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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Assessment of Cloud Mask Forecasts from the WRF-Solar Ensemble Prediction System: Preprint

Numerical weather prediction (NWP) models are important tools used by government agencies and the renewable energy enterprise to forecast solar radiation. Cloud prediction, a key process of NWP models, has the highest impact on the accuracy of solar forecasting. This study uses satellite observations from the National Solar Radiation Data Base (NSRDB) to evaluate the cloud mask forecast by the WRF-Solar Ensemble Prediction System (WRF-Solar EPS). Preliminary analysis of the data in 2018 demonstrates the need of further improvement in predicting thin and low-level clouds. The information obtained from this work will be used to enhance the WRF-Solar EPS in reproducing the cloud field over the contiguous U.S. and reducing solar forecasting errors.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2023

Synthesis and Characterization of a Masked Terminal Nickel-Oxide Complex

In exploring terminal nickel-oxo complexes, postulated to be the active oxidant in natural and non-natural oxidation reactions, we report the synthesis of the pseudo-trigonal bipyramidal Ni II complexes (K)[Ni II (L Ph )(DMF)] (1[DMF]) and (NMe 4 ) 2 [Ni II (L Ph )(OAc)] (1[OAc]) (L Ph =2,2’,2’’-nitrilo-tris-(N-phenylacetamide); DMF=N,N-dimethylformamide; - OAc=acetate). Both complexes were characterized using NMR, FTIR, ESI-MS, and X-ray crystallography, showing the L Ph ligand to bind in a tetradentate fashion, together with an ancillary donor. The reaction of 1[OAc] with peroxyphenyl acetic acid (PPAA) resulted in the formation of [(L Ph )Ni III -O-H∙∙∙OAc] 2- , 2, that displays many of the characteristics of a terminal Ni=O species. 2 was characterized by UV-Vis, EPR, and XAS spectroscopies and ESI-MS. 2 decayed to yield a Ni II -phenolate complex 3 (through aromatic electrophilic substitution) that was characterized by NMR, FTIR, ESI-MS, and X-ray crystallography. 2 was capable of hydroxylation of hydrocarbons and epoxidation of olefins, as well as oxygen atom transfer oxidation of phosphines at exceptional rates. While the oxo-wall remains standing, this complex represents an excellent example of a masked metal-oxide that displays all of the properties expected of the ever elusive terminal M=O beyond the oxo-wall.

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

Xanthohumol Microbiome and Signature in Healthy Adults (the XMaS Trial): Safety and Tolerability Results of a Phase I Triple-Masked, Placebo-Controlled Clinical Trial

Scope Xanthohumol, a prenylflavonoid from hops, has been extensively studied preclinically but has undergone limited research in human subjects. A triple-masked, placebo-controlled phase I clinical trial was conducted to examine the safety and tolerability of xanthohumol. Methods and results Thirty healthy volunteers were randomized to 24?mg/day xanthohumol (99.8% pure) or placebo for eight weeks. Comprehensive metabolic panels, complete blood counts, body weight, vital signs, and health-related quality of life questionnaires were assessed every two weeks. Participants were interviewed for adverse events (AEs) throughout the trial. Thirteen of 14 (93%) and 14 of 16 (88%) participants completed the trial in Group A and B respectively. There were no withdrawals due to AEs. There were no clinically relevant, between-group differences in laboratory biomarkers, body weight, vital signs, or health-related quality of life. There were no severe or FDA-defined serious AEs, but non-serious AEs were documented in both Group A (n=42) and Group B (n=58). Conclusion Over an eight-week period, 24 mg daily xanthohumol was safe and well-tolerated by healthy adults.

Langley, Blake↗
DOE OSTI2021

Tuning Gold Nanoparticle Size with Fixed Interparticle Spacing in Large-Scale Arrays: Implications for Plasmonics and Nanoparticle Etching Masks

Gold nanoparticles play an important role in plasmonic arrays, including, but not limited to, photovoltaic, biomedical, sensing, optical, and catalytic applications. For plasmonic arrays, control over spatial variables of the array, such as nanoparticle size and spacing, must be judiciously cared for. In this work, we present here a multistep deposit/dewet process for nanoparticle ensemble fabrication that exploits thermally driven solid-state diffusional dewetting, coupled with Ostwald ripening, to tune the nanoparticle size or area coverage for a particular particle–particle spacing. The initial dewetting process defines the spatial period of the array, with subsequent dewetting steps growing the nanoparticles while adhering to the “seeded” period from the first dewetting. Following the fifth deposit/dewet cycle, relative to the first cycle, the mean nanoparticle height exhibited a percent increase of 90 and 66% for 5 and 7.5 nm multistep deposit/dewet processes, respectively, while minimally impacting the ensemble period. This provides unprecedented control over the spatial parameters of large-scale nanoparticle ensembles for plasmonic and etching-mask applications where a particular particle size and spacing are desired. Due to the simplicity of the technique and its area scalability, the cyclic deposition/dewetting process is shown here to be a method that can be easily scaled up to apply to large-area (up to meter-size) applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An adaptive masking method for beam halo measurement by a digital micro mirror device

We report that in high intensity particle beams, irregularities in cathode emission, scattering effects, dark current and space charge forces can lead to a common phenomenon termed beam halo with associated emittance growth, beam loss and overall beam quality degradation. It is of fundamental interest and practical importance to precisely monitor such beam halo, which could offer valuable insights into the experimental strategy for the suppression of this undesired feature. A Digital Micro mirror Device (DMD) is a computer-controlled micro-electrical-mechanical system that contains a compact array of physically independent reflective mirrors. Using a standard DMD, we come up with an adaptive masking method for halo measurement, which demonstrates superior robustness and efficiency comparing to the conventional approaches as confirmed by simulation and experimental results. Our development includes an integrated package of hardware, software and a graphic user interface (GUI) that is robust and portable and can be applied as a beam halo diagnostic for electron guns or radiating charged particle beams in general.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Is tokenization needed for masked particle modeling?

In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets relevant to developing foundation models for high-energy physics. In MPM, a model is trained to recover the missing elements of a set, a learning objective that requires no labels and can be applied directly to experimental data. We achieve significant performance improvements over previous work on MPM by addressing inefficiencies in the implementation and incorporating a more powerful decoder. We compare several pre-training tasks and introduce new reconstruction methods that utilize conditional generative models without data tokenization or discretization. We show that these new methods outperform the tokenized learning objective from the original MPM on a new test bed for foundation models for jets, which includes using a wide variety of downstream tasks relevant to jet physics, such as classification, secondary vertex finding, and track identification.

conditional generative models↗
DOE OSTI2026

Particle trajectory representation learning with masked point modeling

Liquid argon time projection chambers (LArTPCs) offer millimeter-scale 3D images of particle trajectories, enabling precision studies of neutrino oscillation, detection of supernova and solar neutrinos, searches for exotic dark matter, and proton decay. Current approaches utilize supervised machine learning models, requiring extensive simulations of particle physics and detector response that can introduce bias. Self-supervised learning (SSL), a machine learning approach that learns useful representations of unlabeled data from the data itself, has significantly advanced how large datasets are utilized for representation learning; however, its potential for applications to sensory data in high precision particle physics experiments remains largely unexplored. We introduce the Point-based liquid argon masked autoencoder (PoLAr-MAE), a self-supervised framework that learns physically meaningful representations directly from unlabeled LArTPC images. PoLAr-MAE achieves remarkable data efficiency for a point-level segmentation task, outperforming fully supervised methods in low data regimes. Linear classifiers on model outputs demonstrate robust performance across multiple downstream tasks. Our results position sensor-level SSL as a practical foundation model strategy for LArTPCs.

Young, Samuel [Stanford Univ., CA (United States)]↗
DOE OSTI2021

Galaxy Zoo: 3D – crowdsourced bar, spiral, and foreground star masks for MaNGA target galaxies

ABSTRACT The challenge of consistent identification of internal structure in galaxies – in particular disc galaxy components like spiral arms, bars, and bulges – has hindered our ability to study the physical impact of such structure across large samples. In this paper we present Galaxy Zoo: 3D (GZ:3D) a crowdsourcing project built on the Zooniverse platform that we used to create spatial pixel (spaxel) maps that identify galaxy centres, foreground stars, galactic bars, and spiral arms for 29 831 galaxies that were potential targets of the MaNGA survey (Mapping Nearby Galaxies at Apache Point Observatory, part of the fourth phase of the Sloan Digital Sky Surveys or SDSS-IV), including nearly all of the 10 010 galaxies ultimately observed. Our crowdsourced visual identification of asymmetric internal structures provides valuable insight on the evolutionary role of non-axisymmetric processes that is otherwise lost when MaNGA data cubes are azimuthally averaged. We present the publicly available GZ:3D catalogue alongside validation tests and example use cases. These data may in the future provide a useful training set for automated identification of spiral arm features. As an illustration, we use the spiral masks in a sample of 825 galaxies to measure the enhancement of star formation spatially linked to spiral arms, which we measure to be a factor of three over the background disc, and how this enhancement increases with radius.

Masters, Karen L. (ORCID:0000000308469578)↗
DOE OSTI2022

AbBERT: Learning Antibody Humanness via Masked Language Modeling

Understanding the degree of humanness of antibody sequences is critical to the therapeutic antibody development process to reduce the risk of failure modes like immunogenicity or poor manufacturability. We introduce AbBERT, a transformer-based language model trained on up to 20 million unpaired heavy/light chain sequences from the Observed Antibody Space database. We first validate AbBERT using a novel “multi-mask” scoring procedure to demonstrate high accuracy in predicting complementary determining regions—including the challenging hypervariable H3 region. We then demonstrate several uses of AbBERT at various points along the antibody design process. AbBERT enhances in silico antibody optimization via deep reinforcement learning by utilizing its learned embeddings as additional observations during optimization. Within a larger computational antibody design platform, AbBERT has been successfully applied as an additional design objective, where it displays strong correlations with computational tools predicting antibody structural stability. Finally, mutant antibody sequences that have been scored as unfavorable by AbBERT have shown corresponding low yields when expressed in cells. These use cases demonstrate the power of language modeling within computational antibody design.

Bioinformatics↗
DOE OSTI2024

Transformer Masked Autoencoders for RF Device Fingerprinting

Machine learning methods for RF device fingerprinting typically rely on CNN-based models. Transformer-based models have outperformed CNNs for modulation classification tasks, but there are few implementations for device fingerprinting. We train a transformer for device fingerprinting with the largest device count to date and explore several variations of the architecture. Additionally, we demonstrate that pre-training an RF transformer as a Masked Autoencoder improves classification accuracy, as has been observed for CNN fingerprinting models and vision transformers.

artificial intelligence↗
DOE OSTI2023

Safe Exploration Reinforcement Learning for Load Restoration using Invalid Action Masking

This paper addresses the load restoration problem after a power outage event. Our primary proposed methodology uses a multi-agent reinforcement learning method to make the optimal sequential decisions on picking up critical loads. Typically, a negative reward is provided to discourage the agents from selecting decisions that violate physical constraints during the restoration process. However, the main disadvantage of this approach is its difficulty in applying it to large-scale systems due to the curse of dimensionality. This paper introduces the invalid action masking technique to overcome this limitation. The features of this technique include zero physical constraint violations, reduced training time, and stabilization of the explo- ration process. Simulation results are performed in IEEE 13-node and IEEE 123-node systems showing the better performance of the proposed algorithm in comparison to the conventional approaches both in terms of restored power and learning curve.

reinforcement learning, blackstart, artificial int↗
DOE OSTI2026

Make the Fastest Faster: Importance Mask Synthesis for Interactive Volume Visualization using Reconstruction Neural Networks

Visualizing a large-scale volumetric dataset with high resolution is challenging due to the substantial computational time and space complexity. Recent deep learning-based image inpainting methods significantly improve rendering latency by reconstructing a high-resolution image for visualization in constant time on GPU from a partially rendered image where only a portion of pixels go through the expensive rendering pipeline. However, existing solutions need to render every pixel of either a predefined regular sampling pattern or an irregular sample pattern predicted from a low-resolution image rendering. Both methods require a significant amount of expensive pixel-level rendering. In this work, we provide Importance Mask Learning (IML) and Synthesis (IMS) networks, which are the first attempts to directly synthesize important regions of the regular sampling pattern from the user’s view parameters, to further minimize the number of pixels to render by jointly considering the dataset, user behavior, and the downstream reconstruction neural network. Our solution is a unified framework to handle various types of inpainting methods through the proposed differentiable compaction/decompaction layers. Experiments show our method can further improve the overall rendering latency of state-of-the-art volume visualization methods using reconstruction neural network for free when rendering scientific volumetric datasets. Our method can also directly optimize the off-the-shelf pre-trained reconstruction neural networks without elongated retraining.

Large-scale data↗
DOE OSTI2022

Xanthohumol microbiome and signature in adults with Crohn’s disease (the XMaS trial): a protocol for a phase II triple-masked, placebo-controlled clinical trial

Xanthohumol (XN), a bioactive flavonoid from Humulus lupulus with anti-inflammatory properties, has potential benefits for patients with Crohn’s disease (CD), a type of inflammatory bowel disease. We recently completed and published results of a placebo-controlled phase I clinical trial demonstrating the safety and tolerability of 24 mg XN daily for 8 weeks. The present study aims to evaluate the safety and tolerability of the same dose of XN adults with clinically active CD in a placebo-controlled phase II clinical trial. Additional aims will assess the impact of XN on inflammatory biomarkers, platelet function, CD clinical activity, and stool microbial composition. The metabolism of XN will also be evaluated. This article provides a model protocol for consideration in investigations of XN or other natural products in disease states. A triple-masked, randomized, placebo-controlled trial will be conducted in adults with clinically active CD. Participants (n ≤ 32) will be randomized to either 24 mg encapsulated XN per day or placebo and followed for 8 weeks. Throughout the trial, participants will be queried for adverse events. Biomarkers of clinical safety, blood and stool markers of inflammation, platelet function, Crohn’s Disease Activity Index score, stool microbial composition, and XN metabolite profiles in blood, urine, and stool will be assessed every 2 weeks. We describe the protocol for a phase II clinical trial that evaluates the safety and tolerability of XN in adults with active CD, as well as evaluate metabolism and mechanisms that are relevant to CD and other diseases with underlying inflammation and/or gut permeability. The effects of XN on inflammatory biomarkers, platelet function, the microbiota, and multi-omics biomarkers measured in this phase II trial of adults with CD will be compared to the effects of XN in healthy adults in our previous phase I trial. The results of the study will advance the evidence guiding the use of XN in patients with CD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Dry heat sterilization as a method to recycle N95 respirator masks: The importance of fit

In times of crisis, including the current COVID-19 pandemic, the supply chain of filtering facepiece respirators, such as N95 respirators, are disrupted. To combat shortages of N95 respirators, many institutions were forced to decontaminate and reuse respirators. While several reports have evaluated the impact on filtration as a measurement of preservation of respirator function after decontamination, the equally important fact of maintaining proper fit to the users’ face has been understudied. In the current study, we demonstrate the complete inactivation of SARS-CoV-2 and preservation of fit test performance of N95 respirators following treatment with dry heat. We apply scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM/EDS), X-ray diffraction (XRD) measurements, Raman spectroscopy, and contact angle measurements to analyze filter material changes as a consequence of different decontamination treatments. We further compared the integrity of the respirator after autoclaving versus dry heat treatment via quantitative fit testing and found that autoclaving, but not dry heat, causes the fit of the respirator onto the users face to fail, thereby rendering the decontaminated respirator unusable. Our findings highlight the importance to account for both efficacy of disinfection and mask fit when reprocessing respirators to for clinical redeployment.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Micropulse Lidar Cloud Mask Machine-Learning Value-Added Product Report

Cloud detection algorithms of various techniques have been developed and applied to atmospheric ground-based lidar data to identify cloud boundaries and produce clouds masks. While these algorithms are able to identify a wide variety of cloud types and conditions, it is often observed that the algorithms can still fail to accurately detect clouds that are readily discernible when inspecting the lidar imagery. Based on this observation, an alternative approach for cloud detection is to take advantage of machine-learning capabilities and the trained human eye as an interpreter of lidar images, and in turn, to train a neural network to recognize the desired features in the lidar data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Securing Environmental IoT Data Using Masked Authentication Messaging Protocol in a DAG-Based Blockchain: IOTA Tangle

The demand for the digital monitoring of environmental ecosystems is high and growing rapidly as a means of protecting the public and managing the environment. However, before data, algorithms, and models can be mobilized at scale, there are considerable concerns associated with privacy and security that can negatively affect the adoption of technology within this domain. In this paper, we propose the advancement of electronic environmental monitoring through the capability provided by the blockchain. The blockchain’s use of a distributed ledger as its underlying infrastructure is an attractive approach to counter these privacy and security issues, although its performance and ability to manage sensor data must be assessed. We focus on a new distributed ledger technology for the IoT, called IOTA, that is based on a directed acyclic graph. IOTA overcomes the current limitations of the blockchain and offers a data communication protocol called masked authenticated messaging for secure data sharing among Internet of Things (IoT) devices. We show how the application layer employing the data communication protocol, MAM, can support the secure transmission, storage, and retrieval of encrypted environmental sensor data by using an immutable distributed ledger such as that shown in IOTA. Finally, we evaluate, compare, and analyze the performance of the MAM protocol against a non-protocol approach.

Gangwani, Pranav (ORCID:0000000159226002)↗
DOE OSTI2025

A Parameter-masked Mock Data Challenge for Beyond-two-point Galaxy Clustering Statistics

The past few years have seen the emergence of a wide array of novel techniques for analyzing high-precision data from upcoming galaxy surveys, which aim to extend the statistical analysis of galaxy clustering data beyond the linear regime and the canonical two-point (2pt) statistics. We test and benchmark some of these new techniques in a community data challenge named “Beyond-2pt,” initiated during the Aspen 2022 Summer Program “Large-Scale Structure Cosmology beyond 2-Point Statistics,” whose first round of results we present here. The challenge data set consists of high-precision mock galaxy catalogs for clustering in real space, in redshift space, and on a light cone. Participants in the challenge have developed end-to-end pipelines to analyze mock catalogs and extract unknown (“masked”) cosmological parameters of the underlying ΛCDM models with their methods. The methods represented are density-split clustering, nearest neighbor statistics, BACCO power spectrum emulator, void statistics, LEFTfield field-level inference using effective field theory (EFT), and joint power spectrum and bispectrum analyses using both EFT and simulation-based inference. In this work, we review the results of the challenge, focusing on problems solved, lessons learned, and future research needed to perfect the emerging beyond-2pt approaches. The unbiased parameter recovery demonstrated in this challenge by multiple statistics and the associated modeling and inference frameworks supports the credibility of cosmology constraints from these methods. The challenge data set is publicly available, and we welcome future submissions from methods that are not yet represented.

Krause, Elisabeth [Univ. of Arizona, Tucson, AZ (U↗
DOE OSTI2022

EASTERN NORTH ATLANTIC (ENA) CONDENSATION PARTICLE COUNTER (CPC) with the AEROSOL MASK (ENA-AM) for the YEAR 2017

The Eastern North Atlantic (ENA) central facility is periodically affected by episodes of local aerosols. High-concentration aerosol events associated with potential local aerosol sources are sampled by the Concentration Particle Counter (CPC) and can be observed in the high-time-resolution data. In an effort to remove aerosol data that is not regionally representative from the CPC data, an aerosol mask (ENA-AM) has been previously developed and validated during the ACE-ENA campaing (Gallo et al., 2020). The ENA-AM is a standard deviation algorithm based on the statistically different behavior of adjacent data points used to determine periods when the measurements are impacted by local aerosols. This file contain one-minute time resolution submicron aerosol particle concentration datasets from the CPC for the year 2017, and the corresponding ENA-AM datasets to remove datapoints affected by local aerosol sources.

54 ENVIRONMENTAL SCIENCES↗