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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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Efficient response to the pandemic through the mobilization of the larger scientific community is challenged by the limited reusability of the available primary genomic data. Here, the Genomic Standards Consortium board highlights the essential need for contextual genomic data FAIRness, for empowering key data-driven biological questions.
Transforming in situ transmission electron microscopy (TEM) imaging into a tool for spatially-resolved operando characterization of solid-state reactions requires automated, high-precision semantic segmentation of dynamically evolving features. However, traditional deep learning methods for semantic segmentation often face limitations due to the scarcity of labeled data, visually ambiguous features of interest, and scenarios involving small objects. To tackle these challenges, we introduce MultiTaskDeltaNet (MTDN), a novel deep learning architecture that creatively reconceptualizes the segmentation task as a change detection problem. By implementing a unique Siamese network with a U-Net backbone and using paired images to capture feature changes, MTDN effectively leverages minimal data to produce high-quality segmentations. Furthermore, MTDN utilizes a multi-task learning strategy to exploit correlations between physical features of interest. In an evaluation using data from in situ environmental TEM (ETEM) videos of filamentous carbon gasification, MTDN demonstrated a significant advantage over conventional segmentation models, particularly in accurately delineating fine structural features. Notably, MTDN achieved a 10.22% performance improvement over conventional segmentation models in predicting small and visually ambiguous physical features. This work bridges key gaps between deep learning and practical TEM image analysis, advancing automated characterization of nanomaterials in complex experimental settings.
Measurements of type Ia supernovae (SNe Ia) in the near-infrared (NIR) have been used both as an alternate path to cosmology compared to optical measurements and as a method of constraining key systematics for the larger optical studies. With the DEHVILS sample, the largest published NIR sample with consistent NIR coverage of maximum light across three NIR bands ( Y, J , and H ), we check three key systematics: (i) the reduction in Hubble residual scatter as compared to the optical, (ii) the measurement of a “mass step” or lack thereof and its implications, and (iii) the ability to distinguish between various dust models by analyzing slopes and correlations between Hubble residuals in the NIR and optical. We produce SN Ia simulations of the DEHVILS sample and find that it is harder to differentiate between various dust models than previously understood. Additionally, we find that fitting with the current SALT3-NIR model does not yield accurate wavelength-dependent stretch-luminosity correlations, and we propose a limited solution for this problem. From the data, we see that (i) the standard deviation of Hubble residual values from NIR bands treated as standard candles are 0.007–0.042 mag smaller than those in the optical, (ii) the NIR mass step is not constrainable with the current sample size of 47 SNe Ia from DEHVILS, and (iii) Hubble residuals in the NIR and optical are correlated in the data. We test a few variations on the number and combinations of filters and data samples, and we observe that none of our findings or conclusions are significantly impacted by these modifications.
At micro- and nanoscales, momentum transfer between surfaces is influenced by various physical mechanisms, including quantum fluctuations, electromagnetic interactions, electric charges, and the dynamics of (rarefied) gases. Under non-isothermal conditions, rarefied gases give rise to thermal Knudsen forces whose magnitudes strongly depend on the gas species and surface characteristics. Knudsen forces are particularly relevant in nanotechnology, optical manipulation, and aerospace systems, where gas rarefaction occurs due to highly confined geometries, sub-micrometer length scales, and reduced particle densities. Despite their significance, predictive modeling of Knudsen forces is limited by a lack of comprehensive experimental data across diverse materials and surface morphologies. Here, in this work, we present a highly sensitive and adaptable measurement platform capable of directly quantifying Knudsen forces using a suspended, interchangeable micro-cantilever within controlled rarefied helium and nitrogen environments. The system integrates optical fiber interferometry to precisely capture out-of-plane displacements at sub-micrometer resolution, driven by Knudsen forces. From the empirical data, we derive a robust correlation linking the magnitudes of Knudsen forces to energy accommodation coefficients, offering deeper insights into the underlying gas–surface interaction mechanisms.
We investigated the ultrafast structural dynamics of cyclobutanone following photoexcitation at λ = 200 nm using gas-phase megaelectronvolt ultrafast electron diffraction. Our investigation complements the simulation studies of the same process within this special issue. It provides information about both electronic state population and structural dynamics through well-separable inelastic and elastic electron scattering signatures. We observe the depopulation of the photoexcited S 2 state of cyclobutanone with n3s Rydberg character through its inelastic electron scattering signature with a time constant of (0.29 ± 0.2) ps toward the S 1 state. The S 1 state population undergoes ring-opening via a Norrish Type-I reaction, likely while passing through a conical intersection with S 0 . The corresponding structural changes can be tracked by elastic electron scattering signatures. These changes appear with a delay of (0.14 ± 0.05) ps with respect to the initial photoexcitation, which is less than the S 2 depopulation time constant. This behavior provides evidence for the ballistic nature of the ring-opening once the S 1 state is reached. The resulting biradical species react further within (1.2 ± 0.2) ps via two rival fragmentation channels yielding ketene and ethylene, or propene and carbon monoxide. Furthermore, our study showcases the value of both gas-phase ultrafast diffraction studies as an experimental benchmark for nonadiabatic dynamics simulation methods and the limits in the interpretation of such experimental data without comparison with such simulations.
The uncertainties in microwave land surface emissivity (MLSE) measurements have long limited the use of spaceborne microwave radiometer data. As an emerging observation method, Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated great potential in several land and ocean applications. In this study, a method for obtaining daily MLSE dataset in the pan-tropical region from Cyclone GNSS (CYGNSS) observations is presented and evaluated. The CYGNSS observations are first aggregated into the Equal-Area-Scalable-Earth (EASE) 2.0 36 km grid by a combined weight function of distance, time, and signal-to-noise ratio variance. Then, the method employs a pixel-by-pixel regression algorithm to conduct the daily MLSE retrieval using reference emissivity derived from the Soil Moisture Active Passive (SMAP) brightness temperature. The CYGNSS MLSE shows good agreement with SMAP MLSE, delivering an overall root-mean-square error (RMSE) of 0.022 and 0.017 for horizontal and vertical polarization, respectively, during the training set spanning the whole year of 2018. Furthermore, on the test set from January 2019 to May 2019, the RMSE values amounted to 0.030 and 0.023 for horizontal and vertical polarization, respectively. Temperature records from the International Soil Moisture Network are employed to calculate the emissivity and for in-situ validation, which yield an RMSE of 0.034 and 0.026 for the two polarizations, respectively. The proposed algorithm provides an encouraging approach to obtain accurate daily MLSE dataset for microwave remote sensing. Compared to the SMAP MLSE, the CYGNSS MLSE has a remarkable improvement of 86% in temporal resolution, greatly complementing the existing microwave emissivity datasets.
Missions like NASA's Imaging X-ray Polarimetry Explorer (IXPE) are poised to provide an unprecedented view of the Universe in polarized X-rays. Polarization probes physical anisotropies, a fact exploited by particle physicists to look for the anisotropic a E∙B operator in the axion-like-particle (ALP) Lagrangian. Such studies have typically focused on polarization in the radio and microwaves, through local or cosmic birefringence effects. To such polarization studies we add X-rays emanating from magnetars — a class of neutron stars with near-critical strength magnetic fields — that are important targets for IXPE. ALPs produced in the neutron star core convert to X-rays in the magnetosphere; such X-rays are polarized along the direction parallel to the dipolar magnetic field at the point of conversion. Here, we develop the full theoretical formalism for ALP-induced polarization in the presence of dipolar magnetic fields. For uncorrelated photon and ALP production mechanisms, we completely disentangle the ALP contributions to the Stokes parameters in terms of the ALP intensity, the ALP-to-photon conversion probability, and the ALP-induced birefringence. In the proper limit, our results demonstrate that the inclusion of ALPs suppresses the observed degree of circular polarization compared to its pure astrophysical value. Our results can also be used to impose limits on ALP couplings with IXPE polarization data from magnetars 4U 0142+61 and 1RXS J170849.0-400910, the subject of upcoming work.
ABSTRACT Various models of modified gravity invoke ‘screening’ mechanisms that are sensitive to the value of the local gravitational potential. This could have observable consequences for galaxies. These consequences might be seen by comparing two proxies for galaxy mass – their luminosity and their internal kinematics – as a function of local galaxy density. Motivated by this prospect, we have compared the observed properties of luminous red galaxies (LRGs) inside and outside of voids in the cosmic large scale structure. We used archival measurements of line widths, luminosities, redshifts, colours, and positions of galaxies in conjunction with recent void catalogues to construct comparison LRG samples inside and outside of voids. We fitted these two samples to the well-established fundamental plane of elliptical galaxies to constrain any differences between the inferred value of the Newtonian gravitational constant G for the two samples. We obtained a null result, with an upper limit on any fractional difference in G within and outside of cosmological voids to be α = δG/G ∼ 40 per cent. This upper bound is dominated by the small-number statistics of our N ∼ 100 within-void LRG sample. With the caveat that environmental effects could influence various parameters such as star formation, we estimate that a 1 per cent statistical limit on α could be attained with data from 105 elliptical galaxies within voids. This is within the reach of future photometric and spectroscopic surveys, both of which are required to pursue this method.
In a seminal paper now a decade old, it was shown that dark-matter detectors geared at probing interactions with nucleons could also be used to probe dark-matter interactions with electrons. In this Letter, we show that new detector concepts designed to probe dark-matter-electron interactions at low masses can similarly be used to probe new parameter space for dark-matter-nucleon interactions. We demonstrate the power of this approach by using existing data from superconducting detectors to place new limits on the interactions of nuclei with MeV-scale dark matter. Further, we show that advances in detector technology that have been anticipated for electronic interactions will automatically extend sensitivity deep into uncharted territory for nuclear interactions. This doubles the effective science output of future low-threshold experiments.
Intermetallic GdNi adopts a CrB type of crystal structure (space group Cmcm), and it orders ferromagnetically via a second-order phase transition at 70 K, exhibiting unusually strong spontaneous striction along the three independent crystallographic axes in the ferromagnetically ordered state. Here we introduce a microscopic model to describe anisotropic changes of lattice parameters and elastic contribution to magnetocaloric effect of GdNi. In the model, results of density functional theory (DFT) calculations are used as inputs into a Hamiltonian that includes elastic energy of an anisotropic crystal lattice, exchange interactions, and Zeeman effect. The magnetic and elastic Hamiltonians are coupled through an anisotropic Bean-Rodbell model of magnetoelastic interactions. This coupling gives rise to anisotropic changes in the lattice parameters observed experimentally, and the model reveals good to reasonable agreements between the current theoretical results and earlier experimental data, thus validating the model within the limits of assumptions made. We also show that DFT calculations with 4f electrons of Gd treated as core electrons lead to a more adequate estimate of elastic constants of GdNi in comparison with the LDA + U method where 4f electrons are treated as valence electrons.
The Large Underground Xenon (LUX) dark matter search was a 250-kg active mass dual-phase time projection chamber that operated by detecting light and ionization signals from particles incident on a xenon target. In December 2015, LUX reported a minimum 90% upper C.L. of 6 x 10 -46 cm 2 on the spin-independent WIMP-nucleon elastic scattering cross section based on a 1.4 x 10 4 kg ∙ day exposure in its first science run. Tension between experiments and the absence of a definitive positive detection suggest it would be prudent to search for WIMPs outside the standard spin-independent/spin-dependent paradigm. Recent theoretical work has identified a complete basis of 14 independent effective field theory (EFT) operators to describe WIMP-nucleon interactions. In addition to spin-independent and spin-dependent nuclear responses, these operators can produce novel responses such as angular-momentum-dependent and spin-orbit couplings. Here we report on a search for all 14 of these EFT couplings with data from LUX's first science run. Limits are placed on each coupling as a function of WIMP mass.
The exploration of critical phenomena in phase transitions of strongly interacting matter governed by quantum chromodynamics (QCD) is one of the goals of present ultrarelativistic heavy-ion collision experiments at BNL and CERN. The key research direction is to locate the putative critical point on the phase diagram of QCD linked to the chiral symmetry restoration at finite temperature and/or density. One of the main theoretical tools used for this purpose is the fluctuations of conserved charges, such as the net-baryon number. However, due to experimental limitations, analyses of heavy-ion collision data suffer from a very doubtful basing of the net-proton number being a proxy for the total net-baryon number fluctuations. In this work, we use the parity doublet model to investigate the fluctuations of the net-baryon number density in hot and dense hadronic matter. The model accounts for chiral criticality within the mean-field approximation. We focus on the qualitative properties and systematics of the first- and second-order susceptibility of the net-baryon number density, and their ratios for nucleons of positive and negative parity, as well as their correlator. We show that the fluctuations of the positive-parity nucleon do not necessarily reflect the fluctuations of the total net-baryon number density at the phase boundary of the chiral phase transition. We also investigate the nontrivial structure of the correlator. Furthermore, we discuss and quantify the differences between the fluctuations of the net-baryon number density in the vicinity of the chiral and liquid-gas phase transition in nuclear matter. We indicate a possible relevance of our results with the interpretation of the experimental data on net-proton number fluctuations in heavy-ion collisions. Published by the American Physical Society 2024
Critical infrastructures such as power grids have become increasingly complex, connected, and vulnerable to adverse scenarios, including cyber and physical attacks and faults. Effective risk mitigation for such cyber-physical energy systems (CPES), requires preemptive knowledge of likely adversarial attack scenarios. Hybrid Attack Graph (HAG) is a structured way to represent an adversarial scenario as an attack sequence using a threat model. However, the scarcity of documented attack sequences hinders analysts and CPES planners’ ability to identify credible attack scenarios for a given CPES. We propose a data-driven Graph Convolutional Deep-Q Network (GCDQ) to address this data challenge through generating HAGs. By leveraging limited real-world observations from the MITRE ATT&CK knowledge base, our GCDQ model synthesizes realistic graphs with the targeted attribute of minimum detectability via reinforcement learning. This generative model is the first step in creating a tool to substantially boost the attack sequence dataset and enhance the performance of CPS defense-related tasks by providing insights into likely attack sequences with given attributes.
Supervised semantic segmentation methods provide state-of-the-art performance, but their performance is limited by the amount of quality labeled data they need for training. Scarcity of labeled data and non-transferablity of models, due to cross-domain discrepancy makes it a bigger challenge for remote sensing imagery analysis. In this work, we approach this problem through adversarial learning, driven by entropy and boundary of region-of-interest for unsupervised domain adaptation. This concept helps with better boundary prediction and encourages target domain entropy maps (probability/uncertainty maps) to be similar to source domains. In particular, we showed that deriving informative entropy through the adversarial learning is essential to enable the adaptation. We used a large scale cross country building extraction dataset to validate the framework. The experimental results show the usefulness of considering boundary and entropy driven adversarial learning for adaptation.
Large-scale capacity expansion models typically rely on estimates of the power transfer limits between modeled zones. Accurate estimation of these interface transfer limits (ITLs) requires modeling the underlying transmission network. Here we expand on a maximum flow optimization method that uses linearized power flow to estimate transfer limits. We apply this method to a data set of the U.S. transmission network to estimate ITLs between U.S. counties. By calculating ITLs using different subsets of the network, we evaluate how the size of the network used in the estimation affects the results. The results show diminishing returns to ITL accuracy after six hops, suggesting that a network subset can reasonably be used to approximate ITLs. The county-level estimates produced in this study will support more spatially resolved capacity expansion modeling and will help inform policy making at local and national levels.
Whole slide imaging (WSI), also called digital virtual microscopy, is a new imaging modality. It allows for the application of AI and machine learning methods to cancer pathology to help establish a means for the automatic diagnosis of cancer cases. However, designing machine-learning models for WSI is computationally challenging due to its required ultra-high resolution. The current state-of-the-art models use multiple instance learning (MIL). MIL is a weakly-supervised learning method in which the model uses an array of inferences from many smaller instances to make a final classification about the entire set. In the context of WSI, researchers divide the ultra-high-resolution image into many patches. The model then classifies the slide based on an array of inferences from the patches. Among several ways of making the final classification, attention-based mechanisms have resulted in superb accuracy scores. The Transformer, one attention-based algorithm, has reported substantial improvements for WSI comprehension tasks. In this project, we studied and compared several WSI comprehension algorithms. We used the following three datasets: CAMELYON16+17, TCGALung, and TCGA-Kidney. We found that attention-based MIL algorithms performed better than standard MIL algorithms for classifying WSI images, achieving a higher mean accuracy and AUC. However, none of the attention-based algorithms performed significantly better than the others, reporting accuracy scores that varied widely. Presumably, it is due to the limited availability of training samples in the data corpus. Since it is not easy to increase the samples from human subjects, some machine learning techniques like transfer learning could help mitigate this issue.
The microscopic mechanisms underpinning the spontaneous surface passivation of metals from ubiquitous water have remained largely elusive. Here, using in situ environmental electron microscopy to atomically monitor the reaction dynamics between aluminum surfaces and water vapor, we provide direct experimental evidence that the surface passivation results in a bilayer oxide film consisting of a crystalline-like Al(OH) 3 top layer and an inner layer of amorphous Al 2 O 3 . The Al(OH) 3 layer maintains a constant thickness of ~5.0 Å, while the inner Al 2 O 3 layer grows at the Al 2 O 3 /Al interface to a limiting thickness. On the basis of experimental data and atomistic modeling, we show the tunability of the dissociation pathways of H 2 O molecules with the Al, Al 2 O 3 , and Al(OH) 3 surface terminations. The fundamental insights may have practical significance for the design of materials and reactions for two seemingly disparate but fundamentally related disciplines of surface passivation and catalytic H 2 production from water.
We present a method for ordering two-nucleon interactions based upon their scaling with the number of QCD colors, N c , in the limit that N c becomes large. Available data in the two-nucleon sector show general agreement with this ordering, indicating that the method may be useful in other contexts where data are less readily available. However, several caveats and potential pitfalls can make the large-N c ordering fragile and/or vulnerable to misinterpretation. We discuss the application of the large-N c analysis to two- and three-nucleon interactions, including those originating from weak and BSM (beyond the Standard Model) interactions, as well as two-nucleon external currents. Finally, we discuss some open questions in the field.