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DOE OSTI2026

JetGP: A derivative enhanced Gaussian process library

Derivative enhanced Gaussian Processes (DEGPs) can significantly improve surrogate model accuracy over standard Gaussian Process (GP) formulations by incorporating derivative information. However, standard implementations scale poorly with dimension, limiting their use in high dimensional engineering problems. JetGP is a Python framework that unifies existing derivative enhanced GP methodologies into a single library and extends them to support arbitrary order derivative information. The library implements four complementary formulations: standard derivative enhanced Gaussian Processes (DEGP), directional DEGP (DDEGP), generalized directional DEGP (GDDEGP), and weighted DEGP (WDEGP). By unifying these approaches in a consistent interface with robust numerical implementations, JetGP enables practitioners to balance predictive accuracy and computational efficiency for high dimensional optimization, uncertainty quantification, and sensitivity analysis in engineering design.

Derivative enhanced Gaussian process↗

NASA NTRS2012

Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation

At the request of NASA, the National Research Council's (NRC's) Committee for Evaluation of Space Radiation Cancer Risk Model1 reviewed a number of changes that NASA proposes to make to its model for estimating the risk of radiation-induced cancer in astronauts. The NASA model in current use was last updated in 2005, and the proposed model would incorporate recent research directed at improving the quantification and understanding of the health risks posed by the space radiation environment. NASA's proposed model is defined by the 2011 NASA report Space Radiation Cancer Risk Projections and Uncertainties--2010 . The committee's evaluation is based primarily on this source, which is referred to hereafter as the 2011 NASA report, with mention of specific sections or tables. The overall process for estimating cancer risks due to low linear energy transfer (LET) radiation exposure has been fully described in reports by a number of organizations. The approaches described in the reports from all of these expert groups are quite similar. NASA's proposed space radiation cancer risk assessment model calculates, as its main output, age- and gender-specific risk of exposure-induced death (REID) for use in the estimation of mission and astronaut-specific cancer risk. The model also calculates the associated uncertainties in REID. The general approach for estimating risk and uncertainty in the proposed model is broadly similar to that used for the current (2005) NASA model and is based on recommendations by the National Council on Radiation Protection and Measurements. However, NASA's proposed model has significant changes with respect to the following: the integration of new findings and methods into its components by taking into account newer epidemiological data and analyses, new radiobiological data indicating that quality factors differ for leukemia and solid cancers, an improved method for specifying quality factors in terms of radiation track structure concepts as opposed to the previous approach based on linear energy transfer, the development of a new solar particle event (SPE) model, and the updates to galactic cosmic ray (GCR) and shielding transport models. The newer epidemiological information includes updates to the cancer incidence rates from the life span study (LSS) of the Japanese atomic bomb survivors, transferred to the U.S. population and converted to cancer mortality rates from U.S. population statistics. In addition, the proposed model provides an alternative analysis applicable to lifetime never-smokers (NSs). Details of the uncertainty analysis in the model have also been updated and revised. NASA's proposed model and associated uncertainties are complex in their formulation and as such require a very clear and precise set of descriptions. The committee found the 2011 NASA report challenging to review largely because of the lack of clarity in the model descriptions and derivation of the various parameters used. The committee requested some clarifications from NASA throughout its review and was able to resolve many, but not all, of the ambiguities in the written description.

Source record↗

NASA NTRS2015

2014 Space Radiation Standing Review Panel

The 2014 Space Radiation Standing Review Panel (from here on referred to as the SRP) participated in a WebEx/teleconference with members of the Space Radiation Program Element, representatives from the Human Research Program (HRP), the National Space Biomedical Research Institute (NSBRI), and NASA Headquarters on November 21, 2014 (list of participants is in Section XI of this report). The SRP reviewed the updated Research Plan for the Risk of Cardiovascular Disease and Other Degenerative Tissue Effects from Radiation Exposure (Degen Risk). The SRP also received a status update on the Risk of Acute and Late Central Nervous System Effects from Radiation Exposure (CNS Risk), the Risk of Acute Radiation Syndromes Due to Solar Particle Events (ARS Risk), and the Risk of Radiation Carcinogenesis (Cancer Risk). The SRP thought the teleconference was very informative and that the Space Radiation Program Element did a great job of outlining where the Element is with respect to our state of knowledge on the risks of carcinogenesis, central nervous system effects, and the risk of cardiovascular disease and other degenerative tissue effects from exposure to space radiation. The SRP was impressed with the quality of research that is being conducted and the progress the Space Radiation Program Element has made in the past year. While much work has been done, the SRP had a few remaining questions regarding the broad applicability of these findings to a manned deep space mission (in terms of cognitive function, the paradigms were still hippocampal based and also using Alzheimer disease models). The SRP believes that NASA should consider developing an approach to follow astronauts long-term (beyond retirement) for potential side-effects/risks of space exposure that may be unknown. Radiation toxicities often occur decades after exposure, and potential consequences would be missed if intensified exams stop after retirement of the astronauts. In addition, while cancer is one consequence of radiation exposure that is monitored, potential other side effects (CNS, Alzheimer Disease, loss of cognitive function, etc.) are not included in long-term studies and would be missed. Inclusion of long-term data would be of benefit to the astronauts themselves who have given their service to the corps but also to future astronauts and the future of space exploration.

Steinberg, Susan↗

NASA NTRS2020

Enabling Space Exploration Medical System Development Using a Tool Ecosystem

The NASA Human Research Program’s (HRP) Exploration Medical Capability (ExMC) Element is utilizing a Model Based Systems Engineering (MBSE) approach to enhance the development of systems engineering products that will be used to advance medical system designs for exploration missions beyond Low Earth Orbit. In support of future missions, the team is capturing content such as system behaviors, functional decompositions, architecture, system requirements and interfaces, and recommendations for clinical capabilities and resources in Systems Modeling Language (SysML) models. As these products mature, SysML models provide a way for ExMC to capture relationships among the various products, which includes supporting more integrated and multi-faceted views of future medical systems. In addition to using SysML models, HRP and ExMC are developing supplementary tools to support two key functions: 1) prioritizing current and future research activities for exploration missions in an objective manner; and 2) enabling risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This paper will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include dynamic Probabilistic Risk Assessment (PRA) capabilities, additional SysML models, a database of system component options, and data visualizations. It also includes a review of an initial Pilot Project focused on enabling medical system trade studies utilizing data that is coordinated across tools for consistent outputs (e.g., mission risk metrics that are associated with medical system mass values and medical conditions addressed). This first Pilot Project demonstrated successful operating procedures and integration across tools. Finally, the paper will also cover a second Pilot Project that utilizes tool enhancements such as medical system optimization capabilities, post-processing, and visualization of generated data for subject matter expert review, and increased integration amongst the tools themselves.

Amador, Jennifer R.↗

NASA NTRS2020

Enabling Space Exploration Medical System Development Using a Tool Ecosystem

The NASA Human Research Program's (HRP) Exploration Medical Capability (ExMC) Element is utilizing a Model Based Systems Engineering (MBSE) approach to enhance the development of systems engineering products that will be used to advance medical system designs for exploration missions beyond Low Earth Orbit. In support of future missions, the team is capturing content such as system behaviors, functional decompositions, architecture, system requirements and interfaces, and recommendations for clinical capabilities and resources in Systems Modeling Language (SysML) models. As these products mature, SysML models provide a way for ExMC to capture relationships among the various products, which includes supporting more integrated and multi-faceted views of future medical systems. In addition to using SysML models, HRP and ExMC are developing supplementary tools to support two key functions: 1) prioritizing current and future research activities for exploration missions in an objective manner; and 2) enabling risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This paper will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include dynamic Probabilistic Risk Assessment (PRA) capabilities, additional SysML models, a database of system component options, and data visualizations. It also includes a review of an initial Pilot Project focused on enabling medical system trade studies utilizing data that is coordinated across tools for consistent outputs (e.g., mission risk metrics that are associated with medical system mass values and medical conditions addressed). This first Pilot Project demonstrated successful operating procedures and integration across tools. Finally, the paper will also cover a second Pilot Project that utilizes tool enhancements such as medical system optimization capabilities, post-processing, and visualization of generated data for subject matter expert review, and increased integration amongst the tools themselves.

Amador, Jennifer R.↗

NASA NTRSDate not supplied

Real-Time Considerations for A Source-Time Dominant Auralization Scheme

A well-designed recording system can capture a moving source without risk of distortions, knowledge of the source or path, or transmission of information back to the source (i.e., a smartphone can reasonably record a plane flying overhead). This necessarily happens in real time. It would be good if signal processing schemes for auralization possessed these properties. Recent work on the NoTAP method of auralization proposed an asynchronous sample rate conversion scheme that keeps track of the (nonuniform) rate of incoming samples to formulate an effective incoming sampling frequency. This value allows the method to predict what frequency regions at the receiver are vulnerable to aliasing or imaging artifacts. Strategies of oversampling and filtering can be used to eliminate these problem regions while preserving as much of the original content as possible given the desired receiver sampling frequency. This approach creates a situation where the receiver processing can run independently of the source/path processing making it attractive for real-time implementation. This presentation discusses the challenges associated with producing a truly real-time scheme. A three-way tradeoff emerges between an interpolation mechanism that generates decorrelated noise, the computational burden, and the nearness to absolute real-time with which one wants the scheme to run.

Auralization↗

DOE OSTI2026

American Made Infrastructure: Evolution of Federal Incentives and Requirements

Foreign Entity of Concern (FEOC) restrictions in the One Big Beautiful Bill Act (OBBB) represent the latest evolution of a multi-year legislative trajectory responding to national security concerns about foreign control – and particularly FEOC control – of energy infrastructure. Beginning with Executive Order 14017 (February 2021), which initiated comprehensive federal review of critical supply chain vulnerabilities in semiconductors, battery energy storage systems, and critical minerals, policymakers have progressively expanded restrictions on foreign participation. The National Defense Authorization Act (NDAA) 2019 established precedent for component-level prohibitions on foreign information and communications technology procurement, while NDAA 2024 extended these restrictions to six major People’s Republic of China (PRC) battery manufacturers. Complementary measures such as the Build America, Buy America (BABA) Act and the Infrastructure Investment and Jobs Act (IIJA) introduced domestic content thresholds and FEOC eligibility criteria for federal funding programs. The Inflation Reduction Act (IRA) 2022 further operationalized FEOC restrictions through electric vehicle tax credit requirements, creating a scalable framework for excluding foreign-controlled components. Recent executive actions and state-level policies have reinforced this trajectory, reflecting sustained alignment across federal and state governments. Collectively, these developments demonstrate a bipartisan policy approach that pairs incentives for advanced energy deployment with safeguards designed to prevent subsidizing adversaries or entities that present foreign-sourcing risk.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

A Process-based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily Culex pipiens/restuans Mosquito Density

While the number of human cases of mosquito-borne diseases has increased in North America in the last decade, accurate modeling of mosquito population density has remained a challenge. Longitudinal mosquito trap data over the many years needed for model calibration, and validation is relatively rare. In particular, capturing the relative changes in mosquito abundance across seasons is necessary for predicting the risk of disease spread as it varies from year to year. We developed a discrete, semi-stochastic, mechanistic process-based mosquito population model that captures life-cycle egg, larva, pupa, adult stages, and diapause for Culex pipiens (Diptera, Culicidae) and Culex restuans (Diptera, Culicidae) mosquito populations. This model combines known models for development and survival into a fully connected age-structured model that can reproduce mosquito population dynamics. Mosquito development through these stages is a function of time, temperature, daylight hours, and aquatic habitat availability. The time-dependent parameters are informed by both laboratory studies and mosquito trap data from the Greater Toronto Area. The model incorporates city-wide water-body gauge and precipitation data as a proxy for aquatic habitat. This approach accounts for the nonlinear interaction of temperature and aquatic habitat variability on the mosquito life stages. We demonstrate that the full model predicts the yearly variations in mosquito populations better than a statistical model using the same data sources. This improvement in modeling mosquito abundance can help guide interventions for reducing mosquito abundance in mitigating mosquito-borne diseases like West Nile virus.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Long-Term Impacts of Constrained Transmission Deployment on the Cost-Reliability Tradeoff

Traditional Resource Adequacy (RA) frameworks in the U.S. undervalue the contributions of inter-regional transmission to resource adequacy during stress periods, focusing on the availability of nameplate capacity instead. However, availability of nameplate capacity does not always translate into electricity delivery, especially during tail events. Moreover, the rapid deployment of energy-limited resources and increasing electricity demand challenge existing resource adequacy frameworks and couple regional electricity demand and availability of supply via transmission. We propose a two-stage framework that goes beyond the existing capacity-centered approaches to reveal the RA contributions of transmission. In the first stage we introduce a multi-objective optimization framework to quantify the merits of transmission expansion via Pareto Frontiers under alternative futures of no transmission investment, primary energy resources availability and demand growth. The second stage focuses on tail events and leverages the results of the first stage to characterize the risk profile of regional consumers across the U.S. under the alternative energy futures. We find that no new transmission can lead to a more expensive and less reliable national grid across scenarios, however, the impact on regional RA can vary. The probabilistic analysis reveals that transmission investments can alleviate the tail risk of consumers, however, the availability of fuel resources does not always alleviate regional tail risks. Our findings inform policymakers and utilities on the prioritization of transmission investments to mitigate the risk of widespread outages, also for tail events, and ensure reliable and affordable electricity delivery to all.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2019

New Era, New Opportunity, Is GES DISC Ready for Big Data Challenge?

The new era of Big Data has opened doors for many new opportunities, as well as new challenges, for both Earth science research/application and data communities. As one of the twelve NASA data centers - Goddard Earth Sciences Data and Information Services Center (GES DISC), one of our great challenges has been how to help research/application community efficiently (quickly and properly) accessing, visualizing and analyzing the massive and diverse data in natural hazard research, management, or even prediction. GES DISC has archived over 2000 TB data on premises and distributed over 23,000 TB of data since 2010. Our data has been widely used in every phase of natural hazard management and research, i.e. long term risk assessment and reduction, forecasting and predicting, monitoring and detection, early warning, damage assessment and response. The big data challenge is not just about data storage, but also about data discoverability and accessibility, and even more, about data migration/mirroring in the cloud. This paper is going to demonstrate GES DISC’s efforts and approaches of evolving our overall Web services and powerful Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) tool into further improving data discoverability and accessibility. Prototype works will also be presented.

Li, A.↗

DOE OSTI2026

Poster: Responsible Adoption of Artificial Intelligence (AI) in Electric Grid Operations

The rapid integration of artificial intelligence (AI) in the utility transmission and distribution (T&D) sector is revolutionizing traditional grid management practices. As utilities encounter complexities from evolving consumer behaviors and energy integration, AI becomes a critical solution for enhancing grid monitoring, fault detection, and operational optimization. However, increased reliance on interconnected technologies introduces significant cybersecurity risks, regulatory compliance challenges, and human factors concerns. This study proposes a strategic, responsible and consequence-driven approach to AI implementation, examining the dual nature of AI adoption by highlighting its transformative benefits for utilities and associated risks. It provides utilities with a framework for evaluating AI integration, enabling them to navigate challenges and capitalize on opportunities to achieve greater reliability, efficiency, and resilience in an increasingly complex energy landscape.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Bridging Equipment Reliability Data and Robust Decisions in a Plant Operation Context

In order to reduce operation and maintenance (O&M) costs, nuclear power plants (NPPs) are moving from corrective and periodic maintenance to predictive maintenance strategies. Such transition requires changes on the data that needs to be retrieved and on the type of decision processes to be employed. Advanced monitoring and data analysis technologies are essential to support predictive strategies. They can in fact provide precise information about health of a component, track its degradation trends, and provide information of its expected failure time. With such information, maintenance operations for a component can be performed right before its expected failure time. This dynamic context of O&M operations requires new methods to analyze data, propagate component health information from the component to the system level, and optimize plant resources. In this respect, the risk informed asset management (RIAM) project has been tasked to develop and test this new class of methods into a risk analytics toolset. This toolset consists of data analytics tools coupled with reliability methods designed to manage plant assets and performances in a predictive maintenance context. This report shows the latest improvements on such development and the initial testing of our methods on the three main research areas that the RIAM project is focusing on. These areas are the following: equipment reliability data analytics, system reliability modeling, and plant resources optimization methods. We show how the methods developed in these areas can support predictive maintenance strategies by: 1) analyzing equipment reliability data (either in numeric and textual form), 2) assessing component and system health through an innovative margin-based reliability approach, and 3) identifying the most critical components and set optimal maintenance schedule based on plant economic and operational constraints.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2001

Micro-Power Sources Enabling Robotic Outpost Based Deep Space Exploration

Robotic outpost based exploration represents a fundamental shift in mission design from conventional, single spacecraft missions towards a distributed risk approach with many miniaturized semi-autonomous robots and sensors. This approach can facilitate wide-area sampling and exploration, and may consist of a web of orbiters, landers, or penetrators. To meet the mass and volume constraints of deep space missions such as the Europa Ocean Science Station, the distributed units must be fully miniaturized to fully leverage the wide-area exploration approach. However, presently there is a dearth of available options for powering these miniaturized sensors and robots. This group is currently examining miniaturized, solid state batteries as candidates to meet the demand of applications requiring low power, mass, and volume micro-power sources. These applications may include powering microsensors, battery-backing rad-hard CMOS memory and providing momentary chip back-up power. Additional information is contained in the original extended abstract.

West, W. C.↗

NASA NTRS2019

Method for Tracking and Communicating Aggregate Risk Through the Use of Model-Based Systems Engineering (MBSE) Tools

Large, complex projects can identify a significant number and variety of risks, throughout the project life cycle. These risks are analyzed, mitigated, closed or accepted as independent uncertainties. Once closed or accepted, it is easy for projects to lose awareness of their impact. In reality, each of these risks contributes some amount to the overall risk posture of the project. The ability to track and effectively communicate this aggregate risk has represented a challenge to project management. There have been previous attempts to create a schema to communicate the aggregate effect of risks, without notable success. Most of these attempts have centered on some additive metric derived from the scoring of likelihood and consequence values. This, in and of itself, is a logical approach, but all too often the scores were then aggregated to a level where all context was lost. One weakness has been a lack of attempt to create linkages or logical groups of the risks upon which useful aggregation could then occur. The overall move to model-based (systems) engineering (MBSE) has opened up a vast frontier of opportunities to better integrate all project data. MBSE provides an underlying layer that links data items to each other. Objectives link to requirements, which then link to functions, functions to physical architecture items, and so on, as far down as projects want to model. While it started with a focus on modeling requirements based on things like use cases, efforts are now underway to integrate safety and mission assurance (S&MA) information and analyses, such as risks. This effort, called Model Based Mission Assurance (MBMA), is yielding models that are more useful and are a more accurate representations of the systems. MBSE models, with this ability to link related items, provide a new means of tracking and communicating aggregate risks. In the proposed method, risks are added into the models as distinct items, having attributes that communicate a scoring derived from the likelihood and consequence values as charted on the standard NASA 5x5 risk matrix. Like earlier efforts, each box in the 5x5 has an associated scoring, which may include both a current score and potential post-mitigation/control score. The risk items are then linked to elements of the model, such as system objectives/goals, requirements, functions, or physical architecture items, with "Risk to" relationships. These risks will then be communicated by use of reports generated from the model, detailing all risks and/or hazards linked to model elements. These reports can include aggregate impacts, including a current scoring and potential future state scoring based on the planned mitigations and/or controls. These reports will show all risks, open, accepted, and closed, linked to project objectives or requirements. When run as part of an upcoming risk acceptance discussion, these reports will serve to remind the team of all previous risks that relate to the effected portion of the system. When included as part of periodic program or project reviews, risk reviews, and safety reviews, this method can improve the overall understanding of the system's true risk posture. This proposed method takes full advantage of the advances that modern modeling techniques provide, with a minimal investment of additional time. Utilizing the model environment also enables a near constant access to current state of aggregate risks.

model based mission assurance↗

DOE OSTI2022

Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction

Abstract Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model ( $${\textsc {TransMED}}$$ T R A N S MED ) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of $${\textsc {TransMED}}$$ T R A N S MED ’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. $${\textsc {TransMED}}$$ T R A N S MED ’s superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Seismic Elastic Double-Beam Characterization of Faults and Fractures for CO₂ Storage Site Selection

Site characterization for underground injection and storage of gigatonne-scale CO₂ requires reliable and cost-effective methods to detect and characterize faults and fractures and to assess their stress state and fault activation potential. This is critical, as wastewater injection and disposal have been shown to activate faults and induce earthquakes, and CO₂ leakage remains a key concern for long-term storage. In this project, we developed seismic methods to detect and characterize large-scale sedimentary and crystalline basement faults and associated small-scale fractures below conventional seismic imaging resolution using multicomponent (9C) surface seismic data. Machine learning was used to automatically interpret large-scale faults, providing key information for estimating the maximum magnitude of potential induced earthquakes. High-fidelity imaging was achieved by exploiting redundancy across multiple elastic wave modes, where independent images from different modes and frequencies cross-validate each other. We also used our nonlinear signal comparison (NLSC) method for ground roll removal, improving data quality in complex near-surface conditions. The methods were validated using field data acquired in central Montana. Results show that basement faults extend into the sedimentary section and that small-scale fractures are widespread above the basement. The inferred stress orientation is consistent with regional stress data, and the estimated maximum induced earthquake magnitude is small (Mw ~2.3). The developed workflow provides a practical approach for fault and fracture characterization and for assessing induced seismicity and leakage risk. It is directly applicable to CO₂ storage site selection and to other subsurface systems.

02 PETROLEUM↗

NASA NTRS2007

All Source Solution Decision Support Products Created for Stennis Space Center in Response to Hurricane Katrina

In the aftermath of Hurricane Katrina and in response to the needs of SSC (Stennis Space Center), NASA required the generation of decision support products with a broad range of geospatial inputs. Applying a systems engineering approach, the NASA ARTPO (Applied Research and Technology Project Office) at SSC evaluated the Center's requirements and source data quality. ARTPO identified data and information products that had the potential to meet decision-making requirements; included were remotely sensed data ranging from high-spatial-resolution aerial images through high-temporal-resolution MODIS (Moderate Resolution Imaging Spectroradiometer) products. Geospatial products, such as FEMA's (Federal Emergency Management Agency's) Advisory Base Flood Elevations, were also relevant. Where possible, ARTPO applied SSC calibration/validation expertise to both clarify the quality of various data source options and to validate that the inputs that were finally chosen met SSC requirements. ARTPO integrated various information sources into multiple decision support products, including two maps: Hurricane Katrina Inundation Effects at Stennis Space Center (highlighting surge risk posture) and Vegetation Change In and Around Stennis Space Center: Katrina and Beyond (highlighting fire risk posture).

Ross, Kenton W.↗

NASA NTRS2012

Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation

At the request of NASA, the National Research Council's (NRC's) Committee for Evaluation of Space Radiation Cancer Risk Model reviewed a number of changes that NASA proposes to make to its model for estimating the risk of radiation-induced cancer in astronauts. The NASA model in current use was last updated in 2005, and the proposed model would incorporate recent research directed at improving the quantification and understanding of the health risks posed by the space radiation environment. NASA's proposed model is defined by the 2011 NASA report Space Radiation Cancer Risk Projections and Uncertainties 2010 (Cucinotta et al., 2011). The committee's evaluation is based primarily on this source, which is referred to hereafter as the 2011 NASA report, with mention of specific sections or tables cited more formally as Cucinotta et al. (2011). The overall process for estimating cancer risks due to low linear energy transfer (LET) radiation exposure has been fully described in reports by a number of organizations. They include, more recently: (1) The "BEIR VII Phase 2" report from the NRC's Committee on Biological Effects of Ionizing Radiation (BEIR) (NRC, 2006); (2) Studies of Radiation and Cancer from the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR, 2006), (3) The 2007 Recommendations of the International Commission on Radiological Protection (ICRP), ICRP Publication 103 (ICRP, 2007); and (4) The Environmental Protection Agency s (EPA s) report EPA Radiogenic Cancer Risk Models and Projections for the U.S. Population (EPA, 2011). The approaches described in the reports from all of these expert groups are quite similar. NASA's proposed space radiation cancer risk assessment model calculates, as its main output, age- and gender-specific risk of exposure-induced death (REID) for use in the estimation of mission and astronaut-specific cancer risk. The model also calculates the associated uncertainties in REID. The general approach for estimating risk and uncertainty in the proposed model is broadly similar to that used for the current (2005) NASA model and is based on recommendations by the National Council on Radiation Protection and Measurements (NCRP, 2000, 2006). However, NASA's proposed model has significant changes with respect to the following: the integration of new findings and methods into its components by taking into account newer epidemiological data and analyses, new radiobiological data indicating that quality factors differ for leukemia and solid cancers, an improved method for specifying quality factors in terms of radiation track structure concepts as opposed to the previous approach based on linear energy transfer, the development of a new solar particle event (SPE) model, and the updates to galactic cosmic ray (GCR) and shielding transport models. The newer epidemiological information includes updates to the cancer incidence rates from the life span study (LSS) of the Japanese atomic bomb survivors (Preston et al., 2007), transferred to the U.S. population and converted to cancer mortality rates from U.S. population statistics. In addition, the proposed model provides an alternative analysis applicable to lifetime never-smokers (NSs). Details of the uncertainty analysis in the model have also been updated and revised. NASA's proposed model and associated uncertainties are complex in their formulation and as such require a very clear and precise set of descriptions. The committee found the 2011 NASA report challenging to review largely because of the lack of clarity in the model descriptions and derivation of the various parameters used. The committee requested some clarifications from NASA throughout its review and was able to resolve many, but not all, of the ambiguities in the written description.

Source record↗