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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Protecting Privacy of Shared Epidemiologic Data without Compromising Analysis Potential

Objective . Ensuring privacy of research subjects when epidemiologic data are shared with outside collaborators involves masking (modifying) the data, but overmasking can compromise utility (analysis potential). Methods of statistical disclosure control for protecting privacy may be impractical for individual researchers involved in small-scale collaborations. Methods . We investigated a simple approach based on measures of disclosure risk and analytical utility that are straightforward for epidemiologic researchers to derive. The method is illustrated using data from the Japanese Atomic-bomb Survivor population. Results . Masking by modest rounding did not adequately enhance security but rounding to remove several digits of relative accuracy effectively reduced the risk of identification without substantially reducing utility. Grouping or adding random noise led to noticeable bias. Conclusions . When sharing epidemiologic data, it is recommended that masking be performed using rounding. Specific treatment should be determined separately in individual situations after consideration of the disclosure risks and analysis needs.

Cologne, John↗
DOE OSTI2014

Reconstructing the local potential of inflation with BICEP2 data

We locally reconstruct the inflationary potential by using the current constraints on r and n{sub s} from BICEP2 data. Assuming small and negligible α{sub s}, the inflationary potential is approximately linear in Δφ∼ M{sub pl} range but becomes non-linear in Δφ∼ 10 M{sub pl} range. However if we vary the value of α{sub s} within the range given by constraints from Planck measurement, the local reconstruction is only valid in the range of Δφ∼ 0.4 M{sub pl}, which challenges the inflationary background from the point of view of effective field theory. We show that, within the range of Δ φ ∼ 0.4 M{sub pl}, the inflation potential can be precisely reconstructed. With the current reconstruction, we show that V(φ) ∼ φ{sup 2} and φ{sup 3} are consistent, while φ model is ruled out by 95% confidence level of the reconstructed range of potential. This sets up a strong limit of large-field inflation models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

National Geothermal Data System: Case Studies on Exploration and Development of Potential Geothermal Sites Through Distributed Data Sharing

The NGDS released version 1 of the system on April 30, 2014 using the US Geoscience Information Network (USGIN) as its data integration platform. NGDS supports the 2013 Open Data Policy, and as such, the launch was featured at the 2014 Energy Datapalooza. Currently, the NGDS features a comprehensive user interface for searching and accessing nearly 41,000 documents and more than 9 million data points shared by scores of data providers across the U.S. The NGDS supports distributed data sharing, permitting the data owners to maintain the raw data that is made available to the consumer. Researchers and industry have been utilizing the NGDS as a mechanism for promoting geothermal development across the country, from hydrothermal to ground source heat pump applications. Case studies in geothermal research and exploration from across the country are highlighted.

15 GEOTHERMAL ENERGY↗
DOE OSTI2013

Stochastic joint inversion of hydrogeophysical data for salt tracer test monitoring and hydraulic conductivity imaging

The assessment of hydraulic conductivity of heterogeneous aquifers is a difficult task using traditional hydrogeological methods (e.g., steady state or transient pumping tests) due to their low spatial resolution. Geophysical measurements performed at the ground surface and in boreholes provide additional information for increasing the resolution and accuracy of the inverted hydraulic conductivity field. We used a stochastic joint inversion of Direct Current (DC) resistivity and Self-Potential (SP) data plus in situ measurement of the salinity in a downstream well during a synthetic salt tracer experiment to reconstruct the hydraulic conductivity field between two wells. The pilot point parameterization was used to avoid over-parameterization of the inverse problem. Bounds on the model parameters were used to promote a consistent Markov chain Monte Carlo sampling of the model parameters. To evaluate the effectiveness of the joint inversion process, we compared 8 cases in which the geophysical data are coupled or not to the in situ sampling of the salinity to map the hydraulic conductivity. We first tested the effectiveness of the inversion of each type of data alone (concentration sampling, self-potential, and DC resistivity), and then we combined the data two by two. We finally combined all the data together to show the value of each type of geophysical data in the joint inversion process because of their different sensitivity map. We also investigated a case in which the data were contaminated with noise and the variogram unknown and inverted stochastically. The results of the inversion revealed that incorporating the self-potential data improves the estimate of hydraulic conductivity field especially when the self-potential data were combined to the salt concentration measurement in the second well or to the time-lapse cross-well electrical resistivity data. Various tests were also performed to quantify the uncertainty in the inverted hydraulic conductivity field.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

San Jose, California: Evaluating Local Solar Energy Generation Potential (City Energy: From Data to Decisions)

This fact sheet "San Jose, California: Evaluating Local Solar Energy Generation Potential" explains how the City of San Jose used data from the U.S. Department of Energy's Cities Leading through Energy Analysis and Planning (Cities-LEAP) and the State and Local Energy Data (SLED) programs to inform its city energy planning. It is one of ten fact sheets in the "City Energy: From Data to Decisions" series.

Cities leading through energy analysis and plannin↗
DOE OSTI2016

Ab initio studies of the Rg–NO{sup +}(X{sup 1}Σ{sup +}) van der Waals complexes (Rg = He, Ne, Ar, Kr, and Xe)

We used the explicitly correlated variant of the coupled clusters method with single, double, and noniterative triple excitations [CCSD(T)-F12] to compute two-dimensional potential energy surfaces of van der Waals complexes formed by rare gas atoms (Rg) and NO{sup +}(X{sup 1}Σ{sup +}) cations. We used the correlation-consistent, triple-zeta (cc-pVTZ-F12) atomic basis sets, and for Kr and Xe rare gases, we employed corresponding pseudopotential cc-pVTZ-PP-F12 atomic basis sets. These basis sets were additionally augmented with mid-bond functions. The complexes are all of skewed T-shape type with Rg atom being closer to the N-side. Using analytical representation of the potentials, we have estimated zero-point energy corrected dissociation energies from anharmonic calculations with BOUND program and also from the harmonic approximation. The binding energies increase with the polarization of the Rg atom in series from He to Xe and are 196 cm{sup −1}, 360 cm{sup −1}, 1024 cm{sup −1}, 1434 cm{sup −1}, and 2141 cm{sup −1}, respectively. Their corresponding dissociation energies are 132 cm{sup −1}, 300 cm{sup −1}, 927 cm{sup −1}, 1320 cm{sup −1}, and 1994 cm{sup −1} for the complexes with He to Xe, respectively. We find good agreement with previous theoretical and experimental results. The harmonic vibrational frequencies were calculated for the bending and stretching modes of the Rg–NO{sup +} complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Federated Quantum Machine Learning

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The importance of Aitken mode aerosol particles for cloud sustenance in the summertime high Arctic – a simulation study supported by observational data

The potential importance of Aitken mode particles (diameters ~ 25–80 nm) for stratiform mixed-phase clouds in the summertime high Arctic (>80° N) has been investigated using two large-eddy simulation models. We find that, in both models, Aitken mode particles significantly affect the simulated microphysical and radiative properties of the cloud and can help sustain the cloud when accumulation mode concentrations are low (< 10–20 cm –3 ), even when the particles have low hygroscopicity (hygroscopicity parameter – κ=0.1). However, the influence of the Aitken mode decreases if the overall liquid water content of the cloud is low, either due to a higher ice fraction or due to low radiative cooling rates. An analysis of the simulated supersaturation (ss) statistics shows that the ss frequently reaches 0.5 % and sometimes even exceeds 1 %, which confirms that Aitken mode particles can be activated. The modelling results are in qualitative agreement with observations of the Hoppel minimum obtained from four different expeditions in the high Arctic. Our findings highlight the importance of better understanding Aitken mode particle formation, chemical properties and emissions, particularly in clean environments such as the high Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Self‐Potential Tomography Preconditioned by Particle Swarm Optimization—Application to Monitoring Hyporheic Exchange in a Bedrock River

Abstract A self‐potential (SP) data‐inversion algorithm was developed and tested on an analytical model of electrical‐potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP‐monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock‐lined riverbed. The algorithm combined stochastic source‐localization by particle‐swarm‐optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least‐squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical‐model data to evaluate the algorithm performance. Results indicated that true parameters of single‐source models were inverted and approximated with small residual error, whereas inversion of analytical‐model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non‐uniqueness of the inverse‐model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point‐scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP‐monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data for "Examining Organic Acid Production Potential and Growth-Coupled Strategies in Issatchenkia orientalis Using Constraint-Based Modeling"

Growth-coupling product formation can facilitate strain stability by aligning industrial objectives with biological fitness. Organic acids make up many building block chemicals that can be produced from sugars obtainable from renewable biomass. Issatchenkia orientalis is a yeast strain tolerant to acidic conditions and is thus a promising host for industrial production of organic acids. Here, we use constraint-based methods to assess the potential of computationally designing growth-coupled production strains for I. orientalis that produce 22 different organic acids under aerobic or microaerobic conditions. We explore native and engineered pathways using glucose or xylose as the carbon substrates as proxy constituents of hydrolyzed biomass. We identified growth-coupled production strategies for 37 of the substrate-product pairs, with 15 pairs achieving production for any growth rate. We systematically assess the strain design solutions and categorize the underlying principles involved.

Bioproducts↗
DOE OSTI2026

Methods for safely sharing dual-use genetic data

Background: Some genetic data has dual-use potential. Sharing pathogen data has shown tremendous value. For example therapeutic development and lineage tracking during the COVID pandemic. This data sharing is complicated by the fact that these data have the potential to be used for harm. The genome sequence of a pathogen can be used to enable malicious genetic engineering approaches or to recreate the pathogen from synthetic DNA. Standard data security methods can be applied to genetic data, but when data is shared between institutions, ensuring appropriate security can be difficult. Sensitive data that is shared internationally among a wide array of institutions can be especially difficult to control. Methods for securely storing and sharing genetic data with potential for dual-use are needed to mitigate this potential harm.Results: Here we propose new methods that allow genetic data to be shared in a data format that prevents a nefarious actor from accessing sensitive aspects of the data. Our methods obfuscate raw sequence data by pooling reads from different samples. This approach can ensure that data is secure while stored and during electronic transfer. We demonstrate that by pooling raw sequence data from multiple samples of the same organism, the ability to fully reconstruct any individual sample is prevented. In the pooled data, most genomic information remains, but reads or mutations cannot be directly attributed to any individual sample. To further restrict access to information, regions of a genome can be removed from the reads.Conclusion: Our methods obscure genomic information within raw sequence reads. This method can allow genetic data to be stored and shared while preventing a nefarious actor from being able to perfectly reconstruct an organism. Broad-scale sequence information remains, while fine scale details about specific samples are difficult or impossible to reconstruct. Our software is available at https://github.com/Geneinfosec-Inc/ReadMixer.

59 BASIC BIOLOGICAL SCIENCES↗