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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Enhancing disease surveillance with novel data streams: challenges and opportunities

Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

An Atlas-Based Electron Density Mapping Method for Magnetic Resonance Imaging (MRI)-Alone Treatment Planning and Adaptive MRI-Based Prostate Radiation Therapy

Purpose: Prostate radiation therapy dose planning directly on magnetic resonance imaging (MRI) scans would reduce costs and uncertainties due to multimodality image registration. Adaptive planning using a combined MRI-linear accelerator approach will also require dose calculations to be performed using MRI data. The aim of this work was to develop an atlas-based method to map realistic electron densities to MRI scans for dose calculations and digitally reconstructed radiograph (DRR) generation. Methods and Materials: Whole-pelvis MRI and CT scan data were collected from 39 prostate patients. Scans from 2 patients showed significantly different anatomy from that of the remaining patient population, and these patients were excluded. A whole-pelvis MRI atlas was generated based on the manually delineated MRI scans. In addition, a conjugate electron-density atlas was generated from the coregistered computed tomography (CT)-MRI scans. Pseudo-CT scans for each patient were automatically generated by global and nonrigid registration of the MRI atlas to the patient MRI scan, followed by application of the same transformations to the electron-density atlas. Comparisons were made between organ segmentations by using the Dice similarity coefficient (DSC) and point dose calculations for 26 patients on planning CT and pseudo-CT scans. Results: The agreement between pseudo-CT and planning CT was quantified by differences in the point dose at isocenter and distance to agreement in corresponding voxels. Dose differences were found to be less than 2%. Chi-squared values indicated that the planning CT and pseudo-CT dose distributions were equivalent. No significant differences (p > 0.9) were found between CT and pseudo-CT Hounsfield units for organs of interest. Mean {+-} standard deviation DSC scores for the atlas-based segmentation of the pelvic bones were 0.79 {+-} 0.12, 0.70 {+-} 0.14 for the prostate, 0.64 {+-} 0.16 for the bladder, and 0.63 {+-} 0.16 for the rectum. Conclusions: The electron-density atlas method provides the ability to automatically define organs and map realistic electron densities to MRI scans for radiotherapy dose planning and DRR generation. This method provides the necessary tools for MRI-alone treatment planning and adaptive MRI-based prostate radiation therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities

An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Roy, Binata [Univ. of Virginia, Charlottesville, V↗
DOE OSTI2021

Misuse Detection for a Generalized SFR Test Reactor

Sodium-cooled Fast Reactors (SFRs) present unique challenges for international safeguards. SFRs possess neutron physics characteristics that if configured appropriately could produce more fissile material than consumed. An adversary state may choose to build an SFR, justified by a lack of domestic natural uranium and limited access or interest to procuring uranium from international markets. Once constructed, the state may choose to misuse the SFR for the purpose of diverting fissile plutonium from declared operation. This work shows that a demonstration SFR does not need to be configured as a plutonium breeder to create one Significant Quantity (SQ) of plutonium in a short amount of time (e.g., one to few years). However, such an extreme case of misuse would change the core reactivity in such a way as to be easily indicated by deviations of control rod position compared to declared operation. In this work a contrived SFR demonstration reactor was modeled for the purpose of exploring proliferation scenarios and how such misuse could be detected using the SFR's Reactor Data Acquisition System (RDAS). Typically, the International Atomic Energy Agency (IAEA) does not have access to the control rod position, power, thermal, pressure sensing and indicating systems of nuclear power plants. However, this work shows that such data streams can be compared against a parallel detailed simulation model (a Digital Twin) to detect possible misuse.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Archival records housed at USTUR support radium dial worker dosimetry

The American radium dial worker (RDW) cohort of over 3200 persons is being revisited as part of the Million Person Study (MPS) to include a modern approach to RDW dosimetry. An exceptional source of data and contextualization in this project is an extensive collection of electronic records (digitized from existing microfilm and microfiche) housed at the United States Transuranium and Uranium Registries (USTUR). Although the type, extent, and quality (e.g. legibility) of record(s) varies between individuals, the remarkable occupational, medical and demographic data include in vivo radiation measurements (e.g. radon breath, whole body counts), autopsy results, medical records (including copies of radiographs), interviews over the years, and correspondence. Of particular dosimetric interest are the details of radiation measurements. For example, there are some instances where hand-written and transcribed values are both available, along with notes providing context for why a particular measurement in a series of measurements was chosen to assign an intake, or if there were concerns about a particular measurement. Born prior to 1935, RDW have nearly all passed away. Thus, the updated dosimetry, especially for the skeletal tissues, will allow the correlation of lifetime cumulative dose with radiation risk. Here we review typical information available in this collection of historical records and highlight some interesting finds. Additionally, we discuss the relevance to current and ongoing work related to updating the dosimetry of the RDW in the MPS, including providing an example of the usefulness of information contained in these records. The RDW cohort provides a unique historical perspective on occupational exposure to radium, making it a valuable dataset for understanding long-term health effects and improving current radiation protection standards.

Million Person Study↗
DOE OSTI2020

Atomic structures determined from digitally defined nanocrystalline regions

Nanocrystallography has transformed our ability to interrogate the atomic structures of proteins, peptides, organic molecules and materials. By probing atomic level details in ordered sub-10 nm regions of nanocrystals, scanning nanobeam electron diffraction extends the reach of nanocrystallography and in principle obviates the need for diffraction from large portions of one or more crystals. Scanning nanobeam electron diffraction is now applied to determine atomic structures from digitally defined regions of beam-sensitive peptide nanocrystals. Using a direct electron detector, thousands of sparse diffraction patterns over multiple orientations of a given crystal are recorded. Each pattern is assigned to a specific location on a single nanocrystal with axial, lateral and angular coordinates. This approach yields a collection of patterns that represent a tilt series across an angular wedge of reciprocal space: a scanning nanobeam diffraction tomogram. Using this diffraction tomogram, intensities can be digitally extracted from any desired region of a scan in real or diffraction space, exclusive of all other scanned points. Intensities from multiple regions of a crystal or from multiple crystals can be merged to increase data completeness and mitigate missing wedges. It is demonstrated that merged intensities from digitally defined regions of two crystals of a segment from the OsPYL/RCAR5 protein produce fragment-based ab initio solutions that can be refined to atomic resolution, analogous to structures determined by selected-area electron diffraction. In allowing atomic structures to now be determined from digitally outlined regions of a nanocrystal, scanning nanobeam diffraction tomography breaks new ground in nanocrystallography.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep learning methods for obtaining photometric redshift estimations from images

ABSTRACT Knowing the redshift of galaxies is one of the first requirements of many cosmological experiments, and as it is impossible to perform spectroscopy for every galaxy being observed, photometric redshift (photo-z) estimations are still of particular interest. Here, we investigate different deep learning methods for obtaining photo-z estimates directly from images, comparing these with ‘traditional’ machine learning algorithms which make use of magnitudes retrieved through photometry. As well as testing a convolutional neural network (CNN) and inception-module CNN, we introduce a novel mixed-input model that allows for both images and magnitude data to be used in the same model as a way of further improving the estimated redshifts. We also perform benchmarking as a way of demonstrating the performance and scalability of the different algorithms. The data used in the study comes entirely from the Sloan Digital Sky Survey (SDSS) from which 1 million galaxies were used, each having 5-filtre (ugriz) images with complete photometry and a spectroscopic redshift which was taken as the ground truth. The mixed-input inception CNN achieved a mean squared error (MSE) =0.009, which was a significant improvement ($30{{\ \rm per\ cent}}$) over the traditional random forest (RF), and the model performed even better at lower redshifts achieving a MSE = 0.0007 (a $50{{\ \rm per\ cent}}$ improvement over the RF) in the range of z < 0.3. This method could be hugely beneficial to upcoming surveys, such as Euclid and the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), which will require vast numbers of photo-z estimates produced as quickly and accurately as possible.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Recovery of coincident frequency domain multiplexed detector pulses using sequential deconvolution

Multiplexing of radiation detector signals into a single channel significantly reduces the need for a large number of digitizer channels, which reduces the cost and the power consumption of a data acquisition system. We previously demonstrated frequency domain multiplexing by convolution using a prototype system that multiplexed two EJ-309 organic scintillators signals into a single channel. Each detector pulse was converted to a damped sinusoid which was then combined into a single channel. The combined signal was digitized and the original detector signal was recovered from the damped sinusoid by deconvolution. In this paper, we demonstrate the recovery of multiple detector signals that arrive during the same digitized record via a new sequential deconvolution method. Here, when two detectors produce signals in the same digitized record and their pulses do not overlap in time, we found that the charge, arrival time, and particle type can be estimated fairly precisely for the first pulse, but the second pulse exhibits substantial degradation in the precision of the estimated charge and arrival time. When the pulses overlap in time, we demonstrate both theoretically and experimentally that the part of the first pulse that does not overlap with the second can be recovered accurately, so the arrival time and amplitude of the first pulse can be estimated fairly precisely, but not the charge or particle type. None of these quantities can be estimated precisely for the second pulse when the two pulses overlap.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Universal Workflow Language and Software Enable Geometric Learning and FAIR Scientific Protocol Reporting

Written language and conventional data structures for representing scientific procedures suffer from low process detail, often fail to accurately represent protocols, and lack universality. New strategies for the handling of experimental data are needed to provide viable process information for both humans and machines. In this work, we present the universal workflow language (UWL) and interface (UWLi). UWL is a findable, accessible, interoperable, and reusable (FAIR)-compatible, graph-based data architecture that can capture arbitrary scientific procedures through workflow representation, and UWLi is an accompanying software package for building, manipulating, and interpreting UWL entries. The UWL format was found to be highly effective in identifying deficiencies in the reported process details of high-impact, peer-reviewed scientific journals, and in simulated scenarios, the graph format was shown to be more effective than conventional methods in predictively modeling the outcome of diverse scientific protocols. Implementation of UWL could enable more accurate scientific communication and more impactful process datasets.

14 SOLAR ENERGY↗
DOE OSTI2022

Self-Sensing, Stretchable, Active Circuit Arrays: Liquid Metal Paste as a Combination Interconnect and Strain Sensor

Stretchable electrical interconnects and substrate materials are increasingly used to create flexible and con-formable electronic systems for soft robotics, smart textiles, and wearable devices. Wires fabricated using composite or liquid conductive inks encapsulated in silicone enable high strain and are mechanically robust. The resistance of these inter-connects increases with strain, which can present challenges for power delivery and signaling among electronic components. However, this effect can be used directly to estimate strain within a stretchable circuit without the need for additional sensor components. In this work, we present an approach for fabricating fully-stretchable, multi-layer active circuit arrays in silicone using liquid metal paste interconnects, in which power and data wires are re-purposed as strain sensors to estimate substrate deformation. As a proof-of-principle, we demonstrate a 3×3 active circuit array that self-senses relative voltage supply at each node to estimate deformation and consumes less than 10 mA per node. Each node is digitally addressable and performs both analog measurement and digital conversion. All interconnects are fabricated using liquid metal paste, and power, data, and sensing are performed over a shared, four-wire interface in this multi-layer stretchable printed circuit board.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Self-supervised Representation Learning for Astronomical Images

Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity. We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks. These representations can be directly used as features, or fine-tuned, to outperform supervised methods trained only on labeled data. We apply a contrastive learning framework on multiband galaxy photometry from the Sloan Digital Sky Survey (SDSS), to learn image representations. We then use them for galaxy morphology classification and fine-tune them for photometric redshift estimation, using labels from the Galaxy Zoo 2 data set and SDSS spectroscopy. In both downstream tasks, using the same learned representations, we outperform the supervised state-of-the-art results, and we show that our approach can achieve the accuracy of supervised models while using 2-4 times fewer labels for training. The codes, trained models, and data can be found at https://portal.nersc.gov/project/dasrepo/self-supervised-learning-sdss.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Identifying multiscale zonation and assessing the relative importance of polygon geomorphology on carbon fluxes in an Arctic tundra ecosystem

Abstract We develop a multiscale zonation approach to characterize the spatial variability of Arctic polygonal ground geomorphology and to assess the relative controls of these elements on land surface and subsurface properties and carbon fluxes. Working within an ice wedge polygonal region near Barrow, Alaska, we consider two scales of zonation: polygon features (troughs, centers, and rims of polygons) that are nested within different polygon types (high, flat, and low centered). In this study, we first delineated polygons using a digital elevation map and clustered the polygons into four types along two transects, using geophysical and kite‐based landscape‐imaging data sets. We extrapolated those data‐defined polygon types to all the polygons over the study site, using the polygon statistics extracted from the digital elevation map. Based on the point measurements, we characterized the distribution of vegetation, hydrological, thermal, and geochemical properties, as well as carbon fluxes, all as a function of polygon types and polygon features. Results show that nested polygon geomorphic zonation—polygon types and polygon features—can be used to represent distinct distributions of carbon fluxes and associated properties, as well as covariability among those properties. Importantly, the results indicate that polygon types have more power to explain the variations in those properties than polygon features. The approach is expected to be useful for improved system understanding, site characterization, and parameterization of numerical models aimed at predicting ecosystem feedbacks to the climate.

Wainwright, Haruko M.↗
DOE OSTI2012

Digital discrimination of neutrons and gamma-rays in organic scintillation detectors using moment analysis

Digital discrimination of neutron and gamma-ray events in an organic scintillator has been investigated by moment analysis. Signals induced by an americium-beryllium (Am/Be) isotropic neutron source in a stilbene crystal detector have been sampled with a flash analogue-to-digital converter (ADC) of 1 GSamples/s sampling rate and 10-bit vertical resolution. Neutrons and gamma-rays have been successfully discriminated with a threshold corresponding to gamma-ray energy about 217 keV. Moment analysis has also been verified against the results assessed by a time-of-flight (TOF) measurement. It is shown that the classification of neutrons and gamma-rays afforded by moment analysis is consistent with that achieved by digital TOF measurement. This method has been applied to analyze the data acquired from the stilbene crystal detector in mixed radiation field of the HL-2A tokamak deuterium plasma discharges and the results are described.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

TURBO: Terabits/s Using Reconfigurable Bandwidth Optics (Final Report)

Large scale, big science applications are generating petabytes of data that need to be shared among multiple locations. Moving a petabyte of data at 100 Gb/s takes 22 hours. Reducing this to single digit hours or below is clearly attractive and with future applications anticipating exabyte scale transfers, multi-terabit per second capacities are needed. This scale of capacity is available using optical systems. Core optical systems have aggregate per fiber capacities on the order of 10 Tb/s and more than 100 Tb/s system experiments have been realized in the lab. But large optical capacities are not just reserved for the core as similar system capacities are available in metro networks right up to the enterprise, campus, or data center. In general, the same technology is used today in the metro area as in the long-haul networks—reconfigurable optical add drop multiplexing (ROADM) node-based wavelength division multiplexing (WDM) systems. Data centers already support massive internal capacities on the scale of 100’s of Tb/s. As new photonic integrated technologies mature, for example through initiatives such as the Integrated Photonics Innovative Manufacturing Institute (IP-IMI), the cost of optical interfaces is expected to decrease, and higher capacity and higher performance interfaces will become more affordable. This set of circumstances creates the potential that Tb/s capacities will be available at the enterprise and campus level to support large scale science network applications.

42 ENGINEERING↗
DOE OSTI2020

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra: Supporting Data

Remote sensing data collected from Brookhaven National Laboratory's (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform - the Osprey - operated by the Terrestrial Ecosystem Science and Technology (TEST) group. Data was collected from a single flight over the Kougarok hillslope site on 26 July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface "skin" temperature imagery, as well as surface reflectance at 1nm intervals in the visible to near-infrared spectral range from ~350-1000 nm measured at regular intervals along each flight path. Derived image products include ortho-mosaiced RGB and TIR images, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, digital terrain model (DTM), and a canopy height model. Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. This dataset includes *.pdf, *.txt, *.tif, *.dat, and *.csv with most zipped in *.tar.gz files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data Readiness for AI: A 360-Degree Survey

Artificial Intelligence (AI) applications critically depend on data. Poor-quality data produces inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Evaluation of data readiness is a crucial step in improving the quality and appropriateness of data usage for AI. R&D efforts have been spent on improving data quality. However, standardized metrics for evaluating data readiness for use in AI training are still evolving. In this study, we perform a comprehensive survey of metrics used to verify data readiness for AI training. This survey examines more than 140 papers published by ACM Digital Library, IEEE Xplore, journals such as Nature, Springer, and Science Direct, and online articles published by prominent AI experts. This survey aims to propose a taxonomy of data readiness for AI (DRAI) metrics for structured and unstructured datasets. We anticipate that this taxonomy will lead to new standards for DRAI metrics that would be used for enhancing the quality, accuracy, and fairness of AI training and inference.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Brochure for the DOE Office of Science Workshop on Envisioning Frontiers in AI and Computing for Biological Research

In February of 2025 a joint ASCR/BER workshop was held to identify key transformational research directions for understanding biology using artificial intelligence (AI), digital twins and high-performance (HPC) computational methods to facilitate scientific discovery and innovation in support of the Department of Energy mission. AI technologies offer exciting new groundbreaking methods to analyze large volumes of complex biological data, thereby greatly accelerating the ability to understand, predict, and design biological processes for beneficial purposes. In the laboratory, the bridging of AI-enabled automated experimental technologies, HPC and digital twins will provide potent tools for researchers to explore the fundamental nature of biology and harness its inherent metabolic potential for a variety of beneficial purposes. The focus of this workshop was on how high-performance computational methods can impact this objective by exploring digital twins, foundational models, and data-driven approaches with applications to advance automated laboratory experiments, modeling of complex living systems and engineering new functions into plants and microbial systems relevant to DOE mission. Workshop attendees with expertise in plant science, microbiology, mathematics, computer science, and AI assessed the current state of the science, trends, and AI challenges at the interface of plant and microbial systems biology and computational science to identify opportunities for high-impact research. This collaborative effort capitalized on ASCR's advancements in applied mathematics, computer science, and Exascale systems, and BER's expertise in basic genomics-enabled research on DOE relevant plant and microbial systems. The workshop culminated in four key priority research directions to guide future research and development within DOE Office of Science programs.

59 BASIC BIOLOGICAL SCIENCES↗