size and are interpreted to be sandstones. As seen from orbit, some boulders are light-toned and others are dark-toned. Rover-based observations show that both have the same gray appearance from the ground and their apparently different albedos in orbital observations result from relatively flat skyfacing surfaces. Chemical observations show that two clasts of fine sandstone at Bimbe have similar compositions and morphologies to nine ChemCam targets observed early in the mission, near Yellowknife Bay, including the Bathurst Inlet outcrop, and to at least one target (Pyramid Hills, Sol 692) and possibly a cap rock unit just north of Hidden Valley, locations that are several kilometers apart in distance and tens of meters in elevation. These findings may suggest the earlier existence of draping strata, like the Stimson formation, that would have overlain the current surface from Bimbe to Yellowknife Bay. Compositionally these extinct strata could be related to the Siccar Point group to which the Stimson formation belongs.

58 GEOSCIENCES↗