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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Dissemination of Global Flood Severity and Surface Water Mapping using Remote Sensing Data to Global Stakeholders

Flooding is a natural event that occurs frequently with high severity worldwide, responsible for significant societal and economic impacts. Disaster managers face significant challenges managing essential information for preparedness, response, and recovery efforts. The development of an open access, global flood alerting system for effective identification of flood impacted areas, classification of potential impacts, and the formulation of effective emergency response measures requires the incorporation of a wide variety of flood models and remote sensing data sources from multiple platforms. NASA is currently funding projects focused on flood forecasting, post-event flood mapping, flood depth estimation and pre-event flood severity estimation using Earth observation (EO) datasets and derived flood products. A new initiative in the Disasters Program is underway to disseminate flood products from different hydrologic models and sensors to global stakeholders via Pacific Disaster Center’s DisasterAWARE®, NASA’s Disasters Mapping Portal and potentially other mechanisms. This initiative focuses on improving response capacity and use of EO products in near real-time by a broader community for resource planning in case of extreme events. As part of this initiative, we have deployed Model of Models (MoM) – an open-source ensemble approach, that integrates outputs from hydrologic models and EO data from optical imagery to assess flood severity daily at sub-watershed level globally. The MoM output is integrated with the incident event system of DisasterAWARE to generate flood severity risk and flood impact boundaries, which are disseminated via the DisasterAWARE platform to different stakeholders globally for decision-making and response efforts. The next step will focus on using MoM outputs to estimate flood depth and extent mapping using high-resolution Synthetic Aperture Radar imagery, impact assessment using optical imagery and population datasets, and damage estimation using critical infrastructure datasets, which would be disseminated via DisasterAWARE to decision-makers, emergency managers and first responders around the world.

flood↗
NASA NTRS1992

Advanced stitching technology

In the design of textile composites, the selection of materials and constructional techniques must be matched with product performance, productivity, and cost requirements. Constructional techniques vary. A classification of various textile composite systems is given. In general, the chopped fiber system is not suitable for structural composite applications because of fiber discontinuity, uncontrolled fiber orientation and a lack of fiber integration or entanglement. Linear filament yarn systems are acceptable for structural components which are exposed to simple tension in their applications. To qualify for more general use as structural components, filament yarn systems must be multi-directionally positioned. With the most sophisticated filament winding and laying techniques, however, the Type 2 systems have limited potential for general load-bearing applications because of a lack of filament integration or entanglement, which means vulnerability to splitting and delamination among filament layers. The laminar systems (Type 3) represented by a variety of simple fabrics (woven, knitted, braided and nonwoven) are especially suitable for load-bearing panels in flat form and for beams in a roled up to wound form. The totally integrated, advanced fabric system (Type 4) are thought to be the most reliable for general load-bearing applications because of fiber continuity and because of controlled multiaxial fiber orientation and entanglement. Consequently, the risk of splitting and delamination is minimized and practically omitted. Type 4 systems can be woven, knitted, braided or stitched through with very special equipment. Multiaxial fabric technologies are discussed.

Scardino, Frank L.↗
DOE OSTI2018

CAT: computer aided triage improving upon the Bayes risk through ε-refusal triage rules

Manual extraction of information from electronic pathology (epath) reports to populate the Surveillance, Epidemiology, and End Result (SEER) database is labor intensive. Systematizing the data extraction automatically using machine-learning (ML) and natural language processing (NLP) is desirable to reduce the human labor required to populate the SEER database and to improve the timeliness of the data. This enables scaling up registry efficiency and collection of new data elements. To ensure the integrity, quality, and continuity of the SEER data, the misclassification error of ML and NPL algorithms needs to be negligible. Current algorithms fail to achieve the precision of human experts who can bring additional information in their assessments. Differences in registry format and the desire to develop a common information extraction platform further complicate the ML/NLP tasks. The purpose of our study is to develop triage rules to partially automate registry workflow to improve the precision of the auto-extracted information. This paper presents a mathematical framework to improve the precision of a classifier beyond that of the Bayes classifier by selectively classifying item that are most likely to be correct. This results in a triage rule that only classifies a subset of the item. We characterize the optimal triage rule and demonstrate its usefulness in the problem of classifying cancer site from electronic pathology reports to achieve a desired precision. From the mathematical formalism, we propose a heuristic estimate for triage rule based on post-processing the soft-max output from standard machine learning algorithms. We show, in test cases, that the triage rule significantly improve the classification accuracy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Multi-Artifact Analysis of Self-Admitted Technical Debt in Scientific Software

Context: Self-admitted technical debt (SATD) occurs when developers acknowledge shortcuts in code. In scientific software (SSW), such debt poses unique risks to the validity and reproducibility of results. Objective: This study aims to identify, categorize, and evaluate scientific debt, a specialized form of SATD in SSW, and assess the extent to which traditional SATD categories capture these domain-specific issues. Method: We conduct a multi-artifact analysis across code comments, commit messages, pull requests, and issue trackers from 23 open-source SSW projects. We construct and validate a curated dataset of scientific debt, develop a multi-source SATD classifier to guide SATD management, and conduct a practitioner validation to assess the practical relevance of scientific debt. Results: Our classifier performs strongly across 900,358 artifacts from 23 SSW projects. SATD is most prevalent in pull requests and issue trackers, underscoring the value of multi-artifact analysis. Models trained on traditional SATD often miss scientific debt, emphasizing the need for its explicit detection in SSW. Practitioner validation confirmed that scientific debt is both recognizable and useful in practice. Conclusions: Scientific debt represents a unique form of SATD in SSW that that is not adequately captured by traditional categories and requires specialized identification and management. Our dataset, classification analysis, and practitioner validation results provide the first formal multi-artifact perspective on scientific debt, highlighting the need for tailored SATD detection approaches in SSW.

Melin, Eric [Boise State University]↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or aerial wildfire operations reports to better understand the risks present. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement. These applications can benefit from the use of state-of-the-art natural language processing techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of adaptation of NLP tools to the aviation domain by fine-tuning transformer based models using aviation data sets. In 2018, a novel language model based on neural units (also called transformers) was created and became known as “Bidirectional Encoder Representations from Transformers” or BERT. This architecture combined with large amounts of English training data and innovative semi-supervised training tasks set the standard for what would later emerge as Large Language Models. The performance of these models was further improved by hyperparameter tuning and refinement of the semi-supervised training task and resulted in “Robustly Optimized BERT Pre-training Approach through hyperparameter tuning” or RoBERTa models. These pre-trained Large Language Models proved to be useful for a wide variety of natural language processing tasks such as text classification and question answering through a process called fine-tuning. The transformer architecture with pre-trained weights served as the basis with the last few layers replaced with layers fine-tuned to perform a new task e.g., a layer that provides a label for the entire input text. This process of fine-tuning can also be used to adapt the models to new domains; e.g., BioBERT started with the pre-trained BERT model and was completed by additional fine-tuning and training on biomedical documents. Transformer-based architectures can also be used to create rich representations of text called embeddings which can serve as the input to other machine learning models. This allows simpler algorithms such as logistic regression to use context-rich representations of the text while still remaining quick to train and evaluate. In the world of aviation, there is a growing demand for natural language processing and understanding but the domain presents unique challenges. Due to the technical content (and specialized language) of most aviation documents, fine-tuning pre-trained Large Language Models to specific tasks has not met the benchmark on natural language processing tasks set by simpler models trained from scratch on the data. To address this deficiency, this paper evaluates the improvements from fine-tuning a Large Language Model on a large set of aviation documents using the original semi-supervised training tasks before performing specific natural language tasks. In fine-tuning, a domain-specific dataset is used on the original training task but with the pre-trained Large Language Model instead of starting from a random initialization. This approach allows the model to be adapted to the specific domain language without discarding the information gained from training on general English data. This paper utilized two major dataset types to train and assess the RoBERTa fine-tuning performance. The first are 7,057 Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the semi-supervised fine-tuning. The second type is the 494 document classification labels to be used for evaluation. This down-stream evaluation aims to show the performance of the fine-tuned model, better understand how much data is needed for an effective fine-tuning, and how fine-tuning can be adapted for different applications in-the domain. After semi-supervised training, evaluation begins by encoding the documents for classification using the fine-tuned RoBERTa model. Then a logistic regression classifier is trained to label the document type and compared against our ground truth labels. This currently leads to a 82.8% accuracy on 10-fold cross validation showing improvement over baseline RoBERTa which achieved 81.0%. We plan to measure the improvements on additional tasks and it is expected that these improvements will lead to more robust models that can tackle the natural language processing challenges present in aviation datasets.

ATM↗
DOE OSTI2017

Risks of Hazardous Substances in Radioactive Waste for the Konrad Repository in Germany - 17522

When disposing of radioactive waste with negligible heat generation, the activity of the radionuclides is limited. However, the waste including radioactive material comprises a certain proportion of non-radioactive material. The risk of hazardous contamination of the groundwater by non-radioactive substances must be taken into account. It must be ascertained that the non-radioactive material cannot affect near-surface groundwater, which is a resource for drinking water supply. To achieve this, limit values on the amount of hazardous substances in the total waste volume shall be determined. In 2002 a regulatory approval was granted to the 'Konrad' final storage facility for radioactive waste with negligible heat generation according to the German Water Resources Act. This approval is embedded in the license (plan-approval decision) based on the German Atomic Energy Act. The approval is based on the model of a possible discharge of contaminants from the final storage facility. In a long-term simulation spanning 300,000 years, contact of the waste with the groundwater in the surrounding rock formations over the postoperational phase was conservatively assumed. The maximum amount of allowable disposal of substances - such that no concern of affecting the near-surface groundwater arises - can be derived from the solubility of the respective substances. Implementing the permit according to the Water Resources Act requires: - classification of the waste through delivery declaration obligations - monitoring the substance inspection in the waste to assess its suitability for the disposal, and - stock keeping procedures for the disposal waste by the operator. While the repository is operated by the Federal Office for Radiation Protection (BfS) (http://www.bfs.de./EN/home/home{sub n}ode;), reporting to the Federal Environment Ministry, the statutory water permit is issued and applied by the State of Lower Saxony as responsible state authority. A waste package/container list supplements a comprehensive list of substances. From this - depending on the respectively conditioned waste - material vectors can be derived. For this purpose, BfS provides a database with a substances and container list to the waste owners. In the meantime, first experiences have been collected in applying the approval. More than 540 waste containers with a total mass of almost 6,000 Mg have been controlled and are ready for disposal. In our presentation we will show the recent development in supervising the preparation for the disposal of mixed waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2014

Injury Surveillance Among NASA Astronauts Using the Barell Injury Diagnosis Matrix

Astronauts perform physically demanding tasks and risk incurring musculoskeletal injuries during both groundbased training and missions. Increased injury rates throughout the history of the U.S. space program have been attributed to numerous factors, including an aging astronaut corps, increased Weightless Environment Training Facility (WETF) and Neutral Buoyancy Laboratory (NBL) training to construct the International Space Station, and improved clinical operations that promote injury prevention and reporting. With NASA program changes through the years (including retirement of the Shuttle program) and an improved training environment (including a new astronaut gym), there is no surveillance program to systematically track injury rates. A limited number of research projects have been conducted over the past 20 years to evaluate musculoskeletal injuries: (1) to evaluate orthopedic injuries from 1987 to 1995, (2) to describe upper extremity injuries, (3) to evaluate EVA spacesuit training related injuries, and (4) to evaluate in-flight musculoskeletal injuries. Nevertheless, there has been no consistently performed comprehensive assessment of musculoskeletal injuries among astronauts. The Barell Injury Diagnosis Matrix was introduced at the 2001 meeting of the International Collaborative Effort (ICE) on Injury Statistics. The Matrix proposes a standardized method of classifying body region by nature of injury. Diagnoses are coded using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding system. The purpose of this study is to assess the usefulness and complexity of the Barell Injury Diagnosis Matrix to classify and track musculoskeletal injuries among NASA astronauts.

Murray, J. D.↗
DOE OSTI2018

Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective

Mammographic breast density has been established as an independent risk marker for developing breast cancer. Breast density assessment is a routine clinical need in breast cancer screening and current standard is using the Breast Imaging and Reporting Data System (BI-RADS) criteria including four qualitative categories (i.e., fatty, scattered density, heterogeneously dense, or extremely dense). In each mammogram examination, a breast is typically imaged with two different views, i.e., the mediolateral oblique (MLO) view and cranial caudal (CC) view. The BI-RADS-based breast density assessment is a qualitative process made by visual observation of both the MLO and CC views by radiologists, where there is a notable inter- and intra-reader variability. In order to maintain consistency and accuracy in BI-RADS-based breast density assessment, gaining understanding on radiologists’ reading behaviors will be educational. In this study, we proposed to leverage the newly emerged deep learning approach to investigate how the MLO and CC view images of a mammogram examination may have been clinically used by radiologists in coming up with a BI-RADS density category. We implemented a convolutional neural network (CNN)-based deep learning model, aimed at distinguishing the breast density categories using a large (15,415 images) set of real-world clinical mammogram images. Our results showed that the classification of density categories (in terms of area under the receiver operating characteristic curve) using MLO view images is significantly higher than that using the CC view. This indicates that most likely it is the MLO view that the radiologists have predominately used to determine the breast density BI-RADS categories. Our study holds a potential to further interpret radiologists’ reading characteristics, enhance personalized clinical training to radiologists, and ultimately reduce reader variations in breast density assessment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

An Overview of the State-of-the-Art Reactor Consequence Uncertainty Assessment Accident Progression Insights

The U.S. Nuclear Regulatory Commission (NRC) with Sandia National Laboratories (Sandia) have completed three uncertainty analyses (UAs) as part of the State-of-the-Art Reactor Consequence Analyses (SOARCA) program. The SOARCA UAs included an integrated evaluation of uncertainty in accident progression, radiological release, and offsite health consequence projections. The UA for Peach Bottom, a boiling-water reactor (BWR) with a Mark I containment located in the State of Pennsylvania, analyzed the unmitigated long-term station blackout SOARCA scenario. The UA for Sequoyah, a 4-loop Westinghouse pressurized-water reactor (PWR) located in the State of Tennessee, analyzed the unmitigated short-term station blackout SOARCA scenario, with a focus on issues unique to the ice condenser containment and the potential for early containment failure due to hydrogen deflagration. The UA for Surry, a 3-loop Westinghouse PWR with a sub-atmospheric large dry containment located in the State of Virginia, analyzed the unmitigated short-term station blackout SOARCA scenario including the potential for thermally-induced steam-generator tube rupture. These three UAs are currently documented in three NUREG/CR reports. This report provides input to planned NRC documentation on the insights and findings from the SOARCA UA program. The purpose of the summary report is to provide a useful reference for regulatory applications that require the evaluation of offsite consequence risk from beyond design basis event severe accidents. This report focuses on the accident progression and source term insights developed from the MELCOR analyses. MELCOR is the NRC's best-estimate, severe accident computer code used in the SOARCA UAs. In anticipation of the SOARCA UA insights work, NRC and Sandia benchmarked the response of the Peach Bottom model to selected reference calculations from the Peach Bottom SOARCA UA. Peach Bottom was the first SOARCA UA performed and was completed in 2015 using the MELCOR 1.8.6 code. The PWR SOARCA UAs evolved the original methodology and utilized the updated MELCOR 2.2 computer code. The Peach Bottom model has been systematically updated for other NRC research efforts and has been updated to MELCOR 2.2. computer code. The findings from the new reference calculations using the updated model with the MELCOR 2.2 code are also integrated into the report. A second objective is an assessment of the applicability of the results to the other nuclear reactors in the U.S. As the key findings are reviewed, judgments are presented on the applicability of the results to other U.S. nuclear power plants. An important objective of the SOARCA program relied on high- fidelity plant-specific modeling. However, the nature of the insights and conclusions allowed judgements to be made on the applicability of the various insights to the same general classification of plant (i.e., BWR or PWR) or the entire fleet of plants. Finally, the results from the SOARCA UA accident progression calculations contain a wealth of information not previously documented in the NUREG/CRs. This report includes new but related information that can be used to benchmark past or support future regulatory decisions related to severe accidents. The new work includes a benchmark of the NUREG-1465 licensing source term definitions, the variability of key accident progression events and timing to radionuclide release, and an improved understanding of the timing and source terms from consequential steam generator tube ruptures. iii ACKNOWLEDGEMENTS The Sandia authors gratefully acknowledge the significant technical and programmatic contributions from the NRC SOARCA team which are reflected throughout the report. Dr. Tina Ghosh has been involved throughout the SOARCA UAs, providing the primary managerial and technical oversight. The long lists of NRC and Sandia contributors from the SOARCA UAs are cited in the three NUREG/CRs and are also gratefully acknowledged by the small team of authors compiling the results of their efforts. Significant technical contributions, advice, and reviews were provided by Dr. Hossein Esmaili, Dr. Alfred Hathaway, and Dr. Edward Fuller (retired) of the NRC. Dr. Randal Gauntt (retired), Mr. Patrick Mattie, Mr. Joseph Jones (retired), and Dr. Doug Osborn from Sandia are recognized as the SOARCA UA managers guiding the past efforts. There is a comparable list of project managers at the NRC including Ms. Patricia Santiago, Dr. Salman Haq, and Mr. Jon Barr. Sadly, we have lost Mr. Charlie Tinkler and Mr. Robert Prato, who were important contributors to the original SOARCA project. Finally, Mr. Kyle Ross and Mr. Mark Leonard have also retired but were significant technical contributors. Mr. Kyle Ross was the technical lead on all three SOARCA UAs and the original pressurized water reactor SOARCA study. Mr. Leonard was the technical lead on the original boiling water reactor SOARCA study and a key contributor to the first Peach Bottom SOARCA UA. iv

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Machine-Learning Enabled Evaluation of Probability of Piping Degradation In Secondary Systems of Nuclear Power Plants

The transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. Furthermore, of the operations and maintenance costs in U.S. plants, approximately 80% are labor costs. To address the issue of rising operating costs and economic viability, technologies used to perform online monitoring of piping and other secondary system structural components in commercial nuclear power plants (NPPs) are under evaluation. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real time measurements, rather than at a pre-determined inspection interval thus reducing the maintenance cost. This paper describes distributed high-temperature stable fiber sensors fabricated in optical fibers through a roll-to-roll laser direct writing process using femtosecond lasers. Using phase-sensitive optical time domain reflectometry, distributed acoustic and vibration sensors can be developed and deployed to critical components and systems in NPPs to perform active measurements with spatial resolution down to 0.5-meter throughout the piping systems. Complex acoustic and vibration signatures harnessed by distributed sensors are registered and analyzed by artificial intelligence algorithms for degradation detection and flaw identification. Piping elbows with machined-in flaws were instrumented with fiber sensors. High-spatial-resolution data were used to develop and validate machine learning algorithms, including both linear and nonlinear regression, and classification. Additionally, classification and sensor analysis were also performed for data analysis. The paper concludes with recommendations and future work on applications of machine learning enabled high-resolution fiber sensors for piping degradation monitoring in current or future NPPs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

VIC-Global Parameter Dataset Sensitivity with the Variable Infiltration Capacity Model: Evaluating the importance of dynamic land surface parameters when using the VIC-Global parameter dataset

Accurate prediction of runoff is essential to water resources management, flood risk assessment, and ecosystem protection. However, many hydrological models still have relatively substantial limitations when representing the influence of land use and land cover (LULC) on runoff generation and routing. Changes in LULC, such as deforestation, urban expansion, agricultural intensification, and wetland loss, have been shown to alter the water balance at the land surface through fundamental hydrologic processes (e.g., interception, infiltration, evapotranspiration, and soil storage). However, it remains an open question what the exact magnitude and timing of these impacts are for the spatial and temporal scales commonly used in engineering applications. In this analysis we focus on one aspect of recent LULC change for assessing human impacts, which is urbanization. Specifically we seek to determine the impacts of urbanization on the magnitude and timing of surface runoff and baseflow in HUC-12 basins in Clark County, Nevada which has experienced rapid urbanization. We use the Variable Infiltration Capacity (VIC) hydrology model with a widely used off-the-shelf dataset of land surface parameters, VIC-Global, both of which have been commonly used in the past for water and energy balance modeling for large scale hydrologic studies. We examine two scenarios where the first scenario removes all urbanized land cover and parameterizes those areas of the basins as barren or open shrubland. The second scenario tests the opposite case where all areas of the basins are classified as urban regardless of their present classification. The results from the VIC model show there is a low sensitivity for daily surface runoff between scenarios. The daily baseflow values indicate similar low sensitivity to the classification change during specific periods, but then have substantial differences during other period when large precipitation events are occurring. This is likely due to the assumed parameter values for the urban land cover classification made by the VIC-Global dataset. Using a static land cover parameterization is reasonable for large domain hydrology models that are being used for near-term planning horizons (<30 years). However, longer planning horizons where feedbacks between the atmosphere and land surface are important, especially in transient climate situations, considerations for how to update land surface parameters should be incorporated.

42 ENGINEERING↗
NASA NTRS2018

Cardiovascular Disease Outcomes Among the NASA Astronaut Corps

BACKGROUND: Acute effects of spaceflight on the cardiovascular system have been studied extensively, but the combined chronic effects of spaceflight and aging are not well understood. Preparation for and participation in spaceflight activities are associated with changes in the cardiovascular system such as decreased carotid artery distensibility and decreased ventricular mass which may lead to an increased risk of cardiovascular disease. Additionally, astronauts who travel into space multiple times or for longer durations may be at an increased risk across their lifespan. To that end, the purpose of this study was to determine the incidence of common cardiovascular disease (CVD) outcomes among the NASA astronaut corps during their active career and through retirement. METHODS: Cardiovascular disease outcomes were defined as reports of any of the following: myocardial infarction (MI), revascularization procedures (coronary artery bypass graft surgery [CABG] or percutaneous coronary intervention [PCI]), hypertension, stroke or transient ischemic attack [TIA], heart failure, or total CVD (as defined by the AHA - combined outcome of MI, Angina Pectoris, heart failure, stroke, and hypertension). Each outcome was identified individually from review of NASA's Electronic Medical Record (EMR), EKG reports, and death certificates using ICD-9 codes as well as string searches of physician notes of astronaut exams that occurred between 1959 and 2016. RESULTS: Of 338 NASA astronauts selected as of 2016, 9 reported an MI, 12 reported a revascularization procedure, (7 PCI and 5 CABG), 4 reported Angina (without MI), 5 reported heart failure, 9 reported stroke/TIA, and 96 reported hypertension. Total CVD was reported in 105 astronauts. No astronaut who had an MI or revascularization procedure flew a spaceflight mission following the event. All MI, revascularization, and stroke events occurred in male astronauts. When reviewing astronaut ECG reports, abnormal ECG reports were found in only 8% of records (n=430) and mainly among retired astronauts (82%), with marked sinus bradycardia being the reason for the abnormal classification.

Charvat, Jacqueline M.↗
DOE OSTI2014

Computerized detection of noncalcified plaques in coronary CT angiography: Evaluation of topological soft gradient prescreening method and luminal analysis

Purpose: The buildup of noncalcified plaques (NCPs) that are vulnerable to rupture in coronary arteries is a risk for myocardial infarction. Interpretation of coronary CT angiography (cCTA) to search for NCP is a challenging task for radiologists due to the low CT number of NCP, the large number of coronary arteries, and multiple phase CT acquisition. The authors conducted a preliminary study to develop machine learning method for automated detection of NCPs in cCTA. Methods: With IRB approval, a data set of 83 ECG-gated contrast enhanced cCTA scans with 120 NCPs was collected retrospectively from patient files. A multiscale coronary artery response and rolling balloon region growing (MSCAR-RBG) method was applied to each cCTA volume to extract the coronary arterial trees. Each extracted vessel was reformatted to a straightened volume composed of cCTA slices perpendicular to the vessel centerline. A topological soft-gradient (TSG) detection method was developed to prescreen for NCP candidates by analyzing the 2D topological features of the radial gradient field surface along the vessel wall. The NCP candidates were then characterized by a luminal analysis that used 3D geometric features to quantify the shape information and gray-level features to evaluate the density of the NCP candidates. With machine learning techniques, useful features were identified and combined into an NCP score to differentiate true NCPs from false positives (FPs). To evaluate the effectiveness of the image analysis methods, the authors performed tenfold cross-validation with the available data set. Receiver operating characteristic (ROC) analysis was used to assess the classification performance of individual features and the NCP score. The overall detection performance was estimated by free response ROC (FROC) analysis. Results: With our TSG prescreening method, a prescreening sensitivity of 92.5% (111/120) was achieved with a total of 1181 FPs (14.2 FPs/scan). On average, six features were selected during the tenfold cross-validation training. The average area under the ROC curve (AUC) value for training was 0.87 ± 0.01 and the AUC value for validation was 0.85 ± 0.01. Using the NCP score, FROC analysis of the validation set showed that the FP rates were reduced to 3.16, 1.90, and 1.39 FPs/scan at sensitivities of 90%, 80%, and 70%, respectively. Conclusions: The topological soft-gradient prescreening method in combination with the luminal analysis for FP reduction was effective for detection of NCPs in cCTA, including NCPs causing positive or negative vessel remodeling. The accuracy of vessel segmentation, tracking, and centerline identification has a strong impact on NCP detection. Studies are underway to further improve these techniques and reduce the FPs of the CADe system.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Regulatory and Licensing Strategy for Microreactor Technology

With electricity demand on the rise, an increase in the use of nuclear energy is needed in order to meet future energy demands. The use of nuclear energy will not only begin to positively address this demand, but will also mitigate the negative impacts of carbon-induced climate change and provide energy security to the United States. An appropriate option to address the increasing energy demand is the use of advanced reactor systems like microreactors. Microreactors, often referred to as special-purpose reactors or vSMRs, are neutronically simple, factory manufacturable, easily transportable, and designed to produce less than 20 MWt (for classification as Hazard Category 2 per 10 CFR 830, DOE STD 1027). These reactors are decentralized energy sources that have the ability to provide sustainable and affordable power to remote communities and to industrial users, while having self-contained geometry that requires very low maintenance care. In order for microreactors to be approved for operation, they must abide by and successfully complete a rigorous regulatory and licensing process monitored by the Nuclear Regulatory Commission (NRC), the Department of Energy (DOE), or the Department of Defense (DoD). Because microreactors are a relatively new and disruptive technology, there is no clear licensing path for them to follow. Current licensing frameworks are tailored specifically to large, traditional light water reactors (LWRs) that have been developed and understood by the nuclear industry for many years. Unlike LWRs, microreactors have major design differences, specifically in the materials, coolant, reflectors and potential technology applications used. They also have higher in-core operating temperatures, which have the potential to push the materials used past their allowable limits. Because they are much smaller, safety margins, the amount of space required for assembly, and the distances required to meet dose-based regulatory criteria can technically be altered to better fit their new size. The following strategies and recommendations are intended to assure that key licensing issues do not influence the path to microreactor development, demonstration, and commercialization: Nuclear Regulatory Commission • The NRC should address the lack of knowledge and support of risk informed decision-making concepts from staff members and continue to advance and communicate risk initiatives using their Risk Informed Steering Committee • More research should be done into whether or not microreactors, which are small Class 103 facilities, can be licensed using Class 104-type guidance (e.g., NUREG 1537) • The NRC should use frequency-consequence curves to incrementally reduce regulatory uncertainty and risk to facilitate future construction and operation of advanced reactor designs Department of Energy • The DOE should continue to implement their multi-step Phenomena Identification and Ranking Table process in order to help detect and recommend the major design and technology work needed for licensing future reactor systems • The DOE should consider a joint licensing approach with the NRC where microreactors first go through the DOE process, then finish with the NRC process, providing an advantage for both applicants and NRC staff Department of Defense • The DoD should invest in personnel with sufficient expertise, incorporate their military reactors into tactical power systems, and self-regulate using Section 91b of the Atomic Energy Act Transportation • Licensees should convert enriched uranium hexafluoride into metal or oxide at an enrichment facility and then transport them to a fabrication facility • To avoid transportation issues altogether, licensees should consider positioning the enrichment and fabrication facilities on the same site • The DOE should provide their support in developing new shipping packages for safe transport, criticality benchmark data needed to license high-assay low-enriched uranium fuel facilities and transport packages, and provide financial and technical help to the Department of Transportation; and, • The NRC should help transportation efforts by finalizing and developing guidance for implementing 10 CFR 73 and 10 CFR 74 specifically for Category 2 Special Nuclear Material.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Protecting Customer Privacy Through Distributed Energy Resource Anonymization

Due to their stochastic nature, the increase of Renewable Energy Resources (RERs) as a primary source of energy for power grids creates challenges regarding the reliability and resilience of the system. In order to combat these obstacles, expansion of Distributed Energy Resources (DERs) and their participation in Demand Response (DR) programs is necessary. Widespread participation requires prioritizing customer privacy and addressing concerns that may arise regarding communication between DERs and the Grid Service Provider (GSP). This paper discusses the use of flow reservation resources to split the operating cycles of DER load profiles into unique phases. The splitting of phases increases anonymization of the DERs by making it more difficult to determine the individual characteristics of the device. We discuss an example of this using simulated DER load profile data and examine the resulting effectiveness by using a machine learning algorithm for classification, called Support Vector Machine (SVM).

Distributed Energy Resource, Anonymization, Renewa↗
DOE OSTI2013

Alternative Endpoints and Approaches for the Remediation of Contaminated Groundwater at Complex Sites - 13426

The goal of United States (U.S.) Department of Energy's (DOE)'s environmental remediation programs is to restore groundwater to beneficial use, similar to many other Federal and state environmental cleanup programs. Based on past experience, groundwater remediation to pre-contamination conditions (i.e., drinking water standards or non-detectable concentrations) can be successfully achieved at many sites. At a subset of the most complex sites, however, complete restoration is not likely achievable within the next 50 to 100 years using today's technology. This presentation describes several approaches used at complex sites in the face of these technical challenges. Many complex sites adopted a long-term management approach, whereby contamination was contained within a specified area using active or passive remediation techniques. Consistent with the requirements of their respective environmental cleanup programs, several complex sites selected land use restrictions and used risk management approaches to accordingly adopt alternative cleanup goals (alternative endpoints). Several sites used long-term management designations and approaches in conjunction with the alternative endpoints. Examples include various state designations for groundwater management zones, technical impracticability (TI) waivers or greater risk waivers at Superfund sites, and the use of Monitored Natural Attenuation (MNA) or other passive long-term management approaches over long time frames. This presentation will focus on findings, statistics, and case studies from a recently-completed report for the Department of Defense's Environmental Security Technology Certification Program (ESTCP) (Project ER-0832) on alternative endpoints and approaches for groundwater remediation at complex sites under a variety of Federal and state cleanup programs. The primary objective of the project was to provide environmental managers and regulators with tools, metrics, and information needed to evaluate alternative endpoints for groundwater remediation at complex sites. A statistical analysis of Comprehensive Environmental Response, Compensation and Liability Act (CERCLA) sites receiving TI waivers will be presented as well as case studies of other types of alternative endpoints and alternative remedial strategies that illustrate the variety of approaches used at complex sites and the technical analyses used to predict and document cost, time frame, and potential remedial effectiveness. This presentation is intended to inform DOE program managers, state regulators, practitioners and other stakeholders who are evaluating technical cleanup challenges within their own programs, and establishing programmatic approaches to evaluating and implementing long-term management approaches. Case studies provide examples of long-term management designations and strategies to manage and remediate groundwater at complex sites. At least 13 states consider some designation for groundwater containment in their corrective action policies, such as groundwater management zones, containment zones, and groundwater classification exemption areas. Long-term management designations are not a way to 'do nothing' or walk away from a site. Instead, soil and groundwater within the zone is managed to be protective of human health and the environment. Understanding when and how to adopt a long-term management approach can lead to cost savings and the more efficient use of resources across DOE and at numerous other industrial and military sites across the U.S. This presentation provides context for assessing the use and appropriate role of alternative endpoints and supporting long-term management designations in final remedies. (authors)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Prediction of inherited genomic susceptibility to 20 common cancer types by a supervised machine-learning method

Prevention and early intervention are the most effective ways of avoiding or minimizing psychological, physical, and financial suffering from cancer. However, such proactive action requires the ability to predict the individual’s susceptibility to cancer with a measure of probability. Of the triad of cancer-causing factors (inherited genomic susceptibility, environmental factors, and lifestyle factors), the inherited genomic component may be derivable from the recent public availability of a large body of whole-genome variation data. However, genome-wide association studies have so far showed limited success in predicting the inherited susceptibility to common cancers. We present here a multiple classification approach for predicting individuals’ inherited genomic susceptibility to acquire the most likely phenotype among a panel of 20 major common cancer types plus 1 “healthy” type by application of a supervised machine-learning method under competing conditions among the cohorts of the 21 types. This approach suggests that, depending on the phenotypes of 5,919 individuals of “white” ethnic population in this study, (i) the portion of the cohort of a cancer type who acquired the observed type due to mostly inherited genomic susceptibility factors ranges from about 33 to 88% (or its corollary: the portion due to mostly environmental and lifestyle factors ranges from 12 to 67%), and (ii) on an individual level, the method also predicts individuals’ inherited genomic susceptibility to acquire the other types ranked with associated probabilities. These probabilities may provide practical information for individuals, heath professionals, and health policymakers related to prevention and/or early intervention of cancer.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2011

Environmental Signatures for Habitability: What to Measure and How to Rank the Habitability Potential of Mars

The environmental signatures for habitability are not necessarily biosignatures, even though on Earth, they are definitive proof of habitability. It is the constant overprint of the chemical signatures of life that makes it difficult to recognize the chemical and physical properties of a potentially habitable environment as distinct from an inhabited one. Mars Science Laboratory (MSL) will soon embark on a mission to Mars to assess its past or present habitability, so it is useful to examine how we measure habitability on Earth and prepare for how that approach may differ for Mars. This exercise includes: (a) articulation of fundamental assumptions about habitability, (b) an inventory of factors that affect habitability, (c) development of metrics, measurement approach and implementation, and (d) a new classification scheme for planetary habitability that goes beyond the binary "yes" or "no." There may be dozens of factors that affect habitability and they can be weighted as a function of specific environment. However a robotic, in situ investigation even on Earth has constraints that prevent the measurement of every environmental factor, so metrics must be reduced to the most relevant subset, given available time, cost, technical feasibility and scientific importance. Many of the factors could be measured with a combination of orbital data and the MSL payload. We propose that, at a minimum, a designation of high habitability potential requires the following conditions be met: (a) thermally stable with respect to extremes and frequency of fluctuation, (b) has more than one energy source, (c) sufficient chemical diversity to make compounds with covalent and hydrogen bonding, (d) can moderate ionizing radiation enough to allow a stable or evolving pool of organic molecules, (e) must have water or other high quality polar solvent, (f) must be able to renew chemical resources (e.g., plate tectonics, volcanism or something else we haven't envisioned). A measurement approach we have taken to measure habitability on Earth is : 1. Study remote sensing data, maps, etc. 2. Decide how big an area to measure. 3. Determine the spatial sampling rate. 4. Determine the temporal sampling rate. 5. Determine the order of measurements 6. Decide where to begin measurements 7. Select locations at field site and proceed While science drives each of the steps, there are additional constraints, e.g., technical, time, cost, safety (risk). This approach is also executable on Mars. Measurement of past habitability is more challenging both for Earth and Mars where access to the past means subsurface access and confrontation with unknowns about preservation of the martian past. Some environments preserve evidence of past habitability better than others, and this is where selection of the landing site to maximize the preservation potential of habitability indicators will be key. Mars presents an opportunity to discover transitional states between habitable or not, and we offer a ranking scale for planetary habitability with Mars as the second test subject: CLASS ONE Uninhabitable and likely has never been so CLASS TWO Has a high potential but no confirmed observation of life (as defined above) CLASS THREE Inhabited (we find life) 3-A Globally inhabited 3-B Primitive life; early in its evolution, but not yet globally established 3-C Exists only in refugia -- planet heading toward class four CLASS FOUR Post-habitable (there once was life, but now it's gone) MSL provides an opportunity to carefully investigate the habitability of at least one site on Mars and it will reveal much about the possible states of planetary habitability

Conrad, Pamela G.↗