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At least 685 records · Page 38

A Summary of Coupled, Uncoupled, and Hybrid Tectonic Models for the Yakima Fold Belt

This document is one in a series of topical reports compiled by the Pacific Northwest National Laboratory to summarize technical information on selected topics important to the performance of a probabilistic seismic hazard analysis of the Hanford Site. The purpose of this report is to summarize the range of opinions and supporting information expressed by the expert community regarding whether a coupled or uncoupled model, or a combination of both, best represents structures in the Yakima Fold Belt. This issue was assessed to have a high level of contention with up to moderate potential for impact on the hazard estimate. This report defines the alternative conceptual models relevant to this technical issue and the arguments and data that support those models. It provides a brief description of the technical issue and principal uncertainties; a general overview on the nature of the technical issue, along with alternative conceptual models, supporting arguments and information, and uncertainties; and finally, suggests some possible approaches for reducing uncertainties regarding this issue.

Chamness, Michele A.↗

Satellite broadcasting system study

The study to develop a system model and computer program representative of broadcasting satellite systems employing community-type receiving terminals is reported. The program provides a user-oriented tool for evaluating performance/cost tradeoffs, synthesizing minimum cost systems for a given set of system requirements, and performing sensitivity analyses to identify critical parameters and technology. The performance/ costing philosophy and what is meant by a minimum cost system is shown graphically. Topics discussed include: main line control program, ground segment model, space segment model, cost models and launch vehicle selection. Several examples of minimum cost systems resulting from the computer program are presented. A listing of the computer program is also included.

Source record↗

Toward Integrated Seasonal Predictions of Land and Ocean Carbon Flux: Lessons from the 2015-16 El Nino

Seasonal forecasts made by coupled atmosphere-ocean general circulation models (GCMs) are increasingly able to provide skillful forecasts of climate anomalies. At some centers, the capabilities of these models are being expanded to represent carbon-climate feedbacks including ocean biogeochemistry (OB), terrestrial biosphere (TB) interactions, and fires. These advances raise the question of whether such models can support skillful forecasts of carbon fluxes.Here, we examine whether land and ocean carbon flux anomalies associated with the 2015-16 El Nino could have been predicted months in advance. This El Nino was noteworthy for the magnitude of the ocean temperature perturbation, the skill with which this perturbation was predicted, and the extensive satellite observations that can be used to track its impact. We explore this topic using NASA's Goddard Earth Observing System (GEOS) model, which routinely produces an ensemble of seasonal climate forecasts, and a suite of offline dynamical and statistical models that estimate carbon flux processes. Using GEOS forecast fields from 2015-16 to force flux model hindcasts shows that these models are able to reproduce significant features observed by satellites. Specifically, OB hindcasts are able to predict anomalies in chlorophyll distributions with lead times of 3-4 months. The ability of TB hindcasts to reproduce NDVI anomalies is driven by the skill of the climate forecast, which is greatest at short lead times over tropical landmasses. Statistical fire forecasts driven by ocean climate indices are able to predict burned area in the tropics with lead times of 3-12 months. We also integrate the ocean and land hindcast fluxes into the GEOS GCM to examine the magnitude of the atmospheric carbon dioxide anomaly and compare with satellite and ground-based observations.While seasonal forecasting remains an active area of research, these results demonstrate that forecasts of carbon flux processes can support a variety of applications, potentially allowing scientists to understand carbon-climate feedbacks as they happen and to capitalize on more flexible satellite technologies that allow areas of interest to be targeted with lead times of weeks to months. We also provide a first glimpse at the spring 2019 carbon forecast using the GEOS-based forecasting system.

Ott, L.↗

CO 2 mineral trapping in fractured basalt

Fractures in basalt can provide substantial surface area for reactions, and limited mass transfer in fractures can allow accumulation of cations to form carbonate minerals in geologic carbon sequestration. In this study, flood basalt and serpentinized basalt with engineered fractures were reacted in water equilibrated with 10 MPa CO 2 at 100 °C or 150 °C for up to 40 weeks. Carbonation in basalt fractures was observed as early as 6 weeks, with Mg- and Ca-bearing siderite formed in both basalts reacted at 100 °C and Mg-Fe-Ca carbonate minerals formed in the flood basalt reacted at 150 °C. X-ray μCT segmentation revealed that precipitates filled 5.4% and 15% (by volume) of the flood basalt fracture after 40 weeks of reaction at 100 °C and 150 °C, respectively. Zones of elevated carbonate abundance did not completely seal the fracture. Limited siderite clusters (<1% volume fraction) were found in localized areas in the serpentinized basalt fracture. A 1-dimensional reactive transport model developed in CrunchTope examined how geochemical gradients drive silicate mineral dissolution and carbonate precipitation in the fracture. The model predicts that siderite will form as early as 1 day after the addition of CO 2 . In conclusion, the predicted location of maximum siderite abundance is consistent with experimental observations, and the predicted total carbonate volumes are comparable to estimates derived from CT segmentation.

54 ENVIRONMENTAL SCIENCES↗

Unraveling capacity fading in lithium-ion batteries using advanced cyclic tests: A real-world approach

Battery lifespan estimation is essential for effective battery management systems, aiding users and manufacturers in strategic planning. However, accurately estimating battery capacity is complex, owing to diverse capacity fading phenomena tied to factors such as temperature, charge-discharge rate, and rest period duration. In this work, we present an innovative approach that integrates real-world driving behaviors into cyclic testing. Unlike conventional methods that lack rest periods and involve fixed charge-discharge rates, our approach involves 1000 unique test cycles tailored to specific objectives and applications, capturing the nuanced effects of temperature, charge-discharge rate, and rest duration on capacity fading. This yields comprehensive insights into cell-level battery degradation, unveiling growth patterns of the solid electrolyte interface (SEI) layer and lithium plating, influenced by cyclic test parameters. Here, the results yield critical empirical relations for evaluating capacity fading under specific testing conditions.

25 ENERGY STORAGE↗

Characterization of Red/Green Cyanobacteriochrome NpR6012g4 by Solution Nuclear Magnetic Resonance Spectroscopy: A Hydrophobic Pocket for the C15- E,anti Chromophore in the Photoproduct

Cyanobacteriochromes (CBCRs) are cyanobacterial photosensory proteins distantly related to phytochromes. Like phytochromes, CBCRs reversibly photoconvert between a dark-stable state and a photoproduct via photoisomerization of the 15,16-double bond of their linear tetrapyrrole (bilin) chromophores. CBCRs provide cyanobacteria with complete coverage of the visible spectrum and near-ultraviolet region. One CBCR subfamily, the canonical red/green CBCRs typified by AnPixJg2 and NpR6012g4, can function as sensors of light color or intensity because of their great variation in photoproduct stability. The mechanistic basis for detection of green light by the photoproduct state in this subfamily has proven to be a challenging research topic, with competing hydration and trapped-twist models proposed. Here, we use 13 C-edited and 15 N-edited 1 H- 1 H NOESY solution nuclear magnetic resonance spectroscopy to probe changes in chromophore configuration and protein-chromophore interactions in the NpR6012g4 photocycle. Our results confirm a C15-Z,anti configuration for the red-absorbing dark state and reveal a C15-E,anti configuration for the green-absorbing photoproduct. The photoactive chromophore D-ring is located in a hydrophobic environment in the photoproduct, surrounded by both aliphatic and aromatic residues. Here, characterization of variant proteins demonstrates that no aliphatic residue is essential for photoproduct tuning. Taken together, our results support the trapped-twist model over the hydration model for the red/green photocycle of NpR6012g4.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Microbial community-level regulation explains soil carbon responses to long-term litter manipulations

Climatic, atmospheric, and land-use changes all have the potential to alter soil microbial activity, mediated by changes in plant inputs. Many microbial models of soil organic carbon (SOC) decomposition have been proposed recently to advance prediction of climate and carbon (C) feedbacks. Most of these models, however, exhibit unrealistic oscillatory behavior and SOC insensitivity to long-term changes in C inputs. Here we diagnose the source of these problems in four archetypal models and propose a density-dependent formulation of microbial turnover, motivated by community-level interactions, that limits population sizes and reduces oscillations. We compare model predictions to 24 long-term C-input field manipulations and identify key benchmarks. The proposed formulation reproduces soil C responses to long-term C-input changes and implies greater SOC storage associated with CO 2 -fertilization-driven increases in C inputs over the coming century compared to recent microbial models. This study provides a simple modification to improve microbial models for inclusion in Earth System Models.

59 BASIC BIOLOGICAL SCIENCES↗

Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology

Abstract The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum . The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.

59 BASIC BIOLOGICAL SCIENCES↗

Strain-controlled thermal conductivity in ferroic twinned films

Large reversible changes of thermal conductivity are induced by mechanical stress, and the corresponding device is a key element for phononics applications. We show that the thermal conductivity k of ferroic twinned thin films can be reversibly controlled by strain. Nonequilibrium molecular dynamics simulations reveal that thermal conductivity decreases linearly with the number of twin boundaries perpendicular to the direction of heat flow. Our demonstration of large and reversible changes in thermal conductivity driven by strain may inspire the design of controllable thermal switches for thermal logic gates and all-solid-state cooling devices.

SURFACES, INTERFACES AND THIN FILMS↗

3D Bragg coherent diffractive imaging of five-fold multiply twinned gold nanoparticle

The formation mechanism of five-fold multiply twinned nanoparticles has been a long-term topic because of their geometrical incompatibility. So, various models have been proposed to explain how the internal structure of the multiply twinned nanoparticles accommodates the constraints of the solid-angle deficiency. Here, we investigate the internal structure, strain field and strain energy density of 600 nm sized five-fold multiply twinned gold nanoparticles quantitatively using Bragg coherent diffractive imaging, which is suitable for the study of buried defects and three-dimensional strain distribution with great precision. Our study reveals that the strain energy density in five-fold multiply twinned gold nanoparticles is an order of magnitude higher than that of the single nanocrystals such as an octahedron and triangular plate synthesized under the same conditions. This result indicates that the strain developed while accommodating an angular misfit, although partially released through the introduction of structural defects, is still large throughout the crystal.

36 MATERIALS SCIENCE↗

Nanoscale laser flash measurements of diffuson transport in amorphous Ge and Si

The thermal properties of amorphous materials have attracted significant attention due to their technological importance in electronic devices. In addition, the disorder-induced breakdown of the phonon gas model makes vibrational transport in amorphous materials a topic of fundamental interest. In the past few decades, theoretical concepts, such as propagons, diffusons, and locons, have emerged to describe different types of vibrational modes in disordered solids. However, experiments can struggle to accurately determine which types of vibrational states carry the majority of the heat. In this study, we use nanoscale laser flash measurements (front/back time-domain thermoreflectance) to investigate thermal transport mechanisms in amorphous Ge and amorphous Si thin-films. We observe a nearly linear relationship between the amorphous film’s thermal resistance and the film’s thickness. The slope of the film’s thermal resistance vs thickness corresponds to a thickness-independent thermal conductivity of 0.4 and 0.6 W/(m K) for a-Ge and a-Si, respectively. This result reveals that the majority of heat currents in amorphous Si and Ge thin films prepared via RF sputtering at room temperature are carried by diffusons and/or propagons with mean free paths less than a few nanometers.

36 MATERIALS SCIENCE↗

Prediction of inherited genomic susceptibility to 20 common cancer types by a supervised machine-learning method

Prevention and early intervention are the most effective ways of avoiding or minimizing psychological, physical, and financial suffering from cancer. However, such proactive action requires the ability to predict the individual’s susceptibility to cancer with a measure of probability. Of the triad of cancer-causing factors (inherited genomic susceptibility, environmental factors, and lifestyle factors), the inherited genomic component may be derivable from the recent public availability of a large body of whole-genome variation data. However, genome-wide association studies have so far showed limited success in predicting the inherited susceptibility to common cancers. We present here a multiple classification approach for predicting individuals’ inherited genomic susceptibility to acquire the most likely phenotype among a panel of 20 major common cancer types plus 1 “healthy” type by application of a supervised machine-learning method under competing conditions among the cohorts of the 21 types. This approach suggests that, depending on the phenotypes of 5,919 individuals of “white” ethnic population in this study, (i) the portion of the cohort of a cancer type who acquired the observed type due to mostly inherited genomic susceptibility factors ranges from about 33 to 88% (or its corollary: the portion due to mostly environmental and lifestyle factors ranges from 12 to 67%), and (ii) on an individual level, the method also predicts individuals’ inherited genomic susceptibility to acquire the other types ranked with associated probabilities. These probabilities may provide practical information for individuals, heath professionals, and health policymakers related to prevention and/or early intervention of cancer.

59 BASIC BIOLOGICAL SCIENCES↗

A round Earth for climate models

Sunlight drives the Earth’s weather, climate, chemistry, and biosphere. Recent efforts to improve solar heating codes in climate models focused on more accurate treatment of the absorption spectrum or fractional clouds. A mostly forgotten assumption in climate models is that of a flat Earth atmosphere. Spherical atmospheres intercept 2.5 W·m −2 more sunlight and heat the climate by an additional 1.5 W⋅m −2 globally. Such a systematic shift, being comparable to the radiative forcing change from preindustrial to present, is likely to produce a discernible climate shift that would alter a model’s skill in simulating current climate. Regional heating errors, particularly at high latitudes, are several times larger. Unlike flat atmospheres, constituents in a spherical atmosphere, such as clouds and aerosols, alter the total amount of energy received by the Earth. To calculate the net cooling of aerosols in a spherical framework, one must count the increases in both incident and reflected sunlight, thus reducing the aerosol effect by 10 to 14% relative to using just the increase in reflected. Simple fixes to the current flat Earth climate models can correct much of this oversight, although some inconsistencies will remain.

54 ENVIRONMENTAL SCIENCES↗

Extraction of mechanical properties of materials through deep learning from instrumented indentation

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three-dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.

36 MATERIALS SCIENCE↗

Mixed valent metals

Here, we review the theory of mixed-valent metals and make comparison with experiments. A single-impurity description of the mixed-valent state is discussed alongside the description of the nearly-integer valent or Kondo limit. The degeneracy N of the f-shell plays an important role in the description of the low-temperature Fermi-liquid state. In particular, for large N , there is a rapid cross-over between the mixed-valent and the Kondo limit when the number of f electrons is changed. We discuss the limitations on the application of the single-impurity description to concentrated compounds such as those caused by the saturation of the Kondo effect and those due to the presence of magnetic interactions between the impurities. This discussion is followed by a description of a periodic lattice of mixed-valent ions, including the role of the degeneracy N . The article concludes with a comparison of theory and experiment. Topics covered include the single-impurity Anderson model, Luttinger's theorem, the Friedel sum rule, the Schrieffer–Wolff transformation, the single-impurity Kondo model, Kondo screening, the Wilson ratio, local Fermi-liquids, Fermi-liquid sum rules, the Nozieres exhaustion principle, Doniach's diagram, the Anderson lattice model, the Slave-Boson method, etc.

36 MATERIALS SCIENCE↗

Distributed State Estimation Over Time-Varying Graphs: Exploiting the Age-of-Information

Here, we study the problem of designing a distributed observer for an LTI system over a time-varying communication graph. The limited existing work on this topic imposes various restrictions either on the observation model or on the sequence of communication graphs. In contrast, we propose a single-time-scale distributed observer that works under mild assumptions. Specifically, our communication model only requires strong-connectivity to be preserved over non-overlapping, contiguous intervals that are even allowed to grow unbounded over time. We show that under suitable conditions that bound the growth of such intervals, joint observability is sufficient to track the state of any discrete-time LTI system exponentially fast, at any desired rate. We also develop a variant of our algorithm that is provably robust to worst-case adversarial attacks, provided the sequence of graphs is sufficiently connected over time. The key to our approach is the notion of a "freshness-index" that keeps track of the age-of-information being diffused across the network. Such indices enable nodes to reject stale estimates of the state, and, in turn, contribute to stability of the error dynamics.

42 ENGINEERING↗

Can we use antipredator behavior theory to predict wildlife responses to high-speed vehicles?

Animals seem to rely on antipredator behavior to avoid vehicle collisions. There is an extensive body of antipredator behavior theory that have been used to predict the distance/time animals should escape from predators. These models have also been used to guide empirical research on escape behavior from vehicles. However, little is known as to whether antipredator behavior models are appropriate to apply to an approaching high-speed vehicle scenario. We addressed this gap by (a) providing an overview of the main hypotheses and predictions of different antipredator behavior models via a literature review, (b) exploring whether these models can generate quantitative predictions on escape distance when parameterized with empirical data from the literature, and (c) evaluating their sensitivity to vehicle approach speed using a simulation approach wherein we assessed model performance based on changes in effect size with variations in the slope of the flight initiation distance (FID) vs. approach speed relationship. The slope of the FID vs. approach speed relationship was then related back to three different behavioral rules animals may rely on to avoid approaching threats: the spatial, temporal, or delayed margin of safety. We used literature on birds for goals (b) and (c). Our review considered the following eight models: the economic escape model, Blumstein’s economic escape model, the optimal escape model, the perceptual limit hypothesis, the visual cue model, the flush early and avoid the rush (FEAR) hypothesis, the looming stimulus hypothesis, and the Bayesian model of escape behavior. We were able to generate quantitative predictions about escape distance with the last five models. However, we were only able to assess sensitivity to vehicle approach speed for the last three models. The FEAR hypothesis is most sensitive to high-speed vehicles when the species follows the spatial (FID remains constant as speed increases) and the temporal margin of safety (FID increases with an increase in speed) rules of escape. The looming stimulus effect hypothesis reached small to intermediate levels of sensitivity to high-speed vehicles when a species follows the delayed margin of safety (FID decreases with an increase in speed). The Bayesian optimal escape model reached intermediate levels of sensitivity to approach speed across all escape rules (spatial, temporal, delayed margins of safety) but only for larger (> 1 kg) species, but was not sensitive to speed for smaller species. Overall, no single antipredator behavior model could characterize all different types of escape responses relative to vehicle approach speed but some models showed some levels of sensitivity for certain rules of escape behavior. We derive some applied applications of our findings by suggesting the estimation of critical vehicle approach speeds for managing populations that are especially susceptible to road mortality. Overall, we recommend that new escape behavior models specifically tailored to high-speeds vehicles should be developed to better predict quantitatively the responses of animals to an increase in the frequency of cars, airplanes, drones, etc. they will face in the next decade.

54 ENVIRONMENTAL SCIENCES↗