Lbl/bctc Ldrd Graphical User Interface
This was written in React, using Typescript, Vite, and Material UI
Pluth, AdamJ. [Idaho National Laboratory (INL), Id↗
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Coreii - Scout
COREII Scout employs React, Vite, TypeScript, Tailwind, and Daisy UI for its graphical user interface (GUI), offering both dark and light modes. The code is modular, with components and reusable wrappers to enhance efficiency. The primary goal of COREII Scout is to aid analysts in collecting and analyzing various sources related to cyber attacks, utilizing models to automate the report writing process. It uses Named Entity Recognition (NER), a type of Natural Language Processing (NLP), to extract key entities from each source. Analysts review and classify these entities using the COREII Attack Chain Estimator (ACE), adding their comments. Ultimately, a Large Language Model (LLM) generates a detailed report with user guidance. This setup ensures a streamlined and effective approach to cyber attack analysis and reporting.
Pluth, Adam [Idaho National Laboratory (INL), Idah↗
A Data Processing Pipeline To Extract A Knowledge Graph From Heterogeneous Data For Socio-technical Analysis Of Critical Infrastructure Influence
The code is written in Python and consists of the following pipeline that is implemented in Apache Airflow. This pipeline intends to understand the companies that are directly or indirectly involved with a type of critical infrastructure system at some point in that system's lifecycle. The pipeline takes a configuration file that specifies a list of initial companies to consider, a geographic region of interest, and a set of SEC form types as well as other data sources (e.g. CrunchBase) from which to extract entities and relations. There are four main components to this pipeline as currently implemented: Entity Extraction, Network Construction, Analysis, and Visualization. First, Entity Extraction, is implemented as the `topear-extract_organizations` Apache Airflow workflow. Given an initial query that specifies a geographic region of interest and a time interval, the software will extract CI facilities of interest and organizations that have a direct influence relationship to those facilities (e.g. ownership). During the course of the LDRD, we focused on Electric Vehicle charging stations and this information is available via the Department of Energy (DOE) database on fueling stations maintained by NREL. Within the context of the DOE CESER project, we have focused on Battery Energy Storage Systems (BESS). Second, the Network Extraction component will iteratively construct a social network graph given the set of organizations and people extracted in the previous step. Organizations (and eventually People if desired) are then fed as a query to the `topgear-construct_social_network` Apache Airflow workflow which given a set of initial companies and data sets (e.g. SEC EDGAR form types, OpenCorporates, Crunchbase). This Airflow workflow will iteratively query such data sources to discover relationships with new organizations and people. For example, this module can iteratively query SEC EDGAR for metadata that documents the number of each type of form for the given set of companies and their location. This forms metadata represents a catalog of data sources from SEC EDGAR for the extracted social network knowledge graph. The pipeline then downloads these forms from the website and saves them in a build directory for further processing. These documents are then parsed for entities and relations. Again, we note that in additional to SEC data sources, this step can also pull in information on organizations via API services such as CrunchBase and OpenCorporates or bulk data sources. At the end of this step, the resultant social network, the Critical Infrastructure network, and the edges that encode relationships between organizations and CI facilities, form the Adversarial Socio-Technical Network (ASTN) that informs the analysis. Third, the Analysis component processes these generated ASTN. Previously, that has included the ability to compare prevalence of different vendors for a given infrastructure component type across different regions as well as identify common public and private investors across those vendors. This was demonstrated for EV Charging Stations across several different metropolitan areas within an IEEE PES GridEdge publication. More recently, we have looked at ways to identify infrastructure owners and operators of BESS with the most nameplate capacity across different states as well as other indictors of risk resulting from changes in ownership over time. Finally, the Visualization component consists of an HTML/CSS/JS framework by which users can interact geospatial, operational, and organizational relationships across a given portfolio of Critical Infrastructure facilities. The objective is to provide a library of UI/UX modules that can be repurposed for stakeholder-specific dashboards. All of the modules are related via a common event model that enables UI actions in one view to percolate across the other views.
Weaver, Gabriel [Idaho National Laboratory (INL), ↗
datasight [SWR-26-045]
This software is an AI-powered data exploration with natural language. datasight connects an AI agent to your database and provides a web UI where you can ask questions in natural language. The agent writes SQL, runs queries, and generates interactive Plotly visualizations. Supports DuckDB, PostgreSQL, SQLite, and Flight SQL databases. Also queries local CSV and Parquet files directly — no database setup required. Supports Anthropic Claude (default), GitHub Models (open source), and Ollama (local) as LLM backends.
Thom, Daniel [National Laboratory of the Rockies (↗
Cyote Insights
CyOTE Insights leverages React, Vite, Typescript, Tailwind, and Daisy UI for the Graphical User Interface. It was designed in a particular style with a dark mode and a light mode. All code is broken down into components and reusable wrapper components for efficiency. All data is stored in Deep Lynx as a central data repository using an ontology based schema. The application serves as a main endpoint for the data in the COREII and CyOTE programs. The main purpose of the application is to display historical attack data in the Operational Technology space. At the time of this writing, it supports 27 historical attack reports compiled from OSINT sources. All of the data is publicly available, but what this application offers is the ability to see many years worth of publications in a detailed dashboard. It will also support future reports that are written using the other applications in the COREII program.
Pluth, AdamJ [Idaho National Laboratory (INL), Ida↗
AmeriFlux US-UiE University of Illinois Sorghum-Soy
This is the AmeriFlux version of the carbon flux data for the site US-UiE University of Illinois Sorghum-Soy. Site Description - Agricultural field planted with photoperiod-sensitive ("energy") sorghum bicolor in a three year rotation with soy (sorghum-sorghum-soy). The first soy rotation was in 2019. This field is typically planted in May and harvested for biomass (sorghum) or grain (soy) in October. This site is located at an experimental farm approximately 2 miles south of the University of Illinois at Urbana Champaign and is colocated with (500-1000m distance) all other Us-Ui sites.
Bernacchi, Carl J [Department of Crop Sciences, Un↗
AmeriFlux US-UiF University of Illinois Miscanthus 2
This is the AmeriFlux version of the carbon flux data for the site US-UiF University of Illinois Miscanthus 2. Site Description - Agricultural field planted with miscanthus x giganteus perennial C4 bioenergy feedstock as a control site for Us-UiB when basalt began to be applied to Us-UiB in 2017. This field is typically harvested in Febraury or March. This site is located at an experimental farm approximately 2 miles south of the University of Illinois at Urbana Champaign and is colocated with (500-1000m distance) all other Us-Ui sites.
Bernacchi, Carl J [Department of Crop Sciences, Un↗
AmeriFlux US-UiG University of Illinois Maize-Soy 2
This is the AmeriFlux version of the carbon flux data for the site US-UiG University of Illinois Maize-Soy 2. Site Description - Agricultural field planted with maize in a three year rotation with soy (maize-maize-soy) as a control site for Us-UiC when basalt began to be applied to Us-UiC in 2017. The first soy rotation was in 2019. This field is typically planted in May and harvested in October. This site is located at an experimental farm approximately 2 miles south of the University of Illinois at Urbana Champaign and is colocated with (500-1000m distance) all other Us-Ui sites.
Bernacchi, Carl J [Department of Crop Sciences, Un↗
AmeriFlux FLUXNET-1F US-UiC University of Illinois Maize-Soy
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-UiC University of Illinois Maize-Soy. This is the FLUXNET version of the carbon flux data for the site US-UiC University of Illinois Maize-Soy produced by applying the standard ONEFlux (1F) software. Site Description - Agricultural field planted with maize in a three year rotation with soy (maize-maize-soy). The first soy rotation year was 2010. This field is typically planted in May and harvested in October. This site is located at an experimental farm approximately 2 miles south of the University of Illinois at Urbana Champaign and is colocated with (500-1000m distance) all other Us-Ui sites.
Bernacchi, Carl J [USDA/ARS]↗
AmeriFlux FLUXNET-1F US-UiE University of Illinois Sorghum-Soy
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-UiE University of Illinois Sorghum-Soy. This is the FLUXNET version of the carbon flux data for the site US-UiE University of Illinois Sorghum-Soy produced by applying the standard ONEFlux (1F) software. Site Description - Agricultural field planted with photoperiod-sensitive ("energy") sorghum bicolor in a three year rotation with soy (sorghum-sorghum-soy). The first soy rotation was in 2019. This field is typically planted in May and harvested for biomass (sorghum) or grain (soy) in October. This site is located at an experimental farm approximately 2 miles south of the University of Illinois at Urbana Champaign and is colocated with (500-1000m distance) all other Us-Ui sites.
Bernacchi, Carl J [Department of Crop Sciences, Un↗
AmeriFlux FLUXNET-1F US-UiF University of Illinois Miscanthus 2
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-UiF University of Illinois Miscanthus 2. This is the FLUXNET version of the carbon flux data for the site US-UiF University of Illinois Miscanthus 2 produced by applying the standard ONEFlux (1F) software. Site Description - Agricultural field planted with miscanthus x giganteus perennial C4 bioenergy feedstock as a control site for Us-UiB when basalt began to be applied to Us-UiB in 2017. This field is typically harvested in Febraury or March. This site is located at an experimental farm approximately 2 miles south of the University of Illinois at Urbana Champaign and is colocated with (500-1000m distance) all other Us-Ui sites.
Bernacchi, Carl J [Department of Crop Sciences, Un↗
AmeriFlux FLUXNET-1F US-UiG University of Illinois Maize-Soy 2
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-UiG University of Illinois Maize-Soy 2. This is the FLUXNET version of the carbon flux data for the site US-UiG University of Illinois Maize-Soy 2 produced by applying the standard ONEFlux (1F) software. Site Description - Agricultural field planted with maize in a three year rotation with soy (maize-maize-soy) as a control site for Us-UiC when basalt began to be applied to Us-UiC in 2017. The first soy rotation was in 2019. This field is typically planted in May and harvested in October. This site is located at an experimental farm approximately 2 miles south of the University of Illinois at Urbana Champaign and is colocated with (500-1000m distance) all other Us-Ui sites.
Bernacchi, Carl J [Department of Crop Sciences, Un↗
Utilizing HYSPLIT for Emergency Response Modeling at SRS
At SRS, emergency responders use a variety of tools to detect, track, and mitigate hazardous material releases into the atmosphere. Two models currently used at SRS are Puff-Plume and the Lagrangian Particle Dispersion Model (LPDM), a Gaussian and Lagrangian model, respectively. A decision has been made to replace LPDM with the more widely-supported Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model for evaluating inhalation and ingestion doses following a release. HYSPLIT is designed to compute complex dispersion and deposition simulation To achieve the implementation of HYSPLIT, we have developed a preliminary UI framework that will allow HYSPLIT to be run on ATG computers without the need for active network connections, thus avoiding the loss of capabilities in the event of a network outage during an emergency.
Earley, Ian↗
MACCS User Guide (V.5.0)
MACCS is used by the Nuclear Regulatory Commission (NRC) and various national and international organizations for probabilistic consequence analysis of nuclear power accidents. This user guide is intended to assist analysts in understanding the MACCS/MACCS-UI User Interface (UI) model and to provide information regarding the code. This user guide version describes MACCS Version 5.0, model history, explains how to set up and execute a problem, and informs the user of the definition of various input parameters and any constraints placed on those parameters. This report is part of a series of reports documenting MACCS. Other reports include the MACCS Theory Manual, MACCS Verification Report, Technical Bases for Consequence Analyses Using MACCS, as well as documentation for preprocessor codes including SecPop, MelMACCS, and COMIDA2.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Commercialization of a Non-Intrusive Optical (NIO) Technology to Measure Heliostat Optical Errors in Utility-Scale Concentrating Solar Power Plants: Final TCF Report
The drone-based Non-Intrusive Optical (NIO) Technology has been developed at NREL to allow for efficient and automated optical characterization of heliostats in Concentrating Solar Power (CSP) plants. For this project, the technology will be developed into a commercial tool package, including software and user-interface (UI), operations manual, and training and support services. The project team will partner with Tietronix to perform market assessment and stakeholder engagement, develop the tool package and business model, and perform data collection and analysis to demonstrate and refine the capabilities for use at a commercial plant. The team will collaborate with a commercial plant to conduct the data collection operations and provide optical error deliverables. The goal of the project is to advance the commercialization of the technology to a stage where a beta version can be demonstrated at additional commercial plants and developed into a licensable product.
14 SOLAR ENERGY↗
MACCS User Guide - Version 5.2
MACCS is used by the Nuclear Regulatory Commission (NRC) and various national and international organizations for probabilistic consequence analysis of nuclear power accidents. This user guide is intended to assist analysts in understanding the MACCS/MACCS-UI User Interface (UI) model and to provide information regarding the code. This user guide version describes MACCS Version 5.2, model history, explains how to set up and execute a problem, and informs the user of the definition of various input parameters and any constraints placed on those parameters. This report is part of a series of reports documenting MACCS. Other reports include the MACCS Theory Manual, MACCS Verification Report, Technical Bases for Consequence Analyses Using MACCS, as well as documentation for preprocessor codes including SecPop, MelMACCS, and COMIDA2.
54 ENVIRONMENTAL SCIENCES↗
Computer-Based Procedures Among Older Adults: Findings from the Rancor Microworld Simulator
As with many industries worldwide, nuclear energy is experiencing an aging workforce; older than other energy sources and the national average. For example, while almost one in three nuclear industry professionals are 55+ years, for oil and gas that number is one in five. The challenges brought about by an aging workforce are one of the industry’s top concerns. However, the industry lacks for empirical research that examines the effects of developmental aging in older workers, as well as the interactions between aging and new digital technologies. We present the results of an experiment that tested three different computer-based procedures in a sample of 30 older adults (55+ years). While the industry has traditionally relied on paper-based procedures, these are being modernized by digital technology. Participants were randomly assigned to one of three procedure-types that varied by the level of digitalization, based on the IEEE Standard-1786. Type 1 essentially represents a digital representation of a paper-based procedure, Type 2 adds embedded indicators, and Type 3 adds soft controls. Participants performed two different operational scenarios (startup and loss of feedwater) on a simplified nuclear power plant simulator. Results revealed a weak signal that Type 2 may produce lower workload and lower completion times in some instances. However, there were no significant effects of procedure-type across any other outcomes, including simulator log data, situation awareness, and preference ratings. We discuss our findings in terms of optimal levels of digitalization/automation for an aging nuclear workforce and suggest pathways for future directions.
46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗