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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Building a genome-based understanding of bacterial pH preferences

The environmental preferences of many microbes remain undetermined. This is the case for bacterial pH preferences, which can be difficult to predict a priori despite the importance of pH as a factor structuring bacterial communities in many systems. We compiled data on bacterial distributions from five datasets spanning pH gradients in soil and freshwater systems (1470 samples), quantified the pH preferences of bacterial taxa across these datasets, and compiled genomic data from representative bacterial taxa. While taxonomic and phylogenetic information were generally poor predictors of bacterial pH preferences, we identified genes consistently associated with pH preference across environments. We then developed and validated a machine learning model to estimate bacterial pH preferences from genomic information alone, a model that could aid in the selection of microbial inoculants, improve species distribution models, or help design effective cultivation strategies. More generally, we demonstrate the value of combining biogeographic and genomic data to infer and predict the environmental preferences of diverse bacterial taxa.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

National Cancer Institute Think-Tank Meeting Report on Proteomic Cartography and Biomarkers at the Single-Cell Level: Interrogation of Premalignant Lesions

A Think-Tank Meeting was convened by the National Cancer Institute (NCI) to solicit experts’ opinion on the development and application of multi-omic single-cell analyses, and especially single-cell proteomics, to improve the development of a new generation of biomarkers for cancer risk, early detection, diagnosis, prognosis as well as to discuss the discovery of new targets for prevention and therapy. It is anticipated that such markers and targets will be based on cellular, subcellular, molecular and functional aberrations within the lesion and within individual cells. Additionally, single-cell proteomic data will be essential for the establishment of new tools that with searchable and scalable features that include spatial and temporal cartographies of premalignant and malignant lesions. Challenges and potential solutions that were discussed included: • The best way/s to analyze single-cells from fresh and preserved tissue • Detection and analysis of secreted molecules and from single cells, especially from a tissue slice • Detection of new, previously undocumented cell type/s in the premalignant and early stage cancer tissue microenvironment • Multi-omic integration of data to support and inform proteomic measurements • Subcellular organelles – identifying abnormal structure, function, distribution and location within individual premalignant and malignant cells • How to improve the dynamic range of single-cell proteomic measurements for discovery of differentially expressed proteins and their post translational modifications (PTM) • The depth of coverage measured concurrently using single-cell techniques • Quantitation - absolute or semiquantitative? • Single methodology or multiplexed combinations? • Application of analytical methods for identification of biologically significant subsets • Data visualization of N-dimensional datasets • How to construct intercellular signaling networks in individual cells within premalignant tumor microenvironments (TME) • Associations between intrinsic cellular processes and extrinsic stimuli • How to predict cellular responses to stress inducing stimuli • Identification of new markers for prediction of progression from precursor, benign and localized lesions to invasive cancer, based on spatial and temporal changes within individual cells • Identification of new targets for immunoprevention or immunotherapy – identification of neoantigens and surfactome of individual cells within a lesion

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Müllerian mimicry and the coloration patterns of sympatric coral snakes

Abstract Coral snakes in the genus Micrurus are venomous, aposematic organisms that signal danger to predators through vivid coloration. Previous studies found that they serve as models to several harmless species of Batesian mimics. However, the extent to which Micrurus species engage in Müllerian mimicry remains poorly understood. We integrate detailed morphological and geographical distribution data to investigate if coral snakes are Müllerian mimics. We found that coloration is spatially structured and that Micrurus species tend to be more similar where they co-occur. Though long supposed, we demonstrate for the first time that coral snakes might indeed be Müllerian mimics as they show some convergence in coloration patterns. Additionally, we found that the length of red-coloured rings in Micrurus is conserved, even at large geographic scales. This finding suggests that bright red rings may be under more substantial stabilizing selection than other aspects of coloration and probably function as a generalized signal for deterring predators.

Evolutionary Biology↗
DOE OSTI2024

Up and down quark structure of the proton

We report an improved measurement of the valence u and d quark distributions from the forward-backward asymmetry in the Drell-Yan process using 8.6 fb − 1 of data collected with the D0 detector in p p ¯ collisions at s = 1.96 . This analysis provides the values of new structure parameters that are directly related to the valence up and down quark distributions in the proton. In other experimental results measuring the quark content of the proton, d quark contributions are mixed with those from other quark flavors. In this measurement, the u and d quark contributions are separately extracted by applying a factorization of the QCD and electroweak portions of the forward-backward asymmetry. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Distributed directory of named data elements in coordination namespace

An approach is described that provides a distributed directory structure within a storage of an information handling system (a local node). A request is received with the request corresponding to a shared virtual address. The shared virtual address that is shared amongst a number of nodes that includes the local node and some remote nodes. A Global Address Space Directory (GASD) is retrieved that corresponds to a global virtual address space. The GASD is stored in a Coordination Namespace that is stored in a memory that is distributed amongst the nodes. A mapping that is included in the GASD is used to determine the node where the shared virtual address currently resides. The shared virtual address is then accessed from the node where it currently resides.

Johns, Charles Ray↗
DOE OSTI2018

Distributed Fiber Optic Arrays: Integrated Temperature and Seismic Sensing for Detection of CO 2 Flow, Leakage and Subsurface Distribution (Final Report)

This report describes the seismic and heat-pulse monitoring equipment, surveys, results and accomplishments of the fiber optic (FO) project funded by the U.S. Department of Energy (DOE) under Agreement Number DE-FE0012700. The formal title of the project is “Distributed Fiber Optic Arrays: Integrated Temperature and Seismic Sensing for Detection of CO 2 Flow, Leakage and Subsurface Distribution.” The project focuses on the field acquisition of FO data using Distributed Acoustic Sensing (DAS) arrays for subsurface imaging of geologic structure and to track the CO 2 migration in the injection interval using time-lapse techniques. A second component of the project uses a novel heat-pulse monitoring method that relies on Distributed Temperature Sensing (DTS) to evaluate near-field leakage of CO 2 or brine in and around the wellbore. The Southeast Regional Carbon Sequestration (SECARB) partnership hosted the project at the Anthropogenic Test site near Citronelle, Alabama after the original site host, the Kansas Geological Survey (KGS), experienced significant delays in permitting its CO 2 injection well at the Wellington Oil Field site in Kansas. The KGS project was later terminated in 2017 before an injection permit issued. A second field trial was planned for the Livingston Oil Field, located in Louisiana, but the site host ran into financial troubles and the project was terminated in 2015. A replacement field-site host was not found for the Livingston field trial. Both the Wellington, Kansas and Livingston, Louisiana projects were funded by DOE. A novel sensor platform referred to as the Modular Borehole Monitoring (MBM) system deployed at the Citronelle field site was utilized by the FO project. Two different seismic sensor technologies were incorporated in the Citronelle MBM system design including: (1) a semi-permanent, tubing-deployed, 18-level geophone array with custom hydraulic clamps; and (2) two, single mode optical fibers for Distributed Acoustic Sensing (DAS). Incorporation of the 18-level geophone array in the MBM system provided an ideal opportunity to compare the DAS response to conventional geophones. A heat-pulse monitoring system consisting of two, multi-mode optical fibers for Distributed Temperature Sensing (DTS) and copper heater element were also incorporated into the MBM package. This report provides an overview of the design of the MBM, DAS, and DTS equipment and the surveys performed during the study. The heat-pulse monitoring technique was used to diagnose a completion problem with observation well D-9-8 #2 where the MBM system was installed in 2012, thus successfully demonstrating its application for leak detection. In 2015, CO 2 unexpectedly arrived at the observation well approximately one year after CO 2 injection and heat-pulse monitoring ended. It was determined at this time that saltwater intrusion had likely penetrated the stainless-steel capillary tube containing the fibers and copper heater elements, thus preventing the heater from being used. DTS measurements indicated that the fiber had also degraded probably due to hydrogen darkening. It is recommended that the capillary tube be constructed of a more corrosive resistant material (e.g., Inconel) in the future should it be deployed in similar harsh downhole environments. Geophysical imaging of the subsurface and monitoring of the CO 2 were successful using DAS in the vertical seismic profile (VSP) survey configuration. A high resolution VSP image of the subsurface was obtained in 2014 using DAS, which exceeded our expectations in comparison to a lower resolution image obtained by SECARB in 2012 using an 80-level geophone array. A time-lapse image of the redistribution of CO 2 after injection ended in September 2014 was obtained using two DAS VSP surveys from June 2014 and December 2015, thus successfully demonstrating its application. DAS data were also acquired during a crosswell seismic survey conducted in 2014. Unfortunately, the DAS technique was not success in the crosswell survey configuration because the noise level was too high, increasing by a factor of ten, over the high frequency output (100-1,200) of the piezoelectric source.

58 GEOSCIENCES↗
DOE OSTI2024

e RPCA : Robust Principal Component Analysis for Exponential Family Distributions

Abstract Robust principal component analysis (RPCA) is a widely used method for recovering low‐rank structure from data matrices corrupted by significant and sparse outliers. These corruptions may arise from occlusions, malicious tampering, or other causes for anomalies, and the joint identification of such corruptions with low‐rank background is critical for process monitoring and diagnosis. However, existing RPCA methods and their extensions largely do not account for the underlying probabilistic distribution for the data matrices, which in many applications are known and can be highly non‐Gaussian. We thus propose a new method called RPCA for exponential family distributions (), which can perform the desired decomposition into low‐rank and sparse matrices when such a distribution falls within the exponential family. We present a novel alternating direction method of multiplier optimization algorithm for efficient decomposition, under either its natural or canonical parametrization. The effectiveness of is then demonstrated in two applications: the first for steel sheet defect detection and the second for crime activity monitoring in the Atlanta metropolitan area.

Zheng, Xiaojun↗
DOE OSTI2021

Local structure of Mott insulating iron oxychalcogenides La 2 O 2 Fe 2 OM2(M=S,Se)

Here, we describe the local structural properties of the iron oxychalcogenides, La 2 O 2 Fe 2 OM 2 (M=S,Se), by using pair distribution function analysis applied to total scattering data. Our results from neutron powder diffraction show that M = S and Se possess similar nuclear structures at low and room temperatures. The local crystal structures were studied by investigating deviations in atomic positions and the extent of the formation of orthorhombicity. Analysis of the total scattering data suggests that buckling of the Fe 2 O plane occurs below 100 K. The buckling may occur concomitantly with a change in octahedral height. Furthermore, within a typical range of 1-2 nm, we observed a short-range orthorhombiclike structure suggestive of nematic fluctuations in both of these materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

A Probabilistic Reasoner Based on Bayes Risk for Damage Detection in Structural Systems

Structural health monitoring (SHM) systems are used to inform operation of structural systems subject to loads and environments that may affect their integrity. SHM systems rely on continuous monitoring of the structure to determine its health state. These systems are often coupled with a model of the deployed structure to determine the consequences of changes in the system by forecasting the response to future states. These models, which may be thought of as digital twins, need to be updated to reflect the latest state of the structural system. This work makes use of an uncertainty-aware machine learning model that enforces distance preservation of the original input space to determine deviations from the training data input space distributions. This workflow enables domain shift detection to determine whether damage is present in the structure. The uncertainty metrics generated by this network are then used in a Bayes risk framework to design an optimal damage detector given cost and risk considerations. The approach is demonstrated on a computational example with simulated damage.

Najera-Flores, David [ATA Engineering, Inc.]↗
DOE OSTI2018

Environmental impact assessment follow-up for projects in China: Institution and practice

Highlights: • As an essential part of the environmental management system required by the EIA Law, the EIA follow-up was poorly executed. • The effectiveness of the EIA follow-up could be greatly elevated by enhancing monitoring and public participation. • Environmental information disclosure plays the essential role in promoting the EIA follow-up. - Abstract: The Environmental Impact Assessment (EIA) is one of the important means of environmental management worldwide, and the EIA follow-up is one of the crucial measures to ensure the concrete implementation of the EIA. In this research, systematic reviews of EIA follow-up for projects in China were conducted to comprehensively appraise the implementation of the EIA follow-up and to identify the potential to improve the practice of an EIA follow-up. First, the EIA follow-up reports were collected through various sources. In total, there were 74 EIA follow-up reports that were identified and acquired. Second, a reviewing framework, containing 16 different indicators, was developed to systematically document and classify these EIA follow-up reports. Third, these EIA follow-up reports were grouped into several categories according to various indicators and then were analyzed to recognize their key features, including sources of data, analytic tools adopted, spatial-temporal distribution, industrial distribution, triggering causes, content, structure and effectiveness. The results showed that there were considerable disparities in the quality and the rigor of EIA follow-ups conducted. These features, along with some existing deficiencies of EIA follow-up, such as the lack of corresponding monitoring and management, can greatly limit the overall application and effectiveness of EIA follow-up in China. However, a number of good practices were identified. For example, a list of projects subject to mandatory EIA follow-up was promulgated, and the conditions for projects subject to EIA follow-up were explicitly stipulated in regulations. These good practices would be very helpful for the international EIA community to advance the practice of EIA follow-up, to reduce the disparities in the quality and the rigor of EIA follow-up, to improve the overall effectiveness of EIA follow-up and to promote the future implementation and development of EIA follow-up.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Nondestructive Modular Leak Detection in 3D Printed 316L Stainless Steel Pipes via Laser Powder Bed Fusion

This research investigates the leak detection features of 316L Stainless Steel pipe structures manufactured via Laser Powder Bed Fusion (LPBF). This work involves the design of a modular sensor system integrating nondestructive evaluation (NDE) methods, including thermal imaging and ultrasonic frequency detection to detect and characterize leaks in components. This aims to improve leak detection sensitivity within medium-pressure gas systems, during continuous operation without halting flow or introducing safety risks. The system could be adaptable for use on unmanned aerial vehicles (UAVs), enabling remote leak detection in active environments. A custom pneumatic system incorporating temperature and pressure sensors was assembled to detect leaks in LPBF-printed 316L SS tee pipes. Experimental results and simulations confirm the system’s effectiveness in leak detection and material evaluation. This research program also integrated a Python-based image recognition platform based on a metallography and optical microscopy to assess the porosity and complement the leak detection data on the printed structures. This allows a detailed analysis of pore distribution and internal leak paths, which could compromise structural integrity, critical for quality control during manufacturing. Findings suggest that the investigated approach holds potential for enhancing leak detection technologies and adapt them for advanced manufactured parts.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Scaling Subspace-Driven Approaches Using Information Fusion

In this work, we seek to exploit the deep structure of multi-modal data to robustly exploit the group subspace distribution of the information using the Convolutional Neural Networks (CNNs) formalism. Upon unfolding the set of subspaces constituting each data modality, and learning their corresponding encoders, an optimized integration of the generated inherent information is carried out to yield a characterization of various classes. Referred to as deep Multimodal Robust Group Subspace Clustering (DRoGSuRe), this approach is compared against the independently developed state-of-the-art approach named Deep Multimodal Subspace Clustering (DMSC). Experiments on different multimodal datasets show that our approach is competitive and more robust in the presence of noise.

Ghanem, Sally↗
DOE OSTI2019

Co-location Pattern Mining of Geosocial Data to Characterize Urban Functional Spaces

Spatial Co-location Pattern (SCP) mining continues to play a critical role in understanding the morphology of urban functional spaces of world cities. It requires a large amount of fine-granular data and computing efficiency to handle the combinatorial explosion of co-location patterns. To this end, this work has two main contributions - i) We showcase a novel approach to perform SCP mining to characterize intra-city scale structure of urban functionality or co-located activity patterns using geosocial Points-of-Interest (POI) vector data. ii) We present a generalized and optimized parallel/distributed SCP mining algorithm implemented on a Hadoop MapReduce system and demonstrate the utility of our approach using the city of Berlin (Germany) as an example. The SCPs tend to vary across Berlin's municipal boroughs and at different spatial scales. Our findings on Berlin's functional structure conform to existing urban geography models. Such a data-driven exploration of massive urban POIs using distributed computing is first of its kind and can help better understand the changing dynamics of urban functionality, as well as physical, and social network structure around the world.

Masrur, Arif↗
DOE OSTI2020

TriC: Distributed-memory Triangle Counting by Exploiting the Graph Structure

Graph analytics has emerged as an important tool in the analysis of large scale data from diverse application domains such as social networks, cyber security and bioinformatics. Counting the number of triangles in a graph is a fundamental kernel with several applications such as detecting the community structure of a graph or in identifying important vertices in a graph. The ubiquity of massive datasets is driving the need to scale graph analytics on parallel systems. However, numerous challenges exist in efficiently parallelizing graph algorithms, especially on distributed-memory systems. Irregular memory accesses and communication patterns, low computation to communication ratios, and the need for frequent synchronization are some of the leading challenges. In this paper, we present TriC, our distributed-memory implementation of triangle counting in graphs using the Message Passing Interface (MPI), as a submission to the 2020 GraphChallenge competition. Using a set of synthetic and real-world inputs from the challenge, we demonstrate a speedup of up to 90x relative to previous work on 32 processor-cores of a NERSC Cori node. We also provide details from distributed runs with up to8192 processes along with strong scaling results. The observations presented in this work provide an understanding of the system-level bottlenecks at scale that specifically impact sparse-irregular workloads and will therefore benefit other efforts to parallelize graph algorithms.

Halappanavar, Mahantesh↗
DOE OSTI2018

Crystal and Magnetic Structures of Granular Powder Spinel Mn–Zn and Ni–Zn Ferrites

Some specific features of the crystal and magnetic structures of granular powder spinel-like ferrites Mn{sub 0.160}Mg{sub 0.404}Zn{sub 0.448}Fe{sub 2}O{sub 4}, Mn{sub 0.676}Zn{sub 0.227}Fe{sub 0.09}Fe{sub 2}O{sub 4}, Mn{sub 0.5792}Zn{sub 0.2597}Fe{sub 0.1612}Fe{sub 2}O{sub 4}, and Ni{sub 0.32}Zn{sub 0.68}Fe{sub 2}O{sub 4} have been studied by neutron diffraction. It has been established that the crystal structure of all the studied compounds has a cubic symmetry with space group Fd3̄m. Ferrimagnetic ordering is observed in all the studied structures. Based on the experimental data, the unit cell parameters and interatomic bond lengths of the studied compounds are determined alongside with the distribution of cations between octahedral and tetrahedral crystallographic positions in their cubic crystal structure. Corresponding average magnetic moments are calculated for different positions in their cubic structure. Some structural mechanisms of the formation of magnetic properties depending on the level of doping and the size of powder grains are discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Search for heavy pseudoscalar and scalar bosons decaying to a top quark pair in proton–proton collisions at $\sqrt{s}$ = 13 TeV

A search for pseudoscalar or scalar bosons decaying to a top quark pair ($\textrm{t}$$\bar{\textrm{t}}$) in final states with one or two charged leptons is presented. The analyzed proton–proton collision data was recorded at $\sqrt{s}$ = 13 TeV by the CMS experiment at the CERN LHC and corresponds to an integrated luminosity of 138 fb -1 . The invariant mass $m_{{\textrm{t}}{\bar{\textrm{t}}}}$ of the reconstructed $\textrm{t}$$\bar{\textrm{t}}$ system and variables sensitive to its spin and parity are used to discriminate against the standard model background. Interference between pseudoscalar or scalar boson production and the standard model $\textrm{t}$$\bar{\textrm{t}}$ continuum is included, leading to peak-dip structures in the $m_{{\textrm{t}}{\bar{\textrm{t}}}}$ distribution. An excess of the data above the background prediction, based on perturbative quantum chromodynamics (QCD) calculations, is observed near the kinematic $\textrm{t}$$\bar{\textrm{t}}$ production threshold, while good agreement is found for high $m_{{\textrm{t}}{\bar{\textrm{t}}}}$. The data are consistent with the background prediction if the contribution from a simplified model of a color-singlet $^1\textrm{S}^{|1|}_0$ ${{\textrm{t}}{\bar{\textrm{t}}}}$ quasi-bound state $η_{\textrm{t}}$, inspired by nonrelativistic QCD, is added. Upper limits at 95% confidence level are set on the coupling between the pseudoscalar or scalar bosons and the top quark for boson masses in the range 365–1000 GeV, relative widths between 0.5% and 25%, and two background scenarios with or without $η_{\textrm{t}}$ contribution.

CMS↗
DOE OSTI2021

Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System

Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness. This paper combines the advantages of two sparsity-based state estimation approaches (matrix completion and compressive sensing) that have been proposed recently to address the challenge of unobservability. The proposed approach exploits both the low rank structure and a suitable transform domain representation to leverage the correlation structure of the spatio-temporal data matrix while incorporating the powerflow constraints of the distribution grid. Simulations are carried out on three phase unbalanced IEEE 37 test system to verify the effectiveness of the proposed approach. The performance results reveal - (1) the superiority over traditional matrix completion and (2) very low state estimation errors for high compression ratios representing very low observability.

Dahale, Shweta↗