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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Overview of Algorithms for Using Particle Morphology in Pre-Detonation Nuclear Forensics

A major goal in pre-detonation nuclear forensics is to infer the processing conditions and/or facility type that produced radiological material. This review paper focuses on analyses of particle size, shape, texture (“morphology”) signatures that could provide information on the provenance of interdicted materials. For example, uranium ore concentrates (UOC or yellowcake) include ammonium diuranate (ADU), ammonium uranyl carbonate (AUC), sodium diuranate (SDU), magnesium diuranate (MDU), and others, each prepared using different salts to precipitate U from solution. Once precipitated, UOCs are often dried and calcined to remove adsorbed water. The products can be allowed to react further, forming uranium oxides UO3, U3O8, or UO2 powders, whose surface morphology can be indicative of precipitation and/or calcination conditions used in their production. This review paper describes statistical issues and approaches in using quantitative analyses of measurements such as particle size and shape to infer production conditions. Statistical topics include multivariate t tests (Hotelling’s T 2 ), design of experiments, and several machine learning (ML) options including decision trees, learning vector quantization neural networks, mixture discriminant analysis, and approximate Bayesian computation (ABC). ABC is emphasized as an attractive option to include the effects of model uncertainty in the selected and fitted forward model used for inferring processing conditions.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification

Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Classification of Intensity Distributions of Transmission Eigenchannels of Disordered Nanophotonic Structures Using Machine Learning

Light-matter interaction optimization in complex nanophotonic structures is a critical step towards the tailored performance of photonic devices. The increasing complexity of such systems requires new optimization strategies beyond intuitive methods. For example, in disordered photonic structures, the spatial distribution of energy densities has large random fluctuations due to the interference of multiply scattered electromagnetic waves, even though the statistically averaged spatial profiles of the transmission eigenchannels are universal. Classification of these eigenchannels for a single configuration based on visualization of intensity distributions is difficult. However, successful classification could provide vital information about disordered nanophotonic structures. Emerging methods in machine learning have enabled new investigations into optimized photonic structures. In this work, we combine intensity distributions of the transmission eigenchannels and the transmitted speckle-like intensity patterns to classify the eigenchannels of a single configuration of disordered photonic structures using machine learning techniques. Specifically, we leverage supervised learning methods, such as decision trees and fully connected neural networks, to achieve classification of these transmission eigenchannels based on their intensity distributions with an accuracy greater than 99%, even with a dataset including photonic devices of various disorder strengths. Simultaneous classification of the transmission eigenchannels and the relative disorder strength of the nanophotonic structure is also possible. Our results open new directions for machine learning assisted speckle-based metrology and demonstrate a novel approach to classifying nanophotonic structures based on their electromagnetic field distributions. These insights can be of paramount importance for optimizing light-matter interactions at the nanoscale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A Comparison of Machine Learning Methods to Forecast Tropospheric Ozone Levels in Delhi

Ground-level ozone is a pollutant that is harmful to urban populations, particularly in developing countries where it is present in significant quantities. It greatly increases the risk of heart and lung diseases and harms agricultural crops. This study hypothesized that, as a secondary pollutant, ground-level ozone is amenable to 24 h forecasting based on measurements of weather conditions and primary pollutants such as nitrogen oxides and volatile organic compounds. We developed software to analyze hourly records of 12 air pollutants and 5 weather variables over the course of one year in Delhi, India. To determine the best predictive model, eight machine learning algorithms were tuned, trained, tested, and compared using cross-validation with hourly data for a full year. The algorithms, ranked by R2 values, were XGBoost (0.61), Random Forest (0.61), K-Nearest Neighbor Regression (0.55), Support Vector Regression (0.48), Decision Trees (0.43), AdaBoost (0.39), and linear regression (0.39). When trained by separate seasons across five years, the predictive capabilities of all models increased, with a maximum R 2 of 0.75 during winter. Bidirectional Long Short-Term Memory was the least accurate model for annual training, but had some of the best predictions for seasonal training. Out of five air quality index categories, the XGBoost model was able to predict the correct category 24 h in advance 90% of the time when trained with full-year data. Separated by season, winter is considerably more predictable (97.3%), followed by post-monsoon (92.8%), monsoon (90.3%), and summer (88.9%). These results show the importance of training machine learning methods with season-specific data sets and comparing a large number of methods for specific applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability

Tracking vital signs accurately is critical for triaging a patient and ensuring timely therapeutic intervention. The patient’s status is often clouded by compensatory mechanisms that can mask injury severity. The compensatory reserve measurement (CRM) is a triaging tool derived from an arterial waveform that has been shown to allow for earlier detection of hemorrhagic shock. However, the deep-learning artificial neural networks developed for its estimation do not explain how specific arterial waveform elements lead to predicting CRM due to the large number of parameters needed to tune these models. Alternatively, we investigate how classical machine-learning models driven by specific features extracted from the arterial waveform can be used to estimate CRM. More than 50 features were extracted from human arterial blood pressure data sets collected during simulated hypovolemic shock resulting from exposure to progressive levels of lower body negative pressure. A bagged decision tree design using the ten most significant features was selected as optimal for CRM estimation. This resulted in an average root mean squared error in all test data of 0.171, similar to the error for a deep-learning CRM algorithm at 0.159. By separating the dataset into sub-groups based on the severity of simulated hypovolemic shock withstood, large subject variability was observed, and the key features identified for these sub-groups differed. This methodology could allow for the identification of unique features and machine-learning models to differentiate individuals with good compensatory mechanisms against hypovolemia from those that might be poor compensators, leading to improved triage of trauma patients and ultimately enhancing military and emergency medicine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Classification of Nuclear Reactor Operations Using Spatial Importance and Multisensor Networks

Distributed multisensor networks record multiple data streams that can be used as inputs to machine learning models designed to classify operations relevant to proliferation at nuclear reactors. The goal of this work is to demonstrate methods to assess the importance of each node (a single multisensor) and region (a group of proximate multisensors) to machine learning model performance in a reactor monitoring scenario. This, in turn, provides insight into model behavior, a critical requirement of data-driven applications in nuclear security. Using data collected at the High Flux Isotope Reactor at Oak Ridge National Laboratory via a network of Merlyn multisensors, two different models were trained to classify the reactor’s operational state: a hidden Markov model (HMM), which is simpler and more transparent, and a feed-forward neural network, which is less inherently interpretable. Traditional wrapper methods for feature importance were extended to identify nodes and regions in the multisensor network with strong positive and negative impacts on the classification problem. These spatial-importance algorithms were evaluated on the two different classifiers. The classification accuracy was then improved relative to baseline models via feature selection from 0.583 to 0.839 and from 0.811 ± 0.005 to 0.884 ± 0.004 for the HMM and feed-forward neural network, respectively. While some differences in node and region importance were observed when using different classifiers and wrapper methods, the nodes near the facility’s cooling tower were consistently identified as important—a conclusion further supported by studies on feature importance in decision trees. Node and region importance methods are model-agnostic, inform feature selection for improved model performance, and can provide insight into opaque classification models in the nuclear security domain.

Tibbetts, Jake↗
DOE OSTI2024

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material’s capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure–property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R2 coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors—eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability.

Ascencio-Medina, Estefania↗
DOE OSTI2022

Factors Controlling a Synthetic Aperture Radar (SAR) Derived Root-Zone Soil Moisture Product over The Seward Peninsula of Alaska

Root-zone soil moisture exerts a fundamental control on vegetation, energy balance, and the carbon cycle in Arctic ecosystems, but it is still not well understood in vast, remote, and understudied regions of discontinuous permafrost. The root-zone soil moisture product (30 m resolution) used in this analysis was retrieved from a time-series P-Band (420–440 MHz) synthetic aperture radar (SAR) backscatter observations (August 2017 & October 2017). While similar approaches have been taken to retrieve surface (0 cm to 5 cm) soil moisture from L-Band (1.2 GHz) SAR backscatter, this is one of the first known attempts at reaching the root-zone in permafrost regions. Here, we analyze secondary factors (excluding primary factors, such as precipitation) controlling summer (August) soil moisture at depths of 6 cm, 12 cm, and 20 cm over a 4500 km2 area on the Seward Peninsula of Alaska. Using a random forest model, we quantify the impact of topography, vegetation, and meteorological factors on soil moisture distributions. In developing the random forest model, we explore a variety of feature scales (30 m, 60 m, 90 m, 120 m, 180 m, and 240 m), tune hyperparameters (the structure of individual decision trees making up the ensemble including the number and depth of trees), and perform the final feature selection using cross-validated recursive feature elimination. Results suggest that root-zone soil moisture on the Seward Peninsula is primarily controlled by vegetation at 6 cm, but deeper in the soil column topography and meteorological factors, such as predominant winter wind direction and summer insolation, play a larger role. The random forest model accounts for 40% to 60% of the variation observed (R2 = 0.44 at 6 cm, R2 = 0.52 at 12 cm, R2 = 0.58 at 20 cm). These results indicate that vegetation is the dominant control on soil moisture shallow in the soil column, but the impact of vegetation does not extend to deeper layers retrieved from P-Band SAR backscatter.

Dann, Julian↗
DOE OSTI2021

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The Circular Velocity Curve of the Milky Way from 5–25 kpc Using Luminous Red Giant Branch Stars

We present a sample of 254,882 luminous red giant branch (LRGB) stars selected from the APOGEE and LAMOST surveys. By combining photometric and astrometric information from the Two Micron All Sky Survey and Gaia survey, the precise distances of the sample stars are determined by a supervised machine-learning algorithm: the gradient-boosted decision trees. To test the accuracy of the derived distances, member stars of globular clusters (GCs) and open clusters are used. The tests by cluster member stars show a precision of about 10% with negligible zero-point offsets, for the derived distances of our sample stars. The final sample covers a large volume of the Galactic disk(s) and halo of 0 < R < 30 kpc and |Z| ≤ 15 kpc. The rotation curve (RC) of the Milky Way across the radius of 5 ≲ R ≲ 25 kpc has been accurately measured with ~54,000 stars of the thin disk population selected from the LRGB sample. The derived RC shows a weak decline along R with a gradient of -1.83 ± 0.02 (stat.) ± 0.07 (sys.) km s -1 kpc -1 , in excellent agreement with the results measured by previous studies. The circular velocity at the solar position, yielded by our RC is 234.04 ± 0.08 (stat.) ± 1.36 (sys.) km s -1 , again in great consistency with other independent determinations. From the newly constructed RC, as well as constraints from other data, we have constructed a mass model for our Galaxy, yielding a mass of the dark matter halo of M 200 = (8.05 ± 1.15) × 10 11 M ⊙ with a corresponding radius of R 200 = 192.37 ± 9.24 kpc and a local dark matter density of 0.39 ± 0.03 GeV cm -3 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Distinguishing Orbiting and Infalling Dark Matter Particles with Machine Learning

Dark matter halos are typically defined as spheres that enclose some overdensity, but these sharp, somewhat arbitrary boundaries introduce nonphysical artifacts such as backsplash halos, pseudo-volution, and an incomplete accounting of halo mass. A more physically motivated alternative is to define halos as the collection of particles that are physically orbiting within their potential well. However, existing methods to classify particles as orbiting or infalling suffer from trade-offs between accuracy, computational cost, and generalizability across cosmologies. We present an efficient, yet accurate, supervised machine learning approach using decision trees. The classification is based on only the particle radii and velocities at two epochs. Compared to detailed analysis of particle trajectories, we find that our model matches the classification of 97% of particles. Consequently, we are able to quickly and accurately reproduce the density profiles of the orbiting and infalling components out to many virial radii. We demonstrate that our model generalizes to a significantly different cosmology that lies outside the training data set. We make publicly available both our final model and the code to train similar models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)

Abstract Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

HarDWR - Harmonized Water Rights Records

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on data sourced from WestDAAT - Changed using a Site ID column to identify unique records to using aa combination of Site ID and Allocation ID - Removed the Water Management Area (WMA) column from the harmonized records. The replacement is a separate file which stores the relationship between allocations and WMAs. This allows for allocations to contribute to water right amounts to multiple WMAs during the subsequent cumulative process. - Added a column describing a water rights legal status - Added "Unspecified" was a water source category - Added an acre-foot (AF) column - Added a column for the classification of the right's owner v1.02 - Added a .RData file to the dataset as a convenience for anyone exploring our code. This is an internal file, and the one referenced in analysis scripts as the data objects are already in R data objects. v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description Here we present an updated database of Western U.S. water right records. This database provides consistent unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of seven broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of inter-sectoral water allocation changes though water markets (Grogan et al., *in review*). Specifically, the data were formatted for use as input to a process-based hydrologic model, Water Balance Model (WBM), with a water rights module (Grogan et al., *in review*). While this specific study motivated the development of the database presented here, water management in the U.S. West is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al., 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. West. We produced the water rights database presented here in four main steps: (1) data collection, (2) data quality control, (3) data harmonization, and (4) generation of cumulative water rights curves. Each of steps (1)-(3) had to be completed in order to produce (4), the final product that was used in the modeling exercise in Grogan et al. (*in review*). All data in each step is associated with a spatial unit called a Water Management Area (WMA), which is the unit of water right administration utilized by the state in which the right came from. Steps (2) and (3) required use to make assumptions and interpretation, and to remove records from the raw data collection. We describe each of these assumptions and interpretations below so that other researchers can choose to implement alternative assumptions an interpretation as fits their research aims. Motivation for Changing Data Sources The most significant change has been a switch from collecting the raw water rights directly from each state to using the water rights records presented in WestDAAT, a product of the Water Data Exchange (WaDE) Program under the Western States Water Council (WSWC). One of the main reasons for this is that each state of interest is a member of the WSWC, meaning that WaDE is partially funded by these states, as well as many universities. As WestDAAT is also a database with consistent categorization, it has allowed us to spend less time on data collection and quality control and more time on answering research questions. This has included records from water right sources we had previously not known about when creating v1.0 of this database. The only major downside to utilizing the WestDAAT records as our raw data is that further updates are tied to when WestDAAT is updated, as some states update their public water right records daily. However, as our focus is on cumulative water amounts at the regional scale, it is unlikely most records updates would have a significant effect on our results. The structure of WestDAAT led to several important changes to how HarWR is formatted. The most significant change is that WaDE has calculated a field known as `SiteUUID`, which is a unique identifier for the Point of Diversion (POD), or where the water is drawn from. This separate from `AllocationNativeID`, which is the identifier for the allocation of water, or the amount of water associated with the water right. It should be noted that it is possible for a single site to have multiple allocations associated with it and for an allocation to be able to be extracted from multiple sites. The site-allocation structure has allowed us to adapt a more consistent, and hopefully more realistic, approach in organizing the water right records than we had with HarDWR v1.0. This was incredibly helpful as the raw data from many states had multiple water uses within a single field within a single row of their raw data, and it was not always clear if the first water use was the most important, or simply first alphabetically. WestDAAT has already addressed this data quality issue. Furthermore, with v1.0, when there were multiple records with the same water right ID, we selected the largest volume or flow amount and disregarded the rest. As WestDAAT was already a common structure for disparate data formats, we were better able to identify sites with multiple allocations and, perhaps more importantly, allocations with multiple sites. This is particularly helpful when an allocation has sites which cross WMA boundaries, instead of just assigning the full water amount to a single WMA we are now able to divide the amount of water between the number of relevant WMAs. As it is now possible to identify allocations with water used in multiple WMAs, it is no longer practical to store this information within a single column. Instead the stAllocationToWMATab.csv file was created, which is an allocation by WMA matrix containing the percent Place of Use area overlap with each WMA. We then use this percentage to divide the allocation's flow amount between the given WMAs during the cumulation process to hopefully provide more realistic totals of water use in each area. However, not every state provides areas of water use, so like HarDWR v1.0, a hierarchical decision tree was used to assign each allocation to a WMA. First, if a WMA could be identified based on the allocation ID, then that WMA was used; typically, when available, this applied to the entire state and no further steps were needed. Second was the spatial analysis of Place of Use to WMAs. Third was a spatial analysis of the POD locations to WMAs, with the assumption that allocation's POD is within the WMA it should belong to; if an allocation still had multiple WMAs based on its POD locations, then the allocation's flow amount would be divided equally between all WMAs. The fourth, and final, process was to include water allocations which spatially fell outside of the state WMA boundaries. This could be due to several reasons, such as coordinate errors / imprecision in the POD location, imprecision in the WMA boundaries, or rights attached with features, such as a reservoir, which crosses state boundaries. To include these records, we decided for any POD which was within one kilometer of the state's edge would be assigned to the nearest WMA. Other Changes WestDAAT has Allowed In addition to a more nuanced and consistent method of assigning water right's data to WMAs, there are other benefits gained from using the WestDAAT dataset. Among those is a consistent categorization of a water right's legal status. In HarDWR v1.0, legal status was effectively ignored, which led to many valid concerns about the quality of the database related to the amounts of water the rights allowed to be claimed. The main issue was that rights with legal status' such as "application withdrawn", "non-active", or "cancelled" were included within HarDWR v1.0. These, and other water rights status' which were deemed to not be in use have been removed from this version of the database. Another major change has been the addition of the "unspecified water source category. This is water that can come from either surface water or groundwater, or the source of which is unknown. The addition of this source category brings the total number of categories to three. Due to reviewer feedback, we decided to add the acre-foot (AF) column so that the data may be more applicable to a wider audience. We added the ownerClassification column so that the data may be more applicable to a wider audience. File Descriptions The dataset is a series of various files organized by state sub-directories. In addition, each file begins with the state's name, in case the file is separate from its sub-directory for some reason. After the state name is the text which describes the contents of the file. Here is each file described in detail. Note that st is a placeholder for the state's name. stFullRecords_HarmonizedRights.csv: A file of the complete water records for each state. The column headers for each of this type of file are: state - The name of the state to which the allocations belong to. FIPS - The two digit numeric state ID code. siteID - The site location ID for POD locations. A site may have multiple allocations, which are the actual amount of water which can be drawn. In a simplified hypothetical, a farm stead may have an allocation for "irrigation" and an allocation for "domestic" water use, but the water is drawn from the same pumping equipment. It should be noted that many of the site ID appear to have been added by WaDE, and therefore may not be recognized by a given state's water rights database. allocationID - The allocation ID for the water right. For most states this is the water right ID, and what is recommended to use should a right be looked up on a given state's water rights database. The water amounts associated with these IDs tend to be finer scaled than those associated with siteID. It should be noted that some allocations may be extracted from multiple sites, particularly for larger Places of Use. ownerClassification - A classification of the types of owners for water rights. The most common is `Private` which incorporates a wide range of entities. Several classifications would be grouped into a government category, most of which are for the U.S. Federal Government. These allocations could be listed as "Federal", "United States of America", or as the names of any number of federal agencies. The last major grouping of entities is for "Native American"s. priorityDate - The date we use as the water right priority date for our modeling analysis. This is the legal priority date when it is available. However, for some rights, specifically from California and New Mexico, we used a pseudo priority date (e.g. well completion date or start of well drilling date) when a legal priority date was not available. The most questionable dates come from New Mexico, where the only date associated with certain water right records was the date the allocation was recorded in the database. As the allocation record creation tended to be within a few months of the filing of the application of the water right, from manually double checking the water rights, and our analysis focuses on aggregating water rights on the timescale of years, we determined it was acceptable to use such dates to include as many records as possible. primaryBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories WestDAAT. This column is the original WaDE category for the primary water use at the PoD site. allocationBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories for WestDAAT. This column is the original WaDE category

Economics↗
DOE OSTI2022

Searching for Clues for a Matter Dominated Universe in Liquid Argon Time Projection Chambers

Liquid Argon Time Projection Chambers (LArTPCs) represent one of the most widely utilized neutrino detection techniques in neutrino experiments, for instance, in the Short Baseline Neutrino (SBN) program and the future large-scale LArTPC: Deep Underground Neutrino Experiment (DUNE). The high-end technique, facilitating excellent spatial and calorimetric reconstruction resolution, also enables testing exotic Beyond Standard Model (BSM) theories, such as baryon number violation (BNV) processes (e.g., proton-decay, neutron-antineutron oscillation). At the same time, Machine Learning (ML) techniques have demonstrated their ubiquitous use in recent decades; ML techniques have also become some of the most powerful tools in high-energy physics (HEP) analyses. Furthermore, the development of algorithms to cater to the needs of problems in HEP (i.e., triggering, reconstruction, improving sensitivity, etc.) has also become an active area of research. By developing a combined approach using Convolutional Neural Network (CNN) and Boosted Decision Tree (BDT) techniques, the sensitivity of neutron-antineutron oscillation in DUNE is evaluated for a projected exposure of 400kton&middot;years. Additionally, to meet the triggering requirement to select such rare events in DUNE, such a search is only supported with highly efficient self-triggering algorithms. An ML-based self-triggering scheme for large-scale LArTPCs, such as DUNE, is also developed with the intention of implementation on field-programmable gate arrays (FPGAs). The ML-based approach for searching for neutron-antineutron oscillation can be demonstrated and validated on the current LArTPC MicroBooNE. The analysis in MicroBooNE represents the first-ever search for neutron-antineutron oscillation in a LArTPC. DUNE's projected 90% C.L. sensitivity to the neutron antineutron oscillation lifetime is 6.45&times;10³² years, assuming 1.327&times;10³⁵ neutron&middot;years, equivalent to 10 years of DUNE far detector exposure (400kton&middot;years). For MicroBooNE, assuming 372 seconds of exposure (equivalent to 3.13&times;10³⁶ neutron&middot;years), the 90% C.L. lifetime sensitivity is found at 3.07&times;10²⁵ yrs, after accounting for Monte-Carlo statistical uncertainty and systematic uncertainty from detector effects.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

MicroBooNE&#x27;s First Search for the MiniBooNE Anomalous Excess Under a Photon-Like Hypothesis with High-Sensitivity Search for Neutrino-Induced Neutral Current Delta Production and Radiative Decay

MicroBooNE is a liquid argon time projection chamber that collected neutrino data at Fermilab's Booster Neutrino Beam from 2015 to 2020. One of its primary goals is to investigate the “Low Energy Excess” of neutrino events observed by the MiniBooNE experiment, for which candidate photon-like interpretations include an underestimation of neutrino neutral current (NC) resonant $\Delta$ production with subsequent radiative decay or another anomalous source of single photon production in neutrino interactions. In particular, NC $\Delta$ radiative decay is poorly constrained background process to electron neutrino measurements and could be a sizable contribution to the “Low Energy Excess.” This thesis will present the analysis developed to search for NC $\Delta \rightarrow N\gamma$ events in MicroBooNE, consisting of a boosted decision tree based event selection with an NC neutral pion background constraint, using data from the first three years of operations corresponding to $6.9\times10^{20}$ POT.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1975

A new methodology to integrate planetary quarantine requirements into mission planning, with application to a Jupiter orbiter

A new methodology is proposed for integrating planetary quarantine objectives into space exploration planning. This methodology is designed to remedy the major weaknesses inherent in the current formulation of planetary quarantine requirements. Application of the methodology is illustrated by a tutorial analysis of a proposed Jupiter Orbiter mission. The proposed methodology reformulates planetary quarantine planning as a sequential decision problem. Rather than concentrating on a nominal plan, all decision alternatives and possible consequences are laid out in a decision tree. Probabilities and values are associated with the outcomes, including the outcome of contamination. The process of allocating probabilities, which could not be made perfectly unambiguous and systematic, is replaced by decomposition and optimization techniques based on principles of dynamic programming. Thus, the new methodology provides logical integration of all available information and allows selection of the best strategy consistent with quarantine and other space exploration goals.

Howard, R. A.↗
NASA NTRS1976

Benefit cost analysis in federal agency R and D program formulation

Benefit-cost analysis is defined and some government investment criteria are discussed in relation to federal agency research and development program planning. The benefits of applied research and development are examined on the basis of a decision tree approach, and R and D project selection is considered, with attention paid to the multi-attribute problem.

Greenberg, J. S.↗
NASA NTRS1977

Satellite solar power - Will it pay off

A cost analysis is presented for front-end investments required for the development of a satellite solar power system. The methodology used makes use of risk analysis techniques to quantify the present state of knowledge relevant to the construction and operation of a satellite solar power station 20 years in the future. Results are used to evaluate the 'expected value' of a three-year research program providing additional information which will be used as a basis for a decision to either continue development of the concept at an increasing funding level or to terminate or drastically alter the program. The program is costed phase by phase, and a decision tree is constructed. The estimated probability of success for the research and studies phase is .540. The expected value of a program leading to the construction of 120 systems at a rate of four per year is 12.433 billion dollars.

Hazelrigg, G. A., Jr.↗