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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Shock Hugoniot calculations using on-the-fly machine learned force fields with ab initio accuracy

We present a framework for computing the shock Hugoniot using on-the-fly machine learned force field (MLFF) molecular dynamics simulations. In particular, we employ an MLFF model based on the kernel method and Bayesian linear regression to compute the free energy, atomic forces, and pressure, in conjunction with a linear regression model between the internal and free energies to compute the internal energy, with all training data generated from Kohn–Sham density functional theory (DFT). We verify the accuracy of the formalism by comparing the Hugoniot for carbon with recent Kohn–Sham DFT results in the literature. In so doing, we demonstrate that Kohn–Sham calculations for the Hugoniot can be accelerated by up to two orders of magnitude, while retaining ab initio accuracy. We apply this framework to calculate the Hugoniots of 14 materials in the FPEOS database, comprising 9 single elements and 5 compounds, between temperatures of 10 kK and 2 MK. We find good agreement with first principles results in the literature while providing tighter error bars. In addition, we confirm that the inter-element interaction in compounds decreases with temperature.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1976

A Landsat study of water quality in Lake Okeechobee

This paper uses multiple regression techniques to investigate the relationship between Landsat radiance values and water quality measurements. For a period of over one year, the Central and Southern Florida Flood Control District sampled the water of Lake Okeechobee for chlorophyll, carotenoids, turbidity, and various nutrients at the time of Landsat overpasses. Using an overlay map of the sampling stations, Landsat radiance values were measured from computer compatible tapes using a GE image 100 and averaging over a 22-acre area at each station. These radiance values in four bands were used to form a number of functions (powers, logarithms, exponentials, and ratios), which were then compared with the ground measurements using multiple linear regression techniques. Several dates were used to provide generality and to study possible seasonal variations. Individual correlations were presented for the various water quality parameters and best fit equations were examined for chlorophyll and turbidity. The results and their relationship to past hydrological research were discussed.

Gervin, J. C.↗
NASA NTRS2002

Locally-Based Kernal PLS Smoothing to Non-Parametric Regression Curve Fitting

We present a novel smoothing approach to non-parametric regression curve fitting. This is based on kernel partial least squares (PLS) regression in reproducing kernel Hilbert space. It is our concern to apply the methodology for smoothing experimental data where some level of knowledge about the approximate shape, local inhomogeneities or points where the desired function changes its curvature is known a priori or can be derived based on the observed noisy data. We propose locally-based kernel PLS regression that extends the previous kernel PLS methodology by incorporating this knowledge. We compare our approach with existing smoothing splines, hybrid adaptive splines and wavelet shrinkage techniques on two generated data sets.

Rosipal, Roman↗
DOE OSTI2013

Sirt2 suppresses glioma cell growth through targeting NF-κB–miR-21 axis

Highlights: •Sirt2 expression is down-regulated in human glioma tissues and cell lines. •Sirt2 regresses glioma cell growth and colony formation via inducing apoptosis. •miR-21 is essential for the functions of Sirt2 in glioma cells. •Sirt2 deacetylates p65 to decrease miR-21 expression. -- Abstract: Sirtuins are NAD{sup +}-dependent deacetylases that regulate numerous cellular processes including aging, DNA repair, cell cycle, metabolism, and survival under stress conditions. The roles of sirtuin family members are widely studied in carcinogenesis. However, their roles in glioma remain unclear. Here we report that Sir2 was under expressed in human glioma tissues and cell lines. We found that Sirt2 overexpression decreased cell proliferation and colony formation capacity. In addition, Sirt2 overexpression induced cellular apoptosis via up-regulating cleaved caspase 3 and Bax, and down-regulating anti-apoptotic protein Bcl-2. Sirt2 knockdown obtained opposing results. We showed that Sirt2 overexpression inhibited miR-21 expression, and Sirt2 was not sufficient to reduce cell proliferation and colony formation as well as to induce apoptosis when miR-21 was knocked down in glioma cells. Mechanically, we demonstrated that Sirt2 deacetylated p65 at K310 and blocked p65 binding to the promoter region of miR-21, thus regressing the transcription of miR-21. In summary, Sirt2 is critical in human glioma via NF-κB–miR-21 pathway and Sirt2 activator may serve as candidate drug for glioma therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Probability of Pinching Failure in PWR SNF after Long-Term Storage

A probabilistic assessment was performed on pinching failure of PWR SNF rod cladding under accidental SNF cask transport conditions. a set of 3,000 rod pinching scenarios was analyzed, with different geometrical, material, and loading parameters, using a Latin Hypercube Sampling (LHS). The investigated accidents include train derailments leading to cask collisions or cask drops. Pinching failure was related to cladding offset strain, defined as the plastic deformation interval in radial cladding direction normalized with respect to the outer cladding diameter. Finite element (FE) models were used to compute SNF rod deformations and the offset strain demand OSD in each of the assessed pinching scenarios. The numerical models account for the contribution of the degraded fuel pellets to rod pinching stiffness. The offset strain capacity OSC was computed using regression and interpolation models based on ring compression (RCT) test results, and account for hydride-related effects on cladding ductility. The results show a low probability of pinching failure. Hydride-related cladding embrittlement affects the structural integrity of PWR SNF rods after long-term dry storage of up to 300 years. Radial hydride patterns or large amounts of circumferentially oriented hydrides reduce the cladding ductile deformation capability when pinched at low material temperatures. The probability of hydride-related pinching failure of PWR cladding is estimated after long-term dry storage to address: How do PWR SNF rod conditions like hydrogen content, vacuum drying peak cladding hoop stress σ{sub θ}, and cladding material temperature affect cladding ductility? Is hydride-related cladding embrittlement a material degradation mechanism that affects the structural integrity of PWR SNF rods under pinching loading? What are the controlling parameters for deformation demands in PWR SNF rods under pinching loading? Is the pinching load amplitude an important parameter that controls the probability of pinching failure? How do parameters like pellet-cladding gap or fuel pellet degradation affect the pinching loading capacity of PWR SNF rods? Statistical modelling of PWR SNF rod conditions in U.S. inventory: A pseudo rod database was generated based on EIA database. Analysis of train accident scenarios: a probability density function (pdf) of a pinching load distribution P{sub 9m} of a 9-m cask drop is linearly scaled to generate a more representative: distribution P{sub AS}. The new distribution considers train collision velocities, cask drops for on- and off-bridge accidents, and cask impact orientation. LHS generates 3,000 rod pinching scenarios based on nine independent geometrical, material, and loading parameters. Probabilistic FE Analysis: The LHS realizations are used to generate 3,000 FE models. The FE analysis is conducted for P{sub 9m}, and P{sub AS}. The workflow is automatized. The OSC is computed with regression and interpolation models, as a function of the hydrogen content C{sub H}, vacuum drying peak σ{sub θ}, and SNF temperature T{sub FR}: The regression data was obtained in RCTs on PWR cladding. The OSD is derived from the cladding deformations in the FE models. Cladding failure was identified via a comparison of OSC and OSD. A 300 and a 30 years-of-dry-storage duration is investigated: The dry-storage period affects the expected SNF temperature and consequently, the OSC and the OSD. The material temperature LHS population is adapted to account for a SNF temperature variation. The probability of hydride-related, ductile cladding failure during a severe transportation accident is not significantly affected by the pinching load amplitude: The probability is slightly reduced from a range of less than 0.0333% to 0.1667% for P{sub 9m}, to a range of less than 0.0333% to 0.1333% for P{sub AS}, depending on the vacuum drying peak cladding hoop stress modelling assumptions. Further, the SNF dry-storage duration does not significantly affect the probability of ductile pinching failure. Based on available train accidents data, the pinching failure probability for a 5,000 km long transportation route ranges from less than 1.62 E-7 to a value of 6.47 E-7, depending on the vacuum drying peak cladding hoop stress modelling assumptions. The pinching load amplitude is not a controlling parameter for the probability of ductile cladding pinching failure, and does not control the cladding deformations once pellet-cladding contact is established, The pellet-cladding gap width, however, plays a key role. Fuel pellet degradation only affects the deformation capacity, if the pellet stiffness is significantly reduced (more than 90%). Only highly embrittled cladding is likely to fail. The deformation demands on the cladding under pinching loading are small.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2011

Novel Analog For Muscle Deconditioning

Existing models (such as bed rest) of muscle deconditioning are cumbersome and expensive. We propose a new model utilizing a weighted suit to manipulate strength, power, or endurance (function) relative to body weight (BW). Methods: 20 subjects performed 7 occupational astronaut tasks while wearing a suit weighted with 0-120% of BW. Models of the full relationship between muscle function/BW and task completion time were developed using fractional polynomial regression and verified by the addition of pre-and postflightastronaut performance data for the same tasks. Splineregression was used to identify muscle function thresholds below which task performance was impaired. Results: Thresholds of performance decline were identified for each task. Seated egress & walk (most difficult task) showed thresholds of leg press (LP) isometric peak force/BW of 18 N/kg, LP power/BW of 18 W/kg, LP work/BW of 79 J/kg, isokineticknee extension (KE)/BW of 6 Nm/kg, and KE torque/BW of 1.9 Nm/kg.Conclusions: Laboratory manipulation of relative strength has promise as an appropriate analog for spaceflight-induced loss of muscle function, for predicting occupational task performance and establishing operationally relevant strength thresholds.

Ploutz-Snyder, Lori↗
DOE OSTI2020

TreeSAPP: the Tree-based Sensitive and Accurate Phylogenetic Profiler

Microbial communities drive matter and energy transformations integral to global biogeochemical cycles, yet many taxonomic groups facilitating these processes remain poorly represented in biological sequence databases. Due to this missing information, taxonomic assignment of sequences from environmental genomes remains inaccurate. We present the Tree-based Sensitive and Accurate Phylogenetic Profiler (TreeSAPP) software for functionally and taxonomically classifying genes, reactions and pathways from genomes of cultivated and uncultivated microorganisms using reference packages representing coding sequences mediating multiple globally relevant biogeochemical cycles. TreeSAPP uses linear regression of evolutionary distance on taxonomic rank to improve classifications, assigning both closely related and divergent query sequences at the appropriate taxonomic rank. TreeSAPP is able to provide quantitative functional and taxonomic classifications for both assembled and unassembled sequences and files supporting interactive tree of life visualizations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

Altered central nervous system processing of baroreceptor input following hindlimb unloading in rats

The effect of cardiovascular deconditioning on central nervous system processing of baroreceptor afferent activity was evaluated following 14 days of hindlimb unloading (HU). Inactin-anesthetized rats were instrumented with catheters, renal sympathetic nerve electrodes, and aortic depressor nerve electrodes for measurement of mean arterial pressure, heart rate, renal sympathetic nerve activity (RSNA), and aortic depressor nerve activity (ADNA). Baroreceptor and baroreflex functions were assessed during infusion of phenylephrine and sodium nitroprusside. Central processing of baroreceptor afferent input was evaluated by linear regression relating RSNA to ADNA. The maximum baroreflex-elicited increase in RSNA was significantly reduced in HU rats (122 +/- 3.8 vs. 144 +/- 4.9% of baseline RSNA), whereas ADNA was not altered. The slope (-0.18 +/- 0.04 vs. -0.40 +/- 0.04) and y-intercept (121 +/- 3.2 vs. 146 +/- 4.3) of the linear regression relating increases in efferent RSNA to decreases in afferent ADNA during hypotension were significantly reduced in HU rats. There were no differences during increases in arterial pressure. Results demonstrate that the attenuation in baroreflex-mediated increases in RSNA following HU is due to changes in central processing of baroreceptor afferent information rather than aortic baroreceptor function.

NASA Discipline Cardiopulmonary↗
DOE OSTI2023

Array-Based Machine Learning for Functional Group Detection in Electron Ionization Mass Spectrometry

Mass spectrometry is a ubiquitous technique capable of complex chemical analysis. The fragmentation patterns that appear in mass spectrometry are an excellent target for artificial intelligence methods to automate and expedite the analysis of data to identify targets such as functional groups. To develop this approach, we trained models on electron ionization (a reproducible hard fragmentation technique) mass spectra so that not only the final model accuracies but also the reasoning behind model assignments could be evaluated. The convolutional neural network (CNN) models were trained on 2D images of the spectra using transfer learning of Inception V3, and the logistic regression models were trained using array-based data and Scikit Learn implementation in Python. Our training dataset consisted of 21,166 mass spectra from the United States’ National Institute of Standards and Technology (NIST) Webbook. The data was used to train models to identify functional groups, both specific (e.g., amines, esters) and generalized classifications (aromatics, oxygen-containing functional groups, and nitrogen-containing functional groups). We found that the highest final accuracies on identifying new data were observed using logistic regression rather than transfer learning on CNN models. It was also determined that the mass range most beneficial for functional group analysis is 0–100 m/z. We also found success in correctly identifying functional groups of example molecules selected from both the NIST database and experimental data. Beyond functional group analysis, we also have developed a methodology to identify impactful fragments for the accurate detection of the models’ targets. The results demonstrate a potential pathway for analyzing and screening substantial amounts of mass spectral data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Effect of tamoxifen on spermatogenesis and testicular steroidogenesis

The aim of this study was to evaluate the effects of in vivo and in vitro treatments with selective estrogen receptor modulator (SERM), tamoxifen on testicular functions. The testis treated with tamoxifen, in vivo or in vitro, showed dose-dependent regressive changes in spermatogenesis. This study showed that the decreased estrogenic effect due to tamoxifen may be directly responsible for decreased testicular expression of aromatase, which in turn may be responsible for decreased synthesis of estradiol in the testis. The decreased endogenous estradiol through cAMP-CREB signaling mechanism may decline germ cells proliferation (PCNA) and survival (Bcl2). The tamoxifen-induced decreased estrogenic effect may also be responsible for increased expression of testicular NOS and consequently increased production of NO, which may cause increased germ cells apoptosis (Caspase-3) and impaired spermatogenesis. Both in vivo and in vitro studies showed the inhibitory effect on testicular steroidogenic factors. Thus, tamoxifen inhibits testicular spermatogenesis and steroidogenesis either directly acting on testis or indirectly through gonadotropin release. - Highlights: •Treatment of tamoxifen, in vivo or in vitro, caused dose-dependent regressive changes in spermatogenesis. •The tamoxifen induced decreased estradiol suppresses germ cells proliferation and survival via cAMP-CREB signaling. • The tamoxifen-induced decreased estrogen may increase production of NO by increasing expression of iNOS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Addressing bias in bagging and boosting regression models

As artificial intelligence (AI) becomes widespread, there is increasing attention on investigating bias in machine learning (ML) models. Previous research concentrated on classification problems, with little emphasis on regression models. This paper presents an easy-to-apply and effective methodology for mitigating bias in bagging and boosting regression models, that is also applicable to any model trained through minimizing a differentiable loss function. Our methodology measures bias rigorously and extends the ML model's loss function with a regularization term to penalize high correlations between model errors and protected attributes. We applied our approach to three popular tree-based ensemble models: a random forest model (RF), a gradient-boosted model (GBT), and an extreme gradient boosting model (XGBoost). We implemented our methodology on a case study for predicting road-level traffic volume, where RF, GBT, and XGBoost models were shown to have high accuracy. Despite high accuracy, the ML models were shown to perform poorly on roads in minority-populated areas. Our bias mitigation approach reduced minority-related bias by over 50%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Image Analysis for Rapid Assessment and Quality-Based Sorting of Corn Stover

Imaging in the visible spectrum is a low-cost tool that can be readily deployed for in-field or over-belt monitoring of biomass quality for bio-refining operations. Rapid image analysis coupled with innovative preprocessing may reduce the impacts of feedstock variability through identification of contaminants or other material attributes to guide selective sorting and quality management. Image analysis was employed to evaluate the quality of corn stover in red-green-blue (RGB) chromatic space. This study used controlled, bench-scale imaging as a proof-of-concept for rapid quality assessment of corn stover based on variations in material attributes, including chemical and physical attributes, that relate to biological degradation and soil contamination. Additionally, logistic regression-based classification algorithms were used to develop a method for biomass screening as a function of biological degradation or soil contamination. This study demonstrated the use of image analysis to extract features from RGB color space to investigate variations in critical material attributes from chemical composition of corn stover. Fourier transform infrared (FT-IR) suggested a correlation between red band intensity and biological degradation, while detailed surface texture analysis was found to distinguish among variations in ash. These insights offer promise for development of a rapid screening tool that could be deployed by farmers for in-field assessment of biomass quality or biorefinery operators for in-line sorting and process optimization.

09 BIOMASS FUELS↗
NASA NTRS1975

Prediction of elemental creep

Cyclic and steady-state creep tests were performed to provide data which were used to develop predictive equations. These equations, describing creep as a function of stress, temperature, and time, were developed through the use of a least squares regression analyses computer program for both the steady-state and cyclic data sets. Comparison of the data from the two types of tests, revealed that there was no significant difference between the cyclic and steady-state creep strains for the L-605 sheet under the experimental conditions investigated (for the same total time at load). Attempts to develop a single linear equation describing the combined steady-state and cyclic creep data resulted in standard errors of estimates higher than obtained for the individual data sets. A proposed approach to predict elemental creep in metals uses the cyclic creep equation and a computer program which applies strain and time hardening theories of creep accumulation.

Davis, J. W.↗
NASA NTRS1981

Estimation of airplane stability and control derivatives at high angles of attack

A technique for extracting approximate aircraft stability and control derivatives over a broad range of angle of attack from a single large amplitude maneuver which encompasses that range is presented. The results from the application of this technique to flight data are shown to agree well with derivatives extracted from several maneuvers which are more restricted in range of angle of attack. The technique consists of dividing the data from the large maneuver into bins that are a function of angle of attack. The binned data is then analyzed with a modified stepwise regression. The identification of nonlinear terms in the aerodynamic model is shown to discern regions of rapid change in aircraft behavior.

Batterson, J. G.↗
NASA NTRS2011

CO2 Washout Testing of the REI and EM-ACES Space Suits

Requirements for using a space suit during ground testing include providing adequate carbon dioxide (CO2) washout for the suited subject. Acute CO2 exposure can lead to symptoms including headache, dyspnea, lethargy and eventually unconsciousness or even death. Symptoms depend on several factors including partial pressure of CO2 (ppCO2), duration of exposure, metabolic rate of the subject and physiological differences between subjects. The objective of this test was to characterize inspired oronasal ppCO2 in the Rear Entry I-Suit (REI) and the Enhanced Mobility Advanced Crew Escape Suit (EM-ACES) across a range of workloads and flow rates for which ground testing is nominally performed. Three subjects were tested in each suit. In all but one case, each subject performed the test twice to allow for comparison between tests. Suit pressure was maintained at 4.3 psid. Subjects wore the suit while resting, performing arm ergometry, and walking on a treadmill to generate metabolic workloads of approximately 500 to 3000 BTU/hr. Supply airflow was varied at 6, 5 and 4 actual cubic feet per minute (ACFM) at each workload. Subjects wore an oronasal mask with an open port in front of the mouth and were allowed to breathe freely. Oronasal ppCO2 was monitored real-time via gas analyzers with sampling tubes connected to the oronasal mask. Metabolic rate was calculated from the total CO2 production measured by an additional gas analyzer at the air outlet from the suit. Real-time metabolic rate was used to adjust the arm ergometer or treadmill workload to meet target metabolic rates. In both suits, inspired CO2 was primarily affected by the metabolic rate of the subject, with increased metabolic rate resulting in increased inspired ppCO2. Suit flow rate also affected inspired ppCO2, with decreased flow causing small increases in inspired ppCO2. The effect of flow was more evident at metabolic rates greater than or equal to 2000 BTU/hr. Results were consistent between suits, with the EM-ACES demonstrating slightly better CO2 washout than the REI suit, but not statistically significant. Regression equations were developed for each suit to predict the mean inspired ppCO2 as a function of metabolic rate and suit flow rate. This paper provides detailed descriptions of the test hardware, methodology and results, as well as implications for future ground testing in the REI and EM-ACES.

Mitchell, Kate↗
NASA NTRS2012

CO2 Washout Testing of the REI and EM-ACES Space Suits

When a space suit is used during ground testing, adequate carbon dioxide (CO2) washout must be provided for the suited subject. Symptoms of acute CO2 exposure depend on partial pressure of CO2 (ppCO2), metabolic rate of the subject, and other factors. This test was done to characterize inspired oronasal ppCO2 in the Rear Entry I-Suit (REI) and the Enhanced Mobility Advanced Crew Escape Suit (EM-ACES) for a range of workloads and flow rates for which ground testing is nominally performed. Three subjects were tested in each suit. In all but one case, each subject performed the test twice. Suit pressure was maintained at 4.3 psid. Subjects wore the suit while resting, performing arm ergometry, and walking on a treadmill to generate metabolic workloads of about 500 to 3000 BTU/hr. Supply airflow was varied between 6, 5, and 4 actual cubic feet per minute (ACFM) at each workload. Subjects wore an oronasal mask with an open port in front of the mouth and were allowed to breathe freely. Oronasal ppCO2 was monitored in real time by gas analyzers with sampling tubes connected to the mask. Metabolic rate was calculated from the total CO2 production measured by an additional gas analyzer at the suit air outlet. Real-time metabolic rate was used to adjust the arm ergometer or treadmill workload to meet target metabolic rates. In both suits, inspired CO2 was affected mainly by the metabolic rate of the subject: increased metabolic rate significantly (P < 0.05) increased inspired ppCO2. Decreased air flow caused small increases in inspired ppCO2. The effect of flow was more evident at metabolic rates . 2000 BTU/hr. CO2 washout values of the EM-ACES were slightly but not significantly better than those of the REI suit. Regression equations were developed for each suit to predict the mean inspired ppCO2 as a function of metabolic rate and suit flow rate. This paper provides detailed descriptions of the test hardware, methodology, and results as well as implications for future ground testing in the REI-suit and EM-ACES.

Mitchell, Kathryn C.↗
NASA NTRS2016

Automation of Flight Software Regression Testing

NASA is developing the Space Launch System (SLS) to be a heavy lift launch vehicle supporting human and scientific exploration beyond earth orbit. SLS will have a common core stage, an upper stage, and different permutations of boosters and fairings to perform various crewed or cargo missions. Marshall Space Flight Center (MSFC) is writing the Flight Software (FSW) that will operate the SLS launch vehicle. The FSW is developed in an incremental manner based on "Agile" software techniques. As the FSW is incrementally developed, testing the functionality of the code needs to be performed continually to ensure that the integrity of the software is maintained. Manually testing the functionality on an ever-growing set of requirements and features is not an efficient solution and therefore needs to be done automatically to ensure testing is comprehensive. To support test automation, a framework for a regression test harness has been developed and used on SLS FSW. The test harness provides a modular design approach that can compile or read in the required information specified by the developer of the test. The modularity provides independence between groups of tests and the ability to add and remove tests without disturbing others. This provides the SLS FSW team a time saving feature that is essential to meeting SLS Program technical and programmatic requirements. During development of SLS FSW, this technique has proved to be a useful tool to ensure all requirements have been tested, and that desired functionality is maintained, as changes occur. It also provides a mechanism for developers to check functionality of the code that they have developed. With this system, automation of regression testing is accomplished through a scheduling tool and/or commit hooks. Key advantages of this test harness capability includes execution support for multiple independent test cases, the ability for developers to specify precisely what they are testing and how, the ability to add automation, and the ability of the harness and cases to be executed continually. This test concept is an approach that can be adapted to support other projects.

Tashakkor, Scott B.↗