Search NASA⌕ Search

SEARCH · Search NASA

Results for “Methods:statistical”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Destructive Analysis of TRISO Particles: Crush/Burn/Leach Followed by Davies-Gray Titration and IDMS

The accurate accounting of nuclear materials is a cornerstone of international nuclear safeguards. One emerging challenge in this domain is the fabrication of TRIstructural ISOtropic (TRISO) particle fuels. Although these innovative fuel forms are critical for advanced reactor applications, their robust refractory ceramics and coating compositions present significant obstacles to destructive analysis (DA) methods. Ensuring full and quantitative recovery from these particles is essential for accurate mass accountancy. The current study was initiated to address these challenges, first by validating a previously established destructive method developed by Oak Ridge National Laboratory (ORNL) for the quantitative recovery of uranium from TRISO particles and then following that process with uranium content determination through isotope dilution mass spectrometry (IDMS) and Davies-Gray titration. This study expands on the scope of a digestive method that was developed under the Advanced Gas Reactor Fuel Development and Qualification program and is currently implemented in both the Coated Particle Fuel Development Laboratory and Irradiated Fuels Examination Laboratory at ORNL. The success of the previous Advanced Gas Reactor work relied on developing a DA method to evaluate the fabrication process and reactor experiments. The methodology described in this report was designed to rigorously investigate the efficacy of the crush/burn/leach sample preparation of TRISO particles; it aims to quantify uranium recovery while also assessing the effects of TRISO constituents (e.g., silicon and zirconium) on analytical precision and accuracy. By comparing the results from the titration method and IDMS, we sought to determine whether existing analytical procedures accepted by the International Atomic Energy Agency (IAEA) could be effectively translated to TRISO fuel forms. The team employed an approach that involved processing replicate TRISO samples, optimizing the milling (i.e., crushing) step and performing serial leaches. The elemental composition of the analytical samples was examined to prepare for interference studies in the second year of this project. The integration of gamma spectrometry to verify residual uranium activity further strengthened the validation. Statistical methods were applied to the collected data to evaluate the uncertainties arising from sampling, sample preparation and uranium quantification. These uncertainties were then compared to the IAEA’s international target values (ITVs). Additional data collected in upcoming project work will strengthen the uncertainty estimates. Ultimately, it is hoped that this project will contribute materially to the body of work related to characterization of TRISO based fuels for the purpose of material accountancy and its applications to international safeguards.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

A machine learning model for predicting the minimum miscibility pressure of CO 2 and crude oil system based on a support vector machine algorithm approach

CO 2 enhanced oil recovery (EOR) is a potential way for carbon capture, utilization and storage (CCUS). Though, the effect of CO 2 injection is greatly influenced by the reservoir conditions. Typically, Minimum miscible pressure (MMP) is selected as one of the key parameters for the screening and evaluation of prospective CO 2 flooding. Conventional slim tube test is both accurate and widely accepted but it is inefficient. Existing empirical formulas for MMPs are easy to be used but have been proved inaccurate and unreliable. Machine learning-based methods have great advantages in predicting MMP. However, only predication accuracy is discussed for most models without the screening of the main control factors and further validation of the model reliability. In this paper, a new prediction model based on support vector machine (SVM) was developed for pure/impure CO 2 and crude oil system. This study was based on 147 sets of MMP data from the literature with full information on reservoir temperature, oil composition and gas composition. The main control factors were screened by several statistical methods. Unlike the conventional prediction models that verified by only prediction accuracy, learning curve and single factor control variable analysis are further validated to obtain the optimum model.

02 PETROLEUM↗
DOE OSTI2015

SU-E-T-508: Internal Organ Motion Effect On Radiation Dose to a Point Under Half-Beam Block Match Line

Purpose Half-beam block is a field matching technique frequently used in radiotherapy. With no setup error, a well calibrated linac, and no internal organ motion, two photon fields can be matched seamlessly dosimetry-wise with their central axes passing the match line. However, in actual clinical situations, internal organ motion is often inevitable. This study was conducted to investigate its influence on radiation dose to patient internal points directly under the matching line. Methods A clinical setting is modeled as two half-space (x<0 and x<0) radiation fields that are turned on sequentially with a time gap of integer times of the patient internal organ motion period (T{sub 0}). Our point of interest moves with patient internal organs periodically and evenly in and out of the radiation fields, resulting in an average location at x=0. When the fields are delivered without any motion management, the initial phase of the point’s movement is unknown. Statistical methods are used to compute the expected value ( ) and variance (σ) of the point dose given the uncertainty. Results Analytical solutions are obtained for and s of dose received by a point directly under the match line. is proportional to the total beam-on time (T1), and σ demonstrates previously unknown periodic behavior. /<Dσ has local maximums (minimums) when T{sub 1} is 2n*T{sub 0} ((2n+1)*T{sub 0}) (n is an integer) with an upper envelope scales as 1/T{sub 1}. Worst case scenario the point dose can be zero or double of the expected value. Conclusion We have analytically analyzed the internal organ motion effect on radiation dose received by a patient internal point directly under a half-beam block match line. Our results help us to better understand this phenomenon and facilitate the reduction of point dosimetric uncertainties in our clinical practice.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics

Abstract Background The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report. Purpose The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain‐relevant assessments via the analysis of relevant image statistics. Methods As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis. To create the training dataset, an established 3D virtual breast phantom was adapted. The resulting dataset comprised about 108 000 images of size 512 512. For the evaluation of submissions to the Challenge, an ensemble of 10 000 DGM‐generated images from each submission was employed. The evaluation procedure consisted of two stages. In the first stage, a preliminary check for memorization and image quality (via the Fréchet Inception Distance [FID]) was performed. Submissions that passed the first stage were then evaluated for the reproducibility of image statistics corresponding to several feature families including texture, morphology, image moments, fractal statistics, and skeleton statistics. A summary measure in this feature space was employed to rank the submissions. Additional analyses of submissions was performed to assess DGM performance specific to individual feature families, the four classes in the training data, and also to identify various artifacts. Results Fifty‐eight submissions from 12 unique users were received for this Challenge. Out of these 12 submissions, 9 submissions passed the first stage of evaluation and were eligible for ranking. The top‐ranked submission employed a conditional latent diffusion model, whereas the joint runners‐up employed a generative adversarial network, followed by another network for image superresolution. In general, we observed that the overall ranking of the top 9 submissions according to our evaluation method (i) did not match the FID‐based ranking, and (ii) differed with respect to individual feature families. Another important finding from our additional analyses was that different DGMs demonstrated similar kinds of artifacts. Conclusions This Grand Challenge highlighted the need for domain‐specific evaluation to further DGM design as well as deployment. It also demonstrated that the specification of a DGM may differ depending on its intended use.

Radiology, Nuclear Medicine & Medical Imaging↗
NASA NTRS1995

Integrated design and manufacturing for the high speed civil transport (a combined aerodynamics/propulsion optimization study)

This report documents the efforts of a Georgia Tech High Speed Civil Transport (HSCT) aerospace student design team in completing a design methodology demonstration under NASA's Advanced Design Program (ADP). Aerodynamic and propulsion analyses are integrated into the synthesis code FLOPS in order to improve its prediction accuracy. Executing the integrated product and process development (IPPD) methodology proposed at the Aerospace Systems Design Laboratory (ASDL), an improved sizing process is described followed by a combined aero-propulsion optimization, where the objective function, average yield per revenue passenger mile ($/RPM), is constrained by flight stability, noise, approach speed, and field length restrictions. Primary goals include successful demonstration of the application of the response surface methodolgy (RSM) to parameter design, introduction to higher fidelity disciplinary analysis than normally feasible at the conceptual and early preliminary level, and investigations of relationships between aerodynamic and propulsion design parameters and their effect on the objective function, $/RPM. A unique approach to aircraft synthesis is developed in which statistical methods, specifically design of experiments and the RSM, are used to more efficiently search the design space for optimum configurations. In particular, two uses of these techniques are demonstrated. First, response model equations are formed which represent complex analysis in the form of a regression polynomial. Next, a second regression equation is constructed, not for modeling purposes, but instead for the purpose of optimization at the system level. Such an optimization problem with the given tools normally would be difficult due to the need for hard connections between the various complex codes involved. The statistical methodology presents an alternative and is demonstrated via an example of aerodynamic modeling and planform optimization for a HSCT.

Baecher, Juergen↗
DOE OSTI2017

Play fairway analysis of geothermal resources across the State of Hawaii: 1. Geological, geophysical, and geochemical datasets

Phase 1 of a Play Fairway Analysis (PFA) of geothermal resource potential across the State of Hawaii was recently completed. The final products of this work include a statewide geothermal resource probability map, a map of confidence in this probability, an assessment of the viability of development in areas of interest, and a prioritized list of recommended future exploration activities. The intersection of subsurface heat (H), permeability (P), and fluid (F) is necessary for an optimum geothermal play. This study: (1) identified and compiled all legacy and current data relevant to Hawaii’s geothermal resource; (2) ranked these datasets in terms of their relevance to subsurface H, P, F; (3) developed a Bayesian statistical method to incorporate the data and their rankings, and produce a statewide resource probability map; (4) developed a method to assess confidence in the probability values; and (5) assessed what we term ‘development viability’ in resulting areas of interest across the state. This paper details the basic project workflow, and activities (1) and (2). It describes the rationale for including this study’s datasets: surface geologic mapping data (calderas, rift zones, volcanic vents, dikes, faults), groundwater data (temperature, Chloride:Magnesium, SiO 2 ) and geophysical data (gravity, magnetotelluric, seismic, geodetic strain), and justifies their relative rankings in terms of H, P, and F. A second paper describes activities (3) and (4) related to the statistical methodology, and a third paper describes activity (5), the development viability criteria, as well as our suggested roadmap for future exploration activities. Overall, we find that the likelihood of abundant of geothermal resources is highest on the youngest island of Hawaii, but groundwater indicators suggest there may also be resources on the other, older islands. Furthermore, the higher demand for renewable energy on the more populated islands of Maui and Oahu, as well as a high viability of development on Lanai, also motivate further exploration on these islands.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

SU-E-P-48: Evaluation of Intensity Modulated Radiotherapy (IMRT) with Three Different Commercial Planning Systems for the Treatment of Cervical Cancer

Purpose: To investigate the performances of three commercial treatment planning systems (TPS) for intensity modulated radiotherapy (IMRT) optimization regarding cervical cancer. Methods: For twenty cervical cancer patients, three IMRT plans were retrospectively re-planned: one with Pinnacle TPS,one with Oncentra TPS and on with Eclipse TPS. The total prescribed dose was 50.4 Gy delivered for PTV and 58.8 Gy for PTVnd by simultaneous integrated boost technique. The treatments were delivered using the Varian 23EX accelerator. All optimization schemes generated clinically acceptable plans. They were evaluated based on target coverage, homogeneity (HI) and conformity (CI). The organs at risk (OARs) were analyzed according to the percent volume under some doses and the maximum doses. The statistical method of the collected data of variance analysis was used to compare the difference among the quality of plans. Results: IMRT with Eclipse provided significant better HI, CI and all the parameters of PTV. However, the trend was not extension to the PTVnd, it was still significant better at mean dose, D50% and D98%, but plans with Oncentra showed significant better in the hight dosage volume, such as maximum dose and D2%. For the bladder wall, there were not notable difference among three groups, although Pinnacle and Oncentra systems provided a little lower dose sparing at V50Gy of bladder and rectal wall and V40Gy of bladder wall, respectively. V40Gy of rectal wall (p=0.037), small intestine (p=0.001 for V30Gy, p=0.010 for maximum dose) and V50Gy of right-femoral head (p=0.019) from Eclipse plans showed significant better than other groups. Conclusion: All SIB-IMRT plans were clinically acceptable which were generated by three commercial TPSs. The plans with Eclipse system showed advantages over the plans with Oncentra and Pinnacle system in the overwhelming majority of the dose coverage for targets and dose sparing of OARs in cervical cancer.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2001

Methods for Discerning Cloud Reflectivity Changes due to the Indirect Effect of Aerosol: A Pilot-study for Triana

Understanding the effect of aerosol on cloud systems is one of the major challenges in atmospheric and climate research. Local studies suggest a multitude of influences on cloud properties. Yet the overall effect on cloud albedo, a critical parameter in climate simulations, remains uncertain. NASA's Triana mission will provide, from its EPIC multi-spectral imager, simultaneous data on aerosol properties and cloud reflectivity. With Triana's unique position in space these data will be available not only globally but also over the entire daytime, well suited to accommodate the often short lifetimes of aerosol and investigations around diurnal cycles. This pilot study explores the ability to detect relationships between aerosol properties and cloud reflectivity with sophisticated statistical methods. Sample results using data from the EOS Terra platform to simulate Triana are presented.

Kinne, S.↗
DOE OSTI2019

A unified approach to evaluating precipitation frequency estimates with uncertainty quantification: Application to Florida and California watersheds

Although a variety of climate datasets are now available, including observational and model products, there remains a pressing need for the development of a unified, automated and application-focused approach for evaluating and comparing climate extremes within and across these datasets. Precipitation frequency (PF) es- timates for extreme precipitation events are useful decision-relevant quantities among water managers. Our objective and quantitative framework for computing PF estimates of any duration uses known techniques in a novel combination. Our framework consists of three steps: (1) use of a regionalization-based statistical method for precipitation frequency estimation at observed sites; this involves hierarchical clustering (here we used Ward’s method) for delineating homogeneous regions, and regional frequency analysis using L-moments; (2) use of kriging for spatial mapping because of its advantages over other conventional interpolation methods; (3) use of z-scores accounts for estimation uncertainties when comparing PF estimates between datasets. The 24-h PF estimates in the Kissimmee-Southern Florida watershed (flat topography) and the Sacramento-San Joaquin watershed (complex topography) demonstrate the applicability of this approach in regions of any geographical complexity. Our results are shown to be reasonably calibrated using the probability integral transform (PIT) histogram of the best fitted distribution. Our results are compared with that of the NOAA Atlas 14 reports and show that for most of the stations the two estimates are statistically indistinguishable; and, the confidence intervals of our estimates are narrower than those of the NOAA estimates. Our approach is applicable to a variety of datasets and provides a baseline for assessing performance of climate models in historical simulations – a necessary first step towards analyzing future projections.

58 GEOSCIENCES↗
NASA NTRS1982

Precipitation measurements from space - Workshop summary 28 April-1 May 1981, Greenbelt, MD

The goals and methods for global precipitation measurements from space are examined. Records currently exist only for visible and IR scans of cloud properties, and are applied from GEO for detecting diurnal variations in precipitation. Microwave radiometry is noted to be a suitable method for supplementing the visible and IR data for measuring stratiform oceanic precipitation, and when used at up to 3 microns can detect areas, if not amounts, of precipitation from GEO. Applications of radar altimeters are proposed in terms of modifications to the Seasat-type 2.2 cm radar, the use of surface target attentuation radar, of frequency agile rain radar, or of adaptive pointing radar. Soil moisture sensing is available with passive microwave radiometry in the 20-50 cm bands, or active sensing in the 5-8 cm bands. The utilization of GARP ground truth data is explored, along with statistical methods for treating the data samples.

Atlas, D.↗
DOE OSTI2024

Dynamic data-driven multiscale modeling for predicting the degradation of a 316L stainless steel nuclear cladding material

Here, we have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dynamic Data-Driven Multiscale Modeling for Predicting Microstructure and Mechanical Properties of 316L Stainless Steel

We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Quantitative Uncertainty Analysis for Diffraction

Current software uses a least squares statistical method. For each model parameter, current software yields a point estimate and estimated standard error for each point estimate. In contrast, we have employed a Bayesian approach with sampling algorithm to provide a full posterior probability density function on each model parameter. This yields a different type of solution (probabilistic) with better fidelity. The Quantitative Uncertainty Analysis for Diffraction (QUAD) package returns a matrix of affect model parameters as a function of iteration through the MCMC analysis. QUAD is a Python code that allows for analysis of X-ray and neutron diffraction data to infer the structure of materials with quantifiable certainty. The code uses Bayesian statistics and Markov chain sampling algorithms to create posterior probability distributions on all material structure parameters that are modeled by a researcher. The statistical packages use the open source GSASII project as a library to calculate model diffraction patterns.

Fancher, ChristopherM↗
DOE OSTI2024

Feature-agnostic metabolomics for determining effective subcytotoxic doses of common pesticides in human cells

Although classical molecular biology assays can provide a measure of cellular response to chemical challenges, they rely on a single biological phenomenon to infer a broader measure of cellular metabolic response. These methods do not always afford the necessary sensitivity to answer questions of subcytotoxic effects, nor do they work for all cell types. Likewise, boutique assays such as cardiomyocyte beat rate may indirectly measure cellular metabolic response, but they too, are limited to measuring a specific biological phenomenon and are often limited to a single cell type. For these reasons, toxicological researchers need new approaches to determine metabolic changes across various doses in differing cell types, especially within the low-dose regime. Here, the data collected herein demonstrate that LC-MS/MS-based untargeted metabolomics with a feature-agnostic view of the data, combined with a suite of statistical methods including an adapted environmental threshold analysis, provides a versatile, robust, and holistic approach to directly monitoring the overall cellular metabolomic response to pesticides. When employing this method in investigating two different cell types, human cardiomyocytes and neurons, this approach revealed separate subcytotoxic metabolomic responses at doses of 0.1 and 1 µM of chlorpyrifos and carbaryl. These findings suggest that this agnostic approach to untargeted metabolomics can provide a new tool for determining effective dose by metabolomics of chemical challenges, such as pesticides, in a direct measurement of metabolomic response that is not cell type-specific or observable using traditional assays.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Mapping Quasar Light Echoes in 3D with Ly α Forest Tomography

The intense radiation emitted by luminous quasars dramatically alters the ionization state of their surrounding IGM. This so-called proximity effect extends out to tens of Mpc, and manifests as large coherent regions of enhanced Ly (Ly) forest transmission in absorption spectra of background sightlines. In this work, we present a novel method based on Ly forest tomography, which is capable of mapping these quasar "light echoes" in three dimensions. Using a dense grid (10-100) of faint (m r ≈ 24.7 mag) background galaxies as absorption probes, one can measure the ionization state of the IGM in the vicinity of a foreground quasar, yielding detailed information about the quasar's radiative history and emission geometry. An end-to-end analysis-combining cosmological hydrodynamical simulations post-processed with a quasar emission model, realistic estimates of galaxy number densities, and instrument + telescope throughput-is conducted to explore the feasibility of detecting quasar light echoes. We present a new, fully Bayesian statistical method that allows one to reconstruct quasar light echoes from thousands of individual low-S/N transmission measurements. Armed with this tool, we undertake an exhaustive parameter study and show that light echoes can be convincingly detected for luminous (M 1450 <-27.5 mag, corresponding to m 1450 < 18.4 mag at z≃3.6) quasars at redshifts 3 < z QSO < 5, and that a relative precision better than 20% on the quasar age can be achieved for individual objects in the expected range of ages between 1 and 100 Myr. The observational requirements are relatively modest: Moderate-resolution (R ≳ 750), multiobject spectroscopy at a low signal-to-noise ratio (S/N > 5) is sufficient, requiring three-hour integrations using existing instruments on 8 m class telescopes.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2000

Finite Element Analysis of Reverberation Chambers

The primary motivating factor behind the initiation of this work was to provide a deterministic means of establishing the validity of the statistical methods that are recommended for the determination of fields that interact in -an avionics system. The application of finite element analysis to reverberation chambers is the initial step required to establish a reasonable course of inquiry in this particularly data-intensive study. The use of computational electromagnetics provides a high degree of control of the "experimental" parameters that can be utilized in a simulation of reverberating structures. As the work evolved there were four primary focus areas they are: 1. The eigenvalue problem for the source free problem. 2. The development of a complex efficient eigensolver. 3. The application of a source for the TE and TM fields for statistical characterization. 4. The examination of shielding effectiveness in a reverberating environment. One early purpose of this work was to establish the utility of finite element techniques in the development of an extended low frequency statistical model for reverberation phenomena. By employing finite element techniques, structures of arbitrary complexity can be analyzed due to the use of triangular shape functions in the spatial discretization. The effects of both frequency stirring and mechanical stirring are presented. It is suggested that for the low frequency operation the typical tuner size is inadequate to provide a sufficiently random field and that frequency stirring should be used. The results of the finite element analysis of the reverberation chamber illustrate io-W the potential utility of a 2D representation for enhancing the basic statistical characteristics of the chamber when operating in a low frequency regime. The basic field statistics are verified for frequency stirring over a wide range of frequencies. Mechanical stirring is shown to provide an effective frequency deviation.

Bunting, Charles F.↗
NASA NTRS2010

Comparison of Postural Recovery Following Short and Long Duration Spaceflights

INTRODUCTION: Post-flight postural ataxia reflects adaptive changes to vestibulo-spinal reflexes and control strategies adopted for movement in weightlessness. Quantitative measures obtained during computerized dynamic posturography (CDP) from US and Russian programs provide insight into the effect of spaceflight duration in terms of both the initial decrements and recovery of postural stability. METHODS: CDP was obtained on 117 crewmembers following Shuttle flights lasting 4-17 days, and on 64 crewmembers following long-duration missions lasting 48-380 days. Although the number and timing of sessions varied, the goal was to characterize postural recovery pooling similar measures from different research and flight medicine programs. This report focuses on eyes closed, head erect conditions with either a fixed or sway-referenced base of support. A smaller subset of subjects repeated the sway-referenced condition while making pitch head movements (+/- 20deg at 0.33Hz). Equilibrium scores were derived from peak-to-peak anterior-posterior sway. Fall probability was modeled using Bayesian statistical methods to estimate parameters of a logit function. RESULTS: The standard Romberg condition was the least sensitive. Longer duration flights led to larger decrements in stability with sway-reference support during the first 1-2 days, although the timecourse of recovery was similar across flight duration with head erect. Head movements led to increased incidence of falls during the first week, with a significantly longer recovery following long duration flights. CONCLUSIONS: The diagnostic assessment of postural instability, and differences in the timecourse of postural recovery between short and long flight durations, are more pronounced during unstable support conditions requiring active head movements.

Wood, S. J.↗
DOE OSTI2018

Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony

Background: As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. Objective: We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA).Methods: Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. Results: We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. Conclusions: Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

97 MATHEMATICS AND COMPUTING↗