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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Predicting Postoperative Injury and Military Discharge Status After Knee Surgery in the US Army

Background: Researchers have assessed postoperative injury or disability predictors in the military setting but typically focused on 1 type of surgical procedure at a time, used relatively small sample sizes, or investigated mixed cohorts with civilian populations. Purpose: To identify the relationship between baseline variables and injury incidence or military discharge status in US Army soldiers after knee surgery. Study Design: Case-control study; Level of evidence, 3. Methods: Data were obtained from a repository containing personnel, performance, and medical records for all active-duty US Army soldiers. Multivariate logistic regressions were used to estimate the effects of numerous variables on postoperative injury or on medical discharge. Variable selection and model validation were conducted using the k-fold method. Results: A total of 7567 soldiers underwent knee surgery between 2017 and 2019. Meniscal procedures were the most common type of surgery (39%), and approximately 71% of the cohort had a postoperative injury. Significant predictors for sustaining a postoperative injury included having a previous nonknee injury (odds ratio [OR], 1.5), female sex (OR, 1.3), and Black race (OR, 1.2). Within 4 years after surgery, 17% of soldiers were discharged from the military because of knee-related disability. Significant predictors for discharge from duty included enlisted rank (OR, 2.3), recent fitness test failure (OR, 1.9), number of previous knee surgeries (OR, 1.7), and having a previous nonknee injury (OR, 1.6). Conclusion: After knee surgery, nearly three-fourths of the soldiers in this cohort sustained a postoperative injury and almost one-fifth of soldiers were medically discharged from the military within 4 years. This study identified variables that indicate statistically increased risk for these postoperative outcomes and highlighted potentially modifiable factors.

Orthopedics↗
DOE OSTI2023

COVID-19 vaccination status, side effects, and perceptions among breast cancer survivors: a cross-sectional study in China

Introduction Breast cancer is the most prevalent malignancy in patients with coronavirus disease 2019 (COVID-19). However, vaccination data of this population are limited. Methods A cross-sectional study of COVID-19 vaccination was conducted in China. Multivariate logistic regression models were used to assess factors associated with COVID-19 vaccination status. Results Of 2,904 participants, 50.2% were vaccinated with acceptable side effects. Most of the participants received inactivated virus vaccines. The most common reason for vaccination was “fear of infection” (56.2%) and “workplace/government requirement” (33.1%). While the most common reason for nonvaccination was “worry that vaccines cause breast cancer progression or interfere with treatment” (72.9%) and “have concerns about side effects or safety” (39.6%). Patients who were employed (odds ratio, OR = 1.783, p = 0.015), had stage I disease at diagnosis (OR = 2.008, p = 0.019), thought vaccines could provide protection (OR = 1.774, p = 0.007), thought COVID-19 vaccines were safe, very safe, not safe, and very unsafe (OR = 2.074, p < 0.001; OR = 4.251, p < 0.001; OR = 2.075, p = 0.011; OR = 5.609, p = 0.003, respectively) were more likely to receive vaccination. Patients who were 1–3 years, 3–5 years, and more than 5 years after surgery (OR = 0.277, p < 0.001; OR = 0.277, p < 0.001, OR = 0.282, p < 0.001, respectively), had a history of food or drug allergies (OR = 0.579, p = 0.001), had recently undergone endocrine therapy (OR = 0.531, p < 0.001) were less likely to receive vaccination. Conclusion COVID-19 vaccination gap exists in breast cancer survivors, which could be filled by raising awareness and increasing confidence in vaccine safety during cancer treatment, particularly for the unemployed individuals.

Xu, Yali↗
NASA NTRS1972

Information extraction techniques for multispectral scanner data

The applicability of recognition-processing procedures for multispectral scanner data from areas and conditions used for programming the recognition computers to other data from different areas viewed under different measurement conditions was studied. The reflective spectral region approximately 0.3 to 3.0 micrometers is considered. A potential application of such techniques is in conducting area surveys. Work in three general areas is reported: (1) Nature of sources of systematic variation in multispectral scanner radiation signals, (2) An investigation of various techniques for overcoming systematic variations in scanner data; (3) The use of decision rules based upon empirical distributions of scanner signals rather than upon the usually assumed multivariate normal (Gaussian) signal distributions.

Malila, W. A.↗
NASA NTRS2001

Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction

Electroencephalographic (EEG) recordings were made while 16 participants performed versions of a personal-computer-based flight simulation task of low, moderate, or high difficulty. As task difficulty increased, frontal midline theta EEG activity increased and alpha band activity decreased. A participant-specific function that combined multiple EEG features to create a single load index was derived from a sample of each participant's data and then applied to new test data from that participant. Index values were computed for every 4 s of task data. Across participants, mean task load index values increased systematically with increasing task difficulty and differed significantly between the different task versions. Actual or potential applications of this research include the use of multivariate EEG-based methods to monitor task loading during naturalistic computer-based work.

NASA Discipline Space Human Factors↗
DOE OSTI2024

STITCHES: a Python package to amalgamate existing Earth system model output into new scenario realizations

Understanding the interaction between humans and the Earth system is a computationally daunting task, with many possible approaches depending on resources available and questions of interest. For example, state-of-the-art impact models require decade-long time series of relatively high frequency, spatially resolved and often multiple variables representing climatic impact-drivers (Ruane et al., 2022). Most commonly these are derived from the outputs of detailed, computationally expensive Earth System Models (ESMs) run according to a standard, limited set of future scenarios, the latest being the SSP-RCPs run under CMIP6/ScenarioMIP (Eyring et al., 2016; O’Neill et al., 2016). At the time of writing, O’Neill et al. (2016) has been cited more than 1750 times and Eyring et al. (2016) more than 5000 times, highlighting the broad, general applications of this data. Often, however, impact modeling seeks to explore new scenarios that were not part of the ScenarioMIP protocol, and/or needs a larger set of initial condition ensemble members than are typically available to quantify the effects of ESM internal variability. In addition, the recognition that the human and Earth systems are fundamentally intertwined, and may feature potentially significant feedback loops, is making integrated, simultaneous modeling of the coupled human-Earth system increasingly necessary, if computationally challenging with most existing tools (Thornton et al., 2017). For impact modelers, climate model emulators can be the answer to meet both the needs of: 1) creating realizations for novel scenarios and 2) achieving a simplified, computationally tractable representation of ESM behavior in a coupled human-Earth system modeling framework. We proposed a new, comprehensive approach to such emulation of gridded, multivariate ESM outputs for novel scenarios without the computational cost of a full ESM, STITCHES (Tebaldi et al., 2022). The approach outlined in Tebaldi et al. (2022) should be extensible to future CMIP eras, although the STITCHES software at present is strictly focused on CMIP6/ScenarioMIP data hosted on Pangeo (https://gallery.pangeo.io/repos/pangeo-gallery/cmip6/). The corresponding STITCHES Python package uses existing archives of ESMs’ scenario experiments from CMIP6/ScenarioMIP to construct gridded, multivariate realizations of new scenarios provided by reduced complexity climate models (Hartin et al., 2015; Meinshausen et al., 2011; Smith et al., 2018), or to enrich existing initial condition ensembles. Its output provides the same characteristics as the emulated ESM output: multivariate (spanning potentially all variables that the ESM has saved), spatially resolved (down to the native grid of the ESM), and preserving the same high frequency as the original data. A new realization of multiple variables can be generated on the order of minutes with STITCHES, rather than the hours or sometimes days that ESMs require.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Multi-application controls: Robust nonlinear multivariable aerospace controls applications

This viewgraph presentation describes the general methodology used to apply Honywell's Multi-Application Control (MACH) and the specific application to the F-18 High Angle-of-Attack Research Vehicle (HARV) including piloted simulation handling qualities evaluation. The general steps include insertion of modeling data for geometry and mass properties, aerodynamics, propulsion data and assumptions, requirements and specifications, e.g. definition of control variables, handling qualities, stability margins and statements for bandwidth, control power, priorities, position and rate limits. The specific steps include choice of independent variables for least squares fits to aerodynamic and propulsion data, modifications to the management of the controls with regard to integrator windup and actuation limiting and priorities, e.g. pitch priority over roll, and command limiting to prevent departures and/or undesirable inertial coupling or inability to recover to a stable trim condition. The HARV control problem is characterized by significant nonlinearities and multivariable interactions in the low speed, high angle-of-attack, high angular rate flight regime. Systematic approaches to the control of vehicle motions modeled with coupled nonlinear equations of motion have been developed. This paper will discuss the dynamic inversion approach which explicity accounts for nonlinearities in the control design. Multiple control effectors (including aerodynamic control surfaces and thrust vectoring control) and sensors are used to control the motions of the vehicles in several degrees-of-freedom. Several maneuvers will be used to illustrate performance of MACH in the high angle-of-attack flight regime. Analytical methods for assessing the robust performance of the multivariable control system in the presence of math modeling uncertainty, disturbances, and commands have reached a high level of maturity. The structured singular value (mu) frequency response methodology is presented as a method for analyzing robust performance and the mu-synthesis method will be presented as a method for synthesizing a robust control system. The paper concludes with the author's expectations regarding future applications of robust nonlinear multivariable controls.

Enns, Dale F.↗
DOE OSTI2022

Dietary B group vitamin intake and the bladder cancer risk: a pooled analysis of prospective cohort studies

Abstract Purpose Diet may play an essential role in the aetiology of bladder cancer (BC). The B group complex vitamins involve diverse biological functions that could be influential in cancer prevention. The aim of the present study was to investigate the association between various components of the B group vitamin complex and BC risk. Methods Dietary data were pooled from four cohort studies. Food item intake was converted to daily intakes of B group vitamins and pooled multivariate hazard ratios (HRs), with corresponding 95% confidence intervals (CIs), were obtained using Cox-regression models. Dose–response relationships were examined using a nonparametric test for trend. Results In total, 2915 BC cases and 530,012 non-cases were included in the analyses. The present study showed an increased BC risk for moderate intake of vitamin B1 (HR B1 : 1.13, 95% CI: 1.00–1.20). In men, moderate intake of the vitamins B1, B2, energy-related vitamins and high intake of vitamin B1 were associated with an increased BC risk (HR (95% CI): 1.13 (1.02–1.26), 1.14 (1.02–1.26), 1.13 (1.02–1.26; 1.13 (1.02–1.26), respectively). In women, high intake of all vitamins and vitamin combinations, except for the entire complex, showed an inverse association (HR (95% CI): 0.80 (0.67–0.97), 0.83 (0.70–1.00); 0.77 (0.63–0.93), 0.73 (0.61–0.88), 0.82 (0.68–0.99), 0.79 (0.66–0.95), 0.80 (0.66–0.96), 0.74 (0.62–0.89), 0.76 (0.63–0.92), respectively). Dose–response analyses showed an increased BC risk for higher intake of vitamin B1 and B12. Conclusion Our findings highlight the importance of future research on the food sources of B group vitamins in the context of the overall and sex-stratified diet.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Lattice Strain Measurement of Core@Shell Electrocatalysts with 4D Scanning Transmission Electron Microscopy Nanobeam Electron Diffraction

Strain engineering enables the direct modification of atomic bonding and is currently an active area of research aimed at improving electrocatalytic activity. However, directly measuring the lattice strain of individual catalyst nanoparticles is challenging, especially at the scale of a single unit cell. In this study, we quantitatively map the strain present in rhodium@platinum (core@shell) nanocube electrocatalysts using conventional aberration-corrected scanning transmission electron microscopy (STEM) and the recently developed technique of 4D-STEM nanobeam electron diffraction. We demonstrate that 4D-STEM combined with data preconditioning allows for quantitative lattice strain mapping with subpicometer precision and without the influence of scan distortions. When combined with multivariate curve resolution, 4D-STEM allows us to distinguish the nanocube core from the shell and to quantify the unit cell size as a function of distance from the core–shell interface. Our results demonstrate that 4D-STEM has significant precision and accuracy advantages in strain metrology of catalyst materials compared to aberration-corrected STEM imaging and is beneficial for extracting information about the evolution of strain in catalyst nanoparticles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Bio-inspired gas sensing: boosting performance with sensor optimization guided by “machine learning”

The performance of existing gas sensors often degrades in field conditions because of the loss of measurement accuracy in the presence of interferences. Thus, new sensing approaches are required with improved sensor selectivity. We are developing a new generation of gas sensors, known as multivariable sensors, that have several independent responses for multi-gas detection with a single sensor. In this study, we analyze the capabilities of natural and fabricated photonic three-dimensional (3-D) nanostructures as sensors for the detection of different gaseous species, such as vapors and non-condensable gases. We employed bare Morpho butterfly wing scales to control their gas selectivity with different illumination angles. Next, we chemically functionalized Morpho butterfly wing scales with a fluorinated silane to boost the response of these nanostructures to the vapors of interest and to suppress the response to ambient humidity. Further, we followed our previously developed design rules for sensing nanostructures and fabricated bioinspired inorganic 3-D nanostructures to achieve functionality beyond natural Morpho scales. These fabricated nanostructures have embedded catalytically active gold nanoparticles to operate at high temperatures of ≈300 °C for the detection of gases for solid oxide fuel cell (SOFC) applications. Our performance advances in the detection of multiple gaseous species with specific nanostructure designs were achieved by coupling the spectral responses of these nanostructures with machine learning (a.k.a. multivariate analysis, chemometrics) tools. Our newly acquired knowledge from studies of these natural and fabricated inorganic nanostructures coupled with machine learning data analytics allowed us to advance our design rules for sensing nanostructures toward the required gas selectivity for numerous gas monitoring scenarios at room and high temperatures for industrial, environmental, and other applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1989

Sources of error in thematic classification of remotely sensed imagery

From a statistician's point of view, the input datasets are rarely examined to determine their underlying frequency distribution; it is just assumed that the data are normal enough, and that the deviations from normality are unimportant. It is not clear how deviations from a hypothetical multivariate normal might affect the power of the classification process, and there is ample evidence in the literature that, at a minimum, the spectral channels are correlated. From a practioner's point of view, in a supervised classification the number of training fields for developing a statistical description of a given class is usually arbitrary. It is unclear how small changes in the details of the training field selection process affect the quality of the derived thematic information. The start of an examination of this latter problem is discussed.

Star, Jeffrey L.↗
NASA NTRS2014

Using Concurrent Cardiovascular Information to Augment Survival Time Data from Orthostatic Tilt Tests

Orthostatic Intolerance (OI) is the propensity to develop symptoms of fainting during upright standing. OI is associated with changes in heart rate, blood pressure and other measures of cardiac function. Problem: NASA astronauts have shown increased susceptibility to OI on return from space missions. Current methods for counteracting OI in astronauts include fluid loading and the use of compression garments. Multivariate trajectory spread is greater as OI increases. Pairwise comparisons at the same time within subjects allows incorporation of pass/fail outcomes. Path length, convex hull area, and covariance matrix determinant do well as statistics to summarize this spread Missing data problems Time series analysis need many more time points per OTT session treatment of trend? how incorporate survival information?

Feiveson, Alan H.↗
DOE OSTI2023

Search for top squarks in the four-body decay mode with single lepton final states in proton-proton collisions at $ \sqrt{s} $ = 13 TeV

A search for the pair production of the lightest supersymmetric partner of the top quark, the top squark ($ {\overset{\sim }{\textrm{t}}}_1 $), is presented. The search targets the four-body decay of the $ {\overset{\sim }{\textrm{t}}}_1 $, which is preferred when the mass difference between the top squark and the lightest supersymmetric particle is smaller than the mass of the W boson. This decay mode consists of a bottom quark, two other fermions, and the lightest neutralino ($ {\overset{\sim }{\chi}}_1^0 $), which is assumed to be the lightest supersymmetric particle. The data correspond to an integrated luminosity of 138 fb$^{−1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV collected by the CMS experiment at the CERN LHC. Events are selected using the presence of a high-momentum jet, an electron or muon with low transverse momentum, and a significant missing transverse momentum. The signal is selected based on a multivariate approach that is optimized for the difference between m($ {\overset{\sim }{\textrm{t}}}_1 $) and m($ {\overset{\sim }{\chi}}_1^0 $). The contribution from leading background processes is estimated from data. No significant excess is observed above the expectation from standard model processes. The results of this search exclude top squarks at 95% confidence level for masses up to 480 and 700 GeV for m($ {\overset{\sim }{\textrm{t}}}_1 $) − m($ {\overset{\sim }{\chi}}_1^0 $) = 10 and 80 GeV, respectively.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1980

Predicting major element mineral/melt equilibria - A statistical approach

Empirical equations have been developed for calculating the mole fractions of NaO0.5, MgO, AlO1.5, SiO2, KO0.5, CaO, TiO2, and FeO in a solid phase of initially unknown identity given only the composition of the coexisting silicate melt. The approach involves a linear multivariate regression analysis in which solid composition is expressed as a Taylor series expansion of the liquid compositions. An internally consistent precision of approximately 0.94 is obtained, that is, the nature of the liquidus phase in the input data set can be correctly predicted for approximately 94% of the entries. The composition of the liquidus phase may be calculated to better than 5 mol % absolute. An important feature of this 'generalized solid' model is its reversibility; that is, the dependent and independent variables in the linear multivariate regression may be inverted to permit prediction of the composition of a silicate liquid produced by equilibrium partial melting of a polymineralic source assemblage.

Hostetler, C. J.↗
DOE OSTI2022

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↗
DOE OSTI2020

Distribution Development for the RDX Regional Model at Los Alamos National Laboratory - 20385

Representing uncertainty in model inputs often means finding a balance between uncertainty and physical reality. Developing wide distributions may seem conservative in principle, but this approach may lead to unrealistic model results. Characterizing the current state of knowledge of stochastic inputs presents many challenges, especially if the data available are limited or have limited relevance to the site. Relationships among these inputs may also be important to represent but are typically complex or difficult to define. Often special adjustments must be made to account for reduced credibility in particular data. If parameters are strongly related to one another, a correlation structure may be developed for input to the model. Other techniques such as regression models may be used to incorporate relationships between the information available and the desired parameters. The process of developing distributions must consider details of the model in terms of what the distribution is meant to represent. This paper uses the example of a probabilistic fate and transport model for hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in the regional aquifer at Los Alamos National Laboratory (LANL). For many parameters, a single draw is applied to all space and time over which the model is run, for a single iteration. This simplification is often made for many reasons, and can often be beneficial, but also adds additional complexity in the distribution development process. Defining the distributional goals as they relate to the modeling process is an important step, which should take place prior to evaluation of the data. Usually, the distributions developed are meant to characterize the average value of the parameter over the spatial and temporal domain of the model. Distribution development requires consideration of many sources of information on the parameter where available, ideally from multiple references. Examples of different sources include data from different references but also from different conditions, measurement methods, or experimental types. Depending on these conditions and the reliability or relevance of particular references, different sources of data may each contribute valuable information but have varying relevance to the site. In these cases, weighting data unequally is a useful way to incorporate this information. As an example, aqueous dispersivity data are available for a variety of rock types. Only a few values are available for the desired rock type, and this is not enough to develop a distribution. Therefore, dispersivity values from other rock types are included in distribution development but are down-weighted such that the best data have the most influence on the distribution developed. In another case, K{sub d} distributions in the model are meant to represent a known composition of multiple soil types. Data from these materials are weighted accordingly to develop a distribution for the weighted average K{sub d} across soil types. Other cases include varying reliability of different sources, and weighting data according to the confidence in these sources. In some cases, input parameters are correlated with one another. An example is advective porosity, which is positively correlated with total porosity and must be less than total porosity. Paired data with both parameters must exist to discern the relationship between the two parameters and if it is necessary to build a correlation structure into the model. In general, correlated parameters may be represented in the model by a multivariate distribution, or perhaps more desirably, capturing the correlation within the developed distributions which may be treated as independent from one another. In the example of porosity, this can be done by transforming advective porosity into a proportion of total porosity which may be drawn independently from the distribution of total porosity. This paper explores how complex data and correlations can be incorporated to meet the distributional goals of the model, using the RDX regional model as a detailed example. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Resolving the Evolution of Atomic Layer-Deposited Thin-Film Growth by Continuous In Situ X-Ray Absorption Spectroscopy

In situ synchrotron X-ray absorption near-edge structure characterization of thin-film titania growth by atomic layer deposition (ALD) over ZnO nanowires reveals persistent low-coordinated Ti motifs leading to a new picture of ALD growth. Through the design of growth and measurement cycles, Ti K-edge spectral data are continuously recorded so as to characterize the film evolution as a function of ALD cycle number and the surface changes within the time scale of the ALD cycle. A unified set of analysis tools is developed to interpret the time-series of spectral data. A prenucleation stage of growth, a transition region, and then a steady-state growth stage are observed with distinguishable features. Multivariate curve resolution analysis, that is physically constrained, demonstrates two specific spectral components with associated, time-dependent concentrations. The bulk-film component tracks the stages of growth. The surface and interface components, present throughout the stages of growth, reveal a significant coverage of relatively isolated or loosely networked tetrahedrally coordinated Ti atomic motifs. Lastly, spectral signatures for the intra-cycle growth kinetics are reconstructed at a time resolution of ~1 s and demonstrate that the transient Ti motifs on the growing surface stabilize within a few seconds of the Ti precursor pulse.

36 MATERIALS SCIENCE↗
NASA NTRS1974

Recent advancements in information extraction methodology and hardware for earth resources survey systems

The present work discusses some recent developments in preprocessing and extractive processing techniques and hardware and in user applications model development for earth resources survey systems. The Multivariate Interactive Digital Analysis System (MIDAS) is currently being developed, and is an attempt to solve the problem of real time multispectral data processing in an operational system. The main features and design philosophy of this system are described. Examples of wetlands mapping and land resource inventory are presented. A user model developed for predicting the yearly production of mallard ducks from remote sensing and ancillary data is described.

Erickson, J. D.↗