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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 ( https://www.nfdi4health.de/task-force-covid-19-2 ) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Watching Without Seeing a Tool to Surveil Astronaut Health Outcomes While Maintaining Astronaut Medical Privacy

BACKGROUND The Privacy Act of 1974 regulates the use a nd disclosure of personally identifiable information by US Federal agencies. The Act applies to biographical, financial, a nd other identity-linked information, a s well a s personal health information (PHI). As such, the use of astronaut PHI is limited to authorized personnel for preapproved uses, with data reporting often limited to aggregated information about groups. These limitations on the use a nd reporting of astronaut PHI complicates surveillance efforts, wherein epidemiologists a t the National Aeronautics and Space Administration (NASA)monitor the incidence of targeted health conditions in the astronaut population, or to discover emerging trends of aging and disease. Stratification on one or more covariates –particularly time-period, sex, a nd mission participation –can lead to extremely small datasets such that the reporting of results is potentially attributable to individuals. An additional challenge is the small size of the astronaut population, both in terms of numbers of individuals a s well a s in terms of density of exposure time. Such small datasets yield volatile rate estimates that are difficult to interpret. To a id the epidemiological surveillance efforts, a surveillance tool is required that can (a) satisfy the need for rapid computation of condition-specific incidence and mortality rates; (b) improve the statistical estimates of these estimated rates; and (c) maintain astronaut privacy. Here we describe a nd demonstrate such a tool. METHODS We devised a system that models incidence a nd mortality rates rather than calculating them directly. This ha s the advantage of using all the available data to derive the estimates, lea ding to rates that a re not attributable to any one individual, a nd a re a s numerically stable a s they can be given the extremely limited data. The system models disease endpoints using a Poisson regression model with exposure density (measured in person-years) a s a n offset term. By doing so the model is estimating event counts per person-year, equivalent to modeling the rates directly. It uses a standard (pre-specified)set of covariates; the system does not engage in “model-building” as model parsimony is not the goa l. Instead, it is explicitly recognized that if a covariate is not statistically significant a nd not a confounder then it will likely have very little effect on the estimate of the incidence a nd mortality rates. Users are able to specify the disease endpoint of interest and the covariates over which they would like to stratify. The system then uses the resulting model to compute the estimated rates for the user-chosen configuration of variables as visualizes those either over an age range within a specified time-period, or over time for astronauts with a specified age range. RESULTS The first iteration of the tool computes incidence a nd mortality rates for cardiovascular conditions and cancers. Code ha s been developed to retrieve the appropriate data from the IMPALA analysis platform, compute the models for incidence a nd mortality, a nd then use those models to generate the corresponding rate curves. A companion graphical user interface allows the user to specify the curves and visualize the results. CONCLUSIONS It is important to note that the rapid surveillance tool described here is neither meant to be a definitive assessment of the incidence or mortality of any particular disease or condition in the astronaut population, nor is it meant to be used for research purposes. Rather, it is meant as an early indicator that in-depth investigation may be warranted. By automating a repetitive process and leveraging carefully curated astronaut health outcomes, the tool makes possible a rapid “first look” into known areas of concern, and, if used judiciously, may surface new areas of concern for long-term astronaut health. This work is supported in part by the Translational Research Institute for Space Health (TRISH) through NASA Cooperative Agreement NNX16AO69A.

R J Reynolds↗
NASA NTRS2001

Activities on Space Debris in U.S.

In the U.S. space debris activities are addressed at all government levels, from the Executive Office of the President to the individual federal agencies to specialized centers, laboratories, organizations, and research groups. U.S. Space Policy specifically challenges government agencies to seek to minimize the creation of space debris and to promote debris minimization practices both domestically and internationally. A set of space debris mitigation standard practices has been developed and adopted by relevant US government agencies, and their application by the commercial aerospace community is highly encouraged. A growing number of US government agencies have issued their own space debris mitigation policies, directives, regulations, and standards. Space debris research, including the definition and modeling of the current and future near-Earth space environment and the development of debris protection technologies, is principally conducted by NASA and the Department of Defense. The U.S. Space Surveillance Network continues to provide the most complete and timely characterization of the population of space debris larger than 10 cm. During the past several years major advancements have been achieved in extending this environment definition in LEO to include particles as small as only a few millimeters. The inspection of returned spacecraft surfaces continues to shed light on the even smaller debris population. With improvements in computer technology, new and more capable programs have been and are being developed to solve a number of operational and research problems. Finally, the academic and industrial sectors of the U.S. are also increasing their participation in and contributions to space debris operations and research. The cooperation of satellite and launch vehicle developers and operators is essential to the U.S. objective of promoting the preservation of the space environment for future generations.

Johnson, Nicholas L.↗
NASA NTRS1998

A Modeling Approach to Global Land Surface Monitoring with Low Resolution Satellite Imaging

The effects of changing land use/land cover on global climate and ecosystems due to greenhouse gas emissions and changing energy and nutrient exchange rates are being addressed by federal programs such as NASA's Mission to Planet Earth (MTPE) and by international efforts such as the International Geosphere-Biosphere Program (IGBP). The quantification of these effects depends on accurate estimates of the global extent of critical land cover types such as fire scars in tropical savannas and ponds in Arctic tundra. To address the requirement for accurate areal estimates, methods for producing regional to global maps with satellite imagery are being developed. The only practical way to produce maps over large regions of the globe is with data of coarse spatial resolution, such as Advanced Very High Resolution Radiometer (AVHRR) weather satellite imagery at 1.1 km resolution or European Remote-Sensing Satellite (ERS) radar imagery at 100 m resolution. The accuracy of pixel counts as areal estimates is in doubt, especially for highly fragmented cover types such as fire scars and ponds. Efforts to improve areal estimates from coarse resolution maps have involved regression of apparent area from coarse data versus that from fine resolution in sample areas, but it has proven difficult to acquire sufficient fine scale data to develop the regression. A method for computing accurate estimates from coarse resolution maps using little or no fine data is therefore needed.

Hlavka, Christine A.↗
NASA NTRS2010

Tropospheric Airborne Meteorological Data Reporting (TAMDAR) Sensor Validation and Verification on National Oceanographic and Atmospheric Administration (NOAA) Lockheed WP-3D Aircraft

As part of the National Aeronautics and Space Administration's Aviation Safety and Security Program, the Tropospheric Airborne Meteorological Data Reporting project (TAMDAR) developed a low-cost sensor for aircraft flying in the lower troposphere. This activity was a joint effort with support from Federal Aviation Administration, National Oceanic and Atmospheric Administration, and industry. This paper reports the TAMDAR sensor performance validation and verification, as flown on board NOAA Lockheed WP-3D aircraft. These flight tests were conducted to assess the performance of the TAMDAR sensor for measurements of temperature, relative humidity, and wind parameters. The ultimate goal was to develop a small low-cost sensor, collect useful meteorological data, downlink the data in near real time, and use the data to improve weather forecasts. The envisioned system will initially be used on regional and package carrier aircraft. The ultimate users of the data are National Centers for Environmental Prediction forecast modelers. Other users include air traffic controllers, flight service stations, and airline weather centers. NASA worked with an industry partner to develop the sensor. Prototype sensors were subjected to numerous tests in ground and flight facilities. As a result of these earlier tests, many design improvements were made to the sensor. The results of tests on a final version of the sensor are the subject of this report. The sensor is capable of measuring temperature, relative humidity, pressure, and icing. It can compute pressure altitude, indicated air speed, true air speed, ice presence, wind speed and direction, and eddy dissipation rate. Summary results from the flight test are presented along with corroborative data from aircraft instruments.

Tsoucalas, George↗
NASA NTRS2015

Terminal Sequencing and Spacing (TSS)

The Federal Aviation Administration's (FAA) Next Generation Air Transportation System (or NextGen) is being designed to support the predicted increases in traffic volume and to increase the capacity, efficiency and safety of the National Airspace System (NAS). The Federal Aviation Administration (FAA) identifies Performance-Based Navigation (PBN) as a key enabling capability of NextGen and is actively publishing PBN procedures at major airports throughout the United States. Standard Terminal Arrival Routes (STARs), procedures, and approaches are designed to facilitate fuel-efficient continuous descent operations. However, their use is limited during periods of high traffic demand due to the complexity of merging multiple streams of aircraft to the same airport. As a result, most arrivals in the Terminal Radar Approach Control (TRACON) area continue to be controlled using radar vectoring and step-down descents, resulting in high workload for controllers and diverting aircraft from efficient PBN trajectories. To address this issue, NASA developed the Terminal Sequencing and Spacing (TSS) system, an advanced arrival management technology that combines time-based scheduling and controller-based precision spacing tools. TSS is a ground-based controller automation tool that facilitates sequencing and merging arrivals on Performance-Based Navigation (PBN) routes, especially during highly congested demand periods. The two main components of TSS are: 1) a scheduler that de-conflicts merging arrivals in the terminal area by computing appropriate arrival times to the runway threshold and upstream terminal merge points, and 2) a set of Controller-Managed Spacing (CMS) decision support tools to efficiently assist schedule conformance. Sixteen high-fidelity human-in-the-loop simulations involving more than five hundred hours of evaluation time, were conducted to mature TSS from proof-of-concept design to a fully functional prototype. Results indicate high controller use and acceptability of the CMS tools as well as improved PBN route conformance (Figure 2). The TSS technology was transferred to the FAA in 2014, and it is targeted for deployment to several busy airports in the U.S. starting in 2018. Potential enhancements to TSS using DataComm will also be presented.

controller-managed spacing↗
DOE OSTI2025

FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration

Federated learning (FL) is a distributed machine learning approach that enables multiple local clients and a central server to collaboratively train a model while keeping the data on their own devices. First-order methods, particularly those incorporating variance reduction techniques, are the most widely used FL algorithms due to their simple implementation and stable performance. However, these methods tend to be slow and require a large number of communication rounds to reach the global minimizer. We propose FedOSAA, a novel approach that preserves the simplicity of first-order methods while achieving the rapid convergence typically associated with second-order methods. Our approach applies one Anderson acceleration (AA) step following classical local updates based on first-order methods with variance reduction, such as FedSVRG and SCAFFOLD, during local training. This AA step is able to leverage curvature information from the history points and gives a new update that approximates the Newton-GMRES direction, thereby significantly improving the convergence. We establish a local linear convergence rate to the global minimizer of FedOSAA for smooth and strongly convex loss functions. Numerical comparisons show that FedOSAA substantially improves the communication and computation efficiency of the original first-order methods, achieving performance comparable to second-order methods like GIANT.

Feng, Xue [University of California, Davis]↗
DOE OSTI2020

Opportunities and Challenges from Artificial Intelligence and Machine Learning for the Advancement of Science, Technology, and the Office of Science Missions

In February 2019, the President signed Executive Order 13859, Maintaining American Leadership in Artificial Intelligence. This order launched the American Artificial Intelligence Initiative, a concerted effort to promote and protect AI technology and innovation in the United States. The Initiative implements a government-wide strategy in collaboration and engagement with the private sector, academia, the public, and like-minded international partners. Among other actions, key directives in the Initiative called for Federal agencies to: Prioritize AI research and development investments, Enhance access to high-quality cyberinfrastructure and data, Ensure that the US maintains an international leadership role in the development of technical standards for AI, and Provide education and training opportunities to prepare the American workforce for the new era of AI. The mission of the Department of Energy (DOE) is to ensure America’s security and prosperity by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions. In terms of Science and Innovation, the DOE’s mission is to maintain a vibrant US effort in science and engineering as a cornerstone of our economic prosperity with clear leadership in strategic areas. From July to October in 2019, the Argonne, Oak Ridge, and Berkeley National Laboratories hosted a series of four AI for Science Town Hall meetings in Chicago, Oak Ridge, Berkeley, and Washington DC. The four meetings were attended by over 1300 scientists from the 17 DOE Labs, 39 companies, and over 90 universities. The goal of the Town Hall series was ‘to examine scientific opportunities in the areas of artificial intelligence, Big Data, and high-performance computing (HPC) in the next decade, and to capture the big ideas, grand challenges, and next steps to realizing these.’ The discussions at the meetings were captured in the final report of the AI for Science Town Hall meetings.

42 ENGINEERING↗
NASA NTRS2012

Role of MODIS Vegetation Phenology Products in the ForWarn System for Monitoring of Forest Disturbances in the Conterminous United States

This presentation discusses MODIS vegetation phenology products used in the ForWarn Early Warning System (EWS) tool for near real time regional forest disturbance detection and surveillance at regional to national scales. The ForWarn EWS is being developed by the USDA Forest Service NASA, ORNL, and USGS to aid federal and state forest health management activities. ForWarn employs multiple historical land surface phenology products that are derived from MODIS MOD13 Normalized Difference Vegetation Index (NDVI) data. The latter is temporally processed into phenology products with the Time Series Product Tool (TSPT) and the Phenological Parameter Estimation Tool (PPET) software produced at NASA Stennis Space Center. TSPT is used to effectively noise reduce, fuse, and void interpolate MODIS NDVI data. PPET employs TSPT-processed NDVI time series data as an input, outputting multiple vegetation phenology products at a 232 meter resolution for 2000 to 2011, including NDVI magnitude and day of year products for seven key points along the growing season (peak of growing season and the minima, 20%, and 80% of the peak NDVI for both the left and right side of growing season), cumulative NDVI integral products for the most active part of the growing season and sequentially across the growing season at 8 day intervals, and maximum value NDVI products composited at 24 day intervals in which each product date has 8 days of overlap between the previous and following product dates. MODIS NDVI phenology products are also used to compute nationwide NRT forest change products refreshed every 8 days. These include percent change in forest NDVI products that compare the current NDVI from USGS eMODIS products to historical MODIS MOD13 NDVI. For each date, three forest change products are produced using three different maximum value NDVI baselines (from the previous year, three previous years, and all previous years). All change products are output with a rainbow color table in which forests with the most severe NDVI decreases are assigned hot colors (yellow to red) and forests with prominent NDVI increases are assigned cold colors (blue tones). All mentioned products have been integrated as data layers into ForWarn s geospatial data viewer known as the U.S. Forest Change Assessment Viewer (FCAV). The latter is used to view and assess the context of the mentioned forest change products with respect to ancillary data layers, such as land cover, elevation, hydrologic features, climatic data, storm data, aerial disturbance surveys, fire data, and land ownership. The FCAV also includes a temporal NDVI profiler for viewing phenological change in multi-year NDVI associated with known or suspected regionally apparent forest disturbances (e.g., from fire and insects). ForWarn forest change products have been used to detect, track, and assess several biotic and abiotic regional forest disturbance events across the country, including ephemeral and longer lasting damage from storms, drought, and insects. Such change products are most effective for viewing severe disturbances affecting multiple MODIS pixels. MODIS vegetation phenology products contribute vital current information on forest conditions to the ForWarn system and this role is expected to grow as these products are refined and derivative products are added.

Spruce, Joseph P.↗
NASA NTRS2001

Flight Test Analysis of the Forces and Moments Imparted on a B737-100 Airplane During Wake Vortex Encounters

Aircraft travel has become a major form of transportation. Several of our major airports are operating near their capacity limit, increasing congestion and delays for travelers. As a result, the National Aeronautics and Space Administration (NASA) has been working in conjunction with the Federal Aviation Administration (FAA), airline operators, and the airline industry to increase airport capacity without sacrificing public safety. One solution to the problem is to increase the number of airports and build new. runways; yet, this solution is becoming increasingly difficult due to limited space. A better solution is to increase the production per runway. This solution increases the possibility that one aircraft will encounter the trailing wake of another aircraft. Hazardous wake vortex encounters occur when an aircraft encounters the wake produced by a heavier aircraft. This heavy-load aircraft produces high-intensity wake turbulence that redistributes the aerodynamic loads of trailing smaller aircraft. This situation is particularly hazardous for smaller aircraft during takeoffs and landings. In order to gain a better understanding of the wake-vortex/aircraft encounter phenomena, NASA Langley Research Center conducted a series of flight tests from 1995 through 1997. These tests were designed to gather data for the development a wake encounter and wake-measurement data set with the accompanying atmospheric state information. This data set is being compiled into a database that can be used by wake vortex researchers to compare with experimental and computational results. The purpose of this research is to derive and implement a procedure for calculating the wake-vortex/aircraft interaction portion of that database by using the data recorded during those flight tests. There were three objectives to this research. Initially, the wake-induced forces and moments from each flight were analyzed based on varying flap deflection angles. The flap setting alternated between 15 and 30 degrees while the separation distance remained constant. This examination was performed to determine if increases in flap deflection would increase or decrease the effects of the wake-induced forces and moments. Next, the wake-induced forces and moments from each flight were analyzed based on separation distances of 1-3 nautical miles. In this comparison, flap deflection was held constant at 30 degrees. The purpose of this study was to determine if increased separation distances reduced the effects of the wake vortex on the aircraft. The last objective compared the wake-induced forces and moments of each flight as it executed a series of maneuvers through the wake-vortex. This analysis was conducted to examine the impact of the wake on the B737 as it traversed the wake horizontally and vertically. Results from the first analysis indicated that there was no difference in wake effect at flap deflections of 15 and 30 degrees. This conclusion is evidenced in the cases of the wake-induced sideforce, rolling moment, and yawing moment. The wake-induced lift, drag, and pitching moment cases yielded less conclusive results. The second analysis compared the wake-induced forces and moments at separation distances of 1-3 nautical miles. Results indicated that there was no significant difference in the wake-induced lift, drag, sideforce, or yawing moment coefficients. The analysis compared the wake-induced forces and moments based on different flight maneuvers. It was found that the wake-induced forces and moments had the greatest impact on out-to-in and in-to-out maneuvers.

Roberts, Chistopher L.↗
DOE OSTI2023

A database of synthetic inelastic neutron scattering spectra from molecules and crystals

Abstract Inelastic neutron scattering (INS) is a powerful tool to study the vibrational dynamics in a material. The analysis and interpretation of the INS spectra, however, are often nontrivial. Unlike diffraction, for which one can quickly calculate the scattering pattern from the structure, the calculation of INS spectra from the structure involves multiple steps requiring significant experience and computational resources. To overcome this barrier, a database of INS spectra consisting of commonly seen materials will be a valuable reference, and it will also lay the foundation of advanced data-driven analysis and interpretation of INS spectra. Here we report such a database compiled for over 20,000 organic molecules and over 10,000 inorganic crystals. The INS spectra are obtained from a streamlined workflow, and the synthetic INS spectra are also verified by available experimental data. The database is expected to greatly facilitate INS data analysis, and it can also enable the utilization of advanced analytics such as data mining and machine learning. Notice: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Advances in geophysical forensic event monitoring

Forensic analysis of man-made, non-nuclear events (such as industrial accidents, explosion experiments and mine collapses) has become more frequent and detailed owing to advancements in geophysical monitoring. Here, in this Technical Review, we demonstrate how geophysical forensic monitoring using seismic, infrasound and hydroacoustic recordings provides insights on events in the solid earth, atmosphere and underwater. Advanced techniques, including machine-learning-based models, have been developed to detect, identify and investigate these events, providing information on location, subevents, sources and explosive yield. The increase in data availability, application of advanced methods and computation and the growth of multitechnology approaches have increased the accuracy of forensic event analysis and enabled more realistic characterization of uncertainties. For example, the 2020 Beirut explosion in Lebanon demonstrated that various seismic, acoustic and other methods could be used to estimate explosive yield (and yield uncertainties) of about 1 ktonne, providing confidence in the application of these methods to smaller events where data are available. However, forensic investigations remain largely limited to known events with identified sources. Increased access to data, sophisticated analysis methods and high-resolution earth models will improve forensic event analysis further, enabling civil and scientific applications, such as localization in the search for the lost ARA San Juan submarine.

geophysics↗
NASA NTRS1989

An error-resistant linguistic protocol for air traffic control

The research results described here are intended to enhance the effectiveness of the DATALINK interface that is scheduled by the Federal Aviation Administration (FAA) to be deployed during the 1990's to improve the safety of various aspects of aviation. While voice has a natural appeal as the preferred means of communication both among humans themselves and between humans and machines as the form of communication that people find most convenient, the complexity and flexibility of natural language are problematic, because of the confusions and misunderstandings that can arise as a result of ambiguity, unclear reference, intonation peculiarities, implicit inference, and presupposition. The DATALINK interface will avoid many of these problems by replacing voice with vision and speech with written instructions. This report describes results achieved to date on an on-going research effort to refine the protocol of the DATALINK system so as to avoid many of the linguistic problems that still remain in the visual mode. In particular, a working prototype DATALINK simulator system has been developed consisting of an unambiguous, context-free grammar and parser, based on the current air-traffic-control language and incorporated into a visual display involving simulated touch-screen buttons and three levels of menu screens. The system is written in the C programming language and runs on the Macintosh II computer. After reviewing work already done on the project, new tasks for further development are described.

Cushing, Steven↗
DOE OSTI2024

Considerations for Introducing Artificial Intelligence into Nuclear Power Plants

Advanced computational tools and techniques such as artificial intelligence and machine learning (AI/ML) can transform the nuclear power industry. This is necessary given that the economic viability of the existing fleet is in jeopardy and its labor-centric approach to operations and maintenance. Currently, AI/ML research is being undertaken for reactor system design and analysis including fault and accident prognosis, nuclear risk analysis such as plant safety and security evaluation, and plant operations and maintenance including predictive maintenance. Applications include both existing and advanced reactor technologies with the aim of improving operational and business efficiencies. Most every aspect of the organization can benefit, from instrumentation and control, to work planning, to human-machine interactions and business management. AI/ML in nuclear can simplify complex problems and produce more effective decision-making. Nonetheless, careful consideration must be given to the implementation of an AI/ML initiative. The aims of this research are to 1) review barriers to AI/ML adoption within the nuclear power industry, and 2) suggest potential solutions. These barriers are organized along five distinct categories (Figure 1) that are interconnected. The first are historical barriers that track the industry’s development over the decades including worldwide nuclear events that shaped public perceptions. The resulting federal scrutiny and intense safety culture that emerged are discussed. Technical barriers to AI/ML adoption are considerable, and include data privacy concerns, data governance, and the current lack of AI/ML expert knowledge at the plants. The main business case barrier remains cost, but an absence of an industry-wide vision and wide-scale adoption also produces reluctance. Stakeholder readiness is reviewed with special attention given to regulatory readiness. The 5-year strategic plan for AI readiness recently published by the U.S. Nuclear Regulatory Commission is highlighted. Last, adoption barriers at the user level are addressed including the importance of user experience and explainable AI. The AI adoption barriers described here are inter-related and ideally should be addressed in a holistic fashion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Network Slicing for Federated Learning in Operational Technology Environment

Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) environments are essential to modern infrastructure, facing challenges in ensuring low-latency, high-throughput communication while mitigating cyber threats. This paper presents a framework integrating Federated Learning (FL) and network slicing with Quality of Service (QoS) to enable real-time monitoring without disrupting OT operations. Leveraging digital twin technology and Network Function Virtualization (NFV), the architecture supports predictive analytics and Industry 4.0 requirements. FL facilitates decentralized model training, preserving data privacy and scalability, though it introduces potential throughput constraints. Network slicing addresses this by creating dedicated virtualized segments optimized for performance and security. Advanced fault tolerance at the container and instance levels enhances system reliability. The proposed architecture ensures high throughput, low latency, and secure orchestration for real-time anomaly detection in OT networks. Performance evaluations validate its efficiency in throughput, deployment, and learning accuracy, providing a robust foundation for future ICS automation and data-driven decision-making.

Delgado, Brian G. Rodiles [University of Texas at ↗
DOE OSTI2026

Draft Feasibility Assessment for Use of AI in Preparing Transportation Safety Analysis Reports

Preparing transportation safety analysis reports for microreactors is time and labor intensive, requiring extensive cross referencing to Federal regulations, previously approved documents, and expert review comments across structural, thermal, criticality, shielding, containment, and security. These burdens are magnified by the novelty of microreactor technologies and the evolving regulatory landscape, as well as current workforce constraints. Generative AI and supporting machine learning tools present an opportunity to accelerate drafting timelines, lift generalized writing burdens, and systematically enforce regulatory adherence through retrieval augmented generation and other knowledge retrieval and mapping methods. This draft report presents a preliminary feasibility assessment of the use of AI to expedite the preparation of microreactor transportation safety analysis reports and proposes an initial methodology for doing so.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Vehicle-Cycle Inventory for Medium- and Heavy-Duty Vehicles

The United States has witnessed multiple attempts to improve fuel economy and reduce pollutant emissions in the transportation sector, guided chiefly via technological interventions by original equipment manufacturers (OEMs) and by specific policies of the U.S. Federal Government, like Corporate Average Fuel Economy (CAFE) norms (Burnham et al., 2006; U.S. DoT, 2013). These efforts have resulted in the growing adoption of alternatives to conventional materials, fuels, and vehicle propulsion technologies across various transportation modes, while also helping to lower global pollution (greenhouse gas, or GHG, emissions). Yet, a holistic evaluation of the ecofriendliness of these alternatives, particularly their energy use and emissions, merits detailed focus on their entire life-cycle, and thereby, on the life-cycle of associated transport modes. This is vital since alternative fuels and materials can differ vastly in energy sources and production methods employed for their processing vis-à-vis their existing counterparts — and this difference causes significant variation in their respective upstream emissions.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2002

A Virtual Mission Operations Center - Collaborative Environment

Development of technologies that enable significant reductions in the cost of space mission operations is critical if constellations, formations, federations and sensor webs, are to be economically feasible. One approach to cost reduction is to infuse automation technologies into mission operations centers so that fewer personnel are needed for mission support. But missions are more culturally and politically adverse to the risks of automation. Reducing the mission risk associated with increased use of automation within a MOC is therefore of great importance. The belief that mission risk increases as more automation is used stems from the fact that there is inherently less direct human oversight to investigate and resolve anomalies in an unattended MOC. The Virtual Missions Operations Center - Collaborative Environment (VMOC-CE) project was launched to address this concern. The goal of the VMOC-CE project is to identify, develop, and infuse technology to enable mission operations between onsite operators and on-call personnel in geographically dispersed locations. VMOC-CE enables missions to more readily adopt automation because off-site operators and engineers can more easily identify, investigate, and resolve anomalies without having to be present in the MOC. The VMOC-CE intent is to have a single access point for all resources used in a collaborative mission operations environment. Team members will be able to interact during spacecraft operations, specifically for resolving anomalies, utilizing a desktop computer and the Internet. Mission operations management can use the VMOC-CE as a tool to participate in and monitor status of anomaly resolution or other mission operations issues. In this paper we present the VMOC-CE project, system capabilities and technologies, operations concept, and results of its pilot in support of the Earth Science Mission Operations System (ESMOS).

Medina, Barbara↗