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At least 919 records · Page 51

Cooperative satellite-based flood detection, mapping, and river monitoring in near real time

The North Atlantic Oscillation (NAO), the Pacific-North American (PNA) teleconnection pattern, and the El Nino-Southern Oscillation (ENSO) combine to influence the planetary wave structure over the northern hemisphere. Floods and droughts are associated around the world with ENSO through such teleconnections, and improved flood prediction relies on understanding them better. The scientific study of floods, and consistent measurements thereof, are needed in order to allow 'Greenhouse warming' predictions about flooding to be tested, and the hydrologic effects of other phenomena such as ENSO to be evaluated. The needed tasks are: 1) detection/warning of flooding, 2) flood magnitude assessment, 3) flood inundation mapping, and 4) preservation of the record of flooding. Accomplishing these same tasks provides direct local societal benefits as well: they can save lives and reduce economic loss. We emphasize that the basic science observations need not be divorced from the immediate practical applications: both can occur together, and just as is the case for meteorological remote sensing.

Shuttle Radar Topography Mission (SRTM)↗

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗

Preliminary Evidence for a Virial Shock around the Coma Galaxy Cluster

Galaxy clusters, the largest gravitationally bound objects in the universe, are thought to grow by accreting mass from their surroundings through large-scale virial shocks. Due to electron acceleration in such a shock, it should appear as a γ-ray, hard X-ray, and radio ring, elongated toward the large-scale filaments feeding the cluster, coincident with a cutoff in the thermal Sunyaev–Zel’dovich (SZ) signal. However, no such signature was found until now, and the very existence of cluster virial shocks has remained a theory. We find preliminary evidence for a large γ-ray ring of ∼5 Mpc minor axis around the Coma cluster, elongated toward the large-scale filament connecting Coma and Abell 1367, detected at the nominal 2.7σ confidence level (5.1σ using control signal simulations). The γ-ray ring correlates both with a synchrotron signal and with the SZ cutoff, but not with Galactic tracers. The γ-ray and radio signatures agree with analytic and numerical predictions if the shock deposits ∼1% of the thermal energy in relativistic electrons over a Hubble time and ∼1% in magnetic fields. The implied inverse Compton and synchrotron cumulative emission from similar shocks can contribute significantly to the diffuse extragalactic γ-ray and low-frequency radio backgrounds. Our results, if confirmed, reveal the prolate structure of the hot gas in Coma, the feeding pattern of the cluster, and properties of the surrounding large-scale voids and filaments. The anticipated detection of such shocks around other clusters would provide a powerful new cosmological probe.

79 ASTRONOMY AND ASTROPHYSICS↗

Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

Shiffman, Smadar↗

Microbial Diffraction Gratings as Optical Detectors for Heavy Metal Pollutants

As a significant industrial pollutant, cadmium is implicated as the cause of itai-itai disease. For biological detection of cadmium toxicity, an assay device has been developed using the motile response of the protozoa species, Tetrahymena pyriformis. This mobile protozoa measures 50 microns in diameter, swims at 10 body lengths per second, and aggregates into macroscopically visible patterns at high organism concentrations. The assay demonstrates a Cd(+2) sensitivity better than 1 micro-M and a toxicity threshold to 5 micro-M, thus encouraging the study of these microbial cultures as viable pollution detectors. Using two-dimensional diffraction patterns within a Tetrahymena culture, the scattered light intensity varies with different organism densities (population counts). The resulting density profile correlates strongly with the toxic effects at very low dosages for cadmium (less than 5 ppm) and then for poison protection directly (with nickel and copper antagonists competing with cadmium absorption). In particular, copper dosages as low as 0.1-0.5 mM Cu have shown protective antagonism against cadmium, have enhanced density variability for cultures containing 1 mM Cd(+2) and therefore have demonstrated the sensitivity of the optical detection system. In this way, such microbial diffraction patterns give a responsive optical measure of biological culture changes and toxicity determination in aqueous samples of heavy metals and industrial pollutants.

Noever, David↗

Lipid Decontamination Procedures for Life Detection Missions

Molecular biosignatures are key targets for current, proposed, and future life detection missions. With the high accuracy and low limit of detection (LOD) that new and future instruments will require, decontamination of life detection hardware is necessary to prevent false positives. Lipids are a molecular biosignature of interest, as they are ubiquitous to all life as we know it, can survive unaltered in the geologic record for longer than any other biomolecule (i.e. billions of years), and form through both biotic and abiotic processes. Lipids display origin-diagnostic molecular patterns that can reveal biotic or abiotic synthesis, so finding them and ascertaining their molecular features is important for potentially detecting evidence of life elsewhere. Traditional methods of decontamination, or contamination control (CC), primarily clean hardware through fabrication in sterile (cleanroom) environments, killing microbes, and removing/flushing contaminants off instrument and spacecraft components. However, research suggests that some standard cleaning methods are either unlikely to remove lipid contaminants or are incompatible with life detection instrument materials. To solve this problem, I propose to find, test, and verify a decontamination method that thoroughly cleans instruments by destroying lipid molecules, but is simultaneously compatible with major materials used in these instruments. I will study the effects of traditional CC methods (including Dry Heat Microbial Reduction and Vapor phase Hydrogen Peroxide) and experimental CC methods (Electron Beam Irradiation) on lipid molecules for application to life detection instrumentation. I will then develop a CC plan for a novel lipid detector (ExCALiBR, Extractor for Chemical Analysis of Lipid Biomarkers in Regolith) searching for lipids in either soil or icy world scenarios. This plan will uphold planetary protection regulation requirements and validate experimental analyses of in-situ life detection tests.

Decontamination↗

Investigation of the environmental change pattern of Japan

The author has identified the following significant results. ERTS-1 imagery clearly identifies the relationships between the status of erosion, effluent patterns affected by the coastal current, and the cultural construction activities. Simple photographic techniques can be used for detecting water mass distribution separately from cloud cover and also noise caused by reflected sunlight from wave surfaces. Polluted water does not diffuse continuously into the oceanic water, but forms masses in the water in the Kuroshio area. The polluted or turbid water in the area just north of the Tomogashima Channel, the south outlet of the Osaka Bay, shows that the northward tidal current runs in a clockwise eddy at the tidal period when the imagery was taken. Such an eddy-like pattern of tidal current had never been revealed by conventional oceanographic data. A front between an oceanic water mass and a polluted water mass runs in a NW-SE direction in the central part of the Osaka Bay. The patterns of turbid water discharged from the Kii River and Yoshino River show a northward tidal current in the North Kii Straits. The pattern of lighter turbid or polluted water located in the northwest region of the North Kii straits suggests the existence of a clockwise eddy in the straits.

Maruyasu, T.↗

3D Localization of Defects in Facility Inspection

Wind tunnels are crucial facilities that support the aerospace industry. However, these facilities are large, complex, and pose unique maintenance and inspection requirements. Manual inspections to identify defects such as cracks, missing fasteners, leaks, and foreign objects are important but labor and schedule intensive. Our goal is to utilize small Unmanned Aircraft Systems (sUAS) and computer vision-based analysis to automate the inspection of the interior and exterior of NASA’s critical wind tunnel facilities. We detect missing fasteners as our defect class, and detect existing fasteners to provide potential future missing fastener sites for preventative maintenance. These detections are done on both 2D raw images and in 3D space to provide a visual reference and real world location to facilitate repairs. A dataset was created consisting of images taken along a grid-like pattern of an interior tunnel section in the AEDC National Full-Scale Aerodynamics Complex (NFAC) at NASA Ames Research Center. Our method uses object detection to create image level bounding boxes of the fasteners and missing fasteners, then uses photogrammetry to create a mapping from 2D image locations to 3D real world locations. The image level bounding boxes and the 2D to 3D mapping are then combined to determine the 3D location of the defects. We describe the data collection, photogrammetry, and computer vision techniques used for object detection as well as a quantitative analysis of the method.

Small Unmanned Aircraft Systems (sUAS)↗

Expert system validation in prolog

An overview of the Expert System Validation Assistant (EVA) is being implemented in Prolog at the Lockheed AI Center. Prolog was chosen to facilitate rapid prototyping of the structure and logic checkers and since February 1987, we have implemented code to check for irrelevance, subsumption, duplication, deadends, unreachability, and cycles. The architecture chosen is extremely flexible and expansible, yet concise and complementary with the normal interactive style of Prolog. The foundation of the system is in the connection graph representation. Rules and facts are modeled as nodes in the graph and arcs indicate common patterns between rules. The basic activity of the validation system is then a traversal of the connection graph, searching for various patterns the system recognizes as erroneous. To aid in specifying these patterns, a metalanguage is developed, providing the user with the basic facilities required to reason about the expert system. Using the metalanguage, the user can, for example, give the Prolog inference engine the goal of finding inconsistent conclusions among the rules, and Prolog will search the graph intantiations which can match the definition of inconsistency. Examples of code for some of the checkers are provided and the algorithms explained. Technical highlights include automatic construction of a connection graph, demonstration of the use of metalanguage, the A* algorithm modified to detect all unique cycles, general-purpose stacks in Prolog, and a general-purpose database browser with pattern completion.

Stock, Todd↗

Rain footprints on C-band synthetic aperture radar images of the ocean - Revisited

It is well known that rain leaves footprints on the sea surface that sometimes become visible on synthetic aperture radar (SAR) images. Rain cells can easily be detected on SAR images at all radar frequencies when they are associated with a downdraft pattern. But rain cells are not always associated with downdraft and rain can also occur in other forms, as stratified rain, rain bands, and squall lines. It turns out that radar signatures of rain at C-band are much more complex than at L- or X-band radar and that it is particularly difficult to identify unambiguously rain events on C-band SAR images acquired over the ocean. This is because C-band lies in the transition region where raindrops impinging onto the sea surface can increase (usually) or decrease the backscattered radar power and where volume scattering and attenuation by rain drops in the atmosphere are not always negligible (at very high rain rates). In order to get an insight into the physical mechanisms causing the C-band radar signatures of rain, we first revisit results obtained from historic laboratory and field experiments and multi-frequency/multi-polarization SAR data acquired during the SIR-C/X-SAR spaceshuttle mission in 1994. Then we analyze several C-band SAR images acquired by the European satellites Envisat and Sentinel-1A, and the Canadian satellite Radarsat-2 and compare them, whenever possible, with quasi-coincident and collocated weather radar images. The observational data show that, at low to medium rain rates, the main physical mechanism causing C-band radar signatures of rain is Bragg scattering at ring waves generated by the rain drops impinging onto the sea surface, which increase the radar backscatter. However, areas of increased radar backscatter are often accompanied by adjacent areas of decreased radar backscatter, which is due to attenuation of the Bragg waves by turbulence also generated by the impinging rain drops. Furthermore, we present a full-polarimetric Radarsat-2 SAR image of a rain cell together with a polarimetric decomposition analysis, which shows that the C-band radar signature of a rain cell is caused by surface scattering. The observation show that radar signatures of rain cells often contain segments, where the co-polarized as well the cross-polarized radar backscatter are strongly enhanced, which indicates non-Bragg scattering contributions to the scattering process. Furthermore, the polarimetric decomposition analysis shows that the C-band radar signature of a rain cell is dominated by surface scattering. Possible mechanisms, like scattering at splash products, are discussed. Whether the normalized radar cross section (NRCS) due to rain is increased or decreased depends on rain rate, wind speed, incidence angle, and history of the rain event. At low to moderate wind speeds (< 10 ms - 1 ) and low to medium high rain rates (< 50 mm h - 1 ), the NRCS is usually increased by up to 8 dB, and at high wind speeds (> 10 m s - 1 ) and low to high rain rates (but < 50 mm h - 1 ), the NRCS is usually decreased by up to 3 dB.

54 ENVIRONMENTAL SCIENCES↗

Salivary mycobiome dysbiosis and its potential impact on bacteriome shifts and host immunity in oral lichen planus

The biodiversity of the mycobiome, an important component of the oral microbial community, and the roles of fungal–bacterial and fungal–immune system interactions in the pathogenesis of oral lichen planus (OLP) remain largely uncharacterized. In this study, we sequenced the salivary mycobiome and bacteriome associated with OLP. First, we described the dysbiosis of the microbiome in OLP patients, which exhibits lower levels of fungi and higher levels of bacteria. Significantly higher abundances of the fungi Candida and Aspergillus in patients with reticular OLP and of Alternaria and Sclerotiniaceae_unidentified in patients with erosive OLP were observed compared to the healthy controls. Aspergillus was identified as an “OLP-associated” fungus because of its detection at a higher frequency than in the healthy controls. Second, the co-occurrence patterns of the salivary mycobiome–bacteriome demonstrated negative associations between specific fungal and bacterial taxa identified in the healthy controls, which diminished in the reticular OLP group and even became positive in the erosive OLP group. Moreover, the oral cavities of OLP patients were colonized by dysbiotic oral flora with lower ecological network complexity and decreased fungal–Firmicutes and increased fungal–Bacteroidetes sub-networks. Third, several keystone fungal genera (Bovista, Erysiphe, Psathyrella, etc.) demonstrated significant correlations with clinical scores and IL-17 levels. Thus, we established that fungal dysbiosis is associated with the aggravation of OLP. Fungal dysbiosis could alter the salivary bacteriome or may reflect a direct effect of host immunity, which participates in OLP pathogenesis.

59 BASIC BIOLOGICAL SCIENCES↗

Observables for recoil identification in high-definition Gas Time Projection Chambers

Directional detection of nuclear recoils is broadly desirable in nuclear and particle physics. At low recoil energies, this capability may be used to confirm the cosmological origin of a dark matter signal, to penetrate the so-called neutrino floor, or to distinguish between different neutrino sources. Gas Time Projection Chambers (TPCs) can enable directional recoil detection if the readout granularity is sufficiently high, as is the case when micro-pattern gaseous detectors (MPGDs) are utilized. A key challenge in such detectors is identifying and rejecting background electron recoil events caused by gamma rays from radioactive contaminants in the detector materials and the environment. We define new observables that can distinguish electron and nuclear recoils, even at keV-scale energies, based on the simulated ionization's topology. Here we perform a simulation study that shows these observables outperform the traditionally used discriminant, dE/dx, by up to three orders of magnitude. Furthermore, these new observables work well even at ionization energies well below 10keV and remain robust even in the regime where directionality fails.

79 ASTRONOMY AND ASTROPHYSICS↗

Linking repeat lidar with Landsat products for large scale quantification of fire-induced permafrost thaw settlement in interior Alaska

The permafrost–fire–climate system has been a hotspot in research for decades under a warming climate scenario. Surface vegetation plays a dominant role in protecting permafrost from summer warmth, thus, any alteration of vegetation structure, particularly following severe wildfires, can cause dramatic top–down thaw. A challenge in understanding this is to quantify fire-induced thaw settlement at large scales (>1000 km 2 ). In this study, we explored the potential of using Landsat products for a large-scale estimation of fire-induced thaw settlement across a well-studied area representative of ice-rich lowland permafrost in interior Alaska. Six large fires have affected ~1250 km 2 of the area since 2000. We first identified the linkage of fires, burn severity, and land cover response, and then developed an object-based machine learning ensemble approach to estimate fire-induced thaw settlement by relating airborne repeat lidar data to Landsat products. The model delineated thaw settlement patterns across the six fire scars and explained ~65% of the variance in lidar-detected elevation change. Our results indicate a combined application of airborne repeat lidar and Landsat products is a valuable tool for large scale quantification of fire-induced thaw settlement.

54 ENVIRONMENTAL SCIENCES↗

Decay of the proton-unbound superradiant state in 13 N

Here, the 12 C ( 3 He, 𝑑)⁢ 13 N* reaction is studied in an experiment with a high-resolution magnetic spectrograph, in coincidence with protons detected in silicon detectors near the target. This allows for the observation of angular correlation patterns between the proton transfer and proton decays from populated unbound resonances. A formalism describing the spin polarization of direct reactions is developed to analyze these correlations, and is verified on the known directionally asymmetric decay distributions arising from parity mixing in the 13 N ⁡($\frac{3}{2}$ − , $\frac{5}{2}$ + ) doublet. The same formalism is used to study the decays from the continuum-aligned, broad 3/2 + resonance at 7.9 MeV excitation energy, which arises from superradiant coupling. The observed asymmetric angular correlation patterns are approximately reproduced by adding an “artificial” 3/2 − resonance with strength equal to that of the reaction formalism. This parity-mixing approach serves as a first approximation to a more advanced reaction model of rapid reaction and decay sequences.

13N↗

Topic modeling to discern irregular order patterns in unlabeled electronic health records

This paper presents an application of topic modeling on event sequences of Electronic Health Record (EHR) orders. Metaphorically, we approach clinical order event sequences of unlabeled data as if they are documents where words are the events that occurred in the history of an order. We demonstrate the approach leveraging Consult orders data. The details of the data preprocessing and the data structures are described along with the data sources. Latent Dirichlet Allocation (LDA) is leveraged to fit against the limited datasets prepared. Another open source tool – LDAvis is used for exploratory analysis of the LDA results. The preliminary results revealed some order patterns that are qualitatively evaluated as potential irregular transitions. The goal of this analysis is to provide unsupervised learning application to domain experts in the absence of labeled data where they can investigate captured patterns and identify irregular transitions of orders. Ultimately, such efforts will guide formalization of hazard detection algorithms that monitor EHR data to identify health information technology related hazards.

Ozmen, Ozgur↗

Kensor: Coordinated Intelligence from Co-Located Sensors

Internet of Things (IoT) is becoming more pervasive in many installations, including homes, manufacturing plants, and industrial facilities of all kinds. The data that IoT produces is a reflection of usual behavior such as daily routines and scheduled tasks, but also from unexpected behavior due to unintentional or undesirable abnormalities. Here, we focus on achieving coordinated intelligence about normal and abnormal phenomena from multiple sensors that are geographically colocated in close proximity, monitoring and controlling a set of co-located devices. Given a set of co-located sensors, we seek an intelligent approach that would automatically determine the “normal” patterns of behaviors among the correlated sensors. After normal behavior is extracted, later monitoring should detect any deviant variations over time. An example application is an entry monitoring and alert system for facilities such as nuclear reactors, where badge readers, door locks, lights, weight trackers and other co-located sensors at the entry point are collectively tracked. To address this problem, we identify the possible solution approach that can be used to solve its different variants. The implemented model is developed as a combination of rules and Markov Chain methods.

Kotevska, Olivera↗

SU-F-T-91: Development of Real Time Abdominal Compression Force (ACF) Monitoring System

Purpose: Hard-plate based abdominal compression is known to be effective, but no explicit method exists to quantify abdominal compression force (ACF) and maintain the proper ACF through the whole procedure. In addition, even with compression, it is necessary to do 4D CT to manage residual motion but, 4D CT is often not possible due to reduced surrogating sensitivity. In this study, we developed and evaluated a system that both monitors ACF in real time and provides surrogating signal even under compression. The system can also provide visual-biofeedback. Methods: The system developed consists of a compression plate, an ACF monitoring unit and a visual-biofeedback device. The ACF monitoring unit contains a thin air balloon in the size of compression plate and a gas pressure sensor. The unit is attached to the bottom of the plate thus, placed between the plate and the patient when compression is applied, and detects compression pressure. For reliability test, 3 volunteers were directed to take several different breathing patterns and the ACF variation was compared with the respiratory flow and external respiratory signal to assure that the system provides corresponding behavior. In addition, guiding waveform were generated based on free breathing, and then applied for evaluating the effectiveness of visual-biofeedback. Results: We could monitor ACF variation in real time and confirmed that the data was correlated with both respiratory flow data and external respiratory signal. Even under abdominal compression, in addition, it was possible to make the subjects successfully follow the guide patterns using the visual biofeedback system. Conclusion: The developed real time ACF monitoring system was found to be functional as intended and consistent. With the capability of both providing real time surrogating signal under compression and enabling visual-biofeedback, it is considered that the system would improve the quality of respiratory motion management in radiation therapy. This research was supported by the Mid-career Researcher Program through NRF funded by the Ministry of Science, ICT & Future Planning of Korea (NRF-2014R1A2A1A10050270) and by the Radiation Technology R&D program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2013M2A2A7038291)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Photonic crystal nanobeam biosensors based on porous silicon

Photonic crystal (PhC) nanobeams (NB) patterned on porous silicon (PSi) waveguide substrates are demonstrated for the specific, label-free detection of oligonucleotides. These photonic structures combine the large active sensing area intrinsic to PSi sensors with the high-quality (Q) factor and low-mode volume characteristic of compact resonant silicon-on-insulator (SOI) PhC NB devices. The PSi PhC NB can achieve a Q-factor near 9,000 and has an approximately 40-fold increased active sensing area for molecular attachment, compared to traditional SOI PhC NB sensors. The PSi PhC NB exhibits a resonance shift that is more than one order of magnitude larger than that of a similarly designed SOI PhC NB for the detection of small chemical molecules and 16-base peptide nucleic acids. Furthermore the design and fabrication of PSi PhC NB sensors are compatible with CMOS processing, sensor arrays, and integration with lab-on-chip systems.

36 MATERIALS SCIENCE↗