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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process

As water treatment technology has improved, the amount of available process data has substantially increased, making real-time, data-driven fault detection a reality. One shortcoming of the fault detection literature is that methods are usually evaluated by comparing their performance on hand-picked, short-term case studies, which yields no insight into long-term performance. In this work, we first evaluate multiple statistical and machine learning approaches for detrending process data. Then, we evaluate the performance of a PCA-based fault detection approach, applied to the detrended data, to monitor influent water quality, filtrate quality, and membrane fouling of an ultrafiltration membrane system for indirect potable reuse. Based on two short case studies, the adaptive lasso detrending method is selected, and the performance of the multivariate approach is evaluated over more than a year. The method is tested for different sets of three critical tuning parameters, and we find that for long-term, autonomous monitoring to be successful, these parameters should be carefully evaluated. However, in comparison with industry standards of simpler, univariate monitoring or daily pressure decay tests, multivariate monitoring produces substantial benefits in long-term testing.

ammonia↗
NASA NTRS1984

Prediction of the space adaptation syndrome

The univariate and multivariate relationships of provocative measures used to produce motion sickness symptoms were described. Normative subjects were used to develop and cross-validate sets of linear equations that optimally predict motion sickness in parabolic flights. The possibility of reducing the number of measurements required for prediction was assessed. After describing the variables verbally and statistically for 159 subjects, a factor analysis of 27 variables was completed to improve understanding of the relationships between variables and to reduce the number of measures for prediction purposes. The results of this analysis show that none of variables are significantly related to the responses to parabolic flights. A set of variables was selected to predict responses to KC-135 flights. A series of discriminant analyses were completed. Results indicate that low, moderate, or severe susceptibility could be correctly predicted 64 percent and 53 percent of the time on original and cross-validation samples, respectively. Both the factor analysis and the discriminant analysis provided no basis for reducing the number of tests.

Reschke, M. F.↗
NASA NTRS2010

The Response of African Land Surface Phenology to Large Scale Climate Oscillations

Variations in agricultural production due to rainfall and temperature fluctuations are a primary cause of food insecurity on the African continent. Analysis of changes in phenology can provide quantitative information on the effect of climate variability on growing seasons in agricultural regions. Using a robust statistical methodology, we describe the relationship between phenology metrics derived from the 26 year AVHRR NDVI record and the North Atlantic Oscillation index (NAO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO), and the Multivariate ENSO Index (MEI). We map the most significant positive and negative correlation for the four climate indices in Eastern, Western and Southern Africa between two phenological metrics and the climate indices. Our objective is to provide evidence of whether climate variability captured in the four indices has had a significant impact on the vegetative productivity of Africa during the past quarter century. We found that the start of season and cumulative NDVI were significantly affected by large scale variations in climate. The particular climate index and the timing showing highest correlation depended heavily on the region examined. In Western Africa the cumulative NDVI correlates with PDO in September-November. In Eastern Africa the start of the June-October season strongly correlates with PDO in March-May, while the PDO in December-February correlates with the start of the February-June season. The cumulative NDVI over this last season relates to the MEI of March-May. For Southern Africa, high correlations exist between SOS and NAO of September-November, and cumulative NDVI and MEI of March-May. The research shows that climate indices can be used to anticipate late start and variable vigor in the growing season of sensitive agricultural regions in Africa.

Brown, Molly E.↗
DOE OSTI2023

Concordance

Dimension reduction techniques have long been an important topic in statistics, and active subspaces (AS) have received much attention this past decade in the computer experiments literature. The most common approach towards estimating the AS is to use Monte Carlo with numerical gradient evaluation. This software takes a new approach towards estimating the active subspace, in which Multivariate Adaptive Regression Splines (MARS) is used as a surrogate model, and relevant calculation are obtained analytically. Using a MARS surrogate has many advantages including improved scaling, better estimation of active subspaces in high dimensions and the ability to handle a large number of prior distributions in closed form. This software is also capable of conducting a so-called "concordance analysis", using a generalization of ASs which we refer to as "Co-Active" subspaces.

Rumsey, Kelin↗
NASA NTRS1982

The McMillan and Newton polygons of a feedback system and the construction of root loci

The local behaviour of root loci around zeros and poles is investigated. This is done by relating the Newton diagrams which arise in the local analysis to the McMillan structure of the open-loop system, by means of what we shall call the McMillan polygon. This geometric construct serves to clarify the precise relationship between the McMillan structure, the principal structure, and the branching patterns of the root loci. In addition, several rules are obtained which are useful in the construction of the root loci of multivariable control systems.

Byrnes, C. I.↗
DOE OSTI2024

Regularized Differentiation for Bioburden Density Estimation in Planetary Protection

In this paper, we propose and investigate the performance of two novel shrinkage estimators for bioburden density estimation in planetary protection. The estimators are based on the regularized differentiation of a cumulative count of colony forming units collected throughout the data collecting session or the life cycle of the entire mission. The regularized differentiation recasts the problem of bioburden density estimation as a linear least squares problem. The least squares problem is then solved through regularization techniques, such as truncated singular value decomposition and penalized least squares. The regularization is necessary to avoid noise amplification during the differentiation of noisy data. The two regularization estimators are compared with four other commonly used estimators to simultaneously evaluate the means of multivariable independent Poisson distributions: the maximum likelihood, noninformative Bayes estimator with Jeffreys prior, Empirical Bayes using conjugate gamma-Poisson model with gamma parameters selected by method of moments, and the Clevenson-Zidek estimator. It is shown through computer-simulated data that the regularized differentiation based on ridge regression has the smallest mean-squared error among all estimators. The analysis of shrinkage mechanism implemented by regularized differentiation is performed, and it is shown that the regularized differentiation amounts to performing a weighted averaging of all the samples. The weights are determined by the regularization parameter automatically selected by the L-curve technique. Since the method of least squares makes no distributional assumptions about the data, it presents an attractive technique for bioburden density estimation when there are concerns about the misspecification of the distributional model. The paper concludes with the analysis of the bioburden data collected during InSight mission and directions for future work.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series

Abstract In this article we propose an extension of singular spectrum analysis for interval‐valued time series. The proposed methods can be used to decompose and forecast the dynamics governing a set‐valued stochastic process. The resulting components on which the interval time series is decomposed can be understood as interval trendlines, cycles, or noise. Forecasting can be conducted through a linear recurrent method, and we devised generalizations of the decomposition method for the multivariate setting. The performance of the proposed methods is showcased in a simulation study. We apply the proposed methods so to track the dynamics governing the Argentina Stock Market (MERVAL) in real time, in a case study over a period of turbulence that led to discussions of the government of Argentina with the International Monetary Fund.

de Carvalho, Miguel↗
DOE OSTI2026

Measurement of the top quark pair production cross section in PbPb collisions at $\sqrt{s_{\mathrm{NN}}}=5.36$ TeV

The inclusive cross section for top quark pair ($\mathrm{t}\overline{\mathrm{t}}$) production in lead-lead (PbPb) collisions is reported for the first time at a center-of-mass energy per nucleon pair of 5.36 TeV. The analysis uses data corresponding to an integrated luminosity of 1.58 nb −1 collected by the CMS experiment at the CERN LHC in 2023. The $\mathrm{t}\overline{\mathrm{t}}$ production cross section, ${\sigma}_{\mathrm{t}\overline{\mathrm{t}}}={3.42}_{-0.51}^{+0.54}{\left(\mathrm{stat}\right)}_{-0.43}^{+0.50}\left(\mathrm{syst}\right)$ μb, is measured in dilepton final states using a fit to a multivariate discriminator that combines the decay electron and muon kinematic properties with the multiplicity of bottom quark jets. The result is consistent with perturbative quantum chromodynamics calculations at next-to-next-to-leading order (NNLO) accuracy employing several nuclear parton distribution functions. In addition, the Drell–Yan production cross section (σ DY ) for dilepton masses above 10 GeV and the ratio of $\mathrm{t}\overline{\mathrm{t}}$ to DY cross sections $\left({R}_{\mathrm{t}\overline{\mathrm{t}}/\mathrm{DY}}\right)$ are found to be compatible with the NNLO predictions. The observables ${\sigma}_{\mathrm{t}\overline{\mathrm{t}}}$, σ DY , and ${R}_{\mathrm{t}\overline{\mathrm{t}}/\mathrm{DY}}$ are measured separately for central and semicentral PbPb collisions to investigate for the first time the dependence of top quark production on the collision impact parameter.

Heavy Ion Experiments↗
DOE OSTI2025

Search for the production of a Higgs boson in association with a single top quark in pp collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

A search for the production of a Higgs boson in association with a single top quark, tH, is presented. The analysis uses proton-proton collision data corresponding to an integrated luminosity of 140 fb −1 at a centre-of-mass energy of 13 TeV, collected by the ATLAS detector at the LHC. The search targets Higgs-boson decays into $b\bar{b}$, WW * , ZZ * , and ττ, accompanied by an isolated lepton (electron or muon) from the top-quark decay. Multivariate techniques are employed to enhance the separation between signal and background processes. The observed signal strength, μ tH , defined as the ratio between the measured cross-section and the predicted Standard Model value, is μ tH = 8.1 ± 2.6 (stat.) ± 2.0 (syst.). The significance of the observed (expected) signal above the background-only expectation is 2.8 (0.4) standard deviations. The corresponding observed (expected) upper limit at the 95% confidence level on the tH cross-section is found to be 13.9 (6.1) times the value predicted by the Standard Model. An interpretation with an inverted sign of the top-quark Yukawa coupling is performed, and the signal strength and corresponding limit are reported.

Hadron-Hadron Scattering↗
DOE OSTI2025

Sensitive detection of structural dynamics using a statistical framework for comparative crystallography

Chemical and conformational changes are crucial to protein function and its pharmacological control. X-ray crystallography can reveal these changes in atomic detail, but standard analysis methods, which refine separate datasets, often overlook differences that are subtle or arise in only a subset of molecules. Direct comparison of crystallographic datasets is, in principle, more powerful, but systematic errors (“scales”) often mask changes in the crystallographic observables (“structure factors”). Machine learning algorithms that jointly estimate scales and structure factors can address this limitation. Here, we augment this approach with multivariate, structured priors derived from crystallographic theory, implemented in the variational deep learning framework Careless. Doing so strongly improves the detection of protein dynamics, element-specific anomalous signals, and the binding of drug candidates, offering a robust approach to comparative crystallography and, potentially, to detection of protein dynamics by other structure determination methods.

Hekstra, Doeke R. [Harvard Univ., Cambridge, MA (U↗
DOE OSTI2023

Contraceptive Sabotage and Contraceptive Use at the Time of Pregnancy: An Analysis of People with a Recent Live Birth in the United States

Contraceptive sabotage and other forms of intimate partner violence (IPV) can interfere with contraceptive use. We used 2012 to 2015 Pregnancy Risk Assessment Monitoring System data from 8,981 people residing in five states who reported that when they became pregnant, they were not trying to get pregnant. We assessed the relationships between ever experiencing contraceptive sabotage and physical IPV 12 months before pregnancy (both by the current partner) and contraceptive use at the time of pregnancy using multivariable logistic regression. We also assessed the joint associations between physical IPV 12 months before pregnancy and ever experienced contraceptive sabotage with contraceptive use at the time of pregnancy. Few people ever experienced contraceptive sabotage (1.8%; 95% confidence interval [CI]: 1.4, 2.3) or physical IPV 12 months before pregnancy (2.8%; 95% CI: 2.3, 3.3). In models adjusted for age, race/ethnicity, marital status, education, and state of residence, ever experiencing contraceptive sabotage was associated with contraceptive use at the time of pregnancy (adjusted odds ratio [aOR]: 1.73; 95% CI: 1.06, 2.82), but not with physical IPV 12 months before pregnancy (aOR: 0.69; 95% CI: 0.46, 1.02). When examining the joint association, compared to not ever experiencing contraceptive sabotage or physical IPV 12 months before pregnancy, ever experiencing contraceptive sabotage was significantly related to contraceptive use at the time of pregnancy (aOR: 1.72; 95% CI: 1.00, 2.95). However, it was not associated with experiencing physical IPV 12 months before pregnancy (aOR: 0.68; 95% CI: 0.45, 1.04) or with experiencing both contraceptive sabotage and physical IPV 12 months before pregnancy (aOR: 1.21; 95% CI: 0.42, 3.50), compared to not ever experiencing contraceptive sabotage or physical IPV 12 months before pregnancy. Our study highlights that current partner contraceptive sabotage may motivate those not trying to get pregnant to use contraception; however, all people in our sample still experienced a pregnancy.

Huber-Krum, Sarah↗
NASA NTRS1992

Visualization of second order tensor fields and matrix data

We present a study of the visualization of 3-D second order tensor fields and matrix data. The general problem of visualizing unsymmetric real or complex Hermitian second order tensor fields can be reduced to the simultaneous visualization of a real and symmetric second order tensor field and a real vector field. As opposed to the discrete iconic techniques commonly used in multivariate data visualization, the emphasis is on exploiting the mathematical properties of tensor fields in order to facilitate their visualization and to produce a continuous representation of the data. We focus on interactively sensing and exploring real and symmetric second order tensor data by generalizing the vector notion of streamline to the tensor concept of hyperstreamline. We stress the importance of a structural analysis of the data field analogous to the techniques of vector field topology extraction in order to obtain a unique and objective representation of second order tensor fields.

Delmarcelle, Thierry↗
DOE OSTI2022

Predictability and empirical dynamics of fisheries time series in the North Pacific

Previous studies have documented a strong relationship between marine ecosystems and large-scale modes of sea surface height (SSH) and sea surface temperature (SST) variability in the North Pacific such as the Pacific Decadal Oscillation and the North Pacific Gyre Oscillation. In the central and western North Pacific along the Kuroshio-Oyashio Extension (KOE), the expression of these modes in SSH and SST is linked to the propagation of long oceanic Rossby waves, which extend the predictability of the climate system to ~3 years. Using a multivariate physical-biological linear inverse model (LIM) we explore the extent to which this physical predictability leads to multi-year prediction of dominant fishery indicators inferred from three datasets (i.e., estimated biomasses, landings, and catches). We find that despite the strong autocorrelation in the fish indicators, the LIM adds dynamical forecast skill beyond persistence up to 5-6 years. By performing a sensitivity analysis of the LIM forecast model, we find that two main factors are essential for extending the dynamical predictability of the fishery indicators beyond persistence. The first is the interaction of the fishery indicators with the SST/SSH of the North and tropical Pacific. The second is the empirical relationship among the fisheries time series. This latter component reflects stock-stock interactions as well as common technological and human socioeconomic factors that may influence multiple fisheries and are captured in the training of the LIM. These results suggest that empirical dynamical models and machine learning algorithms, such as the LIM, provide an alternative and promising approach for forecasting key ecological indicators beyond the skill of persistence.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1983

Vegetation classification based on Advanced Very High Resolution Radiometer /AVHRR/ satellite imagery

Data from the NOAA-6 spacecraft Advanced Very High Resolution Radiometer (AVHRR) were tested for effectiveness for vegetation classification. Vegetation, climatological, and meteorological data were gathered for three days over 12 locations, and the normalized differences between the AVHRR bands 1 and 2 were determined. A vegetative greenness index was compared with a hydrologic factor and vegetation characteristics as measured by ground truth. A multivariate vegetation gradient model was formulated, incorporating AVHRR and climatological data. The hydrologic factor was calculated in terms of the precipitation, evaporation, maximum and minimum temperatures, and the hydrologic capacity. The observations were taken over Texas, which has a wide range of climates. A high correlation was found in the vegetation-HF index. The AVHRR data are concluded to be an effective tool for analysis of vegetation/climate relationships.

Norwine, J.↗
NASA NTRS2020

Building a landslide hazard indicator with machine learning and land surface models

The U.S. Pacific Northwest has a history of frequent and occasionally deadly landslides caused by various factors. Using a multivariate, machine-learning approach, we combined a Pacific Northwest Landslide Inventory with a 36-year gridded hydrologic dataset from the National Climate Assessment – Land Data Assimilation System to produce a landslide hazard indicator (LHI) on a daily 0.125-degree grid. The LHI identified where and when landslides were most probable over the years 1979–2016, addressing issues of bias and completeness that muddy the analysis of multi-decadal landslide inventories. The seasonal cycle was strong along the west coast, with a peak in the winter, but weaker east of the Cascade Range. This lagging indicator can fill gaps in the observational record to identify the seasonality of landslides over a large spatiotemporal domain and show how landslide hazard has responded to a changing climate.

XGBoost↗
DOE OSTI2024

Real-time confinement regime detection in fusion plasmas with convolutional neural networks and high-bandwidth edge fluctuation measurements

Abstract A real-time detection of the plasma confinement regime can enable new advanced plasma control capabilities for both the access to and sustainment of enhanced confinement regimes in fusion devices. For example, a real-time indication of the confinement regime can facilitate transition to the high-performing wide-pedestal (WP) quiescent H-mode, or avoid unwanted transitions to lower confinement regimes that may induce plasma termination. To demonstrate real-time confinement regime detection, we use the 2D beam emission spectroscopy (BES) diagnostic system to capture localized density fluctuations of long wavelength turbulent modes in the edge region at a 1 MHz sampling rate. BES data from 330 discharges in either L-mode, H-mode, quiescent H (QH)-mode, or WP QH-mode were collected from the DIII-D tokamak and curated to develop a high-quality database to train a deep-learning classification model for real-time confinement detection. We utilize the 6×8 spatial configuration with a time window of 1024 µ s and recast the input to obtain spectral-like features via fast Fourier transform preprocessing. We employ a shallow 3D convolutional neural network for the multivariate time-series classification task and utilize a softmax in the final dense layer to retrieve a probability distribution over the different confinement regimes. Our model classifies the global confinement state on 44 unseen test discharges with an average F 1 score of 0.94, using only ∼1 ms snippets of BES data at a time. This activity demonstrates the feasibility for real-time data analysis of fluctuation diagnostics in future devices such as ITER, where the need for reliable and advanced plasma control is urgent.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2002

Visualizing 2D Probability Distributions from Satellite Image-Derived Data

Creating maps of biophysical and geophysical variables using Earth Observing System (EOS) satellite image data is an important component of Earth science. These 2D maps have a single value at every location and standard techniques are used to visualize them. Current tools fall short, however, when it is necessary to describe a distribution of values at each location. Distributions may represent a frequency of occurrence over time, frequency of occurrence from multiple runs of an ensemble forecast or possible values from an uncertainty model. 'Distribution data sets' are described, then a case study is presented to visualize such 2D distributions. Distribution data sets are different from multivariate data sets in the sense that the values are for a single variable instead of multiple variables. Our case study data consists of multiple realizations of percent forest cover, generated using a geostatistical technique that combines ground measurements and satellite imagery to model uncertainty about forest cover. We present several approaches for analyzing and visualizing such data sets. The first is a pixel-wise analysis of the probability density functions for the 2D image while the second is an analysis of features identified within the image. Such pixel-wise and feature-wise views will give Earth scientists a more complete understanding of distribution data sets.

Kao, David↗