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At least 1,081 records · Page 60

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DOE OSTI2018

Investigation of soil properties using different techniques of mid‐infrared spectroscopy

Summary Diffuse reflectance spectroscopy (DRF), attenuated total reflectance spectroscopy (ATR) and Fourier transform infrared photoacoustic spectroscopy (PAS) are useful techniques that are commonly used to characterize soil properties. Here, these techniques were used to evaluate individual properties of 1456 paddy soil samples from Nanjing, China, to explore their features in the mid‐infrared range. The diffuse reflectance spectroscopy showed broad and shouldered peaks that ranged from 4000 to 500 cm −1 . Attenuated total reflectance spectroscopy produced main peaks around 1000 cm −1 . Fourier transform infrared photoacoustic spectroscopy spectra showed several moderate intensity peaks in the regions of 4000–2600 and 2500–500 cm −1 . The effective variables (wavenumbers) were selected using an uninformative variable elimination (UVE) algorithm to predict soil pH, and soil organic matter (SOM), total nitrogen (TN) and available phosphorus (AP) contents; it used a self‐adaptive partial least squares model (SAM–PLS). The results showed that selected wavenumbers improved the accuracy of prediction for pH, and SOM, TN and AP contents. For the prediction of pH, ATR spectra showed marginally better performance than the other spectra. The DRF spectroscopy was the optimal method for SOM and TN prediction. Available phosphorus contents were predicted poorly by all three spectra. Furthermore, mid‐infrared incident light paths, absorption modes, sensing styles, as well as peak interference and the prediction model, were the main factors that affected the assessment of soil properties in the three types of spectroscopy. Highlights Soil spectra data were measured with DRF, ATR and PAS spectrometers over the range 4000–500 cm −1 . A UVE algorithm was used to select effective wavenumbers from the full spectrum. The pH, SOM, TN and AP were estimated using three techniques based on a SAM–PLS model. Mid‐infrared incident light paths and absorption modes affected prediction performance of the three techniques.

Ma, F.↗

DOE OSTI2022

Cold acclimation threshold induction temperatures of switchgrass ecotypes grown under a long and short photoperiod

Abstract Plants can cold acclimate to enhance their freezing tolerance by sensing declining temperature and photoperiod cues. However, the factors influencing genotypic variation in the induction of cold acclimation are poorly understood among perennial grasses. We hypothesized that the more northern upland switchgrass ( Panicum virgatum L.) ecotype develops a higher degree of freezing tolerance by initiating cold acclimation at higher temperatures as compared with the coastal and southern lowland ecotypes. First, we determined the optimal method for assessing freezing tolerance and the length of exposure to 8/4°C required to induce the maximum level of freezing tolerance in the most northern upland and most southern lowland genotypes. We characterized the maximum freezing tolerance of eight uplands, three coastal and five lowland genotypes grown for 21 days at 8/4°C and a 10 or 16 h photoperiod. Next, we identified the temperature required to induce cold acclimation by exposing the 16 genotypes for 7 days at 20–6°C constant temperatures under a 10 or 16 h photoperiod. Cold acclimation initiated at temperatures 5 and 7°C higher in upland than in coastal and lowland genotypes. Among upland genotypes the shorter photoperiod induced cold acclimation at a 1°C higher temperature. Genotypes originating from a more northern latitude initiate cold acclimation at higher temperatures and develop higher maximum freezing tolerances. An earlier response to declining temperatures may provide the upland ecotype with additional time to prepare for winter and provide an advantage when plants are subjected to the rapid changes in fall temperature associated with injurious frosts.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-J-195: On the Performance of Four Dual Energy CT Formalisms for Extracting Proton Stopping Powers

Purpose: Dual energy CT can predict stopping power ratios (SPR) for ion therapy treatment planning. Several approaches have been proposed recently, however accuracy and practicability in a clinical workflow are unaddressed. The aim of this work is to provide a fair comparison of available approaches in a human-like phantom to find the optimal method for tissue characterization in a clinical situation. Methods: The SPR determination accuracy is investigated using simulated DECT images. A virtual human-like phantom is created containing 14 different standard human tissues. SECT (120 kV) and DECT images (100 kV and 140 kV Sn) are simulated using the software ImaSim. The single energy CT (SECT) stoichiometric calibration method and four recently published calibration-based DECT methods are implemented and used to predict the SPRs from simulated images. The difference between SPR predictions and theoretical SPR are compared pixelwize. Mean, standard deviation and skewness of the SPR difference distributions are used as measures for bias, dispersion and symmetry. Results: The average SPR differences and standard deviations are (0.22 ± 1.27)% for SECT, and A) (−0.26 ± 1.30)%, B) (0.08 ± 1.12)%, C) (0.06 ± 1.15)% and D) (−0.05 ± 1.05)% for the four DECT methods. While SPR prediction using SECT is showing a systematic error on SPR, the DECT methods B, C and D are unbiased. The skewness of the SECT distribution is 0.57%, and A) −0.19%, B) −0.56%, C) −0.29% and D) −0.07% for DECT methods respectively. Conclusion: The here presented DECT methods B, C and D outperform the commonly used SECT stoichiometric calibration. These methods predict SPR accurately without a bias and within ± 1.2% (68th percentile). This indicates that DECT potentially improves accuracy of range predictions in proton therapy. A validation of these findings using clinical CT images of real tissues is necessary.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-T-589: HybridArc Planning Criteria for Brain SRS

Purpose: To compare VMAT SRS plans, dynamic conformal arc (DCA) plans, and Brainlab iPlan’s capability of planning and delivering brain SRS plans by employing HybridArc. HybridArc utilizes both DCA and IMRT. Using HybridArc, the amount of DCA versus IMRT needs to be optimized. Methods: Four SRS patients with the aim of reducing brainstem dose were selected for this study. All patients were contoured in iPlan and transferred to Eclipse for VMAT planning. In iPlan, DCA plans were created for each case. Moreover, nine HybridArc plans with DCA-IMRT ratios between 9:1 through 1:9 were created with a single ring structure generated by subtracting 3 mm expansion of target from a 10 mm expansion of the target. Two static IMRT beams were used in each of the five DCA arcs for HybridArc. The dose was prescribed to DCA only and HybridArc plans and normalized so that the target volume (TV) receives 100% dose to 99.5% of the TV to achieve 120% ∼ 130% max dose within targets. Following metrics were compared: PITV, V12Gy, CGIc, CGIg, CGI, brainstem max dose, and total monitor units (MUs). Results: A brainstem max dose comparable with VMAT from 30% IMRT and less with 50% or more IMRT could be achieved. PITV decreased with increasing IMRT portion and begins to saturate past an IMRT portion of 30%. The CGIg index, which represents how fast the dose falls off, was better with HybridArc in all HybridArc plans. Total MUs increased with increasing IMRT but less than VMAT in all cases. Conclusion: Overall, a lower brainstem max dose and a lower V12Gy with fewer MUs can be achieved with HybridArc. Considering all factors, it would be best to use a DCA-IMRT ratio of either 7:3 or 6:4.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Large Circular Plasmids from Groundwater Plasmidomes Span Multiple Incompatibility Groups and Are Enriched in Multimetal Resistance Genes

ABSTRACT Naturally occurring plasmids constitute a major category of mobile genetic elements responsible for harboring and transferring genes important in survival and fitness. A targeted evaluation of plasmidomes can reveal unique adaptations required by microbial communities. We developed a model system to optimize plasmid DNA isolation procedures targeted to groundwater samples which are typically characterized by low cell density (and likely variations in the plasmid size and copy numbers). The optimized method resulted in successful identification of several hundred circular plasmids, including some large plasmids (11 plasmids more than 50 kb in size, with the largest being 1.7 Mb in size). Several interesting observations were made from the analysis of plasmid DNA isolated in this study. The plasmid pool (plasmidome) was more conserved than the corresponding microbiome distribution (16S rRNA based). The circular plasmids were diverse as represented by the presence of seven plasmid incompatibility groups. The genes carried on these groundwater plasmids were highly enriched in metal resistance. Results from this study confirmed that traits such as metal, antibiotic, and phage resistance along with toxin-antitoxin systems are encoded on abundant circular plasmids, all of which could confer novel and advantageous traits to their hosts. This study confirms the ecological role of the plasmidome in maintaining the latent capacity of a microbiome, enabling rapid adaptation to environmental stresses. IMPORTANCE Plasmidomes have been typically studied in environments abundant in bacteria, and this is the first study to explore plasmids from an environment characterized by low cell density. We specifically target groundwater, a significant source of water for human/agriculture use. We used samples from a well-studied site and identified hundreds of circular plasmids, including one of the largest sizes reported in plasmidome studies. The striking similarity of the plasmid-borne ORFs in terms of taxonomical and functional classifications across several samples suggests a conserved plasmid pool, in contrast to the observed variability in the 16S rRNA-based microbiome distribution. Additionally, the stress response to environmental factors has stronger conservation via plasmid-borne genes as marked by abundance of metal resistance genes. Last, identification of novel and diverse plasmids enriches the existing plasmid database(s) and serves as a paradigm to increase the repertoire of biological parts that are available for modifying novel environmental strains.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Plateau Phenomenon in Gradient Descent Training of RELU Networks: Explanation, Quantification, and Avoidance

The ability of neural networks to provide ‘best in class’ approximation across a wide range of applications is well-documented. Nevertheless, the powerful expressivity of neural networks comes to naught if one is unable to effectively train (choose) the parameters defining the network. In general, neural networks are trained by gradient descent type optimization methods,a stochastic variant thereof. In practice, such methods result in the loss function decreases rapidly at the beginning of training but then, after a relatively small number of steps, significantly slow down. The loss may even appear to stagnate over the period of a large number of epochs, only to then suddenly start to decrease fast again for no apparent reason. This so-called plateau phenomenon manifests itself in many learning tasks. The present work aims to identify and quantify the root causes of plateau phenomenon.analysis is carried out in the setting of univariate ReLU networks. No assumptions are made on the number of neurons relative to the number of training data, and our results hold for both the lazy and adaptive regimes. Here, the main findings are: plateaux correspond to periods during which activation patterns remain constant, where activation pattern refers to the number of data points that activate a given neuron; quantification of convergence of the gradient flow dynamics; and, characterization stationary points in terms solutions of local least squares regression lines over subsets of the training data. Based on these conclusions, we propose a new iterative training method, the Active Neuron Least Squares (ANLS), characterised by the explicit adjustment of the activation pattern at each step, which is designed to enable a quick exit from a plateau. Illustrative numerical examples are included throughout.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

waveSZ: A Hardware-Algorithm Co-Design of Efficient Lossy Compression for Scientific Data

Error-bounded lossy compression is critical to the success of extreme-scale scientific research because of ever-increasing volumes of data produced by today's high-performance computing (HPC) applications. Not only can error-controlled lossy compressors significantly reduce the I/O and storage burden but they can retain high data fidelity for post analysis. Existing state-of-the-art lossy compressors, however, generally suffer from relatively low compression and decompression throughput (up to hundreds of megabytes per second on a single CPU core), which considerably restrict the adoption of lossy compression by many HPC applications especially those with a fairly high data production rate. In this paper, we propose a highly efficient lossy compression approach based on field programmable gate arrays (FPGAs) under the state-of-the-art lossy compression model SZ. Our contributions are fourfold. (1) We adopt a wavefront memory layout to alleviate the data dependency during the prediction for higher-dimensional predictors, such as the Lorenzo predictor. (2) We propose a co-design framework named WAVESZ based on the wavefront memory layout and the characteristics of SZ algorithm and carefully implement it by using high-level synthesis. (3) We propose a hardware-algorithm co-optimization method to improve the performance. (4) We evaluate our proposed WAVESZ on three real-world HPC simulation datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and FPGAs. Experiments show that our WAVESZ can improve SZ's compression throughput by 6.9x similar to 8.7x over the production version running on a state-of-the-art CPU and improve the compression ratio and throughput by 2.1x and 5.8x on average, respectively, respectively, compared with the state-of-the-art FPGA design.

Compression Ratio↗

DOE OSTI2023

Exploring Benchmarks for Self-Driving Labs using Color Matching

Self Driving Labs (SDLs) that combine automation of experimental procedures with autonomous decision making are gaining popularity as a means of increasing the throughput of scientific workflows. The task of identifying quantities of supplied colored pigments that match a target color, the color matching problem, provides a simple and flexible SDL test case, as it requires experiment proposal, sample creation, and sample analysis, three common components in autonomous discovery applications. We present a robotic solution to the color matching problem that allows for fully autonomous execution of a color matching protocol. Our solution leverages the WEI science factory platform to enable portability across different robotic hardware, the use of alternative optimization methods for continuous refinement, and automated publication of results for experiment tracking and post-hoc analysis.

Artificial intelligence↗

DOE OSTI2013

DAKOTA Driver (WISDEM®) [SWR-13-16]

OpenMDAO driver for DAKOTA (Design Analysis Kit for Optimization and Terascale Applications). This code connects the SANDIA National Laboratory's C++-based DAKOTA optimization and analysis tool with NASA’s python-based openMDAO systems engineering framework. It allows DAKOTA to be used as an openMDAO “Driver”. DAKOTA contains a wide array of advanced sensitivity, uncertainty quantification, and optimization methods. These are now available in a “pluggable” way to any openMDAO based workflow.

Graf, Peter↗

DOE OSTI2023

JuliaOceanWaves

SAND2023-05107O JuliaOceanWaves is a collection of packages and functions for modeling wave energy converters. The open-source software is designed to be used by the wave energy community to model new concepts, perform design optimization, and more. Capabilities include: • A representation of ocean wave spectra and input/output (e.g. read from National Oceanic and Atmospheric Administration buoys) and post-processing functionalities; • A solver for the linear potential flow radiation and diffraction equations for wave forcing on floating structures. • Solving the floating body linear dynamics in the frequency domain. Future additions to the software will include solving non-linear floating body dynamics in the time-domain through time-marching Ordinary Differential Equation solvers and the pseudo-spectral optimization method. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Michelen Strofer, Carlos↗

DOE OSTI2014

Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign

This study presents radar-based precipitation estimates collected during the two-month DOE ARM - NASA Midlatitude Continental Convective Clouds Experiment (MC3E). Emphasis is on the usefulness of radar observations from the C-band and X-band scanning ARM precipitation radars (CSAPR, XSAPR) for rainfall estimation products to distances within 100 km of the Oklahoma SGP facility. A dense collection of collocated ARM, NASA GPM and nearby surface Oklahoma Mesonet gauge records are consulted to evaluate potential ARM radar-based hourly rainfall products and campaign optimized methods over individual gauge and areal characterizations. Rainfall products are evaluated against the performance of the regional operational NWS NEXRAD S-band radar polarimetric product. Results indicate that the ARM C-band system may achieve similar point and areal-gauge bias and root mean square (rms) error performance to the NEXRAD standard for the variety of MC3E deep convective events sampled when capitalizing on differential phase measurements. The best campaign rainfall performance was achieved when applying radar relations capitalizing on estimates of the specific attenuation from the CSAPR system. The ARM X-band systems only demonstrate solid capabilities as compared to NEXRAD standards for hourly point and areal rainfall accumulations under 10 mm. Here, all methods exhibit a factor of 1.5 to 2.5 reduction in rms errors for areal accumulations over a 15 km2 NASA dense network housing 16 sites having collocated bucket gauges, with the higher error reductions best associated with polarimetric methods.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression

Background Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype. Results We develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus generate an ensemble of models (each model having its rate constants perturbed), and then analyze the models by identifying archetypal time-course metabolite concentration profiles. Furthermore, we use a metaheuristic optimization method to calibrate ME model parameters using time-course measurements such as from a (fed-) batch culture. Finally, we show that constraints on protein concentration dynamics (“inertia”) alter the metabolic response to environmental fluctuations, including increased substrate-level phosphorylation and lowered oxidative phosphorylation. Conclusions Overall, DynamicME provides a novel method for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.

Mathematical & Computational Biology↗

DOE OSTI2023

Solving multiphysics-based inverse problems with learned surrogates and constraints

Abstract Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome these challenges by combining computationally cheap learned surrogates with learned constraints. Not only does this combination lead to vastly improved inversions for the important fluid-flow property, permeability, it also provides a natural platform for inverting multimodal data including well measurements and active-source time-lapse seismic data. By adding a learned constraint, we arrive at a computationally feasible inversion approach that remains accurate. This is accomplished by including a trained deep neural network, known as a normalizing flow, which forces the model iterates to remain in-distribution, thereby safeguarding the accuracy of trained Fourier neural operators that act as surrogates for the computationally expensive multiphase flow simulations involving partial differential equation solves. By means of carefully selected experiments, centered around the problem of geological carbon storage, we demonstrate the efficacy of the proposed constrained optimization method on two different data modalities, namely time-lapse well and time-lapse seismic data. While permeability inversions from both these two modalities have their pluses and minuses, their joint inversion benefits from either, yielding valuable superior permeability inversions and CO 2 plume predictions near, and far away, from the monitoring wells.

Yin, Ziyi (ORCID:0000000250248771)↗

DOE OSTI2022

Numerical simulations of noisy quantum circuits for computational chemistry

The opportunities afforded by near-term quantum computers to calculate the ground-state properties of small molecules depend on the structure of the computational ansatz as well as the errors induced by device noise. Here we investigate the behavior of these noisy quantum circuits using numerical simulations to estimate the accuracy and fidelity of the prepared quantum states relative to the ground truth obtained by conventional means. We implement several different types of ansatz circuits derived from unitary coupled cluster theory for the purposes of estimating the ground-state energy of sodium hydride using the variational quantum eigensolver algorithm. We show how relative error in the energy and the fidelity scale with the levels of gate-based noise, the internuclear configuration, the ansatz circuit depth, and the parameter optimization methods.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Sensitivity-based Experiment Design Optimization for a Molybdenum Critical Experiment

A lack of intermediate molybdenum benchmarks in the ICSBEP has been identified by LANL, Y-12, and IRSN. This lacking adversely effects criticality safety operations and leaves new differential molybdenum data unvalidated. NCERC is proposing a series of intermediate integral experiments to better the understanding of molybdenum systems. Using MCNP6.2 with the ENDF/B-VIII.0 nuclear data library a single unmoderated and four moderated system designs were identified using a sensitivity optimization method. Each proposed system was found to be at least twice as sensitive to the 95 Mo capture cross section in the URR as the sole existing intermediate molybdenum benchmark in the ISCBEP handbook. The addition of a new molybdenum sensitive intermediate system would improve future nuclear data evaluations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Generalized Bayesian Framework for Evaluation of Integral Benchmark Experiments

A recently published generalized Bayesian optimization framework has provided a way to retract any or all of the three common assumptions underlying the conventional Generalized Linear Least Squares (GLLS) optimization method based on the concepts introduced in Ref. [2]. These assumptions are: 1. Perfection: The model used for data evaluation and the prior probability distribution function (PDF) of generalized data are perfect. 2. Normality: The prior and posterior PDF are normal. 3. Linearity: The model is linear. In this work we outline how the framework in [1] could be directly adopted for improved evaluation of nuclear criticality integral benchmark experiments (IBEs) by: 1. Removing the first assumption alone by utilizing the concept of imperfections introduced in [1] to enable evaluation in the presence of discrepancies between the data and model or of missing covariance information by a GLLS method that will be seen as a generalization of the conventional GLLS method employed by the TSURFER code, and by 2. Removing the remaining two assumptions by implementing a Markov Chain Monte Carlo method for computation of the posterior PDF in the SAMPLER code, where TSURFER and SAMPLER are the uncertainty quantification (UQ) codes for IBEs in the SCALE code system based on the GLLS and the stochastic method, respectively. The graphic in Figure 1 categorizes the methods discussed in terms of the assumptions that they employ to determine posterior PDFs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2016

Single-view phase retrieval of an extended sample by exploiting edge detection and sparsity

We propose a new approach to robustly retrieve the exit wave of an extended sample from its coherent diffraction pattern by exploiting sparsity of the sample's edges. This approach enables imaging of an extended sample with a single view, without ptychography. We introduce nonlinear optimization methods that promote sparsity, and we derive update rules to robustly recover the sample's exit wave. We test these methods on simulated samples by varying the sparsity of the edge-detected representation of the exit wave. Finally, our tests illustrate the strengths and limitations of the proposed method in imaging extended samples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

Distributed Acoustic Sensing (DAS) Data for Periodic Hydraulic Tests: Hydraulic Data

Hydraulic responses from periodic hydraulic tests conducted at the Mirror Lake Fractured Rock Research Site, during the summer of 2015. These hydraulic responses were measured also using distributed acoustic sensing (DAS) which is cataloged in a different submission under this grant number. The tests are explained in detail in Matthew Cole's MS Thesis which is cataloged here. The injection and drawdown data and the codes used to analyze the data. Sinusoidal Data is a Matlab data file containing a data table for each period-length test. Within each table is a column labeled: time (seconds since beginning of pumping), Inj_m3pm (formation injection in cubic meters per minute), and head for each observation well (meters). The three Matlab script files (*.m) were used to analyze hydraulic responses from the data file above. High-Pass Sinusoid is a routine for filtering the data, computing the FFT, and extracting phase and amplitude values. Borestore is a routine which contains the borehole storage analytic solution and compares modeled amplitude and phase from this solution to computed amplitude and phase from the data. Patsearch Borestore is a routine containing the built-in pattern search optimization method. This minimizes the total error between modeled and actual amplitude and phase in Borestore. Comments within the script files contain more specific instructions for their use.

15 GEOTHERMAL ENERGY↗