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Results for “Multivariate Analysis”

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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Usefulness of microvolt T-wave alternans for prediction of ventricular tachyarrhythmic events in patients with dilated cardiomyopathy: results from a prospective observational study

OBJECTIVES: This study was designed to evaluate the ability of microvolt-level T-wave alternans (MTWA) to identify prospectively patients with idiopathic dilated cardiomyopathy (DCM) at risk of ventricular tachyarrhythmic events and to compare its predictive accuracy with that of conventional risk stratifiers. BACKGROUND: Patients with DCM are at increased risk of sudden death from ventricular tachyarrhythmias. At present, there are no established methods of assessing this risk. METHODS: A total of 137 patients with DCM underwent risk stratification through assessment of MTWA, left ventricular ejection fraction, baroreflex sensitivity (BRS), heart rate variability, presence of nonsustained ventricular tachycardia (VT), signal-averaged electrocardiogram, and presence of intraventricular conduction defect. The study end point was either sudden death, resuscitated ventricular fibrillation, or documented hemodynamically unstable VT. RESULTS: During an average follow-up of 14 +/- 6 months, MTWA and BRS were significant univariate predictors of ventricular tachyarrhythmic events (p < 0.035 and p < 0.015, respectively). Multivariate Cox regression analysis revealed that only MTWA was a significant predictor. CONCLUSIONS: Microvolt-level T-wave alternans is a powerful independent predictor of ventricular tachyarrhythmic events in patients with DCM.

Evaluation Studies↗
NASA NTRS2022

Warming, Increase in Precipitation, and Irrigation Enhance Greening in High Mountain Asia

High-Mountain Asia (HMA) exhibits one of the highest increases in vegetation greenness on Earth, subsequently influencing the exchange of water and energy between the land surface and the atmosphere. Given the strong interactions between the hydrosphere, the biosphere, and the cryosphere, understanding the drivers of greening in this highly complex region with significant land cover heterogeneity is essential to assess the changes in the regional water budget. Here we perform a holistic multivariate remote sensing analysis to simultaneously examine the primary components of the terrestrial water cycle from 2003 to 2020 and decipher the principal drivers of greening in HMA. We identified three drivers of greening: (1) precipitation drives greening in mid and low elevation areas covered by evergreen and mixed forests (e.g., Irrawaddy basin), (2) decreases in snow enhance greening in most of the hydrologic basins, and (3) irrigation induces greening in irrigated lands (Ganges-Brahmaputra and Indus).

Fadji Zaouna Maina↗
DOE OSTI2014

Hyperspectral stimulated emission depletion microscopy and methods of use thereof

A hyperspectral stimulated emission depletion ("STED") microscope system for high-resolution imaging of samples labeled with multiple fluorophores (e.g., two to ten fluorophores). The hyperspectral STED microscope includes a light source, optical systems configured for generating an excitation light beam and a depletion light beam, optical systems configured for focusing the excitation and depletion light beams on a sample, and systems for collecting and processing data generated by interaction of the excitation and depletion light beams with the sample. Hyperspectral STED data may be analyzed using multivariate curve resolution analysis techniques to deconvolute emission from the multiple fluorophores. The hyperspectral STED microscope described herein can be used for multi-color, subdiffraction imaging of samples (e.g., materials and biological materials) and for analyzing a tissue by Forster Resonance Energy Transfer ("FRET").

47 OTHER INSTRUMENTATION↗
DOE OSTI2012

Lifestyle Factors in U.S. Residential Electricity Consumption

A multivariate statistical approach to lifestyle analysis of residential electricity consumption is described and illustrated. Factor analysis of selected variables from the 2005 U.S. Residential Energy Consumption Survey (RECS) identified five lifestyle factors reflecting social and behavioral choices associated with air conditioning, laundry usage, personal computer usage, climate zone of residence, and TV use. These factors were also estimated for 2001 RECS data. Multiple regression analysis using the lifestyle factors yields solutions accounting for approximately 40% of the variance in electricity consumption for both years. By adding the associated household and market characteristics of income, local electricity price and access to natural gas, variance accounted for is increased to approximately 54%. Income contributed only {approx}1% unique variance to the 2005 and 2001 models, indicating that lifestyle factors reflecting social and behavioral choices better account for consumption differences than income. This was not surprising given the 4-fold range of energy use at differing income levels. Geographic segmentation of factor scores is illustrated, and shows distinct clusters of consumption and lifestyle factors, particularly in suburban locations. The implications for tailored policy and planning interventions are discussed in relation to lifestyle issues.

03 NATURAL GAS↗
NASA NTRS1991

Characterization of exopolymers of aquatic bacteria by pyrolysis-mass spectrometry

Exopolymers from a diverse collection of marine and freshwater bacteria were characterized by pyrolysis-mass spectrometry (Py-MS). Py-MS provides spectra of pyrolysis fragments that are characteristic of the original material. Analysis of the spectra by multivariate statistical techniques (principal component and canonical variate analysis) separated these exopolymers into distinct groups. Py-MS clearly distinguished characteristic fragments, which may be derived from components responsible for functional differences between polymers. The importance of these distinctions and the relevance of pyrolysis information to exopolysaccharide function in aquatic bacteria is discussed.

NASA Program CELSS↗
DOE OSTI2015

Adverse Outcomes After Palliative Radiation Therapy for Uncomplicated Spine Metastases: Role of Spinal Instability and Single-Fraction Radiation Therapy

Purpose: Level I evidence demonstrates equivalent pain response after single-fraction (SF) or multifraction (MF) radiation therapy (RT) for bone metastases. The purpose of this study is to provide additional data to inform the incidence and predictors of adverse outcomes after RT for spine metastases. Methods and Materials: At a single institution, 299 uncomplicated spine metastases (without cord compression, prior RT, or surgery) treated with RT from 2008 to 2013 were retrospectively reviewed. The spinal instability neoplastic score (SINS) was used to assess spinal instability. The primary outcome was time to first spinal adverse event (SAE) at the site, including symptomatic vertebral fracture, hospitalization for site-related pain, salvage surgery, interventional procedure, new neurologic symptoms, or cord compression. Fine and Gray's multivariable model assessed associations of the primary outcome with SINS, SF RT, and other significant baseline factors. Propensity score matched analysis further assessed the relationship of SF RT to first SAEs. Results: The cumulative incidence of first SAE after SF RT (n=66) was 6.8% at 30 days, 16.9% at 90 days, and 23.6% at 180 days. For MF RT (n=233), the incidence was 3.5%, 6.4%, and 9.2%, respectively. In multivariable analysis, SF RT (hazard ratio [HR] = 2.8, 95% confidence interval [CI] 1.5-5.2, P=.001) and SINS ≥11 (HR=2.5 , 95% CI 1.3-4.9, P=.007) were predictors of the incidence of first SAE. In propensity score matched analysis, first SAEs had developed in 22% of patients with SF RT versus 6% of those with MF RT cases (HR=3.9, 95% CI 1.6-9.6, P=.003) at 90 days after RT. Conclusion: In uncomplicated spinal metastases treated with RT alone, spinal instability with SINS ≥11 and SF RT were associated with a higher rate of SAEs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Application of Partial Least Squares Approaches to Pyroprocessing ER Data

Multivariate approaches show promise for application to process monitoring for safeguards of pyroprocessing. Past MPACT work explored the application of Principal Component Analysis (PCA) to detect off-normal conditions in pyroprocessing electrorefiner (ER) data from in the Hot Fuel Examination Facility (HFEF) at Idaho National Laboratory (INL) known as the Scalable Pyrochemical Recycling testbed (SPyRe) ER. PCA, however, does not consider the output variables. In FY24, multivariate analysis was extended from PCA to Partial Least Squares (PLS) analysis. PLS maximizes the variance between both the input signals and output variables. In the case of this work, PLS was applied in two different manners: Predictive PLS and Discriminant PLS. Predictive PLS maximizes the covariance between the process variables of the ER and the measured U concentration from in-situ voltammetry. Discriminant PLS maximizes the covariance between the process variables and a set of training process “states” such as known off-normal conditions. By projecting into the latent variable space in PLS, the process variables can be regressed onto the outputs and predictions can be made for new data sets. In this work, by applying predictive PLS, a penalized non-linear PLS approach was able to make predictions of concentration based on test and training data and detect when operations were off-normal. However, the predictive PLS does not classify the signals to which off-normal operations are attributable. Discriminant PLS can be used to classify off-normal operations but is inadequate to properly classify specific off-normal classes like power supply faults when the Discriminant PLS model is only specifically trained to detect that off-normal class. When all faults are trained against the observation data, all three operational classes are accurately classified and distinguished. Thus, future application of latent variable techniques should not select any given method, but should use a mixture of PCA, Predictive PLS, and Discriminant PLS.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1989

New multivariable capabilities of the INCA program

The INteractive Controls Analysis (INCA) program was developed at NASA's Goddard Space Flight Center to provide a user friendly, efficient environment for the design and analysis of control systems, specifically spacecraft control systems. Since its inception, INCA has found extensive use in the design, development, and analysis of control systems for spacecraft, instruments, robotics, and pointing systems. The (INCA) program was initially developed as a comprehensive classical design analysis tool for small and large order control systems. The latest version of INCA, expected to be released in February of 1990, was expanded to include the capability to perform multivariable controls analysis and design.

Bauer, Frank H.↗
NASA NTRS1978

A sensitivity analysis for the F100 turbofan engine using the multivariable Nyquist array

In the feedback control design of multivariable systems, closed loop performance evaluations must include the dynamic behavior of variables unavailable to the feedback controller. For the multivariable Nyquist array method, a set of sensitivity functions are proposed to simplify the adjustment of compensator parameters when the dynamic response of the unmeasurable output variables is unacceptable. A sensitivity study to improve thrust and turbine temperature responses for the Pratt-Whitney F100 turbofan engine demonstrates the utility of the proposed method.

Leininger, G. G.↗
DOE OSTI2015

Identification of phases, symmetries and defects through local crystallography

Here we report that advances in electron and probe microscopies allow 10 pm or higher precision in measurements of atomic positions. This level of fidelity is sufficient to correlate the length (and hence energy) of bonds, as well as bond angles to functional properties of materials. Traditionally, this relied on mapping locally measured parameters to macroscopic variables, for example, average unit cell. This description effectively ignores the information contained in the microscopic degrees of freedom available in a high-resolution image. Here we introduce an approach for local analysis of material structure based on statistical analysis of individual atomic neighbourhoods. Clustering and multivariate algorithms such as principal component analysis explore the connectivity of lattice and bond structure, as well as identify minute structural distortions, thus allowing for chemical description and identification of phases. This analysis lays the framework for building image genomes and structure–property libraries, based on conjoining structural and spectral realms through local atomic behaviour.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Plaque Brachytherapy for Uveal Melanoma: A Vision Prognostication Model

Purpose: To generate a vision prognostication model after plaque brachytherapy for uveal melanoma. Methods and Materials: All patients with primary single ciliary body or choroidal melanoma treated with iodine-125 or ruthenium-106 plaque brachytherapy between January 1, 2005, and June 30, 2010, were included. The primary endpoint was loss of visual acuity. Only patients with initial visual acuity better than or equal to 20/50 were used to evaluate visual acuity worse than 20/50 at the end of the study, and only patients with initial visual acuity better than or equal to 20/200 were used to evaluate visual acuity worse than 20/200 at the end of the study. Factors analyzed were sex, age, cataracts, diabetes, tumor size (basal dimension and apical height), tumor location, and radiation dose to the tumor apex, fovea, and optic disc. Univariate and multivariable Cox proportional hazards were used to determine the influence of baseline patient factors on vision loss. Kaplan-Meier curves (log rank analysis) were used to estimate freedom from vision loss. Results: Of 189 patients, 92% (174) were alive as of February 1, 2011. At presentation, visual acuity was better than or equal to 20/50 and better than or equal to 20/200 in 108 and 173 patients, respectively. Of these patients, 44.4% (48) had post-treatment visual acuity of worse than 20/50 and 25.4% (44) had post-treatment visual acuity worse than 20/200. By multivariable analysis, increased age (hazard ratio [HR] of 1.01 [1.00-1.03], P=.05), increase in tumor height (HR of 1.35 [1.22-1.48], P<.001), and a greater total dose to the fovea (HR of 1.01 [1.00-1.01], P<.001) were predictive of vision loss. This information was used to develop a nomogram predictive of vision loss. Conclusions: By providing a means to predict vision loss at 3 years after treatment, our vision prognostication model can be an important tool for patient selection and treatment counseling.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Stereotactic Body Radiation Therapy for Locally Advanced Pancreatic Cancer: A Systematic Review and Pooled Analysis of 19 Trials

Purpose: Although surgery is the standard of care for resectable pancreatic cancer (PC), standard-dose chemoradiation therapy and chemotherapy alone are suitable for patients with unresectable disease. Stereotactic body radiation therapy (SBRT) is an alternative, focused local therapy that delivers high radiation doses within a few fractions to the cancer, sparing the surrounding critical tissue. We performed a systematic review and pooled analysis of published trials to evaluate the efficacy and safety of this emerging treatment modality. Methods and Materials: We searched the Cochrane Central Register of Controlled Trials, PubMed, EMBASE, SCOPUS, the Web of Science, and CINAHL for publications regarding SBRT for locally advanced PC. The 1-year overall survival (OS) rate was the primary endpoint, and the median OS, 2-year OS rate, 1-year locoregional control (LRC) rate, and grade 3 to 4 toxicities were the secondary endpoints. A multivariate random-effects meta-analysis was performed to calculate the aggregated OS rates at 1 and 2 years and the 1-year LRC rate. Results: A total of 19 studies, encompassing 1009 patients, were included in the present analysis. The pooled 1-year OS was 51.6% in 13 trials with data available. The median OS ranged from 5.7 to 47 months (median 17). The LRC rate at 1 year was 72.3%. Overall, the occurrence of severe adverse events did not exceed 10%. LRC appeared to correlate with the total SBRT dose and the number of fractions. Conclusions: The advantages of SBRT in terms of treatment time, satisfactory OS, and LRC indicate that it is an effective option for inoperable PC. However, a definitive validation of this treatment modality in large randomized studies is required, owing to the nonrandomized nature of the included studies and the limitations of small single-center series that include mixed populations.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Interpreting mixing relationships in energetic melts to estimate vapor contribution and composition

Heterogeneous, glassy debris can be produced by energetic events such as near surface nuclear explosions and asteroid impacts. In these events, the resultant debris comprises a rapidly-cooled mixture of the projectile or bomb material with proximate materials such as soil and anthropogenic structures. Glasses formed in these events preserve inter and/or intra sample compositional variation reflecting complex mixtures of the precursor materials or their chemically fractionated products. Understanding formation mechanisms and chemical processes relating to energetic glass formation based on study of the end products requires an objective method for calculating the precursor components that mixed together to produce the final product. Here, we compare a set of multivariate linear-least squares approaches to address the unmixing problem of heterogeneous glasses including classical least squares (CLS), principal component analysis (PCA) and two multivariate curve resolution – alternating least squares (MCR-ALS) approaches. We generate a synthetic data set representing spatially resolved, compositional data in a set of heterogeneously mixed glasses to understand model sensitivity to precursor abundance and composition. We explore the impact of incomplete knowledge of the system prior to the formation of the melts on the resulting confidence and accuracy of the solution (i.e., the number, composition, and abundances of the precursor materials). We conclude that a closure and equality constrained MCR-ALS approach succeeds in identifying unrecognized precursor components and is a suitable alternative to CLS approaches when the investigator has incomplete knowledge of the starting precursor chemical compositions. Finally, we demonstrate application of this MCR-ALS approach to measurements of Trinitite.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2014

Heterogeneous recurrence monitoring and control of nonlinear stochastic processes

Recurrence is one of the most common phenomena in natural and engineering systems. Process monitoring of dynamic transitions in nonlinear and nonstationary systems is more concerned with aperiodic recurrences and recurrence variations. However, little has been done to investigate the heterogeneous recurrence variations and link with the objectives of process monitoring and anomaly detection. Notably, nonlinear recurrence methodologies are based on homogeneous recurrences, which treat all recurrence states in the same way as black dots, and non-recurrence is white in recurrence plots. Heterogeneous recurrences are more concerned about the variations of recurrence states in terms of state properties (e.g., values and relative locations) and the evolving dynamics (e.g., sequential state transitions). This paper presents a novel approach of heterogeneous recurrence analysis that utilizes a new fractal representation to delineate heterogeneous recurrence states in multiple scales, including the recurrences of both single states and multi-state sequences. Further, we developed a new set of heterogeneous recurrence quantifiers that are extracted from fractal representation in the transformed space. To that end, we integrated multivariate statistical control charts with heterogeneous recurrence analysis to simultaneously monitor two or more related quantifiers. Experimental results on nonlinear stochastic processes show that the proposed approach not only captures heterogeneous recurrence patterns in the fractal representation but also effectively monitors the changes in the dynamics of a complex system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

State space parameterization of explosive eigenvalues during autoignition

Explosive modes such as ignition and extinction are characterized by an eigenvalue of the chemical Jacobian matrix with positive real part, representing the transient instability of chain-branching chemistry and thermal feedback. Formation and eigen-decomposition of the Jacobian matrix are expensive operations whose cost increases cubically with chemical mechanism size. As an alternative to directly computing the eigenvalues of the Jacobian, we explore principal component analysis (PCA) along with nonlinear regression as a methodology to parameterize the eigenvalues by state variables (or linear combinations thereof). We evaluate this modeling strategy using homogeneous autoignition data on two different applications: pseudotransient continuation (Ψtc)-based ODE solvers and chemical explosive mode analysis (CEMA). Results indicate that the PCA-based parameterization of the eigenvalues appears feasible for Ψtc solvers in autoignition calculations over a range of temperatures and pressures. Our results also show that eigenvalue models are capable of tracking sharp discontinuities (such as ignition or extinction) in the eigenvalue for computational flame diagnostics such as CEMA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1980

Interfaces between statistical analysis packages and the ESRI geographic information system

Interfaces between ESRI's geographic information system (GIS) data files and real valued data files written to facilitate statistical analysis and display of spatially referenced multivariable data are described. An example of data analysis which utilized the GIS and the statistical analysis system is presented to illustrate the utility of combining the analytic capability of a statistical package with the data management and display features of the GIS.

Masuoka, E.↗
DOE OSTI2015

Exploratory Data analysis ENvironment eXtreme scale (EDENx)

EDENx is a multivariate data visualization tool that allows interactive user driven analysis of large-scale data sets with high dimensionality. EDENx builds on our earlier system, called EDEN to enable analysis of more dimensions and larger scale data sets. EDENx provides an initial overview of summary statistics for each variable in the data set under investigation. EDENx allows the user to interact with graphical summary plots of the data to investigate subsets and their statistical associations. These plots include histograms, binned scatterplots, binned parallel coordinate plots, timeline plots, and graphical correlation indicators. From the EDENx interface, a user can select a subsample of interest and launch a more detailed data visualization via the EDEN system. EDENx is best suited for high-level, aggregate analysis tasks while EDEN is more appropriate for detail data investigations.

Steed, Chad Allen↗
DOE OSTI2017

In situ Analysis of Fireworks Using Laser-Induced Breakdown Spectroscopy and Chemometrics

Different types of fireworks are analyzed using the laser-induced breakdown spectroscopy (LIBS) technique. The system employed for spectral acquisition consists of a Nd:YAG laser (532 nm, FWHM = 4 ns) and an Andor Mechelle ME 5000 echelle spectrometer. The presence of Ba, Ca, Mg, Fe, Na, Sr, Si, and Al is identified in the LIBS spectra of different fireworks. These elements can mix easily into the surroundings and thus pollute the environment. In combination with LIBS, multivariate statistical methods, such as principal component analysis and partial least square discriminant analysis, are employed for qualitative classification, regression, and prediction purposes. These methods show good applicability for the classification and prediction of a large data set.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗