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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Monte Carlo Bayesian Inference on a Statistical Model of Sub-gridcolumn Moisture Variability Using High-resolution Cloud Observations : Sensitivity Tests and Results - Part II

Part I presented a Monte Carlo Bayesian method for constraining a complex statistical model of GCM sub-gridcolumn moisture variability using high-resolution MODIS cloud data, thereby permitting large-scale model parameter estimation and cloud data assimilation. This part performs some basic testing of this new approach, verifying that it does indeed significantly reduce mean and standard deviation biases with respect to the assimilated MODIS cloud optical depth, brightness temperature and cloud top pressure, and that it also improves the simulated rotational-Ramman scattering cloud optical centroid pressure (OCP) against independent (non-assimilated) retrievals from the OMI instrument. Of particular interest, the Monte Carlo method does show skill in the especially difficult case where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach allows finite jumps into regions of non-zero cloud probability. In the example provided, the method is able to restore marine stratocumulus near the Californian coast where the background state has a clear swath. This paper also examines a number of algorithmic and physical sensitivities of the new method and provides guidance for its cost-effective implementation. One obvious difficulty for the method, and other cloud data assimilation methods as well, is the lack of information content in the cloud observables on cloud vertical structure, beyond cloud top pressure and optical thickness, thus necessitating strong dependence on the background vertical moisture structure. It is found that a simple flow-dependent correlation modification due to Riishojgaard (1998) provides some help in this respect, by better honoring inversion structures in the background state.

Monte Carlo Bayesian inference↗
NASA NTRS2005

Dependence of Ru2O3 Activity on Composition of Silicate Melts: Using Statistical Correlations to Infer Thermodynamic Behavior in the Melt

Understanding variations in activity with composition is an essential step in improving prediction of partition coefficients during magma evolution. Variations in activity with composition are complex and do not generally exhibit ideal behavior relative to a traditional melt-component set. Although deviations from component ideality can be modeled numerically by simply fitting to compositional variables (such as in a regular or subregular solution model), such models have not been particularly successful for describing variations in trace component activities. A better approach might be to try to identify components that do a better job of describing the behavior of the species in the melt. Electrochemical Measurement of Ru2O3 activities: Electrodes were inserted into silicate melt beads of various compositions (Table 1) suspended on Ptwire loops in a 1-atm gas mixing furnace. An electrical potential was imposed between the electrodes, the imposed potential increasing along a step ramp with a pulse imposed on each step (Fig. 1). Current flows between electrodes when electroactive species in the melt are oxidized or reduced at the electrodes. The resulting current was measured at the top and bottom of the voltage pulse, and the difference (the differential current) was plotted against potential. The peak of the resulting curve is related to the activity coefficient for the particular electroactive species (Ru2O3) in the melt [1, 2, 3]. A significant part of the nonideal contribution to activity is due not to intrinsic properties of the component in the melt, but to our ignorance about the state and mixing properties of the component in the melt.

Colson, R. O.↗
DOE OSTI2020

Inverse methods for design of soft materials

Functional soft materials, comprising colloidal and molecular building blocks that self-organize into complex structures as a result of their tunable interactions, enable a wide array of technological applications. Inverse methods provide a systematic means for navigating their inherently high-dimensional design spaces to create materials with targeted properties. Furthermore, while multiple physically motivated inverse strategies have been successfully implemented in silico, their translation to guiding experimental materials discovery has thus far been limited to a handful of proof-of-concept studies. In this perspective, we discuss recent advances in inverse methods for design of soft materials that address two challenges: (1) methodological limitations that prevent such approaches from satisfying design constraints and (2) computational challenges that limit the size and complexity of systems that can be addressed. Strategies that leverage machine learning have proven particularly effective, including methods to discover order parameters that characterize complex structural motifs and schemes to efficiently compute macroscopic properties from the underlying structure. We also highlight promising opportunities to improve the experimental realizability of materials designed computationally, including discovery of materials with functionality at multiple thermodynamic states, design of externally directed assembly protocols that are simple to implement in experiments, and strategies to improve the accuracy and computational efficiency of experimentally relevant models.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Automated Resonance Fitting for Nuclear Data Evaluation

Global and national efforts to deliver high-quality nuclear data to users have a wide-ranging impact, affecting applications in national security, reactor operations, basic science, medicine, and more. Cross section evaluation is a major part of this effort, combining theory and experimentation to produce recommended values and uncertainties for reaction probabilities. Resonance region evaluation is a specialized type of nuclear data evaluation that can require significant manual effort and months of time from expert scientists. In this article, non-convex non-linear optimization methods are combined with concepts of inferential statistics to infer a resonance model from experimental data in an automated manner that is not dependent on prior evaluation(s). This methodology aims to enhance the workflow of a resonance evaluator by minimizing time, effort, and the potential for bias from prior assumptions, while enhancing reproducibility and documentation, thereby addressing well-known challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

A review of imputation strategies for isobaric labeling-based shotgun proteomics

The throughput efficiency and increased depth of coverage provided by isobaric-labeled proteomics measurements have led to increased usage of these techniques. However, the structure of missing data is uniquely different than unlabeled studies. In this review, we compare the efficacy of nine imputation methods on a CPTAC proteomics iTRAQ dataset. Imputation methods were evaluated with regard to accuracy, variability, statistical hypothesis test inference and run time over datasets consisting of varying number of iTRAQ plexes and percentages of missing data. In general, expectation maximization and random forest imputation methods yielded the best performances, and constant-based methods performed poorly consistently across all dataset sizes and percentages of missing values. For datasets with small sample sizes and higher percentages of missing data, results indicate that statistical inference with no imputation may be preferable. Based on the findings in this review, there are core imputation methods that perform higher for isobaric-labeled proteomics data, but great care and consideration as to whether imputation should be used should be given for datasets comprised of a small number of samples, as well as to factors such as computational time and reproducibility of imputation values.

Bramer, Lisa M.↗
DOE OSTI2020

Detecting differential protein abundance by combining peptide level P -values

The majority of methods for detecting differentially abundant proteins between samples in label-free LC-MS bottom-up proteomics experiments rely on statistically testing inferred protein abundances derived from peptide ionization intensities or averaging peptide level statistics. Here in this paper, we statistically test peptide ionization intensities directly and combine the resulting dependent P-values using the Empirical Brown's Method (EBM), avoiding error introduced through the estimation of protein abundances or summarizing test statistics. We show that on a spike-in proteomics dataset, a peptide level approach using EBM outperforms differential abundance detection using a protein level approach and several analysis workflows, including MSstats. Additionally, we demonstrate the effectiveness of this approach by detecting enriched proteins from an activity-based protein profiling dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1995

Chromospheric Evaporation and Decimetric Radio Emission in Solar Flares

We have discovered decimetric signatures of the chromospheric evaporation process. Evidence for the radio detection of chromospheric evaporation is based on the radio-inferred values of (1) the electron density, (2) the propagation speed, and (3) the timing, which are found to be in good agreement with statistical values inferred from the blueshifted Ca xix soft X-ray line. The physical basis of our model is that free-free absorption of plasma emission is strongly modified by the steep density gradient and the large temperature increase in the upflowing flare plasma. The steplike density increase at the chromospheric evaporation front causes a local discontinuity in the plasma frequency, manifested as almost infinite drift rate in decimetric type III bursts. The large temperature increase of the upflowing plasma considerably reduces the local free-free opacity (due to the T-(exp -3/2) dependence) and thus enhances the brightness of radio bursts emitted at the local plasma frequency near the chromospheric evaporation front, while a high-frequency cutoff is expected in the high-density regions behind the front, which can be used to infer the velocity of the upflowing plasma. From model calculations we find strong evidence that decimetric bursts with a slowly drifting high-frequency cutoff are produced by fundamental plasma emission, contrary to the widespread belief that decimetric bursts are preferentially emitted at the harmonic plasma level. We analyzed 21 flare episodes from 1991-1993 for which broadband (100-3000 MHz) radio dynamic spectra from Phoenix, hard X-ray data from BATSE/CGRO, and soft X-ray data from GOES were available. We detected slowly drifting high-frequency cutoffs between 1.1 and 3.0 GHz, with drift rates of -41 +/- 32 MHz/s, extending over time intervals of 24 +/- 23 s. Developing a density model for type III-emitting flare loops based on the statistically observed drift rate of type III bursts by Alvarez & Haddock, we infer velocities of up to 360 km/s for the upflowing plasma, with an average of v(sub CE) = 236 +/- 130 km /s for episodes with 5-15 s duration. The mean electron density of the upflowing plasma is n(sub e) = 5.2(+/-3.1) x 10(exp 10) /cu cm when it is first detected in radio, at coronal altitudes of h(sub 0) = 9.2 +/- 2.3 Mm.

Aschwanden, Markus J.↗
NASA NTRS1995

Chromospheric evaporation and decimetric radio emission in solar flares

We have discovered decimetric signatures of the chromospheric evaporation process. Evidence for the radio detection of chromospheric evaporation is based on the radio-inferred values of (1) the electron density, (2) the propagation speed, and (3) the timing, which are found to be in good agreement with statistical values inferred from the blueshifted Ca XIX soft X-ray line. The physical basis of our model is that free-free absorption of plasma emission is strongly modified by the steep density gradient and the large temperature increase in the upflowing flare plasma. The steplike density increase at the chromospheric evaporation front causes a local discontinuity in the plasma frequency, manifested as almost infinite drift rate in decimetric type III bursts. The large temperature increase of the upflowing plasma considerably reduces the local free-free opacity (due to the T(exp -3/2) dependence) and thus enhances the brightness of radio bursts emitted at the local plasma frequency near the chromospheric evaporation front, while a high-frequency cutoff is expected in the high-density regions behind the front, which can be used to infer the velocity of the upflowing plasma. From model calculations we find strong evidence that decimetric bursts with a slowly drifting high-frequency cutoff are produced by fundamental plasma emission, contrary to the widespread belief that decimetric bursts are preferentially emitted at the harmonic plasma level. We analyze 21 flare episodes from 1991-1993 for which broadband (100-3000 MHz) radio dynamic spectra from Pheonix, hard X-ray data from (BATSE/CGRO) and soft X-ray data from Burst and Transient Source Experiment/Compton Gamma Ray Observatory (GOES) were available.

Aschwanden, Markus J.↗
NASA NTRS2002

A statistical model for interpreting computerized dynamic posturography data

Computerized dynamic posturography (CDP) is widely used for assessment of altered balance control. CDP trials are quantified using the equilibrium score (ES), which ranges from zero to 100, as a decreasing function of peak sway angle. The problem of how best to model and analyze ESs from a controlled study is considered. The ES often exhibits a skewed distribution in repeated trials, which can lead to incorrect inference when applying standard regression or analysis of variance models. Furthermore, CDP trials are terminated when a patient loses balance. In these situations, the ES is not observable, but is assigned the lowest possible score--zero. As a result, the response variable has a mixed discrete-continuous distribution, further compromising inference obtained by standard statistical methods. Here, we develop alternative methodology for analyzing ESs under a stochastic model extending the ES to a continuous latent random variable that always exists, but is unobserved in the event of a fall. Loss of balance occurs conditionally, with probability depending on the realized latent ES. After fitting the model by a form of quasi-maximum-likelihood, one may perform statistical inference to assess the effects of explanatory variables. An example is provided, using data from the NIH/NIA Baltimore Longitudinal Study on Aging.

NASA Discipline Neuroscience↗
DOE OSTI2020

Tractable minor-free generalization of planar zero-field Ising models

In this work, we present a new family of zero-field Ising models over N binary variables/spins obtained by consecutive 'gluing' of planar and O(1)-sized components and subsets of at most three vertices into a tree. The polynomial time algorithm of the dynamic programming type for solving exact inference (computing partition function) and exact sampling (generating i.i.d. samples) consists of sequential application of an efficient (for planar) or brute-force (for O(1)-sized) inference and sampling to the components as a black box. To illustrate the utility of the new family of tractable graphical models, we first build a polynomial algorithm for inference and sampling of zero-field Ising models over K 33 -minor-free topologies and over K 5 -minor-free topologies—both of which are extensions of the planar zero-field Ising models—which are neither genus- nor treewidth-bounded. Second, we empirically demonstrate an improvement in the approximation quality of the NP-hard problem of inference over the square-grid Ising model in a node-dependent nonzero 'magnetic' field.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Deep Generative Model for Non-Intrusive Identification of EV Charging Profiles

The proliferation of electric vehicles (EVs) brings environmental benefits and technical challenges to power grids. An identification algorithm which can accurately extract individual EV charging profiles out of widely available smart meter measurements has attracted great interests. This paper proposes a non-intrusive identification framework for EV charging profile extraction, which is driven by deep generative models (DGM). First, the proposed DGM is designed as a representation layer embedded into the Markov process and used to model the joint probability distribution of available time-series data. A novel contribution is to approximate posterior distributions by neural networks whose parameters are obtained by variational inference and supervised learning. Second, the EV charging status is inferred from the DGM via dynamic programming. Lastly, the desired EV charging profile can be reconstructed by the rated power of EV models and inferred status. Compared with the benchmark Hidden Markov Models, the proposed framework can better handle noise in data with less computational complexity and better overall accuracy performances with smaller recall. The proposed framework is validated by numerical experiments on the Pecan Street dataset.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Quantification of early nonpharmaceutical interventions aimed at slowing transmission of Coronavirus Disease 2019 in the Navajo Nation and surrounding states (Arizona, Colorado, New Mexico, and Utah)

During an early period of the Coronavirus Disease 2019 (COVID-19) pandemic, the Navajo Nation, much like New York City, experienced a relatively high rate of disease transmission. Yet, between January and October 2020, it experienced only a single period of growth in new COVID-19 cases, which ended when cases peaked in May 2020. The daily number of new cases slowly decayed in the summer of 2020 until late September 2020. In contrast, the surrounding states of Arizona, Colorado, New Mexico, and Utah all experienced at least two periods of growth in the same time frame, with second surges beginning in late May to early June. Here, we investigated these differences in disease transmission dynamics with the objective of quantifying the contributions of non-pharmaceutical interventions (NPIs) (e.g., behaviors that limit disease transmission). We considered a compartmental model accounting for distinct periods of NPIs to analyze the epidemic in each of the five regions. We used Bayesian inference to estimate region-specific model parameters from regional surveillance data (daily reports of new COVID-19 cases) and to quantify uncertainty in parameter estimates and model predictions. Our results suggest that NPIs in the Navajo Nation were sustained over the period of interest, whereas in the surrounding states, NPIs were relaxed, which allowed for subsequent surges in cases. Our region-specific model parameterizations allow us to quantify the impacts of NPIs on disease incidence in the regions of interest.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Community detection in hypergraphs via mutual information maximization

Abstract The hypergraph community detection problem seeks to identify groups of related vertices in hypergraph data. We propose an information-theoretic hypergraph community detection algorithm which compresses the observed data in terms of community labels and community-edge intersections. This algorithm can also be viewed as maximum-likelihood inference in a degree-corrected microcanonical stochastic blockmodel. We perform the compression/inference step via simulated annealing. Unlike several recent algorithms based on canonical models, our microcanonical algorithm does not require inference of statistical parameters such as vertex degrees or pairwise group connection rates. Through synthetic experiments, we find that our algorithm succeeds down to recently-conjectured thresholds for sparse random hypergraphs. We also find competitive performance in cluster recovery tasks on several hypergraph data sets.

97 MATHEMATICS AND COMPUTING↗