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DOE OSTI2026

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Crystal structure↗

DOE OSTI2022

Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry

Estimation of riverbed profiles, also known as bathymetry, plays a vital role in many applications, such as safe and efficient inland navigation, prediction of bank erosion, land subsidence, and flood risk management. The high cost and complex logistics of direct bathymetry surveys, i.e, depth imaging, have encouraged the use of indirect measurements such as surface flow velocities. However, estimating high-resolution bathymetry from indirect measurements is an inverse problem that can be computationally challenging. Here, we propose a reduced-order model (ROM) based approach that utilizes a variational autoencoder (VAE), a type of deep neural network with a narrow layer in the middle, to compress bathymetry and flow velocity information and accelerate bathymetry inverse problems from flow velocity measurements. In our application, the shallow-water equations (SWE) with appropriate boundary conditions (BCs), e.g., the discharge and/or the free surface elevation, constitute the forward problem, to predict flow velocity. Then, ROMs of the SWEs are constructed on a nonlinear manifold of low dimensionality through a variational encoder and the bathymetry inversion problem is derived on the low-dimensional latent space in a Hierarchical Bayesian setting. Further, the reformulation allows variational inference with a small number (e.g., $\mathscr{O}$ (100) of ROM runs and efficient uncertainty quantification. We have tested our inversion approach on a one-mile reach of the Savannah River, GA, USA. Once the neural network is trained (offline stage), the proposed technique can perform the inversion operation orders of magnitude faster than traditional inversion methods that are commonly based on linear projections, such as principal component analysis (PCA), or the principal component geostatistical approach (PCGA). Furthermore, tests show that the algorithm can estimate the bathymetry with good accuracy even with sparse flow velocity measurements.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

ReVise: A Human-AI Interface for Incremental Algorithmic Recourse

The recent adoption of artificial intelligence in socio-technical systems raises concerns about the black-box nature of the resulting decisions in fields such as hiring, finance, admissions, etc. If data subjects—such as job applicants, loan applicants, and students—receive an unfavorable outcome, they may be interested in algorithmic recourse, which involves updating certain features to yield a more favorable result when re-evaluated by algorithmic decision-making. Unfortunately, when individuals do not fully understand the incremental steps needed to change their circumstances, they risk following misguided paths that can lead to significant, long-term adverse consequences. Existing recourse approaches focus exclusively on the final recourse goal but neglect the possible incremental steps to reach the goal with real-life constraints, user preferences, and model artifacts. To address this gap, we formulate a visual analytic workflow for incremental recourse planning in collaboration with AI/ML experts and contribute an interactive visualization interface that helps data subjects efficiently navigate the recourse alternatives and make an informed decision. We also present one of the many usage scenarios, developed during exploratory feedback sessions with twelve graduate students using a real-world dataset, which demonstrates that our approach can be instrumental for data subjects in choosing a suitable recourse path.

algorithmic recourse↗

DOE OSTI2021

Comparison of Deterministic and Statistical Models for Water Quality Compliance Forecasting in the San Joaquin River Basin, California

Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2023

Full Lunar Surface Visualization and Simulation Platform

This project created a visualization and simulation software platform for the entire lunar surface. Given the polar Artemis landing targets, the challenging terrain, lighting, and associated integrated crew / vehicle risk assessment, a more integrated approach to mission safety certification, potentially impacting equipment design and crew activity is required. This integrated capability will enable Safety and Mission Assurance (SMA), Flight Operations (FOD), and EVA to effectively collaborate to reduce human lunar exploration operational risk utilizing the identical tools to perform mission planning, rehearsal simulations, and providing the Safety Review Panel with integrated risk analysis capability.

information fusion↗

DOE OSTI2023

Method and apparatus for constructing informative outcomes to guide multi-policy decision making

In Multi-Policy Decision-Making (MPDM), many computationally-expensive forward simulations are performed in order to predict the performance of a set of candidate policies. In risk-aware formulations of MPDM, only the worst outcomes affect the decision making process, and efficiently finding these influential outcomes becomes the core challenge. Recently, stochastic gradient optimization algorithms, using a heuristic function, were shown to be significantly superior to random sampling. In this disclosure, it was shown that accurate gradients can be computed-even through a complex forward simulation—using approaches similar to those in dep networks. The proposed approach finds influential outcomes more reliably, and is faster than earlier methods, allowing one to evaluate more policies while simultaneously eliminating the need to design an easily-differentiable heuristic function.

Olson, Edwin↗

DOE OSTI2021

Integrating static PRA information with risk informed safety margin characterization (RISMC) simulation methods

The overall objective of the project was to develop a computationally feasible and user-friendly mechanized process to integrate traditional probabilistic risk assessment (PRA) and dynamic PRA (DPRA) results. Starting with the systematic identification of items in an existing PRA that need dynamic augmentation, the project used a generic 4-loop pressurized reactor (PWR) and 3-loop PWR as example plants. Station blackout (SBO) and large break loss of coolant accident SBLOCA) were selected as the example initiating events. Using the traditional event-tree (ET)/fault-tree (FT) methodology augmented by dynamic evet tree approach, the potential consequences of the initiating events were simulated with RELAP-3D and MELCOR/RASCAL codes to cover Level 1 through Level 3 of PRA. RAVEN and ADAPT software were used to generate Level 1 simulations with RELAP-3D and Level 2/3 simulations with MELCOR (Level 2)/RASCAL (Level 3), respectively. Example branching conditions (BCs) for SBO included AC power recovery time, valve repair failure time, reactor coolant pump leak time/break size and emergency power supply duration to a total of 9. Example BCs for LOCA included off-site power recovery time, diesel generator power recovery time, auxiliary feed water system operation time, safety relief valve failure to open upon demand, reactor coolant pump seal break time and size to a total of 21. Each RELAP-3D simulation (9,587 scenarios) was labelled OK or Core Damage based on the maximum allowed peak clad temperature (2,100oF). Each MELCOR simulation (4610 scenarios) was labeled as Bin over 10rem or Bin 0-10rem based on the dose at the site boundary. The scenarios were clustered based on the criteria above using the mean shift methodology. Classical PRA (CPRA) and DPRA results were compared to identify the ET sequences that need DPRA augmentation. Several approaches were proposed for the incorporation of these sequences into CPRA using clustering with the mean shift methodology, restructuring the CPRA ETs by adding new BCs/sequences, and using the concept of a limit surface. Procedures for decision making regarding the possible consequences of an initiating event (e.g. core damage or not, site evacuation or not) were developed using a convolutional neural network (CNN), a recurrent neural network (RNN) and a transformer neural network (TNN). The project has led to two PhD degrees, three archival journal papers and five refereed conference proceedings.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

From Text to Maps: LLM-Driven Extraction and Geotagging of Epidemiological Data

Epidemiological datasets are essential for public health analysis and decision-making, yet they remain scarce and often difficult to compile due to inconsistent data formats, language barriers, and evolving political boundaries. Traditional methods of creating such datasets involve extensive manual effort and are prone to errors in accurate location extraction. To address these challenges, we propose utilizing large language models (LLMs) to automate the extraction and geotagging of epidemiological data from textual documents. Our approach significantly reduces the manual effort required, limiting human intervention to validating a subset of records against text snippets and verifying the geotagging reasoning, as opposed to reviewing multiple entire documents manually to extract, clean, and geotag. Additionally, the LLMs identify information often overlooked by human annotators, further enhancing the dataset’s completeness. Our findings demonstrate that LLMs can be effectively used to semi-automate the extraction and geotagging of epidemiological data, offering several key advantages: (1) comprehensive information extraction with minimal risk of missing critical details; (2) minimal human intervention; (3) higher-resolution data with more precise geotagging; and (4) significantly reduced resource demands compared to traditional methods.

Harrod, Karly↗

NASA NTRS2018

An Integrated Platform for Mission Performance: Goddard Space Flight Center's Meta Information System

Successful management of NASA Goddard Space Flight Centers portfolio of mission projects (i.e. spacecraft, spacecraft instruments / subsystems, and ground systems) depends upon numerous processes, data and information resources coming together throughout the life-cycle of the projects to enable sound decision-making, risk reduction, conformance with requirements and continual improvement. In the summer of 2012, GSFC SMA management recognized that the set of legacy data systems used to support the SMA mission and responsibilities at GSFC were not effective, integrated nor on par with best practices. Accordingly, GSFC SMA management launched a project to transform the GSFC electronic management system to create and deploy an integrated, state-of-the-market information system for process performance, data management and analytics that is currently operating in support of GSFC overall mission performance. The information system, known as Meta, provides controlled, process-based applications, integrated data management, and analytics for a robust set of integrated applications. Since the inception of the project, a lean, award-winning SMA project team has thus far designed, configured and managed the system to serve as an integrated platform for 18 applications that support key processes for GSFC and the Science Mission Directorate at NASA headquarters, such as internal Management System assessments, Supplier Assessments, Supplier Research & Analysis, Problem Reporting for in-house mission projects, On-orbit Anomaly Reporting for GSFC-operated spacecraft, GSFC Project Life-Cycle Reviews, Risk Management and Reporting, and SMA Project Team Reporting. The system has experienced steady growth in utilization with 1,917 users (NASA employees and support contractor personnel) and 44 gigabytes of data and related files managed by the system as of the end of April 2018. Overall, Meta has achieved increasing levels of process management and real-time data / information integration to enable improved process performance, richer analytics and informed decision-making. This paper will present and examine the need, goals, approach and design driving the management and operation of the Meta information system and its ongoing growth as an integrated platform for advancing NASA mission performance.

Integration↗

NASA NTRS2021

Inertial Transfer Concept for Autonomous In-Space Assembly

Rocket payload and fairing size have placed strict mass and volume limitations on single launch in-space structures. These limitations are what motivates In-Space Assembly (ISA)and where Inertial Transfer has potential to improve or augment ISA capabilities by utilizing an efficient multi-agent autonomous system to transport untethered payloads. Adoption of an inertial transfer approach significantly reduces system complexity, energy and time needed for assembly compared to conventional ISA capabilities, increasing the likelihood of early adoption for assembly. The Inertial Transfer concept is presented including autonomy capabilities, potential risks, and three system configurations. These configurations are based on the autonomous system’s available sensor coverage that tracks the payload’s state. Participating agents have well-defined roles, expectations, and assumptions in their physical organization and coordination of actions depending the mode of operation. The sensor information available to participating agents is considered as it affects the uncertainty in the estimated state and theactions required to capture the payload.

Multi-Agent↗

NASA NTRS2024

A High-Performance Computing Predictive GNSS Performance Monitor for Autonomous Air Vehicles in Urban Environments

This report offers analysis and design insights for leveraging High-Performance Computing (HPC) to predict line-of-sight (LOS) Global Navigation Satellite System (GNSS) availability in a city. This work is motivated by the emerging fields of Advanced and Urban Air Mobility (AAM/UAM), where regulatory authorities are seeking city-scale, meter-resolution risk forecasting in order to safely integrate new flight missions with existing urban life and infrastructure. This work addresses the technical challenge of efficiently computing urban GNSS satellite visibility to predict GNSS performance metrics under these requirements. We present a new HPC-optimized shadow casting algorithm variant as a ray-based approach to forecasting satellite visibility. We apply this algorithm variant in a software-defined prognostic service which generates a GNSS navigation risk-correlated map as a path planning-style potential field. We detail dominant computational burdens, viable simplifying assumptions, and different algorithmic implementations, intending to demonstrate a baseline of computation time needed by each stage in such a service. We conclude by analyzing the prototype service’s prediction accuracy compared to receiver data from Corpus Christi, Texas. This informs design trade-offs along the dimensions of hardware, computation time, and tolerable forecasting error (including proportions of false positives and false negatives).

GNSS↗

NASA NTRS1995

Spacecraft Availability Enhancement by In-flight Testing of Spare Parts

This paper describes an approach to improve availability by testing redundant parts at pre-determined intervals. The purpose of the testing is to detect nonfunctional back-up equipment and develop work-around measures or replacement spacecraft before a failure of the primary equipment reduces availability. The work reported here is an outgrowth of the NASA Space Network's program for the maintenance and replenishment of the Tracking and Data Relay Satellite System. The approach is based on a standby factor and a cyclic stress factor. The standby factor accounts for the effects of adverse storage conditions encountered as part of the in-flight environment. The stress factor accounts for the effects of physical or thermal cycling due to the application of force or power that characterizes the operation or use of a component. By the quantitative consideration of standby and cycling risks, a regular testing interval can be calculated - an interval for testing that furnishes information for availability planning but does not subject the spacecraft to undue risk. This paper includes quantitative solutions for appropriate testing intervals for equipment configurations and failure rates that are representative of a Tracking and Relay Data Satellite. The effect of a single test on the availability of certain equipment is also illustrated.

Cutler, Robert M.↗

NASA NTRS1998

High Speed Research: Propulsion Project Accomplishments

This past year has been one of great accomplishment for the propulsion element of NASA's High Speed Research (HSR) Program. The HSR Program is a NASA/industry partnership to develop the high-risk/high-payoff airframe and propulsion technologies applicable to a second-generation supersonic commercial transport, or High Speed Civil Transport (HSCT). The propulsion element, which also involves industry partners, is managed by the NASA Lewis Research Center. These technologies will contribute greatly to U.S. industry's ability to make an informed product launch decision for an HSCT vehicle. Specific NASA Lewis accomplishments in 1997 include: 1. Small-scale combustor sector tests conducted in Lewis' Engine Research Building contributed to the evolution of approaches to developing a combustor with ultralow NOx emissions. 2. Components were tested in Lewis' CE-9 facility (in Lewis' Engine Research Building) to assess the performance of candidate ceramic matrix composite (CMC) materials in this realistic combustion environment. Test results were promising, and acceptable levels of structural durability were demonstrated for the ceramic matrix composite material tested. Ceramic matrix composites continue to show great promise for use in HSCT combustor liners. 3. Engine emissions tests in Lewis' Propulsion Systems Laboratory provided insight into other classes of emissions (e.g., particulates and aerosols) which will be important to control in HSCT propulsion system designs. 4. Small-scale nozzle tests conducted in Lewis' Aero-Acoustic Propulsion Laboratory are contributing to the design of a low-noise, high-performance mixer/ejector nozzle configuration for HSCT engines. Over 18,000 hours of durability testing were completed in Lewis' materials laboratories to evaluate superalloy and g-titanium aluminide performance for HSCT nozzle applications. A two-dimensional supersonic inlet concept was tested in Lewis' 10- by 10-Foot Supersonic Wind Tunnel. The extensive database and the knowledge gained contributed to the selection of a two-dimensional inlet as the preferred inlet concept for the HSR Program.

Shaw, Robert J.↗

DOE OSTI2024

Recent Advances in Assessing Environmental Effects of Marine Renewable Energy Around the World

Marine renewable energy (MRE) is increasingly of interest to coastal nations as a source of renewable energy that can support climate change mitigation goals as well as provide secure locally-produced energy for coastal and island communities. MRE extracts power from tidal streams, waves, ocean currents, run of rivers, and gradients in the ocean, with specialized devices developed and tested for each energy resource. Alongside development of MRE technologies and systems, first in Europe and then in North America, Australia, Asia, and other regions, it has been universally recognized that there is also a need to examine potential effects on marine animals, habitats, ecosystem processes, local communities and other sea users, to ensure that the MRE industry can be developed in a responsible and sustainable manner. This paper looks at the status of assessment and monitoring for potential environmental effects associated with MRE projects around the world. Over 80 projects were identified worldwide as having been tested, demonstrated, or commercially deployed with associated environmental monitoring. Five of the projects that represent tidal stream, wave, and run of river projects are examined in more detail to determine the types of data and information collected for those projects, the outputs of the monitoring campaigns, and the actions taken as a result of the data collection and analysis. Recommendations are provided for standardization of the monitoring approaches, instruments, and analysis methods at MRE project sites worldwide.

marine renewable energy, environmental effects, ri↗

DOE OSTI2024

Datasets and U-Net Model for "A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: a Case Study for California and Oklahoma"

This dataset has results and the model associated with the publication Ciulla et al., (2024). It contains a U-Net semantic segmentation model (unet_model.h5) and associated code implemented in tensorflow 2.0 for the model training and identification of oil and gas well symbols in USGS historical topographic maps (HTMC). Given a quadrangle map (7.5 minutes), downloadable at this url: https://ngmdb.usgs.gov/topoview/, and a list of coordinates of the documented wells present in the area, the model returns the coordinates of oil and gas symbols in the HTMC maps. For reproducibility of our workflow, we provide a sample map in California and the documented well locations for the entire State of California (CalGEM_AllWells_20231128.csv) downloaded from https://www.conservation.ca.gov/calgem/maps/Pages/GISMapping2.aspx. Additionally, the locations of 1,301 potential undocumented orphaned wells identified using our deep learning framework or the counties of Los Angeles and Kern in California, and Osage and Oklahoma in Oklahoma are provided in the file found_potential_UOWs.zip. The results of the visual inspection of satellite imagery in Osage County is in the file visible_potential_UOWs.zip. The dataset also includes a custom tool to validate the detected symbols in the HTMC maps (vetting_tool.py). More details about the methodology can be found in the associated paper: Ciulla, F., Santos, A., Jordan, P., Kneafsey, T., Biraud, S.C., and Varadharajan, C. (2024) A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: a Case Study for California and Oklahoma. Accepted for publication in Environmental Science and Technology. The geographical coordinates provided correspond to the locations of potential undocumented orphaned oil and gas wells (UOWs) extracted from historical maps. The actual presence of wells need to be confirmed with on-the-ground investigations. For your safety, do not attempt to visit or investigate these sites without appropriate safety training, proper equipment, and authorization from local authorities. Approaching these well sites without proper personal protective equipment (PPE) may pose significant health and safety risks. Oil and gas wells can emit hazardous gasses including methane, which is flammable, odorless and colorless, as well as hydrogen sulfide, which can be fatal even at low concentrations. Additionally, there may be unstable ground near the wellhead that may collapse around the wellbore. This dataset was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or the Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.

Artificial Intelligence↗

DOE OSTI2020

Sea-Ice Effects on the Alaskan Wave Climate

This study investigates the uncertainty of wave resource assessments due to sea ice in the nearshore region of Alaska using an innovative approach of combining remotely sensed data and a third-generation phase-averaged wave model. Modeling analysis focused on winter months, when high wave energy is present along with sea ice. This study includes recommendations to improve the accuracy of wave resource assessments and optimize marine and hydrokinetic device siting with consideration of sea-ice effects. Additional recommendations, based on preliminary sea-ice information in Cook Inlet, Alaska, are provided for a trajectory analysis of drifting sea ice and a collision risk assessment of sea ice and tidal turbines. The outcome of this project will provide valuable information about the level of sea-ice impact on the accuracy of wave resource assessments in the Alaska nearshore region, and of sea-ice collision risk with MHK devices and ocean observation systems during winter months when sea ice is present.

16 TIDAL AND WAVE POWER↗

DOE OSTI2023

In silico analyses of diversity and dissemination of antimicrobial resistance genes and mobile genetics elements, for plasmids of enteric pathogens

The antimicrobial resistance (AMR) mobilome plays a key role in the dissemination of resistance genes encoded by mobile genetics elements (MGEs) including plasmids, transposons (Tns), and insertion sequences (ISs). These MGEs contribute to the dissemination of multidrug resistance (MDR) in enteric bacterial pathogens which have been considered as a global public health risk. To further understand the diversity and distribution of AMR genes and MGEs across different plasmid types, we utilized multiple sequence-based computational approaches to evaluate AMR-associated plasmid genetics. A collection of 1,309 complete plasmid sequences from Gammaproteobacterial species, including 100 plasmids from each of the following 14 incompatibility (Inc) types: A/C, BO, FIA, FIB, FIC, FIIA, HI1, HI2, I1, K, M, N, P except W, where only 9 sequences were available, was extracted from the National Center for Biotechnology Information (NCBI) GenBank database using BLAST tools. The extracted FASTA files were analyzed using the AMRFinderPlus web-based tools to detect antimicrobial, disinfectant, biocide, and heavy metal resistance genes and ISFinder to identify IS/Tn MGEs within the plasmid sequences. In silico prediction based on plasmid replicon types showed that the resistance genes were diverse among plasmids, yet multiple genes were widely distributed across the plasmids from enteric bacterial species. These findings provide insights into the diversity of resistance genes and that MGEs mediate potential transmission of these genes across multiple plasmid replicon types. This notion was supported by the observation that many IS/Tn MGEs and resistance genes known to be associated with them were common across multiple different plasmid types. Our results provide critical insights about how the diverse population of resistance genes that are carried by the different plasmid types can allow for the dissemination of AMR across enteric bacteria. The results also highlight the value of computational-based approaches and in silico analyses for the assessment of AMR and MGEs, which are important elements of molecular epidemiology and public health outcomes.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2024

Framework for Seismic Vulnerability Assessment of Nonstructural Elements Inside an Inflatable Lunar Habitat

The pursuit of knowledge and curiosity are once again driving humanity to seek a human presence on extraterrestrial bodies. While numerous challenges must be overcome on the moon to achieve this goal, the moonquake hazard has often been overlooked. Using NASA’s Moon to Mars Architecture requirements for surface habitation and accounting for the midterm/long-term mission duration, a framework for vulnerability assessment is developed for essential nonstructural elements (NSEs) inside an inflatable habitat subjected to moonquakes. The aim of this study is to develop an approach to assess the vulnerability of typical NSEs that support essential equipment, such as the environmental control and life support system, under a paucity of information related to the seismic hazard. We also emphasize that the launch dynamic environment and lunar seismic environment exhibit notable differences in their characteristics, which can lead to higher lateral acceleration levels than initially expected. This acceleration may be linked to high-occurrence seismic events. However, due to the lack of information regarding the frequency of these events, the risk of NSEs being subjected to unforeseen loads increases. It is imperative to equip future lunar habitats with seismic mitigation measures until additional seismometers are deployed near upcoming surface human outposts.

Oscar D Forero↗