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At least 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

DISCOVERY OF LOW-METALLICITY STARS IN THE CENTRAL PARSEC OF THE MILKY WAY

We present a metallicity analysis of 83 late-type giants within the central 1 pc of the Milky Way. K-band spectroscopy of these stars was obtained with the medium spectral resolution integral-field spectrograph NIFS on Gemini North using laser-guided star adaptive optics. Using spectral template fitting with the MARCS synthetic spectral grid, we find that there is a large variation in the metallicity, with stars ranging from [M/H] < −1.0 to above solar metallicity. About 6% of the stars have [M/H] < −0.5. This result is in contrast to previous observations with smaller samples that show stars at the Galactic center having approximately solar metallicity with only small variations. Our current measurement uncertainties are dominated by systematics in the model, especially at [M/H] > 0, where there are stellar lines not represented in the model. However, the conclusion that there are low-metallicity stars, as well as large variations in metallicity, is robust. The metallicity may be an indicator of the origin of these stars. The low-metallicity population is consistent with that of globular clusters in the Milky Way, but their small fraction likely means that globular cluster infall is not the dominant mechanism for forming the Milky Way nuclear star cluster. The majority of stars are at or above solar metallicity, which suggests they were formed closer to the Galactic center or from the disk. In addition, our results indicate that it will be important for star formation history analyses using red giants at the Galactic center to consider the effect of varying metallicity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Private Tabular Survey Data Products through Synthetic Microdata Generation

We propose two synthetic microdata approaches to generate private tabular survey data products for public release. We adapt a pseudo posterior mechanism that downweights by-record likelihood contributions with weights ∈[0,1] based on their identification disclosure risks to producing tabular products for survey data. Our method applied to an observed survey database achieves an asymptotic global probabilistic differential privacy guarantee. Our two approaches synthesize the observed sample distribution of the outcome and survey weights, jointly, such that both quantities together possess a privacy guarantee. The privacy-protected outcome and survey weights are used to construct tabular cell estimates (where the cell inclusion indicators are treated as known and public) and associated standard errors to correct for survey sampling bias. Through a real data application to the Survey of Doctorate Recipients public use file and simulation studies motivated by the application, we demonstrate that our two microdata synthesis approaches to construct tabular products provide superior utility preservation as compared to the additive noise approach of the Laplace Mechanism. Moreover, our approaches allow the release of microdata to the public, enabling additional analyses at no extra privacy cost.

Mathematical Methods In Social Sciences↗
DOE OSTI2018

Quench Detection for High-Temperature Superconductor Conductors Using Acoustic Thermometry

Detecting local heat-dissipating zones in high-temperature superconductor (HTS) magnets is a challenging task due to slow propagation of such zones in HTS conductors. For long conductor lengths, voltage-based methods may not provide a sufficient sensitivity or redundancy, and therefore nonvoltage-based detection alternatives are being sought. One of those is the recently proposed method of Eigen Frequency Thermometry (EFT), which is an active acoustic technique for a fast and nonintrusive detection of 'hot spots,' utilizing temperature dependence of the conductor elastic moduli. In this work, we demonstrate the efficiency of EFT for detecting localized heating in a 1.2-m-long sample of REBCO tape immersed in liquid nitrogen, and benchmark sensitivity of the acoustic detection with respect to voltage, hot spot temperature, and power dissipation in the conductor. Modifying the original technique for differential mode of operation enables a much improved sensitivity, and adds a hot spot localization capability. Furthermore, we adapt this technique to subscale coils wound with REBCO CORC conductor built in the framework of U.S. Magnet Development Program. A successful thermal-based detection of dissipation onset at the critical current for a two-layer canted CORC dipole assembly is discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

TULIP: An RNA-seq-based Primary Tumor Type Prediction Tool Using Convolutional Neural Networks

Background: With cancer as one of the leading causes of death worldwide, accurate primary tumor type prediction is critical in identifying genetic factors that can inhibit or slow tumor progression. There have been efforts to categorize primary tumor types with gene expression data using machine learning, and more recently with deep learning, in the last several years. Methods In this paper, we developed four 1-dimensional (1D) Convolutional Neural Network (CNN) models to classify RNA-seq count data as one of 17 highly represented primary tumor types or 32 primary tumor types regardless of imbalanced representation. Additionally, we adapted the models to take as input either all Ensembl genes (60,483) or protein coding genes only (19,758). Unlike previous work, we avoided selection bias by not filtering genes based on expression values. RNA-seq count data expressed as FPKM-UQ of 9,025 and 10,940 samples from The Cancer Genome Atlas (TCGA) were downloaded from the Genomic Data Commons (GDC) corresponding to 17 and 32 primary tumor types respectively for training and validating the models. Results: All 4 1D-CNN models had an overall accuracy of 94.7% to 97.6% on the test dataset. Further evaluation indicates that the models with protein coding genes only as features performed with better accuracy compared to the models with all Ensembl genes for both 17 and 32 primary tumor types. For all models, the accuracy by primary tumor type was above 80% for most primary tumor types. Conclusions: We packaged all 4 models as a Python-based deep learning classification tool called TULIP (TUmor CLassIfication Predictor) for performing quality control on primary tumor samples and characterizing cancer samples of unknown tumor type. Further optimization of the models is needed to improve the accuracy of certain primary tumor types.

Jones, Sara↗
DOE OSTI2016

Strengthening IAEA Safeguards for Research Reactors

During their December 10-11, 2013, workshop in Grenoble France, which focused on the history and future of safeguarding research reactors, the United States, France and the United Kingdom (UK) agreed to conduct a joint study exploring ways to strengthen the IAEA’s safeguards approach for declared research reactors. This decision was prompted by concerns about: 1) historical cases of non-compliance involving misuse (including the use of non-nuclear materials for production of neutron generators for weapons) and diversion that were discovered, in many cases, long after the violations took place and as part of broader pattern of undeclared activities in half a dozen countries; 2) the fact that, under the Safeguards Criteria, the IAEA inspects some reactors (e.g., those with power levels under 25 MWt) less than once per year; 3) the long-standing precedent of States using heavy water research reactors (HWRR) to produce plutonium for weapons programs; 4) the use of HEU fuel in some research reactors; and 5) various technical characteristics common to some types of research reactors that could provide an opportunity for potential proliferators to misuse the facility or divert material with low probability of detection by the IAEA. In some research reactors it is difficult to detect diversion or undeclared irradiation. In addition, infrastructure associated with research reactors could pose a safeguards challenge. To strengthen the effectiveness of safeguards at the State level, this paper advocates that the IAEA consider ways to focus additional attention and broaden its safeguards toolbox for research reactors. This increase in focus on the research reactors could begin with the recognition that the research reactor (of any size) could be a common path element on a large number of technically plausible pathways that must be considered when performing acquisition pathway analysis (APA) for developing a State Level Approach (SLA) and Annual Implementation Plan (AIP). To broaden the IAEA safeguards toolbox, the study recommends that the Agency consider closing potential gaps in safeguards coverage by, among other things: 1) adapting its safeguards measures based on a case-by-case assessment; 2) using more frequent and expanded/enhanced mailbox declarations (ideally with remote transmission of the data to IAEA Headquarters in Vienna) coupled with short-notice or unannounced inspections; 3) putting more emphasis on the collection and analysis of environmental samples at hot cells and waste storage tanks; 4) taking Safeguards by Design into account for the construction of new research reactors and best practices for existing research reactors; 5) utilizing fully all legal authorities to enhance inspection access (including a strengthened and continuing DIV process); and 6) utilizing new approaches to improve auditing activities, verify reactor operating data history, and track/monitor the movement and storage of spent fuel.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Hyperdimensional Imaging Contrast Using an Optical Fiber

Fluorescence properties of a molecule can be used to study the structural and functional nature of biological processes. Physical properties, including fluorescence lifetime, emission spectrum, emission polarization, and others, help researchers probe a molecule, produce desired effects, and infer causes and consequences. Correlative imaging techniques such as hyperdimensional imaging microscopy (HDIM) combine the physical properties and biochemical states of a fluorophore. Here we present a fiber-based imaging system that can generate hyper-dimensional contrast by combining multiple fluorescence properties into a single fluorescence lifetime decay curve. Fluorescence lifetime imaging microscopy (FLIM) with controlled excitation polarization and temporally dispersed emission can generate a spectrally coded, polarization-filtered lifetime distribution for a pixel. This HDIM scheme generates a better contrast between different molecules than that from individual techniques. This setup uses only a single detector and is simpler to implement, modular, cost-efficient, and adaptable to any existing FLIM microscope. We present higher contrast data from Arabidopsis thaliana epidermal cells based on intrinsic anthocyanin emission properties under multiphoton excitation. This work lays the foundation for an alternative hyperdimensional imaging system and demonstrates that contrast-based imaging is useful to study cellular heterogeneity in biological samples.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

SU-E-J-191: Automated Detection of Anatomic Changes in H'N Patients

Purpose: To develop a novel statistics-based method for automated detection of anatomical changes using cone-beam CT data. A method was developed that can provide a reliable and automated early warning system that enables a “just-in-time” adaptation of the treatment plan. Methods: Anatomical changes were evaluated by comparing the original treatment planning CT with daily CBCT images taken prior treatment delivery. The external body contour was computed on a given CT slice and compared against the corresponding contour on the daily CBCT. In contrast to threshold-based techniques, a statistical approach was employed to evaluate the difference between the contours using a given confidence level. The detection tool used the two-sample Kolmogorov-Smirnov test, which is a non-parametric technique that compares two samples drawn from arbitrary probability distributions. 11 H'N patients were retrospectively selected from a clinical imaging database with a total of 186 CBCT images. Six patients in the database were confirmed to have anatomic changes during the course of radiotherapy. Five of the H'N patients did not have significant changes. The KS test was applied to the contour data using a sliding window analysis. The confidence level of 0.99 was used to moderate false detection. Results: The algorithm was able to correctly detect anatomical changes in 6 out of 6 patients with an excellent spatial accuracy as early as at the 14th elapsed day. The algorithm provided a consistent and accurate delineation of the detected changes. The output of the anatomical change tool is easy interpretable, and can be shown overlaid on a 3D rendering of the patient's anatomy. Conclusion: The detection method provides the basis for one of the key components of Adaptive Radiation Therapy. The method uses tools that are readily available in the clinic, including daily CBCT imaging, and image co-registration facilities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Dimension-adaptive machine learning-based quantum state reconstruction

Here, we introduce an approach for performing quantum state reconstruction on systems of n qubits using a machine learning-based reconstruction system trained exclusively on m qubits, where m ≥ n. This approach removes the necessity of exactly matching the dimensionality of a system under consideration with the dimension of a model used for training. We demonstrate our technique by performing quantum state reconstruction on randomly sampled systems of one, two, and three qubits using machine learning-based methods trained exclusively on systems containing at least one additional qubit. The reconstruction time required for machine learning-based methods scales significantly more favorably than the training time; hence this technique can offer an overall saving of resources by leveraging a single neural network for dimension-variable state reconstruction, obviating the need to train dedicated machine learning systems for each Hilbert space.

42 ENGINEERING↗
DOE OSTI2014

An efficient Bayesian inversion of a geothermal prospect using a multivariate adaptive regression spline method

In this study, an efficient Bayesian framework equipped with a multivariate adaptive regression spline (MARS) technique is developed to alleviate computational burdens encountered in a conventional Bayesian inversion of a geothermal prospect. Fast MARS models are developed from training dataset generated by CPU-intensive hydrothermal models and used as surrogate of high-fidelity physical models in Markov Chain Monte Carlo (MCMC) sampling. This Bayesian inference with MARS-enabled MCMC method is used to reduce prior estimates of uncertainty in structural or characteristic hydrothermal flow parameters of the model to posterior distributions. A geothermal prospect near Superstition Mountain in Imperial County of California in USA is used to illustrate the proposed framework and demonstrate the computational efficiency of MARS-based Bayesian inversion. The developed MARS models are also used to efficiently drive calculation of Sobol’ total sensitivity indices. Furthermore, only top sensitive parameters are included in Bayesian inference to further improve the computational efficiency of inversion. Sensitivity analysis also confirms that water circulation through high permeable structures, rather than heat conduction through impermeable granite, is the primary heat transfer method. The presented framework is demonstrated an efficient tool to update knowledge of geothermal prospects by inversing field data. Although only thermal data is used in this study, other type of data, such as flow and transport observations, can be jointly used in this method for underground hydrocarbon reservoirs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Sequential ensemble transform for Bayesian inverse problems

In this work, we present the Sequential Ensemble Transform (SET) method, an approach for generating approximate samples from a Bayesian posterior distribution. The method explores the posterior distribution by solving a sequence of discrete optimal transport problems to produce a series of transport plans which map prior samples to posterior samples. We prove that the sequence of Dirac mixture distributions produced by the SET method converges weakly to the true posterior as the sample size approaches infinity. Furthermore, our numerical results indicate that, when compared to standard Sequential Monte Carlo (SMC) methods, the SET approach is more robust to the choice of Markov mutation kernels and requires less computational efforts to reach a similar accuracy when used to explore complex posterior distributions. Finally, we describe adaptive schemes that allow to completely automate the use of the SET method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Wavefront Preservation in Soft X-Ray Beamlines for the Advanced Light Source Upgrade

In the 4th generation of X-ray light sources, including Free-Electron Lasers (FEL) and Diffraction-Limited Storage Rings (DLSR), the emittance of the electron beam is small or negligible compared to the emittance of the photon beam produced by insertion devices, thus generating coherent beams of light. Over the next few years, several lightsource facilities will be built or upgraded to produce X-ray beams of unprecedented coherent flux. Delivering these exceptional beams to the sample chambers requires beamline optical systems that are designed to preserve the wavefront properties. Our goal in the Advanced Light Source upgrade project (ALS-U) is to create a new class of beamlines that reach diffraction-limited X-ray optical quality through tightly integrated, efficient optical systems, diagnostics, and adaptive mirrors, thus providing users with new scientific capabilities offered by coherent X-ray light interactions with matter. Herein we describe aspects of the beamlines’ design as they enter the project’s final design phase.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Luminous red galaxies: selection and classification by combining optical and infrared photometry

We describe a new method of combining optical and infrared photometry to select luminous red galaxies (LRGs) at redshifts z>0.6. We explore this technique using a combination of optical photometry from CFHTLS and Hubble Space Telescope, infrared photometry from the Wide-Field Infrared Survey Explorer satellite, and spectroscopic or photometric redshifts from the DEEP2 Galaxy Redshift Survey or COSMOS. We present a variety of methods for testing the success of our selection and present methods for optimization given a set of rest-frame color and redshift requirements. We have tested this selection in two different regions of the sky, the COSMOS and Extended Groth Strip (EGS) fields, to reduce the effect of cosmic/sample variance. We have used these methods to assemble large samples of LRGs for two different ancillary programs as a part of the Sloan Digital Sky Survey (SDSS)-III/Baryon Oscillation Spectroscopic Survey. This technique is now being used to select ∼600,000 LRG targets for SDSS-IV/Extended Baryon Oscillation Spectroscopic Survey, which began observations in Fall 2014, and will be adapted for the proposed Dark Energy Spectroscopic Instrument survey. We have found that these methods can select high-redshift LRGs efficiently with minimal stellar contamination; this is extremely difficult to achieve with selections that rely on optical photometry alone.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Complete genome sequence of Desulfurivibrio alkaliphilus strain AHT2 T , a haloalkaliphilic sulfidogen from Egyptian hypersaline alkaline lakes

Desulfurivibrio alkaliphilus strain AHT2 T is a strictly anaerobic sulfidogenic haloalkaliphile isolated from a composite sediment sample of eight hypersaline alkaline lakes in the Wadi al Natrun valley in the Egyptian Libyan Desert. D. alkaliphilus AHT2 T is Gram-negative and belongs to the family Desulfobulbaceae within the Deltaproteobacteria . Here we report its genome sequence, which contains a 3.10 Mbp chromosome. Here, D. alkaliphilus AHT2 T is adapted to survive under highly alkaline and moderately saline conditions and therefore, is relevant to the biotechnology industry and life under extreme conditions. For these reasons, D. alkaliphilus AHT2 T was sequenced by the DOE Joint Genome Institute as part of the Community Science Program.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Acute Exposure to Aerosolized Nanoplastics Modulates Redox-Linked Immune Responses in Human Airway Epithelium

Micro- and nanoplastics (MPs and NPs) are pervasive environmental pollutants detected in aquatic ecosystems, with emerging evidence suggesting their presence in airborne particles generated by water body motion. Inhalation exposure to airborne MPs and NPs remains understudied despite documented links between occupational exposure to these particles and adverse respiratory outcomes, including airway inflammation, oxidative stress, and chronic respiratory diseases. This study explored the effects of acute NP exposure on a fully differentiated 3D human airway epithelial model derived from 14 healthy donors. Airway epithelium was exposed to aerosolized 50 nm polystyrene NPs at concentrations ranging from 2.5 to 2500 µg/mL for three minutes per day over three days. Functional assays revealed no significant alterations in tissue integrity, cell survival, mucociliary clearance, or cilia beat frequency, suggesting intact epithelial function post-exposure. However, cytokine and chemokine profiling identified a significant five-fold increase in CCL3 (MIP-1α), a neutrophilic chemoattractant, in NP-exposed samples compared to controls. This was corroborated by increased neutrophil chemotaxis in response to conditioned media from NP-exposed tissues, indicating a pro-inflammatory neutrophilic response. Conversely, levels of interleukins (IL-21, IL-2, IL-15), CXCL10, and TGF-β were significantly reduced, suggesting immunomodulatory effects that may impair adaptive immune responses and tissue repair mechanisms. These findings demonstrate that short-term exposure to NP-containing aerosols induces a distinct pro-inflammatory response in airway epithelium, characterized by enhanced neutrophil recruitment and reduced secretion of key immune modulators. These findings underscore the potential for aerosolized NPs to induce oxidative and inflammatory stress, raising concerns about their long-term impact on respiratory health and redox regulation.

Biochemistry & Molecular Biology↗
NASA NTRS1989

Neuromorphic learning of continuous-valued mappings in the presence of noise: Application to real-time adaptive control

The ability of feed-forward neural net architectures to learn continuous-valued mappings in the presence of noise is demonstrated in relation to parameter identification and real-time adaptive control applications. Factors and parameters influencing the learning performance of such nets in the presence of noise are identified. Their effects are discussed through a computer simulation of the Back-Error-Propagation algorithm by taking the example of the cart-pole system controlled by a nonlinear control law. Adequate sampling of the state space is found to be essential for canceling the effect of the statistical fluctuations and allowing learning to take place.

Troudet, Terry↗
NASA NTRS1994

Massively Parallel Algorithms for Real-Time Wavefront Control of a Dense Adaptive Optics System

In this paper massively parallel algorithms and architectures for real-time wavefront control of a dense adaptive optic system (SELENE) are presented. We have already shown that the computation of a near optimal control algorithm for SELENE can be reduced to the solution of a discrete Poisson equation on a regular domain. Although this represents an optimal computation, due the large size of the system and the high sampling rate requirement, the implementation of this control algorithm poses a computationally challenging problem since it demands a sustained computational throughput of the order of 10 GFlops. We develop a novel algorithm, designated as Fast Invariant Imbedding algorithm, which offers a massive degree of parallelism with simple communication and synchronization requirements. Due to these features, our algorithm is significantly more efficient than other Fast Poisson Solvers for implementation on massively parallel architectures.

massively↗
DOE OSTI2020

Safeguards Technical Objectives for Thorium Molten Salt Reactor Fuel Cycles

China, Denmark, France, India, Indonesia, the United Kingdom, and the United States, among others, are exploring commercial thorium fuel cycles for domestic power production and/or international export. For any thorium fuel cycle facilities built and/or operated in a non-nuclear weapon State with a comprehensive safeguards agreement, those facilities and any associated nuclear material would be subject to international nuclear safeguards by the International Atomic Energy Agency (IAEA). Thorium fuel cycle facilities offered for the application of safeguards under a voluntary offer agreement by a nuclear weapon State would also be subject to safeguards, if selected by the IAEA. Compared to uranium/plutonium fuel cycle facilities, thorium fuel cycle facilities are comparatively diverse from an inspection and accounting perspective, differing greatly in material type, form, and design from uranium/plutonium fuel cycle facilities and also from each other. Of these, the most complex thorium fuel cycles – and therefore likely the most challenging to the implementation of international nuclear safeguards – are those that include thorium molten salt reactors. With thorium fuel cycles approaching near-term commercial deployment timelines, it is imperative that the international nuclear safeguards community begins to think about how to adapt existing international nuclear safeguards concepts and approaches to thorium fuel cycles, as well as mitigate any gaps that may exist. This paper focuses on the development of recommended safeguards technical objectives for efficient and effective application of international nuclear safeguards measures (e.g., containment / surveillance, non-destructive assay, destructive assay, environmental sampling) to thorium molten salt reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection

Abstract Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present a universal domain adaptation method, DeepAstroUDA , as an approach to overcome this challenge. This algorithm performs semi-supervised domain adaptation (DA) and can be applied to datasets with different data distributions and class overlaps. Non-overlapping classes can be present in any of the two datasets (the labeled source domain, or the unlabeled target domain), and the method can even be used in the presence of unknown classes. We apply our method to three examples of galaxy morphology classification tasks of different complexities (three-class and ten-class problems), with anomaly detection: (1) datasets created after different numbers of observing years from a single survey (Legacy Survey of Space and Time mock data of one and ten years of observations); (2) data from different surveys (Sloan Digital Sky Survey (SDSS) and DECaLS); and (3) data from observing fields with different depths within one survey (wide field and Stripe 82 deep field of SDSS). For the first time, we demonstrate the successful use of DA between very discrepant observational datasets. DeepAstroUDA is capable of bridging the gap between two astronomical surveys, increasing classification accuracy in both domains (up to 40 % on the unlabeled data), and making model performance consistent across datasets. Furthermore, our method also performs well as an anomaly detection algorithm and successfully clusters unknown class samples even in the unlabeled target dataset.

79 ASTRONOMY AND ASTROPHYSICS↗