Results for “likelihood” — Page 17

NASA NTRS2015

New York City Panel on Climate Change 2015 Report: Climate Observations and Projections - Chapter 1

During 2013 and 2014, numerous international (IPCC, 2013) and national (Melillo et al., 2014; Gordon, 2014) reports have concluded that human activities are changing the climate, leading to increased vulnerability and risk. Since the industrial revolution, fossil fuel burning, industrial activity, and land use changes have led to a 40% increase in heat-trapping carbon dioxide (CO2), and an approximately 150% increase in methane (CH4), another powerful greenhouse gas (GHG), has been observed. Global temperatures have increased by close to 1 C since 1880 as the upper oceans have warmed and polar ice has retreated. These and other climate changes are projected to accelerate as greenhouse gas concentrations continue to rise. In the coming decades, climate change is extremely likely to bring warmer temperatures in the New York metropolitan region (see Box. 1.1 and Fig.1.1 for key definitions and terms). Heat waves are very likely to increase; total annual precipitation will likely increase and brief, intense rainstorms are very likely to increase. Because of incomplete knowledge about exactly how much climate change will occur, choosing among policies for reducing future damages requires prudent risk management (Yohe and Leichenko, 2010; Kunreuther et al., 2013). Given differing risk tolerances among stakeholders, a risk management approach allows for a range of possible climate change outcomes to be examined with associated uncertainties surrounding their likelihoods. The New York City Panel on Climate Change 2 (NPCC2) projections can be used to inform planning across multiple governmental scales (e.g., city, county, state) in the New York metropolitan region. Such coordinated efforts can serve as test cases for successful local, state, and federal coordination for integrated climate adaptation initiatives. This chapter describes the global climate system, and presents observed temperature and precipitation trends and projections for the region. Chapter 2 (NPCC, 2015) focuses on sea level rise and possible changes in coastal storms. Chapter 3 and Chapter 4 (NPCC, 2015) describe efforts to better understand the region's vulnerability to coastal flooding during coastal storms. The treatment of likelihood related to the NPCC projections is similar to that developed by the Intergovernmental Panel on Climate Change Fourth and Fifth Assessment Reports (IPCC, 2007; 2013), with six likelihood categories (Box 1.1 and Fig. 1.1). The assignment of climate hazards to these categories is based on observed data, global climate model simulations, published literature, and expert judgment.

climate change↗

NASA NTRS2016

SC-228 Inclusion of DAA Warning Alert for TCAS Interoperability

This white paper summarizes NASA research results that have informed Special Committee 228 (SC-228) discussions and decisions regarding the inclusion of a warning-level alert within the detect and avoid (DAA) alerting structure for unmanned aircraft systems (UAS). For UAS, the removal of the pilot from onboard the aircraft has eliminated the ability of the ground-based pilot in command (PIC) to use out-the-window visual information to make judgments about a potential threat of a loss of well clear with another aircraft. As a result, the DAA traffic display will be the primary source of information that the PIC can use to execute the three primary well clear functions: 1) detect a potential loss of well clear, 2) determine a resolution maneuver, and 3) upload that maneuver to the aircraft via the ground control station (GCS). In addition, pilots are required to coordinate with air traffic control (ATC) prior to maneuvering off of their approved flight plan. In determining an appropriate resolution maneuver to avoid a loss of well clear, the PIC must decide both when and how to maneuver, and both the timeliness and the accuracy (i.e., correctness) of the maneuver are critical to reducing the likelihood and/or severity of a loss of well clear. Alerting information is one of three critical components of the DAA display, along with traffic information elements (e.g., relative heading, speed and altitude) and maneuver guidance. Alerting information and maneuver guidance, in particular, have been found to have a significant impact, both statistically and practically, on pilots' ability to avoid and minimize the severity of losses of well clear While all three display components are key to pilots performing the traffic avoidance task of remaining well clear, in general, alerting information provides crucial information about when a resolution maneuver is required while maneuver guidance assists the pilot in determining how best to maneuver. A fundamental task of the DAA alerting system is to provide critical timing information to the pilot about the potential for a loss of well clear with another aircraft. This is done by employing both temporal and spatial thresholds that indicate to the pilot the likelihood and imminence of a loss of well clear. The design of the DAA alerting thresholds is a balancing act between eliciting the desired pilot response in real loss of well clear threat events and reducing excessive, unnecessary, and/or uncoordinated UAS maneuvering within the air traffic environment; larger thresholds, both spatially and temporally, may increase the likelihood of a pilot avoiding a loss of well clear, but it can also increase the frequency of maneuvering - especially in cases where a maneuver is not actually needed to maintain well clear. A series of human in the loop (HITL) simulations have been conducted as part of NASA's Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) project. The purpose of these HITLs has been to provide empirical results in order to inform development of the minimum human-machine interface requirements for the DAA system. This white paper will present those results which provide evidence of a human performance benefit (in terms of response times and ability to remain well clear of other aircraft) of the DAA warning alert both with and without a collision avoidance system on board the aircraft.

TCAS interoperability↗

NASA NTRS2017

Consideration of Collision "Consequence" in Satellite Conjunction Assessment and Risk Analysis

Classic risk management theory requires the assessment of both likelihood and consequence of deleterious events. Satellite conjunction risk assessment has produced a highly-developed theory for assessing collision likelihood but holds a completely static solution for collision consequence, treating all potential collisions as essentially equally worrisome. This may be true for the survival of the protected asset, but the amount of debris produced by the potential collision, and therefore the degree to which the orbital corridor may be compromised, can vary greatly among satellite conjunctions. This study leverages present work on satellite collision modeling to develop a method by which it can be estimated, to a particular confidence level, whether a particular collision is likely to produce a relatively large or relatively small amount of resultant debris and how this datum might alter conjunction remediation decisions. The more general question of orbital corridor protection is also addressed, and a preliminary framework presented by which both collision likelihood and consequence can be jointly considered in the risk assessment process.

Hejduk, M.↗

NASA NTRSDate not supplied

Improving Efficacy and Safety of Pharmacological Treatment Through Precision Medicine and Pharmacogenomics for Human Deep Space Exploration

INTRODUCTION: Future spaceflight will require increased crew medical autonomy as exploration class missions expanding duration and distance from Earth, especially for Mars missions. As mission duration increases, it will be even more essential to have appropriate amounts of effective medication to ensure the maintenance of crew health and performance. Conversely, mass and volume constraints will become more severe as future spaceflight expands beyond low Earth orbit, where resupply is difficult or becomes impossible. These constraints thus convey an urgency to further tailor medications included in the spacecraft formulary and increased examination of appropriate dosing regimens. BACKGROUND: Precision Health is an exciting area of cutting-edge research and medicine focused on maintaining an individual’s health and performance through in-depth understanding of an individual’s unique factors and molecular profiles. This approach can be adapted to better predict, monitor, and address physiological responses to the spaceflight environment. One example is the field of pharmacogenomics (PGX),the study of how the expressed genome impacts drug responses with the goal of prescribing the right dose of the right drug at the right time. Specifically, PGX testing provides valuable information on an individual’s precise allelic variations to guide physicians in making informed decisions on pharmaceutical choice and dosing to avoid adverse drug events and maximize pharmacological efficacy. The goal of this study was to evaluate which drugs in the current space pharmacy could be evaluated using PGX testing and to understand the potential impact on the health and wellness of the astronaut population. Additionally, we sought to evaluate clinically available FDA-approved PGX testing solutions to better understand its applicability. METHODS: A complete list of drugs onboard the International Space Station (ISS) was analyzed for risk and likelihood of drug failure and PGX actionability. This analysis encompassed both personal astronaut medications, including supplements and over the counter drugs (n=151) and ISS MedKit formulary medications (n=95). Duplicate medications and different formulations were removed, which resulted in 157 total drugs used in the subsequent analysis. A 5x5 risk assessment table was produced by examining the likelihood of drug failure compared to the consequence of drug failure. Likelihood of individual drug failure was defined by whether existing processes are sufficient to prevent adverse events, as ranked from 1 (very low, can easily be prevented) to 5 (very high, cannot be prevented) during a Mars mission. In contrast, the consequence of drug failure was defined by impact to safety, schedule, cost or technical and ranked from 1 (very low) to 5 (very high).A comprehensive assessment of commercially available PGX solutions is currently underway to evaluate specimen requirements, cost/benefit analysis (cost vs. number of alleles assessed), utility of variant analysis, relevance to inflight medication usage, quality of reporting in enabling clinical application, and ease of integration into electronic medical records. RESULTS: Risk assessments(LxC 5x5 table) indicated29medicationswere in the yellow or red zone driven predominantly by drug failure or safety concerns, with the remainder(n=128)of the medications in the green zone where risk is acceptable. We found that current PGX testing results could impact 21% of the total medications in the ISS MedKit and IMAK; of these, 9 medications currently have direct clinically actionable guidance available. Results of the clinical PGX solution evaluations as related to these medications will be presented. CONCLUSION: PGX testing has demonstrated clear benefits in terrestrial medicine and clinical environments for the selection of proper medications, avoiding adverse drug reactions, and maximizing drug efficacy. We propose that similar benefits would be bestowed on the astronaut and commercial spaceflight passenger population by performing preemptive pre-flight PGX testing to reduce risk of mission failure due to ineffective or toxic medications, improve targeting drug efficacy and safety, and further open the door to countermeasure research exploring PGX-related allelic variants. For example, PGX results could allow tailoring of specific medications at optimal doses more precisely to each individual astronaut, particularly in areas of space motion sickness, sleep aids, and analgesics. An additional benefit is that PGX results could provide information for better planning of the components of a space pharmacy for deep space missions to be more cost effective and more efficient in the utilization of limited pharmaceutical resources. Finally, while PGX testing of the astronaut corps is not currently conducted, this approach could provide immediate impact in support of mission success by reducing risks, optimizing astronaut performance, and providing valuable insights into long-term astronaut health. Such advancements in clinical decision making are important next steps in building dynamic individual risk profiles for astronauts, increasing crew autonomy and providing tailored countermeasures

Alice R W Tang↗

NASA NTRSDate not supplied

Improving Efficacy and Safety of Pharmacological Treatment Through Precision Health and Pharmacogenomics

INTRODUCTION: Future spaceflight will require increased crew medical autonomy as exploration class missions expand in duration and distance from Earth, especially for Mars missions. As mission duration increases, it will be essential to have appropriate amounts of effective medication to ensure the maintenance of crew health and performance. Conversely, mass and volume constraints will become more severe as future spaceflight expands beyond low Earth orbit, where resupply is difficult or becomes impossible. These constraints thus convey an urgency to tailor medications for individual crewmembers and further examine appropriate dosing regimens. BACKGROUND: Precision Health is an exciting area of medicine focused on maintaining an individual’s health and performance through in-depth understanding of an individual’s unique clinical and environmental history, genetic makeup, and molecular profiles. This approach can be adapted to better predict, monitor, and address physiological responses to the spaceflight environment. A subset of this field is pharmacogenomics (PGX), the study of how the expressed genome impacts drug responses with the goal of prescribing the right dose of the right drug at the right time. Specifically, PGX testing provides valuable information on an individual’s precise allelic variations to guide physicians in making informed decisions on drug choice and dosing to avoid adverse events and maximize efficacy. The study goal was to identify which current space pharmacy drugs could be evaluated using PGX testing and to understand the potential impact on the health and wellness of the astronaut population. Additionally, we sought to evaluate clinically available FDA-approved PGX testing solutions to better understand its applicability. METHODS: A complete list of drugs on the ISS was analyzed for risk and likelihood of drug failure and PGX actionability. This analysis encompassed both astronauts’ personal medications, including supplements and over the counter drugs (n=151) contained in the ISS medical accessory kit (IMAK), and ISS MedKit formulary medications (n=95). Duplicate medications and different formulations were removed, which resulted in a total of 157 drugs used in the subsequent analysis. A 5x5 risk assessment table was produced by examining the likelihood of drug failure compared to the consequence of drug failure (LxC). Likelihood of individual drug failure was defined by whether existing processes are sufficient to prevent ineffective treatment or impactful side effect events, as ranked from 1 (very low, can easily be prevented) to 5 (very high, cannot be prevented) during a Mars mission. In contrast, the consequence of drug failure was defined by impact to safety, schedule, cost, or technical criteria and ranked from 1 (very low) to 5 (very high). An assessment of PGX reference laboratories is currently underway to evaluate sample requirements, benefit analysis (cost vs. utility of allele variant analysis), relevance to inflight medication usage, quality of reporting in enabling clinical application, and ease of integration into electronic medical records. RESULTS: Risk assessments (LxC 5x5 table) indicated 128 medications were in the green zone where risk is acceptable, with the remaining 29 of the medications in the yellow or red zone driven predominantly due to drug failure or safety concerns. We found that current PGX testing results could impact 21% of the total medications in the ISS MedKit and IMAK; of these, 9 medications currently have direct clinically actionable guidance available. Results of the clinical PGX solution evaluations as related to these medications will be presented. CONCLUSION: PGX testing has demonstrated clear benefits in terrestrial medicine and clinical environments for the selection of proper medications, avoiding adverse drug reactions, and maximizing drug efficacy. We propose that similar benefits would be bestowed on the astronaut and commercial spaceflight passenger population by performing preemptive preflight PGX testing to reduce risk of mission failure due to ineffective or toxic medications, improve drug efficacy, and further open the door to countermeasure research. For example, PGX results could allow tailoring of specific medications at optimal doses more precisely to each individual astronaut, particularly in areas of space motion sickness, sleep aids, and analgesics. An additional benefit is that PGX results could provide information for better planning of the components of a space pharmacy for deep space missions to be more effective and efficient in the utilization of limited pharmaceutical resources. Finally, while PGX testing of the astronaut corps is not currently conducted, this approach could provide immediate impact in support of mission success by reducing risks, optimizing astronaut performance, and providing valuable insights into long-term astronaut health. Such advancements in clinical decision making are important next steps in building dynamic individual risk profiles for astronauts, increasing selection of the best treatment choice, and providing tailored countermeasures for individual crewmembers.

Pharmacogenomics↗

NASA NTRS2022

Constraining Warm Dark Matter and Population III Stars with the Global 21 cm Signal

Upcoming ground- and space-based experiments may have sufficient accuracy to place significant constraints upon high-redshift star formation, Reionization, and dark matter (DM) using the global 21 cm signal of the intergalactic medium. In the early universe, when the relative abundance of low-mass DM halos was important, measuring the global signal would place constraints on the damping of structure formation caused by DM having a higher relic velocity (warm dark matter, or WDM) than in cold dark matter. Such damping, however, can be mimicked by altering the star formation efficiency (SFE) and can be difficult to detect because of the presence of Population III stars with unknown properties. We study these various cases and their degeneracies with the WDM mass parameter mX using a Fisher matrix analysis. We study the mX = 7 keV case and a star formation model that parameterizes the SFE as a strong function of halo mass and include several variations of this model along with three different input noise levels for the likelihood; we also use a minimum halo virial temperature for collapse near the molecular cooling threshold. We find that when the likelihood includes only Population II stars, mX is constrained to an uncertainty of ∼0.4 keV for all models and noise levels at the 68% confidence level. When the likelihood includes weak Population III stars, mX ∼ 0.3 keV, and if Population III star formation is relatively efficient, mX ∼ 0.1 keV uncertainty, with tight Population III star formation parameter constraints. Our results show that the global 21 cm signal is a promising test-bed for WDM models, even in the presence of strong degeneracies with astrophysical parameters.

Cosmology↗

NASA NTRS2022

Planetary protection assessment of radioisotope thermoelectric generator (RTG)-powered landed missions to ocean worlds: application to Enceladus

Landed missions to icy worlds with a subsurface liquid water ocean must meet planetary protection requirements, and ensure a sufficiently small likelihood of any microorganism-bearing part of the landed element reaching the ocean. A higher bound on this likelihood is set by the potential for radioisotope thermoelectric generator (RTG) power sources, the hottest possible landed element, to melt through the ice shell and reach the ocean. Here, we quantify this potential as a function of three key parameters: surface temperature, ice shell thickness (i.e., heat flux through the shell), and thickness of a porous (insulating) snow or regolith cover. Although the model we describe can be applied to any ocean world, we present results in the context of a landed mission concept to the south polar terrain of Saturn’s moon Enceladus. In this particular context, we discuss planetary protection considerations for landing site selection. The likelihood of forward microbial contamination of Enceladus’ ocean by an RTG-powered landed mission can be made sufficiently low to not undermine compliance with planetary protection policy.

Marc Neveu↗

NASA NTRSDate not supplied

Selection of Next Priority IMPACT Medical Conditions Based on Available Terrestrial and Spaceflight Data

BACKGROUND: As the era of exploration class missions begins, identification of medical conditions that may occur and require management becomes essential for the modeling of medical risk. To this end, NASA has developed IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces), a suite of tools to assist in assessment of medical risk analysis. It has incorporated an initial list of the 120 conditions of highest concern, labeled the IMPACT condition list 1.0 (ICL 1.0). This abstract describes a method for prioritizing the 92 conditions included on the Proposed Future Conditions List (PFCL) for inclusion in future iterations of the ICL. OVERVIEW: To construct the Prioritized Proposed Future Conditions List (P-PFCL), each condition on the PFCL was scored as “low,” “medium,” or “high” on each of four variables: incidence, likelihood of significant task impairment, diagnostic and treatment complexity, and treatment futility. Qualitative assessment using clinical judgement was utilized to score complexity, futility, and likelihood of impairment. Incidence was assessed quantitatively using spaceflight data and/or analog populations where available then assigned a score using established cutoffs. Logarithmic numerical values were assigned to each category label. A Prioritization Score was generated for each condition by taking the product of incidence and likelihood of task impairment (risk) divided by the product of complexity and futility (difficulty of care), with higher values corresponding to higher priority for future inclusion in the ICL. DISCUSSION: The described methods allow for the generation of a ranked P-PFCL to act as a decision support tool for selection of the next generation of modeled medical conditions. Some of the conditions ranked highly on the P-PFCL include EVA-related upper and lower extremity sprain/strain, iron deficiency, delirium, and hypertension, among others. While this effort does not attempt to quantify the absolute risk associated with each condition, it does attempt to semi-quantitatively estimate the risk of each condition relative to the other possible conditions. This tool in concert with subject matter expert opinion could optimize the future use of limited resources thereby producing a more accurate medical risk model, which will be essential to the upcoming exploration class missions.

Michael Pohlen↗

NASA NTRS2023

IMPACTing Medical System Design with a Risk Analysis Tool [“IMPACT” sur la Conception du Système Médical avec un Outil d'Analyse des Risques]

Background: Following the success of Artemis I, NASA is preparing for human extended duration missions. Ongoing efforts are focused on mitigating mission-related risks, including those affecting crew health and performance. Communication latency, logistics of resupply and time frame of medical evacuation are barriers to provision of healthcare for these missions, especially with respect to constraints in mass, volume, and crew training. An in-depth assessment of medical risks, capabilities and resources for a specific mission design is necessary to determine an optimal balance that maximizes likelihood of mission success. Overview: IMPACT (Informed Mission Planning via Analysis of Complex Tradespaces) is a dynamic tool designed to estimate medical risk and outcomes for a specific mission design. In its current iteration, a list of medical conditions selected based on likelihood of occurrence and/or consequence was linked to a set of clinical capabilities and resources necessary for diagnosis and management. A probabilistic risk analysis tool was then used to identify and estimate the likelihood and consequence of risks through the following outcome metrics: loss of crew life (inflight mortality due to medical conditions), need for medical evacuation (return to definitive care), and crew disability (task time affected based on how medical conditions influence the ability to perform specific exploration mission crew tasks). Finally, the model’s optimization algorithm provides recommendations for medical capabilities that maximize risk mitigation relative to mass and volume constraints. In the Spring of 2023, IMPACT was utilized to estimate outcome metrics for a design reference mission that would be representative of an extended duration Artemis mission. Notional data generated were then used to determine a recommended set of medical capabilities and resources relative to user-defined mass and volume constraints. A multidisciplinary team has also been updating IMPACT to strengthen the model’s fidelity. Figure 1 shows how updates to outcome metric inputs for the conditions resulted in different capability and resource allocation recommendations. Discussion: This presentation will discuss the IMPACT tool and share the latest data generated for a representative extended duration Artemis mission. Efforts to improve the fidelity of data generated by the model’s algorithm will also be discussed.

K A Shair↗

NASA NTRSDate not supplied

Earth-Independent Medical Operations (EIMO) Concept of Operations

Compared to the current paradigm for crew health in low-Earth orbit and Lunar missions that rely on constant communication with Mission Control, there is an anticipated shift in medical operations for deep-space exploration missions. This shift stems from mission constraints imposed by the considerable distance from Earth, which include resource limitations due to a lack of resupply, mass, power, volume, and data limitations, challenges imposed by communication latency and the inability to evacuate in case of emergencies. To transition towards a more self-reliant medical approach, a comprehensive strategy is essential to progressively enable crew autonomy and mitigate mission success risks in the challenging environment of space. This transformative shift is collectively referred to as "Earth-Independent Medical Operations" (EIMO), signifying the gradual transfer of medical care and decision-making from terrestrial resources to space-based assets. This transition is aimed at bolstering astronaut health and performance while simultaneously reducing the overall risks associated with space missions. The constraints related to EIMO necessitate an integrated development of medical systems, featuring interoperability with mission planning, vehicle design, spacesuit design, and data architecture. This integration is vital in establishing a robust medical infrastructure that not only supports the well-being of astronauts but also ensures the success of the mission as a whole. The Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has developed a model-based Concept of Operations (ConOps) outlining an initial vision for EIMO. Within this ConOps, a comprehensive view is presented, encompassing stakeholder needs, system objectives, and system goals associated with EIMO. Additionally, it provides illustrative examples of the various activities (scenarios) for which the system will be employed during missions. The selection of these activities has been meticulous, aiming to encompass a wide spectrum of medical conditions, including those falling under different risk categories, such as low-likelihood-low-consequence, low-likelihood-high-consequence, and high-likelihood-low-consequence. The selection of these activities (scenarios) effectively encompasses the wide range of medical events situated within an assumed probability-consequence bell curve. In each scenario, at least one of the five main EIMO components identified is captured. Those EIMO components are: Pre-mission Planning, Acute and Emergent Management Decision Making, Prolonged Medical Management Decision Making, Supplies and Resource Management, and Task Load Management. The ConOps was developed by a multidisciplinary team consisting of systems engineers, scientists, and clinicians across NASA and aims to serve as an initial recommendation to gradually and safely enabling crew autonomy for Mars missions and beyond.

Earth Independent Medical Operations↗

NASA NTRSDate not supplied

IMPACTing Medical System Design with a Risk Analysis Tool

Background: Following the success of Artemis I, NASA is preparing for human extended duration missions. Ongoing efforts are focused on mitigating mission-related risks, including those affecting crew health and performance. Communication latency, logistics of resupply and time frame of medical evacuation are barriers to provision of healthcare for these missions, especially with respect to constraints in mass, volume, and crew training. An in-depth assessment of medical risks, capabilities and resources for a specific mission design is necessary to determine an optimal balance that maximizes likelihood of mission success. Overview: IMPACT (Informed Mission Planning via Analysis of Complex Tradespaces) is a dynamic tool designed to estimate medical risk and outcomes for a specific mission design. In its current iteration, a list of medical conditions selected based on likelihood of occurrence and/or consequence was linked to a set of clinical capabilities and resources necessary for diagnosis and management. A probabilistic risk analysis tool was then used to identify and estimate the likelihood and consequence of risks through the following outcome metrics: loss of crew life (inflight mortality due to medical conditions), need for medical evacuation (return to definitive care), and crew disability (task time affected based on how medical conditions influence the ability to perform specific exploration mission crew tasks). Finally, the model’s optimization algorithm provides recommendations for medical capabilities that maximize risk mitigation relative to mass and volume constraints. In the Spring of 2023, IMPACT was utilized to estimate outcome metrics for a design reference mission that would be representative of an extended duration Artemis mission. Notional data generated were then used to determine a recommended set of medical capabilities and resources relative to user-defined mass and volume constraints. A multidisciplinary team has also been updating IMPACT to strengthen the model’s fidelity. Figure 1 shows how updates to outcome metric inputs for the conditions resulted in different capability and resource allocation recommendations. Discussion: This presentation will discuss the IMPACT tool and share the latest data generated for a representative extended duration Artemis mission. Efforts to improve the fidelity of data generated by the model’s algorithm will also be discussed.

K A Shair↗

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 NTRS2024

Earth-Independent Medical Operations (EIMO) Concept of Operations (ConOps)

Compared to the current paradigm for crew health in low-Earth orbit and Lunar missions that rely on constant communication with Mission Control, there is an anticipated shift in medical operations for deep-space exploration missions. This shift stems from mission constraints imposed by the considerable distance from Earth, which include resource limitations due to a lack of resupply, mass, power, volume, and data limitations, challenges imposed by communication latency and the inability to evacuate in case of emergencies. To transition towards a more self-reliant medical approach, a comprehensive strategy is essential to progressively enable crew autonomy and mitigate mission success risks in the challenging environment of space. This transformative shift is collectively referred to as "Earth-Independent Medical Operations" (EIMO), signifying the gradual transfer of medical care and decision-making from terrestrial resources to space-based assets. This transition is aimed at bolstering astronaut health and performance while simultaneously reducing the overall risks associated with space missions. The constraints related to EIMO necessitate an integrated development of medical systems, featuring interoperability with mission planning, vehicle design, spacesuit design, and data architecture. This integration is vital in establishing a robust medical infrastructure that not only supports the well-being of astronauts but also ensures the success of the mission as a whole. The Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has developed a model-based Concept of Operations (ConOps) outlining an initial vision for EIMO. Within this ConOps, a comprehensive view is presented, encompassing stakeholder needs, system objectives, and system goals associated with EIMO. Additionally, it provides illustrative examples of the various activities (scenarios) for which the system will be employed during missions. The selection of these activities has been meticulous, aiming to encompass a wide spectrum of medical conditions, including those falling under different risk categories, such as low-likelihood-low-consequence, low-likelihood-high-consequence, and high-likelihood-low-consequence. The selection of these activities (scenarios) effectively encompasses the wide range of medical events situated within an assumed probability-consequence bell curve. In each scenario, at least one of the five main EIMO components identified is captured. Those EIMO components are: Pre-mission Planning, Acute and Emergent Management Decision Making, Prolonged Medical Management Decision Making, Supplies and Resource Management, and Task Load Management. The ConOps was developed by a multidisciplinary team consisting of systems engineers, scientists, and clinicians across NASA and aims to serve as an initial recommendation to gradually and safely enabling crew autonomy for Mars missions and beyond.

Earth Independent Medical Operations↗

NASA NTRSDate not supplied

Safety Benefit Analysis of Conformance Monitoring for Situation Awareness in UTM

This work presents an analytical approach to address situations in drone operations when strategic coordination alone cannot ensure safety, requiring support from the Conformance Monitoring for Situation Awareness service and potentially other deconfliction services. This study focuses on evaluating the impact of off-nominal operations on both UA-to-UA collision risk and ground injury risk. It first establishes a relationship between the likelihood of off-nominal flights and the effectiveness of strategic coordination. The analysis then derives operational conditions where strategic coordination alone is insufficient to meet target safety requirements, particularly when the likelihood of off-nominal operations exceeds a certain threshold. The results indicate that higher operational tempo or density requires a lower likelihood of offnominal operations for strategic coordination to remain effective and meet safety targets. These findings provide quantitative operational thresholds for when CMSA services are necessary. This work contributes to the safety case for both strategic coordination and CMSA services, offering analysis on when CMSA should be employed to support strategic coordination in achieving the desired safety levels. It also establishes a framework for incorporating other deconfliction services in future safety case analyses.

Strategic deconfliction↗

NASA NTRSDate not supplied

Safety Benefit Analysis of Conformance Monitoring for Situation Awareness in UTM

This work presents an analytical approach to address situations in drone operations when strategic coordination alone cannot ensure safety, requiring support from the Conformance Monitoring for Situation Awareness service and potentially other deconfliction services. This study focuses on evaluating the impact of off-nominal operations on both UA-to-UA collision risk and ground injury risk. It first establishes a relationship between the likelihood of off-nominal flights and the effectiveness of strategic coordination. The analysis then derives operational conditions where strategic coordination alone is insufficient to meet target safety requirements, particularly when the likelihood of off-nominal operations exceeds a certain threshold. The results indicate that higher operational tempo or density requires a lower likelihood of offnominal operations for strategic coordination to remain effective and meet safety targets. These findings provide quantitative operational thresholds for when CMSA services are necessary. This work contributes to the safety case for both strategic coordination and CMSA services, offering analysis on when CMSA should be employed to support strategic coordination in achieving the desired safety levels. It also establishes a framework for incorporating other deconfliction services in future safety case analyses.

Strategic deconfliction↗

NASA NTRS1996

Testing of hypotheses about altitude decompression sickness by statistical analyses

This communication extends a statistical analysis of forced-descent decompression sickness at altitude in exercising subjects (J Appl Physiol 1994; 76:2726-2734) with a data subset having an additional explanatory variable, rate of ascent. The original explanatory variables for risk-function analysis were environmental pressure of the altitude, duration of exposure, and duration of pure-O2 breathing before exposure; the best fit was consistent with the idea that instantaneous risk increases linearly as altitude exposure continues. Use of the new explanatory variable improved the fit of the smaller data subset, as indicated by log likelihood. Also, with ascent rate accounted for, replacement of the term for linear accrual of instantaneous risk by a term for rise and then decay made a highly significant improvement upon the original model (log likelihood increased by 37 log units). The authors conclude that a more representative data set and removal of the variability attributable to ascent rate allowed the rise-and-decay mechanism, which is expected from theory and observations, to become manifest.

NASA Center JSC↗

NASA NTRS2020

Image Moment-Based Extended Object Tracking for Complex Motions

A novel image moment-based model for shape estimation and tracking of an extended target moving with a complex trajectory is presented. The proposed extended object tracking algorithm is based on multiple noisy measurement points sampled from the target at each time step. The shape of the object, approximated by an ellipse, is estimated using a combination of image moments. Dynamic models of image moments for constant velocity and coordinated turn motions are mathematically derived. An unscented Kalman filter - interacting multiple model (UKF-IMM) method is used to track the object and estimate its shape. A likelihood function based on average log-likelihood is derived for the IMM filter. Simulation results of the proposed UKF-IMM algorithm with the image momentbased models are presented that show the estimation of the shape of the object moving in a complex trajectory. The intersection over union (IoU), and the root mean square errors (RMSEs) of the position and velocity of the centroid of the ellipse are used as metrics. The comparison results of the proposed algorithm with a benchmark algorithm from literature based on the IoU and RMSE metrics are presented.

Extended object tracking↗

NASA NTRS1966

Generalized minimum distance decoding.

Generalized minimum distance decoding that permits likelihood information to be used, yielding same probability of error as maximum likelihood decoding

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