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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

CW EC-QCL-based sensor for simultaneous detection of H 2 O, HDO, N 2 O and CH 4 using multi-pass absorption spectroscopy

A sensor system based on a continuous wave, external-cavity quantum-cascade laser (CW EC-QCL) was demonstrated for simultaneous detection of atmospheric H 2 O, HDO, N 2 O and CH 4 using a compact, dense pattern multi-pass gas cell with an effective path-length of 57.6 m. The EC-QCL with a mode-hop-free spectral range of 1225-1285 cm -1 operating at similar to 7.8 mu m was scanned covering four neighboring absorption lines, for H 2 O at 1281.161 cm -1 , HDO at 1281.455 cm -1 , N 2 O at 1281.53 cm -1 and CH 4 at 1281.61 cm -1 . A first-harmonic-normalized wavelength modulation spectroscopy with second-harmonic detection (WMS-2f/1f) strategy was employed for data processing. An Allan-Werle deviation analysis indicated that minimum detection limits of 1.77 ppmv for H 2 O, 3.92 ppbv for HDO, 1.43 ppbv for N 2 O, and 2.2 ppbv for CH 4 were achieved with integration times of 50-s, 50-s, 100-s and 129-s, respectively. In conclusion, experimental measurements of ambient air are also reported.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)

The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.

42 ENGINEERING↗
NASA NTRS2019

On the Coronal Mass Ejection Detection Rate during Solar Cycles 23 and 24

The Solar and Heliospheric Observatory (SOHO) mission's white light coronagraphs have observed more than25,000 coronal mass ejections (CMEs) from 1996 January to the end of 2015 July. This period of time covers almost two solar cycles (23 and 24). The basic attributes of CMEs, reported in the SOHO/Large Angle and Spectrometric Coronagraph (LASCO) catalog, during these solar cycles were statistically analyzed. The question of the CME detection rate and its connection to the solar cycles was considered in detail. Based on the properties and detection rate, CMEs can be divided into two categories: regular and specific events. The regular events are pronounced and follow the pattern of sunspot number. On the other hand, the special events are poorer and more correlated with the general conditions of heliosphere and corona. Nevertheless, both groups of CMEs are the result of the same physical phenomenon, viz. release of magnetic energy from closed field regions. It was demonstrated that the enhanced CME rate, since the solar cycle 23 polar-field reversal, is due to a significant decrease of total(magnetic and plasma) heliospheric pressure as well as the changed magnetic pattern of solar corona. CMEs expel free magnetic energy and helicity from the Sun; therefore, they are related to complex solar magnetic field structure. It is also worth emphasizing that the CMEs listed in the SOHO/LASCO catalog are real ejections (not false identification). Their detection rate reflects the global evolution of the magnetic field on the Sun, and not only changes in the magnetic structures associated with sunspots.

Michalek, Grzegorz↗
NASA NTRS2006

Detection of Storm Damage Tracks with EOS Data

The damage surveys conducted by the NWS in the aftermath of a reported tornadic event are used to document the location of the tornado ground damage track (pathlength and width) and an estimation of the tornado intensity. This study explores the possibility of using near-real-time medium and high spatial resolution satellite imagery from the NASA Earth Observing System satellites to provide additional information for the surveys. Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were used to study the damage tracks from three tornadic storms: the La Plata, Maryland, storm of 28 April 2002 and the Ellsinore and Marquand, Missouri, storms of 24 April 2002. These storms varied in intensity and occurred over regions with significantly different land cover. It was found that, depending on the nature of the land cover, tornado damage tracks from intense storms (F1 or greater) and hail storms may be evident in ASTER, Landsat, and MODIS satellite imagery. In areas where the land cover is dominated by forests, the scar patterns can show up very clearly, while in areas of grassland and regions with few trees, scar patterns are not as obvious or cannot be seen at all in the satellite imagery. The detection of previously unidentified segments of a damage track caused by the 24 April 2002 Marquand, Missouri, tornado demonstrates the utility of satellite imagery for damage surveys. However, the capability to detect tornado tracks in satellite imagery depends on the ability to observe the ground without obstruction from space and appears to be as much dependent on the nature of the underlying surface and land cover as on the severity of the tornadic storm.

Jedlovec, Gary J.↗
DOE OSTI2018

Siderophore profiling of co-habitating soil bacteria by ultra-high resolution mass spectrometry

The chemical structure of organic molecules profoundly impacts their interactions with metal ions and mineral phases in soils. Understanding the sources and cycling of metal-chelating compounds is therefore essential for predicting the bioavailability and transport of metals throughout terrestrial environments. We investigate the molecular speciation of organic molecules that solubilize trace metals in calcareous soils from Eastern Washington. Ultra-high performance Fourier transform ion cyclotron resonance mass spectrometry at 21 Tesla enabled fast and confident detection and identification of metal chelators that are produced by microbes that inhabit these soils based on screening for features that match diagnostic metal isotope patterns. We compared two approaches, one based on direct infusion using the incorporation of a rare isotope to validate true iron-binding features, and another based on separation with liquid chromatography and detection of isotopologues with coherent elution profiles. While the isotopic exchange method requires significantly shorter analysis time, nearly twice as many features were observed with liquid chromatography mass spectrometry (LCMS), mostly due to the reduction in ion suppression where major features limit the sensitivity of minor features. In addition, LCMS enabled the collection of higher quality fragmentation spectra and facilitated feature identification. Siderophores belonging to four major classes were identified, including ferrioxamines, pseudobactins, enterobactins, and arthrobactins. Each of these siderophores likely derives from a unique member of the microbial community, and each possesses different chemical characteristics and uptake pathways, likely contributing to fierce competition for iron within these soils. Our results provide insight into the metabolic pathways by which microbes that co-inhabit calcareous soils compete for this essential micronutrient.

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

Material transfers detection with seismic observations

We are exploring the use of data from a seismic network around a research nuclear reactor and isotope production facility at Oak Ridge National Laboratory to study activity patterns related to the transfer of nuclear material. A sensor network with eight seismometers was installed around the High Flux Isotope Reactor and the Radiochemical Engineering Development Center and started operating in October 2019. These data are used to detect and characterize operational events around facility. We are using those data to extract signals related to the movement of vehicles involved in the transport of nuclear materials (e.g., transportation of reactor fuel, targets, and product isotopes). Large vehicles produce mechanical energy that can be observed as seismic signals, either as the result of sound emanating from the vehicle engine or by the generation of surface waves as a response of the ground to the load of the vehicles. In particular, the station located near the entrance gate of the facility displays a clear seismic signature as vehicles cross a metal platform on the ground. This signal is characterized by sharp energy bursts that correspond to the number of axles on the vehicle. We developed and are testing an algorithm to identify sequences of energy bursts to count and characterize vehicles of different sizes. We are also exploring the use of seismic polarization analysis to measure the degree of polarization of the observed signals. These two techniques for detection and characterization of vehicles used for nuclear material transfers will be validated with seismic data from a targeted collection using different vehicles and routes similar to the ones used in real-life scenarios at the High Flux Isotope Reactor.

Marcillo, Omar↗
DOE OSTI2022

Regional epithelial cell diversity in the small intestine of pigs

Abstract Understanding regional distribution and specialization of small intestinal epithelial cells is crucial for developing methods to control appetite, stress, and nutrient uptake in swine. To establish a better understanding of specific epithelial cells found across different regions of the small intestine in pigs, we utilized single-cell RNA sequencing (scRNA-seq) to recover and analyze epithelial cells from duodenum, jejunum, and ileum. Cells identified included crypt cells, enterocytes, BEST4 enterocytes, goblet cells, and enteroendocrine (EE) cells. EE cells were divided into two subsets based on the level of expression of the EE lineage commitment gene, NEUROD1. NEUROD1hi EE cells had minimal expression of hormone-encoding genes and were dissimilar to EE cells in humans and mice, indicating a subset of EE cells unique to pigs. Recently discovered BEST4 enterocytes were detected in both crypts and villi throughout the small intestine via in situ staining, unlike in humans, where BEST4 enterocytes are found only in small intestinal villi. Proximal-to-distal gradients of expression were noted for hormone-encoding genes in EE cells and nutrient transport genes in enterocytes via scRNA-seq, demonstrating regional specialization. Regional gene expression in EE cells and enterocytes was validated via quantitative PCR (qPCR) analysis of RNA isolated from epithelial cells of different small intestinal locations. Though many genes had similar patterns of regional expression when assessed by qPCR of total epithelial cells, some regional expression was only detected via scRNA-seq, highlighting advantages of scRNA-seq to deconvolute cell type-specific regional gene expression when compared to analysis of bulk samples. Overall, results provide new information on regional localization and transcriptional profiles of epithelial cells in the pig small intestine.

Agriculture↗
DOE OSTI2024

Spread Spectrum Time Domain Reflectivity for String Monitoring in PV Power Plants (Final Technical Report)

This final report describes the methods and results of applying Spread Spectrum Time Domain Reflectivity (SSTDR) for String Monitoring in PV Power Plants for DE-EE0008169. The project created a new system for both detecting and locating electrical faults in photovoltaic systems. In this work, we address photovoltaic electric faults that are both common and costly. Based on interviews with photovoltaic power plant owners, operators, and maintainers, three types of faults are common and of significant interest: disconnects, ground faults, and arc faults. Disconnects can originate from many sources. They are often due to everyday events, such as lawnmowing (accidentally running over a cable), animals eating through the cables, or degradation that occurs over time due to corrosion or general degradation. Ground faults occur when the cables (for example, due to frayed insolation) connect to the ground, relaying current into the ground. These faults are particularly problematic since the ground faults are often intermittent. That is, ground faults commonly appear during rain storms due to a change in soil conductivity and then disappear when the rain ends. This makes the ground fault difficult to find because while current systems can detect the overall change in voltage and current associated with a ground fault, technicians are necessary to locate the fault. As a result, ground faults may disappear before the technician arrives at the power plant. Hence, locating and fixing ground faults often require multiple trips. We also study arc faults, which can result when nearby conductors create an arc of electrical current through the air. While less common, arc faults can be extremely dangerous. The energetic electrical arc can cause fires and destroy equipment, costing significant damage. Overall all three types of faults cost owners and operators money, either from the destruction of equipment or from technician time. Furthermore, while devices exist for detecting ground faults (ground fault circuit interrupters) and arc faults (arc fault circuit interrupter), these systems only search patterns of electrical current that correspond to each fault. This information cannot be used to locate the fault. In addition, these protection systems experience nuisance trips due to nearby electromagnetic interference, such as from a lawn mower or other motors that produce significant amounts of electromagnetic radiation. Hence, the overall goal of this project is to create an SSTDR tool that provides photovoltaic power plants with more reliable fault detection in addition to the localization of faults. SSTDR works by transmitting electrical signals into the photovoltaic string. Those signals reflect from impedance discontinuities (i.e., disconnects, ground faults, and arc faults). These faults are then detected by measuring the presence of a reflection at the SSTDR and can be located by identifying the location of that reflection in time. In addition, unlike current protection systems, these systems do not experience nuisance trips since their low amplitude, high frequency, and coded signal can by analyzed without interference from the regular operational voltage on the photovoltaic string.

14 SOLAR ENERGY↗
DOE OSTI2018

Detecting outliers in streaming time series data from ARM distributed sensors

The Atmospheric Radiation Measurement (ARM) Data Center at ORNL collects data from a number of permanent and mobile facilities around the globe. The data is then ingested to create high level scientific products. High frequency streaming measurements from sensors and radar instruments at ARM sites require high degree of accuracy to enable rigorous study of atmospheric processes. Outliers in collected data are common due to instrument failure or extreme weather events. Thus, it is critical to identify and flag them. We employed multiple univariate, multivariate and time series techniques for outlier detection methods and studied their effectiveness. First, we examined Pearson correlation coefficient which is used to measure the pairwise correlations between variables. Singular Spectrum Analysis (SSA) was applied to detect outliers by removing the anticipated annual and seasonal cycles from the signal to accentuate anomalies. K-means was applied for multivariate examination of data from collection of sensors to identify any deviation from expected and known patterns and identify abnormal observation. The Pearson correlation coefficient, SSA and K-means methods were later combined together in a framework to detect outliers through a range of checks. We applied the developed method to data from meteorological sensors at ARM Southern Great Plains site and validated against existing database of known data quality issues.

Lu, Yuping↗
NASA NTRS2000

The Use of Water Vapor for Detecting Environments that Lead to Convectively Produced Heavy Precipitation and Flash Floods

This Tech Report summarizes years of study and experiences on using GOES Water vapor (6.7 micron and precipitable water) and Special Sensor Microwave Imager (SSM/1) from the Defense Meteorological Satellite Program (DMSP) derived Precipitable Water (PNAI) for detecting environments favorable for convectively produced flash floods. An emphasis is on the moisture. upper air flow, and equivalent potential temperature (Theta(sub e)) patterns that lead to devastating flood events. The 15 minute 6.7 micron water vapor imagery is essential for tracking middle to upper tropospheric disturbances that produce upward vertical motion and initiate flash flood producing systems. Water vapor imagery at 6.7 micron is also used to detect surges of upper level moisture (called tropical water vapor plumes) that have been associated with extremely heavy rainfall. Since the water vapor readily depicts lifting mechanisms and upper level moisture, water vapor imagery is often an excellent source of data for recognizing patterns of heavy precipitation and flash floods. In order to analyze the depth of the moisture, the PW aspects of the troposphere must be measured. The collocation (or nearby location) of high values ofP\V and instability are antecedent conditions prior to the flash flood or heavy rainfall events. Knowledge of PW magnitudes have been used as thresholds for impending flash flood events, PW trends are essential in flash flood prediction. Conceptual models and water vapor products are used to study some of the characteristics of convective systems that occurred over the United States of America (USA) during the summer of 1997 and the 1997-1998 El Nino. P\V plumes were associated with most of the \vest coast heavy precipitation events examined during the winter season of 1997 - 1998, In another study, conducted during the summer season of 1997. results showed that the collocation of water vapor (6.7 micron) and P\N' plumes possessed higher correlations with predicted rainfall amounts than when PW plumes occurred by themselves (i.e.. without the presence of 6.7 micron water vapor plumes). Satellite Analysis Branch (SAB) meteorologists use the 6.7 micron water and P\V products for their QPE's (interactive Flash Flood Analyzer (IFFA) and Auto-Estimator precipitation estimates), Outlooks, and heavy precipitation briefings with the Hydrometeorological Prediction Center/National Center for Environmental Prediction.

Scofield, Rod↗
NASA NTRS2012

Gas Debris Disks: A New Way to Produce Dust Patterns

Debris disks like those around Fomalhaut and Beta Pictoris show striking dust patterns often attributed to planets. But adding a bit of gas to our models of these disks--too little to detect-could alter this interpretation. Small amounts of gas lead to new dynamical instabilities that may mimic the narrow eccentric rings and other structures planets would create in a gas-free disk. rll discuss these phenomena and whether or not we can still use dust patterns as indicators of hidden exoplanets.

Kuchner, Marc J.↗
NASA NTRS1999

Dark Matter in the Universe and in the Galaxy

During the past four years, Prof. Kamionkowski and collaborators have made progress in research on the nature and distribution of dark-matter in the Universe and in the Galaxy, and on related topics in astrophysics and cosmology. We have made progress on research on the cosmic microwave background, large-scale structure, issues related to particle dark matter, and the gamma-ray-burst enigma. A significant fraction of the research supported by this ATP has been on the cosmic microwave background (CMB). Prof. Kamionkowski and collaborators showed how the polarization of the CMB could be used to detect long-wavelength gravitational waves, such as those produced by inflation. With Kosowsky, Prof. Kamionkowski calculated the amplitude of a stochastic gravitational-wave background that could be detected for a satellite experiment of a given sensitivity and angular resolution. They showed that polarization should improve the sensitivity oa MAP to these gravity waves, and that the Planck Surveyor should do even better. Prof. Kamionkowski, Caldwell, and a student calculated and illustrated the CMB temperature/polarization pattern produced by a single plane-wave gravitational wave. They calculated the amplitude of such a wave that would be detectable with MAP and Planck, and compared that with the sensitivity of traditional gravitational-wave detectors like LIGO and LISA. With Lue and Wang, the PI showed how parity violation from new high-energy physics could conceivably give rise to an observable signature in the CMB polarization. With Loeb, Prof. Kamionkowski showed how measurement of the polarization of CMB photons scattered by hot gas in a cluster could be used to determine the quadrupole moment of the CMB incident on that cluster. Prof. Kamionkowski and Jaffe calculated the amplitude of secondary anisotropies produced by scattering of CMB photons from reionized regions. Research has also been carried out on probing the large-scale distribution of mass in the Universe today, and on structure-formation theories. They investigated the possibility of determining the large-scale distribution of mass in the Universe via measurement of ellipticity-ellipticity correlations in the FIRST radio survey induced by weak gravitational lensing due to mass inhomogeneities along the line of sight. Dr. Summers, Prof. Kamionkowski, and a student investigated the distribution of protogalactic masses and angular momenta in an effort to understand how the luminosity function and angular-momentum distribution of disk galaxies arises from an initial power spectrum of density perturbations. Several projects related to the distribution and possible detection of dark matter in our Galactic halo were studied. The PI was involved in several projects involving the calculation of nuclear-reaction rates needed for stellar evolution and for predictions of solar-neutrino fluxes.

Kamionkowski, Marc↗
DOE OSTI2017

Fluctuation localization imaging-based fluorescence in situ hybridization (fliFISH) for accurate detection and counting of RNA copies in single cells

Here, quantitative gene expression analysis in intact single cells can be achieved using single molecule-based fluorescence in situ hybridization (smFISH). This approach relies on fluorescence intensity to distinguish between true signals, emitted from an RNA copy hybridized with multiple oligonucleotide probes, and background noise. Thus, the precision in smFISH is often compromised by partial or nonspecific probe binding and tissue autofluorescence, especially when only a small number of probes can be fitted to the target transcript. Here we provide an accurate approach for setting quantitative thresholds between true and false signals, which relies on on-off duty cycles of photoswitchable dyes. This fluctuation localization imaging-based FISH (fliFISH) uses on-time fractions (measured over a series of exposures) collected from transcripts bound to as low as 8 probes, which are distinct from on-time fractions collected from nonspecifically bound probes or autofluorescence. Using multicolor fliFISH, we identified radial gene expression patterns in mouse pancreatic islets for insulin, the transcription factor, NKX2-2 and their ratio ( Nkx2 - 2/Ins2 ). These radial patterns, showing higher values in β cells at the islet core and lower values in peripheral cells, were lost in diabetic mouse islets. In summary, fliFISH provides an accurate, quantitative approach for detecting and counting true RNA copies and rejecting false signals by their distinct on-time fractions, laying the foundation for reliable single-cell transcriptomics.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Formation and Propagation of Atmospheric River and Its Impact on Extreme Precipitation Events in the North Pacific and the Western North America

Seasonal and interannual evolution patterns of atmospheric rivers (AR) in the North Pacific are examined as a function of the formation region where an AR is first detected using 43-year MERRA-2 reanalysis data. Integrated water vapor transport (IVT) is used to detect AR with latitude dependent thresholds of IVT to better detect AR-like features in the high latitudes. Based on 3-hourly AR statistics, three main AR genesis regions in the North Pacific (i.e., South China Sea (SCS), Western North Pacific (WNP) and Central Pacific Ocean and Hawaiian Islands (CPO) are identified. WNP is the main source of AR with 1475 ARs detected for 43 cold seasons (NDJFM). Over 70% of all AR formed in the WNP has longer than 2-day. On the other hand, AR from CPO tends to have shorter lifetime than those from WNP and SCS. While propagation patterns of AR from SCS and WNP are similar, AR from WNP tends to reach mature phase quicker and shows higher change of impacting west coast of North America. Longevity and strength of AR are also examined based on three large-scale circulation modes (e.g. ENSO, WP, and EAJS) over the East Asia identified from eigen analysis of upper-level zonal winds. During El Nino, the number of AR formed in the western Pacific increased by 20%. First two days, the average size and intensity shows little difference compared with ARs in La Nina years. AR appears to grow in size in El Nino vs. La Nina years. Positive phase of WP correlated with less, but larger and stronger AR formation over WNP and SCS regions. EAJS has little impact in the numbers of AR and its size, but ARs in a stronger EAJS tends to grow larger. Atmospheric circulation associated with the initial formation and propagation of AR from the different regions in the North Pacific as well as its impact on extreme precipitation events in the North Pacific and the west coast of North America will be also discussed.

Integrated water vapor transport↗
DOE OSTI2015

Two Pathways for Glutamate Biosynthesis in the Syntrophic Bacterium Syntrophus aciditrophicus

Here, the anaerobic metabolism of crotonate, benzoate, and cyclohexane carboxylate by Syntrophus aciditrophicus grown syntrophically with Methanospirillum hungatei provides a model to study syntrophic cooperation. Recent studies revealed that S. aciditrophicus contains Re -citrate synthase but lacks the common Si-citrate synthase. To establish whether the Re-citrate synthase is involved in glutamate synthesis via the oxidative branch of the Krebs cycle, we have used [1- 13 C]acetate and [1- 14 C]acetate as well as [ 13 C]bicarbonate as additional carbon sources during axenic growth of S. aciditrophicus on crotonate. Our analyses showed that labeled carbons were detected in at least 14 amino acids, indicating the global utilization of acetate and bicarbonate. The labeling patterns of alanine and aspartate verified that pyruvate and oxaloacetate were synthesized by consecutive carboxylations of acetyl coenzyme A (acetyl-CoA). The isotopomer profile and 13 C nuclear magnetic resonance (NMR) spectroscopy of the obtained [ 13 C]glutamate, as well as decarboxylation of [ 14 C]glutamate, revealed that this amino acid was synthesized by two pathways. Unexpectedly, only the minor route used Re -citrate synthase (30 to 40%), whereas the majority of glutamate was synthesized via the reductive carboxylation of succinate. This symmetrical intermediate could have been formed from two acetates via hydration of crotonyl-CoA to 4-hydroxybutyryl-CoA. 4-Hydroxybutyrate was detected in the medium of S. aciditrophicus when grown on crotonate, but an active hydratase could not be measured in cell extracts, and the annotated 4-hydroxybutyryl-CoA dehydratase (SYN_02445) lacks key amino acids needed to catalyze the hydration of crotonyl-CoA. Besides Clostridium kluyveri , this study reveals the second example of a microbial species to employ two pathways for glutamate synthesis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Detection of long-range entanglement in gapped quantum spin liquids by local measurements

Topological order, reflected in long-range patterns of entanglement, is quantified by the topological entanglement entropy γ. We show that for gapped quantum spin liquids it is possible to extract γ using two-spin local correlators. We demonstrate our method for the gapped Z 2 Kitaev spin liquid on a honeycomb lattice with anisotropic interactions. We show that the γ = ln⁡2 for Z 2 topological order can be simply extracted from local two-spin correlators across two different bonds that involve only Majorana fermions, with an accuracy comparable to or higher than the Kitaev-Preskill construction. This implies that even though the ground state can be factorized into the product of Majorana and gauge sectors, the different superselection sectors of Z 2 gauge theory determined by global Wilson loop operators can be reflected locally in the Majorana sector.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1974

Automated thematic mapping and change detection of ERTS-1 images

Results of an automated thematic mapping investigation using ERTS-1 MSS images are presented. A diffraction pattern analysis of MSS images led to the development of spatial signatures for farm land, urban areas, and mountains. Four spatial features are employed to describe the spatial characteristics of image cells in the digital data. Three spectral features are combined with the spatial features to form a seven dimensional vector describing each cell. Then, the classification of the feature vectors is accomplished by using the maximum likelihood criterion. Three ERTS-1 images from the Phoenix, Arizona area were processed, and recognition rates between 85% and 100% were obtained for the terrain classes of desert, farms, mountains and urban areas. To eliminate the need for training data, a new clustering algorithm has also been developed.

Gramenopoulos, N.↗
NASA NTRS1981

Detection of regional air pollution episodes utilizing satellite data in the visual range

A comparative analysis of satellite-observed haze patterns and ground-based aerosol measurements is carried out for July 20-23, 1978. During this period, a significant regional air pollution episode existed across the northeastern United States, accompanied by widespread haze, reduced surface visibility, and elevated sulfate levels measured by the Sulfate Regional Experiment (SURE) network. The results show that the satellite-observed haze patterns correlate closely with the area of reported low surface visibility (less than 4 mi) and high sulfate levels. Quantitative information on total aerosol loading derived from the satellite-digitized data, using an atmospheric radiative transfer model, agrees well with the results obtained from the ground-based measurements.

Bowley, C. J.↗