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At least 181 records · Page 10

Iterative ML and Experiments for Emerging VOCs

SAND2026-17074O Iterative ML and Experiments for Emerging VOCs is a tool that analyzes and predicts the behaviors of SARS-CoV-2 variants. It processes experimental data on ACE2 (the receptor for the SARS-CoV-2 virus that allows it to infect the cell) and antibody binding using machine learning models, including neural networks, to forecast ACE2 interactions and variant expression. The tool employs transfer learning and global epistasis modeling, integrating public datasets with proprietary data to enhance prediction accuracy. Additionally, it fits concentration-response curves to determine dissociation constants and generates visualizations to support research findings, thereby aiding in the identification of new antibodies for emerging variants of concern. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Sheffield, Thomas [Sandia National Lab. (SNL-NM), ↗

Mapping Support for Targeted Critical Minerals Exploration and Extraction

The United States’ dependency on imported minerals poses significant risks to economic stability and national security due to potential supply disruptions. Recognizing the strategic importance of critical minerals, the Department of Energy (DOE) emphasizes the need for a secure and resilient supply chain to support emissions reduction, technology development, and capitalization on clean energy opportunities. The DOE’s Office of Manufacturing and Energy Supply Chains (MESC), in collaboration with the Office of Policy (OP), addresses these vulnerabilities by focusing on upstream domestic critical minerals production, balancing extraction with social and environmental goals, including conservation, environmental justice, and respect for Tribal sovereignty. This report showcases a collaborative effort involving Idaho National Laboratory (INL), Argonne National Laboratory (Argonne), National Renewable Energy Laboratory (NREL), and the U.S. Geological Survey (USGS) to map mineral development potential along with key social and environmental datasets. A geographical information system (GIS)-based web map application was developed as a preliminary tool for environmental analysis, integrating 158 geospatial data layers such as critical habitat, land ownership, economic indicators, and environmental concerns. Data were sourced from agencies like the Bureau of Land Management (BLM) and USGS and processed using GIS technology to enhance visualization and analysis. The proposed analysis framework categorizes areas into high, mid, and low concern based on withdrawn lands, special status species, the Economic Development Capacity Index (EDCI) Mining Composite Index, and the Climate and Economic Justice Screening Tool (CEJST). While the application provides broad visualizations, it is not a substitute for detailed environmental reviews required under the National Environmental Policy Act (NEPA). Users must conduct further analyses and engage with tribal entities and other stakeholders for comprehensive planning. A case study of the Idaho Cobalt Belt (ICB) in Lemhi County, Idaho, has been provided in the report to illustrate the tool's practical use. This report introduces a GIS application and framework to support stakeholders in identifying and prioritizing areas for critical mineral exploration, promoting secure supply chains, and advancing the nation's energy independence through responsible resource stewardship.

54 ENVIRONMENTAL SCIENCES↗

Mesh Computing Remote Automatic Workflow

The software suite uses a microservice architecture using Docker and `docker-compose`. The microservices are as follows: 1. User interface. This interface is written in JavaScript using the Svelte framework. It exposes form elements and a 3D visualizer to prompt the user through the definition of microstructure parameters, and setting parameters for mesh generation and refinement. 2. Mesh generator. This is a container running the Python package for DREAM3D to generate a voxelized mesh that represents a microstructure defined by the user in the interface. 3. Cubit runner. This is a secure shell protocol tool that makes the submitting the DREAM mesh to an HPC instance and starts to run Cubit shell commands to smooth the grain boundaries with its `sculpt` library, applies user-defined boundary node sets, and bundles and returns the simulation-ready meshes and input files as a zipped directory.

Harris, BrennanKay↗

MCNPy

SAND2026-20425O MCNPy runs and analyzes simulations from MCNP, a software that models radiation transport of neutrons and gamma rays. MCNPy uses Python to start MCNP, retrieve event data files, and convert them into graph structures for detailed analysis. It offers visualization tools, including 2D views of particle histories, making complex simulation data easier to interpret for researchers and engineers. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Nowack, Aaron [Sandia National Lab. (SNL-CA), Live↗

Fuel Cycle Research & Development: Technical Monthly – June 2012

Topics are: (1) MPACT Campaign - (a) Management and Integration • Coordination meetings between NE and NA-22, NA-24, and NA-82 were conducted the week of June 11th. Preparations are being made for the next MPACT working group meeting, scheduled for Aug 28-30 at Idaho Falls. In addition to covering accomplishments and discussing future plans, a site tour of INL facilities (MFC, EBR, ATR, INTEC) is being organized. (2) Accounting and Control Technologies - (a) Microcalorimetry • Now operating 256-pixel array at LANL. We are in the process of tuning detector parameters to improve and optimize performance. Preliminary measurements show approximate number of live pixels is similar to that observed previously at NIST. Continuing to study contribution to systematic error from uncertainties in tabulated gamma-ray energies. (b) Electrochemical Sensor • Testing of sensors fashioned from different precursor materials continued. SEM analysis of all used sensors has been or will be performed. (c) Lead Slowing Down Spectrometer • Ongoing perturbation calculations are providing information on the fundamental systematic error limits of LSDS. In order to achieve separating the contribution of Pu and 235U to the signal, there will need to be tight controls on systematic errors. Continuing to look into a He4 detector. Research into local construction of a He4 detector continued. We have started to apply the algorithm to test the LSDS using experimental data from previous RPI measurements. PNNL also developed a plan to address the lack of statistics in the MCNP modeling of the NGSI 64 assemblies. The ISU graduate student built and tested a fission chamber to gain experience with them. (d) Fast Neutron Imaging to Quantify Nuclear Materials • The imaging detector design was modified for each pixel to have an 8 x 8 pixel array. Quotations and purchasing process for components, including the new PSD scintillator are in progress. (e) Fast Neutron Multiplicity Analysis • The team submitted two papers to the upcoming INMM meeting that are related to the fast neutron multiplicity R&D effort. Progress was made on the project's main goal of designing a concept for a prototype fast-neutron multiplicity counter. We started laying out the outline for the final report. We have been working with our ORNL collaborators to develop a new digitizer system to support our experimental campaign planned for next year. (3) MPACT Analysis Tools - (a) Multi-isotope Process Monitor • Fuel characterization framework development continued during June. A report describing the methodologies is being completed. Kenneth Dayman, from University of Texas, spent a week at PNNL wrapping up his master's research and working on a journal submission covering that work. The target journal is the IEEE transactions on Nuclear Science; submission is planned for the end of July. A proposal to instrument H-Canyon is being prepared in conjunction with SRNL and the NNSA's NGSI program. The impact of gamma-ray spectrum counting statistics on the precision of relative radioisotope component intensities as reconstructed via Principal Component Regression (PCR) continued in June with Monte Carlo simulations of a two-component (i.e., two radioisotope) system. This work generalizes earlier studies in FY12 in which Poisson counting variations of only a single spectrum component were simulated. (b) Modeling and Simulation for Analysis of Safeguards Performance (Electrochemical) • Preliminary insights into safeguards challenges and the initial design for an electrochemical plant have been written up into an INMM paper and will be presented at the INMM Summer Meeting. Work is currently adding a new visualization capability for integrating materials accountancy with physical protection. (c) Material Control including Process Monitoring (Pattern Recognition, Sensors) • Fabrication of quartz chips continued at an external foundry. Awaiting delivery of the heat exchange manifold and chip holder. (d) MPACT System Integration and Technical Support • The initial report on cost-basis metrics for nuclear material security risk and proliferation risk was completed as scheduled. (4) Safeguards and Security by Design - (a) Used fuels storage security analysis, guidance and best practices • RFP was finalized and sent out. Awaiting response from World Institute for Nuclear Security. Coordinated with Scott Demuth on joint report • LANL milestone on best practices. Attended Nuclear Energy Institute National Nuclear Security Conference. Developed plan for soliciting input on prioritized issues for used fuel storage security. Worked on setting up a meeting with the Nuclear Regulatory Commission. Worked on FY2013 work package planning.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership

The 2005 Intergovernmental Panel on Climate Change (IPCC) Special Report on CCS raised the profile of CO2 capture and storage (CCS) as an important technology for reducing greenhouse gas (GHG) emissions. CCS is now recognized as a key component of most climate change mitigation scenarios. Since publication of that report the international research, development, and deployment (RD&D) community has advanced key technical aspects, clarified regulatory requirements, explored value chain and infrastructure solutions, and developed incentive paradigms to enable and promote large-scale deployment of CCS. These efforts have included research to better characterize geologic storage resources, to improve injection performance and storage efficiency, to assess and manage subsurface environmental risks, and to advance monitoring technologies to assure system conformance. These efforts have helped to build confidence in the viability of geologic carbon storage (GCS), but stakeholder concerns about long-term risks and liability associated with GCS remain a hurdle to broad acceptance and large-scale deployment of CCS. Since 2010, the U.S. DOE’s National Risk Assessment Partnership (NRAP) – a research collaboration between five contributing national laboratories – has worked to establish and demonstrate methods and tools to quantify and manage the subsurface environmental risks associated with GCS, amidst uncertainty. This work supports the Office of Fossil Energy and Carbon Management Carbon Transport and Storage Program’s goal of advancing safe and secure commercial-scale GCS deployment. To address the technical challenge of simulating the physical response of the GCS site to large-scale CO2 injection, NRAP has adopted an approach that relies on coupling computationally efficient reduced-order and/or data-driven proxy models of important system components (i.e., storage reservoir, sealing caprock, leakage pathways, intermediate formations, overlying groundwater aquifers, and the atmosphere) in integrated assessment framework. That integrated model of the physical system is complemented with fit-for purpose functionality to support site characterization and risk-related decisions. The recently released NRAP Phase II toolset includes the Open-Source Integrated Assessment Model (NRAP-Open-IAM) for evaluation of trends in leakage risk and potential impact, tools to support monitoring design optimization (Designs for Risk Evaluation and Management – DREAM v3.0 and Passive Seismic Monitoring Tool - PSMT), and tools for state of stress evaluation (State-of-Stress Analysis Tool - SOSAT) and forecasting induced seismicity risk. The NRAP team has also released a pair of reports describing conceptual workflows to incorporate physics-based, quantitative risk assessment into many of the design, planning, operation, and closure decisions for GCS projects. An online catalogue highlights published studies where these tools and methods are demonstrated. In this presentation, the utility of these products to assess risks and address key stakeholder questions will be highlighted through examples, and related insights about the safety and security of geologic carbon storage in qualified storage sites will be discussed. The prospect of rapid, large-scale deployment of GCS technology to aggressively reduce anthropogenic CO2 emissions requires careful consideration of interference between multiple commercial-scale storage projects within a basin. Going forward, NRAP is expanding and adapting site-scale risk quantification tools and methods to enable assessment of risks and inform management decisions for basin-scale deployment. Increasingly, this work will leverage next-generation approaches for surrogate modelling, fast prediction, and advanced visualization enabled by machine learning and artificial intelligence to promote virtual learning, scenario evaluation, and augment risk-based decision making.

quantitative risk assessment, geologic carbon stor↗

Survey Report for the Characterization of the Five Tanks Located Near the Old Salvage Yard at the Y-12 National Security Complex, Oak Ridge, Tennessee

This summary report presents analytical results, radiological survey data, and other data/information for disposition planning of the five tanks located west of the Old Salvage Yard (OSY) at the Y-12 National Security Complex (Y-12) in Oak Ridge, Tennessee. Field personnel from Oak Ridge Associated Universities (ORAU) and URS?CH2M Oak Ridge LLC completed data collection in May 2012 per the project-specific plan (PSP) (ORAU 2012). Deviations from the PSP are addressed in the body of this report. Characterization activities included three data collection modes: visual inspection, radiological survey, and volumetric sampling/analysis. This report includes the final validated dataset and updates associated with the Tank 2 residues originally thought to be a biological bloom (e.g., slime mold) but ultimately identified as iron sulfate crystals.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

In-Device Delay: Introduction to Obscurant Physcial Protection System

The Office of Radiological Security (ORS) In-Device Delay (IDD) program has undertaken a project to research and develop a novel protection system for industrial irradiators that contain high-activity Co-60 sources. Based on adversary testing conducted by ORS, it is was determined that to successfully accomplish the theft of the target material, the adversary will require visual contact of the sources and source rack located at the bottom of the pool. Therefore, if a means of obscuring or visually hiding the sources in the pool can be achieved (while adhering to facility operations, safety, and regulatory requirements), then illicit source theft will be significantly hindered. This project aims to develop a low-cost, non-propriety obscurant that, when an adversary action is detected, the obscurant will be deployed into the pool quickly, rendering visual observation of the source problematic; however, this obscurant will not otherwise disturb the sources, source rack, and filtration system. The obscurant will remain in the pool until removed by another process.

61 RADIATION PROTECTION AND DOSIMETRY↗

mvBayes

SAND2026-16980O mvBayes implements multivariate Bayesian regression using MATLAB and decomposes a multivariate or functional response into components based on a user-specified orthogonal basis. This allows for independent modeling of each component with any chosen univariate Bayesian regression model. This tool includes methods for prediction and visualization, facilitating the evaluation of Bayesian surrogate models through the application of Bayesian theory and Markov Chain Monte Carlo (MCMC) sampling techniques. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Tucker, J. Derek [Sandia National Lab. (SNL-CA), L↗

Scalable edge clustering of dynamic graphs via weighted line graphs

Timestamped relational datasets consisting of records (or connections) between pairs of entities are ubiquitous in network science. For applications like peer-to-peer communication, email, various social network interactions, and computer network security, it is useful to organize these records into groups based on how and when they are occurring. Weighted line graphs offer a natural way to model how records are related in such datasets but for large real-world graph topologies, building and utilizing the line graph is prohibitively expensive. Here, we present the framework to cluster the edges of a dynamic graph via the associated line graph that contains two major contributions. The first is a method to work with the line graph implicitly and the second is a distributed scale implementation of an agglomerative hierarchical graph clustering algorithm. We outline a novel hierarchical dynamic graph edge clustering approach that efficiently breaks massive relational datasets into small sets of edges containing events at various timescales. This is in stark contrast to traditional graph clustering algorithms that prioritize highly connected (clique-like) community structures. Our approach relies on constructing a sufficient subgraph of a weighted line graph and applying a hierarchical agglomerative clustering. This approach is related to scalable techniques from spatial clustering, nonlinear-dimension reduction, topological data analysis, and draws particular inspiration from HDBSCAN. As an edge clustering, this method yields an overlapping node clustering. Our algorithm is parallelizable and we demonstrate efficient clustering of a billion-scale, real-world dynamic graph into small edge sets that correlate in topology and time. The entire clustering process for a graph with tens of billions of edges takes just a few minutes of run time on 256 nodes of a distributed compute environment. We argue how the output of the edge clustering is useful for a multitude of data visualization and powerful machine learning tasks, both involving the original massive dynamic graph data and metadata associated with the nodes and edges. Finally, we describe how this approach can be extended to dynamic hypergraphs and dynamic graphs/hypergraphs with unstructured data living on vertices and edges.

Data Analysis↗

EDX ClaiMM: Digital Resources for the Critical Minerals and Materials Community

Securing critical mineral supply chains is essential for transitioning to a clean energy economy and for maintaining national security. Big-data analytics can serve as a cost-effective means of identifying new domestic critical mineral resources but only if data can be easily located and digested. Using ArcGIS Enterprise Sites, EDX ClaiMM was developed to increase the accessibility of critical minerals data, reducing time spent on data collection and integration. Hosted tools provide rapid visualization and exploration of key datasets, unlocking insights to support resource assessments.

Yesenchak, Rachel↗

Guided Text Analysis Using Adaptive Visual Analytics

This paper demonstrates the promise of augmenting interactive visualizations with semi-supervised machine learning techniques to improve the discovery of significant associations and insight in the search and analysis of textual information. More specifically, we have developed a system called Gryffin that hosts a unique collection of techniques that facilitate individualized investigative search pertaining to an ever-changing set of analytical questions over an indexed collection of open-source publications related to national infrastructure. The Gryffin client hosts dynamic displays of the search results via focus+context record listings, temporal timelines, term- frequency views, and multiple coordinated views. Furthermore, as the analyst interacts with the display, the interactions are recorded and used to label the search records. These labeled records are then used to drive semi-supervised machine learning algorithms that re-rank the unlabeled search records such that potentially relevant records are moved to the top of the record listing. Gryffin is described in the context of the daily tasks encountered at the Department of Homeland Securitys Fusion Centers, with whom we are collaborating in its development. The resulting system is capable of addressing the analysts information overload that can be directly attributed to the deluge of information that must be addressed in search and investigative analysis of textual information.

machine learning↗

DNSViz

The Domain Name System Security Extensions (DNSSEC) add authentication to the DNS. However, analyzing and troubleshooting a DNSSEC deployment is difficult in part because of the protocol complexity and in part because authentication involves retrieving dependent components from a number of different Internet locations. DNSViz was written to bring all these DNSSEC components together into a single graph, so they can be represented visually, and nuances can be highlighted for easy identification. DNSViz includes functionality for both analyzing a domain name using active probing and visual representation of the domain name, available currently through a Web interface.

Deccio, Casey↗

DNSViz

The Domain Name System Security Extensions (DNSSEC) add authentication to the DNS. However, analyzing and troubleshooting a DNSSEC deployment is difficult in part because of the protocol complexity and in part because authentication involves retrieving dependent components from a number of different Internet locations. DNSViz was written to bring all these DNSSEC components together into a single graph, so they can be represented visually, and nuances can be highlighted for easy identification. DNSViz includes functionality for both analyzing a domain name using active probing and visual representation of the domain name, available currently through a Web interface.

Deccio, Casey↗

Soil properties and biochemical composition of ground-dwelling bee nests in agricultural settings

Soils deliver underrecognized ecosystem services by supplying habitat for ground-dwelling pollinators, such as wild bees and other organisms, that pollinate 80% of insect-pollinated plants and play a critical role in securing resilient pollination provisions. Our objective is to identify soil properties of ground nesting bee nests in agricultural settings of western Oregon, USA. We confirmed ground nesting bee and sand wasp activity in 7 agricultural sites and 1 recreational park. Soils from 17 bee and sand wasp nests were analyzed for pH, particle size distribution, and carbon and nitrogen content. We visually confirmed that 8 of the nesting bees were sweat bees from the Halictidae family and identified a captured bee specimen as Lasioglossum (Dialictus) (Hymenoptera: Halictidae). We located 2 sites with sand wasps where specimens were identified as Cerceris and Bembix (Hymenoptera: Crabronidae). The organic matter composition of three soil samples scraped from the linings of active nests were assessed using Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The FTICR-MS results identified unknown lipid compounds in the nest soil samples, which we hypothesize are waterproofing lipids secreted to line cell walls. Bee nests occurred in slightly acidic, bare-ground soils with low rock/vegetation coverage, low organic carbon content (<1%), and exhibited significantly higher silt-plus-clay fractions (>75%) versus data published for bee nests in prior work. Our findings present important implications for textural controls on nest site selection in wet, cool environments and demonstrate the importance of integrating soil properties to improve our understanding of ground-dwelling organisms and associated soil habitats.

59 BASIC BIOLOGICAL SCIENCES↗

Lessons Learned from PNNL Support to Army Corp of Engineers Munitions Sites

PNNL developed and demonstrated statistically-based methods for characterizing sites where unexploded ordnance (UXO) is a concern. These methods, incorporated into PNNL’s Visual Sample Plan (VSP) software, can be used to design geophysical transect surveys and conduct data analyses to identify potential target areas, map and estimate anomaly density across the study area, and delineate target areas where UXO are most likely to be found. These methods have been tested and demonstrated on several wide area sites under the Department of Defense Environmental Security Technology Certification Program (ESTCP).

Pulsipher, Brent A.↗

Creating an Interprocedural Analyst-Oriented Data Flow Representation for Binary Analysts (CIAO)

National security missions require understanding third-party software binaries, a key element of which is reasoning about how data flows through a program. However, vulnerability analysts protecting software lack adequate tools for understanding data flow in binaries. To reduce the human time burden for these analysts, we used human factors methods in a rolling discovery process to derive user-centric visual representation requirements. We encountered three main challenges: analysis projects span weeks, analysis goals significantly affect approaches and required knowledge, and analyst tools, techniques, conventions, and prioritization are based on personal preference. To address these challenges, we initially focused our human factors methods on an attack surface characterization task. We generalized our results using a two-stage modified sorting task, creating requirements for a data flow visualization. We implemented these requirements partially in manual static visualizations, which we informally evaluated, and partially in automatically generated interactive visualizations, which have yet to be integrated into workflows for evaluation. Our observations and results indicate that 1) this data flow visualization has the potential to enable novel code navigation, information presentation, and information sharing, and 2) it is an excellent time to pursue research applying human factors methods to binary analysis workflows.

42 ENGINEERING↗

Nutri‐cereal tissue‐specific transcriptome atlas during development: Functional integration of gene expression to identify mineral uptake pathways in little millet ( Panicum sumatrense )

SUMMARY Little millet (Panicum sumatrenseRoth ex Roem. & Schult.) is an essential minor millet of southeast Asia and Africa's temperate and subtropical regions. The plant is stress‐tolerant, has a short life cycle, and has a mineral‐rich nutritional profile associated with unique health benefits. We report the developmental gene expression atlas of little millet (genotype JK‐8) from ten tissues representing different stages of its life cycle, starting from seed germination and vegetative growth to panicle maturation. The developmental transcriptome atlas led to the identification of 342 827 transcripts. The BUSCO analysis and comparison with the transcriptomes of related species confirm that this study presents high‐quality, in‐depth coverage of the little millet transcriptome. In addition, the eFP browser generated here has a user‐friendly interface, allowing interactive visualizations of tissue‐specific gene expression. Using these data, we identified transcripts, the orthologs of which inArabidopsisand rice are involved in nutrient acquisition, transport, and response pathways. The comparative analysis of the expression levels of these transcripts holds great potential for enhancing the mineral content in crops, particularly zinc and iron, to address the issue of “hidden hunger” and to attain nutritional security, making it a valuable asset for translational research.

Plant Sciences↗