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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Identification of Metal Stresses in Arabidopsis thaliana Using Hyperspectral Reflectance Imaging

Industrial accidents, such as the Fukushima and Chernobyl disasters, release harmful chemicals into the environment, covering large geographical areas. Natural flora may serve as biological sensors for detecting metal contamination, such as cesium. Spectral detection of plant stresses typically employs a few select wavelengths and often cannot distinguish between different stress phenotypes. In this study, we apply hyperspectral reflectance imaging in the visible and near-infrared along with multivariate curve resolution (MCR) analysis to identify unique spectral signatures of three stresses in Arabidopsis thaliana : salt, copper, and cesium. While all stress conditions result in common stress physiology, hyperspectral reflectance imaging and MCR analysis produced unique spectral signatures that enabled classification of each stress. As the level of potassium was previously shown to affect cesium stress in plants, the response of A. thaliana to cesium stress under variable levels of potassium was also investigated. Increased levels of potassium reduced the spectral response of A. thaliana to cesium and prevented changes to chloroplast cellular organization. While metal stress mechanisms may vary under different environmental conditions, this study demonstrates that hyperspectral reflectance imaging with MCR analysis can distinguish metal stress phenotypes, providing the potential to detect metal contamination across large geographical areas.

multivariate curve resolution↗
DOE OSTI2017

Toward Development of a Stochastic Wake Model: Validation Using LES and Turbine Loads

Wind turbines within an array do not experience free-stream undisturbed flow fields. Rather, the flow fields on internal turbines are influenced by wakes generated by upwind unit and exhibit different dynamic characteristics relative to the free stream. The International Electrotechnical Commission (IEC) standard 61400-1 for the design of wind turbines only considers a deterministic wake model for the design of a wind plant. This study is focused on the development of a stochastic model for waked wind fields. First, high-fidelity physics-based waked wind velocity fields are generated using Large-Eddy Simulation (LES). Stochastic characteristics of these LES waked wind velocity field, including mean and turbulence components, are analyzed. Wake-related mean and turbulence field-related parameters are then estimated for use with a stochastic model, using Multivariate Multiple Linear Regression (MMLR) with the LES data. To validate the simulated wind fields based on the stochastic model, wind turbine tower and blade loads are generated using aeroelastic simulation for utility-scale wind turbine models and compared with those based directly on the LES inflow. The study’s overall objective is to offer efficient and validated stochastic approaches that are computationally tractable for assessing the performance and loads of turbines operating in wakes.

17 WIND ENERGY↗
DOE OSTI2021

Data Mining of Polymer Phase Transitions upon Temperature Changes by Small and Wide-Angle X-ray Scattering Combined with Raman Spectroscopy

The complex physical transformations of polymers upon external thermodynamic changes are related to the molecular length of the polymer and its associated multifaceted energetic balance. The understanding of subtle transitions or multistep phase transformation requires real-time phenomenological studies using a multi-technique approach that covers several length-scales and chemical states. A combination of X-ray scattering techniques with Raman spectroscopy and Differential Scanning Calorimetry was conducted to correlate the structural changes from the conformational chain to the polymer crystal and mesoscale organization. Current research applications and the experimental combination of Raman spectroscopy with simultaneous SAXS/WAXS measurements coupled to a DSC is discussed. In particular, we show that in order to obtain the maximum benefit from simultaneously obtained high-quality data sets from different techniques, one should look beyond traditional analysis techniques and instead apply multivariate analysis. Data mining strategies can be applied to develop methods to control polymer processing in an industrial context. Crystallization studies of a PVDF blend with a fluoroelastomer, known to feature complex phase transitions, were used to validate the combined approach and further analyzed by MVA.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation

The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m –2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Measurement of CH{sub 3}D on Titan at Submillimeter Wavelengths

We present the first radio/submillimeter detection of monodeuterated methane (CH{sub 3}D) in Titan’s atmosphere, using archival data from of the Atacama Large Millimeter/submillimeter Array (ALMA). The J {sub K} = 2{sub 1}−1{sub 1} and J {sub K} = 2{sub 0}−1{sub 0} transitions at 465.235 and 465.250 GHz (∼0.644 mm) were measured at significance levels of 4.6σ and 5.7σ, respectively. These two lines were modeled using the Non-linear optimal Estimator for MultivariatE spectral analySIS (NEMESIS) radiative transfer code to determine the disk-averaged CH{sub 3}D volume mixing ratio = 6.157 × 10{sup −6} in Titan’s stratosphere (at altitudes >130 km). By comparison with the CH{sub 4} vertical abundance profile measured by Cassini–Huygens mass spectrometry, the resulting value for D/H in CH{sub 4} is (1.033 ± 0.081) × 10{sup −4}. This is consistent with previous ground-based and in situ measurements from the Cassini–Huygens mission, though slightly lower than the average of the previous values. Additional CH{sub 3}D observations at higher spatial resolution will be required to determine a value truly comparable with the Cassini–Huygens CH{sub 4} measurements, by measuring CH{sub 3}D with ALMA close to Titan’s equator. In the post-Cassini era, spatially resolved observations of CH{sub 3}D with ALMA will enable the latitudinal distribution of methane to be determined, making this an important molecule for further studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Modeling Stochastic Variability in Multiband Time-series Data

In preparation for the era of time-domain astronomy with upcoming large-scale surveys, we propose a state-space representation of a multivariate damped random walk process as a tool to analyze irregularly-spaced multifilter light curves with heteroscedastic measurement errors. We adopt a computationally efficient and scalable Kalman filtering approach to evaluate the likelihood function, leading to maximum O(k 3 n) complexity, where k is the number of available bands and n is the number of unique observation times across the k bands. This is a significant computational advantage over a commonly used univariate Gaussian process that can stack up all multiband light curves in one vector with maximum O(k 3 n 3 ) complexity. Using such efficient likelihood computation, we provide both maximum likelihood estimates and Bayesian posterior samples of the model parameters. Three numerical illustrations are presented: (i) analyzing simulated five-band light curves for a comparison with independent single-band fits; (ii) analyzing five-band light curves of a quasar obtained from the Sloan Digital Sky Survey Stripe 82 to estimate short-term variability and timescale; (iii) analyzing gravitationally lensed g- and r-band light curves of Q0957+561 to infer the time delay. Two R packages, Rdrw and timedelay, are publicly available to fit the proposed models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Representativeness-based Sampling Network Design for the State of Alaska

This data set collection consists of data products described in Hoffman et. al., 2013. Resource and logistical constraints limit the frequency and extent of environmental observations, particularly in the Arctic, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent environmental variability at desired scales. A quantitative methodology for stratifying sampling domains, informing site selection, and determining the representativeness of measurement sites and networks is described here. Multivariate spatiotemporal clustering was applied to down-scaled general circulation model results and data for the State of Alaska at 4 km2 resolution to define multiple sets of ecoregions across two decadal time periods. Maps of ecoregions for the present (2000-2009) and future (2090-2099) were produced, showing how combinations of 37 characteristics are distributed and how they may shift in the future. Representative sampling locations are identified on present and future ecoregion maps. A representativeness metric was developed, and representativeness maps for eight candidate sampling locations were produced. This metric was used to characterize the environmental similarity of each site. This analysis provides model-inspired insights into optimal sampling strategies, offers a framework for up-scaling measurements, and provides a down-scaling approach for integration of models and measurements. These techniques can be applied at different spatial and temporal scales to meet the needs of individual measurement campaigns. This dataset contains one zipped file, one .txt file, and one .sh file.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Sensitivity Analysis and Emulation for Functional Data using Bayesian Adaptive Splines

When a computer code is used to simulate a complex system, one of the fundamental tasks is to assess the sensitivity of the simulator to the different input parameters. In the case of computationally expensive simulators, this is often accomplished via a surrogate statistical model, a statistical output emulator. An effective emulator is one that provides good approximations to the computer code output for wide ranges of input values. In addition, an emulator should be able to handle large dimensional simulation output for a relevant number of inputs; it should flexibly capture heterogeneities in the variability of the response surface; it should be fast to evaluate for arbitrary combinations of input parameters, and it should provide an accurate quantification of the emulation uncertainty. In this paper we discuss the Bayesian approach to multivariate adaptive regression splines (BMARS) as an emulator for a computer model that outputs curves. We introduce modifications to traditional BMARS approaches that allow for fitting large amounts of data and allow for more efficient MCMC sampling. We emphasize the ease with which sensitivity analysis can be performed in this situation. We present a sensitivity analysis of a computer model of the deformation of a protective plate used in pressure-driven experiments. Our example serves as an illustration of the ability of BMARS emulators to fulfill all the necessities of computability, flexibility and reliable calculation on relevant measures of sensitivity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Immunohistochemical analysis of cyclin A expression in Wilms tumor

Background: Cyclin A overexpression is found in a variety of human tumors and correlates with unfavorable outcome. We analyzed immunohistochemical expression of cyclin A in Wilms tumor (WT) in relation to clinicopathological characteristics, preoperative chemotherapy (PrOpChTh), and overall survival (OS). Methods: This retrospective study involved 43 patients who underwent nephrectomy from January 1996 to October 2010. Tumor stage and histological subtype were determined by revised Societé International d’Oncologie Pediatrique protocol, based on histological components/alterations caused by PrOpChTh, within the prognostic group of low, intermediate and high risk, and with criteria for anaplasia. The regressive/necrotic changes in total tumor mass of primary tumor and the proportion of epithelial, blastemal, and stromal components in the remaining viable tumor tissue were also determined. Cyclin A expression was evaluated by immunohistochemistry using a polyclonal rabbit, antihuman antibody (H-432). Results: Cyclin A overexpression was found in 34.3% of WTs, with higher frequency in tumors with epithelial (31.3%) and blastemal (37.1%) components than those with stromal component (17.7%). Regarding histological type, cyclin A overexpression was found most often in focal anaplasia (100%), stromal (60%), and diffuse anaplastic (66.7) WTs. The overexpression was also more frequent in stages 3 and 4 (77.8% and 66.7%, respectively) compared to tumors in stages 1 and 2 (13.3% and 12.5%, respectively; p = 0.004) in all components, as well as in blastemal component in stages 3 and 4 (77.8% and 66.7%, respectively) vs. stages 1 and 2 (13.3% and 25%, respectively, p = 0.009). Cyclin A overexpression in all components was 66.7% in WTs with metastasis and 31.3% in WTs without metastasis (p = 0.265, Fisher test). Log-rank testing revealed differences of OS regarding stage (p = 0.000), prognostic groups (p = 0.001), and cyclin A expression in blastemal component (p = 0.025). After univariate analysis, tumor stage (p = 0.001), prognostic group (p = 0.004), and cyclin A expression in blastemal component (p = 0.042) were significant prognostic factors for OS; however, after multivariate analysis, none of these factors were confirmed as independent predictors of survival. Discussion: This study showed that cyclin A overexpression might be associated with the development and progression of WT with anaplasia. Also, cyclin A overexpression was more often observed in advanced stages (3 and 4) of WT, in the group of high-risk WTs, and in focal and diffuse anaplasia WTs. There was no relation of cyclin A overexpression and metastatic ability of WT. Although this study has not confirmed the prognostic value of cyclin A overexpression, its association with unfavorable prognosis should be further evaluated. Copyright 2019 Radojevic-Škodric et al.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2001

Noninvasive assessment of mitral inertness: clinical results with numerical model validation

Inertial forces (Mdv/dt) are a significant component of transmitral flow, but cannot be measured with Doppler echo. We validated a method of estimating Mdv/dt. Ten patients had a dual sensor transmitral (TM) catheter placed during cardiac surgery. Doppler and 2D echo was performed while acquiring LA and LV pressures. Mdv/dt was determined from the Bernoulli equation using Doppler velocities and TM gradients. Results were compared with numerical modeling. TM gradients (range: 1.04-14.24 mmHg) consisted of 74.0 +/- 11.0% inertial forcers (range: 0.6-12.9 mmHg). Multivariate analysis predicted Mdv/dt = -4.171(S/D (RATIO)) + 0.063(LAvolume-max) + 5. Using this equation, a strong relationship was obtained for the clinical dataset (y=0.98x - 0.045, r=0.90) and the results of numerical modeling (y=0.96x - 0.16, r=0.84). TM gradients are mainly inertial and, as validated by modeling, can be estimated with echocardiography.

NASA Discipline Cardiopulmonary↗
NASA NTRS2002

Survival after aortic valve replacement for severe aortic stenosis with low transvalvular gradients and severe left ventricular dysfunction

OBJECTIVE: We sought to assess whether aortic valve replacement (AVR) among patients with severe aortic stenosis (AS), severe left ventricular (LV) dysfunction and a low transvalvular gradient (TVG) is associated with improved survival. BACKGROUND: The optimal management of patients with severe AS with severe LV dysfunction and a low TVG remains controversial. METHODS: Between 1990 and 1998, we evaluated 68 patients who underwent AVR at our institution (AVR group) and 89 patients who did not undergo AVR (control group), with an aortic valve area < or = 0.75 cm(2), LV ejection fraction < or = 35% and mean gradient < or = 30 mm Hg. Using propensity analysis, survival was compared between a cohort of 39 patients in the AVR group and 56 patients in the control group. RESULTS: Despite well-matched baseline characteristics among propensity-matched patients, the one- and four-year survival rates were markedly improved in patients in the AVR group (82% and 78%), as compared with patients in the control group (41% and 15%; p < 0.0001). By multivariable analysis, the main predictor of improved survival was AVR (adjusted risk ratio 0.19, 95% confidence interval 0.09 to 0.39; p < 0.0001). The only other predictors of mortality were age and the serum creatinine level. CONCLUSIONS: Among select patients with severe AS, severe LV dysfunction and a low TVG, AVR was associated with significantly improved survival.

NASA Discipline Cardiopulmonary↗
NASA NTRS1997

Chemical and physical gradients along the OMC-1 ridge

We present a survey of the distribution of 20 chemical and isotopic molecular species along the central ridge of the Orion molecular cloud from 6' north to 6' south of BN-KL observed with the QUARRY focal plane array on the FCRAO 14 m telescope, which provides an angular resolution of approximately 50" in the 3 mm wavelength region. We use standard tools of multivariate analysis for a systematic investigation of the similarities and differences among the maps of integrated intensities of the 32 lines observed. The maps fall in three broad classes: first, those strongly peaked toward BN-KL; second, those having rather flat distributions along the ridge; and third, those with a clear north-south gradient or contrast. We identify six positions or regions where we calculate relative abundances. Line velocities and line widths indicate that the optically thin lines generally trace the same volume of dense gas, except in the molecular bar, where C18O, C34S, H13CO+, CN, C2H, SO, and C3H2 have velocities characteristic of the bar itself, whereas the emission from other detected species is dominated by the background cloud. The strongest abundance variations in our data are the well-known enhancements seen in HCN, CH3OH, HC3N, and SO toward BN-KL and, less strongly, toward the Orion-South outflow 1'.3S. The principal result of this study is that along the extended quiescent ridge the chemical abundances, within factors of 3-4, exhibit an impressive degree of uniformity. The northern part of the ridge has a chemistry closest to that found in quiescent dense clouds. While temperature and density are similar around the northern radical-ion peak near 3'.5N and in the southern core near 4'.2S, some abundances, in particular, those of the ions HCO+ and N2H+, are significantly lower toward 4'.2S. The areas near 4.'2S and the molecular bar itself around (1'.7E, 2'.4S) stand out with peculiar and similar properties probably caused by stronger UV fields penetrating deeper into the clumpy molecular gas. This leads to higher electron abundances and thereby reduced abundances of the ions, as well as a lack of complex molecules.

NASA Discipline Exobiology↗
DOE OSTI2015

Hybrid Statistical Testing for Nuclear Material Accounting Data and/or Process Monitoring Data

The two tests employed in the hybrid testing scheme are Page’s cumulative sums for all streams within a Balance Period (maximum of the maximums and average of the maximums) and Crosier’s multivariate cumulative sum applied to incremental cumulative sums across Balance Periods. The role of residuals for both kinds of data is discussed.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Data-Driven Security Assessment of Power Grids Based on Machine Learning Approach: Preprint

Data-driven security assessment provides key indicators on power system stability using simulations on scheduling models, as opposed to dynamic simulations that are more time-consuming. This paper investigates data-driven security assessment of power grids based on machine learning. Multivariate random forest regression is used as the machine learning algorithm due to its high robustness to the input data. Three stability issues are analyzed using the proposed machine learning tool, including transient stability, frequency stability and small signal stability. The estimation values from machine learning tool are compared with those from dynamic simulations. Results show that the proposed machine learning tool can effectively predict the stability margins for the three stability metrics.

14 SOLAR ENERGY↗
DOE OSTI2013

Physics-based dimension reduction in uncertainty quantification for radiative transfer

We present a physics-based methodology for quantifying the uncertainty in a given quantity of interest (QOI) that is contributed by uncertainties in opacities in radiation transport problems. Typically, opacities are tabulated as a function of density, temperature, and photon energy group. The size of this table makes a study of uncertainties at this level challenging because of the well-known 'curse of dimensionality.' We address this by studying uncertain parameters in the underlying physical model that generates the opacity tables. At this level, there are fewer uncertain parameters but still too many to analyze directly through computationally expensive radiation transport simulations. In order to explore this large uncertain parameter space, we develop two simplified radiation transport problems that are much less computationally demanding than the target problem of interest. An emulator is created for each QOI for each simplified problem using Bayesian Multivariate Adaptive Regression Splines (BMARS). This emulator is used to create a functional relationship between the QOIs and the uncertain parameters. Sensitivity analysis is performed using the emulator to determine which parameters contribute significantly to the uncertainty. This physics-based screening process reduces the dimension of the parameter space that is then studied via the computationally expensive radiation transport calculation to generate distributions of quantities of interest. Results of this research demonstrate that the QOIs for the target problem agree for varying screening criteria determined by the sensitivity analysis, and the QOIs agree well for varying Latin Hypercube Design (LHD) sample sizes for the uncertain space. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Spectroscopic Online Monitoring for Process Control and Safeguarding of Radiochemical Fuel Reprocessing Streams - 13553

There is a renewed interest worldwide to promote the use of nuclear power and close the nuclear fuel cycle. The long term successful use of nuclear power is critically dependent upon adequate and safe processing and disposition of the used nuclear fuel. Liquid-liquid extraction is a separation technique commonly employed for the processing of the dissolved spent nuclear fuel. The instrumentation used to monitor these processes must be robust, require little or no maintenance, and be able to withstand harsh environments such as high radiation fields and aggressive chemical matrices. This paper discusses application of absorption and vibrational spectroscopic techniques supplemented by physicochemical measurements for radiochemical process monitoring. In this context, our team experimentally assessed the potential of Raman and spectrophotometric techniques for on-line real-time monitoring of the U(VI)/nitrate ion/nitric acid and Pu(IV)/Np(V)/Nd(III), respectively, in solutions relevant to spent fuel reprocessing. Both techniques demonstrated robust performance in the repetitive batch measurements of each analyte in a wide concentration range using simulant and commercial dissolved spent fuel solutions. Static spectroscopic measurements served as training sets for the multivariate data analysis to obtain partial least squares predictive models, which were validated using on-line centrifugal contactor extraction tests. Satisfactory prediction of the analytes concentrations in these preliminary experiments warrants further development of the spectroscopy-based methods for radiochemical safeguards and process control. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Vibrational spectroscopy for online monitoring of extraction solvent degradation products

In our research, we are exploring the potential of online monitoring of the organic solvents for the flowsheets relevant to the used nuclear fuel reprocessing and tributyl phosphate (TBP)- based extraction processes in particular. Utilization of vibrational spectroscopic techniques permits the discrimination of the degradation products from the primary constituents of the loaded extraction solvent. Multivariate analysis of the spectral data facilitates development of the regression models for their quantification in real time and potentially enables online implementation of a monitoring system. Raman and FTIR spectral databases were created and used to develop the regression partial least squares (PLS) chemometric models for the quantitative prediction of HDBP (dibutyl phosphoric acid) degradation product, TBP, and UO{sub 2}{sup 2+} extraction organic product phase. It was demonstrated that both these spectroscopic techniques are suitable for the quantification of the Purex solvent components in the presence of UO{sub 2}(NO{sub 3}){sub 2}. Developed PLS models successfully predicted HDBP and TBP organic concentrations in simulated Purex solutions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗