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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Three-phase differential mode converter

A system for charging a battery includes three sub-modules, each receiving a respective phase of a three-phase alternating current (AC) signal. The three sub-modules cooperate to transform the respective phases of the three-phase AC signal to a direct current (DC) signal by passing the respective phases of the three-phase AC signal through a respective semiconductor device configured to discontinuously modulate the respective phase of the three-phase AC signal to convert it to a DC signal provided to the battery to charge the battery.

Mazumder, Sudip K.↗
DOE OSTI2024

Radar transmit bandwidth augmentation

Various technologies pertaining to forming a very high instantaneous bandwidth (IBW) radar signal based upon several radio frequency (RF) signals that have sub-bands of the frequency band of the radar signal are described herein. A radar system is configured to address local oscillator leakage across multiple transmit channels of the radar system, such that the radar signal has a relatively constant or other desired amplitude and phase across frequencies of the radar signal. In addition, the radar system is configured to compute a correction signal that pre-distorts each of the sub-frequency channels such that upon combining enables the generation of desired amplitude and phase response across a transmitted pulse.

Loui, Hung↗
DOE OSTI2023

ADCP Upsample

This R script takes reads the csv file of stream-wise velocity data and generates a new time series of with comparable but distinct noise using the Langevin method and library. It simulates a time series measured at 1/10 the sampling rate by calculating a moving average of 10, though other sampling rates can be calculated by adjusting the moving average window. A stationary signal is calculated by taking the difference of the original signal and the moving average, then normalizing by the standard deviation of the moving average. The stationary signal is used to calculate drift and diffusion coefficients. Those coefficients are used to generate a new stationary signal. The new stationary signal is un-normalized and added to the moving average (or new equivalent signal) to create a new up-sampled velocity time-series.

Green, Nathan↗
DOE OSTI2020

Conformation-specific inhibitors of activated Ras GTPases reveal limited Ras dependency of patient-derived cancer organoids

The small GTPases H, K, and NRAS are molecular switches indispensable for proper regulation of cellular proliferation and growth. Several mutations in the genes encoding members of this protein family are associated with cancer and result in aberrant activation of signaling processes caused by a deregulated recruitment of downstream effector proteins. In this study, we engineered variants of the Ras-binding domain (RBD) of the C-Raf proto-oncogene, Ser/Thr kinase (CRAF). These variants bound with high affinity with the effector-binding site of Ras in an active conformation. Structural characterization disclosed how the newly identified RBD mutations cooperate and thereby enhance affinity with the effector-binding site in Ras compared with WT RBD. The engineered RBD variants closely mimicked the interaction mode of naturally occurring Ras effectors and acted as dominant-negative affinity reagents that block Ras signal transduction. We report experiments with cancer cells showed that expression of these RBD variants inhibits Ras signaling, reducing cell growth and inducing apoptosis. Using these optimized RBD variants, we stratified patient-derived colorectal cancer organoids with known Ras mutational status according to their response to Ras inhibition. These results revealed that the presence of Ras mutations was insufficient to predict sensitivity to Ras inhibition, suggesting that not all of these tumors required Ras signaling for proliferation. In summary, by engineering the Ras/Raf interface of the CRAF-RBD, we identified potent and selective inhibitors of Ras in its active conformation that outcompete binding of Ras-signaling effectors.

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

Sensor Domain of Histidine Kinase VxrA of Vibrio cholerae : Hairpin-Swapped Dimer and Its Conformational Change

VxrA and VxrB are cognate histidine kinase (HK)-response regulator (RR) pairs of a two-component signaling system (TCS) found in Vibrio cholerae, a bacterial pathogen that causes cholera. The VxrAB TCS positively regulates virulence, the type VI secretion system, biofilm formation, and cell wall homeostasis in V. cholerae, providing protection from environmental stresses and contributing to the transmission and virulence of the pathogen. The VxrA HK has a unique periplasmic sensor domain (SD) and, remarkably, lacks a cytoplasmic linker domain between the second transmembrane helix and the dimerization and histidine phosphotransfer (DHp) domain, indicating that this system may utilize a potentially unique signal sensing and transmission TCS mechanism. Here, we have determined several crystal structures of VxrA-SD and its mutants. These structures reveal a novel structural fold forming an unusual β hairpin-swapped dimer. A conformational change caused by relative rotation of the two monomers in a VxrA-SD dimer could potentially change the association of transmembrane helices and, subsequently, the pairing of cytoplasmic DHp domains. Based on the structural observation, we propose a putative scissor-like closing regulation mechanism for the VxrA HK. IMPORTANCE V. cholerae has a dynamic life cycle, which requires rapid adaptation to changing external conditions. Two-component signal transduction (TCS) systems allow V. cholerae to sense and respond to these environmental changes. The VxrAB TCS positively regulates a number of important V. cholerae phenotypes, including virulence, the type six secretion system, biofilm formation, and cell wall homeostasis. Here, we provide the crystal structures of the VxrA sensor histidine kinase sensing domain and propose a mechanism for signal transduction. The cognate signal for VxrAB remains unknown; however, in this work we couple our structural analysis with functional assessments of key residues to further our understanding of this important TCS.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Optimization of peptide amphiphile-lipid raft interaction by changing peptide amphiphile lipophilicity

Various peptide amphiphile (PA) molecules have been developed to promote bone regeneration. Previously we discovered that a peptide amphiphile with a palmitic acid tail (C 16 ) attenuates the signaling threshold of leucine-rich amelogenin peptide (LRAP)-mediated Wnt activation by increasing membrane lipid raft mobility. In the current study, we found that treatment of murine ST2 cells with an inhibitor (Nystatin) or Caveolin-1-specific siRNA abolishes the effect of C 16 PA, indicating that Caveolin-mediated endocytosis is required. Here, to determine whether hydrophobicity of the PA tail plays a role in its signaling effect, we modified the length of the tail (C 12 , C 16 and C 22 ) or composition (cholesterol). While shortening the tail (C 12 ) decreased the signaling effect, lengthening the tail (C 22 ) had no prominent effect. On the other hand, the cholesterol PA displayed a similar function as the C 16 PA at the same concentration of 0.001% w/v. Interestingly, a higher concentration of C 16 PA (0.005%) is cytotoxic while cholesterol PA at the higher concentration (0.005%) is well-tolerated by cells. Use of the cholesterol PA at 0.005% enabled a further reduction of the signaling threshold of LRAP to 0.20 nM, compared to 0.25 nM at 0.001%. Caveolin-mediated endocytosis is also required for cholesterol PA, as evidenced by Caveolin-1 siRNA knockdown experiments. We further demonstrated that the noted effects of cholesterol PA are also observed in human bone marrow mesenchymal stem cells (BMMSCs). Taken together, these results indicate that the cholesterol PA modulates lipid raft/caveolar dynamics, thereby increasing receptor sensitivity for activation of canonical Wnt signaling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Jasmonate activates secondary cell wall biosynthesis through MYC2–MYB46 module

Formation of secondary cell wall (SCW) is tightly regulated spatiotemporally by various developmental and environmental signals. Successful fine-tuning of the trade-off between SCW biosynthesis and stress responses requires a better understanding of how plant growth is regulated under environmental stress conditions. However, the current understanding of the interplay between environmental signaling and SCW formation is limited. The lipid-derived plant hormone jasmonate (JA) and its derivatives are important signaling components involved in various physiological processes including plant growth, development, and abiotic/biotic stress responses. Recent studies suggest that JA is involved in SCW formation but the signaling pathway has not been studied for how JA regulates SCW formation. We tested this hypothesis using the transcription factor MYB46, a master switch for SCW biosynthesis, and JA treatments. Both the transcript and protein levels of MYB46, a master switch for SCW formation, were significantly increased by JA treatment, resulting in the upregulation of SCW biosynthesis. We then show that this JA-induced upregulation of MYB46 is mediated by MYC2, a central regulator of JA signaling, which binds to the promoter of MYB46. We conclude that this MYC2-MYB46 module is a key component of the plant response to JA in SCW formation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Deep learning categorization of infrasound array data

Here we develop a deep learning-based infrasonic detection and categorization methodology that uses convolutional neural networks with self-attention layers to identify stationary and non-stationary signals in infrasound array processing results. Using features extracted from the coherence and direction-of-arrival information from beamforming at different infrasound arrays, our model more reliably detects signals compared with raw waveform data. Using three infrasound stations maintained as part of the International Monitoring System, we construct an analyst-reviewed data set for model training and evaluation. We construct models using a 4-category framework, a generalized noise vs non-noise detection scheme, and a signal-of-interest (SOI) categorization framework that merges short duration stationary and non-stationary categories into a single SOI category. We evaluate these models using a combination of k-fold cross-validation, comparison with an existing “state-of-the-art” detector, and a transportability analysis. Although results are mixed in distinguishing stationary and non-stationary short duration signals, f-scores for the noise vs non-noise and SOI analyses are consistently above 0.96, implying that deep learning-based infrasonic categorization is a highly accurate means of identifying signals-of-interest in infrasonic data records.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one’s peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the “pattern” by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game decides among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make a similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Jefferson Laboratory C100 Superconducting Radio-Frequency Cavity Fault Data, 2020

The dataset was created to train machine learning models for the task of identifying the (1) cavity and (2) fault type from C100-type cryomodules at the Thomas Jefferson National Accelerator Facility (Jefferson Lab), thereby replacing the time-consuming efforts of a subject matter expert. Superconducting radio-frequency (SRF) cavity trips represent a significant source of accelerator downtime. Real-time – rather than post-mortem – identification of the offending cavity and classification of the fault type would give control room operators valuable feedback for corrective action planning. The anticipated benefit is increased beam-on-target time for users and provides performance metrics that can be used to improve future cavity designs. A series of 17 RF signals are recorded for each of the 8 cavities in a C100 cryomodule every time a cavity trips. These time-series signals are written to file using a specially designed data acquisition system. The dataset represents fault events recorded during Continuous Electron Beam Accelerator Facility (CEBAF) beam operations between January 18, 2019 and March 9, 2020. The following filtering steps were applied to collected data; (1) only 4 of the 17 signals per cavity are retained (GMES, GASK, CRFP, DETA2) (2) only events with data from each of the eight cavities in the cryomodule are kept, (3) only events that were sampled at 5 kHz were kept, (4) events from cryomodule 0L04 were neglected, (5) events occurring between February 4, 3PM and February 5, 12PM were neglected. As a result of preprocessing, the dataset is comprised of 2,375 unique events. The full dataset is comprised of three files: features.csv, cavity_labels.csv, fault_labels.csv. Each instance in faults.csv includes a timestamp (“date_time”), a label for the cryomodule which experienced the trip (“zone_label”), and 192 features (“feature_1”, “feature_2”... “feature_192”). The features correspond to 6 autoregressive features for each of 4 signals per cavity for each of the 8 cavities (6 × 4 signals/cavity × 8 cavities/cryomodule = 192). To deal with the large variation of signal amplitudes, time-series standardization via the z-score (standard score) function was applied prior to computing the features. For each instance, there is an associated label for the (1) cavity which faulted first (cavity_labels.csv) and (2) the type of fault that caused the trip (fault_labels.csv). The cavity identification can take values of [0, 1, 2, 3, 4, 5, 6, 7, 8] and the fault type can take values of [‘Microphonics’, ‘Quench_100ms’, ‘Controls_Fault’, ‘E_Quench’, ‘Quench_3ms’, ‘Single_Cav_Turn_Off’ , ‘Heat_Riser_Choke’, ‘Multi_Cav_Turn_Off’].

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Cardinal: Seismic and Geoacoustic Array Processing

Data collected via seismic and infrasound array deployments are leveraged in the geosciences to detect and characterize a myriad of natural and anthropogenic sources. These deployments consist of numerous sensors placed in a predetermined configuration to amplify signal strength and improve the efficacy of array processing techniques used to measure signal directionality and waveform coherence. High‐fidelity feature extraction is often predicated on interstation distance as well as the frequency content and wavelength of an incident signal. Numerous array processing softwares analyze data in sequential frequency bands to obtain a more detailed characterization of a signal. However, current algorithms are limited in their ability to determine optimal array configuration for each band. We introduce an open‐source Python code, called Cardinal, to process seismic and infrasound array data in discretized time–frequency space with the option of applying an adaptive array design to determine optimal subarray configuration for each frequency band. To reduce computational time, the array processing step can be run in parallel using multithreading. Furthermore, the software has the capability to aggregate array processing results from different time–frequency pixels to produce separate sets of detections, or families, with added utility via the application of an adaptive semblance threshold, which aids in isolating signals‐of‐interest from coherent background noise. Upon appropriate configuration, Cardinal exhibits the potential to combine distinct seismic and infrasound phases into separate families.

Adaptive Array↗
DOE OSTI2021

Stellar Activity Manifesting at a One-year Alias Explains Barnard b as a False Positive

Barnard’s star is among the most studied stars given its proximity to the Sun. It is often considered the radial velocity (RV) standard for fully convective stars due to its RV stability and equatorial decl. Recently, an Msin i=3.3M{sub ⊕} super-Earth planet candidate with a 233 day orbital period was announced by Ribas et al. New observations from the near-infrared Habitable-zone Planet Finder (HPF) Doppler spectrometer do not show this planetary signal. We ran a suite of experiments on both the original data and a combined original + HPF data set. These experiments include model comparisons, periodogram analyses, and sampling sensitivity, all of which show the signal at the proposed period of 233 days is transitory in nature. The power in the signal is largely contained within 211 RVs that were taken within a 1000 day span of observing. Our preferred model of the system is one that features stellar activity without a planet. We propose that the candidate planetary signal is an alias of the 145 day rotation period. This result highlights the challenge of analyzing long-term, quasi-periodic activity signals over multiyear and multi-instrument observing campaigns.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

MSU IETC LSTM Ethernet Decode (AN EDGE)

This research explores the ability of machine learning to perform signal separation of an Ethernet style encoded, full-duplex communication. Typical signal separation currently requires an active tap of the communication line, followed by a recombination and retransmission of the data. The purpose of this research is to study a passive approach to data acquisition from a full-duplex signal. The machine learning model used in this research is a long-short-term memory recurrent neural network (LSTM-RNN). The results show that the LSTM was largely successful in recreating the transmission signal from the measured data points, though the separated signals have not yet been tested using a decoding method.

Full Duplex Signals↗
DOE OSTI2020

Absorption of fermionic dark matter by nuclear targets

Absorption of fermionic dark matter leads to a range of distinct and novel signatures at dark matter direct detection and neutrino experiments. We study the possible signals from fermionic absorption by nuclear targets, which we divide into two classes of four Fermi operators: neutral and charged current. In the neutral current signal, dark matter is absorbed by a target nucleus and a neutrino is emitted. This results in a characteristically different nuclear recoil energy spectrum from that of elastic scattering. The charged current channel leads to induced $\beta$ decays in isotopes which are stable in vacuum as well as shifts of the kinematic endpoint of $ \beta$ spectra in unstable isotopes. To confirm the possibility of observing these signals in light of other constraints, we introduce UV completions of example higher dimensional operators that lead to fermionic absorption signals and study their phenomenology. Most prominently, dark matter which exhibits fermionic absorption signals is necessarily unstable leading to stringent bounds from indirect detection searches. Nevertheless, we find a large viable parameter space in which dark matter is sufficiently long lived and detectable in current and future experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Light in the shadows: primordial black holes making dark matter shine

We consider the possibility of indirect detection of dark sector processes by investigating a novel form of interaction between ambient dark matter (DM) and primordial black holes (PBHs). The basic scenario we envisage is that the ambient DM is “dormant”, i.e., it has interactions with the SM, but its potential for an associated SM signal is not realized for various reasons. We argue that the presence of PBHs with active Hawking radiation (independent of any DM considerations) can act as a catalyst in this regard by overcoming the aforementioned bottlenecks. The central point is that PBHs radiate all types of particles, whether in the standard model (SM) or beyond (BSM), which have a mass at or below their Hawking temperature. The emission of such radiation is “democratic” (up to the particle spin), since it is based on a coupling of sorts of gravitational origin. In particular, such shining of (possibly dark sector) particles onto ambient DM can then activate the latter into giving potentially observable SM signals. We illustrate this general mechanism with two specific models. First, we consider asymmetric DM, which is characterized by an absence of ambient anti-DM, and consequently the absence of DM indirect detection signals. In this case, PBHs can “resurrect” such a signal by radiating anti-DM, which then annihilates with ambient DM in order to give SM particles such as photons. In our second example, we consider the PBH emission of dark gauge bosons which can excite ambient DM into a heavier state (which is, again, not ambient otherwise), this heavier state later decays back into DM and photons. Finally, we demonstrate that we can obtain observable signals of these BSM models from asteroid-mass PBHs (Hawking radiating currently with ~ $ \mathcal{O}\left(\textrm{MeV}\right) $ temperatures) at gamma-ray experiments such as AMEGO-X.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Non-resonant anomaly detection with background extrapolation

Complete anomaly detection strategies that are both signal sensitive and compatible with background estimation have largely focused on resonant signals. Non-resonant new physics scenarios are relatively under-explored and may arise from off-shell effects or final states with significant missing energy. In this paper, we extend a class of weakly supervised anomaly detection strategies developed for resonant physics to the non-resonant case. Machine learning models are trained to reweight, generate, or morph the background, extrapolated from a control region. A classifier is then trained in a signal region to distinguish the estimated background from the data. The new methods are demonstrated using a semi-visible jet signature as a benchmark signal model, and are shown to automatically identify the anomalous events without specifying the signal ahead of time.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Earth-catalyzed detection of magnetic inelastic dark matter with photons in large underground detectors

Inelastic dark matter with moderate splittings, $\mathcal{O}$ (few to 150) keV, can upscatter to an excited state in the Earth, with the excited state subsequently decaying, leaving a distinctive monoenergetic photon signal in large underground detectors. The photon signal can exhibit sidereal-daily modulation, providing excellent separation from backgrounds. Using a detailed numerical simulation, we examine this process as a search strategy for magnetic inelastic dark matter with the dark matter mass near the weak scale, where the upscatter to the excited state and decay proceed through the same magnetic dipole transition operator. At lower inelastic splittings, the scattering is dominated by moderate mass elements in the Earth with high spin, especially 27 Al, while at larger splittings, 56 Fe becomes the dominant target. We show that the proposed large volume gaseous detector CYGNUS will have excellent sensitivity to this signal. Xenon detectors also provide excellent sensitivity through the inelastic nuclear recoil signal, and if a future signal is seen, we show that the synergy among both types of detection can provide strong evidence for magnetic inelastic dark matter. In the course we have calculated nuclear response functions for elements relevant for scattering in the Earth, which are publicly available on GitHub.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Earth-scattering induced modulation in low-threshold dark matter experiments

Dark matter particles with sufficiently large interactions with ordinary matter can scatter in the Earth before reaching and scattering in a detector. This induces a modulation in the signal rate with a period of one sidereal day. We calculate this modulation for sub-GeV dark matter particles that interact either with a heavy or an ultralight dark-photon mediator and investigate the resulting signal in low-threshold detectors consisting of silicon, xenon, or argon targets. The scattering in the Earth is dominated by dark matter scatters off nuclei, while the signal in the detector is easiest to observe from dark matter scattering off electrons. We investigate the properties of the modulation signal and provide projections of the sensitivity of future experiments. We find that a search for a modulation signal can probe new regions of parameter space near the energy thresholds of current experiments, where the data are typically dominated by backgrounds.

Models for Dark Matter↗