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NASA NTRS2003

Characterization of Model-Based Reasoning Strategies for Use in IVHM Architectures

Open architectures are gaining popularity for Integrated Vehicle Health Management (IVHM) applications due to the diversity of subsystem health monitoring strategies in use and the need to integrate a variety of techniques at the system health management level. The basic concept of an open architecture suggests that whatever monitoring or reasoning strategy a subsystem wishes to deploy, the system architecture will support the needs of that subsystem and will be capable of transmitting subsystem health status across subsystem boundaries and up to the system level for system-wide fault identification and diagnosis. There is a need to understand the capabilities of various reasoning engines and how they, coupled with intelligent monitoring techniques, can support fault detection and system level fault management. Researchers in IVHM at NASA Ames Research Center are supporting the development of an IVHM system for liquefying-fuel hybrid rockets. In the initial stage of this project, a few readily available reasoning engines were studied to assess candidate technologies for application in next generation launch systems. Three tools representing the spectrum of model-based reasoning approaches, from a quantitative simulation based approach to a graph-based fault propagation technique, were applied to model the behavior of the Hybrid Combustion Facility testbed at Ames. This paper summarizes the characterization of the modeling process for each of the techniques.

Poll, Scott↗

DOE OSTI2015

Synchronizability of random rectangular graphs

Random rectangular graphs (RRGs) represent a generalization of the random geometric graphs in which the nodes are embedded into hyperrectangles instead of on hypercubes. The synchronizability of RRG model is studied. Both upper and lower bounds of the eigenratio of the network Laplacian matrix are determined analytically. It is proven that as the rectangular network is more elongated, the network becomes harder to synchronize. The synchronization processing behavior of a RRG network of chaotic Lorenz system nodes is numerically investigated, showing complete consistence with the theoretical results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Graph identification of proteins in tomograms ( GRIP‐Tomo )

Abstract In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three‐dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo‐multimeric complexes from 10 topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single‐domain protein native topologies as well as distinct single‐domain proteins from multimeric complexes within noisy volumes.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1991

Software reliability report

There are many software reliability models which try to predict future performance of software based on data generated by the debugging process. Unfortunately, the models appear to be unable to account for the random nature of the data. If the same code is debugged multiple times and one of the models is used to make predictions, intolerable variance is observed in the resulting reliability predictions. It is believed that data replication can remove this variance in lab type situations and that it is less than scientific to talk about validating a software reliability model without considering replication. It is also believed that data replication may prove to be cost effective in the real world, thus the research centered on verification of the need for replication and on methodologies for generating replicated data in a cost effective manner. The context of the debugging graph was pursued by simulation and experimentation. Simulation was done for the Basic model and the Log-Poisson model. Reasonable values of the parameters were assigned and used to generate simulated data which is then processed by the models in order to determine limitations on their accuracy. These experiments exploit the existing software and program specimens which are in AIR-LAB to measure the performance of reliability models.

Wilson, Larry↗

DOE OSTI2024

Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots

Most problems within and beyond the scientific domain can be framed into one of the following three levels of complexity of function approximation. Type 1: Approximate an unknown function given input/output data. Type 2: Consider a collection of variables and functions, some of which are unknown, indexed by the nodes and hyperedges of a hypergraph (a generalized graph where edges can connect more than two vertices). Given partial observations of the variables of the hypergraph (satisfying the functional dependencies imposed by its structure), approximate all the unobserved variables and unknown functions. Type 3: Expanding on Type 2, if the hypergraph structure itself is unknown, use partial observations of the variables of the hypergraph to discover its structure and approximate its unknown functions. These hypergraphs offer a natural platform for organizing, communicating, and processing computational knowledge. While most scientific problems can be framed as the data-driven discovery of unknown functions in a computational hypergraph whose structure is known (Type 2), many require the data-driven discovery of the structure (connectivity) of the hypergraph itself (Type 3). We introduce an interpretable Gaussian Process (GP) framework for such (Type 3) problems that does not require randomization of the data, access to or control over its sampling, or sparsity of the unknown functions in a known or learned basis. Its polynomial complexity, which contrasts sharply with the super-exponential complexity of causal inference methods, is enabled by the nonlinear ANOVA capabilities of GPs used as a sensing mechanism.

Science & Technology - Other Topics↗

DOE OSTI2025

Modeling Cyber Supply Chain Incidents with Multilayered Graph Motifs

As noted within the literature, supply chain includes people and organizations---manufacturers, integrators, and third-party vendors---that are involved in one or more stages of a product lifecycle. Since supply chains, by definition, include organizations and people, supply chain risk management activities must consider dependencies between an organization's business processes and third-party resources. Just as adversarial tactics can be implemented via techniques implemented via networked computer systems, so can such tactics be expressed via legal business relationships. A cyber incident may have an exponential impact downstream, for example, by leveraging a product's distribution channel (e.g. malicious updates in SolarWinds, buggy updates in CrowdStrike). Similarly, legitimate and legal business relationships also affect the attack surface exposure of systems, enabling long-term persistence and/or unknown impacts to product quality that are hard to detect. This paper catalogs several recent digital supply chain incidents and applies a multilayered network formalism to develop structural indicators (graph motifs) that reflect potentially-adversarial behavior. Finally, we compare and contrast the characteristics of adversarial tactics (e.g. Loss of Availability, Data Collection) that leverage cyber-physical dependencies to those that leverage legal organizational relationships.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Hamiltonian systems, Toda lattices, solitons, Lax pairs on weighted Z -graded graphs

In this study, we consider discrete one-dimensional nonlinear equations and present the procedure of lifting them to Z -graded graphs. We identify conditions that allow one to lift one-dimensional solutions to solutions on graphs. In particular, we prove the existence of solitons for static potentials on graded fractal graphs. We also show that even for a simple example of a topologically interesting graph, the corresponding non-trivial Lax pairs and associated unitary transformations do not lift to a Lax pair on the Z -graded graph.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

SU-F-T-99: Data Visualization From a Treatment Planning Tracking System for Radiation Oncology

Purpose: A treatment planning process tracker database with input forms and a TV-viewable display webpage was developed and implemented in our clinic to collect time data points throughout the process. Tracking plan times is important because it directly affects the patient quality of care. Simply, the longer a patient waits after their initial simulation CT for treatment to begin, the more time the cancer has to progress. The tracker helps to drive workflow through the clinic, while the data collected can be used to understand and manage the process to find and eliminate inefficiencies. Methods: The overall process steps tracked are CT-simulation, mark patient, draw normal contours, draw target volumes, create plan, and review/approve plan. Time stamps for task completion were extracted and used to generate a set of clinic metrics, among which include average time for each step in the process split apart by type of treatment, average time to completion for plans started in a given week, and individual overall completion time per plan. Results: Trends have been tracked for fourteen weeks of clinical data (196 plans). On average, drawing normal contours and target volumes is taking 2–5 times as long as creating the plan itself. This is potentially an issue because it could mean the process is taking too long initially, and it could be forcing the planning step to be done in a short amount of time. We also saw from our graphs that there appears to be no clear trend on the average amount of time per plan week-to-week. Conclusion: A tracker of this type has the potential to provide insight into how time is utilized in our clinic. By equipping our dosimetrists, radiation oncologists, and physicists with individualized metric sets, the tracker can help provide visibility and drive workflow. Funded in part by CPRIT (RP140105).

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Statistical Learning for Nonlinear Model Reduction from Local Simulations of Stochastic and Particle- and Agent-Based Systems

Stochastic physical systems across the sciences that have very high-dimensional state spaces, with a large number of fast degrees of freedom that force direct simulators to proceed by integration steps that are orders of magnitude smaller than events of interests (e.g., particle collisions). Examples range from molecular motion to dynamics of large populations of cells. A grand challenge in the simulation and understanding of such systems is the systematic construction of accurate, interpretable, reduced models, enabling faster simulations, revealing fundamental properties of the dynamics, and predicting phenomena of interest that the original simulator could not reached with sufficient accuracy or within a given computational budget. In this projected we developed novel statistical estimation/machine learning techniques for analyzing and building empirical reduced models for important families of high-dimensional stochastic systems, in particular: - we developed techniques for estimating interaction kernels in interacting particle- and agent-based systems, which are ubiquitous in Physics, Biology and many other sciences, given observed trajectories of the system; - we developed techniques for nonlinear model reduction for high-dimensional stochastic systems that have a small number of unknown, nonlinear slow variables, and a large number of fast modes, that are possibly of large magnitude, given observed short trajectories of the system in the form of bursts of trajectories from different initial conditions; - we developed novel techniques for estimating linear dynamical systems on graphs when both the dynamics and the underlying graph are unknown, and we have a sparse set of space-time observations; - we considered the problem of estimating an unknown nonlinear observation function of a standard process (e.g. Brownian motion), so that we can recognized if an observed dynamics is "just" a nonlinear version of a known dynamics; we also developed benchmarks for learning algorithms aimed at learning and classifying diffusion processes.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1990

Improving processing and toughness of a high performance composite matrix through an interpenetrating polymer network. VI

The use of a semiinterpenetrating polymer network (SIPN) of the high-performance polyimide NR-150B2 to reduce brittleness and improve processability in the highly crosslinked acetylene-terminated polyimides Thermid LR-600, AL-600, MC-600, and FA-700 is described. The theoretical basis of the SIPN process is reviewed; the preparation and characterization of the neat SIPN resins and unidirectional graphite-fiber composites are explained; and the results are presented in extensive tables, graphs, and micrographs and discussed in detail. Significant increases in fracture energy were observed with SIPN, from 93 J/sq m for unmodified LR-600 to 283-603 J/sq m for the SIPN materials; the room-temperature flexural strength of the unidirectional composites also increased, from 1344 MPa for an unmodified MC-600 composite to 2020-1751 MPa for the SIPN composites. The potential applicability of SIPN-based composites to aerospace structures and electronic components is indicated.

Pater, Ruth H.↗

DOE OSTI2025

InterGraph-CPS: A Graph-Theoretic Approach to Characterize Cross-Domain Cyber-Physical Interdependencies and Uncertainties in Electric Grid Systems for Improved Decision-Making in Operation and Response

Critical infrastructure systems such as the electric grid are increasingly cyber-physical; yet, despite the cyber-physical characteristics of critical infrastructure systems, the physical process system and communication/control network system are traditionally analyzed in siloes. As these systems become more cyber-physical, it is crucial that models and methods are available to assess the cyber physical system (CPS) interdependencies, characteristics, and event propagation for improved planning, operation, and response. Thus, we proposed an integrated structural and temporal CPS interdependency analysis framework, InterGraph-CPS, that provides insight into the CPS function during normal operation as well as disturbances. This integrated structural and temporal interdependency framework is uniquely designed for assessing CPSs by account for the challenges of analyzing cyber and physical data streams together due to data availability, data type, and time scale differences. By leveraging both structural (e.g., graph analysis) and temporal (e.g., data analytics) techniques, different CPS behaviors and configurations can be accounted for.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra

Motivation: Although proteomics has rapidly developed in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a “bird’s eye view” of intact proteoforms. The combinatorial explosion of various alterations on a protein may result in billions of possible proteoforms, making proteoform identification a challenging computational problem. Results: We propose a new data structure, called the mass graph, for efficient representation of proteoforms and design mass graph alignment algorithms. We developed TopMG, a mass graph-based software tool for proteoform identification by top-down mass spectrometry. Experiments on top-down mass spectrometry data sets showed that TopMG outperformed existing methods in identifying complex proteoforms.

Dynamic Programming↗

DOE OSTI2013

On collisional diffusion in a stochastic magnetic field

The effect of particle collisions on the transport in a stochastic magnetic field in tokamaks is investigated. The model of resonant magnetic perturbations generated by external coils at the plasma edge is used for the stochastic magnetic field. The particle collisions are simulated by a random walk process along the magnetic field lines and the jumps across the field lines at the collision instants. The dependencies of the local diffusion coefficients on the mean free path λ{sub mfp}, the diffusion coefficients of field lines D{sub FL}, and the collisional diffusion coefficients, χ{sub ⊥} are studied. Based on these numerical data and the heuristic arguments, the empirical formula, D{sub r}=χ{sub ⊥}+v{sub ||}D{sub FL}/(1+L{sub c}/λ{sub mfp}), for the local diffusion coefficient is proposed, where L{sub c} is the characteristic length of order of the connection length l{sub c}=πqR{sub 0}, q is the safety factor, R{sub 0} is the major radius. The formula quite well describes the results of numerical simulations. In the limiting cases, the formula describes the Rechester-Rosenbluth and Laval scalings.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2016

Final Technical Report on Quantifying Dependability Attributes of Software Based Safety Critical Instrumentation and Control Systems in Nuclear Power Plants

With the current transition from analog to digital instrumentation and control systems in nuclear power plants, the number and variety of software-based systems have significantly increased. The sophisticated nature and increasing complexity of software raises trust in these systems as a significant challenge. The trust placed in a software system is typically termed software dependability. Software dependability analysis faces uncommon challenges since software systems’ characteristics differ from those of hardware systems. The lack of systematic science-based methods for quantifying the dependability attributes in software-based instrumentation as well as control systems in safety critical applications has proved itself to be a significant inhibitor to the expanded use of modern digital technology in the nuclear industry. Dependability refers to the ability of a system to deliver a service that can be trusted. Dependability is commonly considered as a general concept that encompasses different attributes, e.g., reliability, safety, security, availability and maintainability. Dependability research has progressed significantly over the last few decades. For example, various assessment models and/or design approaches have been proposed for software reliability, software availability and software maintainability. Advances have also been made to integrate multiple dependability attributes, e.g., integrating security with other dependability attributes, measuring availability and maintainability, modeling reliability and availability, quantifying reliability and security, exploring the dependencies between security and safety and developing integrated analysis models. However, there is still a lack of understanding of the dependencies between various dependability attributes as a whole and of how such dependencies are formed. To address the need for quantification and give a more objective basis to the review process -- therefore reducing regulatory uncertainty -- measures and methods are needed to assess dependability attributes early on, as well as throughout the life-cycle process of software development. In this research, extensive expert opinion elicitation is used to identify the measures and methods for assessing software dependability. Semi-structured questionnaires were designed to elicit expert knowledge. A new notation system, Causal Mechanism Graphing, was developed to extract and represent such knowledge. The Causal Mechanism Graphs were merged, thus, obtaining the consensus knowledge shared by the domain experts. In this report, we focus on how software contributes to dependability. However, software dependability is not discussed separately from the context of systems or socio-technical systems. Specifically, this report focuses on software dependability, reliability, safety, security, availability, and maintainability. Our research was conducted in the sequence of stages found below. Each stage is further examined in its corresponding chapter. Stage 1 (Chapter 2): Elicitation of causal maps describing the dependencies between dependability attributes. These causal maps were constructed using expert opinion elicitation. This chapter describes the expert opinion elicitation process, the questionnaire design, the causal map construction method and the causal maps obtained. Stage 2 (Chapter 3): Elicitation of the causal map describing the occurrence of the event of interest for each dependability attribute. The causal mechanisms for the “event of interest” were extracted for each of the software dependability attributes. The “event of interest” for a dependability attribute is generally considered to be the “attribute failure”, e.g. security failure. The extraction was based on the analysis of expert elicitation results obtained in Stage 1. Stage 3 (Chapter 4): Identification of relevant measurements. Measures for the “events of interest” and their causal mechanisms were obtained from expert opinion elicitation for each of the software dependability attributes. The measures extracted are presented in this chapter. Stage 4 (Chapter 5): Assessment of the coverage of the causal maps via measures. Coverage was assessed to determine whether the measures obtained were sufficient to quantify software dependability, and what measures are further required. Stage 5 (Chapter 6): Identification of “missing” measures and measurement approaches for concepts not covered. New measures, for concepts that had not been covered sufficiently as determined in Stage 4, were identified using supplementary expert opinion elicitation as well as literature reviews. Stage 6 (Chapter 7): Building of a detailed quantification model based on the causal maps and measurements obtained. Ability to derive such a quantification model shows that the causal models and measurements derived from the previous stages (Stage 1 to Stage 5) can form the technical basis for developing dependability quantification models. Scope restrictions have led us to prioritize this demonstration effort. The demonstration was focused on a critical system, i.e. the reactor protection system. For this system, a ranking of the software dependability attributes by nuclear stakeholders was developed. As expected for this application, the stakeholder ranking identified safety as the most critical attribute to be quantified. A safety quantification model limited to the requirements phase of development was built. Two case studies were conducted for verification. A preliminary control gate for software safety for the requirements stage was proposed and applied to the first case study. The control gate allows a cost effective selection of the duration of the requirements phase.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2012

Feynman rules for Coulomb gauge QCD

The Coulomb gauge in nonabelian gauge theories is attractive in principle, but beset with technical difficulties in perturbation theory. In addition to ordinary Feynman integrals, there are, at 2-loop order, Christ-Lee (CL) terms, derived either by correctly ordering the operators in the Hamiltonian, or by resolving ambiguous Feynman integrals. Renormalization theory depends on the sub-graph structure of ordinary Feynman graphs. The CL terms do not have a sub-graph structure. We show how to carry out renormalization in the presence of CL terms, by re-expressing these as 'pseudo-Feynman' integrals. We also explain how energy divergences cancel. - Highlights: Black-Right-Pointing-Pointer In Coulomb gauge QCD, we re-express Christ-Lee terms in the Hamiltonian as pseudo-Feynman integrals. Black-Right-Pointing-Pointer This gives a subgraph structure, and allows the ordinary renormalization process. Black-Right-Pointing-Pointer It also leads to cancellation of energy-divergences.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Sensory stimulation for upper limb amputations modulates adaptability of cortical large-scale systems and combination of somatosensory and visual inputs

Abstract Touch-like phantom limb sensations can be elicited through targeted transcutaneous electrical nerve stimulation (tTENS) in individuals with upper limb amputation. The corresponding impact of sensory stimulation on cortical activity remains an open question. Brain network research shows that sensorimotor cortical activity is supported by dynamic changes in functional connections between relevant brain regions. These groups of interconnected regions are functional modules whose architecture enables specialized function and related neural processing supporting individual task needs. Using electroencephalographic (EEG) signals to analyze modular functional connectivity, we investigated changes in the modular architecture of cortical large-scale systems when participants with upper limb amputations performed phantom hand movements before, during, and after they received tTENS. We discovered that tTENS substantially decreased the flexibility of the default mode network (DMN). Furthermore, we found increased interconnectivity (measured by a graph theoretic integration metric) between the DMN, the somatomotor network (SMN) and the visual network (VN) in the individual with extensive tTENS experience. While for individuals with less tTENS experience, we found increased integration between DMN and the attention network. Our results provide insights into how sensory stimulation promotes cortical processing of combined somatosensory and visual inputs and help develop future tools to evaluate sensory combination for individuals with amputations.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Is the 80 - 20 Principle a Friend or a Foe? Critical Remarks on the Dominant Flat Gradient - 17206

The well-known 80 - 20 principle, also known as the Pareto principle, describes the fact that usually 80 % of the result is achieved by 20 % of the effort, because many probability distributions are caused by a power-law. This principle is valued in the realm of project management and management in general because of the fast progress which generally takes place in the first fifth of the process. If the final result is not exactly specified or resources are spent wisely, 100 % of the planned result will be reached easily. However, in nuclear business safety is a principal parameter. Therefore precision is inevitable. In long-lasting projects the bad end of the Pareto-distribution is dominant: The very shallowly-inclined gradient at the end of the graph means that results at that point come at a very high effort and cost. The graph reaches the 100% only asymptomatically, meaning the initially projected results are never met. Especially if in earlier project phases the Pareto principle is applied very successfully, a lot of work is transferred to the future. For example, in decommissioning projects the waste often is badly characterized causing an enormous effort in the process of characterization and qualification. Especially if funding or time are limited, it is important to be aware of the asymmetric distribution of applied work and result. The 80 - 20 principle is a great advantage, if you start a project and respect the asymmetric distribution of applied resources and results in your project plan. Especially in long-lasting projects with changing perspectives, this asymmetric distribution may cause massive delays and expansion of costs, because small changes near the end cause large/big effort due to consequences on the existing data. If the proper goal of a project is not entirely known in the beginning, it is very probable that the last 5 - 10 % of the result will cause major financial threat and extensions of the estimated time frame. If funding or time are limited, it is almost impossible to readjust a project in the estimated frame of resources. In this case a complete restart of the project may be more reasonable than a long-lasting change management with uncertain results. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2012

On the use of graph search techniques for the analysis of extreme-scale combustion simulation data

With the continuous increase in high performance computing capabilities, simulations are becoming ever larger and more complex, using bigger domains, tracking more variables, and producing more time steps. This increase in the ranges of spatial and temporal simulation scales results in data that presents significant challenges to as well as new opportunities for the visualization and data analysis community. For example, highly-localized, intermittent events (such as the formation of ignition kernels in turbulent combustion) may be caused by interactions between multiple variables across a series of time steps, making both their definition and their extraction difficult, particularly at scale. This paper introduces an intuitive framework to support the identification, characterization, and tracking of such complex, multivariate, temporally evolving events in large-scale simulations. In a pre-processing step, we use topological techniques to create a hierarchical family of feature definitions for each variable of interest. Subsequently, we select a particular set of features for analysis and, using overlap-based metrics, we generate an attributed relational graph (ARG) capturing the relationships between different variables both within one and across multiple time steps. Finally, we leverage subgraph-isomorphism search heuristics to identify patterns in the ARG that characterize interesting events. We demonstrate the power of this approach by analyzing a large-scale turbulent combustion simulation.

McLendon, W. C.↗