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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Quantum Workflows in Python (QWiP) is a framework for running experiments with superconducting qubit devices. To get started using this library follow the installation instructions below, and then check out the User Guide.(https://qnl-internal.berkeley.edu/QWiP/user-guide/)
Kinetic metabolic models provide invaluable insights into cellular metabolism, supporting applications in synthetic biology, metabolic engineering, and systems biology. However, reproducibility and utility of these models hinge on clear and rigorous documentation, standardized annotation, and accessible visualization. This paper presents a workflow for building, annotating, visualizing, and sharing kinetic metabolic models. Our method integrates community standards and open-source tools to ensure reproducibility, interoperability, and user accessibility. This procedure enables researchers to produce reusable and well-documented kinetic models, advancing their role as powerful tools in metabolic research.
The extracellular matrix (ECM) is a complex network of biomolecules that plays an integral role in the structure, processes, and signaling mechanisms of cells and tissues. Identifying and quantifying changes in these matrix components provides insight into the mechanisms behind specific tissue remodeling processes; however, quantifying these changes is challenging due to difficult imaging conditions, complexity of the ECM, and the subtlety of these changes. Current imaging techniques allow us to visualize these critical remodeling events and developments in image analysis have employed a combination of analysis software and machine learning techniques to improve the efficiency and accuracy with which features are measured. Although image analysis has seen much improvement in recent years, there has been no technique developed to address ambiguity in feature edges in electron microscopy images. Presented here is a new machine learning-based workflow for the analysis of microscopy images named FIRM (Feature Identification from Raw Microscopy) that uses a random forest classifier to identify ECM features of interest and generate binary segmentation masks for quantification with ImageJ-FIJI. FIRM performed with an F1 score of 0.794 and greater than 80% accuracy for number and size of features detected. FIRM had similar deviation from the ground truth in the number of identified fibrils, fibril size, and size distributions when compared to human analyses. The results suggest that FIRM performs as well as manual analysis and requires a fraction of the time. This analysis technique is more efficient, eliminates user bias, and can be easily optimized to identify a variety of features, making it useful for any discipline requiring image analysis.
This report aims to demonstrate workflow-generating algorithms for optimizing dispatch across a broad range of Integrated Energy System applications using the Framework for Optimization of Resources and Economics (FORCE) tool suite. The optimization is performed at two different time scales. In the coarse time scale, the optimization focuses on a class of energy sources and consumers and aims to find the optimal combinations and flows of energy based on real-time price data information. In the fine time scale, the optimization focuses on a specific thermal energy delivery system and aims to find the optimal setpoints of components in order to meet the energy demands from coarse-time-scale optimizations. In this demonstration, the coarse-scale optimization is implemented using the newly developed Dispatch Optimization Variable Engine (DOVE), while the fine-scale optimization used Optimization of Real-Time Capacity Allocation (ORCA).
Carbon capture and storage is a crucial component of climate change mitigation strategies, involving the capture of carbon dioxide (CO2) from point sources and its injection into permeable subsurface formation. Many suitable CO2 storage sites coincide with legacy wells since the conditions that kept hydrocarbons in-situ for thousands of years are also ideal for storage of carbon dioxide. To protect underground sources of drinking water (USDW) during greenhouse gas injection, the Environmental Protection Agency (EPA) mandates area of review evaluations. These evaluations ensure that drinking water sources would not be contaminated by injected fluids. They include identification of legacy wellbores, integrity assessments, and implementing any necessary corrective action. Previous assessment approaches of legacy wells include high-level scoring of regional data and well construction and abandonment evaluation. This work describes a novel methodology that evaluates well construction and abandonment, ranks them based on complexity, and performs a risk assessment with NRAP-Open-IAM. A workflow of the methodology is presented, highlighting its capabilities and limitations.
Project Overview and Goal: Create a Python Toolkit for UQ (PyTUQ) to facilitate the interoperability of FASTMath UQ tools (Dakota, UQTk, KLPC), as well as other ASCR funded capabilities for UQ workflows.
EDGE is a bioinformatics platform developed in 2016 by researchers at Los Alamos National Laboratory (LANL) to facilitate the analysis of next-generation sequencing data by researchers with varying levels of experience in bioinformatics (Li et al., 2017). Users with single-end, paired-end or long-read sequencing data can provide their reads as input to EDGE and select the combination of workflows to run that are most useful for their research (e.g., quality control of reads, genome assembly, or the taxonomic classification of input reads). Table 1 summarizes the modules available in EDGE. EDGE is available as a web platform at https://edgebioinformatics.org, as installable source code maintained on GitHub under a GPLv3 license, and as a publicly hosted Docker image.
The purpose of this project was to continue supporting customizations of algorithms and raw data file structures to enhance software workflows for liquid chromatography (LC), mass spectrometry (MS) and ion mobility mass spectrometry (IM-MS)-based protein and metabolite characterization. PNNL worked with Agilent to design, implement, evaluate, and demonstrate new algorithms and integrated them as functionalities into the PNNL-PreProcessor software. The project augmented PNNL’s capabilities to analyze complex proteomics and metabolomics samples. These capabilities are directly beneficial to DOE and PNNL efforts to characterize and analyze these compounds in microbial and plant communities. The project assisted Agilent in further developing improved instrument-software solutions combining liquid chromatography and ion mobility with mass spectrometry for widespread applications in life sciences and other fields.
NREL will provide technical assistance to help digitize best practice operations and maintenance (“O&M”) procedure guidelines utilizing the Illu platform for specific distributed solar-storage system configurations. NREL shall test Illu workflows in a real inspection environment for clarity of instruction and ease of documentation.
The poster describes the justIN workflow management system funded by the UK for DUNE and now used for all DUNE centrally managed data processing and simulation
Growing demands to understand PV deployment and reliability across an expanding range of climates, along with increasing computational power, are driving the need for simplified computing tools that support large-scale analysis and geospatial workflows. While existing open-source libraries and PV system modeling tools offer extensive collections of empirical and physical models, they often lack the ability to scale to large geospatial datasets. The tool presented here, GeoGridFusion, enables PV modelers to store and utilize gridded geospatial data from sources such as NSRDB, PVGIS, and others. GeoGridFusion harmonizes diverse datasets, taxonomies, and nomenclatures, and includes utilities that support intuitive geospatial area selection.
We explore how visualizations can help users understand what an AI agent is doing as it builds and runs queries over data. As part of the LinkQ system, a natural language interface for querying knowledge graphs with a large language model (LLM), we designed two complementary views: A State Diagram that shows where the agent is within a larger workflow, and a Live Action Display that gives real-time updates about the agent's current task. In a study with 14 practitioners, we found that these visuals helped participants build stronger mental models of the agent's behavior while also increasing their confidence in the system. However, we also observed that users sometimes trusted incorrect outputs simply because the agent appeared to be doing the "right" thing. Our findings point to both the value and risk of visualizing agent behavior in interactive AI systems.
Tutorials for modeling of slot-die and slide-die coating flows with Goma 7, an open source finite element code, are presented. The tutorials cover the workflow to attaining steady state solutions for these flows, and continuation strategies for navigating the operating windows. Advanced topics of coating window prediction, automated multiparameter continuation, non-Newtonian rheology, dynamic contact line modeling, and some more solution strategies are also covered.
The goal of the experiment was to demonstrate that the optimized multiplexed multi-PTM profiling workflow can comprehensively and quantitatively capture dynamic changes in protein abundance, cysteine oxidation, phosphorylation, and acetylation in cytokine-induced inflammatory stress in mouse pancreatic ß-cells. Global proteomic, redox proteomic, phosphoproteomic, and acetylomic were data collected from mouse Beta-TC-6 pancreatic Beta-cells, untreated (mock) and cytokine-treated Beta-cells at 4, 8, and 24 hours with 4 biological replicates. Samples were digested with trypsin and Lys-C, then analyzed by LC-MS/MS. Data were searched with MS-GF+, MASIC, and MaxQuant using PNNL's DMS processing pipeline.
This workflow enables lamella production targeting fluorescently labeled biological structures that are small (<1 μm in axial extent) and rare (1 copy per cell) using a cryogenic tri-coincident imaging platform. In conclusion, this platform integrates fluorescence microscopy, focused ion beam milling, and scanning electron microscopy at a single focal position and enables simultaneous fluorescence microscopy while milling.
The Broadband Automation for Distributed Grid Efficiency and Resilience (BADGER) project aligns with national strategic priorities for integrating emerging wireless technologies and advancing AI-driven security. As critical infrastructure modernizes toward increasingly software-defined and interconnected systems, the ability to leverage 5G/NextG networks and AI-enabled control becomes essential. This report outlines work at the National Laboratory of the Rockies (NLR) to develop a NextG-native security architecture powered by AI-RAN concepts and evaluate workflows that enable efficient and reliable architectures. Together, these efforts position the laboratory to accelerate innovation while directly supporting national security and resilience objectives.
Automating 'paper' workflow processes with electronic forms and email can dramatically improve the efficiency of those processes. However, applications that involve complex forms that are used for a variety of purposes or that require numerous and varied approvals often require additional software tools to ensure that (1) the electronic form is correctly and completely filled out, and (2) the form is routed to the proper individuals and organizations for approval. The prototype electronic purchase request (PEPR) system, which has been in pilot use at NASA Ames Research Center since December 1993, seamlessly links a commercial electronics forms package and a CLIPS-based knowledge system that first ensures that electronic forms are correct and complete, and then generates an 'electronic routing slip' that is used to route the form to the people who must sign it. The PEPR validation module is context-sensitive, and can apply different validation rules at each step in the approval process. The PEPR system is form-independent, and has been applied to several different types of forms. The system employs a version of CLIPS that has been extended to support AppleScript, a recently-released scripting language for the Macintosh. This 'scriptability' provides both a transparent, flexible interface between the two programs and a means by which a single copy of the knowledge base can be utilized by numerous remote users.
During the 1980s, a community of artificial intelligence researchers became interested in formalizing problem solving methods as part of an effort called "second generation expert systems" (2nd GES). How do the motivations and results of this research relate to building tools for the workplace today? We provide an historical review of how the theory of expertise has developed, a progress report on a tool for designing and implementing model-based automation (Brahms), and a concrete example how we apply 2nd GES concepts today in an agent-based system for space flight operations (OCAMS). Brahms incorporates an ontology for modeling work practices, what people are doing in the course of a day, characterized as "activities." OCAMS was developed using a simulation-to-implementation methodology, in which a prototype tool was embedded in a simulation of future work practices. OCAMS uses model-based methods to interactively plan its actions and keep track of the work to be done. The problem solving methods of practice are interactive, employing reasoning for and through action in the real world. Analogously, it is as if a medical expert system were charged not just with interpreting culture results, but actually interacting with a patient. Our perspective shifts from building a "problem solving" (expert) system to building an actor in the world. The reusable components in work system designs include entire "problem solvers" (e.g., a planning subsystem), interoperability frameworks, and workflow agents that use and revise models dynamically in a network of people and tools. Consequently, the research focus shifts so "problem solving methods" include ways of knowing that models do not fit the world, and ways of interacting with other agents and people to gain or verify information and (ultimately) adapt rules and procedures to resolve problematic situations.