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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Hot or Not? An Evaluation of Methods for Identifying Hot Moments of Nitrous Oxide Emissions From Soils

Abstract Effectively quantifying hot moments of nitrous oxide (N 2 O) emissions from agricultural soils is critical for managing this potent greenhouse gas. However, we are challenged by a lack of standard approaches for identifying hot moments, including (a) determining thresholds above which emissions are considered hot moments, and (b) considering seasonal variation in the magnitude and frequency distribution of net N 2 O fluxes. We used one year of hourly N 2 O flux measurements from 16 autochambers that varied in flux magnitude and frequency distribution in a conventionally tilled maize field in central Illinois, USA, to compare three approaches to identify hot moment thresholds: standard deviations (SD) above the mean, 1.5x the interquartile range (IQR), and isolation forest (IF) identification of anomalous values. We also compared these approaches on seasonally subdivided data (early, late, and non‐growing seasons) versus the whole year. Our analyses revealed that 1.5x IQR method best identified N 2 O hot moments. In contrast, using 2 or 4 SD both yielded hot moment threshold values too high, and IF yielded threshold values too low, leading to missed N 2 O hot moments or low net N 2 O fluxes mischaracterized as hot moments, respectively. Furthermore, seasonally subdividing the data set not only facilitated identification of smaller hot moments in the late‐ and non‐growing seasons when N 2 O hot moments were generally smaller but it also increased hot moment threshold values in the early growing season when N 2 O hot moments were larger. Consequently, of the methods evaluated here, we recommend using the 1.5x IQR method on whole year data sets to identify N 2 O hot moments.

Stuchiner, Emily R. [Institute for Sustainability,↗
DOE OSTI2025

An unsupervised machine learning based approach to identify efficient spin-orbit torque materials

Materials with large spin–orbit torque (SOT) hold considerable significance for many spintronic applications because of their potential for energy-efficient magnetization switching. Unfortunately, most of the existing materials exhibit an SOT efficiency factor that is much less than unity, requiring a large current for magnetization switching. The search for new materials that can exhibit an SOT efficiency much greater than unity is a topic of active research, and only a few such materials have been identified using conventional approaches. In this paper, we present a machine learning-based approach using a word embedding model that can identify new results by deciphering non-trivial correlations among various items in a specialized scientific text corpus. We show that such a model can be used to identify materials likely to exhibit high SOT and rank them according to their expected SOT strengths. The model captured the essential spintronics knowledge embedded in scientific abstracts within various materials science, physics, and engineering journals and identified 97 new materials to exhibit high SOT. Among them, 16 candidate materials are expected to exhibit an SOT efficiency greater than unity, and one of them has recently been confirmed with experiments with quantitative agreement with the model prediction.

Sayed, Shehrin↗
DOE OSTI2023

Identifying COVID-19 cases and extracting patient reported symptoms from Reddit using natural language processing

We used social media data from “covid19positive” subreddit, from 03/2020 to 03/2022 to identify COVID-19 cases and extract their reported symptoms automatically using natural language processing (NLP). We trained a Bidirectional Encoder Representations from Transformers classification model with chunking to identify COVID-19 cases; also, we developed a novel QuadArm model, which incorporates Question-answering, dual-corpus expansion, Adaptive rotation clustering, and mapping, to extract symptoms. Our classification model achieved a 91.2% accuracy for the early period (03/2020-05/2020) and was applied to the Delta (07/2021–09/2021) and Omicron (12/2021–03/2022) periods for case identification. We identified 310, 8794, and 12,094 COVID-positive authors in the three periods, respectively. The top five common symptoms extracted in the early period were coughing (57%), fever (55%), loss of sense of smell (41%), headache (40%), and sore throat (40%). During the Delta period, these symptoms remained as the top five symptoms with percent authors reporting symptoms reduced to half or fewer than the early period. During the Omicron period, loss of sense of smell was reported less while sore throat was reported more. Our study demonstrated that NLP can be used to identify COVID-19 cases accurately and extracted symptoms efficiently.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Pan-cancer proteogenomic investigations identify post-transcriptional kinase targets

Identifying genomic alterations of cancer proteins has guided the development of targeted therapies, but proteomic analyses are required to validate and reveal new treatment opportunities. Herein, we develop a new algorithm, OPPTI, to discover overexpressed kinase proteins across 10 cancer types using global mass spectrometry proteomics data of 1,071 cases. OPPTI outperforms existing methods by leveraging multiple co-expressed markers to identify targets overexpressed in a subset of tumors. OPPTI-identified overexpression of ERBB2 and EGFR proteins correlates with genomic amplifications, while CDK4/6, PDK1, and MET protein overexpression frequently occur without corresponding DNA- and RNA-level alterations. Analyzing CRISPR screen data, we confirm expression-driven dependencies of multiple currently-druggable and new target kinases whose expressions are validated by immunochemistry. Identified kinases are further associated with up-regulated phosphorylation levels of corresponding signaling pathways. Collectively, our results reveal protein-level aberrations—sometimes not observed by genomics—represent cancer vulnerabilities that may be targeted in precision oncology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Small molecule microarray identifies inhibitors of tyrosyl-DNA phosphodiesterase 1 that simultaneously access the catalytic pocket and two substrate binding sites

Tyrosyl-DNA phosphodiesterase 1 (TDP1) is a member of the phospholipase D family of enzymes, which catalyzes the removal of both 3'- and 5'-DNA phosphodiester adducts. Importantly, it is capable of reducing the anticancer effects of type I topoisomerase (TOP1) inhibitors by repairing the stalled covalent complexes of TOP1 with DNA. It achieves this by promoting the hydrolysis of the phosphodiester bond between the Y723 residue of human TOP1 and the 3'-phosphate of its DNA substrate. Blocking TDP1 function is an attractive means of enhancing the efficacy of TOP1 inhibitors and overcoming drug resistance. Previously, we reported the use of an X-ray crystallographic screen of more than 600 fragments to identify small molecule variations on phthalic acid and hydroxyquinoline motifs that bind within the TDP1 catalytic pocket. Yet, the majority of these compounds showed limited (millimolar) TDP1 inhibitory potencies. We now report examining a 21 000-member library of drug-like Small Molecules in Microarray (SMM) format for their ability to bind Alexa Fluor 647 (AF647)-labeled TDP1. The screen identified structurally similar N,2-diphenylimidazo[1,2-a]pyrazin-3-amines as TDP1 binders and catalytic inhibitors. We then explored the core heterocycle skeleton using one-pot Groebke–Blackburn–Bienayme multicomponent reactions and arrived at analogs having higher inhibitory potencies. Solving TDP1 co-crystal structures of a subset of compounds showed their binding at the TDP1 catalytic site, while mimicking substrate interactions. Although our original fragment screen differed significantly from the current microarray protocol, both methods identified ligand–protein interactions containing highly similar elements. Importantly inhibitors identified through the SMM approach show competitive inhibition against TDP1 and access the catalytic phosphate-binding pocket, while simultaneously providing extensions into both the substrate DNA and peptide-binding channels. As such, they represent a platform for further elaboration of trivalent ligands, that could serve as a new genre of potent TDP1 inhibitors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Identifiability and characterization of transmon qutrits through Bayesian experimental design

Robust control of a quantum system is essential to utilize the current noisy quantum hardware to its full potential, such as quantum algorithms. To achieve such a goal, a systematic search for an optimal control for any given experiment is essential. The design of optimal control pulses requires accurate numerical models and, therefore, accurate characterization of the system parameters. We present an online Bayesian approach for quantum characterization of qutrit systems, which automatically and systematically identifies optimal experiments that provide maximum information on the system parameters, thereby greatly reducing the number of experiments that need to be performed on the quantum testbed. Unlike most characterization protocols that provide point-estimates of the parameters, the proposed approach is able to estimate their probability distribution. The applicability of the Bayesian experimental design technique was demonstrated on test problems, where each experiment was defined by a parameterized control pulse. In addition to this, we also present an approach for iterative pulse extension, which is robust under uncertainties in transition frequencies and coherence times, and shot noise, despite being initialized with wide uninformative priors. Furthermore, we provide a mathematical proof of the theoretical identifiability of the model parameters and present conditions on the quantum state under which the parameters are identifiable. The proof and conditions for identifiability are presented for both closed and open quantum systems using the Schrödinger equation and the Lindblad master equation, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

AutoCheck: Automatically Identifying Variables for Checkpointing by Data Dependency Analysis

Checkpoint/Restart (C/R) has been widely deployed in numerous HPC systems, Clouds, and industrial data centers, which are typically operated by system engineers. Nevertheless, there is no existing approach that helps system engineers without domain expertise and domain scientists without system fault tolerance knowledge identify those critical variables accounted for correct application execution restoration in a failure for C/R. To address this problem, we propose an analytical model and a tool (AutoCheck) that can automatically identify critical variables to checkpoint for C/R. AutoCheck relies on first, analytically tracking and optimizing data dependency between variables and other application execution state, and second, a set of heuristics that identify critical variables for checkpointing from the refined data dependency graph (DDG). AutoCheck allows programmers to pinpoint critical variables to checkpoint quickly within a few minutes. We evaluate AutoCheck on 13 representative HPC benchmarks, demonstrating that AutoCheck can efficiently identify correct critical variables to checkpoint.

HPC↗
DOE OSTI2022

An Information Theoretic Approach to Identify Dominant Voltage Influencers for Unbalanced Distribution Systems

Smart distribution grid with multiple renewable energy sources can experience random voltage fluctuations due to variable generation, which may result in voltage violations. Traditional voltage control algorithms are inadequate to handle fast voltage variations. Therefore, new dynamic control methods are being developed that can significantly benefit from the knowledge of dominant voltage influencer (DVI) nodes. DVI nodes for a particular node of interest refer to nodes that have a relatively high impact on the voltage fluctuations at that node. Conventional power flow-based algorithms to identify DVI nodes are computationally complex, which limits their use in real-time applications. This paper proposes a novel information theoretic voltage influencing score (VIS) that quantifies the voltage influencing capacity of nodes with DERs/active loads in a three phase unbalanced distribution system. VIS is then employed to rank the nodes and identify the DVI set. VIS is derived analytically in a computationally efficient manner and its efficacy to identify DVI nodes is validated using the IEEE 37-node test system. It is shown through experiments that KL divergence and Bhattacharyya distance are effective indicators of DVI nodes with an identifying accuracy of more than 90%. Additionally, the computation burden is also reduced by an order of 5, thus providing the foundation for efficient voltage control.

42 ENGINEERING↗
DOE OSTI2023

Phylogeography of the blacklegged tick ( Ixodes scapularis ) throughout the USA identifies candidate loci for differences in vectorial capacity

Abstract The blacklegged tick ( Ixodes scapularis ( Journal of the Academy of Natural Sciences of Philadelphia , 1821, 2 , 59)) is a vector of Borrelia burgdorferi sensu stricto ( s.s .) ( International Journal of Systematic Bacteriology , 1984, 34 , 496), the causative bacterial agent of Lyme disease, part of a slow‐moving epidemic of Lyme borreliosis spreading across the northern hemisphere. Well‐known geographical differences in the vectorial capacity of these ticks are associated with genetic variation. Despite the need for detailed genetic information in this disease system, previous phylogeographical studies of these ticks have been restricted to relatively few populations or few genetic loci. Here we present the most comprehensive phylogeographical study of genome‐wide markers in I. scapularis , conducted by using 3RAD (triple‐enzyme restriction‐site associated sequencing) and surveying 353 ticks from 33 counties throughout the species' range. We found limited genetic variation among populations from the Northeast and Upper Midwest, where Lyme disease is most common, and higher genetic variation among populations from the South. We identify five spatially associated genetic clusters of I. scapularis . In regions where Lyme disease is increasing in frequency, the I. scapularis populations genetically group with ticks from historically highly Lyme‐endemic regions. Finally, we identify 10 variable DNA sites that contribute the most to population differentiation. These variable sites cluster on one of the chromosome‐scale scaffolds for I. scapularis and are within identified genes. Our findings illuminate the need for additional research to identify loci causing variation in the vectorial capacity of I. scapularis and where additional tick sampling would be most valuable to further understand disease trends caused by pathogens transmitted by I. scapularis .

3RAD↗
DOE OSTI2023

Deep learning-enabled natural language processing to identify directional pharmacokinetic drug–drug interactions

Background. During drug development, it is essential to gather information about the change of clinical exposure of a drug (object) due to the pharmacokinetic (PK) drug-drug interactions (DDIs) with another drug (precipitant). While many natural language processing (NLP) methods for DDI have been published, most were designed to evaluate if (and what kind of) DDI relationships exist in the text, without identifying the direction of DDI (object vs. precipitant drug). Here we present a method for the automatic identification of the directionality of a PK DDI from literature or drug labels. Methods. We reannotated the Text Analysis Conference (TAC) DDI track 2019 corpus for identifying the direction of a PK DDI and evaluated the performance of a fine-tuned BioBERT model on this task by following the training and validation steps prespecified by TAC. Results. This initial attempt showed the model achieved an F-score of 0.82 in identifying sentences as containing PK DDI and an F-score of 0.97 in identifying object versus precipitant drugs in those sentences. Discussion and conclusion. Despite a growing list of NLP methods for DDI extraction, most of them use a common set of corpora to perform general purpose tasks (e.g., classifying a sentence into one of several fixed DDI categories). There is a lack of coordination between the drug development and biomedical informatics method development community to develop corpora and methods to perform specific tasks (e.g., extract clinical exposure changes due to PK DDI). We hope that our effort can encourage such a coordination so that more “fit for purpose” NLP methods could be developed and used to facilitate the drug development process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Generalizable Evaluated Approach, Applying Advanced Geospatial Statistical Methods, to Identify High Lead Exposure Locations at Census Tract Scale: Michigan Case Study

BACKGROUND: Despite great progress in reducing environmental lead (Pb) levels, many children in the United States are still being exposed. OBJECTIVE: Our aim was to develop a generalizable approach for systematically identifying, verifying, and analyzing locations with high prevalence of children’s elevated blood Pb levels (EBLLs) and to assess available Pb models/indices as surrogates, using a Michigan case study. METHODS: We obtained ~1:9 million BLL test results of children <6 years of age in Michigan from 2006–2016; we then evaluated them for data representativeness by comparing two percentage EBLL (%EBLL) rates (number of children tested with EBLL divided by both number of children tested and total population). We analyzed %EBLLs across census tracts over three time periods and between two EBLL reference values (≥5 vs. ≥10 μg/dL) to evaluate consistency. Locations with high %EBLLs were identified by a top 20 percentile method and a Getis-Ord Gi* geospatial cluster “hotspot” analysis. For the locations identified, we analyzed convergences with three available Pb exposure models/indices based on old housing and sociodemographics. RESULTS: Analyses of 2014–2016 %EBLL data identified 11 Michigan locations via cluster analysis and 80 additional locations via the top 20 percentile method and their associated census tracts. Data representativeness and consistency were supported by a 0.93 correlation coefficient between the two EBLL rates over 11 y, and a Kappa score of ~0:8 of %EBLL hotspots across the time periods (2014–2016) and reference values. Many EBLL hotspot locations converge with current Pb exposure models/indices; others diverge, suggesting additional Pb sources for targeted interventions. DISCUSSION: This analysis confirmed known Pb hotspot locations and revealed new ones at a finer geographic resolution than previously available, using advanced geospatial statistical methods and mapping/visualization. It also assessed the utility of surrogates in the absence of blood Pb data. This approach could be applied to other states to inform Pb mitigation and prevention efforts. https://doi.org/10.1289/EHP9705

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Development and Validation of Algorithms That Analyze Communicating Thermostat Data to Identify Enclosure Retrofit Opportunities

Annual energy savings of up to $\$ 4$ to $\$ 5$ billion could be achieved nationwide through basic insulation and heating system retrofits of existing homes. However, current utility energy efficiency programs are costly and challenging to scale. Customer acquisition occurs primarily through energy bill mailers, mass media, and online advertising that lack specificity about home-specific retrofit opportunities, expected energy savings, and cost-effectiveness. Specific retrofit opportunities are identified via on-site home energy assessments (HEAs) that are inconvenient to homeowners, expensive, and of variable accuracy. We developed computational algorithms that automatically analyze communicating thermostat (CT) heating data that could be used to increase the customer uptake of insulation and air sealing energy conservation measures (ECMs) by identifying homes with the most significant retrofit opportunities, estimating post-retrofit energy savings, and formulating home-specific outreach. The algorithms are based on an extended second-order grey-box model that characterizes a building’s thermal response using lumped elements, coupled with an empirical model of infiltration that accounts for both wind and stack effects. The basic parameters of the model correspond to actual physical parameters of the home, i.e., the home’s overall R-value of and the building envelope ACH50. Unlike the conventional approach, which estimates model parameters based on the best fit to the observed time-dependent room temperature, our approach derives correlations between the daily heating system runtime and temperature difference (indoor-outdoor) that are more robust to data quality issues in real-world applications. We also used HEA data for algorithm development and validation. With the help of our utility partners, Eversource and National Grid, we obtained data sets for hundreds of Massachusetts homes. For each home, these data sets included three sets of information anonymized by the utility: (1) CT data (HVAC runtime, room temperature, and, for some vendors, outdoor temperature and wind speed) collected by the CT vendor (one of three) over a heating season, (2) HEA report performed by the HEA vendor (same vendor for all homes), (3) Monthly utility gas bills coincident with the CT data (3 to 24 per home, depending on availability). For some homes, we also obtained blower-door test results. Initially, we applied the algorithms developed to homes with a single CT and then extended them to homes with two CTs by using an equivalent home approach. Finally, we developed algorithms for prediction of energy savings and a methodology of comparing our predictions with those generated by HEAs. The main technical results indicate that we can reliably identify homes with insulation and/or air sealing retrofit opportunities and provide accurate savings predictions. Our hypothesis is that the algorithms could be applied to utility energy efficiency programs to identify homes that could realize significant energy savings from insulation and/or air sealing retrofits. This information could then be used to reach out to those homes with highly customized outreach, thereby delivering increased program energy savings and cost-effectiveness. This would: Significantly increase the uptake rate of on-site HEAs, and Significantly increase the fraction of HEAs resulting in ECM implementation. To test these hypotheses, we designed and conducted a randomized controlled trial (RCT). The RCT results suggest that personal messaging leads to a two- to five-fold increase in the HEA uptake rate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Identifying Disinformation Using Rhetorical Devices in Natural Language Models

Foreign disinformation campaigns are strategically organized, extended efforts using disinformation – false or misleading information deliberately placed by an adversary – to achieve some goal. Disinformation campaigns pose severe threats to our nation’s security by misinforming decision makers and negatively influencing their actions when they are operating on limited amounts of evidence. Current efforts rely on subject matter experts to manually identify disinformation, or on computers and traditional natural language processing algorithms to identify patterns in data to calculate the probability that something is disinformation or not. While both have their merits and successes, subject matter experts are unable to keep up with the high volumes of global information and traditional natural language algorithms do not do well in identifying “why” something is disinformation or not. Our hypothesis is that we can identify disinformation by looking at the way someone speaks, in the rhetorical devices they use. We have curated and annotated a dataset designed for multiple natural language processing tasks, but specifically useful for disinformation detection algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Framework for Identifying Building Energy Models of Localized Utility Service Areas Using Smart Meter Data

Bottom-up load modeling of buildings offers a versatile approach to simulating baseline demand and scenarios of future technology evolution and adoption at the individual building level. This capability is essential to understanding how future load shapes may change with the adoption of electric equipment and vehicles, particularly as it relates to grid planning and infrastructure investments. Traditionally, grid planning techniques have used historical load data to predict future load and infrastructure needs. However, with the anticipated rise in adoption of electrification technologies such as heat pumps and electric vehicles, historical data become less reliable predictors of the future. By employing ResStock, a high-fidelity building stock modeling tool, we can fine-tune electrification scenarios and aggregate models to represent varying geographic resolutions of the grid system, while considering the underlying features of homes. This may enable a more accurate and responsive approach to anticipate and plan for the evolving landscape of energy demands. We present a new framework that leverages building stock energy modeling to identify building models that align with the load shapes and housing attributes of buildings with AMI data. This approach applies two model layers: (1) a classification step that identifies the presence of air conditioning, electric heating, and electric water heating, and (2) an optimization routine that identifies building energy models aligning with load profile data from advanced metering infrastructure meters. This report demonstrates one approach to deploying this framework, and presents results for three test cases that use both modeled and AMI data to assess performance. For a test case using AMI data in Fort Collins, Colorado, we observed a median monthly electricity load CV-RMSE of 16.6%, and a top ten daily heating and cooling median absolute percent error of 7.7% and 8.3%, respectively. For each AMI meter, we identify a set of potential energy models so that downstream use-cases can account for uncertainty driven by variability of baseline technologies and occupant behavior, which impact the response to electrification and energy efficiency scenarios. Our results indicate that ResStock has potential as a scalable solution for modeling residential energy demand at local grid resolutions. Its performance depends on location-specific factors, underlying building characteristics, and the level of aggregation, offering a path towards more precise and adaptive distribution grid planning for the evolving energy landscape.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Identifying Controlling Variables for Mercury Vapors in Alpha-4 at Y-12: Two Year Data Collection Update

Multiple sensor packages were deployed at Alpha-4 by SRNL, in collaboration with United Cleanup Oak Ridge LLC (UCOR), to monitor mercury vapor concentrations and meteorological parameters. These sensors collected data, inside and outside of the legacy-use facility, for approximately two years. Though data gaps still exist, particularly in colder months, several controlling variables were identified that govern mercury vapor concentrations within Alpha-4. Temperature, barometric pressure gradients, humidity, and wind speed have been identified as controlling variables. A strong positive correlation was seen between mercury vapor concentrations and temperature which generally followed diurnal fluctuations. Temperatures below approximately 10 degrees Celsius did not show any spikes above the PEL, indicating more work can be performed at any time during the winter months – more data should be collected to confirm consistency in this finding. Additionally, spikes in mercury vapor concentrations above that of the permissible exposure limit (PEL; 100 µg/m 3 ) occurred primarily in late afternoon or evening/overnight hours (between 3 PM and 6 AM), which suggests D&D operations might be best scheduled during morning or daytime hours prior to the late afternoon. However, a limited number of spikes did occur outside of the identified window, although this may be attributed to disturbances in air flow and mercury vapor release from work activities performed inside of the Alpha-4 building. The analysis conducted allows for a strong predictive capability for estimating mercury vapor concentrations based upon accurate meteorological parameters. Still, additional data collection, particularly in the winter months, could help to strengthen the predictive power and validate the identified data trends. Further, increased temporal resolution could also help to better characterize the incipient stages of the increases and decreases in the mercury vapor concentration. Continued monitoring support by SRNL at Y-12 is underway at Alpha-4 to further close remaining data gaps and support deactivation and decommissioning work. Within a collaborative effort with UCOR, the SRNL team is collecting mercury vapor data to study the efficacy of a novel mercury suppressant, FerroBlack® which was recently deployed at Alpha-4. In addition, modifications to the current monitoring setup to increase measurement resolution is also being investigated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Identifying Urban Pluvial Frequency Flooding Hotspots Using the Topographic Control Index and Remote Sensing Radar Images for Early Warning Systems

Identifying areas that frequently experience post-rainfall ponding is essential for effective flood mitigation and planning. This study integrates Sentinel-1 radar imagery and the Topographic Control Index (TCI) to identify 378 flood-prone urban depressions in Beaumont, Texas. Out of 159 major rainfall events, only six had Sentinel-1 radar imagery acquired within six hours of peak rainfall, and these were used to generate the flood frequency map; the ground-based flood sensor data were used to verify that these selected events corresponded to actual peak rainfall and to validate radar-detected water pixels. Validation results showed 100% precision, 70.87% recall, an F1-score of 82.95%, and 71.32% overall accuracy. Approximately 84% of medium-to-high TCI depressions overlapped with Beaumont’s two-year inundation map, confirming a strong relationship between TCI and observed flooding. A total of 124 depressions retained significant water, and after excluding 25 engineered detention ponds, 99 natural depressions remained flood vulnerable. Among these, 74 depressions with medium or high TCI were identified as the highest-priority nuisance flooding hotspots. The results demonstrate that combining TCI with radar imagery provides a reliable and cost-effective approach for identifying areas prone to frequent urban ponding. This framework supports practical decision-making for drainage improvements, hotspot identification, and early-warning system development in urban flood-prone regions.

Sentinel-1 radar imagery↗
DOE OSTI2021

Three New Late-type Stellar Companions to Very Dusty WISE Debris Disks Identified with SPHERE Imaging

Debris disk stars are good targets for high-contrast imaging searches for planetary systems, since debris disks have been shown to have a tentative correlation with giant planets. We selected 20 stars identified as debris disk hosts by the WISE mission, with particularly high levels of warm dust. We observed these with the VLT/SPHERE high-contrast imaging instrument with the goal of finding planets and imaging the disks in scattered light. Our survey reaches a median 5σ sensitivity of 10.4 M {sub J} at 25 au and 5.9 M {sub J} at 100 au. We identified three new stellar companions (HD 18378B, HD 19257B, and HD 133778B): two are mid-M-type stars and one is a late-K or early-M star. Three additional stars have very widely separated stellar companions (all at >2000 au) identified in the Gaia catalog. The stars hosting the three SPHERE-identified companions are all older (≳700 Myr), with one having recently left the main sequence and one a giant star. We infer that the high volumes of dust observed around these stars has been caused by a recent collision between the planets and planetesimal belts in the system, although for the most evolved star, mass loss could also be responsible for the infrared excess. Future mid-infrared spectroscopy or polarimetric imaging may allow the positions and spatial extent of these dust belts to be constrained, thereby providing evidence as to the true cause of the elevated levels of dust around these old systems. None of the disks in this survey is resolved in scattered light.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Methods and data structures for efficient cross-referencing of physical-asset spatial identifiers

Described herein are methods, systems, and storage media having computer-processable instructions for cross-referencing in a computationally efficient manner different data sets each having pluralities of n-dimensional identifiers of physical assets. The disclosed cross-referencing approach can be used to determine relationships between different assets and their respective asset identifier strings. The relationships can be established based on user-selected heuristics. For example, at least two asset identifier strings can be compared, and user-defined heuristics can be applied to determine whether “a match” exists. A match can be defined according to the particular heuristic. In one instance, the comparison and heuristic can be applied to determine if two different asset identifier strings refer to a single physical asset.

Borkum, Mark I.↗