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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Lead exposure in young children over a 5-year period from urban environments using alternative exposure measures with the US EPA IEUBK model – A trial

Highlights: • IEUBK simulations of soil with petri dish dust (PDD), soil with handwipes, sweepings with PDD, sweepings with wipes. • Predicted blood lead levels (PbB) generally give good agreement with observed PbB in spite of differences in intake units for PDD and handwipes. • Analyses with dust loadings converted to concentration give higher predicted PbB. • Simulations on aggregated data for each child give similar results to age-stratified data. • Estimating house dust Pb from measured soil Pb concentrations (in the absence of measured house dust concentrations) results in over-prediction of PbB. The US Environmental Protection Agency (EPA) Integrated Exposure Uptake Biokinetic (IEUBK) model has been widely used to predict blood lead (PbB) levels in children especially around industrial sites. Exposure variables have strongly focussed on the major contribution of lead (Pb) in soil and interior dust to total intake and, in many studies, site-specific data for air, water, diet and measured PbB were not available. We have applied the IEUBK model to a comprehensive data set, including measured PbB, for 108 children monitored over a 5-year period in Sydney, New South Wales, Australia. To use this data set, we have substituted available data (with or without modification) for standard inputs as needed. For example, as an alternative measure for soil Pb concentration (μg/g), we have substituted exterior dust sweepings Pb concentration (μg/g). As alternative measures for interior dust Pb concentration (μg/g) we have used 1) 30-day cumulative petri dish deposition data (PDD) (as µg Pb/m{sup 2}/30days), or 2) hand wipe data (as μg Pb/hand). For comparison, simulations were also undertaken with estimates of dust Pb concentration derived from a prior regression of dust Pb concentration (μg/g) on dust Pb loading (μg/ft{sup 2}) as concentration is the unit specified for the Model. Simulations for each subject using observed data aggregated over the 5-year interval of the study, the most usual application of the IEUBK model, showed using Wilcoxon tests that there was a significant difference between the observed values and the values predicted by the Model containing soil with hand wipes (p < 0.001), and soil and PDD (p = 0.026) but not those for the other two sets of predictors, based on sweepings and PDD or sweepings and wipes. Overall, simulations of the Model using alternative exposure measures of petri dish dust (and possibly hand wipes) instead of vacuum cleaner dust and dust sweepings instead of soil provide predicted PbB which are generally consistent with each other and observed values. The predicted geometric mean PbBs were 2.17 ( ± 1.24) μg/dL for soil with PDD, 1.95 ( ± 1.17) μg/dL for soil with hand wipes, 2.36 ( ± 1.75) μg/dL for sweepings with PDD, and 2.15 ( ± 1.69) for sweepings with hand wipes. These results are in good agreement with the observed geometric mean PbB of 2.46 ( ± 0.99) μg/dL. In contrast to all other IEUBK model studies to our knowledge, we have stratified the data over the age ranges from 1 to 5 years. The median of the predicted values was lower than that for the observed values for every combination of age and set of measures; in some cases, the difference was statistically significant. The differences between observed and predicted PbB tended to be greatest for the soil plus wipes measure and for the oldest age group. Use of ‘default dust’ values calculated from the site-specific soil values, a common application of the IEUBK model, results in predicted PbB about 22% (range 0 to 52%) higher than those from soil with PDD data sets. Geometric mean contributions estimated from the Model to total Pb intake for a child aged 1–2 years was 0.09% for air, 42% for diet, 5.3% for water and 42% for soil and dust. Our results indicate that it is feasible to use alternative measures of soil and dust exposure to provide reliable predictions of PbB in urban environments.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

NASA GLDAS Evapotranspiration Data and Climatology

Evapotranspiration (ET) is the water lost to the atmosphere by evaporation and transpiration. ET is a shared component in the energy and water budget, therefore, a critical variable for global energy and water cycle and climate change studies. However, direct ET measurements and data acquisition are difficult and expensive, especially at the global level. Therefore, modeling is one common alternative for estimating ET. With the goal to generate optimal fields of land surface states and fluxes, the Global Land Data Assimilation System (GLDAS) has been generating quality-controlled, spatially and temporally consistent, terrestrial hydrologic data, including ET and other variables that affect evaporation and transpiration, such as temperature, precipitation, humidity, wind, soil moisture, heat flux, and solar radiation. This poster presents the long-term ET climatology (mean and monthly), derived from the 61-year GLDAS-2 monthly 1.0 deg x 1.0 deg. NOAH model Experiment-1 data, and describes the basic characteristics of spatial and seasonal variations of the climatology. The time series of GLDAS-2 precipitation and radiation, and ET are also discussed to show the improvement of GLDAS-2 forcing data and model output over those from GLDAS-1.

Rui, Hualan↗
DOE OSTI2018

Preliminary Long-term Safety Assessment for the Planned LLW Disposal Facility at Al-Tuwaitha, Iraq - 18142

Before 1991, Iraq had a significant nuclear program which encompassed ten nuclear sites spread out across the country. The largest nuclear site was located at the Al-Tuwaitha site near Baghdad where 18 nuclear facilities were concentrated. During the first Gulf War in 1991, all sites suffered significant damage from bombing and subsequent looting. Currently, the Ministry of Science and Technology is responsible for the ten heavily damaged nuclear sites and for the management of radioactive waste in Iraq in general. The task of decommissioning the damaged nuclear facilities and managing the resulting waste is being supported by the International Atomic Energy Agency (IAEA), the European Commission (EC), and the United States (US). In the framework of its Instrument for Nuclear Safety Cooperation (INSC), the EC has launched a cooperation project to support the planned construction of an engineered repository for short-lived low- and intermediate level waste for the safe disposal of radioactive waste resulting from the decommissioning activities, as well as for waste from the continuing use of radioisotopes in medicine and the oil and gas industry in the country. The main objectives of this project, which was awarded to a Consortium led by NUKEM Technologies Engineering Services GmbH and joined by DBE Technology GmbH of Germany and JAVYS a.s. of Slovakia, are the development of an initial conceptual level basic design and preliminary safety assessment report (PSAR) and the subsequent detailed engineering design and supporting pre-construction safety assessment report (PCSAR). The current paper focuses on the development of the PSAR. Special focus is given to the long-term calculations and difficulties associated with the limited data base in regard to site characterization as well as the expected inventory for the future repository. Because of the ongoing security concerns in Iraq, the Ministry of Science and Technology had already selected the Al-Tuwaitha Site as the best suited location for the planned surface repository at the start of the project. Although some site characterization work had already been carried out prior to the start of the project, certain data for modelling the geology and hydrology of the site are still lacking. Missing data have been compensated by conservative assumptions and the assessment of alternative scenarios. Consequently, for the development of the simplified GoldSim{sup R} computer model used to carry out the long-term calculations supporting the PSAR, existing site information was used and, as appropriate, amended or replaced by conservative assumptions. Due to the nature of the waste, data on the expected inventory are associated with large uncertainties. Although certain estimates related to the future volume of the waste exist, little to no information is available on the expected radionuclide vector and respective activities. Therefore, the long-term calculations were carried out for a generic inventory including a broad range of radionuclides that are to be expected as being important considering the kind of waste to be disposed of in the facility. Based on the results of these calculations, the maximum total capacity of the facility was estimated and converted into activity limits for individual radionuclides that will be integrated into the existing general WAC for the facility. Compliance with these WAC will then guarantee the safety of the repository in regard to potential radionuclide releases via the groundwater pathway. In parallel to the determination of activity limits for the WAC, long-term calculations were carried using different estimates for the expected total activity to determine the general suitability of the proposed technical solution of the repository for receiving the total amount of Iraqi radioactive waste principally acceptable for surface disposal. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Non-Electricity Based Renewable Fuels: Theory and Computation for Solar Thermochemical Hydrogen

Dominated by photovoltaics and wind, current renewable energy sources generate mostly electricity, but 80% of the global final energy consumption occurs in form of fuels. Therefore, direct solar fuel generation would be a major breakthrough for the energy transition. Solar thermochemical hydrogen (STCH) is one of the very few potential routes towards scalable renewable fuels, but currently suffers from lack of an oxide working material that could optimally perform energy conversion within the thermodynamic boundary conditions. Theory and computation can contribute in two distinct ways, through materials search and discovery, but also by providing detailed mechanistic models for specific systems so to advance our understanding of possible design strategies. To enable high-throughput materials screening, we developed a defect graph neural network (dGNN) machine learning approach,[1] which accelerates the prediction of defect formation energies by replacing the tedious density functional theory (DFT) supercell calculations for all possible defect sites. This approach enables high-throughput database screening of oxides, which was integrated with thermodynamic modeling to extract the reduction entropies as additional selection criterion for STCH. Once potential candidate materials are identified, detailed models can guide materials design by predicting performance characteristics. One challenge is to quantitatively predict thermochemical equilibria at high concentrations when the redox active defects start to interact with each other, thereby impeding the formation of additional defects. Introducing a model for the free energy of defect interaction, parametrized on the basis of DFT data, we simulated the complete STCH redox cycle for (Sr,Ce)MnO3 alloys, achieving near-quantitative agreement with experimental data.[2] The analysis of these simulations reveals how defect interactions diminish the reduction entropy and H2 yield, suggesting to include these interactions in design considerations. Finally, we revisit the popular van't Hoff method for analyzing reduction enthalpies and entropies. This method is not ideal, as it involves a temperature-dependent convolution of gas-phase and solid-state entropies, causing uncertainties in the same order of magnitude as the physical quantities of interest. To avoid this problem, we suggest a simple alternative approach which can be applied to experimental and simulated data alike.

first-principles calculations↗
DOE OSTI2026

Admissible Powertrain Alternatives for Heavy-Duty Fleets: A Case Study on Resiliency and Efficiency

Heavy-duty vehicles dominate global freight movement and primarily rely on fossil-derived diesel fuel. However, fluctuations in crude oil prices and evolving emissions regulations have prompted interest in alternative powertrains to enhance fleet energy resiliency. This study paired real-world operational data from a large commercial fleet with high-fidelity vehicle models to evaluate the potential for replacing diesel internal combustion engine (ICE) trucks with alternative powertrain architectures. The baseline vehicle for this analysis is a diesel-powered ICE truck. Alternatives include ICE trucks fueled by bio- and renewable diesel, compressed natural gas (CNG) or hydrogen (H 2 ), as well as plug-in hybrid (PHEV), fuel cell electric (FCEV), and battery electric vehicles (BEV). While most alternative powertrains resulted in some payload capacity loss, the overall fleetwide impact was negligible due to underutilized payload capacity for the specific fleet considered in this study. For sleeper cab trucks, CNG-powered trucks achieved the highest replacement potential, covering 85% of the fleet. In contrast, H 2 and BEV architectures could replace fewer than 10% and 1% of trucks, respectively. Day cab trucks, with shorter daily routes, showed higher replacement potential: 98% for CNG, 78% for H 2 , and 34% for BEVs. However, achieving full fleet replacement would still require significant operational changes such as route reassignment and enroute refueling, along with considerable improvements to onboard energy storage capacity. Additionally, the higher total cost of ownership (TCO) for alternative powertrains remains a key challenge. This study also evaluated lifecycle impacts across various fuel sources, both fossil and bio-derived. Bio-derived synthetic diesel fuels emerged as a practical option for diesel displacement without disrupting operations. Conversely, H 2 and electrified powertrains provide limited lifecycle impacts under the current energy scenario. This analysis highlights the complexity of replacing diesel ICE trucks with admissible alternatives while balancing fleet resiliency, operational demands, and emissions goals. These results reflect a US-based fleet’s duty cycles, payloads, GVWR allowances, and an assumption of depot-only refueling/recharging. Applicability to other fleets and regions may differ based on differing routing practices or technical features such as battery swapping.

BEV↗
DOE OSTI2024

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Graph Identification of Proteins in Tomograms (GRIP-Tomo) 2.0: Topologically aware classification for proteins

Cryo-electron tomography (cryo-ET) enables structural characterization of biomolecules under near-native conditions. Existing approaches for interpreting the resulting three-dimensional volumes are computationally expensive and have difficulty interpreting density associated with small proteins/complexes. To explore alternate approaches for identifying proteins in cryo-ET data we pursued a Graph Network and topologically invariant approach. Here, we report on a fast algorithm that classifies particles by searching for nuances of evolutionarily conversed motifs and the geometrical characteristics of protein structure. GRIP-Tomo 2.0 is a machine-learning pipeline that extracts interpretable topological features of protein structures within noisy experimental backgrounds. Compared to version 1.0, the new pipeline includes three upgrades that significantly improve performance including synthetic tomogram generation simulating realistic noise, graph-based persistent feature extraction as protein fingerprints, and high-performance computing acceleration. GRIP-Tomo 2.0 achieves over 90% accuracy in classifying between proteins and noise using both real and synthetic datasets which represents a foundational step toward advancing cryo-ET workflows and empowering automated visual proteomics.

Li, Chengxuan↗
DOE OSTI2017

Climate controls over ecosystem metabolism: insights from a fifteen-year inductive artificial neural network synthesis for a subalpine forest

Eddy covariance (EC) datasets have provided insight into climate determinants of net ecosystem productivity (NEP) and evapotranspiration (ET) in natural ecosystems for decades, but most EC studies were published in serial fashion such that one study’s result became the following study’s hypothesis. Our approach reflects the hypothetico-deductive process by focusing on previously derived hypotheses. A synthesis of this type of sequential inference reiterates subjective biases and may amplify past assumptions about the role, and relative importance, of cont rols over ecosystem metabolism. Long-term EC datasets facilitate an alternative approach to synthesis: the use of inductive data-based analyses to re-examine past deductive studies of the same ecosystem. Here we examined the seasonal climate determinants of NEP and ET by analyzing a 15-year EC time-series from a subalpine forest using an ensemble of Artificial Neural Networks (ANNs) at the half-day (daytime/nighttime) time-step. We also extracted relative rankings of climate drivers and driver–response relationships directly from the dataset with minimal a priori assumptions. The ANN analysis revealed temperature variables as primary climate drivers of NEP and daytime ET, when all seasons are considered, consistent with the assembly of past studies. New relations uncovered by the ANN approach include the role of soil moisture in driving daytime NEP during the snowmelt period, the nonlinear response of NEP to temperature across seasons, and the low relevance of summer rainfall for NEP or ET at the same daytime/nighttime time step. These new results offer a more complete perspective of climate–ecosystem interactions at this site than traditional deductive analyses alone.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Synergy between cosmological and laboratory searches in neutrino physics

The intersection of the cosmic and neutrino frontiers is a rich field where much discovery space still remains. Neutrinos play a pivotal role in the hot big bang cosmology, influencing the dynamics of the universe over numerous decades in cosmological history. Recent studies have made tremendous progress in understanding some properties of cosmological neutrinos, primarily their energy density. Upcoming cosmological probes will measure the energy density of relativistic particles with higher precision, but could also start probing other properties of the neutrino spectra. When convolved with results from terrestrial experiments, cosmology can become even more acute at probing new physics related to neutrinos or even Beyond the Standard Model (BSM). Any discordance between laboratory and cosmological data sets may reveal new BSM physics and/or suggest alternative models of cosmology. Here we give examples of the intersection between terrestrial and cosmological probes in the neutrino sector, and briefly discuss the possibilities of what different laboratory experiments may see in conjunction with cosmological observatories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Tailoring electric dipole of hole-transporting material p-dopants for perovskite solar cells

We report Li-TFSI/t-BP are the most widely employed p-dopants for hole-transporting materials (HTMs) within the state-of-the-art perovskite solar cells (PSCs). The hygroscopicity and migration of these dopants, however, lead to devices with limited stability. To solve this problem, we report here on a diphenyl iodide cation and pentafluorophenyl boric acid anion-based dopant (DIC-PBA) with an oriented interfacial dipole moment as an alternative to Li-TFSI/t-BP. Theoretical and experimental data reveal that DIC-PBA exhibits deep doping of poly[bis(4-phenyl)(2,4,6-triMethylphenyl)aMine] (PTAA) and also creates p-doping of perovskite surface, which originates from ionic interactions-derived dipole arrangement that yields fast interfacial charge transport. The improved intrinsic stability of PSCs originates from the inhibition of dipole moment degeneration on the perovskite surface. Devices prepared with DIC-PBA yielded high efficiency of 22.86%, and the modules (aperture area: 33.2 cm 2 ) efficiency reached 19.13%. Importantly, the storage stability also significantly improved exceeding to 90% after aging 1,200 h under air ambient.

14 SOLAR ENERGY↗
DOE OSTI2024

Modeling plasticity-mediated void growth at the single crystal scale: A physics-informed machine learning approach

Modeling the evolution of voids during plastic flow as well as their effects on plastic dissipation is critical for both component manufacturing and lifetime estimation purposes. To this end, we propose a rate-dependent constitutive model to homogenize the effects of semi-randomly distributed voids on single crystal plasticity whilst capturing void interaction and plastic anisotropy. Here, this present work focuses on the case of face centered cubic crystals to introduce an anisotropic gauge function applicable within the crystal plasticity formalism. The approach combines analytical methods to describe the micromechanics of the system in combination with symbolic regression to capture analytically intractable mechanisms from data. The hybrid framework uses a physics-informed genetic programming-based symbolic regression algorithm to solve a multiform optimization problem simultaneously producing a new gauge function and a new strain rate equation. This is also a multi-objective optimization problem with many competing objectives. A new search and selection step is introduced to the genetic algorithm that promotes convergence toward a global solution that better satisfies all the objectives. Overall, the symbolic equations produced leverage data-driven methods to achieve greater accuracy than comparable alternatives on an analytically intractable problem while maintaining model transparency.

36 MATERIALS SCIENCE↗
DOE OSTI2017

A novel muon detector for borehole density tomography

Muons can be used to image the density of materials through which they pass, including geological structures. Subsurface applications of the technology include tracking fluid migration during injection or production, with increasing concern regarding such timely issues as induced seismicity or chemical leakage into aquifers. Current density monitoring options include gravimetric data collection and active or passive seismic surveys. One alternative, or complement, to these methods is the development of a muon detector that is sufficiently compact and robust for deployment in a borehole. Such a muon detector can enable imaging of density structure to monitor small changes in density – a proxy for fluid migration – at depths up to 1500 m. Such a detector has been developed, and Monte Carlo modeling methods applied to simulate the anticipated detector response. Testing and measurements using a prototype detector in the laboratory and shallow underground laboratory demonstrated robust response. Lastly, a satisfactory comparison with a large drift tube-based muon detector is also presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Structural and functional consequences of the STAT5B N642H driver mutation

Hyper-activated STAT5B variants are high value oncology targets for pharmacologic intervention. STAT5B N642H , a frequently-occurring oncogenic driver mutation, promotes aggressive T-cell leukemia/lymphoma in patient carriers, although the molecular origins remain unclear. Herein, we emphasize the aggressive nature of STAT5B N642H in driving T-cell neoplasia upon hematopoietic expression in transgenic mice, revealing evidence of multiple T-cell subset organ infiltration. Notably, we demonstrate STAT5B N642H -driven transformation of γδ T-cells in in vivo syngeneic transplant models, comparable to STAT5B N642H patient γδ T-cell entities. Importantly, we present human STAT5B and STAT5B N642H crystal structures, which propose alternative mutation-mediated SH2 domain conformations. Our biophysical data suggests STAT5B N642H can adopt a hyper-activated and hyper-inactivated state with resistance to dephosphorylation. MD simulations support sustained interchain cross-domain interactions in STAT5B N642H , conferring kinetic stability to the mutant anti-parallel dimer. This study provides a molecular explanation for the STAT5B N642H activating potential, and insights into pre-clinical models for targeted intervention of hyper-activated STAT5B.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Reduced global plant respiration due to the acclimation of leaf dark respiration coupled with photosynthesis

Summary Leaf dark respiration ( R d ) acclimates to environmental changes. However, the magnitude, controls and time scales of acclimation remain unclear and are inconsistently treated in ecosystem models. We hypothesized that R d and Rubisco carboxylation capacity ( V cmax ) at 25°C ( R d,25 , V cmax,25 ) are coordinated so that R d,25 variations support V cmax,25 at a level allowing full light use, with V cmax,25 reflecting daytime conditions (for photosynthesis), and R d,25 / V cmax,25 reflecting night‐time conditions (for starch degradation and sucrose export). We tested this hypothesis temporally using a 5‐yr warming experiment, and spatially using an extensive field‐measurement data set. We compared the results to three published alternatives: R d,25 declines linearly with daily average prior temperature; R d at average prior night temperatures tends towards a constant value; and R d,25 / V cmax,25 is constant. Our hypothesis accounted for more variation in observed R d,25 over time ( R 2 = 0.74) and space ( R 2 = 0.68) than the alternatives. Night‐time temperature dominated the seasonal time‐course of R d , with an apparent response time scale of c. 2 wk. V cmax dominated the spatial patterns. Our acclimation hypothesis results in a smaller increase in global R d in response to rising CO 2 and warming than is projected by the two of three alternative hypotheses, and by current models.

Plant Sciences↗
DOE OSTI2016

Technical Note: Development of a 3D printed subresolution sandwich phantom for validation of brain SPECT analysis

Purpose: To make an adaptable, head shaped radionuclide phantom to simulate molecular imaging of the brain using clinical acquisition and reconstruction protocols. This will allow the characterization and correction of scanner characteristics, and improve the accuracy of clinical image analysis, including the application of databases of normal subjects. Methods: A fused deposition modeling 3D printer was used to create a head shaped phantom made up of transaxial slabs, derived from a simulated MRI dataset. The attenuation of the printed polylactide (PLA), measured by means of the Hounsfield unit on CT scanning, was set to match that of the brain by adjusting the proportion of plastic filament and air (fill ratio). Transmission measurements were made to verify the attenuation of the printed slabs. The radionuclide distribution within the phantom was created by adding {sup 99m}Tc pertechnetate to the ink cartridge of a paper printer and printing images of gray and white matter anatomy, segmented from the same MRI data. The complete subresolution sandwich phantom was assembled from alternate 3D printed slabs and radioactive paper sheets, and then imaged on a dual headed gamma camera to simulate an HMPAO SPECT scan. Results: Reconstructions of phantom scans successfully used automated ellipse fitting to apply attenuation correction. This removed the variability inherent in manual application of attenuation correction and registration inherent in existing cylindrical phantom designs. The resulting images were assessed visually and by count profiles and found to be similar to those from an existing elliptical PMMA phantom. Conclusions: The authors have demonstrated the ability to create physically realistic HMPAO SPECT simulations using a novel head-shaped 3D printed subresolution sandwich method phantom. The phantom can be used to validate all neurological SPECT imaging applications. A simple modification of the phantom design to use thinner slabs would make it suitable for use in PET.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Bulk Handling Facility Modeling and Simulation for Safeguards Analysis

The Separation and Safeguards Performance Model (SSPM) uses MATLAB/Simulink to provide a tool for safeguards analysis of bulk handling nuclear processing facilities. Models of aqueous and electrochemical reprocessing, enrichment, fuel fabrication, and molten salt reactor facilities have been developed to date. These models are used for designing the overall safeguards system, examining new safeguards approaches, virtually testing new measurement instrumentation, and analyzing diversion scenarios. The key metrics generated by the models include overall measurement uncertainty and detection probability for various material diversion or facility misuse scenarios. Safeguards modeling allows for rapid and cost-effective analysis for Safeguards by Design. The models are currently being used to explore alternative safeguards approaches, including more reliance on process monitoring data to reduce the need for destructive analysis that adds considerable burden to international safeguards. Machine learning techniques are being applied, but these techniques need large amounts of data for training and testing the algorithms. The SSPM can provide that training data. This paper will describe the SSPM and its use for applying both traditional nuclear material accountancy and newer machine learning options.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2017

Forecasting seasonal influenza with a state-space SIR model

Seasonal influenza is a serious public health and societal problem due to its consequences resulting from absenteeism, hospitalizations, and deaths. The overall burden of influenza is captured by the Centers for Disease Control and Prevention’s influenza-like illness network, which provides invaluable information about the current incidence. This information is used to provide decision support regarding prevention and response efforts. Despite the relatively rich surveillance data and the recurrent nature of seasonal influenza, forecasting the timing and intensity of seasonal influenza in the U.S. remains challenging because the form of the disease transmission process is uncertain, the disease dynamics are only partially observed, and the public health observations are noisy. Fitting a probabilistic state-space model motivated by a deterministic mathematical model [a susceptible-infectious-recovered (SIR) model] is a promising approach for forecasting seasonal influenza while simultaneously accounting for multiple sources of uncertainty. A significant finding of this work is the importance of thoughtfully specifying the prior, as results critically depend on its specification. Our conditionally specified prior allows us to exploit known relationships between latent SIR initial conditions and parameters and functions of surveillance data. We demonstrate advantages of our approach relative to alternatives via a forecasting comparison using several forecast accuracy metrics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Rapid estimation of photosynthetic leaf traits of tropical plants in diverse environmental conditions using reflectance spectroscopy

Tropical forests are one of the main carbon sinks on Earth, but the magnitude of CO 2 absorbed by tropical vegetation remains uncertain. Terrestrial biosphere models (TBMs) are commonly used to estimate the CO 2 absorbed by forests, but their performance is highly sensitive to the parameterization of processes that control leaf-level CO 2 exchange. Direct measurements of leaf respiratory and photosynthetic traits that determine vegetation CO 2 fluxes are critical, but traditional approaches are time-consuming. Reflectance spectroscopy can be a viable alternative for the estimation of these traits and, because data collection is markedly quicker than traditional gas exchange, the approach can enable the rapid assembly of large datasets. However, the application of spectroscopy to estimate photosynthetic traits across a wide range of tropical species, leaf ages and light environments has not been extensively studied. Here, we used leaf reflectance spectroscopy together with partial least-squares regression (PLSR) modeling to estimate leaf respiration ( R dark25 ), the maximum rate of carboxylation by the enzyme Rubisco ( V cmax25 ), the maximum rate of electron transport ( J max25 ), and the triose phosphate utilization rate ( T p25 ), all normalized to 25°C. We collected data from three tropical forest sites and included leaves from fifty-three species sampled at different leaf phenological stages and different leaf light environments. Our resulting spectra-trait models validated on randomly sampled data showed good predictive performance for V cmax25 , J max25 , T p25 and R dark25 (RMSE of 13, 20, 1.5 and 0.3 μmol m -2 s -1 , and R 2 of 0.74, 0.73, 0.64 and 0.58, respectively). The models showed similar performance when applied to leaves of species not included in the training dataset, illustrating that the approach is robust for capturing the main axes of trait variation in tropical species. We discuss the utility of the spectra-trait and traditional gas exchange approaches for enhancing tropical plant trait studies and improving the parameterization of TBMs.

54 ENVIRONMENTAL SCIENCES↗