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NASA NTRS1984

A 154-day periodicity in the occurrence of hard solar flares?

An analysis of the temporal distribution of 139 solar flares monitored by the Gamma Ray Spectrometer aboard the Solar Maximum Mission is reported. It is found that, instead of being randomly distributed in time, these events have a tendency to occur in groups with a mean spacing of about 154 days (75 nHz) over the observing interval. A larger sample of flares with an X-ray classification of M 2.5 or larger recorded by the GOES satellite showed a similar regularity.

Rieger, E.↗

NASA NTRS1992

Research Instruments

The GENETI-SCANNER, newest product of Perceptive Scientific Instruments, Inc. (PSI), rapidly scans slides, locates, digitizes, measures and classifies specific objects and events in research and diagnostic applications. Founded by former NASA employees, PSI's primary product line is based on NASA image processing technology. The instruments karyotype - a process employed in analysis and classification of chromosomes - using a video camera mounted on a microscope. Images are digitized, enabling chromosome image enhancement. The system enables karyotyping to be done significantly faster, increasing productivity and lowering costs. Product is no longer being manufactured.

Source record↗

DOE OSTI2021

CryoEM reveals the stochastic nature of individual ATP binding events in a group II chaperonin

Chaperonins are homo- or hetero-oligomeric complexes that use ATP binding and hydrolysis to facilitate protein folding. ATP hydrolysis exhibits both positive and negative cooperativity. The mechanism by which chaperonins coordinate ATP utilization in their multiple subunits remains unclear. Here we use cryoEM to study ATP binding in the homo-oligomeric archaeal chaperonin from Methanococcus maripaludis (MmCpn), consisting of two stacked rings composed of eight identical subunits each. Using a series of image classification steps, we obtained different structural snapshots of individual chaperonins undergoing the nucleotide binding process. We identified nucleotide-bound and free states of individual subunits in each chaperonin, allowing us to determine the ATP occupancy state of each MmCpn particle. We observe distinctive tertiary and quaternary structures reflecting variations in nucleotide occupancy and subunit conformations in each chaperonin complex. Detailed analysis of the nucleotide distribution in each MmCpn complex indicates that individual ATP binding events occur in a statistically random manner for MmCpn, both within and across the rings. Our findings illustrate the power of cryoEM to characterize a biochemical property of multi-subunit ligand binding cooperativity at the individual particle level.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2007

Application of Multi-Satellite Precipitation Analysis to Floods and Landslides

Satellite data acquired and processed in real time now have the potential to provide the spacetime information on rainfall needed to monitor flood and landslide events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models and landslide algorithms. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to flood and landslide forecasts is outlined, with a focus on understanding limitations of the rainfall data and impacts of those limitations on flood/landslide analyses. Case studies of both successes and failures will be shown, as well as comparison with ground comparison data sets both in terms of rainfall and in terms of flood/landslide events. In addition to potential uses in real-time, the nearly ten years of TMPA data allow retrospective running of the models to examine variations in extreme events. The flood determination algorithm consists of four major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin, and river network etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers. In terms of landslides, the satellite rainfall information is combined with a global landslide susceptibility map, derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a weighted linear combination approach. In those areas identified as "susceptible" (based on the surface characteristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. Results are described indicating general agreement with landslide occurrences. However, difficulties in comparing landslide event information (mostly from news reports) with the satellite-based forecasts are analyzed.

Adler, Robert↗

NASA NTRS2000

Gamma-Ray Burst Class Properties

Guided by the supervised pattern recognition algorithm C4.5 developed by Quinlan in 1986, we examine the three gamma-ray burst classes identified by Mukherjee et al. in 1998. C4.5 provides strong statistical support for this classification. However, with C4.5 and our knowledge of the Burst and Transient Source Experiment (BATSE) instrument, we demonstrate that class 3 (intermediate fluence, intermediate duration, soft) does not have to be a distinct source population: statistical/systematic errors in measuring burst attributes combined with the well-known hardness/intensity correlation can cause low peak flux class 1 (high fluence, long, intermediate hardness) bursts to take on class 3 characteristics naturally. Based on our hypothesis that the third class is not a distinct one, we provide rules so that future events can be placed in either class 1 or class 2 (low fluence, short, hard). We find that the two classes are relatively distinct on the basis of Band's work in 1993 on spectral parameters alpha, beta, and E (sub peak) alone. Although this does not indicate a better basis for classification, it does suggest that different physical conditions exist for class 1 and class 2 bursts. In the process of studying burst class characteristics, we identify a new bias affecting burst fluence and duration measurements. Using a simple model of how burst duration can be underestimated, we show how this fluence duration bias can affect BATSE measurements and demonstrate the type of effect it can have on the BATSE fluence versus peak flux diagram.

Hakkila, Jon↗

DOE OSTI2020

Identifications and classifications of human locomotion using Rayleigh-enhanced distributed fiber acoustic sensors with deep neural networks

Abstract This paper reports on the use of machine learning to delineate data harnessed by fiber-optic distributed acoustic sensors (DAS) using fiber with enhanced Rayleigh backscattering to recognize vibration events induced by human locomotion. The DAS used in this work is based on homodyne phase-sensitive optical time-domain reflectometry (φ-OTDR). The signal-to-noise ratio (SNR) of the DAS was enhanced using femtosecond laser-induced artificial Rayleigh scattering centers in single-mode fiber cores. Both supervised and unsupervised machine-learning algorithms were explored to identify people and specific events that produce acoustic signals. Using convolutional deep neural networks, the supervised machine learning scheme achieved over 76.25% accuracy in recognizing human identities. Conversely, the unsupervised machine learning scheme achieved over 77.65% accuracy in recognizing events and human identities through acoustic signals. Through integrated efforts on both sensor device innovation and machine learning data analytics, this paper shows that the DAS technique can be an effective security technology to detect and to identify highly similar acoustic events with high spatial resolution and high accuracies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Benchmark Dose Analysis of DNA Damage Biomarker Responses Provides Compound Potency and Adverse Outcome Pathway Information for the Topoisomerase II Inhibitor Class of Compounds

Genetic toxicology data have traditionally been utilized for hazard identification to provide a binary call for a compound's risk. Recent advances in the scientific field, especially with the development of high‐throughput methods to quantify DNA damage, have influenced a change of approach in genotoxicity assessment. The in vitro MultiFlow® DNA Damage Assay is one such method which multiplexes γH2AX, p53, phospho‐histone H3 biomarkers into a single‐flow cytometric analysis (Bryce et al., [2016]: Environ Mol Mutagen 57:546–558). This assay was used to study human TK6 cells exposed to each of eight topoisomerase II poisons for 4 and 24 hr. Using PROAST v65.5, the Benchmark Dose approach was applied to the resulting flow cytometric datasets. With “compound” serving as covariate, all eight compounds were combined into a single analysis, per time point and endpoint. The resulting 90% confidence intervals, plotted in Log scale, were considered as the potency rank for the eight compounds. The in vitro MultiFlow data showed a maximum confidence interval span of 1Log, which indicates data of good quality. Patterns observed in the compound potency rank were scrutinized by using the expert rule‐based software program Derek Nexus, developed by Lhasa Limited. Compound sub‐classification and structural alerts were considered contributory to the potencies observed for the topoisomerase II poisons studied herein. The Topo II poison Adverse Outcome Pathway was evaluated with MultiFlow endpoints serving as Key Events. The step‐wise approach described herein can be considered as a foundation for risk assessment of compounds within a specific mode of action of interest. Environ. Mol. Mutagen. 2020. © 2020 Wiley Periodicals, Inc.

Wheeldon, Ryan P.↗

DOE OSTI2024

PlasmoData.jl — A Julia framework for modeling and analyzing complex data as graphs

Datasets encountered in scientific and engineering applications appear in complex formats (e.g., images, multivariate time series, molecules, video, text strings, networks). Graph theory provides a unifying framework to model such datasets and enables the use of powerful tools that can help analyze, visualize, and extract value from data. In this work, we present PlasmoData.jl, an open-source, Julia framework that uses concepts of graph theory to facilitate the modeling and analysis of complex datasets. The core of our framework is a general data modeling abstraction, which we call a DataGraph. We show how the abstraction and software implementation can be used to represent diverse data objects as graphs and to enable the use of tools from topology, graph theory, and machine learning (e.g., graph neural networks) to conduct a variety of tasks. We illustrate the versatility of the framework by using real datasets: (i) an image classification problem using topological data analysis to extract features from the graph model to train machine learning models; (ii) a disease outbreak problem where we model multivariate time series as graphs to detect abnormal events; and (iii) a technology pathway analysis problem where we highlight how we can use graphs to navigate connectivity. Further, our discussion also highlights how PlasmoData.jl leverages native Julia capabilities to enable compact syntax, scalable computations, and interfaces with diverse packages. Overall, we show that the DataGraph abstraction and PlasmoData.jl Julia package are able to model data within graphs and enable useful analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS1986

The Maryville meteorite - A 1983 fall of an L6 chondrite

The Maryville chondrite fell on January 28, 1983 in eastern Tennessee. Compositions of olivine (Fa 24.5), orthopyroxene (Fs 20.8), plagioclase (An 10.6), along with the bulk composition and siderophile concentrations, indicate L-group classification. The presence of highly equilibrated minerals, strongly recrystallized matrix, and the development of large, clear plagioclase grains suggest petrologic type 6 classification. Subsequent to metamorphism the meteorite was subjected to high transient pressures that converted some feldspar to glass, deformed the silicates, and caused small amounts of melting to occur. The effects of this shock event correspond to shock facies 'd' or 'e'.

Shervais, J. W.↗

DOE OSTI2021

Novel usage of deep learning and high-performance computing in long-baseline neutrino oscillation experiments

Mención Internacional en el título de doctorDeep-learning methods are playing a crucial role in numerous scientific and industrialapplications. Over the past two decades, these techniques have helped in the collection,reconstruction, and analysis of large data samples in particle physics experiments. Themain topic of this PhD research is the study of deep-learning techniques in long-baselineneutrino oscillation experiments. Neutrinos are mysterious light elementary particles,and their investigation is essential to shed light on some of the remaining open questionsin physics. The work presented here describes an algorithm based on a convolutionalneural network developed to provide highly accurate and efficient selections of electronneutrino and muon neutrino interactions in the Deep Underground Neutrino Experiment(DUNE). With this algorithm, the electron neutrino (antineutrino) selection efficiencypeaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between2-5 GeV. The selection efficiency for muon neutrino (antineutrino) interactions is foundto have a maximum of 96% (97%) and exceeds 90% (95%) efficiency for reconstructedneutrino energies above 2 GeV. When considering all electron neutrino and antineutrinointeractions as signal (both those appearing from oscillations and those intrinsic tothe beam), a selection purity of 90% is achieved. These event selections are criticalto maximise the sensitivity of the experiment to CP-violating effects, key to furtherunderstand the matter-antimatter asymmetry of the Universe.In high-energy physics experiments, deep learning has also been explored for producingfast simulations and physically-motivated manipulations of simulated images. Some ofthose simulations, such as the light production and detection, are very computationallyexpensive and require novel methods to produce the necessary samples while controllingthe varied underlying physics model parameters. To do so, we invented the model-assistedgenerative adversarial network (MAGAN), first validated on simple generic case studiesand then successfully applied to the DUNE photon-detector simulation.Moreover, we also developed graph neural networks for 3D-voxel classification ofambiguities and optical crosstalk for a different particle physics experiment, most preciselyfor the proposed SuperFGD. This novel 3D-granular plastic-scintillator neutrino detectorwill be used to upgrade the near detector of the T2K neutrino oscillation experiment, and our method reports efficiencies and purities of 94-96% per event in the classificationof particle track voxels.Due to the growth and complexity of deep neural networks, researchers have beeninvestigating techniques to train those networks in a more computationally-efficient way.Many efforts have been made by the community to optimise deep-learning models byparallelising or distributing their training computation across multiple devices. In thisthesis, we study an approach based on data locality for those neural networks that cannotbenefit from scaling their computation due to a significant bottleneck in the data I/O.The research also includes a detailed study on the performance of deep neural networkson hardware accelerator boards.Los métodos de aprendizaje profundo son cada vez más utilizados en numerosas aplicacionescientíficas e industriales hoy en día. Durante las dos últimas décadas, estastécnicas se han empleado en la recolección, reconstrucción y análisis de la gran cantidadde datos generados por experimentos de física de partículas. El tema principal de estatesis doctoral es el uso de estos modelos de aprendizaje profundo en experimentos defísica de neutrinos, en concreto en los experimentos de larga distancia DUNE y T2K. Losneutrinos, partículas fundamentales neutras, de las más ligeras del Universo, pueden serclave para explicar algunas de las cuestiones todavía sin resolver en física fundamental.Entre las diferentes contribuciones que esta tesis ha hecho a su estudio, cabe destacar eldesarrollo de un algoritmo basado en una red de neuronas convolucional para seleccionarcon gran eficiencia y precisión las interacciones de neutrinos electrónicos y muónicos enel Deep Underground Neutrino Experiment (DUNE). La eficiencia de selección obtenidapara neutrinos (antineutrinos) electrónicos alcanza un máximo del 90% (94%) y supera el85% (90%) para neutrinos con energías reconstruidas en el rango 2-5 GeV. La selección deneutrinos (antineutrinos) muónicos tiene una eficiencia máxima del 96% (97%) y excedeel 90% (95%) para neutrinos con energías reconstruidas de más de 2 GeV. Considerandocomo señal todas las interacciones de neutrinos y antineutrinos electrónicos (procedentestanto de oscilaciones como intrínsecos en el haz inicial), se logra una pureza en la seleccióndel 90%. Dichas selecciones de eventos son fundamentales para maximizar la sensibilidaddel experimento a los efectos de violació...

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Flash Drought indicators at catchment scale for CONUS

Flash droughts are defined by the rapid onset and intensification of drought conditions - a feature common across various proposed indicators. However, the absence of a standardized definition and detection method makes them particularly difficult to anticipate and manage. This dataset includes flash drought classifications based on six different methods across 222 catchments (4-digit Hydrologic Units, HUC4) in the Contiguous United States (CONUS) from 1983 to 2023. It also includes the pairwise agreement between indicators and the event-level multi-indicator agreement. For a detailed description of each file, refer to README_MSDlive.pdf

Catchment scale↗

NASA NTRS1993

Signal detection theory and methods for evaluating human performance in decision tasks

Signal Detection Theory (SDT) can be used to assess decision making performance in tasks that are not commonly thought of as perceptual. SDT takes into account both the sensitivity and biases in responding when explaining the detection of external events. In the standard SDT tasks, stimuli are selected in order to reveal the sensory capabilities of the observer. SDT can also be used to describe performance when decisions must be made as to the classification of easily and reliably sensed stimuli. Numbers are stimuli that are minimally affected by sensory processing and can belong to meaningful categories that overlap. Multiple studies have shown that the task of categorizing numbers from overlapping normal distributions produces performance predictable by SDT. These findings are particularly interesting in view of the similarity between the task of the categorizing numbers and that of determining the status of a mechanical system based on numerical values that represent sensor readings. Examples of the use of SDT to evaluate performance in decision tasks are reviewed. The methods and assumptions of SDT are shown to be effective in the measurement, evaluation, and prediction of human performance in such tasks.

Obrien, Kevin↗

NASA NTRS2007

A Longitudinal Analysis of the Causal Factors in Major Maritime Accidents in the USA and Canada (1996-2006)

Accident reports provide important insights into the causes and contributory factors leading to particular adverse events. In contrast, this paper provides an analysis that extends across the findings presented over ten years investigations into maritime accidents by both the US National Transportation Safety Board (NTSB) and Canadian Transportation Safety Board (TSB). The purpose of the study was to assess the comparative frequency of a range of causal factors in the reporting of adverse events. In order to communicate our findings, we introduce J-H graphs as a means of representing the proportion of causes and contributory factors associated with human error, equipment failure and other high level classifications in longitudinal studies of accident reports. Our results suggest the proportion of causal and contributory factors attributable to direct human error may be very much smaller than has been suggested elsewhere in the human factors literature. In contrast, more attention should be paid to wider systemic issues, including the managerial and regulatory context of maritime operations.

Johnson, C. W.↗

DOE OSTI2022

Lightweight jet reconstruction and identification as an object detection task

We apply object detection techniques based on deep convolutional blocks to end-to-end jet identification and reconstruction tasks encountered at the CERN large hadron collider (LHC). Collision events produced at the LHC and represented as an image composed of calorimeter and tracker cells are given as an input to a Single Shot Detection network. The algorithm, named PFJet-SSD performs simultaneous localization, classification and regression tasks to cluster jets and reconstruct their features. This all-in-one single feed-forward pass gives advantages in terms of execution time and an improved accuracy w.r.t. traditional rule-based methods. A further gain is obtained from network slimming, homogeneous quantization, and optimized runtime for meeting memory and latency constraints of a typical real-time processing environment. We experiment with 8-bit and ternary quantization, benchmarking their accuracy and inference latency against a single-precision floating-point. We show that the ternary network closely matches the performance of its full-precision equivalent and outperforms the state-of-the-art rule-based algorithm. Finally, we report the inference latency on different hardware platforms and discuss future applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1983

Crosscutting relations and relative ages of ridges and faults in the Tharsis region of Mars

The results of a detailed photogeologic analysis and classification of ridge-fault cross-cutting relationships on ridged plains units of the Tharsis region is reported. The problems involved in using the morphology of ridge-fault intersections to determine relative timing of structural events are discussed. The implications of using intersection angles as well as morphology to determine possible age relationships for the tectonic history of the region are presented. It is concluded that the major ridge-forming events in the Tharsis region were roughly coincident with, and possibly prior to, the extensional events that produced the faulting of the Tempe and Mareotis regions, the Coprates and Memnonia regions, and the rifting of the Valles Marineris. The compressional events that formed most of the ridges are restricted in time both by their relationship to regional extensional events and by the age of the units on which they formed.

Watters, T. R.↗

NASA NTRS1989

Impulsiveness and energetics in solar flares with and without type II radio bursts - A comparison of hard X-ray characteristics for over 2500 solar flares

The hard X-ray characteristics of more than 2500 solar flares are used to study the relative size, impulsiveness, and energetics of flares with and without type II radio bursts. A quantitative definition of the hard X-ray impulsiveness is introduced, which may be applied to a large number of events unambiguously. It is found that the flares with type II bursts are generally not significantly larger, more impulsive, or more energetic than those without type II bursts. Also, no evidence is found to suggest a simple classification of the flares as either 'impulsive' or 'gradual'. Because type II bursts are present even in small flares with relatively unimpulsive energy releases, it is concluded that changes in the ambient conditions of the solar atmosphere causing an unusually low Alfven speed may be important in the generation of the shock wave that produces type II radio bursts.

Pearson, Douglas H.↗

NASA NTRS1993

Arctic coastal polynya observations with ERS-1 SAR and DMSP SSM/I

Work to improve the characterization of the distribution of new and young sea ice types and open water amount within Arctic coastal polynyas through the combined use of ERS-1 SAR (Synthetic Aperture Radar) and DMSP SSM/I (Defense Meteorological Satellite Program Special Sensor Microwave/Imager) data is described. Two St. Lawrence Island polynya events are studied using low resolution, geocoded SAR images and coincident SSM/I data. The SAR images are analyzed in terms of polarization and spectral gradient ratios. Results of the combined analysis show that the SAR ice type classification is consistent with that from SSM/I and that the combined use of SAR and SSM/I can improve the characterization of thin ice better than either data set can do alone.

Cavalieri, D. J.↗

NASA NTRS1988

Anorthosites: Classification, mythology, trivia, and a simple unified theory

An overview was presented of anorthosites. They were classified into six types: (1) Archean megacrystic, (2) Proterozoic massif-type, (3) stratiform, (4) oceanic, (5) inclusions, and (6) extraterrestrial. Some of the anorthosite mythology was discussed, such as the existence of a distinct, catastrophic anorthosite event in the late Proterozoic, the misconception that anorthosite is a major constituent of the lower continental crust, and the misconception that Archean anorthosites represent metamorphosed equivalents of mafic layered intrusions such as Bushveld or Stillwater. A general statement was offered about the origin of all anorthosites: They are cumulates of plagioclase from mantle-derived basaltic magmas.

Ashwal, Lewis D.↗