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This presentation conveys an approach for risk-based safety and mission assurance applied to cubesats. This presentation accompanies a NASA Goddard standard in development that provides guidance for building a mission success plan for cubesats based on the risk tolerance and resources available.
This talk is intended to help small project proposers to navigate the process of deciding what risks would be acceptable in their Class D projects, as well as steering away from misinterpretations of risk to mean “doing something different”. This will be largely NASA centric, largely focused on science missions, but there will be more general takeaways.
The term risk is used ubiquitously in the space community, but more often than not it is used in a counterproductive way. Furthermore, risks are often written to convey some type of concern, but the risk statements are often more emotional in nature than technically substantive. In this talk, we will provide a rigorous view and definition of risk, along with some structured guidelines for writing risk statements centered on critical thinking, which will combine to provide effective risk management tools when dealing with complex space systems.
Abstract not provided.
Abstract not provided.
Develop a report summarizing a structured approach for the incorporation of risk insights when making facility safety assessments and regulatory decisions is proposed to supplement the deterministic approach, with the following expected benefits: Increased confidence that key safety issues have been fully identified and addressed Better understanding of safety margins Facilitates more structured dialogue among international regulators Reduced reliance on “expert judgement” through a rigorous, pre-defined process with more predictable outcomes Proposed outcome is development of a report summarizing the structure and key considerations for applying this approach that: Is inclusive of advanced reactor technologies Provides for flexible implementation recognizing unique and varying sovereign regulatory structures Builds upon existing/current GIF safety approaches (e.g., GIF Basic Safety Approach, Integrated Safety Assessment Methodology)
Artificial intelligence (AI) methods have revolutionized and redefined the landscape of data analysis in business, healthcare, and technology. These methods have innovated the applied mathematics, computer science, and engineering fields and are showing considerable potential for risk science, especially in the disaster risk domain. The disaster risk field has yet to define itself as a necessary application domain for AI implementation by defining how to responsibly balance AI and disaster risk. (1) How is AI being used for disaster risk applications; and how are these applications addressing the principles and assumptions of risk science, (2) What are the benefits of AI being used for risk applications; and what are the benefits of applying risk principles and assumptions for AI-based applications, (3) What are the synergies between AI and risk science applications, and (4) What are the characteristics of effective use of fundamental risk principles and assumptions for AI-based applications? This study develops and disseminates an online survey questionnaire that leverages expertise from risk and AI professionals to identify the most important characteristics related to AI and risk, then presents a framework for gauging how AI and disaster risk can be balanced. This study is the first to develop a classification system for applying risk principles for AI-based applications. This classification contributes to understanding of AI and risk by exploring how AI can be used to manage risk, how AI methods introduce new or additional risk, and whether fundamental risk principles and assumptions are sufficient for AI-based applications.
Identifying and avoiding terrain hazards (e.g., soft soil and pointy embedded rocks) are crucial for the safety of planetary rovers. This paper presents a newly developed groundbased Mars rover operation tool that mitigates risks from terrain by automatically identifying hazards on the terrain, evaluating their risks, and suggesting operators safe paths options that avoids potential risks while achieving specified goals. The tool will bring benefits to rover operations by reducing operation cost, by reducing cognitive load of rover operators, by preventing human errors, and most importantly, by significantly reducing the risk of the loss of rovers.
Mean heart dose (MHD) over 10 Gy and left anterior descending (LAD) coronary artery volume (V) receiving 15 Gy (V15Gy) greater than 10% can significantly increase the risk of major adverse cardiac events (MACE) in patients with non-small cell lung cancer (NSCLC). We sought to characterize the discordance between MHD and LAD dose and the association of this classification on the risk of MACE after radiation therapy.
Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. Objectives: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different endpoints: LD50 value, U.S. Environmental Protection Agency hazard categories, Globally Harmonized System for Classification and Labelling hazard categories, very toxic chemicals (LD50 =50 mg/kg), and non-toxic chemicals (LD50 >2000 mg/kg). Methods: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. Results: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared to in vivo results. Discussion: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for over 800,000 chemicals have been made available via the NTP’s Integrated Chemical Environment. The models are also implemented in a free, standalone open-source tool, OPERA, which allows predictions of new and untested chemicals to be made.
In this paper, the problem of designing a matched filter for image correlation will be treated as a statistical pattern recognition problem. It is shown that, by minimizing a suitable criterion, a matched filter can be estimated which approximates the optimum Bayes discriminant function in a least-squares sense. It is well known that the use of the Bayes discriminant function in target classification minimizes the Bayes risk, which in turn directly minimizes the probability of a false fix. A fast Fourier implementation of the minimum Bayes risk correlation procedure is described.
Nonparametric Bayes risk estimation for measurement classification, using nearest neighbor error rate and Parzen probability density function estimators
The recently published NASA-STD-6030 defines the Additive Manufacturing (AM) Requirements for Spaceflight Systems. Key aspects of the certification approach include the development of a qualified material process (QMP) and material characterization determined by part classification. Nondestructive evaluation (NDE) of the full surface and volume is required for all part classifications except those with negligible risk. NASA is exploring the use of in-process monitoring data to improve risk posture and supplement post-build inspection for complex parts. Currently, the most challenging obstacle to overcome is linking the indications in the monitoring data to the physics of the process and the final material state of the finished part. NASA is undertaking studies to understand and quantify this relationship for various monitoring methods. The desired goal is to develop a protocol to establish this correlation for any monitoring method. Once this correlation is known, the critical defect size can be linked to a representative indication in the monitoring data, and the capability of the monitoring system can be tested using the 90/95 probability of detection requirement for NDE methods. This would enable the use of in-process monitoring as a defect screening activity for AM part certification. Many high-criticality components built with AM have high complexity and therefore limited inspectability, so using in-process monitoring can help address this certification gap. The use of adaptive, closed-loop monitoring systems that alter the locked process will require a new approach to the QMP.
The recently published NASA-STD-6030 defines the Additive Manufacturing (AM) Requirements for Spaceflight Systems. Key aspects of the certification approach include the development of a qualified material process (QMP) and material characterization determined by part classification. Nondestructive evaluation (NDE) of the full surface and volume is required for all part classifications except those with negligible risk. NASA is exploring the use of in-process monitoring data to improve risk posture and supplement post-build inspection for complex parts. Currently, the most challenging obstacle to overcome is linking the indications in the monitoring data to the physics of the process and the final material state of the finished part. NASA is undertaking studies to understand and quantify this relationship for various monitoring methods. The desired goal is to develop a protocol to establish this correlation for any monitoring method. Once this correlation is known, the critical defect size can be linked to a representative indication in the monitoring data, and the capability of the monitoring system can be tested using the 90/95 probability of detection requirement for NDE methods. This would enable the use of in-process monitoring as a defect screening activity for AM part certification. Many high-criticality components built with AM have high complexity and therefore limited inspectability, so using in-process monitoring can help address this certification gap. The use of adaptive, closed-loop monitoring systems that alter the locked process will require a new approach to the QMP.
Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters have occurred almost annually in the urban area of Beijing, the capital of China. Based on a self-organizing map (SOM) artificial neural network (ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product (GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANN is suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors, producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. As a result, the points that were assigned risk grades of IV or V were located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.
Resource Prospector (RP) is an in-situ resource utilization (ISRU) technology demonstration mission under study by the NASA Human Exploration and Operations Mission Directorates (HEOMD). This clever mission is currently planned to launch in 2020 and will demonstrate extraction of oxygen, water and other volatiles, as well measure mineralogical content such as silicon and light metals, like aluminum and titanium, from lunar regolith. Expanding human presence beyond low-Earth orbit to asteroids and Mars will require the maximum possible use of local materials, so-called in-situ resources, and the moon presents a unique destination to conduct robotic investigations that advance ISRU capabilities, as well as providing significant exploration and science value. This mission is equally important; however, for how it executes as a risk-tolerant, cost-effective mission. RP follows on the path-finding approaches of the Lunar Crater Observation and Sensing Satellite (LCROSS) mission. The LCROSS mission confirmed the presence of water-ice on the moon, but also established a new lightweight-approach to project and mission execution which was considerably cheaper and faster than traditional NASA missions. RP has been designated as a Class D mission, just as LCROSS. This mission classification is the most risk-tolerant class of mission within the NASA risk framework and as such, is given more latitude to accept higher-levels of residual risk. The intention is that by saving monies normally spent attempting to assure a single missions success, more missions can be funded. A well-designed portfolio can accept occasional mission failure, as it still gets more done for the same investment of resources. This classification enables tailoring the NASA Policy Requirements (NPRs) to lighter-weight approaches to mission management and execution. RP is also studying both international and commercial partnerships as a means to maximize return on the investment. International partnerships provide both capabilities synergies and cost-sharing opportunities, while the evolving new space commercial options are revealing new approaches to acquiring cost-effective services, including the benefits of bundling services. Even the world of launch vehicles is changing, offering much less expensive access to space, especially if NASA is able to be flexible in how it approaches mission assurance. Finally, leveraging investments being made elsewhere within a program portfolio, can enable cost-savings by enabling two applications with one investment. RP will be the next pathfinder mission to both enable exploration capabilities for future missions, and continue to evolve cost-effective approaches for NASA.
Licensing Modernization Project (LMP) Risk Informed Approach Selection of Licensing Basis Events (LBEs) Frequency-Consequence Target LBE Cumulative Risk Targets Structures, Systems, and Components (SSC) Safety Categories Classification Evaluation of Defense in Depth (DID) Use of Probabilistic Risk Assessment (PRA) in LMP Process PRA Policy Statement American Nuclear Society (ANS) Non-Light-Water Reactor (non-LWR) PRA Standard
Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type has become a necessity in cancer research. A major challenge in cancer management is the classification of patients into appropriate risk groups for better treatment and follow-up. Such risk assessment is critically important in order to optimize the patient’s health and the use of medical resources, as well as to avoid cancer recurrence. This paper focuses on the application of machine learning methods for predicting the likelihood of a recurrence of cancer. It is not meant to be an extensive review of the literature on the subject of machine learning techniques for cancer recurrence modeling. Other recent papers have performed such a review, and I will rely heavily on the results and outcomes from these papers. The electronic databases that were used for this review include PubMed, Google, and Google Scholar. Query terms used include “cancer recurrence modeling”, “cancer recurrence and machine learning”, “cancer recurrence modeling and machine learning”, and “machine learning for cancer recurrence and prediction”. The most recent and most applicable papers to the topic of this review have been included in the references. It also includes a list of modeling and classification methods to predict cancer recurrence.