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DOE OSTI2025

Risk-Aware Reinforcement Learning Framework for User-Centric O-RAN

The evolution of Open Radio Access Networks (O-RAN) presents an opportunity to enhance network performance by enabling dynamic orchestration of configuration and optimization parameters (COPs) through online learning methods. However, leveraging this potential requires overcoming the limitations of traditional cell-centric RAN architectures, which lack the necessary flexibility. On the other hand, despite their recent popularity, the practical deployment of online learning frameworks, such as Deep Reinforcement Learning (DRL)-based COP optimization solutions, remains limited due to their risk of deteriorating network performance during the exploration phase. In this article, we propose and analyze a novel risk-aware DRL framework for user-centric RAN (UC-RAN), which offers both the architectural flexibility and COP optimization to exploit this flexibility. We investigate and identify UC-RAN COPs that can be optimized via a soft actor-critic algorithm implementable as an O-RAN application (rApp) to jointly maximize latency satisfaction, reliability satisfaction, area spectral efficiency, and energy efficiency. We use the offline learning on UC-RAN to reliably accelerate DRL training, thus minimizing the risk of DRL deteriorating cellular network performance. Results show that our proposed solution approaches near-optimal performance in just a few hundred iterations with a decrease in risk score by a factor of ten.

6G and beyond↗

DOE OSTI2024

Application of Banking Scoring and Rating for Coherent Risk Measures in Electricity Systems ABSCORES

This project developed a framework for asset and system risk management that can be incorporated into current electricity system operations to improve economic efficiency and establish an Electric Assets Risk Bureau. We leveraged scoring and ratings from banking and financial institutions alongside current optimization methods in dispatching power systems to help system operators and electricity markets schedule resources. This approach is based on the observation that there are major discrepancies between the power scheduled by a system operator and the actual power generated/consumed. These discrepancies—exacerbated by unplanned contingencies (e.g., natural disasters)—are caused by multiple factors, including the different financial, environmental and risk preferences of power producers, consumers, and aggregators. We developed a framework that counteracts two failures in electricity system operations: imperfect information and missing markets for products. The technical approach included five tasks. Tasks 1 and 2 supported the development of risk scores at the asset level with historical data collected for this project. Tasks 3, 4, and 5 incorporated scoring into decision-making at the system level. The proposed effort achieved PERFORM's Program Objectives because the proposed outputs and algorithms do not exist in the electricity industry and are an innovative approach to managing risk. Since the acknowledged need to better assess and act upon risk profiles for grid assets has not been met by the industry, this project will also impact ARPA-E's Mission Areas, including improving energy efficiency and giving the U.S. a technological lead in advanced energy technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Low responsiveness of machine learning models to critical or deteriorating health conditions

Machine learning (ML) based mortality prediction models can be immensely useful in intensive care units. Such a model should generate warnings to alert physicians when a patient’s condition rapidly deteriorates, or their vitals are in highly abnormal ranges. Before clinical deployment, it is important to comprehensively assess a model’s ability to recognize critical patient conditions. We develop multiple medical ML testing approaches, including a gradient ascent method and neural activation map. We systematically assess these machine learning models’ ability to respond to serious medical conditions using additional test cases, some of which are time series. Guided by medical doctors, our evaluation involves multiple machine learning models, resampling techniques, and four datasets for two clinical prediction tasks. We identify serious deficiencies in the models’ responsiveness, with the models being unable to recognize severely impaired medical conditions or rapidly deteriorating health. For in-hospital mortality prediction, the models tested using our synthesized cases fail to recognize 66% of the injuries. In some instances, the models fail to generate adequate mortality risk scores for all test cases. Our study identifies similar kinds of deficiencies in the responsiveness of 5-year breast and lung cancer prediction models. Using generated test cases, we find that statistical machine-learning models trained solely from patient data are grossly insufficient and have many dangerous blind spots. Most of the ML models tested fail to respond adequately to critically ill patients. How to incorporate medical knowledge into clinical machine learning models is an important future research direction.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1977

Geomorphic and landform survey of Northern Appennine Range (NAR)

The author has identified the following significant results. An approach to landslide hazard detection was developed through the analysis of satellite imagery (LANDSAT 2) showing many landslide areas that occur on marine silts and clays in northern Appennine Range in Italy. A landslide risk score was given for large areas by narrowing and extending well defined areas, whose behavior and reflectivity variation was due to upper surface changes. Results show that this methodology allows evolution pattern of clay outflows to be distinguished.

Marino, C. M.↗

NASA NTRS2009

Strategic Risk Assessment

This slide presentation provides an overview of the attempt to develop and demonstrate a methodology for the comparative assessment of risks across the entire portfolio of NASA projects and assets. It includes information about strategic risk identification, normalizing strategic risks, calculation of relative risk score, and implementation options.

Derleth, Jason↗

NASA NTRS2017

Systemic Response to Microgravity: Utilizing GeneLab Datasets to Identify Molecular Targets for Future Hypotheses-Driven Spaceflight Studies

Biological risks associated with microgravity are a major concern for long-term space travel. Although determination of risk has been a focus for NASA research, data examining systemic (i.e., multi- or pan-tissue) responses to space flight are sparse. To perform our analysis, we utilized the NASA GeneLab database which is a publicly available repository containing a wide array of omics results from experiments conducted with: i) with different flight conditions (space shuttle (STS) missions vs. International Space Station (ISS); ii) a variety of tissues; and 3) assays that measure epigenetic, transcriptional, and protein expression changes. Meta-analysis of the transcriptomic data from 7 different murine and rat data sets, examining tissues such as liver, kidney, adrenal gland, thymus, mammary gland, skin, and skeletal muscle (soleus, extensor digitorum longus, tibialis anterior, quadriceps, and gastrocnemius) revealed for the first time, the existence of potential master regulators coordinating systemic responses to microgravity in rodents. We identified p53, TGF(beta)1 and immune related pathways as the highly prevalent pan-tissue signaling pathways that are affected by microgravity. Some variability in the degree of change in their expression across species, strain and time of flight was also observed. Interestingly, while certain skeletal muscle (gastrocnemius and soleus) exhibited an overall down-regulation of these genes, some other muscle types such as the extensor digitorum longus, tibialis anterior and quadriceps, showed an up-regulated expression, indicative of potential compensatory mechanisms to prevent microgravity-induced atrophy. Key genes isolated by unbiased systems analyses displayed a major overlap between tissue types and flight conditions and established TGF(beta)1 to be the most connected gene across all data sets. Finally, a set of microgravity responsive miRNA signature was identified and based on their predicted functional state and subsequent impact on health, a theoretical health risk score was calculated. The genes and miRNAs identified from our analyses can be targeted for future research involving efficient countermeasure design. Our study thus exemplifies the utility of GeneLab data repository to aid in the process of performing novel hypothesis based spaceflight research aimed at elucidating the global impact of environmental stressors at multiple biological scales.

GeneLab↗

NASA NTRS2017

Systemic Microgravity Response: Utilizing GeneLab to Develop Hypotheses for Spaceflight Risks

Biological risks associated with microgravity are a major concern for long-term space travel. Although determination of risk has been a focus for NASA research, data examining systemic (i.e., multi- or pan-tissue) responses to space flight are sparse. To perform our analysis, we utilized the NASA GeneLab database which is a publicly available repository containing a wide array of omics results from experiments conducted with: i) with different flight conditions (space shuttle (STS) missions vs. International Space Station (ISS); ii) a variety of tissues; and 3) assays that measure epigenetic, transcriptional, and protein expression changes. Meta-analysis of the transcriptomic data from 7 different murine and rat data sets, examining tissues such as liver, kidney, adrenal gland, thymus, mammary gland, skin, and skeletal muscle (soleus, extensor digitorum longus, tibialis anterior, quadriceps, and gastrocnemius) revealed for the first time, the existence of potential master regulators coordinating systemic responses to microgravity in rodents. We identified p53, TGF1 and immune related pathways as the highly prevalent pan-tissue signaling pathways that are affected by microgravity. Some variability in the degree of change in their expression across species, strain and time of flight was also observed. Interestingly, while certain skeletal muscle (gastrocnemius and soleus) exhibited an overall down-regulation of these genes, some other muscle types such as the extensor digitorum longus, tibialis anterior and quadriceps, showed an up-regulated expression, indicative of potential compensatory mechanisms to prevent microgravity-induced atrophy. Key genes isolated by unbiased systems analyses displayed a major overlap between tissue types and flight conditions and established TGF1 to be the most connected gene across all data sets. Finally, a set of microgravity responsive miRNA signature was identified and based on their predicted functional state and subsequent impact on health, a theoretical health risk score was calculated. The genes and miRNAs identified from our analyses can be targeted for future research involving efficient countermeasure design. Our study thus exemplifies the utility of GeneLab data repository to aid in the process of performing novel hypothesis based spaceflight research aimed at elucidating the global impact of environmental stressors at multiple biological scales.

GeneLab↗

NASA NTRS2009

Determining the Relative Criticality of Diverse Exploration Risks in NASA's Human Research Program

The mission of NASA s Human Research Program (HRP) is to understand and reduce the risk to crew health and performance in exploration missions. The HRP addresses 27 specific risks, primarily in the context of Continuous Risk Management. Each risk is evaluated in terms of two missions (a six month stay on the Moon and a thirty month round trip to Mars) and three types of consequences (in-mission crew health, post-mission crew health, and in-mission performance). The lack of a common metric between the three consequence scales, such as financial costs or quality adjusted life years lost, makes it difficult to compare the relative criticality of the risks. We are, therefore, exploring the use of a ternary scale of criticality based on the common metric of influencing an operational decision. The three levels correspond to the level of concern the risk generates for a "go/no-go" decision to launch a mission: 1) no-go; 2) go with significant reservations; 3) go. The criticality of each of the 27 risks is scored for the three types of consequence in both types of mission. The scores are combined to produce an overall criticality rating for each risk. The overall criticality rating can then be used to guide the prioritization of resources to affect the greatest amount of risk reduction.

Kundrot, Craig E.↗

DOE OSTI2025

Novel CHI3L1 ‐Associated Angiogenic Phenotypes Define Glioma Microenvironments: Insights From Multi‐Omics Integration

ABSTRACT The CHI3L1 signaling pathway significantly influences glioma angiogenesis, but its role in the tumor microenvironment (TME) remains elusive. We propose a novelCHI3L1‐associated vascular phenotype classification for glioma through integrative analyses of multiple datasets with bulk and single‐cell transcriptome, genomics, digital pathology, and clinical data. We investigated the biological characteristics, genomic alterations, therapeutic vulnerabilities, and immune profiles within these phenotypes through a comprehensive multi‐omics approach. We constructed the vascular‐related risk (VR) score based onCHI3L1‐associated vascular signatures (CAVS) identified by machine learning algorithms. Utilizing unsupervised consensus clustering, gliomas were stratified into three distinct vascular phenotypes: Cluster A, marked by high vascularization and stromal activation with a relatively low levels of tumor‐infiltrating lymphocytes (TILs); Cluster B, characterized by moderate vascularization and stromal activity, coupled with a high density of TILs; and Cluster C, defined by low vascularization and sparse immune cell infiltration. We observed that the CAVS effectively indicated glioma‐associated angiogenesis and immune suppression by single‐cell RNA‐seq analysis. Moreover, the high‐VR‐score group exhibited enhanced angiogenic activity, reduced immune response, resistance to immunotherapy, and poorer clinical outcomes. The VR score independently predicted glioma prognosis and, combined with a nomogram, provided a robust clinical decision‐making tool. Potential drug prediction based on transcription factors for high‐risk patients was also performed. Our study reveals thatCHI3L1‐associated vascular phenotypes shape distinct immune landscapes in gliomas, offering insights for optimizing therapeutic strategies to improve patient outcomes.

Oncology↗

NASA NTRS2010

Risk Identification and Visualization in a Concurrent Engineering Team Environment

Incorporating risk assessment into the dynamic environment of a concurrent engineering team requires rapid response and adaptation. Generating consistent risk lists with inputs from all the relevant subsystems and presenting the results clearly to the stakeholders in a concurrent engineering environment is difficult because of the speed with which decisions are made. In this paper we describe the various approaches and techniques that have been explored for the point designs of JPL's Team X and the Trade Space Studies of the Rapid Mission Architecture Team. The paper will also focus on the issues of the misuse of categorical and ordinal data that keep arising within current engineering risk approaches and also in the applied risk literature.

Risk Scoring↗

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↗

DOE OSTI2024

The Cybersecurity Value-at-Risk Framework: Informing Cybersecurity Decisions

The Cybersecurity Value-at-Risk Framework is a tool that can be used by hydropower plant manager to make more educated cybersecurity investments. Users can take a self guided assessment allowing the tools to generate risk, impact and cybersecurity scores and be given risk-based recommendations to enhance decision-making.

CVF↗

NASA NTRS2002

Engineering Management Capstone Project EM 697: Compare and Contrast Risk Management Implementation at NASA and the US Army

NASA at Marshall Space Flight Center (MSFC) and the U.S. Army at Redstone Arsenal were analyzed to determine whether they were successful in implementing their risk management program. Risk management implementation surveys were distributed to aid in this analysis. The scope is limited to NASA S&MA (Safety and Mission Assurance) at MSFC, including applicable support contractors, and the US Army Engineering Directorate, including applicable contractors, located at Redstone Arsenal. NASA has moderately higher risk management implementation survey scores than the Army. Accordingly, the implementation of the risk management program at NASA is considered good while only two of five of the survey categories indicated that the risk management implementation is good at the Army.

Brothers, Mary Ann↗

NASA NTRS2012

Factors Contributing to Unsuccessful Re-Proposed Missions

JPL often re-proposes unselected missions after technology advancement and mission concept development. Feedback given as major and minor weaknesses and strengths in prior rounds are addressed in later versions of proposals. This feedback provides insight into the factors that affect perceptions of risk and value. My research involved an in-depth case study of an original mission, Mission-A, and the re-proposed mission, Mission-B, after a multi-year technology development effort.In 2002, Mission-A was rated as Category III, with above average science merit (top score) and High Risk. To reduce risk, NASA invested technology development funds. In 2006 the re-formulated Mission-A mission, renamed Mission-B, was rated Category IV, good to very good science, and Medium Risk. While the risk rating improved from Mission-A to Mission-B, the overall results were worse.

risk↗

DOE OSTI2024

Adoption Readiness Level Assessment of Redox Flow Batteries

Adoption readiness levels (ARLs) were developed by the Department of Energy’s Office of Technology Transitions (OTT) to holistically capture barriers to market adoption for a technology. The framework consists of 17 risk dimensions falling into 4 broad categories: Value Proposition, Market Acceptance, Resource Maturity, and License to Operate. OTT’s Commercial Adoption Readiness Assessment Tool (CARAT) can be used to evaluate a technology’s ARL. This work applies CARAT to redox flow batteries to evaluate the level of risk for this technology class across the 17 dimensions. Redox flow batteries were found to bear 1-2 high risk dimensions, 10-11 medium risk dimensions, 5 low risk dimensions, and scored an overall low readiness on the CARAT scoring scale (ranges reflect variation with flow battery chemistry). Herein, we describe the factors and evaluation across the dimensions leading to this score for redox flow batteries.

25 ENERGY STORAGE↗

NASA NTRSDate not supplied

Inflight Medical Conditions Risk – Overview and Strategic Approach

The risk of adverse health outcomes and decrements in performance due to inflight medical conditions is among NASA’s “red” risks for Mars missions, scored at the highest likelihood and consequence on the rating scale. Importantly, this risk encompasses all possible medical conditions that might occur in spaceflight. At thesame time, exploration spaceflight will have significant resource constraints (mass, power, volume, data, etc.) and supplying the full complement of medical capabilities available on Earth will be impossible. Inevitably, medical conditions will develop during a human Mars mission and having inadequate inflight capabilities will have a negative impact on the health and performance of the astronauts, leading to adverse crew and/or mission outcomes.

B D Easter↗

NASA NTRS2013

Combined Effects of Spaceflight and Age in Astronauts as Assessed by Areal Bone Mineral Density [BMD] and Trabecular Bone Score

Spaceflight is a potential risk factor for secondary osteoporosis in astronauts. Although lumbar spine (LS) BMD declines rapidly, more than expected for age, there have been no fragility fractures in astronauts that can clearly be attributed to spaceflight. Recently, astronauts have been returning from 6‐month spaceflights with absolute BMD still above young adult mean BMD. In spite of these BMD measurements, we project that the rapid loss in bone mass over long‐duration spaceflight affects the bone microarchitecture of the LS which might predispose astronauts to premature vertebral fractures. Thus, we evaluated TBS, a novel texture index correlated with vertebral bone microarchitecture, as a means of monitoring changes to bone microarchitecture in astronauts as they age. We previously reported that TBS detects an effect of spaceflight (~6‐month duration), independent of BMD, in 51 astronauts (47+/‐4 y) (Smith et al, J Clin Densitometry 2014). Hence, TBS was evaluated in serial DXA scans (Hologic Discovery W) conducted triennially in all active and retired astronauts and more frequently (before spaceflight, after spaceflight and until recovery) in the subset of astronauts flying 4‐6‐ month missions. We used non‐linear models to describe trends in observations (BMD or TBS) plotted as a function of astronaut age. We fitted 1175 observations of 311 astronauts, pre‐flight and then postflight starting 3 years after landing or after astronaut's BMD for LS was restored to within 2% of preflight BMD. Observations were then grouped and defined as follows: 1) LD: after exposure to at least one long‐duration spaceflight > 100 days and 2) SD: before LD and after exposure to at least one short-duration spaceflight < 30 days. Data from males and females were analyzed separately. Models of SD observations revealed that TBS and BMD had similar curvilinear declines with age for both male and female astronauts. However, models of LD observations showed TBS declining with age while BMD appeared stable or trending upward. For females (n=8) LD observations were too few to discern a trend. Notably, models describing trends in TBS appeared to be more sensitive to the effects of age than the models for BMD. We conclude that TBS may provide an additional index for the lumbar spine to monitor the combined changes due to spaceflight and due to aging. This increased knowledge may enhance the ability to identify an intervention trigger for premature vertebral fractures in astronauts.

Sibonga, Jean D.↗

DOE OSTI2025

Integrated Metrics for County-Level Resilience Ranking Using Entropy and TOPSIS

In the face of atypical weather events, power infrastructure failures, and limited resources for resilience investment, energy decision-makers need data-driven metrics to allocate resilience investments and maximize the reduction of power outage impacts. For state-level planning, for instance, ranking the resilience of each county is key to ensuring effective distribution of resources. In such cases, resilience for each spatial unit is multifaceted and is captured by a set of indicators (i.e., metrics) that can be combined into an overall score that reduces the complexity of power outage dynamics to a single decision metric. However, weighting of these indicators is often addressed by simplifying assumptions (i.e., equal weights) or semi-subjective methods that rely on user-defined weights that can introduce biases (e.g., weighted average score). Within the disaster risk reduction and resilience engineering community, a recurring challenge in multicriteria decision-making is the objective weighting of indicators for composite indices. To address this issue, we have leveraged a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) combined with an entropy-based weighting approach to calculated the integrated scores. This method objectively determines the importance of each metric, better discerns between spatial units (i.e., counties), and offers a more reliable ranking of counties according to their relative resilience attributes. By improving methods for integrating resilience indicators, our approach helps planners and decision-makers prioritize resources more effectively for more efficient resilience investments.

Bhusal, Narayan [Oak Ridge National Laboratory (OR↗