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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.

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

Evaluating the limitations of Bayesian metabolic control analysis

AbstractBayesian Metabolic Control Analysis (BMCA) has emerged as a promising framework for inferring metabolic control coefficients in data-limited scenarios by integrating Bayesian inference with linlog rate laws. However, its predictive accuracy and limitations remain underexplored. This study systematically evaluates BMCA’s ability to infer elasticity values, flux control coefficients (FCCs), and concentration control coefficients (CCCs) under varying data availability conditions using three synthetic metabolic network models. Our findings highlight the strengths and weaknesses of BMCA, guiding its application in metabolic engineering and emphasizing the need for methodological refinements.Author summaryUnderstanding how enzymes control metabolic pathways is crucial for optimizing biomanufacturing and synthetic biology applications. Bayesian Metabolic Control Analysis (BMCA) is a promising computational method that integrates Bayesian inference with metabolic control analysis to estimate key control parameters, even in cases with limited experimental data. However, the accuracy and limitations of BMCA remain unclear. In this study, we systematically evaluate BMCA using three synthetic metabolic networks to determine how different types of physiological data impact its predictive performance. We find that BMCA requires flux and enzyme concentration data for accurate predictions, while external metabolite concentrations contribute little. Additionally, BMCA fails to predict elasticity values beyond a magnitude of 1.5 and reliably infer allosteric regulation, even when strong regulatory interactions exist. In addition, BMCA does not accurately rank metabolic control points, which may limit its utility in identifying key enzymes in engineered pathways. Our work provides practical insights into when and how BMCA can be applied, guiding future research in metabolic modeling and control analysis.

Shin, Janis (ORCID:0000000216572455)↗
NASA NTRS2014

In-Situ and Remote-Sensing Data Fusion Using Machine Learning Techniques to Infer Urban and Fire Related Pollution Plumes

Airmass type characterization is key in understanding the relative contribution of various emission sources to atmospheric composition and air quality and can be useful in bottom-up model validation and emission inventories. However, classification of pollution plumes from space is often not trivial. Sub-orbital campaigns, such as SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) give us a unique opportunity to study atmospheric composition in detail, by using a vast suite of in-situ instruments for the detection of trace gases and aerosols. These measurements allow identification of spatial and temporal atmospheric composition changes due to various pollution plumes resulting from urban, biogenic and smoke emissions. Nevertheless, to transfer the knowledge gathered from such campaigns into a global spatial and temporal context, there is a need to develop workflow that can be applicable to measurements from space. In this work we rely on sub-orbital in-situ and total column remote sensing measurements of various pollution plumes taken aboard the NASA DC-8 during 2013 SEAC4RS campaign, linking them through a neural-network (NN) algorithm to allow inference of pollution plume types by input of columnar aerosol and trace-gas measurements. In particular, we use the 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) airborne measurements of wavelength dependent aerosol optical depth (AOD), particle size proxies, O3, NO2 and water vapor to classify different pollution plumes. Our method relies on assigning a-priori ground-truth labeling to the various plumes, which include urban pollution, different fire types (i.e. forest and agriculture) and fire stage (i.e. fresh and aged) using cluster analysis of aerosol and trace-gases in-situ and auxiliary (e.g. trajectory) data and the training of a NN scheme to fit the best prediction parameters using 4STAR measurements as input. We explore our misclassification rates as related to our ground-truth labels, and with multi-layered pollution plume cases. The next step in our analysis is to optimize parameter selection for a scheme that can be applied to space-borne aerosol and trace-gas observation platforms such as OMI, and future geostationary satellites such as TEMPO and GEO-CAPE.

Neural-network↗
NASA NTRS1993

Synergistic use of optical and microwave data in agrometeorological applications

Remotely sensed optical and microwave data can be synergistically used to infer land surface properties. Optical data can be used to estimate surface albedo, radiation absorption by vegetation canopies and their photosynthetic efficiencies. Vegetation canopy reflectance at red and near-infrared wavelengths can be used to correct for vegetation effect on microwave emissivities at low frequencies for estimating soil moisture. Optical data can also provide information about surface and air temperatures, precipitable water vapor, cloud top temperature and its water content. This information can be utilized to correct microwave data for atmospheric effects. These points are illustrated with theoretical analyses and by application to satellite data. The basic physical mechanisms operative at the various wavelengths are also discussed.

Myneni, R. B.↗
NASA NTRS1998

Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

Liang, Shoudan↗
NASA NTRS2012

Test of Shi et al. Method to Infer the Magnetic Reconnection Geometry from Spacecraft Data: MHD Simulation with Guide Field and Antiparallel Kinetic Simulation

When analyzing data from an array of spacecraft (such as Cluster or MMS) crossing a site of magnetic reconnection, it is desirable to be able to accurately determine the orientation of the reconnection site. If the reconnection is quasi-two dimensional, there are three key directions, the direction of maximum inhomogeneity (the direction across the reconnection site), the direction of the reconnecting component of the magnetic field, and the direction of rough invariance (the "out of plane" direction). Using simulated spacecraft observations of magnetic reconnection in the geomagnetic tail, we extend our previous tests of the direction-finding method developed by Shi et al. (2005) and the method to determine the structure velocity relative to the spacecraft Vstr. These methods require data from four proximate spacecraft. We add artificial noise and calibration errors to the simulation fields, and then use the perturbed gradient of the magnetic field B and perturbed time derivative dB/dt, as described by Denton et al. (2010). Three new simulations are examined: a weakly three-dimensional, i.e., quasi-two-dimensional, MHD simulation without a guide field, a quasi-two-dimensional MHD simulation with a guide field, and a two-dimensional full dynamics kinetic simulation with inherent noise so that the apparent minimum gradient was not exactly zero, even without added artificial errors. We also examined variations of the spacecraft trajectory for the kinetic simulation. The accuracy of the directions found varied depending on the simulation and spacecraft trajectory, but all the directions could be found within about 10 for all cases. Various aspects of the method were examined, including how to choose averaging intervals and the best intervals for determining the directions and velocity. For the kinetic simulation, we also investigated in detail how the errors in the inferred gradient directions from the unmodified Shi et al. method (using the unperturbed gradient) depended on the amplitude of the calibration errors. For an accuracy of 3 for the maximum gradient direction, the calibration errors could be as large as 3% of reconnection magnetic field, while for the same accuracy for the minimum gradient direction, the calibration errors could only be as large as 0.03% of the reconnection magnetic field. These results suggest that the maximum gradient direction can normally be determined by the unmodified Shi et al. method, while the modified method or some other method must be used to accurately determine the minimum gradient direction. The structure velocity was found with magnitude accurate to 2% and direction accurate to within 5%.

Denton, R.↗
NASA NTRS1987

Program for Automated Real-Time Monitoring

Process-control decisions made by applying rules to process data streams. Expert System Executive computer program demonstrates feasibility of automated real-time monitoring. Using Executives knowledge of system formally coded as set of rules specifying actions taken under various conditions. Program makes inferences about observed data based on rules. Written in C language.

Goodrum, Glenn R.↗
NASA NTRS1990

Estimates of radiative flux divergence in the atmosphere from satellite data

Several options for the inference of the atmospheric radiative flux divergence (ARD) on the basis of satellite data are discussed. Attention is given to the clear-sky case and the cloudy-sky case. LW ARD profiles for different climatological regimes are presented and the effect of cloud base height on LW ARD divergence at various heights is illustrated.

Smith, G. L.↗
NASA NTRS2001

Exploration of Solar Wind Acceleration Region Using Interplanetary Scintillation of Water Vapor Maser Source and Quasars

Single-station observations of interplanetary scintillation (IPS) at three microwave frequencies; 2 GHz, 8 GHz and 22 GHz have been carried out between 1989 and 1998 using a large (34 m farad) radio telescope at the Kashima Space Research Center of the Communications Research Laboratory. The aim of these observations is to explore the near-sun solar wind, which is the key region for the study of the solar wind acceleration mechanism. Strong quasars; 3C279 and 3C273B were used for Kashima IPS observations at 2 GHz and 8 GHz, and a water vapor maser source, IRC20431 was used for the IPS observations at 22 GHz. Solar wind velocities derived from Kashima IPS data suggest that the solar wind acceleration takes place at radial distances between 10 and 30 solar radii (R(sub s)) from the sun. Properties of the turbulence spectrum (e.g. anisotropy, spectral index, inner scale) inferred from Kashima data are found to change systematically in the solar wind acceleration region. While the solar wind in the maximum phase appears to be dominated by the slow wind, fast and rarefied winds associated with coronal holes are found to develop significantly at high latitudes as the solar activity declines. Nevertheless, Kashima data suggests that the location of the acceleration region is stable throughout the solar cycle.

Tokumaru, Munetoshi↗
NASA NTRS1978

The effects of polar stratwarms on the winds at the mesopause level in mid latitudes

The continuous measurement of the wind profile over the 80 to 100 km region by means of radio reflections from meteor trails provides data which can be correlated with stratospheric temperature changes inferred from satellite radiance data. The very preliminary results presented show a tendency for stratospheric warming events to produce dramatic changes in the circulation in the neighborhood of the mesopause at 34 deg N. The mean zonal wind over the height range 80 to 100 km above Atlanta for the period November, 1974 through February, 1975 is presented in a graph.

Roper, R. G.↗
NASA NTRS1992

Observed currents on the earth's high-latitude magnetopause

A survey of electrical currents of the earth's magnetosphere, principally at high latitudes, as inferred from magnetic vector data acquired by the Hawkeye 1 satellite, is reported. A total of 536 candidate crossings of the magnetopause were examined. A reduced data set of 139 selected cases was analyzed in detail though solar wind dynamic pressure data were available for only 117 of these cases. Inferred values of the lineal current densities on the magnetopause are in the range 5.5 to 157.5 mA/m over a wide range of solar wind dynamic pressure from 1.17 to 16.1 nPa. The apparent normal thickness of the magnetopause current sheet ranges from 30 to 850 km with mean and median values of 185 and 158 km, respectively. It is argued that the radial rate of motion of the magnetopause is of the order of 2 km/s and hence that its true thickness is of similar magnitude. The relationship of these results to models of the geomagnetic field and to other related work is discussed.

Van Allen, J. A.↗
NASA NTRS1999

The Second Most Distant Cluster of Galaxies in the Extended Medium Sensitivity Survey

We report on our ASCA, Keck, and ROSAT observations of MS 1137.5+6625, the second most distant cluster of galaxies in the Einstein Extended Medium Sensitivity Survey (EMSS), at redshift 0.78. We now have a full set of X-ray temperatures, optical velocity dispersions, and X-ray images for a complete, high-redshift sample of clusters of galaxies drawn from the EMSS. Our ASCA observations of MS 1137.5 +6625 yield a temperature of 5.7 (+2.1)(-1.1) keV and a metallicity of 0.43 (+40)(-3.7) solar, with 90% confidence limits. Keck II spectroscopy of 22 cluster members reveals a velocity dispersion of 884 (+185)(-124) km 24/s. This cluster is the most distant in the sample with a detected iron line. We also derive a mean abundance at z = 0.8 by simultaneously fitting X-ray data for the two z = 0.8 clusters, and obtain an abundance of Z(sub Fe) = 0.33 (+.26)(-.23). Our ROSAT observations show that MS 1137.5+6625 is regular and highly centrally concentrated. Fitting of a Beta model to the X-ray surface brightness yields a core radius of only 71/h kpc (q(sub o) = 0.1) with Beta = 0.70(+.45)(-.15) The gas mass interior to 0.5/h Mpc is thus 1.2 (+0.2)(-0.3) X 10(exp 13) h(exp - 5/2) Solar Mass (q(sub o) = 0.1). If the cluster's gas is nearly isothermal and in hydrostatic equilibrium with the cluster potential, the total mass of the cluster within this same region is 2.1(+1.5)(-0.8) X 10exp 14)/h Solar Mass, giving a gas fraction of 0.06 +/-0.04 h (exp -3/2). This cluster is the highest redshift EMSS cluster showing evidence for a possible cooling flow (about 20-400 Solar Mass/yr). The velocity dispersion, temperature, gas fraction, and iron abundance of MS 1137.5+6625 are all statistically the same as those properties in lower red- shift clusters of similar luminosity. With this cluster's temperature now in hand, we derive a high-redshift temperature function for EMSS clusters at 0.5 < z < 0.9 and compare it with temperature functions at lower redshifts, showing that the evolution of the temperature function is relatively modest. Supplementing our high-redshift sample with other data from the literature, we demonstrate that neither the cluster luminosity-temperature relation, nor cluster metallicities, nor the cluster gas evolved with redshift. The very modest degree of evolution in the luminosity-temperature relation inferred from these data is inconsistent with the absence of evolution in the X-ray luminosity functions derived from ROSAT cluster surveys if a critical density structure formation model is assumed.

Donahue, Megan↗
DOE OSTI2023

A non-linear solution to the S 8 tension – II. Analysis of DES Year 3 cosmic shear

ABSTRACT Weak galaxy lensing surveys have consistently reported low values of the S8 parameter compared to the Planck lambda cold dark matter (ΛCDM) cosmology. Amon & Efstathiou used KiDS-1000 cosmic shear measurements to propose that this tension can be reconciled if the matter fluctuation spectrum is suppressed more strongly on non-linear scales than assumed in state-of-the-art hydrodynamical simulations. In this paper, we investigate cosmic shear data from the Dark Energy Survey (DES) Year 3. The non-linear suppression of the matter power spectrum required to resolve the S8 tension between DES and the Planck ΛCDM model is not as strong as inferred using KiDS data, but is still more extreme than predictions from recent numerical simulations. An alternative possibility is that non-standard dark matter contributes to the required suppression. We investigate the redshift and scale dependence of the suppression of the matter power spectrum. If our proposed explanation of the S8 tension is correct, the required suppression must extend into the mildly non-linear regime to wavenumbers $k\sim 0.2 \, h\, {\rm Mpc}^{-1}$. In addition, all measures of S8 using linear scales should agree with the Planck ΛCDM cosmology, an expectation that will be testable to high precision in the near future.

Preston, Calvin (ORCID:0000000281136916)↗
DOE OSTI2022

The impact of tomographic redshift bin width errors on cosmological probes

ABSTRACT Systematic errors in the galaxy redshift distribution n(z) can propagate to systematic errors in the derived cosmology. We characterize how the degenerate effects in tomographic bin widths and galaxy bias impart systematic errors on cosmology inference using observational data from the Deep Lens Survey. For this we use a combination of galaxy clustering and galaxy–galaxy lensing. We present two end-to-end analyses from the catalogue level to parameter estimation. We produce an initial cosmological inference using fiducial tomographic redshift bins derived from photometric redshifts, then compare this with a result where the redshift bins are empirically corrected using a set of spectroscopic redshifts. We find that the derived parameter S8 ≡ σ8(Ωm/.3)1/2 decreases from $0.841^{+0.062}_{-0.061}$ to $0.781^{0.061}_{0.054}$ upon correcting the n(z) errors in the second method.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Real-time Anomaly Detection for Liquid Argon Time Projection Chambers

We present a real-time anomaly detection framework for liquid argon time projection chambers (LArTPCs), targeting applications in particle physics experiments such as the Short Baseline Near Detector (SBND) or the future Deep Underground Neutrino Experiment (DUNE). These experiments employ detectors that generate and stream high-resolution but sparse images of neutrino and other particle interactions. Our approach utilizes anomaly detection with autoencoders, compressed through knowledge distillation (KD), to enable the detection of anomalous signals in the data through efficient inference on resource-constrained hardware. The framework is targeted for deployment on computing platforms equipped with field-programmable gate arrays (FPGAs), GPUs, or CPUs, allowing low-latency selection of relevant activity directly from the raw detector data stream. We demonstrate that our approach is suitable for the detection and localization of anomalously "high-multiplicity" activity, and outline promising applications for LArTPC online data filtering and triggering.

FOS: Physical sciences↗
NASA NTRS1999

Inferring Selected Cloud Properties from Satellite Lightning Data

The recent advent of satellite lightning detection programs has introduced a new potential for obtaining global information about other (hard to measure) cloud properties. We have made use of observations together with numerical model studies to show that positive correlations exist between: (1) lightning flashrate (F) and vertical velocity (w); and (2) flashrate (F) and the amount of condensate (water and ice) lofted through the -10 C isotherm (C(sub u)). The lightning flashrate appears to be very sensitive to the magnitude of the updraft velocity, with F increasing rapidly with w above a threshold of w approx. = 5-10 m/s. By contrast, we have found that the flashrate/condensate relationship appears to be approximately linear. We are currently refining the F-updraft and F-condensate relationships with further model studies before applying them to lightning data from the Optical Transient Detector (OTD) and the Lightning Imaging Sensor (LIS). We hope, with this method, to provide estimates of the large scale vertical water transport by continental convective systems over seasonal timescales.

Schroeder, Vicki↗
NASA NTRS2011

Inferring Absorbing Organic Carbon Content from AERONET Data

Black carbon, light-absorbing organic carbon (often called brown carbon) and mineral dust are the major light-absorbing aerosols. Currently the sources and formation of brown carbon aerosol in particular are not well understood. In this study we estimated globally the amount of light absorbing organic carbon and black carbon from AERONET measurements. We find that the columnar absorbing organic carbon (brown carbon) levels in biomass burning regions of South-America and Africa are relatively high (about 15-20 magnesium per square meters during biomass burning season), while the concentrations are significantly lower in urban areas in US and Europe. However, we estimated significant absorbing organic carbon amounts from the data of megacities of newly industrialized countries, particularly in India and China, showing also clear seasonality with peak values up to 30-35 magnesium per square meters during the coldest season, likely caused by the coal and biofuel burning used for heating. We also compared our retrievals with the modeled organic carbon by global Oslo CTM for several sites. Model values are higher in biomass burning regions than AERONET-based retrievals, while opposite is true in urban areas in India and China.

Arola, A.↗