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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Toxin activity assays, devices, methods and systems therefor

Embodiments of the present invention are directed toward devices, system and method for conducting toxin activity assay using sedimentation. The toxin activity assay may include generating complexes which bind to a plurality of beads in a fluid sample. The complexes may include a target toxin and a labeling agent, or may be generated due to presence of active target toxin and/or labeling agent designed to be incorporated into complexes responsive to the presence of target active toxin. The plurality of beads including the complexes may be transported through a density media, wherein the density media has a lower density than a density of the beads and higher than a density of the fluid sample, and wherein the transporting occurs, at least in part, by sedimentation. Signal may be detected from the labeling agents of the complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Active Learning Framework for Non-Intrusive Load Monitoring: Preprint

Non-Intrusive Load Monitoring (NILM) is a set of techniques that estimate the electricity usage of individual appliances from power measurements taken at a limited number of locations in a building. One of the key challenges in NILM is having too much data without class labels yet being unable to label the data manually for cost or time constraints. This paper presents an active learning framework that helps existing NILM techniques to overcome this challenge. Active learning is an advanced machine learning method that interactively queries a user for the class label information. Unlike most existing NILM systems that heuristically request user inputs, the proposed method only needs minimally sufficient information from a user to build a compact and yet highly representative load signature library. Initial results indicate the proposed method can reduce the user inputs by up to 90% while still achieving similar disaggregation performance compared to a heuristic method. Thus, the proposed method can substantially reduce the burden on the user, improve the performance of a NILM system with limited user inputs, and overcome the key market barriers to the wide adoption of NILM technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

C-11 cyanide production system

A method for providing .sup.11C-labeled cyanides from .sup.11C labeled oxides in a target gas stream retrieved from an irradiated high pressure gaseous target containing O.sub.2, wherein .sup.11C labeled oxides are reduced with H.sub.2 in the presence of a nickel catalyst under a pressure and a temperature sufficient to form a product stream comprising at least about 95% .sup.11CH.sub.4, the .sup.11CH.sub.4 is then combined with an excess of NH.sub.3 in a carrier/reaction stream flowing at an accelerated velocity and the combined .sup.11CH4 carrier/reaction stream is then contacted with a platinum (Pt) catalyst particulate supported on a substantially-chemically-nonreactive heat-stable support at a temperature of at least about 900.degree. C., whereby a product stream comprising at least about 60% H.sup.11CN is provided in less than 10 minutes from retrieval of the .sup.11C labeled oxide.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Superpixels for improved structure and terrain classification using multiple synthetic aperture radar image products

Various embodiments presented herein relate to assigning labels to segments of a synthetic aperture radar (SAR) image, where the segments are based upon a speckle-reduced SAR image product. A plurality of SAR images of a scene are co-registered to form a registered stack of SAR images. A speckle-reduced SAR image product is generated based upon at least one registered SAR image in the registered stack of SAR images. The speckle-reduced SAR image product is segmented into a plurality of superpixels, and boundaries of the superpixels are applied to the at least one registered SAR image to form a segmented SAR image. A segment of the SAR image is then labeled as including or not including a feature, wherein the label is assigned based upon values of pixels in the segment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

SATYA: Defending Against Adversarial Attacks Using Statistical HypothesisTesting

The paper presents a new defense against adversarial attacks for deep neural networks. We demonstrate the effectiveness of our approach against the popular adversarial image generation method DeepFool. Our approach uses Wald’s Sequential Probability Ratio Test to sufficiently sample a carefully chosen neighborhood around an input image to determine the correct label of the image. On a benchmark of 50,000 randomly chosen adversarial images generated by DeepFool we demonstrate that our method SATYA is able to recover the correct labels for 95.76% of the images for CaffeNet and 97.43% of the correct label for GoogLeNet.

Raj, Sunny↗
DOE OSTI2019

Detection of metabolites for controlled substances

The various technologies presented herein relate to identifying whether an individual has taken, and/or is under the influence of, a restricted drug. A density separation technique is utilized, wherein a sample (e.g., blood, saliva, urine, etc.,) which may include an analyte is exposed to a first plurality of beads having an analyte attached thereto, a second plurality of beads having a metabolite-specific antibody attached thereto, and a plurality of fluorophore-labelled analyte-specific antibodies. After incubation, any analyte in the sample (e.g., delta-9-THC) is bound to the fluorophore-labelled analyte-specific antibodies, any free fluorophore-labelled analyte-specific antibodies are attached to the analyte of the first beads, and any metabolite in the sample is bound to the second antibody. By applying centrifugal separation, the first beads move to a region which undergoes irradiation. If no fluorescence occurs, the sample includes the analyte; if fluorescence occurs, the sample does not include the analyte.

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

Automatic method of material identification for computed tomography

A method is provided for isolating and labeling discrete features in a spectral radiographic image recorded as a set of images in different energy channels. The disclosed method involves creating a profile for each of at least some pixels in the spectral radiographic image. The profiles are sequences of pixel values, in which each pixel value is a photon count or a similar radiographic exposure value indicative of the attenuation of a portion of the scanning beam in a respective energy channel. Iterative hierarchical clustering is used to cluster the pixels on the basis of their respective profiles. Labels are assigned to one or more of the resulting clusters. In implementations, each label can be associated with an inferred material composition or with an inference that the material composition is unknown.

Jimenez, Jr., Edward Steven↗
DOE OSTI2021

Detection of metabolites for controlled substances

The various technologies presented herein relate to identifying whether an individual has taken, and/or is under the influence of, a restricted drug. A density separation technique is utilized, wherein a sample (e.g., blood, saliva, urine, etc.) which may include an analyte is exposed to a first plurality of beads having an analyte attached thereto, a second plurality of beads having a metabolite-specific antibody attached thereto, and a plurality of fluorophore-labelled analyte-specific antibodies. After incubation, any analyte in the sample (e.g., delta-9-THC) is bound to the fluorophore-labelled analyte-specific antibodies, any free fluorophore-labelled analyte-specific antibodies are attached to the analyte of the first beads, and any metabolite in the sample is bound to the second antibody. By applying centrifugal separation, the first beads move to a region which undergoes irradiation. If no fluorescence occurs, the sample includes the analyte; if fluorescence occurs, the sample does not include the analyte.

Koh, Chung-Yan↗
DOE OSTI2022

Use of Machine Learning on PMU Data for Transmission System Fault Analysis

Synchrophasor technology has been used for monitoring, control, and protection of bulk power system for over 10 years. Deployment of phasor measurement units (PMUs) in the USA power system has surpassed 3000 units installed in the transmission substations as stand-alone intelligent electronic devices (IEDs) or as a software add-on to other devices such as digital protective relays (DPRs) or digital fault recorders (DFRs). By now, thousands of terabytes of PMU data may have been captured and stored by various transmission system operators (TSOs) and independent system operators (ISOs). This creates an opportunity to deploy advanced machine learning (ML) techniques to detect and classify faults recorded by PMUs automatically to be used by the system operators for rapid, critical decision-making when manual analysis of the past or unfolding events is not feasible. In this paper we offer a brief background on how the automated fault analysis may be done using DPR and/or DFR data, and compare some of the legacy approaches to the new ML approaches in the context of the system-wide PMU recordings. We then offer insights from developing practical ML solutions that have been applied on field recordings captured by close to 450 PMUs from all three US interconnections (Western, Eastern and ERCOT) over two years (2016-2017). We identify and illustrate ML challenges we addressed: inaccurate data, data with scarce and temporally imprecise fault labels, data recorded by PMUs sparsely located at substations resulting in the fault records taken afar from the ends of the faulted lines, data containing only positive sequence values, and data taken at different voltage levels. We then illustrate the ML model results for fault analysis under different application scenarios. The novelty of this study is not only in the design, implementation, and performance analysis of the ML algorithms, but also in the use of advanced fault modelling and simulation approaches to improve the training results when developing supervised ML models for fault detection and classification. Extensive simulations of faults were conducted on a 14-bus power system to create a training dataset with over 1400 accurately labelled faults. This dataset was applied to enhance the accuracy of fault detection and classification of machine learning-based models trained with small number of labelled faults in large datasets recorded in the grid interconnections ranging from 5,000 to 70,000 buses.

Synchrophasors, Machine Learning, Fault Analysis, ↗
DOE OSTI2022

Contextual Active Online Model Selection with Expert Advice

How can we collect the most useful labels to learn a model selection policy, when presented with arbitrary heterogeneous data streams? In this paper, we formulate this task as a contextual active model selection problem, where at each round the learner receives an unlabeled data point along with a context. The goal is to output the best model for any given context without obtaining an excessive amount of labels. In particular, we focus on the task of selecting pre-trained classifiers, and propose a contextual active model selection algorithm (CAMS), which relies on a novel uncertainty sampling query criterion defined on a given policy class for adaptive model selection. In comparison to prior art, our algorithm does not assume a globally optimal model. We provide rigorous theoretical analysis for the regret and query complexity under both adversarial and stochastic settings. Our experiments on several benchmark classification datasets demonstrate the algorithm’s effectiveness in terms of both regret and query complexity. Notably, to achieve the same accuracy, CAMS incurs less than 10% of the label cost when compared to the best online model selection baselines on CIFAR10.

Liu, Xuefeng↗
DOE OSTI2022

Methods for detecting and determining levels of monolignol ester conjugates incorporated into lignin and compounds relating thereto

Methods of detecting and, optionally, determining a level of incorporation of monolignol ester conjugates into lignin. The methods include derivatizing lignin to acylate at least a portion of free phenolic and aliphatic hydroxyls and to halogenate at least a portion of benzylic alcohols present in the lignin to yield derivatized lignin, treating the derivatized lignin with a reducing agent to cleave at least a portion of the derivatized lignin to yield lignin cleavage products, acetylating at least a portion of free hydroxyl groups in the lignin cleavage products with a labeled acetylation agent to yield labeled lignin fragments, and detecting the labeled lignin fragments.

09 BIOMASS FUELS↗
DOE OSTI2025

Systems and methods for binary code analysis

Human-readable (HR) code may be derived from a binary. The HR code may be configured to have statistical properties suitable for machine-learned (ML) translation. The HR code may comprise source code, intermediate code, assembly code, or the like. A machine-learned translator may be configured to translate the HR code into labels comprising semantic information pertaining to respective functions of the binary, such as a function name, role, or the like. Execution of the binary may be blocked in response to translating the HR code to a label associated with malware, such as cryptocurrency mining malware or the like. Conversely, the binary may be permitted to proceed to execution in response to determining that the translation is free from labels indicative of malware.

Anderson, Matthew W.↗
DOE OSTI2021

Pyrolysis temperature and soil depth interactions determine PyC turnover and induced soil organic carbon priming

Pyrogenic organic carbon (PyC) is a complex, heterogeneous class of thermally altered organic substrates, but its dynamics and how its behavior changes with soil depth remain poorly understood. We conducted a laboratory incubation study to investigate the interactive effects of pyrolysis temperature and soil depth on the turnover of PyC compared to its precursor wood and native SOC (NSOC). We incubated dual-labeled (13C and 15N) jack pine pyrogenic organic matter produced at 300 °C (PyC300), 450 °C (PyC450), and their precursor pine wood in a fine-loamy, mixed-conifer forest soil for 745 days. A mixture of surface (0–10 cm) and subsurface (50–70 cm) forest soils, with and without labeled biomass were incubated in the dark at 55% soil water field capacity and 25 °C. Total 13C from PyC and wood mineralized as 13C-CO2 (as % of C added to soil) declined with an increase in pyrolysis temperature as follows: 54 ± 7.7% for wood, 3.1 ± 0.2% for PyC300, and 0.94 ± 0.08% for PyC450. Furthermore, after 2 years, soil depth interacted with pyrolysis temperature to affect C turnover, with total wood C losses significantly declining from 70.6% in surface soils to 37.5% in subsurface soil, while total losses of PyC300 and PyC450 were unaffected by differences between surface and subsurface soils. Wood induced negative priming (i.e., decreased mineralization rates) in surface soil at days 3 and 60, while PyC300 induced positive priming (i.e., increased mineralization rates) in subsurface soil at day 60. After 2 years, unlabeled NSOC losses increased from 9.2 ± 0.8% of NSOC in unamended treatments to 16.5 ± = 2.6% of NSOC with PyC450 additions. Our results suggest that PyC pyrolyzed at a given temperature can mineralize at similar rates between soil depths, and high amounts of PyC450 in subsurface soils can stimulate NSOC losses. These findings indicate that soil depth imposes critical controls on PyC dynamics belowground.

13C-labeling↗
DOE OSTI2018

Investigation on the upconversion luminescence in Ho3+/Yb3+ co-doped Ba3Sc4O9 phosphor

Highlights: • The optical properties of novel Ba{sub 3}Y{sub 4}O{sub 9}:Ho{sup 3+}/Yb{sup 3+} phosphor were investigated. • The luminescence color could be tuned from green to yellow-green light. • The energy transfer mechanisms of UC luminescence were investigated in detail. • The phosphor can be a good candidate for green UC material in display fields and fluorescent labeling. - Abstract: A series of Ba{sub 3}Sc{sub 4}O{sub 9}:Ho{sup 3+}/Yb{sup 3+} phosphors were synthesized by a high-temperature solid-state reaction method. The phase purity and upconversion luminescence were characterized by X-ray diffraction and photoluminescence spectroscopy. The sintering temperature and doping concentrations were optimized to obtain the strongest emission intensity. Under 980 nm laser diode excitation, the green (545 nm, {sup 5}F{sub 4}/{sup 5}S{sub 2} → {sup 5}I{sub 8}) and red (665 nm, {sup 5}F{sub 5} → {sup 5}I{sub 8}) emissions were observed. The cross-relaxation and back-energy-transfer processes play an important role to adjust the emission intensity ratio of Green/Red at different dopant concentrations. Based on the energy level diagrams, the energy transfer mechanisms are investigated in detail according to the analysis of pump power dependence and luminescence decay curves. The upconversion luminescence properties of the phosphor were evaluated and the results show good color monochromaticity and relatively high upconversion luminescence intensity. The Ba{sub 3}Sc{sub 4}O{sub 9}:Ho{sup 3+}/Yb{sup 3+} can be a good candidate for green upconversion material in display fields and fluorescent labeling.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Stimulation of anaerobic organic matter decomposition by subsurface organic N addition in tundra soils

We present that increasing nitrogen (N) availability in Arctic soils could stimulate the growth of both plants and microorganisms by relieving the constraints of nutrient limitation. It was hypothesized that organic N addition to anoxic tundra soil would increase CH 4 production by stimulating the fermentation of labile substrates, which is considered the rate-limiting step in anaerobic C mineralization. We tested this hypothesis through both field and lab-based experiments. In the field experiment, we injected a solution of 13 C- and 15 N-labeled glutamate 35 cm belowground at a site near Nome on the Seward Peninsula, Alaska, and observed the resulting changes in porewater geochemistry and dissolved greenhouse gas concentrations. The concentration of free glutamate declined rapidly within hours of injection, and the 15 N label was recovered almost exclusively as dissolved organic N within 62 h. These results indicate rapid microbial assimilation of the added N and transformation into novel organic compounds. We observed increasing concentrations of dissolved CH 4 and Fe(II), indicating rapid stimulation of methanogenesis and Fe(III) reduction. Low molecular weight organic acids such as acetate and propionate accumulated despite increasing consumption through anaerobic C mineralization. A laboratory soil column flow experiment using active layer soil collected from the same site further supported these findings. Glutamate recovery was low compared to a conservative bromide tracer, but concentrations of NO 3 - and NH 4 + remained low, consistent with microbial uptake of the added N. Similar to the field experiment, we observed both increasing Fe(II) and organic acid concentrations. Lastly, together, these results support our hypothesis of increased fermentation in response to organic N addition and suggest that increasing N availability could accelerate CH 4 production in tundra soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Belowground allocation and dynamics of recently fixed plant carbon in a California annual grassland

Plant roots and the organisms that surround them are a primary source for stabilized soil organic carbon (SOC). While grassland soils have a large capacity to store organic carbon (C), few field-based studies have quantified the amount of plant-fixed C that moves into soil and persists belowground over multiple years. Yet this characteristic of the soil C cycle is critical to C storage, soil water holding capacity, nutrient provisions, and the management of soil health. Here, we tracked the fate of plant-fixed C following a five-day 13 CO 2 labeling of a Northern California annual grassland, measuring C pools starting at the end of the labeling period, at three days, four weeks, six months, one year, and two years. Soil organic carbon was fractionated using a density-based approach to separate the free-light fraction (FLF), occluded-light fraction (OLF), and heavy fraction (HF). Using isotope ratio mass spectrometry, we measured 13 C enrichment and total C content for plant shoots, roots, soil, soil dissolved organic carbon (DOC), and the FLF, OLF, and HF. The chemical nature of C in the HF was further analyzed by solid state 13 C nuclear magnetic resonance (NMR) spectroscopy.

13C-NMR↗