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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 19 records

InClass nets: independent classifier networks for nonparametric estimation of conditional independence mixture models and unsupervised classification

Abstract Conditional independence mixture models (CIMMs) are an important class of statistical models used in many fields of science. We introduce a novel unsupervised machine learning technique called the independent classifier networks (InClass nets) technique for the nonparameteric estimation of CIMMs. InClass nets consist of multiple independent classifier neural networks (NNs), which are trained simultaneously using suitable cost functions. Leveraging the ability of NNs to handle high-dimensional data, the conditionally independent variates of the model are allowed to be individually high-dimensional, which is the main advantage of the proposed technique over existing non-machine-learning-based approaches. Two new theorems on the nonparametric identifiability of bivariate CIMMs are derived in the form of a necessary and a (different) sufficient condition for a bivariate CIMM to be identifiable. We use the InClass nets technique to perform CIMM estimation successfully for several examples. We provide a public implementation as a Python package called RainDancesVI.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

The Sylvester Graphical Lasso (SyGlasso)

This paper introduces the Sylvester graphical lasso (SyGlasso) that captures multiway dependencies present in tensor-valued data. The model is based on the Sylvester equation that defines a generative model. The proposed model complements the tensor graphical lasso that imposes a Kronecker sum model for the inverse covariance matrix by providing an alternative Kronecker sum model that is generative and interpretable. A nodewise regression approach is adopted for estimating the conditional independence relationships among variables. The statistical convergence of the method is established, and empirical studies are provided to demonstrate the recovery of meaningful conditional dependency graphs. Here we apply the SyGlasso to an electroencephalography (EEG) study to compare the brain connectivity of alcoholic and nonalcoholic subjects. Here, we demonstrate that our model can simultaneously estimate both the brain connectivity and its temporal dependencies.

Wang, Yu↗

Physics-constrained deep neural network method for estimating parameters in a redox flow battery

Here, in this paper, we present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium redox flow battery (VRFB). In this approach, we use deep neural networks to approximate the model parameters as functions of the operating conditions. This method allows the integration of VRFB computational models as the physical constraints in the parameter learning process, leading to enhanced accuracy of parameter estimation and cell voltage prediction. Using an experimental dataset, we demonstrate that the PCDNN method can estimate model parameters for a range of operating conditions and improve the 0D model prediction of voltage compared to the 0D model prediction with constant operation-condition-independent parameters estimated with traditional inverse methods. We also demonstrate that the PCDNN approach has an improved generalization ability for estimating parameter values for operating conditions not used in the training process.

25 ENERGY STORAGE↗

Inference for Nonparanormal Partial Correlation via Regularized Rank-Based Nodewise Regression

Abstract Partial correlation is a common tool in studying conditional dependence for Gaussian distributed data. However, partial correlation being zero may not be equivalent to conditional independence under non-Gaussian distributions. In this paper, we propose a statistical inference procedure for partial correlations under the high-dimensional nonparanormal (NPN) model where the observed data are normally distributed after certain monotone transformations. The NPN partial correlation is the partial correlation of the normal transformed data under the NPN model, which is a more general measure of conditional dependence. We estimate the NPN partial correlations by regularized nodewise regression based on the empirical ranks of the original data. A multiple testing procedure is proposed to identify the nonzero NPN partial correlations. The proposed method can be carried out by a simple coordinate descent algorithm for lasso optimization. It is easy-to-implement and computationally more efficient compared to the existing methods for estimating NPN graphical models. Theoretical results are developed to show the asymptotic normality of the proposed estimator and to justify the proposed multiple testing procedure. Numerical simulations and a case study on brain imaging data demonstrate the utility of the proposed procedure and evaluate its performance compared to the existing methods. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

97 MATHEMATICS AND COMPUTING↗

Dietary uptake of geosmin in rainbow trout ( Oncorhynchus mykiss )

Geosmin is a primary source of muddy/earthy ‘off-flavors’ in farmed fish, which may render their organoleptic quality unacceptable to consumers. Model systems of geosmin uptake typically comprise exposure to waterborne geosmin that is absorbed via the gills. The present research demonstrates dietary exposure as an alternative route of geosmin uptake in Rainbow Trout (Oncorhynchus mykiss) fillets. Trout (average initial weight of 355 g) were stocked in quadruplicate compartmentalized raceways (N = 4 compartments per treatment, n = 50 fish per compartment) supplied with first-use, flow-through water (4.5 complete turnovers per hour). Fish were fed diets containing 0 (control dose), 0.005 (low dose), 0.05 (medium dose), or 0.5 (high dose) mg geosmin/kg feed at 1% body weight/day for four weeks. Fillets (12 fillets/diet/week plus 12 pre-trial fillet samples) and weekly feed and water samples were analyzed via GC–MS to determine geosmin concentrations. Feeding behavior was documented daily according to a four-point scale. ANOVA with post-hoc Tukey tests and polynomial contrast, regression, correlation, and chi-squared statistical analyses were applied to data (α = 0.05 significance level). Palatability of feed did not hinder consumption of the geosmin-spiked feeds: although fish responded positively to all feeds, the most aggressive feeding behavior was observed among those fed medium and high dose feed. Geosmin was effectively imparted into fillets after one week, and no significant temporal effect was found after four weeks. Mean geosmin concentrations significantly increased in fillets from low (mean of 26 ng/kg geosmin) to medium (202 ng/kg) to high (441 ng/kg) dose feed groups during the trial. Polynomial contrasts and regression modeling validated this significant positive effect of dose on geosmin uptake, with an estimated 166 ng/kg rise in fillet-geosmin for every log 10 increase in feed concentration. Waterborne geosmin levels immediately post-feeding were conditionally independent of concentrations in feed and fillets, therefore dietary uptake was affirmed as the predominant mechanism of absorption in the present experimental system. Finally, based on these findings, geosmin-spiked feeds may be used to induce predictable, repeatable levels of this off-flavor compound in fillets and serve as a model system for further investigation of sensory quality and off-flavor mitigation strategies for farm-raised fish.

59 BASIC BIOLOGICAL SCIENCES↗

Implications of rootless geothermal models: Missing processes, parameter compensation, and imposter convection

Numerical models of geothermal systems commonly capture only the top of a reservoir. Deeper areas of the reservoir are simplified to a boundary at the base of the model domain. Commonly, the basal boundary is given either a heat source or a source of mass and enthalpy. Here we developed and present simple numerical experiments which demonstrate that these approaches do not produce the correct model behavior in comparison to a model that captures the entire convecting domain with a heat flux only. We describe a variety of incorrect types of model behavior that arise directly from the choice of boundary condition, independent of the specific parameterization of the model. Heat sources are sensitive to the thickness of the domain and parameters take unphysical values to compensate for the reduced height. The combined mass/heat boundary can produce temperatures that show similarities to circulating geothermal systems, but with incorrect fluid flow and a strong boundary layer focused at the base of the simulated clay cap. These errors likely cause parameters to adjust their values to compensate for the incorrect physics. We highlight these issues and show an example from a developed reservoir model. Initial calibrations to natural state temperature were unsuitable for history matching. The 3D model required parameter adjustments to achieve a more realistic production model. Appropriate mitigation measures should be considered to reduce parameter compensation and improve decision-support models.

15 GEOTHERMAL ENERGY↗

Transcriptome-wide association analysis identifies candidate susceptibility genes for prostate-specific antigen levels in men without prostate cancer

Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility for prostate cancer (PCa) screening. Using genome-wide association study (GWAS) summary statistics from 95,768 PCa-free men, we conducted a transcriptome-wide association study (TWAS) to examine impacts of genetically predicted gene expression on PSA. Analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10 × 10 –6 ) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61 × 10 –6 ) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses identified 155 statistically significantly (p < 0.05/22,249 = 2.25 × 10 –6 ) genes. Out of 173 unique PSA-associated genes across analyses, we replicated 151 (87.3%) in a TWAS of 209,318 PCa-free individuals from the Million Veteran Program. Based on conditional analyses, we found 20 genes (11 single tissue, nine cross-tissue) that were associated with PSA levels in the discovery TWAS that were not attributable to a lead variant from a GWAS. Ten of these 20 genes replicated, and two of the replicated genes had colocalization probability of >0.5: CCNA2 and HIST1H2BN. Six of the 20 identified genes are not known to impact PCa risk. Fine-mapping based on whole blood and prostate tissue revealed five protein-coding genes with evidence of causal relationships with PSA levels. Of these five genes, four exhibited evidence of colocalization and one was conditionally independent of previous GWAS findings. These results yield hypotheses that should be further explored to improve understanding of genetic factors underlying PSA levels.

60 APPLIED LIFE SCIENCES↗

Holographic entropy cone beyond AdS/CFT

We extend all known area inequalities obeyed by Ryu-Takayanagi surfaces of anti–de Sitter boundary regions—the holographic entropy cone—to static generalized entanglement wedges of bulk regions in arbitrary spacetimes. The generalized holographic entropy cone is subject to a mutual independence condition on the bulk regions: each bulk input region must be outside the entanglement wedge of the union of all others. The condition captures when gravitating regions involve fundamentally distinct degrees of freedom despite the nonlocality inherent in the holographic principle.

Quantum gravity↗

Scaling properties of direct photon yields in heavy ion collisions

Abstract A recent analysis from the PHENIX collaboration of available direct photon measurement results in collisions of various systems such as Au+Au, Cu+Cu, and Pb+Pb, at different beam energies ranging from 39 to 2760 GeV, has shown a universal, within experimental uncertainties, multiplicity scaling, in which direct photon $$p_{T}$$ p T -spectra for transverse momenta up to 2 GeV/ c are scaled with charged hadron pseudorapidity density at midrapidity raised to power $$\alpha =1.25$$ α = 1.25 . On the other hand, those direct photon $$p_{T}$$ p T -spectra also exhibit geometrical scaling in the similar $$p_{T}$$ p T range. Assuming power-law dependence of the scaled photon spectra for both scaling laws, we formulate two independent conditions for the power $$\alpha $$ α , which overshoot experimental data by $$\sim 10\%$$ ∼ 10 % on average. We discuss possible sources that might improve this estimate.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Nitrogen starvation causes lipid remodeling in Rhodotorula toruloides

Abstract Background The oleaginous yeast Rhodotorula toruloides is a promising chassis organism for the biomanufacturing of value-added bioproducts. It can accumulate lipids at a high fraction of biomass. However, metabolic engineering efforts in this organism have progressed at a slower pace than those in more extensively studied yeasts. Few studies have investigated the lipid accumulation phenotype exhibited by R. toruloides under nitrogen limitation conditions. Consequently, there have been only a few studies exploiting the lipid metabolism for higher product titers. Results We performed a multi-omic investigation of the lipid accumulation phenotype under nitrogen limitation. Specifically, we performed comparative transcriptomic and lipidomic analysis of the oleaginous yeast under nitrogen-sufficient and nitrogen deficient conditions. Clustering analysis of transcriptomic data was used to identify the growth phase where nitrogen-deficient cultures diverged from the baseline conditions. Independently, lipidomic data was used to identify that lipid fractions shifted from mostly phospholipids to mostly storage lipids under the nitrogen-deficient phenotype. Through an integrative lens of transcriptomic and lipidomic analysis, we discovered that R. toruloides undergoes lipid remodeling during nitrogen limitation, wherein the pool of phospholipids gets remodeled to mostly storage lipids. We identify specific mRNAs and pathways that are strongly correlated with an increase in lipid levels, thus identifying putative targets for engineering greater lipid accumulation in R. toruloides . One surprising pathway identified was related to inositol phosphate metabolism, suggesting further inquiry into its role in lipid accumulation. Conclusions Integrative analysis identified the specific biosynthetic pathways that are differentially regulated during lipid remodeling. This insight into the mechanisms of lipid accumulation can lead to the success of future metabolic engineering strategies for overproduction of oleochemicals.

59 BASIC BIOLOGICAL SCIENCES↗

MFNets: Multifidelity data-driven networks for Bayesian learning and prediction

This paper presents a multifidelity uncertainty quantification framework called MFNets. We seek to address three existing challenges that arise when experimental and simulation data from different sources are used to enhance statistical estimation and prediction with quantified uncertainty. Specifically, we demonstrate that MFNets can (1) fuse heterogeneous data sources arising from simulations with different parameterizations, e.g simulation models with different uncertain parameters or data sets collected under different environmental conditions; (2) encode known relationships among data sources to reduce data requirements; and (3) improve the robustness of existing multi-fidelity approaches to corrupted data. MFNets construct a network of latent variables (LVs) to facilitate the fusion of data from an ensemble of sources of varying credibility and cost. These LVs are posited as explanatory variables that provide the source of correlation in the observed data. Furthermore, MFNets provide a way to encode prior physical knowledge to enable efficient estimation of statistics and/or construction of surrogates via conditional independence relations on the LVs. We highlight the utility of our framework with a number of theoretical results which assess the quality of the posterior mean as a frequentist estimator and compare it to standard sampling approaches that use single fidelity, multilevel, and control variate Monte Carlo estimators. We also use the proposed framework to derive the Monte Carlo-based control variate estimator entirely from the use of Bayes rule and linear-Gaussian models -- to our knowledge the first such derivation. Finally, we demonstrate the ability to work with different uncertain parameters across different models.

97 MATHEMATICS AND COMPUTING↗

Helical spin structure in iron chains with hybridized boundaries

In this paper, we have compared the magnetic properties of well-controlled ultra-short (≤50 nm) atomic iron (Fe) chains embedded in Fe-phthalocyanine films with those in Fe–hydrogen (H2) phthalocyanine superlattices. Surprisingly, we found that the coercivity of the atomic chains with free boundary conditions is independent of the chain length, whereas the one subject to hybridization of the chain ends exhibits an unexpected length dependence. These findings suggest that ferromagnetism in the free-boundary condition system is caused by an intrinsic indirect exchange. On the other hand, controlled boundary conditions produce a helical spin structure due to an extrinsic indirect exchange, which arises from the interaction between iron atoms at the ends of the chain and the hydrogen in the H 2 phthalocyanine spacer. As a consequence, during magnetic reversal, ultra-short iron chains subject to boundary clamping develop a helical spin structure, leading to increased coercivity. These findings suggest unique insights and ideas for the design of atomic-scale ultra-dense magnetic storage nanodevices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

A consistent and conservative Phase-Field method for multiphase incompressible flows

In the present study, a consistent and conservative Phase-Field method, including both the model and scheme, is developed for multiphase flows with an arbitrary number of immiscible and incompressible fluid phases. The consistency of mass conservation and the consistency of mass and momentum transport are implemented to address the issue of physically coupling the Phase-Field equation, which locates different phases, to the hydrodynamics. These two consistency conditions, as illustrated, provide the “optimal” coupling because (i) the new momentum equation resulting from them is Galilean invariant and implies the kinetic energy conservation, regardless of the details of the Phase-Field equation, and (ii) failures of satisfying the second law of thermodynamics or the consistency of reduction of the multiphase flow model only result from the same failures of the Phase-Field equation but are not due to the new momentum equation. Physical interpretation of the consistency conditions and their formulations are first provided, and general formulations that are obtained from the consistency conditions and independent of the interpretation of the velocity are summarized. Then, the present consistent and conservative multiphase flow model is completed by selecting a reduction consistent Phase-Field equation. Several novel techniques are developed to inherit the physical properties of the multiphase flows after discretization, including the gradient-based phase selection procedure, the momentum conservative method for the surface force, and the general theorems to preserve the consistency conditions on the discrete level. Equipped with those novel techniques, a consistent and conservative scheme for the present multiphase flow model is developed and analyzed. The scheme satisfies the consistency conditions, conserves the mass and momentum, and assures the summation of the volume fractions to be unity, on the fully discrete level and for an arbitrary number of phases. All those properties are numerically validated. Finally, numerical applications demonstrate that the present model and scheme are robust and effective in studying complicated multiphase dynamics, especially for those with large-density ratios.

97 MATHEMATICS AND COMPUTING↗

DIVA/DeviceEditor (DIVA) v6.0.0

The DIVA software interfaces a process in which researchers design their DNA with a web-based graphical user interface (DeviceEditor), submit their designs to a central queue, and a few weeks later receive their sequence-verified clonal constructs. Each researcher independently designs the DNA to be constructed with a web-based BioCAD tool, and presses a button to submit their designs to a central queue. Researchers have web-based access to their DNA design queues, and can track the progress of their submitted designs as they progress from "evaluation", to "waiting for reagents", to "in progress", to "complete". Researchers access their completed constructs through the central DNA repository. Along the way, all DNA construction success/failure rates are captured in a central database. Once a design has been submitted to the queue, a small number of dedicated staff evaluate the design for feasibility and provide feedback to the responsible researcher if the design is either unreasonable (e.g., encompasses a combinatorial library of a billion constructs) or small design changes could significantly facilitate the downstream implementation process. The dedicated staff then use DNA assembly design automation software to optimize the DNA construction process for the design, leveraging existing parts from the DNA repository where possible and ordering synthetic DNA where necessary. Once all requisite process inputs are available, the design progresses from "waiting for reagents" to "in progress" in the design queue. Human-readable and machine-parseable DNA construction protocols output by the DNA assembly design automation software are then executed by the dedicated staff exploiting lab automation devices wherever possible. Since the all employed DNA construction methods are sequence-agnostic, standardized (utilize the same enzymatic master mixes and reaction conditions), completely independent DNA construction tasks can be aggregated into the same multi-well plates and pursued in parallel. The resulting sets of cloned constructs can then be screened by high-throughput next-gen sequencing platforms for sequence correctness. A combination of long read-length (e.g., PacBio) and paired-end read platforms (e.g., Illumina) would be exploited depending the particular task at hand (e.g., PacBio might be sufficient to screen a set of pooled constructs with significant gene divergence). Post sequence verification, designs for which at least one correct clone was identified will progress to a "complete" status, while designs for which no correct clones were identified will progress to a "failure" status. Depending on the failure mode (e.g., no transformants), and how many prior attempts/variations of assembly protocol have been already made for a given design, subsequent attempts may be made or the design can progress to a "permanent failure" state. All success and failure rate information will be captured during the process, including at which stage a given clonal construction procedure failed (e.g., no PCR product) and what the exact failure was (e.g. assembly piece 2 missing). This success/failure rate data can be leveraged to refine the DNA assembly design process.

Plahar, Hector↗

A versatile Johansson-type tender x-ray emission spectrometer

We present a high energy resolution x-ray spectrometer for the tender x-ray regime (1.6–5.0 keV) that was designed and operated at Stanford Synchrotron Radiation Lightsource. The instrument is developed on a Rowland geometry (500 mm of radius) using cylindrically bent Johansson analyzers and a position sensitive detector. By placing the sample inside the Rowland circle, the spectrometer operates in an energy-dispersive mode with a subnatural line-width energy resolution (~0.32 eV at 2400 eV), even when an extended incident x-ray beam is used across a wide range of diffraction angles (~30° to 65°). The spectrometer is enclosed in a vacuum chamber, and a sample chamber with independent ambient conditions is introduced to enable a versatile and fast-access sample environment (e.g., solid/gas/liquid samples, in situ cells, and radioactive materials). The design, capabilities, and performance are presented and discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

TEAMER: Experimental performance characterization of a shrouded axial-flow turbine

Sitkana has developed a shrouded hydrokinetic turbine with a modular, low-cost design that can be scaled to meet the needs of remote communities. With technical support from the University of Washington, Sitkana sought to experimentally characterize the mechanical power and structural loads of various 1:3.3 scale rotor geometries. In all, 11 different rotor geometries were characterized with variations in height-to-diameter ratio, blade number, and blade type (foiled versus flat). All tests were conducted in Reynolds-independent flow conditions in the Alice C. Tyler Flume at the University of Washington. Results allow Sitkana to (1) refine the optimal rotor geometry, (2) validate numerical models, and (3) predict power output for a full-scale system. This project is part of the TEAMER RFTS 8 (request for technical support) program.

16 TIDAL AND WAVE POWER↗

Principled Schedulability Analysis for Distributed Storage Systems Using Thread Architecture Models

In this article, we present an approach to systematically examine the schedulability of distributed storage systems, identify their scheduling problems, and enable effective scheduling in these systems. We use Thread Architecture Models (TAMs) to describe the behavior and interactions of different threads in a system, and show both how to construct TAMs for existing systems and utilize TAMs to identify critical scheduling problems. We specify three schedulability conditions that a schedulable TAM should satisfy: completeness, local enforceability, and independence; meeting these conditions enables a system to easily support different scheduling policies. We identify five common problems that prevent a system from satisfying the schedulability conditions, and show that these problems arise in existing systems such as HBase, Cassandra, MongoDB, and Riak, making it difficult or impossible to realize various scheduling disciplines. We demonstrate how to address these schedulability problems using both direct and indirect solutions, with different trade-offs. To show how to apply our approach to enable scheduling in realistic systems, we develop Tamed-HBase and Muzzled-HBase, sets of modifications to HBase that can realize the desired scheduling disciplines, including fairness and priority scheduling, even when presented with challenging workloads.

Computer Science↗

Exponential Decay of Sensitivity in Graph-Structured Nonlinear Programs

We study solution sensitivity for nonlinear programs (NLPs) whose structures are induced by graphs. These NLPs arise in many applications such as dynamic optimization, stochastic optimization, optimization with partial differential equations, and network optimization. We illustrate that for a given pair of nodes, the sensitivity of the primal-dual solution at one node against a data perturbation at the other node decays exponentially with respect to the distance between these two nodes on the graph. In other words, the solution sensitivity decays as one moves away from the perturbation point. This result, which we call exponential decay of sensitivity, holds under the strong second-order sufficiency condition and the linear independence constraint qualification. We also present conditions under which the decay rate remains uniformly bounded; this allows us to characterize the sensitivity behavior of NLPs defined over subgraphs of infinite graphs. The theoretical developments are illustrated with numerical examples.

97 MATHEMATICS AND COMPUTING↗