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

Incorporation of Human Risk Directed Acyclic Graphs (DAG) With Mishap Investigations to Un-Silo Knowledge

NASA’s Human System Risk Board (HSRB) has been a central driver in efforts to understand, mitigate, and communicate the 29 human systems risks monitored by the board. As a result of the collaboration between research, operations, and technical authorities, large bodies of knowledge have been collected and digested to represent the current understanding of the risks. As a part of these bodies of knowledge, directed acyclic graphs (DAGs) have been developed to communicate the current understanding of the causal relationship of the hazards, contributing factors, countermeasures, other risks, and outcomes that contribute to the overall risk. This risk knowledge is applied in a theoretical sense for potential incidents during exploration even while informed by surveillance data. However, there have been mishaps and close calls during past space exploration that intersect with one or more of the Human System Risks DAGs and knowledge bases. The purpose of this exercise was to un-silo this risk knowledge and connect it to the close call of EVA 23 through the development of a DAG representing the intersection of the HSRB Risks and the events of the close call. The development of the DAG occurred through an iterative process, with each iteration expanding and/or refining the nodes and connections described by the source materials. In addition to the risk documentation developed by the HSRB, lessons learned and other mishap investigation documents were utilized to understand the events that led to water entering the helmet of a crewmember on the EVA. New nodes specific to the events of EVA 23 were interconnected with existing HSRB DAG nodes and edges. Nodes within the DAG were defined within a “DAG-tionary” with any updates to a definition that may have previously existed from the HSRB DAGs, and edges were recorded in a matrix. Both the DAG-tionary and matrix describe where nodes and edges are present across the Risk and Mishap DAG. This DAG will then be reviewed by experts outside of HSRB and HRP to confirm that interpretations of the non-health related events (such as the engineering nodes) are represented accurately. DISCUSSION This process highlighted a method by which the knowledge generated among the contributing members of the HSRB Risks can be effectively adapted and utilized through the tools employed by the Risk Custodian teams. By leveraging these tools, new context and insights to the information at hand can be brought forward to address current spaceflight challenges. Moreover, un-siloing this knowledge through future DAGs and other efforts can drive interprofessional collaboration and foster communication. This will enable teams to work together more effectively, leveraging their diverse expertise to tackle the complex challenges of space exploration and human research. Ultimately, this collaboration will bring NASA closer to achieve agency goals and contribute to the overall shared mission and vision.

Samuel Jacobs↗
NASA NTRS1997

Diagnostic Analyzer for Gearboxes (DAG): User's Guide

This documentation describes the Diagnostic Analyzer for Gearboxes (DAG) software for performing fault diagnosis of gearboxes. First, the user would construct a graphical representation of the gearbox using the gear, bearing, shaft, and sensor tools contained in the DAG software. Next, a set of vibration features obtained by processing the vibration signals recorded from the gearbox using a signal analyzer is required. Given this information, the DAG software uses an unsupervised neural network referred to as the Fault Detection Network (FDN) to identify the occurrence of faults, and a pattern classifier called Single Category-Based Classifier (SCBC) for abnormality scaling of individual vibration features. The abnormality-scaled vibration features are then used as inputs to a Structure-Based Connectionist Network (SBCN) for identifying faults in gearbox subsystems and components. The weights of the SBCN represent its diagnostic knowledge and are derived from the structure of the gearbox graphically presented in DAG. The outputs of SBCN are fault possibility values between 0 and 1 for individual subsystems and components in the gearbox with a 1 representing a definite fault and a 0 representing normality. This manual describes the steps involved in creating the diagnostic gearbox model, along with the options and analysis tools of the DAG software.

Jammu, Vinay B.↗
NASA NTRS2004

DAG-TM Concept Element 11 CNS Performance Assessment: ADS-B Performance in the TRACON

Distributed Air/Ground (DAG) Traffic Management (TM) is an integrated operational concept in which flight deck crews, air traffic service providers and aeronautical operational control personnel use distributed decision-making to enable user preferences and increase system capacity, while meeting air traffic management (ATM) safety requirements. It is a possible operational mode under the Free Flight concept outlined by the RTCA Task Force 3. The goal of DAG-TM is to enhance user flexibility/efficiency and increase system capacity, without adversely affecting system safety or restricting user accessibility to the National Airspace System (NAS). DAG-TM will be accomplished with a human-centered operational paradigm enabled by procedural and technological innovations. These innovations include automation aids, information sharing and Communication, Navigation, and Surveillance (CNS) / ATM technologies. The DAG-TM concept is intended to eliminate static restrictions to the maximum extent possible. In this paradigm, users may plan and operate according to their preferences - as the rule rather than the exception - with deviations occurring only as necessary. The DAG-TM concept elements aim to mitigate the extent and impact of dynamic NAS constraints, while maximizing the flexibility of airspace operations

Raghavan, Rajesh S.↗
DOE OSTI2024

Deeplynx Dag Repository

The DeepLynx DAG repository will contain several Airflow DAGs (Directed Acyclic Graphs) which will be used in the context of DeepLynx's deployed Apache Airflow instance. These DAGs will be used for multiple data management tasks for DeepLynx data, including but not limited to: - bringing data from various sources and tools into DeepLynx - managing sequential data workflows, such as running Python scripts on data to perform analysis and returning the results to DeepLynx - performing any necessary transformation or pre-processing on data coming into DeepLynx from external sources or out of DeepLynx to go to external applications

Brownlee, JarenM.↗
NASA NTRS2003

Airborne Conflict Management within Confined Airspace in a Piloted Simulation of DAG-TM Autonomous Aircraft Operations

A human-in-the-loop experiment was performed at the NASA Langley Research Center to study the feasibility of Distributed Air/Ground Traffic Management (DAG-TM) autonomous aircraft operations in highly constrained airspace. The airspace was constrained by a pair of special use airspace (SUA) regions on either side of the pilot s planned route. The available airspace was further varied by changing the separation standard for lateral separation between 3 nm and 5 nm. The pilot had to maneuver through the corridor between the SUA s, avoid other traffic and meet flow management constraints. Traffic flow management (TFM) constraints were imposed as a required time of arrival and crossing altitude at an en route fix. This is a follow-up study to work presented at the 4th USA/Europe Air Traffic Management R&D Seminar in December 2001. Nearly all of the pilots were able to meet their TFM constraints while maintaining adequate separation from other traffic. In only 3 out of 59 runs were the pilots unable to meet their required time of arrival. Two loss of separation cases are studied and it is found that the pilots need conflict prevention information presented in a clearer manner. No degradation of performance or safety was seen between the wide and narrow corridors. Although this was not a thorough study of the consequences of reducing the en route lateral separation, nothing was found that would refute the feasibility of reducing the separation requirement from 5 nm to 3 nm. The creation of additional, second-generation conflicts is also investigated. Two resolution methods were offered to the pilots: strategic and tactical. The strategic method is a closed-loop alteration to the Flight Management System (FMS) active route that considers other traffic as well as TFM constraints. The tactical resolutions are short-term resolutions that leave avoiding other traffic conflicts and meeting the TFM constraints to the pilot. Those that made use of the strategic tools avoided additional conflicts, whereas, those making tactical maneuvers often caused additional conflicts. Many of these second-generation conflicts could be avoided by improved conflict prevention tools that clearly present to the pilot which maneuver choices will result in a conflict-free path. These results, together with previously reported studies, continue to support the feasibility of autonomous aircraft operations.

Barmore, Bryan↗
DOE OSTI2026

Diacylglycerol enantiomer selectivity of diacylglycerol acyltransferases highlights metabolic specialization in triacylglycerol synthesis across the tree of life

Triacylglycerols are the major energy storage lipids in plants, animals, and microorganisms, and are predominantly produced by acyl-CoA:diacylglycerol (DAG) acyltransferases (DGATs). Two enantiomers of the DAG substrate, sn -1,2 and sn -2,3, can be produced by different biological mechanisms; however, little is known about which species produce each enantiomer, the selectivity of DGAT isoforms for either enantiomer, or whether DGAT enantiomer selectivity varies across organisms. Here, DAG enantiomer selectivity of DGAT1 and DGAT2 was measured from eight seed plants, two mammals, one oleaginous yeast, and one photosynthetic microalga using enantiomer-specific in vitro DGAT assays. Across most plants, DGAT1 favored sn -1,2-DAG, whereas DGAT2 preferentially utilized sn -2,3-DAG. However, there were several exceptions. Mammalian DGAT1, DGAT2, and microbial DGAT1s efficiently used both DAG enantiomers, while microbial DGAT2s had unique selectivity. The selectivity of several DGATs for combined acyl-CoA and DAG enantiomer molecular species were also evaluated for biotechnical applications. Therefore, DGAT DAG enantiomer selectivity is common yet strongly dependent on lineage and isoform and likely shaped in part by species-specific metabolic context of triacylglycerol synthesis, turnover, and remodeling. This work expands our understanding of DGAT function and establishes a foundation for leveraging enantiomer-selective acyltransferases in metabolic engineering of tailored lipid products.

Arabidopsis thaliana↗
NASA NTRSDate not supplied

Machine Learning for the Validation of Expert-Elicited Causal Risk Diagrams

Exposure to spaceflight poses risk to human health in complex ways. To help manage this risk, the Human Systems Risk Board (HSRB) at the National Aeronautics and Space Administration (NASA) maintains a set of causal diagrams that attempt to explain how spaceflight hazards generate health risks and lead to adverse outcomes both in-mission, immediately post-mission, and over the long term. These causal risk diagrams are formulated as directed acyclic graphs (DAGs) and can function as knowledge graphs of connected risks and outcomes. These DAGs have proven useful for communication, and, through network analysis, have allowed for the identification of structurally important factors in the risk network. However, the utility these DAGs provide is directly proportional to their verisimilitude, making assessment of this trait using empirical data – whether from actual human spaceflight or various spaceflight analogue exposures and model organisms – a high priority. In this research we explore the use of machine learning algorithms to learn DAG structure from empirical data as a means of evaluating human-elicited DAG structures. To do so, we test several different graph structure-learning algorithms on data concerning changes in the bones of rats and mice after exposure to either spaceflight or a spaceflight analogue. We explore potential methods for indexing the similarity between each algorithm’s output DAG with all the others and with that of the expert-elicited DAG. We discuss next steps in this ongoing line of research and open science initiatives underway to complete them.

directed acyclic graphs↗
NASA NTRS2003

Cosmogenic Records in 18 Ordinary Chondrites from the Dar Al Gani Region, Libya: Radionclides - 2

In the past decade more than 1000 meteorites have been recovered from the Dar al Gani (DaG) plateau in the Libyan part of the Sahara. The geological setting, meteorite pairings and density are described. So far, only a few terrestrial ages are known for DaG meteorites, e.g. 60+/- 20 kyr for the DaG 476 shergottite shower and 80+/- 20 kyr for the lunar meteorite DaG 262. However, from other desert areas, such as Oman, it is known that achondrites may survive much longer than chondritic meteorites, so the ages of these two achondrites may not be representative of the majority of the DaG meteorite collection, of which more than 90% are ordinary chondrites. In this work we report concentrations of the cosmogenic radionuclides, 14C (half-life = 5,730 yr), 41Ca (1.04x10 superscript 5 yr), Cl-36 (3.01x10 superscript 5 yr), Al-26 (7.05x10 superscript 5 yr) and 10Be (1.5x10 superscript 6 yr) to determine the terrestrial ages of DaG meteorites and constrain their pre-atmospheric size and exposure history.

Welten, K. C.↗
NASA NTRS2022

Directed Acyclic Graphs: A Tool for Understanding the NASA Human Spaceflight System Risks - Human System Risk Board

For over a decade, the National Aeronautics and Space Administration (NASA) has tracked and configuration-managed approximately 30 risks to astronaut health and performance that occur before, during and after spaceflight. The Human System Risk Board (HSRB), a Health and Medical Technical Authority (HMTA) Board at NASA Johnson Space Center, is the entity responsible for identifying, assessing, analyzing, and monitoring the official understanding of the risk or risk posture for each of the Human System Risks and determining – based on evaluation of the available evidence – when that risk posture changes. The ultimate purpose of tracking and researching these risks is to find ways to reduce the risk that astronaut crews face during spaceflight. Historically, research, development and operations relevant to one risk have been conducted in isolation from other risks; these individual risk ‘silos’ enabled initial characterization of each specific risk. In spaceflight however, the impact of exposure to risk for astronaut crews is cumulative, and not independent of exposures or other risks, as all the adverse effects of the spaceflight environment begin at launch, continue throughout the duration of the mission and in some cases across the lifetime of the crews. In January of 2020, the HSRB at NASA embarked on a pilot project designed to assess the potential value of causal diagramming as a tool to facilitate understanding of these cumulative and interdependent effects as applied within Human System Risk management. This process uses directed acyclic graphs as a means of formalizing a shared mental model of the causal flow of risk among Risk Board stakeholders. Initially this model was to improve communication among those stakeholders, but the potential value exceeds communication alone. The causal diagrams are formulated as directed acyclic graphs (DAGs) to function as a type of knowledge graph for reference for the board and its stakeholders. This document is a sister document to NASA/TM 20220006812 Directed Acyclic Graph Guidance Documentation (1). In that document, the basic guidance for creating and standardizing directed acyclic graphs as tools for cross-risk analysis is provided. This document contains the initial configuration managed DAGs that were created as a result of applying those principles. These initial versions were accepted by the HSRB in January of 2022. Each of the Human System Risks are represented by a DAG that has been reviewed by the larger Human Health and Performance community at NASA including life scientists, physical scientists, physicians, nurses, pharmacists, exercise specialists and more. These results show the starting point for Human System Risk DAGs as shared mental models and communication aids across the boundaries of the various expertise needed to understand and mitigate the human risks in spaceflight. Because they are a starting point, each of these DAGs can be expected to change over time as new or refined evidence becomes available. The process for updating these DAGs can be found in the JSC-66705 Human System Risk Management Plan (2) that is publicly available on the NASA Technical Reports Server.

Erik L. Antonsen↗
NASA NTRS2004

Fact Sheets of CTAS and NASA Decision-Support Tools and Concepts

Distributed Air/Ground (DAG) Traffic Management (TM) is an integrated operational concept in which flight deck crews, air traffic service providers and aeronautical operational control personnel use distributed decision-making to enable user preferences and increase system capacity, while meeting air traffic management (ATM) requirements. It is a possible operational mode under the Free Flight concept outlined by the RTCA Task Force 3. The goal of DAG-TM is to enhance user flexibility/efficiency and increase system capacity, without adversely affecting system safety or restricting user accessibility to the National Airspace System (NAS). DAG-TM will be accomplished with a human-centered operational paradigm enabled by procedural and technological innovations. These innovations include automation aids, information sharing and Communication, Navigation, and Surveillance (CNS) / ATM technologies. The DAG-TM concept is intended to eliminate static restrictions to the maximum extent possible. In this paradigm, users may plan and operate according to their preferences - as the rule rather than the exception - with deviations occumng eyond the year 2015. Out of a total of 15 concept elements, 4 have been selected for initial sutidies (see Key Elements in sidebar). DAG-TM research is being performed at Ames, Glenn, and Langley Research Centers.

Lee, Katharine↗
NASA NTRSDate not supplied

Development of A Directed Acyclic Graph for Venous Thromboembolism During Spaceflight

Introduction: Recent studies have reported the development of venous blood flow stasis in astronauts and an occlusive venous thrombosis during spaceflight. Subsequent investigations revealed approximately one quarter of surveilled crew members had some degree of blood flow stasis in the left internal jugular vein. Therefore, NASA’s Human System Risk Board now formally tracks venous thromboembolism (VTE) as a “concern” for human spaceflight. To investigate potential mechanisms by which exposures concomitant with spaceflight (e.g., microgravity, radiation) may contribute to VTE, we developed a causal diagram in the form of a directed acyclic graph (DAG). Methods: The mechanisms by which spaceflight exposures may elevate the risk of VTE and the downstream effects on mission outcomes were critically analyzed, taking into account scientific literature and subject matter expertise consultation, and a DAG was generated. A Level-of-Evidence score for each causal relationship was assigned based on assessing the literature against a set of criteria derived from the A. Bradford Hill Causal Guidelines. Results: The set of three main factors that predispose people to VTE (hypercoagulability, endothelial damage, and blood stasis) is known as Virchow’s Triad. In constructing the DAG for VTE we articulated various mechanisms by which the principal spaceflight hazards (microgravity, radiation, closed hostile environment, isolation and confinement, distance from Earth) are thought to interact with or cause the components in Virchow’s triad. We found sufficient evidence to at least speculate that fluid shifts from microgravity, compensatory alterations in hematologic indices, spaceflight atmospheric conditions, and oxidative stress/inflammation from radiation may be potential contributors to VTE development. Discussion: Developing the DAG entailed a systematic and repeatable approach for visualizing relationships between contributing factors that may lead to VTE in spaceflight. Articulating pathways linking spaceflight exposures to VTE risk factors and possible VTE development enables subject matter experts from different domains to construct a shared mental model. Assignment of levels of evidence scores to the relationships helps identify knowledge and capability gaps that should be considered for further investigation. Furthermore, the DAG highlights modifiable variables and may therefore facilitate the development of new VTE risk mitigation strategies.

Alexander Svoronos↗
NASA NTRSDate not supplied

Human Systems Risk Network - A Ranking Analysis of Risks

INTRODUCTION The Human Systems Risk Board (HSRB) is responsible for understanding, managing, and mitigating the risks associated with spaceflight. For a particular mission, the HSRB assigns each human system risk a rating on a 5x5 grid assessing its likelihood and consequence, which is ultimately used to compare and rank the risks. The HSRB approaches risk management by primarily establishing the context of each human system risk individually with the understanding that mitigating one risk might affect the likelihood, consequence, and mitigation approaches of another. To support this effort the HSRB, subject matter experts, and risk custodian teams created directed acyclic graphs (DAG), often called a causal graph, for the twenty-nine risks. In this presentation, we propose a new ranking algorithm for the risks which includes the downstream influence of each risk according to the information in the DAGs and provide an application of graph theoretic tools. METHODS In 2014, Mindock and Klaus proposed a taxonomy for human system risk influences which we have adopted to categorize the nodes in each DAG. Analyzing the nodes that correspond to the risks in this taxonomy allows us to analyze and understand how each risk influences the others. We construct an auxiliary network, which we call the Primary Risk Network (PRN), where the nodes are the twenty-nine space flight risks and, a directed edge connects Risk A to Risk B if Risk A has some influence on the likelihood or consequence of Risk B as described in the DAGS. We perform a variety of graph theoretic ranking methods on the nodes (or risks) in the PRN, including Katz centrality. RESULTS We rank the nodes in the PRN using the Katz centrality score. The ten risks with the highest score are pictured in Figure 1, colored (light to dark) according to their score. We analyze other centrality measures like betweenness centrality, eigenvector centrality, and the Estrada index, and provide the meaning of the corresponding rankings in terms of the risks. Future work includes analyzing the other categories in the taxonomy defined by Mindock and Klaus [1]. For example, we are interested in analyzing the nodes that are labeled as countermeasures or capabilities and perform similar analysis to measure their effect on certain medical conditions.

dag↗
NASA NTRS2004

ADS-B within a Multi-Aircraft Simulation for Distributed Air-Ground Traffic Management

Automatic Dependent Surveillance Broadcast (ADS-B) is an enabling technology for NASA s Distributed Air-Ground Traffic Management (DAG-TM) concept. DAG-TM has the goal of significantly increasing capacity within the National Airspace System, while maintaining or improving safety. Under DAG-TM, aircraft exchange state and intent information over ADS-B with other aircraft and ground stations. This information supports various surveillance functions including conflict detection and resolution, scheduling, and conformance monitoring. To conduct more rigorous concept feasibility studies, NASA Langley Research Center s PC-based Air Traffic Operations Simulation models a 1090 MHz ADS-B communication structure, based on industry standards for message content, range, and reception probability. The current ADS-B model reflects a mature operating environment and message interference effects are limited to Mode S transponder replies and ADS-B squitters. This model was recently evaluated in a Joint DAG-TM Air/Ground Coordination Experiment with NASA Ames Research Center. Message probability of reception vs. range was lower at higher traffic levels. The highest message collision probability occurred near the meter fix serving as the confluence for two arrival streams. Even the highest traffic level encountered in the experiment was significantly less than the industry standard "LA Basin 2020" scenario. Future studies will account for Mode A and C message interference (a major effect in several industry studies) and will include Mode A and C aircraft in the simulation, thereby increasing the total traffic level. These changes will support ongoing enhancements to separation assurance functions that focus on accommodating longer ADS-B information update intervals.

Barhydt, Richard↗
NASA NTRS2006

Identification of a Common R-Chondrite Impactor on the Ureilite Parent Body

Polymict ureilites are brecciated ultramafic meteorites that contain a variety of single mineral and lithic clasts. They represent the surface debris from a small, differentiated asteroid. We are continuing a detailed petrological study of several polymict ureilites including EET 87720, EET 83309 and FRO93008 (from Antarctica), North Haig, Nilpena (Australia), DaG 976, DaG 999, DaG 1000 and DaG 1023 (Libya). The latter four stones are probably paired. Clast sizes can be 10 mm in diameter, so a thin-section can consist of a single lithic clast.

Downes, H.↗