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Hydropower Cybersecurity Value-at-Risk Framework

Hydropower remains one of the strongest forms of renewable energy generation methods. It is crucial to address the increasing risks associated with the rapid digitization. The push towards decarbonization also factors in the need to ensure security and resilience for grid-connected renewable energy resources. This report summarizes the U.S. Department of Energy's Water Power Technologies Office's effort to develop a cybersecurity valuation methodology that assists hydropower stakeholders in assessing risks associated with plan operations and gathers valuation guidance through a web-based application. The Hydropower Cybersecurity Value-at-Risk Framework delivers a platform for industry members to perform self-assessments and make informed decisions on their cybersecurity investments.

13 HYDRO ENERGY↗

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.↗

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↗

Project: Corbomite - Product: ConsoleWorks REACT

TDi Technologies presents ConsoleWorks REACT, an advanced platform designed to tackle the complexities of cyber and operational risk assessment. This comprehensive solution goes beyond asset-focused approaches by considering the impact of both assets and people have on the security and operation of critical infrastructure, specifically targeting preventing gird mis-operation by evaluating real-time human interaction or commands with critical assets. The hypothesis suggests that by integrating the assessment of user commands into the overall risk assessment process, organizations can make more informed decisions, prioritize resources effectively, and respond promptly to potential threats. This hypothesis forms the basis for the development of a proactive and holistic risk management approach that is more comprehensive and context aware than traditional models which only look at assets, patch levels, configuration and threat intel by collecting that information off the network vs directly from the asset, human, and human interaction all in real-time. ConsoleWorks' unique man-in-the-middle architecture is a key feature that sets it apart in the cybersecurity landscape. This architecture enables the real-time observation, enforcement, and commands or interaction risk transparency of user interaction with critical infrastructure to be risk mitigated and audited as they are aggregated with device and human risk factors for a more comprehensive risk threat score across a device or group of devices. This architecture allows ConsoleWorks to act as a secure intermediary between users and critical assets, monitoring all interactions and ensuring that only authorized commands are sent to the asset to be executed. This not only enhances security but also provides a comprehensive audit trail of all user activities, contributing to compliance efforts and facilitating incident investigation and risk management. By integrating this unique architecture with our comprehensive risk assessment methodology, ConsoleWorks REACT provides a powerful solution for managing cyber and operational risks, enabling organizations to maintain a robust security posture and effectively mitigate potential threats. To that end, the primary objective of this project was to research and develop a robust solution that enables the energy industry to mitigate the risks associated with human actions that can compromise the security or operations of assets critical to energy delivery, generation, transmission and operation. ConsoleWorks REACT plays a pivotal role in achieving this goal by leveraging its unique capabilities to monitor and track all user activity. Through its Zero Trust approach, which emphasizes continuous verification, the platform ensures secure access to assets and serves as the centralized human response and notification platform for addressing cyber and operational issues.

97 MATHEMATICS AND COMPUTING↗

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↗

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↗

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↗

Pre-screening to guide coronary artery calcium scoring for early identification of high-risk individuals in the general population

Abstract Aims To evaluate the ability of Systematic COronary Risk Estimation 2 (SCORE2) and other pre-screening methods to identify individuals with high coronary artery calcium score (CACS) in the general population. Methods and results Computed tomography-based CACS quantification was performed in 6530 individuals aged 45 years or older from the general population. Various pre-screening methods to guide referral for CACS were evaluated. Miss rates for high CACS (CACS ≥300 and ≥100) were evaluated for various pre-screening methods: moderate (≥5%) and high (≥10%) SCORE2 risk, any traditional coronary artery disease (CAD) risk factor, any Risk Or Benefit IN Screening for CArdiovascular Disease (ROBINSCA) risk factor, and moderately (>3 mg/24 h) increased urine albumin excretion (UAE). Out of 6530 participants, 643 (9.8%) had CACS ≥300 and 1236 (18.9%) had CACS ≥100. For CACS ≥300 and CACS ≥100, miss rate was 32 and 41% for pre-screening by moderate (≥5%) SCORE2 risk and 81 and 87% for high (≥10%) SCORE2 risk, respectively. For CACS ≥300 and CACS ≥100, miss rate was 8 and 11% for pre-screening by at least one CAD risk factor, 24 and 25% for at least one ROBINSCA risk factor, and 67 and 67% for moderately increased UAE, respectively. Conclusion Many individuals with high CACS in the general population are left unidentified when only performing CACS in case of at least moderate (≥5%) SCORE2, which closely resembles current clinical practice. Less stringent pre-screening by presence of at least one CAD risk factor to guide CACS identifies more individuals with high CACS and could improve CAD prevention.

Ties, Daan (ORCID:0000000164729886)↗

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↗

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↗

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↗

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↗

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.↗

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↗

Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks

Abstract Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi‐sector dynamics and the design of risk‐management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example, the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (a) stepwise line search, (b) precalibration or screening, and (c) the Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle‐based approach that takes advantage of the massive parallelization afforded by modern high‐performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.

54 ENVIRONMENTAL SCIENCES↗

Hydropower Cybersecurity Risk Management and Valuation

Advancements to DOE WPTO funded Hydropower Cybersecurity Value-at-Risk Framework application allows stakeholder to translate risk-based assessments to quantitative scores allowing to better decision making for cybersecurity investments.

13 HYDRO ENERGY↗

Multidimensional Risk Analysis: MRISK

Multidimensional Risk (MRISK) calculates the combined multidimensional score using Mahalanobis distance. MRISK accounts for covariance between consequence dimensions, which de-conflicts the interdependencies of consequence dimensions, providing a clearer depiction of risks. Additionally, in the event the dimensions are not correlated, Mahalanobis distance reduces to Euclidean distance normalized by the variance and, therefore, represents the most flexible and optimal method to combine dimensions. MRISK is currently being used in NASA's Environmentally Responsible Aviation (ERA) project o assess risk and prioritize scarce resources.

McCollum, Raymond↗

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

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

model based mission assurance↗