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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Recalibration of missing low-frequency variability and trends in the North Atlantic Oscillation

Abstract Multi-decadal trends in the wintertime North Atlantic Oscillation (NAO) are under-represented by coupled general circulation models (CGCMs), consistent with a lack of autocorrelation in their NAO index series. This study proposes and tests two simple “reddening” approaches for correcting this problem in simulated indices based on simple one parameter short-term (AR; Auto-Regressive order 1) and long-term (FD; Fractional-Difference) time series filters. Using CGCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), the FD filter successfully improves the autocorrelation structure of the NAO, and in turn the simulation of extreme trends, while the AR filter is less successful. The 1963–1993 NAO trend is the maximum 31-year trend in the historical period. Raw CGCMs underestimate the likelihood of this trend by a factor of ten but this discrepancy is corrected after reddening. CMIP6 future projections show that long-term (2024–2094) NAO ensemble mean trends systematically increase with the magnitude of radiative forcing: -2.4 to 3.5 hPa/century for low-to-high forcing after reddening (more than double the range using raw output). The related likelihood of future maximum 31year trends comparable to 1963–1993 ranges from 3 to 7% whereas none of these CMIP6 projections simulate this without reddening. Near-term projections of the next 31 years (2024–2054) are less sensitive than long term trends to the future scenario, showing weak-to-no forced trend. However, reddening increases the ensemble range by 74% (to +/-1 standard deviation/decade), which could increase/decrease regional climate change signals in the Northern Hemisphere by magnitudes that are underestimated when using raw CGCM output.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

Stressful crystal histories recorded around melt inclusions in volcanic quartz

Abstract Magma ascent and eruption are driven by a set of internally and externally generated stresses that act upon the magma. We present microstructural maps around melt inclusions in quartz crystals from six large rhyolitic eruptions using synchrotron Laue X-ray microdiffraction to quantify elastic residual strain and stress. We measure plastic strain using average diffraction peak width and lattice misorientation, highlighting dislocations and subgrain boundaries. Quartz crystals across studied magma systems preserve similar and relatively small magnitudes of elastic residual stress (mean 53–135 MPa, median 46–116 MPa) in comparison to the strength of quartz (~ 10 GPa). However, the distribution of strain in the lattice around inclusions varies between samples. We hypothesize that dislocation and twin systems may be established during compaction of crystal-rich magma, which affects the magnitude and distribution of preserved elastic strains. Given the lack of stress-free haloes around faceted inclusions, we conclude that most residual strain and stress was imparted after inclusion faceting. Fragmentation may be one of the final strain events that superimposes stresses of ~ 100 MPa across all studied crystals. Overall, volcanic quartz crystals preserve complex, overprinted deformation textures indicating that quartz crystals have prolonged deformation histories throughout storage, fragmentation, and eruption.

58 GEOSCIENCES↗
DOE OSTI2020

Tradeoffs between phenotypic plasticity and local adaptation influence the ecophysiology of the moss, Sphagnum magellanicum

Bryophytes are a diverse plant group and are functionally different from vascular plants. Yet, their peculiarities are rarely considered in the theoretical frameworks for plants. Currently, we lack information about the magnitude and the importance of intraspecific variability in the ecophysiology of bryophytes and how these might translate to local adaptation—a prerequisite for adaptive evolution. Capitalizing on two ecologically distinct (hummock and hollow) phenotypes of Sphagnum magellanicum, we explored the magnitude and pattern of intraspecific variability in this species and asked whether the environmental-mediated changes in shoot and physiological traits are due to phenotypic plasticity or local adaptation. Size, pigmentation, and habitat type that distinguished the phenotypes in the field did not influence the trait responses under a transplant and factorial experiment. In addition, the magnitude and pattern of trait variability (e.g., branch, stem and capitulum mass) changed with the treatments, which suggest that trait responses were due largely to phenotypic plasticity. The trait responses also suggest that the ecophysiological needs for mosses to grow in clumps, where they maintain a uniform growth may have an overriding effect over the potential for a fixed adaptive response to environmental heterogeneity, which would constrain local adaptation. In this paper, we conclude that extending the trait-based framework to mosses or making comparisons between mosses and vascular plants under any theoretical framework would only be meaningful to the extent that growth form and dispersal strategies are considered.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Hotter droughts alter resource allocation to chemical defenses in piñon pine

Abstract Heat and drought affect plant chemical defenses and thereby plant susceptibility to pests and pathogens. Monoterpenes are of particular importance for conifers as they play critical roles in defense against bark beetles. To date, work seeking to understand the impacts of heat and drought on monoterpenes has primarily focused on young potted seedlings, leaving it unclear how older age classes that are more vulnerable to bark beetles might respond to stress. Furthermore, we lack a clear picture of what carbon resources might be prioritized to support monoterpene synthesis under drought stress. To address this, we measured needle and woody tissue monoterpene concentrations and physiological variables simultaneously from mature piñon pines ( Pinus edulis ) from a unique temperature and drought manipulation field experiment. While heat had no effect on total monoterpene concentrations, trees under combined heat and drought stress exhibited ~ 85% and 35% increases in needle and woody tissue, respectively, over multiple years. Plant physiological variables like maximum photosynthesis each explained less than 10% of the variation in total monoterpenes for both tissue types while starch and glucose + fructose measured 1-month prior explained ~ 45% and 60% of the variation in woody tissue total monoterpene concentrations. Although total monoterpenes increased under combined stress, some key monoterpenes with known roles in bark beetle ecology decreased. These shifts may make trees more favorable for bark beetle attack rather than well defended, which one might conclude if only considering total monoterpene concentrations. Our results point to cumulative and synergistic effects of heat and drought that may reprioritize carbon allocation of specific non-structural carbohydrates toward defense.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation

Abstract Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray computed tomography (CT) enables volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) have been shown to excel at segmentation tasks; however, memory constraints, class imbalance, and lack of sufficient training data often prohibit their deployment in high resolution volumetric domains. Our 3D convolutional NN implementation mitigates these challenges and accurately segments full resolution CT scans of thermal sprayed materials with maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.

Martinez, Carianne↗
DOE OSTI2023

Baryon Anticorrelations in PYTHIA

Abstract We present a computational investigation of a problem of hadron collisions from recent years, that of baryon anticorrelations. This is an experimental dearth of baryons near other baryons in phase space, not seen upon examining numerical Monte Carlo simulations. We have addressed one of the best known Monte Carlo codes, , to see what baryon (anti)correlations it produces, where they are originated at the string-fragmentation level in the underlying Lund model, and what simple modifications could lead to better agreement with data. We propose two ad-hoc alterations of the fragmentation code, a “one-baryon” and an “always-baryon” policies that qualitatively reproduce the data behaviour, i.e anticorrelation, and suggest that lacking Pauli-principle induced corrections at the quark level could be the culprit behind the current disagreement between computations and experiment.

Physics↗
DOE OSTI2021

Local Environment of Superoxide Radical Formed on the TiO 2 Surface Produced From Ti(OiPr) 4 Exposed to H 2 O 2

A selective heterogeneous catalyst with the superoxide anion O 2 •– stable at room temperature, generated by treating Ti(OR) 4 (R=iPr, nBu) with H 2 O 2 , was previously reported and used for the oxidation of organic compounds. Furthermore, it was proposed that exceptional stability of the O 2 •– is resulted from its stabilization near Ti +4 cation on the TiO 2 surface with participation of water molecules and/or OH groups. In this article, we performed high-resolution pulsed EPR study on the O 2 •– in this catalyst with the aim to obtain quantitative information about its interactions with the surrounding molecules. Our data has shown that the O 2 •– is involved in weak hyperfine interactions with protons in the nearest environment, which do not exceed a value of 2 MHz. It corresponds to an Os…H distance of ≥ 3 Å that exclude the formation of hydrogen bonds. The analysis of possible structures of O 2 •– adsorption on TiO 2 surface taking into account our results and lack of radical mobility at room temperature previously observed in EPR spectra led us to suggest that the O 2 •– is stabilized in the form of superoxo bridge coordinated with two titanium atoms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Characterizing Peatland Microtopography Using Gradient and Microform-Based Approaches

Abstract Peatlands represent an important component of the global carbon cycle, storing 180–621 Gt of carbon (C). Small-scale spatial variations in elevation, frequently referred to as microtopography, influence ecological processes associated with the peatland C cycle, including Sphagnum photosynthesis and methane flux. Microtopography can be characterized with measures of topographic variability and by using conceptual classes (microforms) linked to function: most commonly hummocks and hollows. However, the criteria used to define these conceptual classes are often poorly described, if at all, and vary between studies. Such inconsistencies compel development of explicit quantitative methods to classify microforms. Furthermore, gradient-based characterizations that describe spatial variability without the use of microforms are lacking in the literature. Therefore, the objectives of this study were to (1) calculate peatland microtopographical elevation gradients and measures of spatial variability, (2) develop three microform classification methods intended for specific purposes, and (3) evaluate and contrast classification methods. Our results suggest that at spatial scales much larger than microforms, elevation distributions are unimodal and are well approximated with parametric probability density functions. Results from classifications were variable between methods and years and exhibited significant differences in mean hollow areal coverages of a raised ombrotrophic bog. Our results suggest that the conceptualization and classification of microforms can significantly influence microtopographic structural metrics. The three explicit methods for microform classification described here may be used and built upon for future applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A primal–dual algorithm for risk minimization

In this paper, we develop an algorithm to efficiently solve risk-averse optimization problems posed in reflexive Banach space. Such problems often arise in many practical applications as, e.g., optimization problems constrained by partial differential equations with uncertain inputs. Unfortunately, for many popular risk models including the coherent risk measures, the resulting risk-averse objective function is nonsmooth. Here, this lack of differentiability complicates the numerical approximation of the objective function as well as the numerical solution of the optimization problem. To address these challenges, we propose a primal–dual algorithm for solving large-scale nonsmooth risk-averse optimization problems. This algorithm is motivated by the classical method of multipliers and by epigraphical regularization of risk measures. As a result, the algorithm solves a sequence of smooth optimization problems using derivative-based methods. We prove convergence of the algorithm even when the subproblems are solved inexactly and conclude with numerical examples demonstrating the efficiency of our method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

McCormick envelopes in mixed-integer PDE-constrained optimization

McCormick envelopes are a standard tool for deriving convex relaxations of optimization problems that involve polynomial terms. Such McCormick relaxations provide lower bounds, for example, in branch-and-bound procedures for mixed-integer nonlinear programs but have not gained much attention in PDE-constrained optimization so far. This lack of attention may be due to the distributed nature of such problems, which on the one hand leads to infinitely many linear constraints (generally state constraints that may be difficult to handle) in addition to the state equation for a pointwise formulation of the McCormick envelopes and renders bound-tightening procedures that successively improve the resulting convex relaxations computationally intractable. We analyze McCormick envelopes for a model problem class that is governed by a semilinear PDE involving a bilinearity and integrality constraints. We approximate the nonlinearity and in turn the McCormick envelopes by averaging the involved terms over the cells of a partition of the computational domain on which the PDE is defined. This yields convex relaxations that underestimate the original problem up to an a priori error estimate that depends on the mesh size of the discretization. These approximate McCormick relaxations can be improved by means of an optimization-based bound-tightening procedure. We show that their minimizers converge to minimizers to a limit problem with a pointwise formulation of the McCormick envelopes when driving the mesh size to zero. We provide a computational example, for which we certify all of our imposed assumptions. The results point to both the potential of the methodology and the gaps in the research that need to be closed. Our methodology provides a framework first for obtaining pointwise underestimators for nonconvexities and second for approximating them with finitely many linear inequalities in an infinite-dimensional setting.

Approximations and Expansions↗
DOE OSTI2024

Empirical Characterization and Modeling of Cohesive – to – Adhesive Shear Fracture Mode Transition due to Increased Adhesive Layer Thicknesses of Fiber Reinforced Composite Single – Lap Joints

Here, to ensure a strong adhesive bond, most standards and adhesive manufacturers specify a maximum adhesive gap of 1 mm when bonding fiber reinforced composite structures. In manufacturing large components, such as joining two halves of wind turbine blades, meeting this gap tolerance specification is impractical; gaps larger than 10 mm are common in large adhesively bonded composite structures using state-of-the-art manufacturing techniques. Currently, there is a lack of fundamental understanding of the failure mechanics of adhesive gaps larger than 3 mm. To create such understanding, glass fiber - acrylic thermoplastic composite panels bonded using different epoxy adhesives within single-lap joint samples with adhesive thicknesses of 0.1 mm, 0.3 mm, 1 mm, 3 mm, 5 mm, and 10 mm were sheared to failure. A transition from cohesive to adhesive failure was observed to occur about 1 mm to 3 mm joint thicknesses. Plotting the shear stress normalized by the ratio of the joint width to thickness as a function of the joint thickness normalized by the joint length is shown to result in the ability to fit simple empirically derived models of the cohesive-to-adhesive failure transition, regardless of the adhesive. Furthermore, using these normalized variables, all the observed cohesively failed specimens collapse to a single master curve, as do the adhesively failed specimens.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enhancing transcription in Escherichia coli and Pseudomonas putida using bacteriophage lambda anti-terminator protein Q

Functional characterization of metagenomic DNA often involves expressing heterologous DNA in genetically tractable microorganisms such as Escherichia coli . Functional expression of heterologous genes can suffer from limitations due to the lack of recognition of foreign promoters or presence of intrinsic terminators on foreign DNA between a vector-based promoter and the transcription start site. Anti-terminator proteins are a possible solution to overcome this limitation. When bacteriophage lambda infects E. coli , it relies on the host transcription machinery to transcribe and express phage DNA. Lambda anti-terminator protein Q (λQ) regulates the expression of late-genes of phage lambda. E. coli RNA polymerase recognizes the P R ' promoter on the lambda genome and forms a complex with λQ, to overcome the terminator t R '. In this study we show the use of λQ to efficiently transcribe a capsular polysaccharide cluster, cps3 , from Lactobacillus plantarum containing intrinsic terminators in Escherichia coli . In addition, we expand the use of anti-terminator λQ in Pseudomonas putida . The results show ~ fivefold higher expression of a fluorescent reporter locate ~ 12.5kbp downstream from the promoter, when the transcription is driven by P R ' promoter in presence of λQ compared to a lac promoter. These results suggest that λQ could be used in metabolic engineering to enhance expression of heterologous DNA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Comparison of greenhouse gas emission estimates from six hydropower reservoirs using modeling versus field surveys

As with most aquatic ecosystems, reservoirs play an important role in the global carbon (C) cycle and emit greenhouse gases (GHG) as carbon dioxide (CO 2 ) and methane (CH 4 ). However, GHG emissions from reservoirs are poorly quantified, especially in temperate systems, resulting in high uncertainty. We compared reservoir C emission estimates and uncertainty of diffusive, ebullitive, and degassing pathways in six hydropower reservoirs in the southeastern United States among four data sources: two field-based surveys and two models (including the GHG Reservoir “G-res” Tool). We found that CH 4 diffusion was most similar across data sources (modeled minus observed, bias = - 21 g CO 2-eq m -2 y -1 ) and had low relative uncertainty (coefficient of variation, CV = 0.98). On the other hand, CO 2 diffusion was least consistent across data sources (bias = - 518 g CO 2-eq m -2 y -1 ). Both field surveys indicated strong negative CO 2 diffusion (i.e., CO 2 uptake) at all reservoirs, while G-res estimated positive CO 2 diffusion. By extension, total C emissions showed similar discrepancies, leading to high uncertainty in upscaling and interpreting reservoir source-sink dynamics. Finally, CH 4 ebullition had the highest relative uncertainty (CV = 2.77) due to high variability across sites. We discuss limitations of field surveys and these models, including temperature-based annualization methods, varying definitions of ebullition zones, low sampling resolution, and lack of dynamism. Future field efforts focused on capturing variability in CO 2 diffusion and CH 4 ebullition will be especially valuable in reducing uncertainty and improving models to advance our understanding reservoir GHG emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Coupling Mesoscale Budget Components to Large-Eddy Simulations for Wind-Energy Applications

To simulate the airflow through a wind farm across a wide range of atmospheric conditions, microscale models (e.g., large-eddy simulation, LES, models) have to be coupled with mesoscale models, because microscale models lack the atmospheric physical processes to represent time-varying local forcing. Here we couple mesoscale model outputs to a LES solver by applying mesoscale momentum- and temperature-budget components from the Weather Research and Forecasting model to the governing equations of the Simulator fOr Wind Farm Applications model. We test whether averaging the budget components affects the LES results with regard to quantities of interest to wind energy. Our study focuses on flat terrain during a quiescent diurnal cycle. The simulation results are compared with observations from a 200-m tall meteorological tower and a wind-profiling radar, by analyzing time series, profiles, rotor-averaged quantities, and spectra. However, while results show that averaging reduces the spatio-temporal variability of the mesoscale momentum-budget components, when coupled with the LES model, the mesoscale bias (in comparison with observations of wind speed and direction, and potential temperature) is not reduced. In contrast, the LES technique can correct for shear and veer. In both cases, however, averaging the budget components shows no significant impact on the mean flow quantities in the microscale and is not necessary when coupling mesocale budget components to the LES model.

17 WIND ENERGY↗
DOE OSTI2024

Structural determination of a full-length plant cellulose synthase informed by experimental and in silico methods

Three-dimensional structure determination and prediction of proteins with intrinsically disordered regions, unstructured regions, conformational flexibility, and lacking homologous structures are challenging. We previously predicted and refined an in silico structure of a plant cellulose synthase from cotton (GhCESA1), and more recently, cryo-electron microscopy (cryo-EM) has resolved a majority of the lengths of two CESA structures from poplar (PttCESA8) and cotton (GhCESA7). However, 26–30% of these cryo-EM structures remain unresolved, including the N-terminal domain, half of the class-specific region, the gating loop region, and the C-terminal domain. Here, we describe the generation and evaluation of a full-length hybrid GhCESA1 model based on this cryo-EM PttCESA8 structure, with unresolved regions completed using this in silico refined GhCESA1 model. All-atom molecular dynamics simulations and subsequent energy minimizations were performed for the in silico and hybrid GhCESA1 models in a lipid bilayer-water-ion environment, and structural stability, dynamics, energetics, contacts, and quality were evaluated. The unresolved regions were found to be the most dynamic, in agreement with their poor electron density with cryo-EM. The hybrid model exhibited a higher total secondary structure content, more favorable intra-protein and protein-lipid interaction energies, and improved quality metrics. Moreover, hydrogen bonding was revealed to be a primary mechanism for intra-protein and protein-lipid contacts. These results demonstrate that in silico structure prediction and refinement may be useful to augment experimental structure determination, especially for disordered and unstructured regions. Furthermore, this hybrid model can serve as a steppingstone to derive full-length homology models of other CESAs found in more experimentally tractable organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Contribution of advanced fluorescence nano microscopy towards revealing mitotic chromosome structure

The organization of chromatin into higherorder structures and its condensation process represent one of the key challenges in structural biology. This is important for elucidating several disease states. To address this long-standing problem, development of advanced imaging methods has played an essential role in providing understanding into mitotic chromosome structure and compaction. Amongst these are two fast evolving fluorescence imaging technologies, specifically fluorescence lifetime imaging (FLIM) and superresolution microscopy (SRM). FLIM in particular has been lacking in the application of chromosome research while SRM has been successfully applied although not widely. Furthermore, both these techniques are capable of providing fluorescence imaging with nanometer information. SRM or "nanoscopy" is capable of generating images of DNA with less than 50 nm resolution while FLIM when coupled with energy transfer may provide less than 20 nm information. Here, we discuss the advantages and limitations of both methods followed by their contribution to mitotic chromosome studies. Furthermore, we highlight the future prospects of how advancements in new technologies can contribute in the field of chromosome science.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The Earth has humans, so why don’t our climate models?

While climate models have rapidly advanced in sophistication over recent decades, they lack dynamic representation of human behavior and social systems despite strong feedbacks between social processes and climate. The impacts of climate change alter perceptions of risk and emissions behavior that, in turn, influence the rate and magnitude of climate change. Addressing this deficiency in climate models requires a substantial interdisciplinary effort to couple models of climate and human behavior. We suggest a multi-model approach that considers a range of theories and implementations of human behavior and social systems, similar to the multi-model approach that has been used to explore the physical climate system. In this work, we describe the importance of linking social factors with climate processes and identify four priorities essential to advancing the development of coupled social-climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Convergence analysis for a nonlocal gradient descent method via directional Gaussian smoothing

We analyze the convergence of a nonlocal gradient descent method for minimizing a class of high-dimensional non-convex functions, where a directional Gaussian smoothing (DGS) is proposed to define the nonlocal gradient (also referred to as the DGS gradient). The method was first proposed in [Zhang et al., Enabling long-range exploration in minimization of multimodal functions, UAI 2021], in which multiple numerical experiments showed that replacing the traditional local gradient with the DGS gradient can help the optimizers escape local minima more easily and significantly improve their performance. However, a rigorous theory for the efficiency of the method on nonconvex landscape is lacking. In this work, we investigate the scenario where the objective function is composed of a convex function, perturbed by deterministic oscillating noise. We provide a convergence theory under which the iterates exponentially converge to a tightened neighborhood of the solution, whose size is characterized by the noise wavelength. Here, we also establish a correlation between the optimal values of the Gaussian smoothing radius and the noise wavelength, thus justifying the advantage of using moderate or large smoothing radii with the method. Furthermore, if the noise level decays to zero when approaching the global minimum, we prove that DGS-based optimization converges to the exact global minimum with linear rates, similarly to standard gradient-based methods in optimizing convex functions. Several numerical experiments are provided to confirm our theory and illustrate the superiority of the approach over those based on the local gradient.

Tran, Hoang [Oak Ridge National Laboratory (ORNL),↗