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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Planck intermediate results: XL. The Sunyaev-Zeldovich signal from the Virgo cluster

The Virgo cluster is the largest Sunyaev-Zeldovich (SZ) source in the sky, both in terms of angular size and total integrated flux. Planck’s wide angular scale and frequency coverage, together with its high sensitivity, enable a detailed study of this big object through the SZ effect. Virgo is well resolved by Planck, showing an elongated structure that correlates well with the morphology observed from X-rays, but extends beyond the observed X-ray signal. We find good agreement between the SZ signal (or Compton parameter, y c ) observed by Planck and the expected signal inferred from X-ray observations and simple analytical models. Owing to its proximity to us, the gas beyond the virial radius in Virgo can be studied with unprecedented sensitivity by integrating the SZ signal over tens of square degrees. In this paper, we study the signal in the outskirts of Virgo and compare it with analytical models and a constrained simulation of the environment of Virgo. Planck data suggest that significant amounts of low-density plasma surround Virgo, out to twice the virial radius. We find the SZ signal in the outskirts of Virgo to be consistent with a simple model that extrapolates the inferred pressure at lower radii, while assuming that the temperature stays in the keV range beyond the virial radius. The observed signal is also consistent with simulations and points to a shallow pressure profile in the outskirts of the cluster. This reservoir of gas at large radii can be linked with the hottest phase of the elusivewarm/hot intergalactic medium. Taking the lack of symmetry of Virgo into account, we find that a prolate model is favoured by the combination of SZ and X-ray data, in agreement with predictions. In conclusion, based on the combination of the same SZ and X-ray data, we constrain the total amount of gas in Virgo. Under the hypothesis that the abundance of baryons in Virgo is representative of the cosmic average, we also infer a distance for Virgo of approximately 18 Mpc, in good agreement with previous estimates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Arrestin-3 scaffolding of the JNK3 cascade suggests a mechanism for signal amplification

Significance Scaffold proteins are key regulators of signal transduction that bind multiple proteins of a signaling pathway. This simple tethering model explains how the scaffold colocalizes the components for signal propagation, but it is unclear whether scaffolds ensure signal amplification. Here, we use arrestin-3 and the ASK1-MKK4/7-JNK3 kinase cascade as a model system to explore whether scaffolding amplifies signaling. Our studies suggest a model of scaffold-dependent signal amplification that may be conceptually applicable to other scaffold-dependent signaling pathways.

Perry, Nicole A.↗
DOE OSTI2018

Measuring phylogenetic signal between categorical traits and phylogenies

Abstract Motivation Determining whether a trait and phylogeny share some degree of phylogenetic signal is a flagship goal in evolutionary biology. Signatures of phylogenetic signal can assist the resolution of a broad range of evolutionary questions regarding the tempo and mode of phenotypic evolution. However, despite the considerable number of strategies to measure it, few and limited approaches exist for categorical traits. Here, we used the concept of Shannon entropy and propose the δ statistic for evaluating the degree of phylogenetic signal between a phylogeny and categorical traits. Results We validated δ as a measure of phylogenetic signal: the higher the δ-value the higher the degree of phylogenetic signal between a given tree and a trait. Based on simulated data we proposed a threshold-based classification test to pinpoint cases of phylogenetic signal. The assessment of the test’s specificity and sensitivity suggested that the δ approach should only be applied to 20 or more species. We have further tested the performance of δ in scenarios of branch length and topology uncertainty, unbiased and biased trait evolution and trait saturation. Our results showed that δ may be applied in a wide range of phylogenetic contexts. Finally, we investigated our method in 14 360 mammalian gene trees and found that olfactory receptor genes are significantly associated with the mammalian activity patterns, a result that is congruent with expectations and experiments from the literature. Our application shows that δ can successfully detect molecular signatures of phenotypic evolution. We conclude that δ represents a useful measure of phylogenetic signal since many phenotypes can only be measured in categories. Availability and implementation https://github.com/mrborges23/delta_statistic. Supplementary information Supplementary data are available at Bioinformatics online.

Borges, Rui↗
DOE OSTI2024

Periodicity significance testing with null-signal templates: reassessment of PTF’s SMBH binary candidates

Periodograms are widely employed for identifying periodicity in time series data, yet they often struggle to accurately quantify the statistical significance of detected periodic signals when the data complexity precludes reliable simulations. We develop a data-driven approach to address this challenge by introducing a null-signal template (NST). The NST is created by carefully randomizing the period of each cycle in the periodogram template, rendering it non-periodic. It has the same frequentist properties as a periodic signal template, and we show with simulations that the distribution of false positives is the same as with the original periodic template, regardless of the underlying data. Thus, performing a periodicity search with the NST acts as an effective simulation of the null (no-signal) hypothesis, without having to simulate the noise properties of the data. We apply the NST method to the supermassive black hole binaries (SMBHB) search in the Palomar Transient Factory (PTF), where Charisi et al. had previously proposed 33 high signal-to-noise candidates utilizing simulations to quantify their significance. Our approach reveals that these simulations do not capture the complexity of the real data. There are no statistically significant periodic signal detections above the non-periodic background. To improve the search sensitivity, we introduce a Gaussian quadrature based algorithm for the Bayes Factor with correlated noise as a test statistic. We show with simulations that this improves sensitivity to true signals by more than an order of magnitude. However, the Bayes Factor approach also results in no statistically significant detections in the PTF data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Large-Signal Impedance Modeling of Three-Phase Voltage Source Converters

Resonance problems have become a major hurdle in the large-scale grid integration of renewable energy. It is important to be able to predict the amplitude of resonance-induced distortions for the protection and control design of power electronic converters. We recently presented an impedance-based approach for the prediction of resonance amplitude. It was shown that the impedance response of a converter starts changing with the amplitude of resonance; this blocks the amplitude growth beyond a certain point where the converter forms a limit cycle mode of sustained oscillations. The large-signal impedance of a converter captures the change in its impedance response with the resonance amplitude, and it can be used to predict the resonance amplitude under different operation conditions. This paper presents large-signal sequence impedance modeling for three-phase voltage source converters (VSC) with dq-current control and PLL. Modeling challenges encountered in the absence of the small-signal approximation are addressed by a) leveraging the dominating influence of hard nonlinearities such as pulse-width modulation saturation and limiters over soft nonlinearities in shaping the converter large-signal behavior and b) modeling the large-signal gain of hard nonlinearities using appropriate describing functions. Developed large-signal impedance models are validated using numerical simulations. Measurements of the large-signal impedance responses of a commercial 1-MW VSC-based inverter are also presented to experimentally demonstrate the effects of the injected perturbation amplitude on impedance response as predicted by the developed model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Toward Mixed Analog-Digital Quantum Signal Processing: Quantum AD/DA Conversion and the Fourier Transform

Signal processing stands as a pillar of classical computation and modern information technology, applicable to both analog and digital signals. Recently, advancements in quantum information science have suggested that quantum signal processing (QSP) can enable more powerful signal processing capabilities. However, the developments in QSP have primarily leveraged digital quantum resources, such as discrete-variable (DV) systems like qubits, rather than analog quantum resources, such as continuous-variable (CV) systems like quantum oscillators. Consequently, there remains a gap in understanding how signal processing can be performed on hybrid CV-DV quantum computers. Here we address this gap by developing a new paradigm of mixed analog-digital QSP. We demonstrate the utility of this paradigm by showcasing how it naturally enables analog-digital conversion of quantum signals—specifically, the transfer of states between DV and CV quantum systems. We then show that such quantum analog-digital conversion enables new implementations of quantum algorithms on CV-DV hardware. This is exemplified by realizing the quantum Fourier transform of a state encoded on qubits via the free-evolution of a quantum oscillator, albeit with a runtime exponential in the number of qubits due to information theoretic arguments. Collectively, this work marks a significant step forward in hybrid CV-DV quantum computation, providing a foundation for scalable analog-digital signal processing on quantum processors.

42 ENGINEERING↗
DOE OSTI2023

Ectomycorrhizal symbiosis prepares its host locally and systemically for abiotic cue signaling

Tree growth and survival are dependent on their ability to perceive signals, integrate them, and trigger timely and fitted molecular and growth responses. While ectomycorrhizal symbiosis is a predominant tree-microbe interaction in forest ecosystems, little is known about how and to what extent it helps trees cope with environmental changes. We hypothesized that the presence of Laccaria bicolor influences abiotic cue perception by Populus trichocarpa and the ensuing signaling cascade. We submitted ectomycorrhizal or non-ectomycorrhizal P. trichocarpa cuttings to short-term cessation of watering or ozone fumigation to focus on signaling networks before the onset of any physiological damage. Poplar gene expression, metabolite levels, and hormone levels were measured in several organs (roots, leaves, mycorrhizas) and integrated into networks. We discriminated the signal responses modified or maintained by ectomycorrhization. Ectomycorrhizas buffered hormonal changes in response to short-term environmental variations systemically prepared the root system for further fungal colonization and alleviated part of the root abscisic acid (ABA) signaling. Furthermore, the presence of ectomycorrhizas in the roots also modified the leaf multi-omics landscape and ozone responses, most likely through rewiring of the molecular drivers of photosynthesis and the calcium signaling pathway. In conclusion, P. trichocarpa-L. bicolor symbiosis results in a systemic remodeling of the host's signaling networks in response to abiotic changes. In addition, ectomycorrhizal, hormonal, metabolic, and transcriptomic blueprints are maintained in response to abiotic cues, suggesting that ectomycorrhizas are less responsive than non-mycorrhizal roots to abiotic challenges.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-F-CAMPUS-J-03: Sorting 2D Dynamic MR Images Using Internal Respiratory Signal for 4D MRI

Purpose: To develop a novel algorithm to extract internal respiratory signal (IRS) for sorting dynamic magnetic resonance (MR) images in order to achieve four-dimensional (4D) MR imaging. Methods: Dynamic MR images were obtained with the balanced steady state free precession by acquiring each two-dimensional sagittal slice repeatedly for more than one breathing cycle. To generate a robust IRS, we used 5 different representative internal respiratory surrogates in both the image space (body area) and the Fourier space (the first two low-frequency phase components in the anterior-posterior direction, and the first two low-frequency phase components in the superior-inferior direction). A clustering algorithm was then used to search for a group of similar individual internal signals, which was then used to formulate the final IRS. A phantom study and a volunteer study were performed to demonstrate the effectiveness of this algorithm. The IRS was compared to the signal from the respiratory bellows. Results: The IRS computed by our algorithm matched well with the bellows signal in both the phantom and the volunteer studies. On average, the normalized cross correlation between the IRS and the bellows signal was 0.97 in the phantom study and 0.87 in the volunteer study, respectively. The average difference between the end inspiration times in the IRS and bellows signal was 0.18 s in the phantom study and 0.14 s in the volunteer study, respectively. 4D images sorted based on the IRS showed minimal mismatched artifacts, and the motion of the anatomy was coherent with the respiratory phases. Conclusion: A novel algorithm was developed to generate IRS from dynamic MR images to achieve 4D MR imaging. The performance of the IRS was comparable to that of the bellows signal. It can be easily implemented into the clinic and potentially could replace the use of external respiratory surrogates. This research was partially funded by the the Center for Radiation Oncology Research from UT MD Anderson Cancer Center.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

A genome-wide RNA interference screen identifies a role for Wnt/β-catenin signaling during Rift Valley Fever Virus infection

Rift Valley fever virus (RVFV) is an arbovirus within the Bunyaviridae family capable of causing serious morbidity and mortality in humans and livestock. To identify host factors involved in bunyavirus replication, we employed genome-wide RNA interference (RNAi) screening and identified 381 genes whose knockdown reduced infection. The Wnt pathway was the most represented pathway when gene hits were functionally clustered. With further investigation, we found that RVFV infection activated Wnt signaling, was enhanced when Wnt signaling was preactivated, was reduced with knockdown of β-catenin, and was blocked using Wnt signaling inhibitors. Similar results were found using distantly related bunyaviruses La Crosse virus and California encephalitis virus, suggesting a conserved role for Wnt signaling in bunyaviral infection. We propose a model where bunyaviruses activate Wnt-responsive genes to regulate optimal cell cycle conditions needed to promote efficient viral replication. The findings in this study should aid in the design of efficacious host-directed antiviral therapeutics. IMPORTANCE RVFV is a mosquito-borne bunyavirus that is endemic to Africa but has demonstrated a capacity for emergence in new territories (e.g., the Arabian Peninsula). As a zoonotic pathogen that primarily affects livestock, RVFV can also cause lethal hemorrhagic fever and encephalitis in humans. Currently, there are no treatments or fully licensed vaccines for this virus. Using high-throughput RNAi screening, we identified canonical Wnt signaling as an important host pathway regulating RVFV infection. The beneficial role of Wnt signaling was observed for RVFV, along with other disparate bunyaviruses, indicating a conserved bunyaviral replication mechanism involving Wnt signaling. Lastly, these studies supplement our knowledge of the fundamental mechanisms of bunyavirus infection and provide new avenues for countermeasure development against pathogenic bunyaviruses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Dynamic Tuning of Seismic Signal Detector Trigger Levels for Local Networks

The quality of automatic signal detections from sensor networks depends on individual detector trigger levels (TLs) from each sensor. The largely manual process of identifying effective TLs is painstaking and does not guarantee optimal configuration settings, yet achieving superior automatic detection of signals and ultimately, events, is closely related to these parameters. In this paper, we present a Dynamic Detector Tuning (DDT) system that automatically adjusts effective TL settings for signal detectors to the current state of the environment by leveraging cooperation within a local neighborhood of network sensors. After a stabilization period, the DDT algorithm can adapt in near-real time to changing conditions and automatically tune a signal detector to identify (detect) signals from only events of interest. Our current work focuses on reducing false signal detections early in the seismic signal processing pipeline, which leads to fewer false events and has a significant impact on reducing analyst time and effort. This system provides an important new method to automatically tune detector TLs for a network of sensors and is applicable to both existing sensor performance boosting and new sensor deployment. Finally, with ground truth on detections from a local neighborhood of seismic sensors within a network monitoring the Mount Erebus volcano in Antarctica, we show that DDT reduces the number of false detections by 18% and the number of missed detections by 11% when compared with optimal fixed TLs for all sensors.

58 GEOSCIENCES↗
DOE OSTI2021

Utilizing the Dynamic Networks Data Processing and Analysis Experiment (DNE18) to Establish Methodologies for the Comparison of Automatic Infrasonic Signal Detectors

The Dynamic Networks Experiment 2018 (DNE18) was a collaborative effort between Los Alamos National Laboratory (LANL), Sandia National Laboratories (SNL), Lawrence Livermore National Laboratory (LLNL) and Pacific Northwest National Laboratory (PNNL) designed to evaluate methodologies for multi-modal data ingestion and processing. One component of this virtual experiment was a quantitative assessment of current capabilities for infrasound data processing, beginning with the establishment of a baseline for infrasound signal detection. To produce such baselines, SNL and LANL exploited a common dataset of infrasound data recorded across a regional network in Utah from December 2010 through February 2011. We utilize two automated signal detectors, the Adaptive F-Detector (AFD) and the Multivariate Adaptive Learning Detector (MALD) to produce automated signal detection catalogs and an analyst-produced catalog. Comparisons indicate that automatic detectors may be able to identify small amplitude, low SNR events that cannot be identified by analyst review. We document detector performance in terms of precision and recall, demonstrating that the AFD is more precise, but the MALD has higher recall. We use a synthetic dataset of signals embedded in pink noise in order to highlight shortcomings in assessing detection algorithms for low signal to noise ratio signals which are commonly of interest to the nuclear monitoring community. For comparisons utilizing the synthetic dataset, the AFD has higher recall while precision is equal for both detectors. These results indicate that both detectors perform well across a variety of background noise environments; however, both detectors fail to identify repetitive, short duration signals arriving from similar backazimuths. These failures represent specific scenarios that could be targeted for further detector development.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Seismic Signal Detection on International Monitoring System 3-Component Stations using PhaseNet

In this report we discuss training a deep learning seismic signal detection model on 3-component stations from the International Monitoring System (IMS) using the PhaseNet architecture. Using 14 years of associated signals from the International Data Centre’s (IDC) Late Event Bulletin (LEB), we auto-curated training data consisting of signal windows containing associated arrivals, and noise windows that contain no LEB-associated signals. We trained several models using different waveform window durations (30 seconds and 100 seconds), with and without bandpass filtering. We evaluated the effectiveness of our models using associated signals from the Unconstrained Global Event Bulletin (UGEB) and found that several of our models outperformed the signal detections from the IDC’s Selected Event List 3 (SEL3) arrival table. The SEL3 bulletin evaluated on the UGEB dataset with 100-second waveform windows registered a precision and recall of .15 and .48, respectively, versus .19 and .59 for our filtered-data model. For the 30-second waveform window dataset, the SEL3 bulletin achieved a precision and recall of .31 and .47, respectively, versus .32 and .60 for our filtered-data model. Finally, our models detected signals from all source-to-receiver distances, suggesting it is feasible to use a single PhaseNet model for the IMS network.

58 GEOSCIENCES↗
DOE OSTI2017

ARRIVAL TIME DIFFERENCES BETWEEN GRAVITATIONAL WAVES AND ELECTROMAGNETIC SIGNALS DUE TO GRAVITATIONAL LENSING

In this study we demonstrate that general relativity predicts arrival time differences between gravitational wave (GW) and electromagnetic (EM) signals caused by the wave effects in gravitational lensing. The GW signals can arrive earlier than the EM signals in some cases if the GW/EM signals have passed through a lens, even if both signals were emitted simultaneously by a source. GW wavelengths are much larger than EM wavelengths; therefore, the propagation of the GWs does not follow the laws of geometrical optics, including the Shapiro time delay, if the lens mass is less than approximately 10{sup 5} M {sub ⊙}( f /Hz){sup −1}, where f is the GW frequency. The arrival time difference can reach ∼0.1 s ( f /Hz){sup −1} if the signals have passed by a lens of mass ∼8000 M {sub ⊙}( f /Hz){sup −1} with the impact parameter smaller than the Einstein radius; therefore, it is more prominent for lower GW frequencies. For example, when a distant supermassive black hole binary (SMBHB) in a galactic center is lensed by an intervening galaxy, the time lag becomes of the order of 10 days. Future pulsar timing arrays including the Square Kilometre Array and X-ray detectors may detect several time lags by measuring the orbital phase differences between the GW/EM signals in the SMBHBs. Gravitational lensing imprints a characteristic modulation on a chirp waveform; therefore, we can deduce whether a measured arrival time lag arises from intrinsic source properties or gravitational lensing. Determination of arrival time differences would be extremely useful in multimessenger observations and tests of general relativity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The NANOGrav 15 yr Data Set: Search for Signals from New Physics

The 15 yr pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) shows positive evidence for the presence of a low-frequency gravitational-wave (GW) background. In this paper, we investigate potential cosmological interpretations of this signal, specifically cosmic inflation, scalar-induced GWs, first-order phase transitions, cosmic strings, and domain walls. We find that, with the exception of stable cosmic strings of field theory origin, all these models can reproduce the observed signal. When compared to the standard interpretation in terms of inspiraling supermassive black hole binaries (SMBHBs), many cosmological models seem to provide a better fit resulting in Bayes factors in the range from 10 to 100. However, these results strongly depend on modeling assumptions about the cosmic SMBHB population and, at this stage, should not be regarded as evidence for new physics. Furthermore, we identify excluded parameter regions where the predicted GW signal from cosmological sources significantly exceeds the NANOGrav signal. These parameter constraints are independent of the origin of the NANOGrav signal and illustrate how pulsar timing data provide a new way to constrain the parameter space of these models. Finally, we search for deterministic signals produced by models of ultralight dark matter (ULDM) and dark matter substructures in the Milky Way. We find no evidence for either of these signals and thus report updated constraints on these models. In the case of ULDM, these constraints outperform torsion balance and atomic clock constraints for ULDM coupled to electrons, muons, or gluons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1975

The realization of arithmetic processors for delta modulation encoded signals

The design and realization of digital devices which add or multiply delta modulation (DM) encoded signals are presented. These systems operate directly on the DM bit stream. It is shown that the devices can be constructed using standard digital hardware and that the hardware complexity needed to add or multiply the two DM encoded signals is equivalent to that needed for pulse code modulation (PCM) signals. Experimental results are presented showing the operation of these systems. The results obtained by adding or multiplying DM encoded signals are compared with those obtained using PCM encoded signals on the basis of signal-to-noise ratio (SNR). A spectral-analysis technique applicable to a DM encoded signal is developed to obtain SNR curves.

Locicero, J. L.↗
NASA NTRS1977

Unipolar pulse and bipolar noise testing of wideband signal noise conditioner (MC476-0132-0034)

Information is presented on performance characteristics of the shuttle orbiter wideband signal conditioner when subjected to special types of input signals. Design analysis of the signal flow path through the signal conditioning amplifier was performed followed by acutal testing of the amplifier with various signal inputs. Results indicate that the signal conditioner should perform acceptably if the shuttle orbiter flight vibration signal levels are in accord with preflight predictions.

Harris, J. E.↗
NASA NTRS1978

Repeated observation of an uncertain signal

The focus here is on sensory adaptation, or progressively more appropriate attention, as repeated observations yield more information about a signal with an uncertain parameter. The signal was a brief sinusoid; its uncertain parameter was frequency. Detection performance is predicted from data on a signal of known and constant frequency, as a function of the number of frequencies the uncertain signal could assume. A comparison condition presented a signal that varied in a manner not permitting adaptation. Models derived from signal detection theory describe the ideal observation processes for the three signal conditions, and supply quantitative predictions of relative performances. The models are generally supported by the data.

Swets, J. A.↗
NASA NTRS1983

Fiber optic crossbar switch for automatically patching optical signals

A system for automatically optically switching fiber optic data signals between a plurality of input optical fibers and selective ones of a plurality of output fibers is described. The system includes optical detectors which are connected to each of the input fibers for converting the optic data signals appearing at the respective input fibers to an RF signal. A plurality of RF to optical signal converters are arranged in rows and columns. The output of each of the optical detectors are each applied to a respective row of optical signal converted for being converters back to an optical signal when the particular optical signal converter is selectively activated by a dc voltage.

Bell, C. H.↗