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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Hydropower Infrastructure - LAkes, Reservoirs, and RIvers (HILARRI), v4

HILARRI is a database of links between major datasets of operational hydropower dams and powerplants, and inland water bodies. These connections are critical for conducting large-scale analysis of hydropower infrastructure and their associated natural and engineered water systems. Features include: – Dams from the National Inventory of Dams (2025) and the Global Reservoir and Dam Database (GRanD v1.3) – Hydropower plants from the Existing Hydropower Assets dataset (EHA 2025) – Power plants that are listed in the 2025 U.S. Hydropower Development Pipeline Data or were listed in previous versions of the dataset These hydropower infrastructure features are linked to several major datasets that provide hydrologic and hydraulic information relevant for analysis of hydropower systems that includes the integral water resources. That information comes from: – Products from the National Hydrography Dataset (NHD) – NHDPlusV2 Medium Resolution river network flowlines, – NHD waterbodies (limited to lakes and reservoirs), – NHD Watershed Boundary Dataset (HUC12-level for the Conterminous United States (CONUS)) – NHD High Resolution waterbodies – HydroLAKES water bodies (lakes and reservoirs) – LAGOS-US lakes and reservoirs – EPA National Lakes Assessment (2007, 2012, 2017, and 2022) – The Reservoir Sedimentation Database (RESSED) – EPA SuRGE sampling locations Unique identifiers are used to facilitate joining to the original full datasets. For example, characteristics of NHD flowlines such as estimated average flow rate can be joined from the NHDPlusV2 dataset to a dam or power plant listed in HILARRI based on the ID field, “COMID”, that is common to both datasets. HILARRI only includes basic information about identifiers, location, and data quality or usage notes. It does not contain the attributes or time series data associated with these sites. The HILARRI dataset incorporates information from several datasets to facilitate more effective and accurate analysis of hydropower infrastructure and their associated waterbodies. For example, dams were checked against the most recent American Rivers Dam Removal Database to identify and flag facilities that may no longer exist. Additionally, dams that are listed multiple times in the NID are identified and flagged to avoid double-counting when analyzing and summarizing information. Other quality flags include certainty of operational hydropower (i.e., if one or more datasets indicates hydropower at a particular location), whether an associated water body is accurate or composed of multiple polygons, or whether there is a known issue with reported characteristics in one of the underlying datasets. These additional data flags are designed to increase confidence in data usage for individual to large-scale analyses.

Hansen, Carly [ORNL] (ORCID:0000000193280838)↗
DOE OSTI2023

eYGFPuv-Assisted Transgenic Selection in Populus deltoides WV94 and Multiplex Genome Editing in Protoplasts of P. trichocarpa × P. deltoides Clone ‘52-225’

Although CRISPR/Cas-based genome editing has been widely used for plant genetic engineering, its application in the genetic improvement of trees has been limited, partly because of challenges in Agrobacterium-mediated transformation. As an important model for poplar genomics and biotechnology research, eastern cottonwood (Populus deltoides) clone WV94 can be transformed by A. tumefaciens, but several challenges remain unresolved, including the relatively low transformation efficiency and the relatively high rate of false positives from antibiotic-based selection of transgenic events. Moreover, the efficacy of CRISPR-Cas system has not been explored in P. deltoides yet. Here, we first optimized the protocol for Agrobacterium-mediated stable transformation in P. deltoides WV94 and applied a UV-visible reporter called eYGFPuv in transformation. Our results showed that the transgenic events in the early stage of transformation could be easily recognized and counted in a non-invasive manner to narrow down the number of regenerated shoots for further molecular characterization (at the DNA or mRNA level) using PCR. We found that approximately 8.7% of explants regenerated transgenic shoots with green fluorescence within two months. Next, we examined the efficacy of multiplex CRISPR-based genome editing in the protoplasts derived from P. deltoides WV94 and hybrid poplar clone ‘52-225’ (P. trichocarpa × P. deltoides clone ‘52-225’). The two constructs expressing the Trex2-Cas9 system resulted in mutation efficiency ranging from 31% to 57% in hybrid poplar clone 52-225, but no editing events were observed in P. deltoides WV94 transient assay. The eYGFPuv-assisted plant transformation and genome editing approach demonstrated in this study has great potential for accelerating the genome editing-based breeding process in poplar and other non-model plants species and point to the need for additional CRISPR work in P. deltoides.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Distinctive Signals in 1-min Observations of Overshooting Tops and Lightning Activity in a Severe Supercell Thunderstorm

This work examines a severe weather event that took place over central Argentina on December 11, 2018. The evolution of the storm from its initiation, rapid organization into a supercell, and eventual decay was analyzed with high-temporal resolution observations. This work provides insight into the spatio-temporal co-evolution of storm kinematics (updraft area and lifespan), cloud-top cooling rates, and lightning production that led to severe weather. The analyzed storm presented two convective periods with associated severe weather. An overall decrease in cloud-top local minima IR brightness temperature (MinIR10.3), increase in overshooting top area, and lightning jump (LJ) preceded both periods. LJs provided the highest lead time to the occurrence of severe weather, with the ground-based lightning networks providing the maximum warning time of around 30 min. Lightning flash counts from the Geostationary Lightning Mapper (GLM) were underestimated when compared to detections from ground-based lightning networks. Among the possible reasons for GLM’s lower detection efficiency were an optically dense medium located above lightning sources and the occurrence of flashes smaller than GLM’s footprint. The minimum MinIR provided the shorter warning time to severe weather occurrence. However, secondary minima in MinIR10.3 that preceded the absolute minima improved this warning time by more than 10 min. Furthermore, trends in MinIR10.3 for time scales shorter than 6 min revealed shorter cycles of fast cooling and warming, which provided information about the lifecycle of updrafts within the storm. The advantages of using observations with high-temporal resolution to analyze the evolution and intensity of convective storms are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Improved Distributed-memory Triangle Counting by Exploiting the Graph Structure

Graphs are ubiquitous in modeling complex systems and representing interactions between entities to uncover structural information of the domain. Traditionally, graph analytics workloads are challenging to efficiently scale (both strong and weak cases) on distributed memory due to the irregular memory-access driven nature (with little or no computations) of the methods. The structure of graphs and their relative distribution over the processing elements poses another level of complexity, making it difficult to attain sustainable scalability across platforms. In this paper, we discuss enhancements to TriC, a distributed-memory implementation of graph triangle counting using Message Passing Interface (MPI), which was featured in the 2020 Graph Challenge competition. We have made some incremental enhancements to TriC, primarily adopting a user-defined buffering strategy to overcome the startup problem for large graphs (by fixing the memory for intermediate data), and experimenting with probabilistic data structures such as bloom filter to improve the query response time for assessing edge existence, at the expense of increasing the overall false positive rate. These adjustments have led to a modest improvements in most cases, as compared to the previous version.

Graph Analytics, HPC↗
DOE OSTI2021

Analysis of Loss-of-Offsite-Power Events: Update

Loss-of-offsite power (LOOP) can have a negative impact on a nuclear power plant’s ability to achieve and maintain safe shutdown conditions. LOOP event frequencies and times required for subsequent restoration of offsite power are important inputs to plant probabilistic risk assessments. This report presents a statistical and engineering analysis of LOOP frequencies and durations at U.S. commercial nuclear power plants. The data used in this study were based on the operating experience during calendar years 1987–2020, while the most recent 15-year data (i.e., from 2006–2020) were used for most analyses in this report. LOOP events during critical operation that did not result in a reactor trip are not included. Frequencies and durations were determined for four LOOP event categories: plant-centered, switchyard-centered, grid-related, and weather related. Highly significant decreasing trends in the LOOP occurrence rates were identified for All-LOOPs during critical operation (p-value = 0.001) and switchyard-centered LOOPs during critical operation (p-value = 0.002) for the most recent 10-year period (2011–2020). Adverse trends in LOOP durations continue for switchyard-centered LOOPs (p-value = 0.005), All-LOOPs (p-value = 0.019), as well as All-LOOPs during shutdown operation (p-value = 0.003). Statistical tests show the LOOP counts are not uniformly distributed across the 12 months, and variation among the months exists for plant-centered LOOPs (p-value = 0.028), grid-related LOOPs (p-value = 0.021), All-LOOPs (p-value = 0.003), and All-LOOPs during critical operation (p-value = 0.008). The engineering analysis of LOOP data showed for the period of 2006–2020, the equipment failure events were dominated by failures of circuits and relay; human errors have been less frequent and occurred primarily in maintenance; and weather events were dominated by tornadoes and lightning.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Local Power Impact Experiment Design for a New Fuel Type for use in the Advanced Test Reactor

The Advanced Test Reactor (ATR), and complimentary zero-power ATR Critical (ATRC) reactor, located at Idaho National Labs (INL), are undergoing conversion from Highly Enriched Uranium (HEU) to Low Enriched Uranium (LEU). Both have a variety of testing locations that can receive large variations in flux due to its unique serpentine design, consisting of five lobes (see Figure 1). Initial criticality and power distribution throughout the core are controlled by core-external outer shim control cylinders (OSCCs). Distinct test loops allow for testing at specific temperatures, pressures, and irradiation conditions. The ATR is one of the key nuclear engineering research and testing facilities within the DOE National Laboratory Complex, and the ATRC supports its operation [1]. Currently, the Office of Material Management and Minimization (M3) within the National Nuclear Security Administration of the DOE is working to convert the remaining research reactors, including the ATR, from 93% HEU fuel to 19.75% LEU fuel (LEU) to support non-proliferation [2]. Extensive materials testing at INL and internationally has demonstrated that a high-density uranium molybdenum (U 10Mo) alloy can meet the performance requirements of the remaining high powered research reactors. However, there are many technical challenges to address before the conversion to LEU can be successful, including the accurate characterization of the reactor core physics with LEU fuel. Reactor physics safety evaluations currently use Monte Carlo for the 21st Century (MC21), a continuous-energy Monte Carlo radiation transport code [3]. Existing MC21 models of the ATR and ATRC cores have a validation basis for use in neutronics analyses with HEU fuel. The models are used to support safety analyses that include comparisons to the safety requirements for the reactors. However, the use of the LOWE element in the ATR and ATRC is not currently covered by the current model validation basis. To deploy the new fuel type, extensive computational reactor physics support is necessary to support the use of LOWE in the ATR and ATRC. Therefore, LOWE requires a rigorous validation basis, aligned with that of HEU fuel, that takes advantage of the existing software tools and processes currently used for the ATR and ATRC. The experiment to validate of the MC21 models for determining power, the Power Impact Validation Experiment, will consist of two flux runs in the ATRC, one with fully HEU loading and one with a single LOWE element. Both flux runs will be instrumented with 20 sets of azimuthal fission wires and 3 sets of axial fission wires, as shown in Figure 4. Standard flux run methodology will be used [4]. Power Impact Validation Experiment data will be compared against MC21 calculated data, both for absolute fission rate accuracy and to determine the relative change in fission rates between the two runs. The results of the Power Impact Validation Experiment and subsequent evaluations will provide the validation basis for MC21 for use with LOWE elements. Key features of the Power Impact Validation Experiment include: (1) Two flux runs to allow for LOWE perturbed measurements to be compared to already validated measurements taken from a full core of HEU fuel, (2) Optimization of instrumentation to balance analytical needs with practical considerations (e.g., limited time window to count beta particles from fission products), and (3) Standard ATRC core loading, including both driver positions and flux traps, to minimize cost while remaining representative of typical ATR core loading.

42 ENGINEERING↗
DOE OSTI2021

Plant Growth Study: Growth of Three Plant Species in 100-OL-1 Operable Unit Soils

Soil contamination from historical application of lead arsenate pesticides persists in the 100-OL-1 Operable Unit (OU) of the U.S. Department of Energy’s Hanford Site in Benton County, Washington. A remedial investigation used a portable X-ray fluorescence analyzer to estimate and map the concentrations of metals in surface soil in the 133 decision units comprising the OU. There is variability in lead and arsenic concentrations among the non-contiguous decision units, and an incomplete understanding of the ecological and human risks from soil contamination. While general screening criteria for lead are reported in literature, limited information is available on the impacts of heavy metals on site-specific plant and animal life. To address this information gap, a study on plant growth in Hanford soils was conducted using native bluegrass (Poa secunda), invasive cheatgrass (Bromus tectorum), and buttercrunch lettuce (Lactuca sativa). The latter is included because it is a common species used in plant growth studies. Soil samples were collected from a single general location within the 100-OL-1 OU, prepared, and categorized as having low, medium, or high concentrations of lead and arsenic, with the high concentration soil having concentrations of up to 3,400 and 790 mg/kg lead and arsenic, respectively. Additionally, a synthetic soil was prepared and used as a control, in accordance with standard plant growth protocols. Fifty-four seeds of each plant species were planted in each of the four soil types following the procedure outlined by the Washington State Department of Ecology. Germination rates and biomass measurements were recorded for 20 days. Both bluegrass and lettuce germination rates appeared to be reduced at the highest lead and arsenic concentrations (Pb: 3400 mg/kg, As: 790 mg/kg), while cheatgrass germination rates were unaffected. Total biomass for all species appeared to be related to the relative concentrations of lead and arsenic in the soil. Results of this growth study agree reasonably well with previous Hanford Site plant growth studies and provide additional data for assessing ecological risk at the Hanford Site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

ASCR Workshop Position Paper: Challenges and Opportunities in High Energy Physics

High energy particle physics and cosmology concern themselves with estimating fundamental parameters of nature, such as the masses and interactions of fundamental particles like the Higgs boson and the rate of expansion of the universe. In doing so, they analyze exabyte-scale datasets, some of the largest in all of science, and face many challenges in subsequent data analysis. These challenges are shared between the two disciplines, but we focus on particle physics to highlight one specific domain. In particle physics, the standard method for estimating parameters involves performing Monte Carlo (MC) integration as a function of both parameters of interest and nuisance parameters using an expensive simulator, counting the number of observed collision events (i.i.d. samples) from an experiment in the corresponding integration domains, and forming a Poisson likelihood function. This likelihood function is then used in a Frequentist manner to construct a maximum likelihood point estimate (MLE) and confidence set for the parameters. To sufficiently populate the high-dimensional integration domains, simulators consume billions of CPU-hours annually and produce hundreds of petabytes of intermediate output data. Several techniques have been developed to: optimize definitions of the integration domains so as to be maximally sensitive to a particular subset of parameters, efficiently estimate the integrals, and build robust surrogate models by interpolating between integral evaluations at different parameter points. One can view this whole endeavor as classical Simulation-Based Inference (SBI).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Annihilation-gamma-based diagnostic techniques for magnetically confined electron–positron pair plasma

Efforts are underway to magnetically confine electron–positron pair plasmas to study their unique behaviour, which is characterized by significant changes in plasma time and length scales, supported waves and unstable modes. However, use of conventional plasma diagnostics presents challenges with these low-density and annihilating matter–antimatter plasmas. To address this problem, we propose to develop techniques based on the distinct emission provided by annihilation. This emission exhibits two spatial correlations: the distance attenuation of isotropic sources and the back-to-back propagation of momentum-preserving 2 $\gamma$ annihilation. We present the results of our analysis of the $\gamma$ emission rate and the spatial profile of the annihilation in a magnetized pair plasma from direct pair collisions, from the formation and decay of positronium as well as from transport processes. In order to demonstrate the effectiveness of annihilation-based techniques, we tested them on annular $\gamma$ emission profiles produced by a $\beta ^+$ radioisotope on a rotating turntable. Direct and positronium-mediated annihilation result in overlapping volumetric $\gamma$ sources, and the 2 $\gamma$ emission from these volumetric sources can be tomographically reconstructed from coincident counts in multiple detectors. Transport processes result in localized annihilation where field lines intersect walls, limiters or internal magnets. These localized sources can be identified by the fractional $\gamma$ counts on spatially distributed detectors.

Physics↗
DOE OSTI2025

TARP Identifying Critical Thresholds for Acute Response of Plants and Ecosystems to Water Stress at Walker Branch Watershed, 2002-2005

This dataset contains data from a manipulative field study aimed at identifying critical thresholds for acute response of plants and ecosystems to water stress (TARP) that took place at Walker Branch Watershed in Oak Ridge, Tennessee from 2002-2005 (2002-06-20 to 2005-12-16). The study used understory tents for the removal of 100% of the growing-season throughfall and stem flow, to provide data on the impact of acute drought on mechanisms responsible for growth and mortality of deciduous forest canopy trees representative of common plant functional types (Liriodendron and Quercus). Through three years of manipulation (2003, 2004 and 2005; pretreatment measurements in 2002) various measures of tree response to surface moisture deficits were recorded including root and leaf traits, plant nonstructural carbohydrates status, hourly sapflow, basal area, and periodic observations of foliar photosynthesis and conductance. Additionally, environmental data such as air and soil temperature, soil water content, and soil matric potential were recorded. This dataset contains data 16 files in comma-separate (*.csv) format. Additional metadata are provided: 16 data dictionaries and a file-level metadata file in comma separate (*.csv) format and a user guide in PDF (*.pdf) format.

basal area↗
DOE OSTI2021

Exosomes secreted from mesenchymal stem cells mediate the regeneration of endothelial cells treated with rapamycin by delivering pro-angiogenic microRNAs

Highlights: • MSC-derived exosomes play an important role in promoting endothelial regeneration treated with rapamycin in vitro. • A distinct class of exosomal miRNAs is identified to be tightly related to endothelial regeneration. • The therapeutic effects of MSC-exosomes may be ascribed to the delivery of pro-angiogenic miRNAs to endothelial cells. • MSC-exosomes may serve as a cell-free therapy for clinical treatment of delayed endothelial healing after DES implantation. Delayed endothelial healing after drug eluting stent (DES) implantation is a critical clinical problem in treatment of coronary artery diseases. Exosomes exhibit proangiogenic potential in a variety of ischemic diseases. However, the association of exosomes with endothelial regeneration after DES implantation has been rarely reported. In this study, we aimed to investigate the therapeutic effects of mesenchymal stem cell (MSC)-derived exosomes on endothelial cells treated with rapamycin and explore the potential mechanisms of MSC-derived exosomes in promoting endothelial regeneration. Exosomes were isolated from MSCs by ultracentrifugation and identified by transmission electron microscopy, nanoparticle tracking analysis, and Western blot assay. The in vitro effects of MSC-derived exosomes on the proliferation and migration of endothelial cells treated with rapamycin were evaluated by integrated experiment, cell counting kit-8, scratch, tube formation, and transwell assays. And the apoptosis of rapamycin-induced endothelial cells loaded with MSC-derived exosomes was detected using TUNEL and Annexin-V FITC and PI double-staining assays. The microRNA (miRNA) cargo of MSC-derived exosomes was identified by high-throughput RNA sequencing. Pro-angiogenic miRNAs and key pathways were further characterized. Our results indicated that MSC-derived exosomes could be ingested into umbilical vein endothelial cells (HUVECs) and significantly enhanced cell proliferation rate, migratory and tube-forming capabilities in vitro. MSC-derived exosomes also inhibited the apoptosis of HUVECs induced by rapamycin. A distinct class of exosomal miRNAs was further identified, including six miRNAs tightly related to neovasculogenesis. Silencing the expression of exosomal miRNA-21–5p and let-7c-5p attenuated the pro-proliferative and pro-migratory capacity of MSC-derived exosomes. Moreover, functional enrichment analysis indicated that metabolic pathways might contribute to reendothelialization. This study highlights a proregenerative effect of MSC-derived exosomes in vitro, which may be partly explained by the delivery of pro-angiogenic miRNAs to endothelial cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Boron coated straw-based neutron multiplicity counter for neutron interrogation of TRISO fueled pebbles

Pebble bed reactors (PBRs) can improve the safety and economics of the nuclear energy production. PBRs rely on TRIstructural-ISOtropic (TRISO) fuel pebbles for enhanced fission product retention. Accurate characterization of individual fuel pebbles would enable the validation of computational models, efficient use of TRISO fuel, and improve fuel accountability. Here, we have developed and tested a new neutron multiplicity counter (NMC) based on 192 boron coated straw (BCS) detectors optimized for 235 U assay in TRISO fuel. The new design yielded a singles and doubles neutron detection efficiency of 4.71% and 0.174%, respectively, and a die-away time of 16.7 μs. The NMC has a low intrinsic gamma-ray detection efficiency of 8.71 x 10 –8 at an exposure rate of 80.3 mR/h. In simulation, a high-efficiency version of the NMC encompassing 396 straws was able to estimate the 235 U in a pebble with a relative uncertainty and error both below 2% in 100 s.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Negative Muon Count for the FNAL Muon g-2 Experiment

The primary goal of the FNAL Muon g-2 Experiment, located in Batavia, IL, USA, is to measure the anomalous magnetic dipole moment, a μ = (g-2)/2, of a muon to a precision of 0.14 ppm. Initial results from the experiment confirm a discrepancy between the experimental value of the muon anomaly compared with the theoretical value first discovered by the Brookhaven Muon g-2 Experiment in 2001. This discrepancy hints at new physics. However, running the experiment with negative muons is essential in order to verify the experimental value of the muon anomaly. As neither experiments nor simulation studies pertaining to the muon anomaly have been performed with negative muons, the goal of this work is to estimate the rates of negative muons arriving at the FNAL Muon g-2 experiment so that scientists can estimate the time required to run the experiment with negative muons. In order to do this, we performed several simulations of the Muon Campus, which delivers muons to the FNAL Muon g-2 Experiment, using the G4Beamline v. 3.06 at the National Energy Research Computing Center. We performed separate simulations for different sections of the muon campus, and simulations were repeated twice, once for negative muons and once for positive muons. An initial 10 9 protons were shot on target for both the negative and positive muon simulations. We found that the rate of negative muons arriving at the experiment was 0.549 times the rate of positive muons arriving at the experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Atomic Layer Deposition Niobium Nitride Films for High-Q Resonators

Niobium nitride (NbN) is a useful material for fabricating detectors because of its high critical temperature and relatively high kinetic inductance. In particular, NbN can be used to fabricate nanowire detectors and mm-wave transmission lines. When deposited, NbN is usually sputtered, leaving room for concern about uniformity at small thicknesses. In this work, we present atomic layer deposition niobium nitride (ALD NbN) as an alternative technique that allows for precision control of deposition parameters such as film thickness, stage temperature, and nitrogen composition. Atomic-scale control over film thickness admits a high degree of uniformity for films 4–30 nm thick; control over deposition temperature gives rise to growth rate changes, which can be used to optimize film thickness and critical temperature. In order to charac- terize ALD NbN in the radio-frequency regime, we construct single-layer micro- wave resonators and test their performance as a function of stage temperature and input power. ALD processes can admit high resonator quality factors, with ≥ 43% of resonators above Q i = 10 6 , which in turn increase detector multiplexing capabilities. i Furthermore, we find critical temperatures in the range of 7.5–10.9 K that vary as a function of cycle count and deposition temperature.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

FPGA-accelerated SpeckleNN with SNL for real-time X-ray single-particle imaging

We present the implementation of a specialized version of our previously published unified embedding model, SpeckleNN, for real-time speckle pattern classification in X-ray Single-Particle Imaging (SPI), using the SLAC Neural Network Library (SNL) on an FPGA platform. This hardware realization transitions SpeckleNN from a prototypic model into a practical edge solution, optimized for running inference near the detector in high-throughput X-ray free-electron laser (XFEL) facilities, such as those found at the Linac Coherent Light Source (LCLS). To address the resource constraints inherent in FPGAs, we developed a more specialized version of SpeckleNN. The original model, which was designed for broader classification across multiple biological samples, comprised ~5.6 million parameters. The new implementation, while reducing the parameter count to 64.6K (a 98.8% reduction), focuses on maintaining the model's essential functionality for real-time operation, achieving an accuracy of 90%. Furthermore, we compressed the latent space from 128 to 50 dimensions. This implementation was demonstrated on the KCU1500 FPGA board, utilizing 71% of available DSPs, 75% of LUTs, and 48% of FFs, with an average power consumption of 9.4W according to the Vivado post-implementation report. The FPGA performed inference on a single image with a latency of 45.015 microseconds at a 200 MHz clock rate. In comparison, running the same inference on an NVIDIA A100 GPU resulted in an average power consumption of ~73W and an image processing latency of around 400 microseconds. Our FPGA-accelerated version of SpeckleNN demonstrated significant improvements, achieving an 8.9 × speedup and a 7.8 × reduction in power consumption compared to the GPU implementation. Key advancements include model specialization and dynamic weight loading through SNL, which eliminates the need for time-consuming FPGA design re-synthesis, allowing fast and continuous deployment of models (re)trained online. These innovations enable real-time adaptive classification and efficient vetoing of speckle patterns, making SpeckleNN more suited for deployment in XFEL facilities. This implementation has the potential to significantly accelerate SPI experiments and enhance adaptability to evolving experimental conditions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Antimicrobial properties of nanostructured surfaces – demonstrating the need for a standard testing methodology

Bioinspired nanostructured materials that exhibit antimicrobial properties are being synthesized and tested at increasing rates for use in healthcare, manufacturing processes, and diagnostics. Although progress has been made in improving and understanding their bactericidal activity, arguably, the biggest problem currently in the field is the lack of a standard testing methodology that allows for optimal characterization and better comparison of emerging nanostructures. In this work, we examine two forms of nanostructured silicon that vary in their ability to kill certain bacterial species due to different physical mechanisms and derive guidelines for the comparative testing. We perform a comprehensive evaluation of methodologies used extensively in the field (e.g., colony counting and live dead analysis) and the novel application of high-throughput flow cytometry. The data reveal how the techniques are complementary but not always directly equivalent or correlative. Therefore, comparison of results obtained using different methodologies on different materials can be grossly misleading. We report significant variations in bactericidal efficiencies depending on experimental environments (medium type, etc.) and methodologies employed. In addition, we demonstrate how cytometry is yet another powerful complementary tool that can aid the mechanistic understanding of antimicrobial activities of rough surfaces. Besides standardization for comparison, ultimately, evaluation methods need to consider anticipated applications. Then and only then can the true potential (or limitation) of a novel material be determined for its suitability for advancement in a particular field of use.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Hydrogen Plus Other Alternative Fuels Risk Assessment Models (HyRAM+) Version 5.1 Technical Reference Manual

The HyRAM+ software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen, natural gas, and autogas systems. HyRAM+ is designed to facilitate the use of state-of-the-art models to conduct robust, repeatable assessments of safety, hazards, and risk. HyRAM+ integrates deterministic and probabilistic models for quantifying leak sizes and rates, predicting physical effects, characterizing hazards (thermal effects from jet fires, overpressure effects from delayed ignition), and assessing impacts on people. HyRAM+ is developed at Sandia National Laboratories to support the development and revision of national and international codes and standards, and to provide developed models in a publicly-accessible toolkit usable by all stakeholders. This document provides a description of the methodology and models contained in HyRAM+ version 5.1. The most significant changes for HyRAM+ version 5.1 from HyRAM+ version 5.0 are updated default leak frequency values for propane, new default component counts for different fuel types, and an improved fuel specification view in the graphical user interface.

02 PETROLEUM↗
DOE OSTI2021

Mapathons versus automated feature extraction: a comparative analysis for strengthening immunization microplanning

Background: Social instability and logistical factors like the displacement of vulnerable populations, the difficulty of accessing these populations, and the lack of geographic information for hard-to-reach areas continue to serve as barriers to global essential immunizations (EI). Microplanning, a population-based, healthcare intervention planning method has begun to leverage geographic information system (GIS) technology and geospatial methods to improve the remote identification and mapping of vulnerable populations to ensure inclusion in outreach and immunization services, when feasible. We compare two methods of accomplishing a remote inventory of building locations to assess their accuracy and similarity to currently employed microplan line-lists in the study area. Methods: The outputs of a crowd-sourced digitization effort, or mapathon, were compared to those of a machine-learning algorithm for digitization, referred to as automatic feature extraction (AFE). The following accuracy assessments were employed to determine the performance of each feature generation method: (1) an agreement analysis of the two methods assessed the occurrence of matches across the two outputs, where agreements were labeled as “befriended” and disagreements as “lonely”; (2) true and false positive percentages of each method were calculated in comparison to satellite imagery; (3) counts of features generated from both the mapathon and AFE were statistically compared to the number of features listed in the microplan line-list for the study area; and (4) population estimates for both feature generation method were determined for every structure identified assuming a total of three households per compound, with each household averaging two adults and 5 children. Results: The mapathon and AFE outputs detected 92,713 and 53,150 features, respectively. A higher proportion (30%) of AFE features were befriended compared with befriended mapathon points (28%). The AFE had a higher true positive rate (90.5%) of identifying structures than the mapathon (84.5%). The difference in the average number of features identified per area between the microplan and mapathon points was larger (t = 3.56) than the microplan and AFE (t = -2.09) (alpha = 0.05). Conclusions: Our findings indicate AFE outputs had higher agreement (i.e., befriended), slightly higher likelihood of correctly identifying a structure, and were more similar to the local microplan line-lists than the mapathon outputs. These findings suggest AFE may be more accurate for identifying structures in high-resolution satellite imagery than mapathons. However, they both had their advantages and the ideal method would utilize both methods in tandem.

59 BASIC BIOLOGICAL SCIENCES↗