Linear Covariance Analysis Framework for Aerospace Vehicle Trajectory Modeling and Parametric Design.
Abstract not provided.
Heflin, Lucas↗
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Linear Covariance Analysis Framework for Aerospace Vehicle Trajectory Modeling and Parametric Design.
Abstract not provided.
Whitten, William↗
Cardiorespiratory fitness and health‐related quality of life in adolescents: A longitudinal analysis from the LabMed Physical Activity Study
Abstract Objective The purpose of this study was to examine the longitudinal associations between cardiorespiratory fitness and health‐related quality of life (HRQoL) in a sample of adolescents, and to determine whether changes in cardiorespiratory fitness were associated with HRQoL over a 2‐year follow‐up. Methods This is a longitudinal analysis with 571 Portuguese adolescents (274 boys and 297 girls) aged 12‐18 years. HRQoL was measured with the Kidscreen‐10 questionnaire; the 20 m shuttle‐run was used to estimate cardiorespiratory fitness; socioeconomic status was assessed with the Family Affluence Scale; and pubertal stage was assessed with Tanner stages. Linear regression and analysis of covariance ( ancova s) were used to examine the associations between cardiorespiratory fitness and HRQoL. Results HRQoL decreased over a 2‐year period both in boys and girls ( P < .05). Moreover, girls at baseline and at follow‐up reported significantly lower HRQoL values than boys (between subjects P < .001). After adjustments for potential confounders, regression analyses showed significant cross‐sectional associations between cardiorespiratory fitness and HRQoL both at baseline ( B = 0.095; P = .023) and at follow‐up (B = 0.090; P = .012). ancova showed that adolescents whose cardiorespiratory fitness decreased over time exhibited lower scores of HRQoL at follow‐up, compared to those with persistent high cardiorespiratory fitness; this held after adjustments for age, sex, socioeconomic status, and pubertal stage. Conclusions Changes in cardiorespiratory fitness during adolescence were associated with HRQoL over a 2‐year period. This study suggests that improving cardiorespiratory fitness could be an important strategy in improving the HRQoL of adolescents.
Evaristo, Olga S.↗
Large-Scale Inference of Multivariate Regression for Heavy-Tailed and Asymmetric Data
Large-scale multivariate regression is a fundamental statistical tool with a wide range of applications. Here, this study considers the problem of simultaneously testing a large number of general linear hypotheses, encompassing covariate-effect analysis, analysis of variance, and model comparisons. The challenge that accompanies a large number of tests is the ubiquitous presence of heavy-tailed and/or highly skewed measurement noise, which is the main reason for the failure of conventional least squares-based methods. For large-scale multivariate regression, we develop a set of robust inference methods to explore data features such as heavy tailedness and skewness, which are not visible to least squares methods. The new testing procedure is based on the data-adaptive Huber regression and a new covariance estimator of regression estimates. Under mild conditions, we show that our methods produce consistent estimates of the false discovery proportion. Extensive numerical experiments and an empirical study on quantitative linguistics demonstrate the advantage of the proposed method over many state-of-the-art methods when the data are generated from heavy-tailed and/or skewed distributions.
97 MATHEMATICS AND COMPUTING↗
Hopf-algebraic renormalization of QED in the linear covariant gauge
In the context of massless quantum electrodynamics (QED) with a linear covariant gauge fixing, the connection between the counterterm and the Hopf-algebraic approach to renormalization is examined. The coproduct formula of Green’s functions contains two invariant charges, which give rise to different renormalization group functions. All formulas are tested by explicit computations to third loop order. The possibility of a finite electron self-energy by fixing a generalized linear covariant gauge is discussed. An analysis of subdivergences leads to the conclusion that such a gauge only exists in quenched QED.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Consistent Estimation of Gibbs Energy Using Component Contributions
Standard Gibbs energies of reactions are increasingly being used in metabolic modeling for applying thermodynamic constraints on reaction rates, metabolite concentrations and kinetic parameters. The increasing scope and diversity of metabolic models has led scientists to look for genome-scale solutions that can estimate the standard Gibbs energy of all the reactions in metabolism. Group contribution methods greatly increase coverage, albeit at the price of decreased precision. We present here a way to combine the estimations of group contribution with the more accurate reactant contributions by decomposing each reaction into two parts and applying one of the methods on each of them. This method gives priority to the reactant contributions over group contributions while guaranteeing that all estimations will be consistent, i.e. will not violate the first law of thermodynamics. We show that there is a significant increase in the accuracy of our estimations compared to standard group contribution. Specifically, our cross-validation results show an 80% reduction in the median absolute residual for reactions that can be derived by reactant contributions only. We provide the full framework and source code for deriving estimates of standard reaction Gibbs energy, as well as confidence intervals, and believe this will facilitate the wide use of thermodynamic data for a better understanding of metabolism.
59 BASIC BIOLOGICAL SCIENCES↗
Response of Ecosystem Productivity to High Vapor Pressure Deficit and Low Soil Moisture: Lessons Learned From the Global Eddy-Covariance Observations
Although there is mounting concern about how high vapor pressure deficit (VPD) and low soil moisture (SM) affect ecosystem productivity, their relative importance is still under debate. Here, we comprehensively quantified the relative impacts of these two factors on ecosystem gross primary production (GPP) using observations from a global network of eddy-covariance towers and two approaches (sensitivity analysis and linear regression model). Both approaches agree that a higher percentage of sites experience GPP reduction from high VPD than from low SM over the growing season. However, the constraint of high VPD and low SM on GPP reduction is tightly linked with climates and plant functional types. Humid and mesic ecosystems including forests and grasslands are dominated by VPD, while the semi-arid and arid ecosystems including shrublands and savannas are dominated by SM. The varying dominant role of these two factors on GPP is closely related to plant stomatal behavior, as predicted by a stomatal conductance model. Additionally, we highlight the non-linear impact of SM on GPP during droughts and the possible underestimation of the SM effects for deep-rooted plants when only using surface-layer SM. Our results shed light on a better understanding of the impacts of VPD and SM on vegetation productivity, with important implications for modeling the response and feedback of ecosystem dynamics to current and future climates.
54 ENVIRONMENTAL SCIENCES↗
Associations of blood lead, cadmium, and mercury with estimated glomerular filtration rate in the Korean general population: Analysis of 2008-2010 Korean National Health and Nutrition Examination Survey data
Introduction: The objective of this study was to evaluate associations between blood lead, cadmium, and mercury levels with estimated glomerular filtration rate in a general population of South Korean adults. Methods: This was a cross-sectional study based on data obtained in the Korean National Health and Nutrition Examination Survey (KNHANES) (2008-2010). The final analytical sample consisted of 5924 participants. Estimated glomerular filtration rate (eGFR) was calculated using the MDRD Study equation as an indicator of glomerular function. Results: In multiple linear regression analysis of log2-transformed blood lead as a continuous variable on eGFR, after adjusting for covariates including cadmium and mercury, the difference in eGFR levels associated with doubling of blood lead were -2.624 mL/min per 1.73 m Superscript-Two (95% CI: -3.803 to -1.445). In multiple linear regression analysis using quartiles of blood lead as the independent variable, the difference in eGFR levels comparing participants in the highest versus the lowest quartiles of blood lead was -3.835 mL/min per 1.73 m Superscript-Two (95% CI: -5.730 to -1.939). In a multiple linear regression analysis using blood cadmium and mercury, as continuous or categorical variables, as independent variables, neither metal was a significant predictor of eGFR. Odds ratios (ORs) and 95% CI values for reduced eGFR calculated for log2-transformed blood metals and quartiles of the three metals showed similar trends after adjustment for covariates. Discussion: In this large, representative sample of South Korean adults, elevated blood lead level was consistently associated with lower eGFR levels and with the prevalence of reduced eGFR even in blood lead levels below 10 {mu}g/dL. In conclusion, elevated blood lead level was associated with lower eGFR in a Korean general population, supporting the role of lead as a risk factor for chronic kidney disease.
60 APPLIED LIFE SCIENCES↗
A note on the Bayesian analysis of experiments with correlated multiple responses using a matrix-logarithmic covariance model
In the literature, analysis of multiple responses from experiments with replicates has modeled the covariance matrix directly as linear models of the transformed variances and correlations, i.e., covariance modeling. This article considers models based on the matrix- logarithm of the covariance matrix. This so-called log-covariance modeling is illustrated with data from actual experiments and compared with the traditional covariance modeling.
97 MATHEMATICS AND COMPUTING↗
Maternal exposure to selenium and cadmium, fetal growth, and placental expression of steroidogenic and apoptotic genes
Background: Cadmium (Cd) and selenium (Se) antagonistically influence redox balance and apoptotic signaling, with Cd potentially promoting and Se inhibiting oxidative stress and apoptosis. Alterations to placental redox and apoptotic functions by maternal exposure to Cd and Se during pregnancy may explain some of the Cd and Se associations with fetal development. Objectives: Investigate associations between Cd and Se concentrations in maternal toenails with placental expression patterns of tumor necrosis factor (TNF) and steroidogenic genes involved in redox reactions and test associations with fetal growth. Methods: In a sub-sample from the Rhode Island Child Health Study (n = 173), we investigated the relationships between: (1) maternal toenail Cd and Se concentrations and fetal growth using logistic regression, (2) Cd and Se interactions with factor scores from placental TNF and steroidogenic expression patterns (RNAseq) using linear models, and (3) TNF and steroidogenic expression factors with fetal growth via analysis of covariance. Results: Se was associated with decreased odds of intrauterine growth restriction (IUGR) (OR = 0.27, p-value = 0.045). Cd was associated with increased odds of IUGR (OR = 1.95, p-value = 0.13) and small for gestational age (SGA) births (OR = 1.46, p-value = 0.11), though not statistically significant. Cd and Se concentrations were antagonistically associated with placental TNF and steroidogenic expression patterns, which also differed by birth size. Conclusions: Se may act as an antagonist to Cd and as a modifiable protective factor in fetal growth restriction, and these data suggest these effects may be due to associated variations in the regulation of genes involved in placental redox balance and/or apoptotic signaling. - Highlights: • Tested cadmium and selenium associations with fetal growth and gene expression. • Maternal selenium associated with decreased odds of intrauterine growth restriction. • Cadmium may increase oxidative and apoptotic placental expression patterns. • Selenium may decrease oxidative and apoptotic placental expression patterns.
54 ENVIRONMENTAL SCIENCES↗
Comparison of Computed Tomography– and Magnetic Resonance Imaging–based Clinical Target Volume Contours at Brachytherapy for Cervical Cancer
Purpose: We prospectively compared computed tomography (CT)– and magnetic resonance imaging (MRI)–based high-risk clinical target volume (HR-CTV) contours at the time of brachytherapy for cervical cancer in an effort to identify patients who might benefit most from MRI-based planning. Methods and Materials: Thirty-seven patients who had undergone a pretreatment diagnostic MRI scan were included in the analysis. We delineated the HR-CTV on the brachytherapy CT and brachytherapy MRI scans independently for each patient. We then calculated the absolute volumes for each HR-CTV and the Dice coefficient of similarity (DC, a measure of spatial agreement) for the HR-CTV contours. We identified the clinical and tumor factors associated with (1) a discrepancy in volume between the CT HR-CTV and MRI HR-CTV contours; and (2) DC. The mean values were compared using 1-way analysis of variance or paired or unpaired t tests, as appropriate. Simple and multivariable linear regression analyses were used to model the effects of covariates on the outcomes. Results: Patients with International Federation of Gynecology and Obstetrics stage IB to IVA cervical cancer were treated with intracavitary brachytherapy using tandem and ovoid (n=33) or tandem and cylinder (n=4) applicators. The mean CT HR-CTV volume (44.1 cm{sup 3}) was larger than the mean MRI HR-CTV volume (35.1 cm{sup 3}; P<.0001, paired t test). On multivariable analysis, a higher body mass index (BMI) and tumor size ≥5 cm with parametrial invasion on the MRI scan at diagnosis were associated with an increased discrepancy in volume between the HR-CTV contours (P<.02 for both). In addition, the spatial agreement (as measured by DC) between the HR-CTV contours decreased with an increasing BMI (P=.013). Conclusions: We recommend MRI-based brachytherapy planning for patients with tumors >5 cm and parametrial invasion on MRI at diagnosis and for those with a high BMI.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Detecting population-environmental interactions with mismatched time series data
Time series analysis is an essential method for decomposing the influences of density and exogenous factors such as weather and climate on population regulation. However, there has not been much work focused on understanding how well commonly collected data can reconstruct the effects of environmental factors on population dynamics. We show that, analogous to similar scale issues in spatial data analysis, coarsely sampled temporal data can fail to detect covariate effects when interactions occur on timescales that are fast relative to the survey period. We propose a method for modeling mismatched time series data that couples high-resolution environmental data to low-resolution abundance data. We illustrate our approach with simulations and by applying it to Florida's southern Snail kite population. Our simulation results show that our method can reliably detect linear environmental effects and that detecting nonlinear effects requires high-resolution covariate data even when the population turnover rate is slow. In the Snail kite analysis, our approach performed among the best in a suite of previously used environmental covariates explaining Snail kite dynamics and was able to detect a potential phenological shift in the environmental dependence of Snail kites. Our work provides a statistical framework for reliably detecting population–environment interactions from coarsely surveyed time series. An important implication of this work is that the low predictability of animal population growth by weather variables found in previous studies may be due, in part, to how these data are utilized as covariates.
54 ENVIRONMENTAL SCIENCES↗
Rayleigh-Taylor instability at spherical interfaces between viscous fluids: Fluid/vacuum interface
For a spherical interface of radius R separating two different homogeneous regions of incompressible viscous fluids under the action of a radially directed acceleration, we perform a linear stability analysis in terms of spherical surface harmonics Y n to derive the dispersion relation. The instability behavior is investigated by computing the growth rates and the most-unstable modes as a function of the spherical harmonic degree n. This general methodology is applicable to the entire parameter space spanned by the Atwood number, the viscosity ratio, and the dimensionless number B = (α R Ρ² 2 /μ² ² )¹ / ³ R (where α R , Ρ 2 and μ 2 are the local radial acceleration at the interface, and the density and viscosity of the denser overlying fluid, respectively). While the mathematical formulation here is general, this paper focuses on instability that arises at a spherical viscous fluid/vacuum interface as there is a great deal to be learned from the effects of one-fluid viscosity and sphericity alone. To quantify and understand the effect that curvature and radial accelerationhave on the Rayleigh-Taylor instability, a comparison of the growth rates, under homologous driving conditions, between the planar and spherical interfaces is performed. The derived dispersion relation for the planar interface accounts for an underlying finite fluid region of thickness L and normal acceleration α R . Under certain conditions, the development of the most-unstable modes at a spherical interface can take place via the superposition of two adjacent spherical harmonics Y n and Y n+1 . This bimodality in the evolution of disturbances in the linear regime does not have a counterpart in the planar configuration where the most-unstable modes are associated with a unique wave number.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
A Statistical Interpolation Code for Ocean Analysis and Forecasting
Abstract We present a data assimilation package for use with ocean circulation models in analysis, forecasting, and system evaluation applications. The basic functionality of the package is centered on a multivariate linear statistical estimation for a given predicted/background ocean state, observations, and error statistics. Novel features of the package include support for multiple covariance models, and the solution of the least squares normal equations either using the covariance matrix or its inverse—the information matrix. The main focus of this paper, however, is on the solution of the analysis equations using the information matrix, which offers several advantages for solving large problems efficiently. Details of the parameterization of the inverse covariance using Markov random fields are provided and its relationship to finite-difference discretizations of diffusion equations are pointed out. The package can assimilate a variety of observation types from both remote sensing and in situ platforms. The performance of the data assimilation methodology implemented in the package is demonstrated with a yearlong global ocean hindcast with a 1/4° ocean model. The code is implemented in modern Fortran, supports distributed memory, shared memory, multicore architectures, and uses climate and forecasts compliant Network Common Data Form for input/output. The package is freely available with an open source license from www.tendral.com/tsis/ .
Srinivasan, Ashwanth↗
From Data to Insights: A Covariate Analysis of the IARPA BRIAR Dataset for Multimodal Biometric Recognition Algorithms at Altitude and Range
This paper examines covariate effects on fused whole body biometrics performance in the IARPA BRIAR dataset, specifically focusing on UAV platforms, elevated positions, and distances up to 1000 meters. The dataset includes outdoor videos compared with indoor images and controlled gait recordings. Normalized raw fusion scores relate directly to predicted false accept rates (FAR), offering an intuitive means for interpreting model results. A linear model is developed to predict biometric algorithm scores, analyzing their performance to identify the most influential covariates on accuracy at altitude and range. Weather factors like temperature, wind speed, solar loading, and turbulence are also investigated in this analysis. The study found that resolution and camera distance best predicted accuracy and findings can guide future research and development efforts in long-range/elevated/UAV biometrics and support the creation of more reliable and robust systems for national security and other critical domains.
Bolme, David↗
Kernel principal component analysis for stochastic input model generation
Highlights: {yields} KPCA is used to construct a reduced order stochastic model of permeability. {yields} A new approach is proposed to solve the pre-image problem in KPCA. {yields} Polynomial chaos is used to provide a parametric stochastic input model. {yields} Flow in porous media with channelized permeability is considered. - Abstract: Stochastic analysis of random heterogeneous media provides useful information only if realistic input models of the material property variations are used. These input models are often constructed from a set of experimental samples of the underlying random field. To this end, the Karhunen-Loeve (K-L) expansion, also known as principal component analysis (PCA), is the most popular model reduction method due to its uniform mean-square convergence. However, it only projects the samples onto an optimal linear subspace, which results in an unreasonable representation of the original data if they are non-linearly related to each other. In other words, it only preserves the first-order (mean) and second-order statistics (covariance) of a random field, which is insufficient for reproducing complex structures. This paper applies kernel principal component analysis (KPCA) to construct a reduced-order stochastic input model for the material property variation in heterogeneous media. KPCA can be considered as a nonlinear version of PCA. Through use of kernel functions, KPCA further enables the preservation of higher-order statistics of the random field, instead of just two-point statistics as in the standard Karhunen-Loeve (K-L) expansion. Thus, this method can model non-Gaussian, non-stationary random fields. In this work, we also propose a new approach to solve the pre-image problem involved in KPCA. In addition, polynomial chaos (PC) expansion is used to represent the random coefficients in KPCA which provides a parametric stochastic input model. Thus, realizations, which are statistically consistent with the experimental data, can be generated in an efficient way. We showcase the methodology by constructing a low-dimensional stochastic input model to represent channelized permeability in porous media.
97 MATHEMATICS AND COMPUTING↗
140,142 Ce Neutron Cross Section Resolved Resonance Region Evaluation
A resolved resonance region evaluation of 140,142 Ce was conducted by Oak Ridge National Laboratory. Requested by the US Nuclear Criticality Safety Program, this evaluation is based on recent high-resolution transmission and capture high-resolution measurements of nat Ce and 142 Ce conducted at JRC-Geel at the Geel Linear Accelerator facility. It is also based on recently measured thermal constants available from the EX FOR database. Starting from the resonance parameters from the ENDF/B-VIII.0 library and following a preliminary R-matrix analysis, an updated set of resonance parameters and corresponding covariance in formation was derived by the fit of these experimental datasets using the Reich–Moore approximation of the R-matrix theory, as implemented in the SAMMY code system. The resolved resonance region upper energy limit for 140 Ce was kept at 200 keV, whereas the 142 Ce resonance region was extended from 13 to 26 keV. This new evaluation was found to be in good agreement not only with several integral quantities of interest to the reactor physics community, but also with the stellar Maxwellian-averaged cross section.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Water loss through evapotranspiration after precipitation events in bioenergy crops grown in similar climatic conditions
The relationship between precipitation and evapotranspiration (ET) is critical to understanding water cycle related dynamics in ecosystems, including crops. Existing studies of bioenergy crops have primarily focused on annual or seasonal ET rates, with less attention given to the immediate ET response following precipitation events. This study examines the variation in ET rates in the days subsequent to precipitation events across various bioenergy crops—corn, switchgrass, and prairies—utilizing 13 years (2010–2022) of growing season data. Meteorological and eddy covariance flux data were collected from seven eddy covariance flux towers as part of the GLBRC scale-up experiment at the Kellogg Biological Station Long Term Ecological Research sites. The analysis revealed that average ET peaked the day after precipitation and declined linearly over the following days, with a statistically significant relationship (p-value = 0.00027, R2 = 0.96). Neither the type of biofuel vegetation nor the historical land use significantly influenced ET post-precipitation events (p-values = 0.53 and 0.153, respectively). Key predictors of ET following precipitation events include shortwave radiation, season, day of the year, ambient temperature, vapor pressure deficit (VPD), long-wave radiation, precipitation amount, soil moisture, and annual variability. These findings enhance our comprehension of ET responses in bioenergy crop systems, with implications for water management in sustainable agriculture.
09 BIOMASS FUELS↗