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The Federal Energy Management Program's 'Utility Energy Service Contracts: Enabling Documents' provide legislative information and materials that clarify the authority for federal agencies to enter into utility energy service contracts, or UESCs.
Document provides a select set of background information materials that clarify the authority for federal agencies to enter into utility energy service contracts.
The National Reactor Innovation Center (NRIC) is leading a transformative initiative to accelerate advanced reactor deployment by fundamentally reimagining how nuclear safety basis documentation is developed, reviewed, and maintained. Traditional Documented Safety Analysis (DSA) processes for DOE-authorized facilities rely on static, document-centric workflows that consume significant time and resources, exemplified by recent major licensing efforts requiring hundreds of thousands of staff hours and millions of pages of documentation review. These conventional approaches create barriers to the rapid, cost-effective deployment of advanced reactors that America's future energy needs demand. NRIC's DOE Authorization Digital Transformation Project addresses these challenges through an innovative framework that integrates artificial intelligence (AI), digital engineering, and systems-based data management into a cohesive digital ecosystem. This white paper presents NRIC's methodology for evaluating AI-enabled document generation capabilities within this broader digital infrastructure, using the Demonstration of Microreactor Experiments (DOME) facility as a pilot case study. The evaluation will assess an AI tool's ability to generate a Preliminary Documented Safety Analysis (PDSA) through progressive integration stages—from standalone document processing to full digital thread connectivity—while maintaining rigorous verification, validation, and regulatory acceptance standards. By establishing dynamic, traceable connections between design data and safety documentation, NRIC's approach has the potential to reduce both document development time and regulatory review cycles by as much as 50%, while simultaneously improving accuracy, consistency, and traceability. This initiative represents a critical step toward establishing reusable digital infrastructure that reactor developers can leverage to accelerate their path from concept to commercial operation, directly supporting NRIC's mission to demonstrate and deploy advanced nuclear energy technologies.
Safety basis documentation development and review under U.S. Department of Energy (DOE) authorization have emerged as critical constraint throttling deployment of advanced nuclear reactors, with traditional processes demanding extraordinary resource investment that delays the delivery of these technologies. Traditional Documented Safety Analysis (DSA) processes rely on static documents with limited traceability [U.S. DOE]. The regulatory review and engagement processes are similarly constrained, often requiring significant effort and extensive manual verification. The scale of this challenge is exemplified by the U.S. Nuclear Regulatory Commission (NRC) review of the NuScale application, which required over 250,000 staff hours and the evaluation of approximately two million pages of documentation [Bergman 2021]. The volume and complexity of information within nuclear licensing applications or authorization reviews demands innovative approaches to document generation and data management.
PARETO 0.3.0 Release. Highlights: - Fixing Warning in get_data - Clean up of old main function which has been replaced by the run files - Adding plot_scatter() function - Support node capacity - Add Gurobi troubleshooting files to gitignore by @melody-shellman - Set slacks to zero - Water treatment case study - Fix defaultvalue treatement - Fix bug for incorrect number of input arguments in water quality - Pipeline configs documentation - Enable rendering Unicode characters in ReadTheDocs PDF builds - Adding support for units - Add discrete water quality to the model - Updating documentation for strategic model, and corresponding fixes to strategic model - Rework getting started docs build section and more - Enable Codecov reports in CI builds - Correct error in CompletionsPadSupplyBalanceRule
Combinatorial antibody libraries not only effectively reduce antibody discovery to a numbers game, but enable documentation of the history of antibody responses in an individual. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has prompted a wider application of this technology to meet the public health challenge of pandemic threats in the modern era. Herein, a combinatorial human antibody library constructed 20 years before the coronavirus disease 2019 (COVID-19) pandemic is used to discover three highly potent antibodies that selectively bind SARS-CoV-2 spike protein and neutralize authentic SARS-CoV-2 virus. Compared to neutralizing antibodies from COVID-19 patients with generally low somatic hypermutation (SHM), these three antibodies contain over 13–22 SHMs, many of which are involved in specific interactions in their crystal structures with SARS-CoV-2 spike receptor binding domain. The identification of these somatically mutated antibodies in a pre-pandemic library raises intriguing questions about the origin and evolution of these antibodies with respect to their reactivity with SARS-CoV-2.
This report draws together activities related to wet and gaseous waste feedstocks into a single document. It enables an amplified focus on feedstocks in the relevant technology and potential markets category. Also, this report helps to inform and support ongoing wet and gaseous resource recovery activities in the Bioenergy Technologies Office (BETO) and in the broader federal space. Historically, the office has identified wet and gaseous waste feedstocks as potentially advantageous, but has not pursued them with a sustained focus. This document seeks to position these waste streams appropriately alongside more traditional feedstocks in BETO efforts.
This data package is associated with the publication "Integrated, Coordinated, Open, and Networked (ICON) Science to Advance the Geosciences: Introduction and Synthesis of a Special Collection of Commentary Articles" in Earth and Space Science (Goldman et al. 2022; https://doi.org/10.1029/2021EA002099). The manuscript is an introductory article for a special collection of commentary articles across 19 geoscience disciplines that explore the challenges and opportunities associated with the use of ICON science principles. These principles focus on research intentionally designed to be Integrated, Coordinated, Open, and Networked (ICON) with the goal of maximizing mutual benefit (among stakeholders) and cross-system transferability of science outcomes. This data package contains data, figures, and R scripts associated with the cost/benefit analysis presented in the manuscript. The writing teams involved in the special collection placed each letter of ICON on a plot with perceived cost on one axis and perceived benefit on the other to summarize their perceptions of pursuing each principle of ICON science. These data were subsequently quantified and analyzed. Files are saved as .csv, .R, and .pdf. This data package also contains (1) the public foundational and instructional documents that enabled the crowdsourced creation of the special collection; (2) file-level metadata (flmd) that lists each file in the data package with a description; (3) data dictionary (dd) that defines column headers that appear in csv files. Files are saved as .pdf and .csv.
Software Defined Networking for Operational Technologies, referred to as OT-SDN, is a leading technology to secure critical infrastructure and command and control (C2) systems. As the name implies, OT-SDN networks are programmable, which allows system owners to utilize the characteristics of their physical process to inform the security of their network. There are best practices for deploying OT-SDN into an environment, whether it is all at once or over time (hybrid) that the network is converted to SDN technologies. Through the development of data mining tools and standardized process control, OT-SDN can be deployed reliably. These tools will minimize or eliminate any communication failures during the transition and provide the network owner with complete documentation of their environment. This documentation could enable or facilitate the network owner to pass any audits or policy checks (Authority to Operate) before being allowed to utilize the OT-SDN infrastructure.
This report documents the technical assistance provided to Orison as part of the American-Made Challenges Round 2 Voucher: Orison Enables Solar project. A testbed was developed and used to demonstrate, using modeling and simulation, the capability of controlled behind-the-meter energy storage to (1) reduce net load variability caused by appliances and distributed generation and (2) enable customers to respond to the time-of-use pricing. We demonstrate that load leveling can shave peak loads or limit solar photovoltaic export and that load shifting enables customers to reduce load during “peak” pricing intervals. In the scenarios that we simulated, load leveling was effective up to the charging and discharging limits of the storage systems and the effectiveness of load shifting was a function of the energy capacity of the storage systems. This report describes the co-simulation testbed and the scenarios simulated, including controller setup and simulation results. Considerations for a multi-objective controller are also discussed.
Mitochondria are essential components of eukaryotic cells, responsible for ATP production through oxidative phosphorylation. Despite their biological importance, unique challenges have hindered the adoption of automated mitochondrial genome (mitogenome) annotation methods, obstructing mitochondrial comparative genomics in a broad evolutionary context. Using Fungi as a study system and a Joint Genome Institute (JGI) annotated high-quality reference set, we observed broad patterns of mitochondrial evolution across the kingdom. We found that the median fungal mitogenome size is 58 kb and identified exceptionally large examples over 1 Mb in Pezizomycetes. All 14 expected oxidative phosphorylation protein-coding genes, plus rps3, were generally conserved. We found evidence of major evolutionary transitions within the Ascomycota, including the transfer of mitochondrially encoded atp8 and atp9 to the nuclear genomes across the Pezizomycotina and shifts in mitogenome tRNA patterns across the kingdom. We found substantial concordance between mitochondrial and nuclear evolution, enabling us to document 3131 total fungal mitogenomes from JGI-derived metagenomic datasets. We also identified 6467 total undeclared mitogenomes embedded in Genbank fungal nuclear assemblies. We provide interactive tools for mitogenome analysis through the JGI MycoCosm platform. Collectively, this work generated nearly 10 000 new fungal mitogenome annotations, providing a foundation and resources for future exploration of comparative fungal mitogenomics.
Establishing a solid taxonomic framework is crucial for enabling discovery and documentation efforts. This ensures effective communication between scientists as well as reproducibility of results between laboratories, and facilitates the exchange and preservation of biological material. Such framework can only be achieved by establishing clear criteria for taxa characterization and rank assignment. Within the anaerobic fungi (phylum Neocallimastigomycota), the need for such criteria is especially vital. Difficulties associated with their isolation, maintenance and long-term storage often result in limited availability and loss of previously described taxa. To this end, we provide here a list of morphological, microscopic, phylogenetic and phenotypic criteria for assessment and documentation when characterizing newly obtained Neocallimastigomycota isolates. We also recommend a polyphasic rank-assignment scheme for novel genus-, species- and strain-level designations for newly obtained Neocallimastigomycota isolates.
ABSTRACT Metagenomics is a powerful method for interpreting the ecological roles and physiological capabilities of mixed microbial communities. Yet, many tools for processing metagenomic data are neither designed to consider eukaryotes nor are they built for an increasing amount of sequence data. EukHeist is an automated pipeline to retrieve eukaryotic and prokaryotic metagenome-assembled genomes (MAGs) from large-scale metagenomic sequence data sets. We developed the EukHeist workflow to specifically process large amounts of both metagenomic and/or metatranscriptomic sequence data in an automated and reproducible fashion. Here, we applied EukHeist to the large-size fraction data (0.8–2,000 µm) from Tara Oceans to recover both eukaryotic and prokaryotic MAGs, which we refer to as TOPAZ (Tara Oceans Particle-Associated MAGs). The TOPAZ MAGs consisted of >900 environmentally relevant eukaryotic MAGs and >4,000 bacterial and archaeal MAGs. The bacterial and archaeal TOPAZ MAGs expand upon the phylogenetic diversity of likely particle- and host-associated taxa. We use these MAGs to demonstrate an approach to infer the putative trophic mode of the recovered eukaryotic MAGs. We also identify ecological cohorts of co-occurring MAGs, which are driven by specific environmental factors and putative host-microbe associations. These data together add to a number of growing resources of environmentally relevant eukaryotic genomic information. Complementary and expanded databases of MAGs, such as those provided through scalable pipelines like EukHeist, stand to advance our understanding of eukaryotic diversity through increased coverage of genomic representatives across the tree of life. IMPORTANCE Single-celled eukaryotes play ecologically significant roles in the marine environment, yet fundamental questions about their biodiversity, ecological function, and interactions remain. Environmental sequencing enables researchers to document naturally occurring protistan communities, without culturing bias, yet metagenomic and metatranscriptomic sequencing approaches cannot separate individual species from communities. To more completely capture the genomic content of mixed protistan populations, we can create bins of sequences that represent the same organism (metagenome-assembled genomes [MAGs]). We developed the EukHeist pipeline, which automates the binning of population-level eukaryotic and prokaryotic genomes from metagenomic reads. We show exciting insight into what protistan communities are present and their trophic roles in the ocean. Scalable computational tools, like EukHeist, may accelerate the identification of meaningful genetic signatures from large data sets and complement researchers’ efforts to leverage MAG databases for addressing ecological questions, resolving evolutionary relationships, and discovering potentially novel biodiversity.
Haystack is a software agent platform for user emulation. We are leveraging python and the SPADE software packages to develop an architecture and platform where software agents can be sent actuation behaviors, configured with different behavior profiles (time based, event based, probability based, etc.), and controlled/monitored from a central control unit. The haystack agent platform provides the ability to model and simulate user behavior in cyber systems. This eliminates the need to fund real users to provide this interaction, provides control such that researchers can randomize, hold-steady, or model whatever behavior that want to study without running afoul of IRB requirements, and provides a documented truth that enables understanding what happened in and experiment and provides for repeatability.
Idaho National Laboratory (INL) developed the Consequence-driven Cyber-informed Engineering (CCE) methodology to provide public and private organizations with steps to work collaboratively and establish a working relationship to protect critical infrastructure and other national assets. This process is a considerable undertaking, iterative in nature, and—as time and resources allow—should become a part of a company’s culture. By focusing on the impact of potentially negative Events, CCE provides a better understanding of how and why adversaries can affect critical functions and services using cyber-enabled sabotage. This document describes Phase 1 of the CCE process.
A modeling system to simulate fate and transport of contaminant plumes in groundwater beneath the Central Plateau to support the Cumulative Impact Evaluation (CIE) and Composite Analysis (CA) is being developed. The CIE will evaluate the cumulative effect of multiple Central Plateau radiological and chemical sources on groundwater quality (DOE/RL-2018-69, Cumulative Impact Evaluation Technical Approach Document ). This will enable cleanup decisions to be evaluated in the context of all Central Plateau waste sites and existing groundwater conditions. The CA evaluates the cumulative effect of all sources of radioactive materials on radiological dose to future members of the public (DOE/RL-2018-60, Annual Status Report (FY 2018): Composite Analysis of Low Level Waste Disposal in the Central Plateau of the Hanford Site ). Fate and transport modeling will be used to forecast future groundwater concentrations for both the CIE and CA. The CIE/CA subsurface modeling system is divided into source (inventory), vadose zone, and saturated zone facets. Source inventory is a necessary input to the vadose zone facet, and releases from the vadose zone to groundwater are inputs to the saturated zone facet. The vadose zone facet simulates historical and future releases from the vadose zone to groundwater for both liquid waste and solid waste disposal sites. Effluent release volumes and radionuclide inventory estimates for liquid waste sites are contained in ECFHANFORD- 17-0079, Hanford Soil Inventory Model (SIM-v2) Calculated Radionuclide Inventory of Direct Liquid Discharges to Soil in the Hanford Site’s 200 Areas , herein referred to as SIM-v2. The liquid waste sites in SIM-v2 include the 216-B-3 Pond near 200 East Area and the 216-U-10 Pond in 200 West Area. The 216-B-3 Pond system consisted of a main pond and 3 expansion lobes that were operational during overlapping time periods. It also included influent ditches, but these are not listed as liquid waste sources in SIM-v2 and are not included in the CIE/CA modeling. The total effluent volume released to the pond system was measured, but the partitioning of this volume to the various expansion lobes was not determined and is not accounted for in SIM-v2. Regarding the 216-U-10 Pond, it received water from several influent ditches. The pond and the influent ditches are listed as liquid waste sites in SIM-v2 and are included as sources in the CIE/CA modeling. Because water in the ditches flowed into the pond, only a portion of the effluent volumes and contaminant inventories assigned to the ditches in SIM-v2 infiltrated from the ditches. Thus, partitioning of infiltration and contaminant inventories between the ditches and the pond is not fully accounted for in SIM-v2. Further, some of the water in the 216-U-10 Pond entered overflow ditches and the amount of this overflow was not recorded and is not accounted for in SIM-v2. Thus, the purpose of this Environmental Calculation File (ECF) is to partition the infiltration of wastewater and contaminant inventory between the main pond and each expansion lobe of the 216-B-3 Pond system, and to partition the infiltration of wastewater and contaminant inventory between the ditches and pond of the 216-U-10 Pond system.
Haystack is a software agent platform for user emulation. We are leveraging python and the SPADE software packages to develop an architecture and platform where software agents can be sent actuation behaviors, configured with different behavior profiles (time based, event based, probability based, etc.), and controlled/monitored from a central control unit. The haystack agent platform provides the ability to model and simulate user behavior in cyber systems. This eliminates the need to fund real users to provide this interaction, provides control such that researchers can randomize, hold-steady, or model whatever behavior that want to study without running afoul of IRB requirements, and provides a documented truth that enables understanding what happened in and experiment and provides for repeatability.