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DOE OSTI2016

Fuel hardware considerations for BWR PCI mitigation

Pellet-clad interaction (PCI) remains a potential cause of fuel failures and is a major consideration in the design of new BWR fuel assemblies. The design and material properties of both the pellet and the cladding have been investigated and optimized to mitigate the impact of PCI. The creep behavior of standard and ADOPT (Advanced Doped Pellet Technology) fuel pellets has been measured across a wide range of temperatures. Dopants have been found to increase the high temperature creep properties of the fuel and can explain the improved PCI resistance of ∼5 kW/m in ramp tests. Other effects, such as chemical trapping of fission products and modified crack patterns cannot be ruled out, but these are at an earlier stage of investigation. It is shown that the enhanced creep effect occurs at the high centerline fuel temperatures during a ramp test, but is not present during the much lower temperatures of commercial fuel operation. Thus low temperature PCI benefits in routine commercial operation are not expected. The development of cladding materials with liner provided a great improvement for PCI mitigation in BWRs. With the softer Westinghouse liner, stepwise ramp tests have shown a benefit of ca 15-20 kW/m. Differences between stepwise and single step ramp tests are discussed. Notably the benefit of the liner remains under all types of fuel operation and temperatures. It is also thought to provide significant benefit in the case of fuel relocation or pellet defects such as missing pellet chips. The results of post irradiation examination (PIE) of a commercial fuel rod that experienced fuel fragment relocation are presented in the paper. To provide the maximum PCI benefit in the next generation BWR fuel design, TRITON11, the proven benefits of both the liner and ADOPT pellets will be included. To reduce the hydrogen pickup, below that of Zircaloy-2, the outer cladding component will be made from HiFi. TRITON11, ADOPT and HiFi are registered trademarks of Westinghouse Electric Company LLC in the United States. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Topography-Induced TKE Budget Behavior Over an Amazon Forest

Data from three different heights (35, 50 and 81 m) of one of the ATTO Project towers in the Amazon forest were used to calculate the TKE (turbulence kinetic energy) budget and some other statistics within the RSL (roughness sublayer). The statistical analyses were carried out for unstable and stable cases. The vertical transport and the vertical advection terms do not explain the imbalances found in the TKE budget, highlighting the impor- tance of horizontal terms in complex terrain. Indeed, patterns independent of the time of day were found for vertical transport, horizontal and vertical velocity skewness, the mean vertical component velocity, and momentum and vertical TKE flux. Here, the highest values of mean vertical velocity were observed from the direction where there is a valley near the tower, which may indicate a topographical effect. Monin-Obukhov Similarity Theory (MOST) predictions for the mean velocity gradient, strictly applicable only in the inertial sublayer (ISL), gave reasonable predictions at 35 and 50 m, but dimensionless dissipation rates displayed large scatter. At 81 m, there were clear deviations from MOST for the two functions, disclosing the effect of topography. In addition, a neutral LES (large-eddy simu- lation) study showed good agreement with tower data for the same general wind direction.

ATTO project↗

DOE OSTI2022

Building a Framework to Understand Transition Metals' Behavior in Euxinic Conditions (Final Technical Report)

This project focuses first and foremost on metal sulfide geochemistry and mineralogy as controlled by a complex matrix of environmental factors. The principal investigator’s group aim to illuminate the metal-sulfide reaction mechanisms, rates, and pathways through systematic experimentation and data collection and analyzing the relationships between the characteristics of the produced metal sulfide solid-phase/aqueous complexes and the environmental factors. This understanding is essential for obtaining a full picture of the complex cycling patterns of single or multi metal species in sulfidic environments ranging from deep-see basins, hydrothermal vents, inland seas, terrestrial water bodies, to engineered remediation systems. The specific goal of this past project was to illuminate the reaction mechanisms and kinetics of metal anions and sulfide in mixed metal cation-metal anion-sulfide systems under various aqueous conditions (which resembled a range of naturally occurring euxinic settings). For the period of this contract, we investigated the molybdenum-iron-sulfide system, with an emphasis on the conditions that caused solid phase formation. We focused on quantifying the mobility/sequestration of molybdenum under each experimental condition and identified the changes of valence states for each involved element (i.e., Mo, Fe, and S) in the precipitate. We also proposed pathways for the electron transfer that occurred in aqueous chemistry. The major analytical tools used for this study include UV-visible light spectroscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, and synchrotron-based X-ray absorption spectroscopy (access gained through facility proposals to the Canadian Light Source). The biggest finding of this project was that besides pH, the iron-sulfur chemistry has a dominant control of the thiolation kinetics and subsequent reduction of Mo(VI), which are likely prerequisites of molybdenum sequestration in anoxic conditions. The results have been written up as manuscript by the end of this project (see Phillips et al.). The experimental results of this project may be critical for advancing our understanding of (1) basic chemistry involving transition metals and reduced sulfur species, (2) the validity of certain geochemical proxies, and (3) the stability and evolution of euxinic geochemical environments. It is noted that the basic results obtained through this project also have implications for Mo-S cluster-based catalyst development in inorganic chemistry and materials sciences.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Spectroscopic Evidence of Pore Geometry Effect on Axial Coordination of Guest Molecules in Metalloporphyrin-Based Metal Organic Frameworks

A systematic comparison of host–guest interactions in two iron porphyrin-based metal–organic frameworks (MOFs), FeCl-PCN222 and FeCl-PCN224, with drastically different pore sizes and geometries is reported in this fundamental spectroscopy study. Guest molecules (acetone, imidazole, and piperidine) of different sizes, axial binding strengths, and reactivity with the iron porphyrin centers are employed to demonstrate the range of possible interactions that occur at the porphyrin sites inside the pores of the MOF. Binding patterns of these guest species under the constraints of the pore geometries in the two frameworks are established using multiple spectroscopy methods, including UV–vis diffuse reflectance, Raman, X-ray absorption, and X-ray emission spectroscopy. Line shape analysis applied to the latter method provides quantitative information on axial ligation through its spin state sensitivity. The observed coordination behaviors derived from the spectroscopic analyses of the two MOF systems are compared to those predicted using space-filling models and relevant iron porphyrin molecular analogues. While the space-filling models show the ideal axial coordination behavior associated with these systems, the spectroscopic results provide powerful insight into the actual binding interactions that occur in practice. Evidence for potential side reactions occurring within the pores that may be responsible for the observed deviation from model coordination behavior in one of the MOF/guest molecule combinations is presented and discussed in the context of literature precedent.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Visualization of Multifractal Superconductivity in a Two-Dimensional Transition Metal Dichalcogenide in the Weak-Disorder Regime

Eigenstate multifractality is a distinctive feature of noninteracting disordered metals close to a metal–insulator transition, whose properties are expected to extend to superconductivity. While multifractality in three dimensions (3D) only develops near the critical point for specific strong-disorder strengths, multifractality in 2D systems is expected to be observable even for weak disorder. Here we provide evidence for multifractal features in the superconducting state of an intrinsic, weakly disordered single-layer NbSe 2 by means of low-temperature scanning tunneling microscopy/spectroscopy. The superconducting gap, characterized by its width, depth, and coherence peaks’ amplitude, shows a characteristic spatial modulation coincident with the periodicity of the quasiparticle interference pattern. The strong spatial inhomogeneity of the superconducting gap width, proportional to the local order parameter in the weak-disorder regime, follows a log-normal statistical distribution as well as a power-law decay of the two-point correlation function, in agreement with our theoretical model. Furthermore, the experimental singularity spectrum f(α) shows anomalous scaling behavior typical from 2D weakly disordered systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Mechanisms and Impacts of Earth System Tipping Elements

Tipping elements are components of the Earth system which may respond nonlinearly to anthropogenic climate change by transitioning toward substantially different long-term states upon passing key thresholds or “tipping points.” In some cases, such changes could produce additional greenhouse gas emissions or radiative forcing that could compound global warming. Improved understanding of tipping elements is important for predicting future climate risks and their impacts. Here we review mechanisms, predictions, impacts, and knowledge gaps associated with 10 notable Earth system components proposed to be tipping elements. We evaluate which tipping elements are approaching critical thresholds and whether shifts may manifest rapidly or over longer timescales. Some tipping elements have a higher risk of crossing tipping points under middle-of-the-road emissions pathways and will possibly affect major ecosystems, climate patterns, and/or carbon cycling within the 21st century. However, literature assessing different emissions scenarios indicates a strong potential to reduce impacts associated with many tipping elements through climate change mitigation. The studies synthesized in our review suggest most tipping elements do not possess the potential for abrupt future change within years, and some proposed tipping elements may not exhibit tipping behavior, rather responding more predictably and directly to the magnitude of forcing. Nevertheless, uncertainties remain associated with many tipping elements, highlighting an acute need for further research and modeling to better constrain risks.

58 GEOSCIENCES↗

DOE OSTI2023

Characterizing Performance of Freshwater Wetland Methane Models Across Time Scales at FLUXNET-CH 4 Sites Using Wavelet Analyses

Process-based land surface models are important tools for estimating global wetland methane (CH 4 ) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site-level patterns of freshwater wetland CH 4 fluxes (FCH 4 ) at different time scales. A Monte Carlo approach was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model-observation disagreements are mainly at multi-day time scales (<15 days); (b) most of the models can capture the CH 4 variability at monthly and seasonal time scales (>32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales <5 days could contribute to persistent systematic biases on longer time scales; and (d) differences in error pattern are related to model structure (e.g., proxy of CH 4 production). Our evaluation suggests the need to accurately replicate FCH 4 variability, especially at short time scales, in future wetland CH 4 model developments.

58 GEOSCIENCES↗

DOE OSTI2026

Subseasonal Forecasting and MJO Teleconnections in Machine Learning Weather Prediction Models

Abstract In recent years, machine‐learning (ML) models trained on reanalysis data have rivaled physics‐based forecast models in terms of performance skill for global weather forecasting. With increased rollout stability, the question of how these models perform for subseasonal to seasonal (S2S, week 3–8) forecasting has emerged. In this study we run a large set of subseasonal hindcasts over 2004–2023 to evaluate two ML weather forecast models at the S2S time scale, SFNO‐HENS (Nvidia, fully ML) and NeuralGCM (Google Research, hybrid). Corresponding hindcasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) are used as a baseline for comparison to a physics‐based model. Because our focus is on predicting moisture transport over the Western United States between October and March, we evaluate the models' prediction skill for the Madden‐Julian Oscillation (MJO) and its associated teleconnections in the North Pacific. We find that both ML models are competitive with the ECWMF model, with comparable skill in predicting the North Pacific large‐scale circulation and the MJO at week 3 and beyond. Even though overall the mid‐latitude subseasonal prediction skill remains low, the ML models exhibit interesting behavior such as a realistic propagation of the MJO across the Maritime Continent and realistic teleconnections. A SFNO‐HENS sensitivity experiment with altered initial conditions in the tropics demonstrates the stability of the model, and it illustrates the capability of ML models to represent important physical processes of the atmosphere at the S2S time scale. Plain Language Summary Predicting weather patterns and precipitation a few weeks in advance (subseasonal time scale) is of great interest for stakeholders such as water managers in the Southwest United States (US), where arid conditions prevail. Subseasonal forecasts from traditional weather forecast models exhibit low skill in the region, limiting their applicability. Here we examine whether the recent breakthrough in weather forecasting made with machine learning/artificial intelligence models can translate to improved subseasonal forecasts. Recently‐developed machine learning models exhibit comparable skill to a state‐of‐the‐art physics‐based model for predicting weather patterns in the North Pacific/North America region, and associated moisture transport. The same applies to their skill in predicting the tropical pattern, the Madden‐Julian Oscillation, and its important remote perturbations over the midlatitude East Pacific and Southwest US. Additionally, a perturbation experiment carried out with one of the machine learning models illustrates their ability to not only predict the evolution of atmospheric fields, but also to learn and represent physical processes such as tropics‐extratropics Rossby wave propagation. Key Points Two machine learning weather forecast models exhibit state‐of‐the‐art prediction skill at the subseasonal time scale in the Pacific sector The models equal ECWMF in terms of Madden‐Julian oscillation (MJO) prediction skill, and they accurately predict the MJO propagation and associated teleconnections The two machine‐learning models represent key physical processes for subseasonal prediction, despite being trained for weather forecasting

Peings, Yannick↗

DOE OSTI2020

Enhancement of chemical oscillations by self-generated convective flows

Chemical feedback loops in fluids can produce not only chemical oscillations, but also density variations that generate solutal buoyancy forces, which in turn initiate fluid flow. Using analytical and computational models, we herein examine how the reaction-induced flows alter the chemical oscillations in a fluid-filled chamber whose top and bottom walls are coated with different enzymes. Due to this chemo-fluidic coupling, the systems form oscillating flow patterns, which combine the characteristic size of the buoyancy-driven convection rolls with the frequency of the chemical oscillations. With changes in the distance between the enzyme-coated walls, the convective flows not only enhance or suppress the chemical oscillations, but also substantially increase the amplitude and frequency of the oscillations and extend the regime of the oscillatory behavior. These design principles can facilitate the development of artificial biochemical networks that act as chemical clocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Unconventional magnetism in ThCr2Si2-type phosphides, La1 xNdxCo2P2

Quaternary phases La1 xNdxCo2P2 (x = 0, 0.12, 0.25, 0.37, 0.50, 0.63, 0.75, 0.88, 1.0) have been synthesized from Sn flux to investigate the origins of drastic differences in properties between ferromagnetic LaCo2P2 and antiferromagnetic NdCo2P2. Powder and single-crystal X-ray diffraction indicate that all La1 xNdxCo2P2 samples are isostructural and crystallize in the ThCr2Si2 structure type. The unit cell parameters and volume change non-linearly with the Nd content (x), with the x < 0.50 10 samples being closer to LaCo2P2 and the ones with x > 0.50 being closer to NdCo2P2. These structural differences are also reflected in the magnetic behavior. The samples with lower Nd content are characterized by ferromagnetic ordering in the Co sublattice with the TC increasing from 132 K for x = 0 to 262 K for x = 0.50, while the samples with higher Nd content exhibit suppressed magnetization in the Co sublattice and canted antiferromagnetic ordering with TC ~ 270 K. Refinement of neutron powder 15 diffraction patterns for x = 0.50 and 0.75 reveals a gradual ordering of the Nd 4f moments under the influence of Co 3d moments below 100 K. At low temperatures and zero field, these samples exhibit antiferromagnetic ordering of both Nd and Co magnetic moments, but under applied field they demonstrate the stabilization of a ferrimagnetic state with antiparallel alignment of the 4f and 3d moments, as indicated by isothermal magnetization measurements. The re-entrant ferrimagnetic transition 20 is also observed in samples with x > 0.50 if the temperature is lowered below 5 K. The occurrence of this low-temperature magnetic transition was confirmed by alternating-current susceptibility measurements.

Thompson, Corey↗

DOE OSTI2014

Rationalizing the role of structural motif and underlying electronic structure in the finite temperature behavior of atomic clusters

Melting in finite size systems is an interesting but complex phenomenon. Many factors affect melting and owing to their interdependencies it is a challenging task to rationalize their roles in the phase transition. In this work, we demonstrate how structural motif of the ground state influences melting transition in small clusters. Here, we report a case with clusters of aluminum and gallium having same number of atoms, valence electrons, and similar structural motif of the ground state but drastically different melting temperatures. We have employed Born-Oppenheimer molecular dynamics to simulate the solid-like to liquid-like transition in these clusters. Our simulations have reproduced the experimental trends fairly well. Further, the detailed analysis of isomers has brought out the role of the ground state structure and underlying electronic structure in the finite temperature behavior of these clusters. For both clusters, isomers accessible before cluster melts have striking similarities and does have strong influence of the structural motif of the ground state. Further, the shape of the heat capacity curve is similar in both the cases but the transition is more spread over for Al{sub 36} which is consistent with the observed isomerization pattern. Our simulations also suggest a way to characterize transition region on the basis of accessibility of the ground state at a specific temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Structural, dielectric and magnetic properties of nickel substituted cobalt ferrite nanoparticles: Effect of nickel concentration

Nickel substituted cobalt ferrite nanoparticles with composition Co{sub 1−x}Ni{sub x}Fe{sub 2}O{sub 4} (0.0 ≤ x ≤ 1.0) was synthesized using simple, low temperature auto combustion method. The X-ray diffraction patterns reveal the formation of cubic phase spinel structure. The crystallite size varies from 30-44 nm with the nickel content. Porous and agglomerated morphology of the bulk sample was displayed in the scanning electron microscopy. Micro Raman spectroscopy reveals continuous shift of E{sub g} and E{sub g}(2) stokes line up to 0.8 Ni substitution. The dispersion behavior of the dielectric constant with frequency and the semicircle nature of the impedance spectra show the cobalt nickel ferrite to have high resistance. The ferromagnetic nature is observed in all the samples, however, the maximum saturation magnetization was achieved by the 0.4 Ni substituted cobalt ferrite, which is up to the 92.87 emu/gm at 30K.

36 MATERIALS SCIENCE↗

DOE OSTI2014

Living liquid crystals

Collective motion of self-propelled organisms or synthetic particles, often termed •active fluid,• has attracted enormous attention in the broad scientific community because of its fundamentally nonequilibrium nature. Energy input and interactions among the moving units and the medium lead to complex dynamics. Here,we introduce a class of active matter-living liquid crystals (UCs} that combines living swimming bacteria with a lyotropic liquid crystal. The physical properties of LLCs can be controlled by the amount of oxygen available to bacteria, by concentration of ingredients, or by temperature. Our studies reveal a wealth of intriguing dynamic phenomena. caused by the coupling between the activity-triggered flow and long-range orientational order of the medium. Among these are (i) nonlinear trajectories of bacterial motion guided by nonuniform director, (ii) local melting of the liquid crystal caused by the bacteria-produced shear flows, (iii) activity-triggered transition from a nonflowing uniform state into a flowing one-dimensional periodic pattern and its evolution into a turbulent array of topological defects, and (iv) birefringence enabled visualization of microflow generated by the nanometers thick bacterial flagella. Unlike their isotropic counterpart, the LLCs show collective dynamic effects at very low volume fraction of bacteria, on the order of 0.2%. Our work suggests an unorthodox design concept to control and manipulate the dynamic behavior of soft active matter and opens the door for potential biosensing and biomedical applications.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Visualizing strange metallic correlations in the two-dimensional Fermi-Hubbard model with artificial intelligence

Strongly correlated phases of matter are often described in terms of straightforward electronic patterns. This has so far been the basis for studying the Fermi-Hubbard model realized with ultracold atoms. Here, we show that artificial intelligence (AI) can provide an unbiased alternative to this paradigm for phases with subtle, or even unknown, patterns. Long- and short-range spin correlations spontaneously emerge in filters of a convolutional neural network trained on snapshots of single atomic species. In the less well-understood strange metallic phase of the model, we find that a more complex network trained on snapshots of local moments produces an effective order parameter for the non-Fermi-liquid behavior. We report our technique can be employed to characterize correlations unique to other phases with no obvious order parameters or signatures in projective measurements, and has implications for science discovery through AI beyond strongly correlated systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Insights on carbon dioxide adsorption in a flexible 1-D coordination polymer from in situ X-ray scattering and density functional theory

A combination of in situ small-angle X-ray scattering (SAXS) microstructure characterization over scales ranging from 1 nm to 10 µm and powder X-ray diffraction (XRD) structure characterization under various gas/pressure/temperature conditions with density functional theory (DFT) calculations provides new insights for the CO2 sorption behavior of a one-dimensional porous coordination polymer: catena-bis(dibenzoylmethanato)(4,4′-bipyridyl)nickel(II), denoted NiDBM-Bpy. The NiDBM-Bpy chains are held together by van der Waals forces, but the structure of guest-free NiDBM-Bpy is unsolved due to a lack of suitable crystals and high-quality powder XRD patterns. Nevertheless, SAXS and powder XRD can follow microstructural and structural changes as a function of gas pressure, composition and temperature. Both mixed-gas flow and static supercritical CO2 regimes are explored experimentally. DFT calculations are used to model the structural variation associated with XRD changes and hysteresis in the sorption isotherms. XRD and DFT calculations suggest that an orthorhombic Fddd structure with two CO2 per Ni emerges following a transition from the structure with lower molar volume and symmetry that exists without CO2 present.

Allen, Andrew J.↗

DOE OSTI2024

Intrinsic and environmental drivers of pairwise cohesion in wild Canis social groups

Animals within social groups respond to costs and benefits of sociality by adjusting the proportion of time they spend in close proximity to other individuals in the group (cohesion). Variation in cohesion between individuals, in turn, shapes important group-level processes such as subgroup formation and fission–fusion dynamics. Although critical to animal sociality, a comprehensive understanding of the factors influencing cohesion remains a gap in our knowledge of cooperative behavior in animals. We tracked 574 individuals from six species within the genus Canis in 15 countries on four continents with GPS telemetry to estimate the time that pairs of individuals within social groups spent in close proximity and test hypotheses regarding drivers of cohesion. Pairs of social canids (Canis spp.) varied widely in the proportion of time they spent together (5%–100%) during seasonal monitoring periods relative to both intrinsic characteristics and environmental conditions. The majority of our data came from three species of wolves (gray wolves, eastern wolves, and red wolves) and coyotes. For these species, cohesion within social groups was greatest between breeding pairs and varied seasonally as the nature of cooperative activities changed relative to annual life history patterns. Across species, wolves were more cohesive than coyotes. For wolves, pairs were less cohesive in larger groups, and when suitable, small prey was present reflecting the constraints of food resources and intragroup competition on social associations. Pair cohesion in wolves declined with increased anthropogenic modification of the landscape and greater climatic variability, underscoring challenges for conserving social top predators in a changing world. We show that pairwise cohesion in social groups varies strongly both within and across Canis species, as individuals respond to changing ecological context defined by resources, competition, and anthropogenic disturbance. Our work highlights that cohesion is a highly plastic component of animal sociality that holds significant promise for elucidating ecological and evolutionary mechanisms underlying cooperative behavior.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Modeling the Pseudomonas Sulfur Regulome by Quantifying the Storage and Communication of Information

ABSTRACT Bacteria are not simply passive consumers of nutrients or merely steady-state systems. Rather, bacteria are active participants in their environments, collecting information from their surroundings and processing and using that information to adapt their behavior and optimize survival. The bacterial regulome is the set of physical interactions that link environmental information to the expression of genes by way of networks of sensors, transporters, signal cascades, and transcription factors. As bacteria cannot have one dedicated sensor and regulatory response system for every possible condition that they may encounter, the sensor systems must respond to a variety of overlapping stimuli and collate multiple forms of information to make “decisions” about the most appropriate response to a specific set of environmental conditions. Here, we analyze Pseudomonas fluorescens transcriptional responses to multiple sulfur nutrient sources to generate a predictive, computational model of the sulfur regulome. To model the regulome, we utilize a transmitter-channel-receiver scheme of information transfer and utilize principles from information theory to portray P. fluorescens as an informatics system. This approach enables us to exploit the well-established metrics associated with information theory to model the sulfur regulome. Our computational modeling analysis results in the accurate prediction of gene expression patterns in response to the specific sulfur nutrient environments and provides insights into the molecular mechanisms of Pseudomonas sensory capabilities and gene regulatory networks. In addition, modeling the bacterial regulome using the tools of information theory is a powerful and generalizable approach that will have multiple future applications to other bacterial regulomes. IMPORTANCE Bacteria sense and respond to their environments using a sophisticated array of sensors and regulatory networks to optimize their fitness and survival in a constantly changing environment. Understanding how these regulatory and sensory networks work will provide the capacity to predict bacterial behaviors and, potentially, to manipulate their interactions with an environment or host. Leveraging the information theory provides useful quantitative metrics for modeling the information processing capacity of bacterial regulatory networks. As our model accurately predicted gene expression profiles in a bacterial model system, we posit that the information theory-based approaches will be important to enhance our understanding of a wide variety of bacterial regulomes and our ability to engineer bacterial sensory and regulatory networks.

Larsen, Peter E.↗

NASA NTRS1990

Continuous monitoring of the lunar or Martian subsurface using on-board pattern recognition and neural processing of Rover geophysical data

The ultimate goal is to create an extraterrestrial unmanned system for subsurface mapping and exploration. Neural networks are to be used to recognize anomalies in the profiles that correspond to potentially exploitable subsurface features. The ground penetrating radar (GPR) techniques are likewise identical. Hence, the preliminary research focus on GPR systems will be directly applicable to seismic systems once such systems can be designed for continuous operation. The original GPR profile may be very complex due to electrical behavior of the background, targets, and antennas, much as the seismic record is made complex by multiple reflections, ghosting, and ringing. Because the format of the GPR data is similar to the format of seismic data, seismic processing software may be applied to GPR data to help enhance the data. A neural network may then be trained to more accurately identify anomalies from the processed record than from the original record.

Mcgill, J. W.↗