Search NASA⌕ Search

SEARCH · Search NASA

Results for “Fuzzy”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A density-based continuous local symmetry measure

Although continuous symmetry theory has attracted increasing attention in modern chemistry, local symmetry remains under-investigated. As a consequence, the relationship between symmetry and chemical behavior is often obscured, limiting the practical use of fuzzy symmetry measures. Here, in this study, we introduce a novel framework for evaluating local symmetry based on electron density localization and present continuous symmetry representations for several representative molecules. Our approach not only quantitatively captures global symmetry but also reveals distinctive features of symmetry in a local chemical environment. The related concept, local chirality or chirotopicity, is also discussed. Overall, the proposed local symmetry and chirality measures provide valuable insights into molecular structure and structure–property relationships.

Lai, Duc Anh [Southern Methodist Univ., Dallas, TX↗
DOE OSTI2022

Cryo-EM structure of RNA-induced tau fibrils reveals a small C-terminal core that may nucleate fibril formation

In neurodegenerative diseases including Alzheimer’s and amyotrophic lateral sclerosis, proteins that bind RNA are found in aggregated forms in autopsied brains. Evidence suggests that RNA aids nucleation of these pathological aggregates; however, the mechanism has not been investigated at the level of atomic structure. Here, we present the 3.4-Å resolution structure of fibrils of full-length recombinant tau protein in the presence of RNA, determined by electron cryomicroscopy (cryo-EM). The structure reveals the familiar in-register cross-β amyloid scaffold but with a small fibril core spanning residues Glu391 to Ala426, a region disordered in the fuzzy coat in all previously studied tau polymorphs. RNA is bound on the fibril surface to the positively charged residues Arg406 and His407 and runs parallel to the fibril axis. The fibrils dissolve when RNase is added, showing that RNA is necessary for fibril integrity. While this structure cannot exist simultaneously with the tau fibril structures extracted from patients’ brains, it could conceivably account for the nucleating effects of RNA cofactors followed by remodeling as fibrils mature.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Integrative mapping reveals molecular features underlying the mechanism of nucleocytoplasmic transport

Nuclear pore complexes (NPCs) enable rapid, selective, and robust nucleocytoplasmic transport. To explain how transport emerges from the system components and their interactions, we used experimental data and theoretical information to construct an integrative Brownian dynamics model of transport through an NPC, coupled to a kinetic model of transport in the cell. The model recapitulates key aspects of transport for a wide range of molecular cargoes, including preribosomes and viral capsids. Our model quantifies how flexible phenylalanine-glycine (FG) repeat proteins create an entropic barrier to passive diffusion and how this barrier is selectively lowered in facilitated diffusion by the many transient interactions of nuclear transport receptors with the FG repeats. Selective transport is enhanced by “fuzzy” multivalent interactions, redundant FG repeat mass, coupling to the energy-dependent RanGTP concentration gradient, and exponential dependence of transport kinetics on the transport barrier. Our model will facilitate rational modulation of the NPC and its artificial mimics.

Raveh, Barak [School of Computer Science and Engin↗
DOE OSTI2023

Novel constraints on fifth forces and ultralight dark sector with asteroidal data

Here, we study for the first time the possibility of probing long-range fifth forces utilizing asteroid astrometric data, via the fifth force-induced orbital precession. We examine nine Near-Earth Object (NEO) asteroids whose orbital trajectories are accurately determined via optical and radar astrometry. Focusing on a Yukawa-type potential mediated by a new gauge field (dark photon) or a baryon-coupled scalar, we estimate the sensitivity reach for the fifth force coupling strength and mediator mass in the mass range m ≃ (10 -21 -10 -15 ) eV, near the "fuzzy" dark matter region. Our estimated sensitivity is comparable to leading limits from equivalence principle tests, potentially exceeding these in a specific mass range. The fifth force-induced precession increases with the orbital semi-major axis in the small m limit, motivating the study of objects further away from the Sun. We also demonstrate that precession tests are particularly strong in probing long-range forces which approximately conserve the equivalence principle. We discuss future prospects for extending our study to more than a million asteroids, including NEOs, main-belt asteroids, Hildas, and Jupiter Trojans, as well as trans-Neptunian objects and exoplanets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Cosmic neutrino decoupling and its observable imprints: insights from entropic-dual transport

Abstract Very different processes characterize the decoupling of neutrinos to form the cosmic neutrino background (CνB) and the much later decoupling of photons from thermal equilibrium to form the cosmic microwave background (CMB). The CνB emerges from the fuzzy, energy-dependent neutrinosphere and encodes the physics operating in the early universe in the temperature rangeT∼ 10 MeV toT∼ 10 keV. This is the epoch where beyond Standard Model (BSM) physics, especially in the neutrino sector, may be influential in setting the light element abundances, the necessarily distorted fossil neutrino energy spectra, and other light particle energy density contributions. Here we use techniques honed in extensive CMB studies to analyze the CνB as calculated in detailed neutrino energy transport and nuclear reaction simulations of the protracted weak decoupling and primordial nucleosynthesis epochs. Our moment method, relative entropy, and differential visibility approach can leverage future high precision CMB and light element primordial abundance measurements to provide new insights into the CνB and any BSM physics it encodes. We demonstrate that the evolution of the energy spectrum of the CνB throughout the weak decoupling epoch is accurately captured in the Standard Model by only three parameters per species, a non-trivial conclusion given the deviation from thermal equilibrium and the impact of the decrease of electron-positron pairs. Furthermore, we can interpret each of the three parameters as physical characteristics of a non-equilibrium system. Though the treatment presented here makes some simplifying assumptions including ignoring neutrino flavor oscillations, the success of our compact description within the Standard Model motivates its use also in BSM scenarios. We further demonstrate how observations of primordial light element abundances can be used to place constraints on the CνB energy spectrum, deriving response functions that can be applied for general deviations from a thermal spectrum. Combined with the description of those deviations that we develop here, our methods provide a convenient and powerful framework to constrain the impact of BSM physics on the CνB.

Astronomy & Astrophysics↗
DOE OSTI2021

Material migration in W and Mo during bubble growth and fuzz formation

Growth of helium (He) induced bubbles and fuzz in tungsten (W) and molybdenum (Mo) is investigated using samples of W films on Mo substrates and Mo films on W substrates exposed to He-containing plasma in the temperature range of 340 to 1075 K, fluence range of 1.0–14 × 10 25 He·m -2 , and incident ion energy of <50 eV. No fuzz (only up to 2 nm diameter bubbles) and no material transport occur in W films at ≤750 K, while precursors-of or fully-developed fuzz and material mixing occur in W and Mo films at ≥800 K. This suggests that fuzz forms in multi-material systems as long as one material meets the conditions for fuzz formation, namely T s / T m ~ 0.27–0.5 where T s and T m are the sample exposure and material melting temperatures, respectively. Larger He bubbles, more material mixing, and further-developed fuzz occur at higher temperature due to increased mobility of He atoms and small He clusters. Accumulation of substrate material at the surface of fuzzy W and Mo thin-film (<80 nm) samples suggests fuzz growth by material transport from the bubble layer in the bulk up to the fiber tip, likely by a two-step process: (i) diffusion of punched dislocation loops in the bulk toward the fuzz base and (ii) diffusion of adatoms along the fuzz base and fiber surface (with effective transport of adatoms upwards due to trapping of adatoms at curved surfaces of fiber tips and/or due to the continuous generation of adatoms at the fuzz base). While the bubble size and fuzz thickness increase with reduced W concentration in Mo thin-film samples at 838 K likely due to an increase in trap mutation and dislocation loop punching in Mo compared to W, the fuzz thickness decreases with reduced W concentration at 1075 K despite an increase in the bubble size likely due to slower diffusion of interstitial loops in Mo.

Physics↗
DOE OSTI2020

Automatic microseismic event picking via unsupervised machine learning

SUMMARY Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.

Chen, Yangkang↗
DOE OSTI2020

A semblance measure for model comparison

Algorithmic and computational advances have made it possible that geophysical survey and earth model design can be aided by many systematic trial inverse-modelling runs with synthetic data. Such may, for example, come up in machine-learning approaches. Automated image appraisal pertaining to such applications will involve common statistical tests for goodness-of-data fit as a primary evaluation method. However, solution non-uniqueness may render multiple images equivalent in terms of their data fit, requiring secondary categorizers. A logical choice for classifying synthetic-imaging results quantifies the goodness of model fit where a known reference model replaces the observational input. The task of model intercomparison in terms of measuring the resemblance to the reference model poses challenges to common distance-based metrics like root mean square error and mean absolute error. First, distance-based metrics can introduce spurious contributions when smooth models with fuzzy target contours are to be compared against a sharp reference. Second, large differences due to parameter-estimation overshoots can dominate distance metrics. Here, we propose a remedy that is referred to as semblance and is based on the idea of logistic functions, where a binary-dependent variable adds non-zero or zero accumulation terms for the, respectively, passing or failing of preset target thresholds. This classifying approach is amenable to an objective where model feature recognition is primary. Numerical comparisons to distance-based metrics provide evidence for the advantages of the semblance in view of this objective. Geophysical imaging in conjunction with machine-learning is seen as a benefitting upcoming application area.

58 GEOSCIENCES↗
DOE OSTI2026

Monitored Fluctuating Hydrodynamics

We introduce a hydrodynamic framework for describing monitored classical stochastic processes. We study the conditional ensembles for these monitored processes—i.e., we compute spacetime correlation functions conditioned on a fixed, typical measurement record. In the presence of global symmetries we show that these conditional ensembles can undergo measurement-induced “sharpening” phase transitions as a function of the monitoring rate; moreover, even weak monitoring can give rise to novel critical phases, derived entirely from a classical perspective. We give a simple hydrodynamic derivation of the known “charge-fuzzy phase” for weakly monitored diffusive many-body quantum systems. We show that although the unmonitored symmetric and asymmetric exclusion processes are in different universality classes of transport, the fluctuations in their conditional ensembles flow to the same fixed point with emergent relativistic invariance under monitoring. On the other hand, weakly monitored systems with non-Abelian symmetries enter a novel strongly coupled fixed point with nontrivial dynamical exponent, which we characterize. Our formalism naturally accounts for monitoring general observables, such as currents or density gradients, and allows for a direct calculation of information-theoretic diagnostics of sharpening transitions, including the Shannon entropy of the measurement record.

classical statistical mechanics↗
DOE OSTI2022

High-temperature fractional quantum Hall state in the Floquet kagome flat band

A fractional quantum Hall effect (FQHE) has been predicted in a topological flat band (FB) by a single-particle band structure combined with phenomenological theory or solution of a many-body lattice Hamiltonian with fuzzy parameters. A long-standing roadblock toward the realization of a FB-FQHE is lacking the many-body solution of specific materials under realistic conditions. Here, we demonstrate a combined study of single-particle Floquet band theory with exact diagonalization (ED) of a many-body Hamiltonian. We show that a time-periodic circularly polarized laser inverts the sign of second-nearest-neighbor hopping in a kagome lattice and enhances spin-orbit coupling in one spin channel to produce a Floquet FB with a high flatness ratio of bandwidth over band gap, as exemplified in monolayer Pt 3 C 36 S 12 H 12 . The ED of the resultant Floquet-kagome lattice Hamiltonian gives a one-third-filling ground state with a laser-dependent excitation gap of a FQH state, up to an estimated temperature above 70 K. Our findings pave the way for exploring the alluding high-temperature FB-FQHE.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Quantum fields, geometric fluctuations, and the structure of spacetime

Quantum fluctuations of the vacuum stress-energy tensor are highly non-Gaussian, and can have unexpectedly large effects on spacetime geometry. In this paper, we study a two-dimensional dilaton gravity model coupled to a conformal field, in which the distribution of vacuum fluctuations is well understood. In this model, the fluctuations of the matter field are responsible for the fluctuations of the geometry itself. Here, by analyzing the geodesic deviation in this model, we show that a pencil of massive particles propagating on this fuzzy spacetime eventually converges and collapses. This is consistent with our earlier analysis of null geodesics in [Phys. Rev. Lett. 107, 021303 (2011)].

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Mitigating foreground bias to the CMB lensing power spectrum for a CMB-HD survey

A promising way to measure the distribution of matter on small scales (k~10 h Mpc –1 ) is to use gravitational lensing of the cosmic microwave background (CMB). CMB-HD, a proposed high-resolution, low-noise millimeter survey over half the sky, can measure the CMB lensing auto spectrum on such small scales enabling measurements that can distinguish between a cold dark matter (CDM) model and alternative models designed to solve problems with CDM on small scales. However, extragalactic foregrounds can bias the CMB lensing auto spectrum if left untreated. Here, we present a foreground mitigation strategy that provides a path to reduce the bias from two of the most dominant foregrounds, the thermal Sunyaev-Zel’dovich effect and the cosmic infrared background. Given the level of realism included in our analysis, we find that the thermal Sunyaev-Zel’dovich alone and the cosmic infrared background alone bias the lensing auto spectrum by 0.6σ and 1.1σ, respectively, in the lensing multipole range of Lϵ[5000,20000] for a CMB-HD survey; combined these foregrounds yield a bias of only 1.3σ. Including these foregrounds, we also find that a CMB-HD survey can distinguish between a CDM model and a 10 –22 eV fuzzy dark matter model at the 5σ level. These results provide an important step in demonstrating that foreground contamination can be sufficiently reduced to enable a robust measurement of the small-scale matter power spectrum with CMB-HD.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Kinetic relaxation and nucleation of Bose stars in self-interacting wave dark matter

We revisit kinetic relaxation and soliton/boson star nucleation in fuzzy scalar dark matter featuring short-ranged self-interactions H int = − λ | ψ | 4 / 2 m 2 , alongside gravitational self-interactions. We map out the full curve of nucleation timescale for both repulsive ( λ < 0 ) and attractive ( λ > 0 ) short-ranged self-interaction strength and in doing so reveal two new points. Firstly, besides the two usual terms, ∝ G 2 and ∝ λ 2 , in the total relaxation rate Γ relax , there is an additional cross term ∝ G λ arising due to interference between gravitational and short-ranged self-interaction scattering amplitudes. This yields a critical repulsive interaction strength λ cr ≃ − 2 π G m 2 / v 0 2 , at which the relaxation rate is smallest and serves as the transition point between typical net attractive self-interaction ( λ ≳ λ cr ) and net repulsive self-interaction ( − λ ≳ − λ cr ). Secondly, while in the net attractive regime, nucleation timescale is similar to inverse relaxation timescale τ nuc ∼ Γ relax − 1 ; in the net repulsive regime, nucleation occurs at a delayed time τ nuc ∼ ( λ / λ cr ) Γ relax − 1 . We confirm our analytical understanding by performing 3D field simulations with varying average mass density ρ ¯ , box size L and grid size N . Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2024

Unbiased particle conformation extraction from scattering spectra using orthonormal basis expansions

A strategy is outlined for quantitatively evaluating the particle density profiles from small-angle scattering spectra of dilute solutions. The approach employs an orthonormal basis function expansion method, enabling the determination of characteristic mass distributions in self-assembled structures without the need for a specific structural model. Through computational benchmarking, the efficacy of this approach is validated by effectively reconstructing the density profile of soft-ball systems with varying fuzziness from their scattering signatures. Further, the feasibility of the method is demonstrated by fitting small-angle neutron scattering data obtained from Pluronic L64 micelles at different temperatures. This proposed approach is both simple and analytical, eliminating the requirement for a presumptive structural model in scattering analysis. The new method could therefore facilitate quantitative descriptions of complex nanoscopic structures inherent to numerous soft-matter systems using small-angle scattering techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Human-Machine Shared Control Framework Considering Time-Varying Driver Characteristics

The uncertainties of driver's behavior seriously affect road safety and bring significant challenges to the human-machine cooperative control. Here, this paper proposes a human-machine shared control framework considering driver's time-varying characteristics to improve the co-driving cooperation performance. Firstly, the driving intention is introduced to describe the driver's involvement level through using Gauss-Bernoulli restricted Boltzmann machine method. And the index of driving ability is proposed to evaluate driver skills based on path-tracking errors. Then, a novel human-machine authority allocation strategy is designed by combining the two driving behavior characteristics and used to construct the driver-vehicle interaction system. Subsequently, a T-S fuzzy robust state-feedback shared control system is developed considering time-varying driver behaviors and vehicle states. Finally, the proposed shared steering system is validated by the driver-in-the-loop test bench. The results show that the proposed control method can reduce human-machine conflicts and has obvious superiority in improving performance of driving comfort, path tracking, and vehicle stability for the co-driving vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Robust System Separation Strategy Considering Online Wide-Area Coherency Identification and Uncertainties of Renewable Energy Sources

With the fast growth of renewable energy sources (RES), more and more uncertainties are involved and influencing the stable operation of power systems. Controlled islanding is the last measure to prevent power system blackouts, thus this paper aims to propose a novel model of system separation based on Online Coherency Identification and Adjustable Robust Optimization Programming (OCI-AROP) for minimizing load shedding considering the uncertainties of RES. First, Fuzzy C-Means (FCM) clustering method with F-statistics is utilized to identify the coherent generator groups with the frequency data measured by Phasor Measurement Units (PMUs). Then, the OCI-AROP model considering coherent group constraints, connectivity constraints and robustness constraints about RES are presented. Finally, the case studies on IEEE-39 bus system and WECC-179 bus system are employed to demonstrate the effectiveness of the proposed OCI-AROP model, and comparisons among the OCI-AROP model and the other models are also given to show its superiority.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A Multi-Objective Bayesian Optimized Human Assessed Multi-Target Generated Spectral Recommender System for Rapid Pareto Discoveries of Material Properties

Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameter and function spaces need a rapid strategic search through active learning. However, in all cases prior to optimization, the target material properties are assumed known and fixed, which mostly deviates from real-world scenarios in material synthesis. This can be critical for running expensive experiments on new materials, when the experimental results are fuzzy for any scientific outcomes due to improper target setting, ultimately wasting time and cost. The failure rate and cost are even higher over exploring on multi-target space, where we want to learn the pareto among multiple properties, to jointly optimize during material synthesis for desired applications. To address the challenge, here we introduce the human-operator attempt flexibility in the active learning based automated experiment framework, with generating multiple human assessed targets through a voting-based recommender system during real-time microscope measurements over the large material image space, sequentially learn/update multiple desired targets through a weighting system, and adaptively search in multiple material properties functional space for non-dominated pareto discoveries to maximize the custom structural similarity based acquisition function. We term this a multi-objective Bayesian optimized human assessed multi-target generated spectral recommender systems (MOBO-HAM-SRS). The approach has been demonstrated to peizoresponse force spectroscopy of a ferroelectric thin film, exploring with different kernels and acquisition functions. This work shows an advancement towards human-AI collaborated automated experiments, steering optimization trajectories through human overpowering AI at the early stage when uncertainty is high and AI overpowering human at the later stage with rapid exploration towards optimal goal, following human-assessed multiple targets properties.

Biswas, Arpan↗