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DOE OSTI2022

Continental United States may lose 1.8 petagrams of soil organic carbon under climate change by 2100

Abstract Aims High‐resolution information on soils’ vulnerability to climate‐induced soil organic carbon (SOC) loss can enable environmental scientists, land managers, and policy makers to develop targeted mitigation strategies. This study aims to estimate baseline and decadal changes in continental US surface SOC stocks under future emission scenarios. Location Continental United States. Time period 2014–2100. Methods We used recent SOC field observations ( n = 6,213 sites), environmental factors ( n = 32), and an ensemble machine learning (ML) approach to estimate baseline SOC stocks in surface soils across the continental United States at 100‐m spatial resolution, and decadal changes under the projected climate scenarios of Coupled Model Intercomparison Project Phase Six (CMIP6) earth system models (ESMs). Results Baseline SOC projections from ML approaches captured more than 50% of variability in SOC observations, whereas ESMs represented only 6–16% of observed SOC variability. ML estimates showed a mean total loss of 1.8 Pg C from US surface soils under the high‐emission scenario by 2100, whereas ESMs showed no significant change in SOC stocks with wide variation among ESMs. Both ML and ESM predictions agree on the direction of SOC change (net emissions or sequestration) across 46–51% of continental US land area. These differences are attributable to the high‐resolution site‐specific data used in the ML models compared to the relatively coarse grid represented in CMIP6 ESMs. Main conclusions Our high‐resolution estimates of baseline SOC stocks, identification of key environmental controllers, and projection of SOC changes from US land cover types under future climate scenarios suggest the need for high‐resolution simulations of SOC in ESMs to represent the heterogeneity of SOC. We found that the SOC change is sensitive to key soil related factors (e.g. soil drainage and soil order) that have not been historically considered as input parameters in ESMs, because currently more than 95% variability in the SOC of CMIP6 ESMs is controlled by net primary productivity, temperature, and precipitation. Using additional environmental factors to estimate the baseline SOC stocks and predict the future trajectory of SOC change can provide more accurate results.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1983

Effects of vegetation cover on the microwave radiometric sensitivity to soil moisture

The reduction in sensitivity of the microwave brightness temperature to soil moisture content due to vegetation cover is analyzed using airborne observations made at 1.4 and 5 GHz. The data were acquired during six flights in 1978 over a test site near Colby, Kansas. The test site consisted of bare soil, wheat stubble, and fully mature corn fields. The results for corn indicate that the radiometric sensitivity to soil moisture S decreases in magnitude with increasing frequency and with increasing angle of incidence (relative to nadir). The sensitivity reduction factor, defined in terms of the radiometric sensitivities for bare soil and canopy-covered conditions Y = 1 - Scan/Ss was found to be equal to 0.65 for normal incidence at 1.4 GHz, and increases to 0.89 at 5 GHz. These results confirm previous conclusions that the presence of vegetation cover may pose a serious problem for soil moisture detection with passive microwave sensors.

Ulaby, F. T.↗

NASA NTRS1994

A Comparison of Atomic Oxygen Erosion Yields of Carbon and Selected Polymers Exposed in Ground Based Facilities and in Low Earth Orbit

A comparison of the relative erosion yields (volume of material removed per oxygen atom arriving) for FEP Teflon, polyethylene, and pyrolytic graphite with respect to Kapton HN was performed in an atomic oxygen directed beam system, in a plasma asher, and in space on the EOIM-III (Evaluation of Oxygen Interaction with Materials-III) flight experiment. This comparison was performed to determine the sensitivity of material reaction to atomic oxygen flux, atomic oxygen fluence, and vacuum ultraviolet radiation for enabling accurate estimates of durability in ground based facilities. The relative erosion yield of pyrolytic graphite was found not to be sensitive to these factors, that for FEP was sensitive slightly to fluence and possibly ions, and that for polyethylene was found to be partially VUV and flux sensitive but more sensitive to an unknown factor. Results indicate that the ability to use these facilities for material relative durability prediction is great as long as the sensitivity of particular materials to conditions such as VUV, and atomic oxygen flux and fluence are taken into account. When testing materials of a particular group such as teflon, it may be best to use a witness sample made of a similar material that has some available space data on it. This would enable one to predict an equivalent exposure in the ground based facility.

Rutledge, Sharon K.↗

NASA NTRS1995

Constraints on the gamma-ray burst luminosity function from Pioneer Venus Orbiter and BATSE observations

We examine the width of the gamma ray burst luminosity function through the distribution of Gamma Ray Burst (GRB) peak fluxes as detected by the Pioneer Venus Orbiter (PVO) and the Burst and Transient Source Experiment (BATSE). The strength of the analysis is greatly enhanced by using a merged catalog of peak fluxes from both instruments with good cross-calibration of their sensitivities. The range of peak fluxes is increased by approximately a factor of 20 relative to the BATSE catalog. Thus, more sensitive investigations of the log N - log P distribution are possible. We place constraints on the width of the luminosity function of gamma-ray bursts brighter than the BATSE completeness limit by comparing the intensity distribution in the merged catalog with those produced by a variety of spatial density and luminosity functions. For the models examined, 90% of the detectable bursts have peak luminosities within a range of 10, indicating that the peak luminosities of gamma-ray bursts span a markedly less wide range of values than many other of their measurable properties. We also discuss for which slopes of a power-law luminosity function the observed width is at the upper end of the constrained range. This is essential in determining the power-law slopes for which luminosity-duration correlations could be important.

Ulmer, Andrew↗

NASA NTRS1991

The IRAS faint source survey

The principal features of the IRAS Faint Source Survey (FSS), a new product resulting from the extended IRAS mission, are reviewed. The FSS has achieved an increase in sensitivity of about a factor of 2.5 relative to the IRAS Point Source Catalog by coadding the data before extracting sources. The FSS was produced by point-source filtering the individual detector data streams and then coadding the data streams using a trimmed-average algorithm. The discussion covers FSS production methods; reliability, completeness, and positional accuracy of the FSS; and FSS view of the IR sky.

Moshir, Mehrdad↗

NASA NTRS1981

Nature of the annoyance and noise annoyance relation around airports

A survey of 5,000 individuals living around Orly Airport is described. The psobic index was used as the noise index which indicated the intensity of the annoyance experienced by people living around the airport. The results indicate that sensitivity to noise is related to certain personal factors.

Francois, J.↗

DOE OSTI2021

PastoralScape: An Environment-Driven Model of Vaccination Decision Making Within Pastoralist Groups in East Africa

Economic and cultural resilience among pastoralists in East Africa is threatened by the interconnected forces of climate change, contagious diseases spread and evolving national and international trade. A key factor in the resilience of livestock that communities depend on is human decision making regarding vaccination against prevalent diseases such as Rift Valley fever and Contagious Bovine Pleuropneumonia. This paper describes an agent-based model that couples models of disease propagation, animal health, human decision making, and external GIS data sources capturing measures of foraging condition. We describe the design of the sub-models, their coupling, and demonstrate the sensitivity of the model to parameters that relate to controllable factors such as government and NGO information sources that can influence human decision making patterns. This model is intended to form the basis upon which richer economic and human factor models can be built.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Systematic discovery of pseudomonad genetic factors involved in sensitivity to tailocins

Tailocins are bactericidal protein complexes produced by a wide variety of bacteria that kill closely related strains and may play a role in microbial community structure. Thanks to their high specificity, tailocins have been proposed as precision antibacterial agents for therapeutic applications. Compared to tailed phages, with whom they share an evolutionary and morphological relationship, bacterially produced tailocins kill their host upon production but producing strains display resistance to self-intoxication. Though lipopolysaccharide (LPS) has been shown to act as a receptor for tailocins, the breadth of factors involved in tailocin sensitivity, and the mechanisms behind resistance to self-intoxication, remain unclear. Here, we employed genome-wide screens in four non-model pseudomonads to identify mutants with altered fitness in the presence of tailocins produced by closely related pseudomonads. Our mutant screens identified O-antigen composition and display as most important in defining sensitivity to our tailocins. In addition, the screens suggest LPS thinning as a mechanism by which resistant strains can become more sensitive to tailocins. Furthermore, we validate many of these novel findings, and extend these observations of tailocin sensitivity to 130 genome-sequenced pseudomonads. This work offers insights into tailocin–bacteria interactions, informing the potential use of tailocins in microbiome manipulation and antibacterial therapy.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1997

Psychosocial value of space simulation for extended spaceflight

There have been over 60 studies of Earth-bound activities that can be viewed as simulations of manned spaceflight. These analogs have involved Antarctic and Arctic expeditions, submarines and submersible simulators, land-based simulators, and hypodynamia environments. None of these analogs has accounted for all the variables related to extended spaceflight (e.g., microgravity, long-duration, heterogeneous crews), and some of the stimulation conditions have been found to be more representative of space conditions than others. A number of psychosocial factors have emerged from the simulation literature that correspond to important issues that have been reported from space. Psychological factors include sleep disorders, alterations in time sense, transcendent experiences, demographic issues, career motivation, homesickness, and increased perceptual sensitivities. Psychiatric factors include anxiety, depression, psychosis, psychosomatic symptoms, emotional reactions related to mission stage, asthenia, and postflight personality, and marital problems. Finally, interpersonal factors include tension resulting from crew heterogeneity, decreased cohesion over time, need for privacy, and issues involving leadership roles and lines of authority. Since future space missions will usually involve heterogeneous crews working on complicated objectives over long periods of time, these features require further study. Socio-cultural factors affecting confined crews (e.g., language and dialect, cultural differences, gender biases) should be explored in order to minimize tension and sustain performance. Career motivation also needs to be examined for the purpose of improving crew cohesion and preventing subgrouping, scapegoating, and territorial behavior. Periods of monotony and reduced activity should be addressed in order to maintain morale, provide meaningful use of leisure time, and prevent negative consequences of low stimulation, such as asthenia and crew member withdrawal. Leadership roles and lines of authority need to be studied further to understand the factors leading to status leveling, leadership competition, and role confusion. Finally, the relationship between crews and ground personnel should be characterized in order to minimize the displacement of anger and tension to the outside, to counter the effects of inter-group miscommunications, and to develop support strategies that can help to counter in-group/out-group conflicts. Ground-based space simulations still have a role to play in terms of understanding the impact of these factors and ways of dealing with them. In particular, issues involving language, cultural differences, gender biases, career motivation, monotonous conditions, use of free time, leadership, lines of authority, and the relationship between crews and outside monitoring personnel need to be further characterized and examined under controlled conditions. Until such time as these factors can be studied directly in space, simulations provide an opportunity to learn more about these psychosocial issues and to plan ways of minimizing their negative consequences during actual space missions.

NASA Discipline Number 00-00↗

DOE OSTI2023

Data Assimilation using Non-invasive Monte Carlo Sensitivity Analysis of Reactor Kinetics Parameters

Accurately predicting the criticality of an experiment before interacting with the experimental components is very important for criticality safety. Radiation transport software can be utilized to calculate the effective neutron multiplication factor of a nuclear system. Because of the integral nature of the effective neutron multiplication factor, the value calculated contains various sources of nuclear-data induced uncertainty. The sensitivity analysis and data assimilation technique presented in this paper exhibit one possible method of identifying and reducing the effective neutron multiplication factor nuclear-data induced uncertainty. The results presented in this work show that it is possible to use relative sensitivity coefficients of the prompt neutron decay constant and the effective delayed neutron fraction to 239 Pu nuclear data to reduce nuclear-data induced uncertainties in the effective neutron multiplication factor. This work has been utilized by members of the Los Alamos National Laboratory project EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) for optimally designing a new experiment, which will be used to reduce compensating errors in 239 Pu nuclear data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers

The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure–property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.

quantitative structure–property relationship (QSPR↗

NASA NTRS1990

Spectral synthesis in the ultraviolet. III - The spectral morphology of normal stars in the mid-ultraviolet

The morphology of 218 mid-UV spectra of stars ranging from O through K in spectral type is examined. Several new line and continuum indices are defined and their usefulness as temperature, luminosity, and metallicity discriminants is discussed. Mid-UV stellar continua are found to be markedly affected by abundance. A UV excess, delta(2600-V), is computed which is more sensitive by a factor of 10 to (Fe/H) than is delta(U-B). The relative strength of spectral lines in the mid-UV is not as strongly affected by abundance. Mid-UV spectra are much more sensitive to the temperature of the stellar population than to either metallicity or the dwarf/giant ratio. Mg II 2800 shows unexpected behavior, displaying no sensitivity to abundance for cool stars and a reversed sensitivity in FG dwarfs such that metal-poor stars have stronger Mg II strengths at the same temperature than more metal-rich stars.

Fanelli, Michael N.↗

NASA NTRS1982

Dipole-field sums and Lorentz factors for orthorhombic lattices, and implications for polarizable molecules

A method for computing the Lorentz tensor components in single crystals via rapidly convergent sums of Bessels functions is developed using the relationship between dipole-field sums and the tensor components. The Lorentz factors for simple, body-centered, and base-centered orthorhombic lattices are computed using this method, and the derivative Lorentz factors for simple orthorhombic lattices are also determined. Both the Lorentz factors and their derivatives are shown to be very sensitive to a lattice structure. The equivalent of the Clausius-Mossotti relation for general orthorhombic lattices is derived using the Lorentz-factor formalism, and the permanent molecular dipole moment is related to crystal polarization for the case of a ferroelectric of polarizable point dipoles. It is concluded that the polarization enhancement due to self-polarization familiar from classical theory may actually be a reduction in consequences of negative Lorentz factors in one or two lattice directions for noncubic crystals.

Purvis, C. K.↗

NASA NTRS1991

Acquisition and production of skilled behavior in dynamic decision-making tasks: Modeling strategic behavior in human-automation interaction: Why and aid can (and should) go unused

Advances in computer and control technology offer the opportunity for task-offload aiding in human-machine systems. A task-offload aid (e.g., an autopilot, an intelligent assistant) can be selectively engaged by the human operator to dynamically delegate tasks to an automated system. Successful design and performance prediction in such systems requires knowledge of the factors influencing the strategy the operator develops and uses for managing interaction with the task-offload aid. A model is presented that shows how such strategies can be predicted as a function of three task context properties (frequency and duration of secondary tasks and costs of delaying secondary tasks) and three aid design properties (aid engagement and disengagement times, aid performance relative to human performance). Sensitivity analysis indicates how each of these contextual and design factors affect the optimal aid aid usage strategy and attainable system performance. The model is applied to understanding human-automation interaction in laboratory experiments on human supervisory control behavior. The laboratory task allowed subjects freedom to determine strategies for using an autopilot in a dynamic, multi-task environment. Modeling results suggested that many subjects may indeed have been acting appropriately by not using the autopilot in the way its designers intended. Although autopilot function was technically sound, this aid was not designed with due regard to the overall task context in which it was placed. These results demonstrate the need for additional research on how people may strategically manage their own resources, as well as those provided by automation, in an effort to keep workload and performance at acceptable levels.

Kirlik, Alex↗

DOE OSTI2021

Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA) (Final Scientific/Technical Report, Subcontract Award No. 6953691)

The overarching objective the Baliga lab is to develop a mechanistic understanding of the field relevant metabolic processes underlying community partitioning of respiration pathways and physiological state shifts. In order to dissect how environmental perturbations influence microbial regulatory networks, we have constructed data-driven models that capture the dynamic interplay between abiotic and biotic factors during laboratory simulations. Specifically, we have developed multiple computational models (i.e., cMonkey2, EGRIN 2.0, and multiple genome-scale metabolic models) connecting environmental influence to genome-encoded regulatory programs. In addition, we have generated new tools such as Live Anaerobic Cell Sorting (LAnCS) and Fluidized Bed Reactors (FBRs) to investigate the dynamics of community partitioning of metabolic processes supporting sulfate or nitrate respiration, which are two critical activities observed from the field site at Oak Ridge National Labs (ORNL). This framework of connecting field phenomena with laboratory simulations and computational modeling has created the Environmental Simulations and Modelling (EnvSim) campaign within ENIGMA. The campaign, which spans multiple laboratories across ENIGMA and is led by the Baliga group, has established synthetic communities (SynComs) to discover, characterize, and dissect key microbial processes relevant to field observations, such as emissions of the greenhouse gas nitrous oxide (N 2 O). For example, the EnvSim campaign so far has deduced four potential mechanisms that may account for the N 2 O emissions at the FRC and are currently being investigated by labs across ENIGMA. Denitrification may be driven by complete denitrifiers, however, their NosZ enzymes, which catalyze the final step in denitrification by converting N 2 O to N 2 , may be sensitive at a lower pH. Additionally, the denitrification process could be partitioned among organisms and some may have pH-sensitive NosZ genes. Another factor contributing to variable N 2 O emissions relates to the metal co-factors involved in the denitrification pathway. For instance, excess Cu, Al, Mn, U, Ni, Co, Cu, and/or Cd may have inhibitory effects on multiple enzymatic steps during denitrification, while the enzymatic production of nitrite, the precursor of N 2 O, can be limited by the essential metal Mo. Lastly, abiotic production of N 2 O may occur as a result of chemodenitrification, in which metals like Fe, Mn, and some organic compounds can drive redox reactions that convert nitrogen cycle intermediates to N 2 O under the right conditions. Current investigations and analyses have generated multiple transcriptomic profiles aiding the refinement of new metabolic and gene regulatory network model for our denitrifying SynCom. In sum, the previous funding cycle has generated 33 peer-reviewed publications spanning predictive network biology to co-operativity of mutualistic interspecies interactions of evolved communities. Our integration of multiple omics datasets and the development of new network modelling tools has revealed important insights that can explain field observed phenomenon and generated new hypotheses to be investigated in the field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1950

Antiknock evaluation of hydrocarbons and ethers as aviation fuel components

The results of a NACA investigation conducted over a period of several years to evaluate the anti-knock characteristics of organic compounds are summarized. Included are data for 18 branched paraffins and olefins, 27 aromatics, and 22 ethers. The factors of performance investigated were blending characteristics, temperature sensitivity, lead response, and relation between molecular structure and antiknock ratings. Four engines were used.

Barnett, Henry C↗

DOE OSTI2024

CTA and SWGO can discover Higgsino dark matter annihilation

Thermal Higgsino dark matter (DM), with a mass near 1.1 TeV, is one of the most well-motivated and untested DM candidates. Leveraging recent hydrodynamic cosmological simulations that give DM density profiles in Milky Way analog galaxies we show that the linelike gamma-ray signal predicted from Higgsino annihilation in the Galactic Center could be detected at high significance with the upcoming Cherenkov Telescope Array (CTA) and Southern Wide-field Gamma-ray Observatory (SWGO) for all but the most pessimistic DM profiles. We perform the most sensitive search to-date for the linelike signal using 15 years of data from the Fermi Large Area Telescope, coming within an order one factor of the necessary sensitivity to detect the Higgsino for some Milky Way analog DM density profiles. We show that H.E.S.S. has subleading sensitivity relative to Fermi for the Higgsino at present. In contrast, we analyze H.E.S.S. inner Galaxy data for the thermal wino model with a mass near 2.8 TeV; we find no evidence for a DM signal and exclude the wino by over a factor of two in cross section for all DM profiles considered. In the process, we identify and attempt to correct what appears to be an inconsistency in previous H.E.S.S. inner Galaxy analyses for DM annihilation related to the analysis effective area, which may weaken the DM cross-section sensitivity claimed in those works by around an order of magnitude.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

The Prediction Model of Risk Factors for COVID-19 Developing into Severe Illness Based on 1046 Patients with COVID-19

This study analyzed the risk factors for patients with COVID-19 developing severe illnesses and explored the value of applying the logistic model combined with ROC curve analysis to predict the risk of severe illnesses at COVID-19 patients’ admissions. The clinical data of 1046 COVID-19 patients admitted to a designated hospital in a certain city from July to September 2020 were retrospectively analyzed, the clinical characteristics of the patients were collected, and a multivariate unconditional logistic regression analysis was used to determine the risk factors for severe illnesses in COVID-19 patients during hospitalization. Based on the analysis results, a prediction model for severe conditions and the ROC curve were constructed, and the predictive value of the model was assessed. Logistic regression analysis showed that age (OR = 3.257, 95% CI 10.466–18.584), complications with chronic obstructive pulmonary disease (OR = 7.337, 95% CI 0.227–87.021), cough (OR = 5517, 95% CI 0.258–65.024), and venous thrombosis (OR = 7322, 95% CI 0.278–95.020) were risk factors for COVID-19 patients developing severe conditions during hospitalization. When complications were not taken into consideration, COVID-19 patients’ ages, number of diseases, and underlying diseases were risk factors influencing the development of severe illnesses. The ROC curve analysis results showed that the AUC that predicted the severity of COVID-19 patients at admission was 0.943, the optimal threshold was −3.24, and the specificity was 0.824, while the sensitivity was 0.827. The changes in the condition of severe COVID-19 patients are related to many factors such as age, clinical symptoms, and underlying diseases. This study has a certain value in predicting COVID-19 patients that develop from mild to severe conditions, and this prediction model is a useful tool in the quick prediction of the changes in patients’ conditions and providing early intervention for those with risk factors.

Lian, Zhichuang↗