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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The relationship between below average cognitive ability at age 5 years and the child’s experience of school at age 9

Background At age 5, while only embarking on their educational journey, substantial differences in children’s cognitive ability will already exist. The aim of this study was to examine the causal association between below average cognitive ability at age 5 years and child-reported experience of school and self-concept, and teacher-reported class engagement and emotional-behavioural function at age 9 years. Methods This longitudinal cohort study used data from 7,392 children in the Growing Up in Ireland Infant Cohort, who had completed the Picture Similarities and Naming Vocabulary subtests of the British Abilities Scales at age 5. Principal components analysis was used to produce a composite general cognitive ability score for each child. Children with a general cognitive ability score more than 1 standard deviation (SD) below the mean at age 5 were categorised as ‘Below Average Cognitive Ability’ (BACA), and those scoring above this as ‘Typical Cognitive Development’ (TCD). The outcomes of interest, measured at age 9, were child-reported experience of school, child’s self-concept, teacher-reported class engagement, and teacher-reported emotional behavioural function. Binary and multinomial logistic regression models were used to examine the association between BACA and these outcomes. Results Compared to those with TCD, those with BACA had significantly higher odds of never liking school [Adjusted odds ratio (AOR) 1.82, 95% CI 1.37–2.43, p < 0.001], of being picked on (AOR 1.27, 95% CI 1.09–1.48) and of picking on others (AOR 1.53, 95% CI 1.27–1.84). They had significantly higher odds of experiencing low self-concept (AOR 1.20, 95% CI 1.02–1.42) and emotional-behavioural difficulties (AOR 1.34, 95% CI 1.10–1.63, p = 0.003). Compared to those with TCD, children with BACA had significantly higher odds of hardly ever or never being interested, motivated and excited to learn (AOR 2.29, 95% CI 1.70–3.10). Conclusion Children with BACA at school-entry had significantly higher odds of reporting a negative school experience and low self-concept at age 9. They had significantly higher odds of having teacher-reported poor class engagement and problematic emotional-behavioural function at age 9. The findings of this study suggest BACA has a causal role in these adverse outcomes. Early childhood policy and intervention design should be cognisant of the important role of cognitive ability in school and childhood outcomes.

Bowe, Andrea K.↗
NASA NTRS1980

Eigenvector analysis of some observed and model-generated Climatological fields

An empirical orthogonal function analysis of monthly mean synoptic fields computed with a general circulation model and the corresponding observed climatological patterns is presented. A computer program was used to compute the principal components of the data matrices representing global maps of various observed and model-generated climatological fields. The variables analyzed were: 500mb geopotential height, 850 mb temperature, and sea level pressure. It is concluded that eigenvector analysis of observed and model-generated synoptic climatological fields is a useful diagnostic aid in the evaluation of global model simulation. The maps of any climatological variables are readily decomposed into a few orthogonal components which account for a large fraction of the total spatial variance, and the observed and model-generated patterns may then be compared in terms of these principal components of the original data matrices.

Dennis, M.↗
DOE OSTI2014

TU-F-18C-09: Mammogram Surveillance Using Texture Analysis for Breast Cancer Patients After Radiation Therapy

Purpose: To study the feasibility of applying cancer risk model established from treated patients to predict the risk of recurrence on follow-up mammography after radiation therapy for both ipsilateral and contralateral breast. Methods: An extensive set of textural feature functions was applied to a set of 196 Mammograms from 50 patients. 56 Mammograms from 28 patients were used as training set, 44 mammograms from 22 patients were used as test set and the rest were used for prediction. Feature functions include Histogram, Gradient, Co-Occurrence Matrix, Run-Length Matrix and Wavelet Energy. An optimum subset of the feature functions was selected by Fisher Coefficient (FO) or Mutual Information (MI) (up to top 10 features) or a method combined FO, MI and Principal Component (FMP) (up to top 30 features). One-Nearest Neighbor (1-NN), Linear Discriminant Analysis (LDA) and Nonlinear Discriminant Analysis (NDA) were utilized to build a risk model of breast cancer from the training set of mammograms at the time of diagnosis. The risk model was then used to predict the risk of recurrence from mammogram taken one year and three years after RT. Results: FPM with NDA has the best classification power in classifying the training set of the mammogram with lesions versus those without lesions. The model of FPM with NDA achieved a true positive (TP) rate of 82% compared to 45.5% of using FO with 1-NN. The best false positive (FP) rates were 0% and 3.6% in contra-lateral breast of 1-year and 3-years after RT, and 10.9% in ipsi-lateral breast of 3-years after RT. Conclusion: Texture analysis offers high dimension to differentiate breast tissue in mammogram. Using NDA to classify mammogram with lesion from mammogram without lesion, it can achieve rather high TP and low FP in the surveillance of mammogram for patient with conservative surgery combined RT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

A variance-based decomposition and global sensitivity index method for uncertainty quantification: Application to retrieved ice cloud properties

This study develops a novel uncertainty quantification (UQ) method for cloud microphysical property retrievals using variance-based decomposition and global sensitivity index. In this UQ framework, empirical orthogonal function (EOF) analysis is applied to the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) ground-based observations, which are the inputs for the cloud retrieval studied here. The principal components (PCs) in the EOF expansion are parameterized as random input variables, and hence, the input dimension is greatly reduced (up to a factor of 50), allowing large ensemble of random samplings. The EOF expansion improves the accuracy of the uncertainty estimation by taking into account the cross correlations in the input data profiles. Here, this method enables a probabilistic representation of a retrieval process by adding normally distributed perturbations into PCs of sample means of input data profiles within a time window. Therefore, it effectively facilitates objective validation of climate models against cloud retrievals under a probabilistic framework for rigorous statistical inferences. Moreover, the variance-based global sensitivity index analysis, part of this method, attributes the output uncertainties to each individual source, thus providing directions for improving retrieval algorithms and observation strategies. For demonstration, we apply this method to quantify the uncertainties of the ARM program's baseline cloud retrieval algorithm for an ice cloud case observed at the Southern Great Plains site on 9 March 2000.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Indirect dark matter signatures in the cosmic dark ages. I. Generalizing the bound on s -wave dark matter annihilation from Planck results

Recent measurements of the cosmic microwave background (CMB) anisotropies by Planck provide a sensitive probe of dark matter annihilation during the cosmic dark ages, and specifically constrain the annihilation parameter f eff <σv>=m χ . Using new results (paper II) for the ionization produced by particles injected at arbitrary energies, we calculate and provide f eff values for photons and e + e - pairs injected at keV-TeV energies; the f eff value for any dark matter model can be obtained straightforwardly by weighting these results by the spectrum of annihilation products. This result allows the sensitive and robust constraints on dark matter annihilation presented by the Planck collaboration to be applied to arbitrary dark matter models with s-wave annihilation. Here, we demonstrate the validity of this approach using principal component analysis. As an example, we integrate over the spectrum of annihilation products for a range of Standard Model final states to determine the CMB bounds on these models as a function of dark matter mass, and demonstrate that the new limits generically exclude models proposed to explain the observed high-energy rise in the cosmic ray positron fraction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1998

Using Neural Networks for Sensor Validation

This paper presents the results of applying two different types of neural networks in two different approaches to the sensor validation problem. The first approach uses a functional approximation neural network as part of a nonlinear observer in a model-based approach to analytical redundancy. The second approach uses an auto-associative neural network to perform nonlinear principal component analysis on a set of redundant sensors to provide an estimate for a single failed sensor. The approaches are demonstrated using a nonlinear simulation of a turbofan engine. The fault detection and sensor estimation results are presented and the training of the auto-associative neural network to provide sensor estimates is discussed.

Mattern, Duane L.↗
DOE OSTI2016

Simulation methodology for fuel assembly drop accident during handling

Fresh and irradiated fuel assembly handling is a routine part of Nuclear Power Plant activities. Fuel Assembly handling occurs during fresh fuel receipt, core refueling, spent fuel assemblies transfer from spent fuel pool to the storage containers, etc. Westinghouse, ENUSA and KEPCO-NF have collaborated in the technical area of Fuel Assembly Drop Accident (FADA) Simulation to update the approach for estimating the number of failed fuel rods as a consequence of a fresh or irradiated fuel assembly drop, which might occur during a handling accident in a PWR-type reactor. The proposed FADA simulation methodology starts with an assessment of fuel assembly impact velocity at the final stage of the drop in water or air. The vertical, horizontal or inclined drop directions are considered. This velocity is an input to determine a fuel assembly structural response during impact which defines the loading function for the fuel assembly components including spacer grids. The dynamic load on the fuel rod is calculated from the detailed spacer grid model and the corresponding loading function. Finally, the load from the spacer grid spring and dimples are applied to the fuel rod model to determine the cladding parameters for use in compliance to fuel rod cladding failure criteria. A combination of first principal, finite element analysis (FEA) and computational fluid dynamic (CFD) methods has been utilized to create a technically sound methodology. A number of specific and traditional tests for the fuel assembly and its components has been used to confirm that the proposed approaches which describe the physical phenomena related to the fuel assembly drop accident are reasonably conservative.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Star/galaxy separation at faint magnitudes: application to a simulated Dark Energy Survey

We address the problem of separating stars from galaxies in future large photometric surveys. We focus our analysis on simulations of the Dark Energy Survey (DES). In the first part of the paper, we derive the science requirements on star/galaxy separation, for measurement of the cosmological parameters with the gravitational weak lensing and large-scale structure probes. These requirements are dictated by the need to control both the statistical and systematic errors on the cosmological parameters, and by point spread function calibration. We formulate the requirements in terms of the completeness and purity provided by a given star/galaxy classifier. In order to achieve these requirements at faint magnitudes, we propose a new method for star/galaxy separation in the second part of the paper. We first use principal component analysis to outline the correlations between the objects parameters and extract from it the most relevant information. We then use the reduced set of parameters as input to an Artificial Neural Network. This multiparameter approach improves upon purely morphometric classifiers (such as the classifier implemented in SExtractor), especially at faint magnitudes: it increases the purity by up to 20 per cent for stars and by up to 12 per cent for galaxies, at i-magnitude fainter than 23.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1994

Understanding software faults and their role in software reliability modeling

This study is a direct result of an on-going project to model the reliability of a large real-time control avionics system. In previous modeling efforts with this system, hardware reliability models were applied in modeling the reliability behavior of this system. In an attempt to enhance the performance of the adapted reliability models, certain software attributes were introduced in these models to control for differences between programs and also sequential executions of the same program. As the basic nature of the software attributes that affect software reliability become better understood in the modeling process, this information begins to have important implications on the software development process. A significant problem arises when raw attribute measures are to be used in statistical models as predictors, for example, of measures of software quality. This is because many of the metrics are highly correlated. Consider the two attributes: lines of code, LOC, and number of program statements, Stmts. In this case, it is quite obvious that a program with a high value of LOC probably will also have a relatively high value of Stmts. In the case of low level languages, such as assembly language programs, there might be a one-to-one relationship between the statement count and the lines of code. When there is a complete absence of linear relationship among the metrics, they are said to be orthogonal or uncorrelated. Usually the lack of orthogonality is not serious enough to affect a statistical analysis. However, for the purposes of some statistical analysis such as multiple regression, the software metrics are so strongly interrelated that the regression results may be ambiguous and possibly even misleading. Typically, it is difficult to estimate the unique effects of individual software metrics in the regression equation. The estimated values of the coefficients are very sensitive to slight changes in the data and to the addition or deletion of variables in the regression equation. Since most of the existing metrics have common elements and are linear combinations of these common elements, it seems reasonable to investigate the structure of the underlying common factors or components that make up the raw metrics. The technique we have chosen to use to explore this structure is a procedure called principal components analysis. Principal components analysis is a decomposition technique that may be used to detect and analyze collinearity in software metrics. When confronted with a large number of metrics measuring a single construct, it may be desirable to represent the set by some smaller number of variables that convey all, or most, of the information in the original set. Principal components are linear transformations of a set of random variables that summarize the information contained in the variables. The transformations are chosen so that the first component accounts for the maximal amount of variation of the measures of any possible linear transform; the second component accounts for the maximal amount of residual variation; and so on. The principal components are constructed so that they represent transformed scores on dimensions that are orthogonal. Through the use of principal components analysis, it is possible to have a set of highly related software attributes mapped into a small number of uncorrelated attribute domains. This definitively solves the problem of multi-collinearity in subsequent regression analysis. There are many software metrics in the literature, but principal component analysis reveals that there are few distinct sources of variation, i.e. dimensions, in this set of metrics. It would appear perfectly reasonable to characterize the measurable attributes of a program with a simple function of a small number of orthogonal metrics each of which represents a distinct software attribute domain.

Munson, John C.↗
DOE OSTI2014

A limited-angle intrafraction verification (LIVE) system for radiation therapy

Purpose: Currently, no 3D or 4D volumetric x-ray imaging techniques are available for intrafraction verification of target position during actual treatment delivery or in-between treatment beams, which is critical for stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT) treatments. This study aims to develop a limited-angle intrafraction verification (LIVE) system to use prior information, deformation models, and limited angle kV-MV projections to verify target position intrafractionally. Methods: The LIVE system acquires limited-angle kV projections simultaneously during arc treatment delivery or in-between static 3D/IMRT treatment beams as the gantry moves from one beam to the next. Orthogonal limited-angle MV projections are acquired from the beam's eye view (BEV) exit fluence of arc treatment beam or in-between static beams to provide additional anatomical information. MV projections are converted to kV projections using a linear conversion function. Patient prior planning CT at one phase is used as the prior information, and the on-board patient volume is considered as a deformation of the prior images. The deformation field is solved using the data fidelity constraint, a breathing motion model extracted from the planning 4D-CT based on principal component analysis (PCA) and a free-form deformation (FD) model. LIVE was evaluated using a 4D digital extended cardiac torso phantom (XCAT) and a CIRS 008A dynamic thoracic phantom. In the XCAT study, patient breathing pattern and tumor size changes were simulated from CT to treatment position. In the CIRS phantom study, the artificial target in the lung region experienced both size change and position shift from CT to treatment position. Varian Truebeam research mode was used to acquire kV and MV projections simultaneously during the delivery of a dynamic conformal arc plan. The reconstruction accuracy was evaluated by calculating the 3D volume percentage difference (VPD) and the center of mass (COM) difference of the tumor in the true on-board images and reconstructed images. Results: In both simulation and phantom studies, LIVE achieved substantially better reconstruction accuracy than reconstruction using PCA or FD deformation model alone. In the XCAT study, the average VPD and COM differences among different patient scenarios for LIVE system using orthogonal 30° scan angles were 4.3% and 0.3 mm when using kV+BEV MV. Reducing scan angle to 15° increased the average VPD and COM differences to 15.1% and 1.7 mm. In the CIRS phantom study, the VPD and COM differences for the LIVE system using orthogonal 30° scan angles were 6.4% and 1.4 mm. Reducing scan angle to 15° increased the VPD and COM differences to 51.9% and 3.8 mm. Conclusions: The LIVE system has the potential to substantially improve intrafraction target localization accuracy by providing volumetric verification of tumor position simultaneously during arc treatment delivery or in-between static treatment beams. With this improvement, LIVE opens up a new avenue for margin reduction and dose escalation in both fractionated treatments and SRS and SBRT treatments.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2004

Satellite Observations of Spatial and Interannual Variability of Lightning and Radar Reflectivity

The authors use satellite data to examine the relationship between lightning and upper-level radar reflectivity. They find correlations between average flash rates and upper-level reflectivities over both land and ocean, although both flash rates and reflectivities are much lower over ocean than land. Analysis of the data using Empirical Orthogonal Functions (EOFs) shows similar EOFs for averaged lightning and reflectivity. In contrast, the EOFs of the anomalies of lightning and reflectivity have different spatial patterns; however, both have principal component time series that are correlated with the Southern Oscillation Index and, hence, El Nino. Differences in behavior of the lightning and reflectivity anomaly EOFs and principal components suggest that El Nino plays a smaller role in lightning anomaly than precipitation anomaly.

lightning↗
DOE OSTI2020

Data Analytics Applied to Coal Fired Boilers for Detecting Leaks

Data analytics were used to detect boiler leaks from five different coal-fired boilers including both subcritical and supercritical systems. Discriminant functions were developed that detected leaks up to two weeks prior to forced plant shutdowns for repairs. The leaks were identified to occur at different sections of the boiler for each plant, including waterwalls, economizer and superheater using conventional process measurement data. Leaking conditions were detected with a high degree of confidence (&NestedLessLess; 1% misclassified observations) and were able to distinguish normal operations from those time periods with steam leaks even while operating the power plants in power cycling mode.Multivariable statistical analyses, including Principal Component (PCA), cluster, and Fischer Discriminant Analysis (FDA) were used to characterize the leak occurrence. Normal and operational states with steam leaks were provided in the original process datasets. These datasets were split into two different groups for training and validation purposes. The data were sorted chronologically, and every third observation was assigned to training the Discriminant Function Model (DFM) while the rest were reserved for validation. PCA was used to reduce dimensionality of the original datasets. Canonical and FDA analyses were used to investigate the relationship between process variables. The outcome of the analyses revealed that nearly 35,000 observations were classified correctly; less than 0.05% of total observations were misclassified to be leaking, i.e. both false positives and false negatives.

Indrawan, Natarianto↗
NASA NTRS1985

Preliminary Evaluation of Thematic Mapper Image Data Quality

Improvements in the ability to monitor renewable resources/vegegation due to improvements in the spatial, spectral and radiometric resolution of TM data were evaluated. Results presented from the first 4 months of analysis presented include: (1) geometric performance; (2) band-to-band registration; (3) modulation transfer function; and (4) crop separabililty performance. Crop separability in Webster County, Iowa and in Mississippi County, Arkansas as determined by cluster and principal components analyses is assessed.

Macdonald, R. B.↗
DOE OSTI2019

A hierarchical analysis of the impact of methodological decisions on statistical downscaling of daily precipitation and air temperatures

Despite the widespread application of statistical downscaling tools, uncertainty remains regarding the role of model formulation in determining model skill for daily maximum and minimum temperature (Tmax and Tmin), and precipitation occurrence and intensity. Impacts of several key aspects of statistical transfer function form on model skill are evaluated using a framework resistant to model overspecification. We focus on: (a) model structure: simple (generalized linear models, GLMs) versus complex (artificial neural networks, ANNs) models. (b) Predictor selection: Fixed number of predictors chosen a priori versus stepwise selection of predictors and inclusion of grid point values versus predictors derived from application of principal components analysis (PCA) to spatial fields. We also examine the influence of domain size on model performance. For precipitation downscaling, we consider the role of the threshold used to characterize a wet day and apply three approaches (Poisson and Gamma distributions in GLM and ANN) to downscale wet-day precipitation amounts. While no downscaling formulation is optimal for all predictands and at 10 locations representing diverse U.S. climates, and due to the exclusion of variance inflation all of the downscaling formulations fail to reproduce the range of observed variability, models with larger suites of prospective predictors generally have higher skill. For temperature downscaling, ANNs generally outperform GLM, with greater improvements for T min than T max . Furthermore, use of PCA-derived predictors does not systematically improve model skill, but does improve skill for temperature extremes. Model skill for precipitation occurrence generally increases as the wet-day threshold increases and models using PCA-derived predictors tend to outperform those based on grid cell predictors. Each model for wet-day precipitation intensity overestimates annual total precipitation and underestimates the proportion derived from extreme precipitation events, but ANN-based models and those with larger predictor suites tend to have the smallest bias.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Designing clay-polymer nanocomposite sorbents for water treatment: A review and meta-analysis of the past decade

Clay-polymer nanocomposites (CPNs) have been studied for two decades as sorbents for water pollutants, but their applicability remains limited. Our aim in this review is to present the latest progress in CPN research using a meta-analysis approach and identify key steps necessary to bridge the gap between basic research and CPN application. Based on results extracted from 99 research articles on CPNs and 8 review articles on other widely studies sorbents, CPNs had higher adsorption capacities for several inorganic and organic pollutant classes (including heavy metals, oxyanions, and dyes, n = 308 observations). We applied principal component analysis, analysis of variance, and multiple linear regressions to test how CPN and pollutant properties correlated with Langmuir adsorption model coefficients. While adsorption was, surprisingly, not influenced by mineral properties, it was influenced by CPN fabrication method, polymer functional groups, and pollutant properties. For example, among the pollutant classes, heavy metals had the highest adsorption capacity but the lowest adsorption affinity. Conversely, dyes had high adsorption affinities, as reflected by the linear correlation between adsorption affinity and pollutant molecular weight. Scaling from ‘basic research’ to ‘technological application’ requires testing CPN performance in real water, application in columns, comparison to commercial sorbents, regeneration, and cost evaluation. However, our survey indicates that of the 158 observations, only 20 compared the CPN's performance to that of a commercial sorbent. We anticipate that this review will promote the design of smart and functional CPNs, which can then evolve into an effective water treatment technology.

42 ENGINEERING↗
DOE OSTI2016

Principal shapes and squeezed limits in the effective field theory of large scale structure

We apply an orthogonalization procedure on the effective field theory of large scale structure (EFT of LSS) shapes, relevant for the angle-averaged bispectrum and non-Gaussian covariance of the matter power spectrum at one loop. Assuming natural-sized EFT parameters, this identifies a linear combination of EFT shapes—referred to as the principal shape—that gives the dominant contribution for the whole kinematic plane, with subdominant combinations suppressed by a few orders of magnitude. For the covariance, our orthogonal transformation is in excellent agreement with a principal component analysis applied to available data. Additionally we find that, for both observables, the coefficients of the principal shapes are well approximated by the EFT coefficients appearing in the squeezed limit, and are thus measurable from power spectrum response functions. Employing data from N-body simulations for the growth-only response, we measure the single EFT coefficient describing the angle-averaged bispectrum with Ο (10%) precision. These methods of shape orthogonalization and measurement of coefficients from response functions are valuable tools for developing the EFT of LSS framework, and can be applied to more general observables.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1977

VLBI (Very Long Baseline Interferometry) instrumental effects, part 1

Very Long Baseline Interferometry (VLBI) is a method for observation of extragalactic radio sources which appears to have potential for precise long-distance earth surveying, clock synchronization and spacecraft navigation. Many researchers have been working to establish the accuracy of VLBI observations. The intent of the work reported here is to review the principal components of the VLBI instrument in order to estimate and/or bound the systematic error contributions. In this first of a series of articles, the definitions and tools which are needed in order to apply filter transfer-function analysis to the VLBI receiver are established and they are used to estimate the sensitivity of the VLBI receiver to plausible filter variations.

Layland, J. W.↗
NASA NTRS1994

Development of a low-aspect ratio fin for flight research experiments

A second-generation flight test fixture, developed at NASA Dryden Flight Research Center, offers a generic testbed for aerodynamic and fluid mechanics research. The new fixture, a low-aspect ratio vertical fin shape mounted on the centerline of an F-15B aircraft lower fuselage, is designed for flight research at Mach numbers up to 2.0. The new fixture is a composite structure with a modular configuration and removable components for functional flexibility. This report describes the multidisciplinary design and analysis approach used to develop the fixture. The approach integrates conservative assumptions with simple analysis techniques to minimize the time and cost associated with its development. Presented are the principal disciplines required for this effort, which include aerodynamics, structures, stability, and operational considerations. In addition, preliminary results from the first phase of flight testing are presented. Acceptable directional stability and flow quality are documented and show agreement with predictions. Future envelope expansion activities will minimize current limitations so that the fixture can be used for a wide variety of high-speed aerodynamic and fluid mechanics research experiments.

Richwine, David M.↗