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DOE OSTI2022

Width-Based Discharge Partitioning in Distributary Networks: How Right We Are

River deltas are home to large populations and can be composed of complex channel networks which convey flows of matter to the shoreline. Knowledge of flow within individual channels is needed to quantify the distribution of discharge across the delta, and thus its sustainability over time. Due to a lack of field measurements at the local channel scale, researchers leverage remote sensing data to estimate the partitioning of flow. We compare data from 15 river deltas to discharge partitioning estimates based on channel network graphs derived from remote sensing imagery. We quantify errors in the common width-based method and test alternative partitioning techniques to find that width-based discharge partitioning is universally applicable, suggesting that absent any site-specific information, discharge partitioning by average channel width is an appropriate approach. We also provide networks, streamflow measurements, and flux partitioning estimates for 28 delta networks as the Discharge In Distributary NeTworks (DIDNT) dataset.

58 GEOSCIENCES↗

DOE OSTI2022

A Semi-Automated Approach for Curating a Glossary of Key Terms for Open-Source Data Queries

In FY20, the Savannah River National Laboratory (SRNL) was funded by the National Nuclear Security Administration’s Office of Defense Nuclear Non-Proliferation Research and Development (NA-22) to build a machine learning based modeling pipeline that could extract proliferation events of interest from open text-based data sources. As a test case, the research team targeted the identification/fusion of events and indicators that fissile core fabrication would be executed at the Savannah River Site prior to its official announcement in May of 2018. The demonstration prototype proved successful by applying natural language processing and graph theoretical techniques to identify contextual shifts in key words and phrases that acted as indicators that pit production would be carried out at the Savannah River Site up to two years prior to the official announcement.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2013

Adaptive AMG with Coarsening Based on Compatible Weighted Matching

We introduce a new composite adaptive Algebraic Multigrid (composite αAMG) method to solve systems of linear equations without a-priori knowledge or assumption on characteristics of near-null components of the AMG preconditioned problem referred to as algebraic smoothness. Our version of αAMG is a composite solver built through a bootstrap strategy aimed to obtain a desired convergence rate. The coarsening process employed to build each new solver component relies on a pairwise aggregation scheme based on maximum weighted matching in a graph and on principles of compatible relaxation. The latter replaces the commonly used characterization of strength of connection in both the coarse space selection and in the interpolation scheme. The goal is to design a method leading to scalable AMG for a wide class of problems that go beyond the standard elliptic Partial Differential Equations (PDEs). In the present work, we introduce the method and demonstrate its potential when applied to symmetric positive definite linear systems arising from finite element discretization of highly anisotropic elliptic PDEs on structured and unstructured meshes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

The Strategic Environment Assessment bibliographic network: A quantitative literature review analysis

Academic literature has been continuously growing at such a pace that it can be difficult to follow the progression of scientific achievements; hence, the need to dispose of quantitative knowledge support systems to analyze the literature of a subject. In this article we utilize network analysis tools to build a literature review of scientific documents published in the multidisciplinary field of Strategic Environment Assessment (SEA). The proposed approach helps researchers to build unbiased and comprehensive literature reviews. We collect information on 7662 SEA publications and build the SEA Bibliographic Network (SEABN) employing the basic idea that two publications are interconnected if one cites the other. We apply network analysis at macroscopic (network architecture), mesoscopic (sub graph) and microscopic levels (node) in order to i) verify what network structure characterizes the SEA literature, ii) identify the authors, disciplines and journals that are contributing to the international discussion on SEA, and iii) scrutinize the most cited and important publications in the field. Results show that the SEA is a multidisciplinary subject; the SEABN belongs to the class of real small world networks with a dominance of publications in Environmental studies over a total of 12 scientific sectors. Christopher Wood, Olivia Bina, Matthew Cashmore, and Andrew Jordan are found to be the leading authors while Environmental Impact Assessment Review is by far the scientific journal with the highest number of publications in SEA studies. - Highlights: • We utilize network analysis to analyze scientific documents in the SEA field. • We build the SEA Bibliographic Network (SEABN) of 7662 publications. • We apply network analysis at macroscopic, mesoscopic and microscopic network levels. • We identify SEABN architecture, relevant publications, authors, subjects and journals.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Probabilistic Modeling of Microbial Metabolic Networks for Integrating Partial Quantitative Knowledge Within the Nitrogen Cycle

Understanding the interactions between microbial communities and their environment sufficiently to predict diversity on the basis of physicochemical parameters is a fundamental pursuit of microbial ecology that still eludes us. However, modeling microbial communities is problematic, because (i) communities are complex, (ii) most descriptions are qualitative, and (iii) quantitative understanding of the way communities interact with their surroundings remains incomplete. One approach to overcoming such complications is the integration of partial qualitative and quantitative descriptions into more complex networks. Here we outline the development of a probabilistic framework, based on Event Transition Graph (ETG) theory, to predict microbial community structure across observed chemical data. Using reverse engineering, we derive probabilities from the ETG that accurately represent observations from experiments and predict putative constraints on communities within dynamic environments. These predictions can feedback into the future development of field experiments by emphasizing the most important functional reactions, and associated microbial strains, required to characterize microbial ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Figure Descriptive Text Extraction Using Ontological Representation

Abstract Experimental research publications provide figure form resources including graphs, charts, and any type of images to effectively support and convey methods and results. To describe figures, authors add captions, which are often incomplete, and more descriptions reside in body text. This work presents a method to extract figure descriptive text from the body of scientific articles. We adopted ontological semantics to aid concept recognition of figure-related information, which generates human- and machine-readable knowledge representations from sentences. Our results show that conceptual models bring an improvement in figure descriptive sentence classification over word-based approaches.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

A computational approach for the annotation of hydrogen-bonded base interactions in crystallographic structures of the ribozymes

A fundamental understanding of the atomic level interactions in ribonucleic acid (RNA) and how they contribute towards RNA architecture is an important knowledge platform to develop through the discovery of motifs from simple arrangements base pairs, to more complex arrangements such as triples and larger patterns involving non-standard interactions. The network of hydrogen bond interactions is important in connecting bases to form potential tertiary motifs. Therefore, there is an urgent need for the development of automated methods for annotating RNA 3D structures based on hydrogen bond interactions. COnnection tables Graphs for Nucleic ACids (COGNAC) is automated annotation system using graph theoretical approaches that has been developed for the identification of RNA 3D motifs. This program searches for patterns in the unbroken networks of hydrogen bonds for RNA structures and capable of annotating base pairs and higher-order base interactions, which ranges from triples to sextuples. COGNAC was able to discover 22 out of 32 quadruples occurrences of the Haloarcula marismortui large ribosomal subunit (PDB ID: 1FFK) and two out of three occurrences of quintuple interaction reported by the non-canonical interactions in RNA (NCIR) database. These and several other interactions of interest will be discussed in this paper. These examples demonstrate that the COGNAC program can serve as an automated annotation system that can be used to annotate conserved base-base interactions and could be added as additional information to established RNA secondary structure prediction methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Bringing Fenton Hill into the Digital Age: Data Conversion in Support of the Geothermal Technologies Office Code Comparison Study Challenge Problems

The Geothermal Technologies Office Code Comparison Study (GTO-CCS) was established by the U.S. Department of Energy to facilitate collaboration among members of the geothermal modeling community and to evaluate and improve upon the ability of existing codes to simulate thermal, hydrological, mechanical, and chemical processes associated with complex enhanced geothermal systems (EGS). The first stage of the project, which has been completed, involved comparing simulations for seven benchmark problems that were primarily designed using well-prescribed, simplified data sets. In the second stage, the participating teams are tackling two challenge problems based on the EGS research conducted in hot dry rock (HDR) at Fenton Hill, near Los Alamos, New Mexico. The Fenton Hill project, conducted by Los Alamos National Laboratory (LANL) from 1970 to 1995, was the world’s first HDR demonstration project. One of the criteria for selecting this experiment as the basis for the challenge problems was the amount and availability of data for generating model inputs. The Fenton Hill HDR system consisted of two reservoirs – an earlier Phase I reservoir tested from 1974 to 1981 and a deeper Phase II reservoir tested from 1980 to 1995. Detailed accounts of both phases of the HDR project have been presented in a number of books and reports, including a recently published summary of the lessons learned and a final report with a chronological description of the Fenton Hill project, prepared by LANL. Project documents and records have been archived and made public through the National Geothermal Data System (NGDS). Some of the data acquired from Phase II are available in electronic format readable on modern computers. These include the microseismic data from some of the important experiments (e.g. the massive hydraulic fracturing test conducted in 1983) and the injection/production wellhead data from the circulation tests conducted between 1992-1995. However, much of the data collected during the project, while publicly available, currently only exist in the form of tables or graphs within scanned documents. Therefore, in support of the GTO-CCS, the data needed for developing simulation inputs are being compiled and converted to platform independent, open readable formats so that all participating teams will have access to the same electronic data set. In some cases this requires conversion using optical character recognition, digitizing existing images, and generating the appropriate metadata from project documents. The GTO-Velo knowledge management framework, developed by Pacific Northwest National Laboratory (PNNL), was used for the benchmark problem stage of the comparison study and will also be used as the data repository for the challenge problem data sets. It is staggering and impractical to convert all published data for the Fenton Hill site, so the focus is on data that supports simulations for the three topical areas defined by the study for the challenge problems: 1) reservoir creation/stimulation, 2) reactive and passive transport, and 3) thermal recovery. Conversion of these data provide value not only to GTO-CCS participants, but also to members of the geothermal community at large who may be interested in revisiting the Fenton Hill experiment in the future.

Code Comparison Study↗

DOE OSTI2012

Public Communication of Technical Issues in Today's Changing Visual Language - 12436

Communication regarding the management of radioactive materials is a well-established challenge. Residents and consumers have suspected for years that companies and governments place short-term economic concerns ahead of health and safety. This skepticism is compounded with increased attention to safety issues at nuclear power plants everywhere after Fukushima. Nonetheless, today's environment presents unexpected opportunities to transform public fear into teachable moments that bring knowledge and facts to discussions on nuclear energy. In the weeks following Japan's crisis, the lack of reliable information on radiation levels saw citizens taking to the streets with dosimeters and Geiger counters in crowd-sourced radiation monitoring efforts. Efforts, based mainly online, represent a growing set of examples of how internet and cell-phone technology are being put to use in emergency situations. The maps, graphs and tables created to meet public interest also exemplify some of the psychological priorities of audiences and present learning tools that can improve future education efforts in non-emergency situations. Industry outreach efforts often consist of technical details and quantitative data that are difficult for lay audiences to interpret. The intense attention to nuclear energy issues since last March has produced a wide array of visual samples. Citizen monitors, news organizations, government agencies and others have displayed quantitative information in innovative ways. Their efforts offer new perspective on what charts, maps and info graphics do - or need to do - to illustrate requirements, record assessments and promote understanding of nuclear-waste issues. Surveying the best examples, nuclear communicators can improve their offerings of easy-to-use, evidence-based visuals to inform stakeholders. Familiar to most communications professionals in the nuclear industry, risk communication is a science-based approach with over three decades of research evidence informing the discipline. Risk communication principles address the fact that often perception is reality. Scientific evidence for decision making must be approached differently in situations where stakeholders have high levels of concern and low levels of trust. Visual communications must take this into account to be successful. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2012

A Space-Filling Visualization Technique for Multivariate Small World Graphs

We introduce an information visualization technique, known as GreenCurve, for large sparse graphs that exhibit small world properties. Our fractal-based design approach uses spatial cues to approximate the node connections and thus eliminates the links between the nodes in the visualization. The paper describes a sophisticated algorithm to order the neighboring nodes of a large sparse graph by solving the Fiedler vector of its graph Laplacian, and then fold the graph nodes into a space-filling fractal curve based on the Fiedler vector. The result is a highly compact visualization that gives a succinct overview of the graph with guaranteed visibility of every graph node. We show in the paper that the GreenCurve technology is (1) theoretically sustainable by introducing an error estimation metric to measure the fidelity of the new graph representation, (2) empirically rigorous by conducting a usability study to investigate its strengths and weaknesses against the traditional graph layout, and (3) pragmatically feasible by applying it to analyze stressed conditions of the large scale electric power grid on the west coast.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Automated descriptor selection, volcano curve generation, and active site determination using the DescMAP software

The material space for catalyst discovery is expansive. Volcano curves are traditionally employed to provide physical insights into optimal catalyst characteristics for new material selection. Their generation lies on a single descriptor picked using expert knowledge. Here we present DescMAP, a Python-based software, to automate the selection of descriptors, the generation of volcano maps, and the identification of active sites for structure-sensitive reactions. Here, we consider traditional energy-based and geometric descriptors for structure-sensitive reactions. DescMAP is integrated with the Virtual Kinetic Laboratory (VLab) to provide multiple functionalities. It inputs spreadsheets or template files for flexibility and outputs interactive graphs for post-processing. We demonstrate its features using the non-oxidative dehydrogenation of ethane to ethylene over (111) closed-packed surfaces and the methane total oxidation over various Pt facets. It can be easily applied to other complex chemistries and achieves quick screening of potential catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

GRAPH — an readout ASIC for large MCP based detectors

We present a programmable 16 channel, mixed signal, low power readout ASIC, having the project historically named Gigasample Recorder of Analog waveforms from a PHotodetector (GRAPH). It is designed to read large aperture single photon imaging detectors using micro channel plates for charge multiplication, and measuring the detector's response on crossed strips anodes to extrapolate the incoming photon position. Each channel consists of a fast, low power and low noise charge sensitive amplifier, which provides a myriad of coarse and fine programmable options for gain and shaping settings. Further, the amplified signal is recorded using, to our knowledge novel, the Hybrid Universal sampLing Architecture (HULA) ADC. A kind of mixed signal double buffer memory, that enables concurrent waveform recording, and selected event digitized data extraction. The sampling frequency is freely adjustable between few kHz up to 125 MHz, while the chip's internal digital memory holds a history 2048 samples for each channel, with a digital headroom of 12 bits. An optimized region of interest sample-read algorithm allows to extract the information just around the event pulse peak, while selecting the next event, thus substantially reducing the operational dead time. The chip is designed in 130 nm TSMC CMOS technology, and its power consumption is around 47 mW per channel.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

AutoCheck: Automatically Identifying Variables for Checkpointing by Data Dependency Analysis

Checkpoint/Restart (C/R) has been widely deployed in numerous HPC systems, Clouds, and industrial data centers, which are typically operated by system engineers. Nevertheless, there is no existing approach that helps system engineers without domain expertise and domain scientists without system fault tolerance knowledge identify those critical variables accounted for correct application execution restoration in a failure for C/R. To address this problem, we propose an analytical model and a tool (AutoCheck) that can automatically identify critical variables to checkpoint for C/R. AutoCheck relies on first, analytically tracking and optimizing data dependency between variables and other application execution state, and second, a set of heuristics that identify critical variables for checkpointing from the refined data dependency graph (DDG). AutoCheck allows programmers to pinpoint critical variables to checkpoint quickly within a few minutes. We evaluate AutoCheck on 13 representative HPC benchmarks, demonstrating that AutoCheck can efficiently identify correct critical variables to checkpoint.

HPC↗

DOE OSTI2017

Sampling-Based Motion Planning Algorithms for Replanning and Spatial Load Balancing

The common theme of this dissertation is sampling-based motion planning with the two key contributions being in the area of replanning and spatial load balancing for robotic systems. Here, we begin by recalling two sampling-based motion planners: the asymptotically optimal rapidly-exploring random tree (RRT*), and the asymptotically optimal probabilistic roadmap (PRM*). We also provide a brief background on collision cones and the Distributed Reactive Collision Avoidance (DRCA) algorithm. The next four chapters detail novel contributions for motion replanning in environments with unexpected static obstacles, for multi-agent collision avoidance, and spatial load balancing. First, we show improved performance of the RRT* when using the proposed Grandparent-Connection (GP) or Focused-Refinement (FR) algorithms. Next, the Goal Tree algorithm for replanning with unexpected static obstacles is detailed and proven to be asymptotically optimal. A multi-agent collision avoidance problem in obstacle environments is approached via the RRT*, leading to the novel Sampling-Based Collision Avoidance (SBCA) algorithm. The SBCA algorithm is proven to guarantee collision free trajectories for all of the agents, even when subject to uncertainties in the knowledge of the other agents’ positions and velocities. Given that a solution exists, we prove that livelocks and deadlock will lead to the cost to the goal being decreased. We introduce a new deconfliction maneuver that decreases the cost-to-come at each step. This new maneuver removes the possibility of livelocks and allows a result to be formed that proves convergence to the goal configurations. Finally, we present a limited range Graph-based Spatial Load Balancing (GSLB) algorithm which fairly divides a non-convex space among multiple agents that are subject to differential constraints and have a limited travel distance. The GSLB is proven to converge to a solution when maximizing the area covered by the agents. The analysis for each of the above mentioned algorithms is confirmed in simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Comparison of Machine Learning Approaches for Prediction of the Equivalent Alkane Carbon Number for Microemulsions Based on Molecular Properties

The chemical properties of oils are vital in the design of microemulsion systems. The hydrophilic–lipophilic difference equation used to predict microemulsions’ phase behavior expresses the oils’ physiochemical properties as the equivalent alkane carbon number (EACN). The experimental determination of EACN requires knowledge of the temperature dependence of the microemulsion system and the effects of different surfactant concentrations. Thus, the experimental determination is time-intensive and tedious, requiring days to months for proper separations. Furthermore, the experiments require high purity of chemicals because microemulsions are sensitive to impurities. Our work focuses on the quick and reliable predictions of the EACN with machine learning (ML) models. Due to the immaturity of ML chemical predictions, we compare three graph neural networks (GNNs) and a gradient-boosted tree algorithm, known as XGBoost. The GNNs use the molecular structures represented as simplified molecular-input line-entry system (SMILES) codes for the initial input, which allows us to assess whether geometry optimization is necessary for reliable results. The XGBoost model also begins with the SMILES representations of the molecules but uses molecular descriptors instead of geometry optimizations. As a result, the best model tested (crystal graph convolutional neural network with Merck molecular force field-94) has an error of 1.15 EACN units of the true EACN for unknown data with the errors skewed toward zero and an R² score of 0.9

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Document Retrieval and Ranking using Similarity Graph Mean Hitting Times

We present a novel approach to information retrieval and document analysis based on graph analytic methods. Traditional information retrieval methods use a set of terms to define a query that is applied against a document corpus to identify the documents most related to those terms. In contrast, we define a query as a set of documents of interest and apply the query by computing mean hitting times between this set and all other documents on a document similarity graph abstraction of the semantic relationships between all pairs of documents. We present the steps of our approach along with a simple example application illustrating how this approach can be used to find documents related to two or more documents or topics of interest.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

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 OSTI2014

A Visual Analytics Approach to Structured Data Analysis to Enhance Nonproliferation and Arms Control Verification Activities

Analysis activities for Nonproliferation and Arms Control verification require the use of many types of data. Tabular structured data, such as Excel spreadsheets and relational databases, have traditionally been used for data mining activities, where specific queries are issued against data to look for matching results. The application of visual analytics tools to structured data enables further exploration of datasets to promote discovery of previously unknown results. This paper discusses the application of a specific visual analytics tool to datasets related to the field of Arms Control and Nonproliferation to promote the use of visual analytics more broadly in this domain. Visual analytics focuses on analytical reasoning facilitated by interactive visual interfaces (Wong and Thomas 2004). It promotes exploratory analysis of data, and complements data mining technologies where known patterns can be mined for. Also with a human in the loop, they can bring in domain knowledge and subject matter expertise. Visual analytics has not widely been applied to this domain. In this paper, we will focus on one type of data: structured data, and show the results of applying a specific visual analytics tool to answer questions in the Arms Control and Nonproliferation domain. We chose to use the T.Rex tool, a visual analytics tool developed at PNNL, which uses a variety of visual exploration patterns to discover relationships in structured datasets, including a facet view, graph view, matrix view, and timeline view. The facet view enables discovery of relationships between categorical information, such as countries and locations. The graph tool visualizes node-link relationship patterns, such as the flow of materials being shipped between parties. The matrix visualization shows highly correlated categories of information. The timeline view shows temporal patterns in data. In this paper, we will use T.Rex with two different datasets to demonstrate how interactive exploration of the data can aid an analyst with arms control and nonproliferation verification activities. Using a dataset from PIERS (PIERS 2014), we will show how container shipment imports and exports can aid an analyst in understanding the shipping patterns between two countries. We will also use T.Rex to examine a collection of research publications from the IAEA International Nuclear Information System (IAEA 2014) to discover collaborations of concern. We hope this paper will encourage the use of visual analytics structured data analytics in the field of nonproliferation and arms control verification. Our paper outlines some of the challenges that exist before broad adoption of these kinds of tools can occur and offers next steps to overcome these challenges.

arms control↗