Evaluation of methane release from coals from the San Juan basin and Powder River basin
Not provided.
Energy & Fuels↗
SEARCH · Engineering Papers
Results for “Adaptive Sampling”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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THE NATURE OF TRANSITION CIRCUMSTELLAR DISKS. II. SOUTHERN MOLECULAR CLOUDS
Transition disk objects are pre-main-sequence stars with little or no near-IR excess and significant far-IR excess, implying inner opacity holes in their disks. Here we present a multifrequency study of transition disk candidates located in Lupus I, III, IV, V, VI, Corona Australis, and Scorpius. Complementing the information provided by Spitzer with adaptive optics (AO) imaging (NaCo, VLT), submillimeter photometry (APEX), and echelle spectroscopy (Magellan, Du Pont Telescopes), we estimate the multiplicity, disk mass, and accretion rate for each object in our sample in order to identify the mechanism potentially responsible for its inner hole. We find that our transition disks show a rich diversity in their spectral energy distribution morphology, have disk masses ranging from {approx}<1 to 10 M{sub JUP}, and accretion rates ranging from {approx}<10{sup -11} to 10{sup -7.7} M{sub Sun} yr{sup -1}. Of the 17 bona fide transition disks in our sample, three, nine, three, and two objects are consistent with giant planet formation, grain growth, photoevaporation, and debris disks, respectively. Two disks could be circumbinary, which offers tidal truncation as an alternative origin of the inner hole. We find the same heterogeneity of the transition disk population in Lupus III, IV, and Corona Australis as in our previous analysis of transition disks in Ophiuchus while all transition disk candidates selected in Lupus V, VI turned out to be contaminating background asymptotic giant branch stars. All transition disks classified as photoevaporating disks have small disk masses, which indicates that photoevaporation must be less efficient than predicted by most recent models. The three systems that are excellent candidates for harboring giant planets potentially represent invaluable laboratories to study planet formation with the Atacama Large Millimeter/Submillimeter Array.
79 ASTRONOMY AND ASTROPHYSICS↗
Following black hole scaling relations through gas-rich mergers
We present black hole mass measurements from kinematic modeling of high-spatial resolution integral field spectroscopy of the inner regions of nine nearby (ultra-)luminous infrared galaxies in a variety of merger stages. These observations were taken with OSIRIS and laser guide star adaptive optics on the Keck I and Keck II telescopes, and reveal gas and stellar kinematics inside the spheres of influence of these supermassive black holes. We find that this sample of black holes are overmassive (∼10{sup 7–9} M) compared to the expected values based on black hole scaling relations, and suggest that the major epoch of black hole growth occurs in early stages of a merger, as opposed to during a final episode of quasar-mode feedback. The black hole masses presented are the dynamical masses enclosed in ∼25 pc, and could include gas which is gravitationally bound to the black hole but has not yet lost sufficient angular momentum to be accreted. If present, this gas could in principle eventually fuel active galactic nucleus feedback or be itself blown out from the system.
79 ASTRONOMY AND ASTROPHYSICS↗
Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation
Many material and biological samples in scientific imaging are characterized by nonlocal repeating structures. These are studied using scanning electron microscopy and electron tomography. Sparse sampling of individual pixels in a two-dimensional image acquisition geometry, or sparse sampling of projection images with large tilt increments in a tomography experiment, can enable high speed data acquisition and minimize sample damage caused by the electron beam. In this paper, we present an algorithm for electron tomographic reconstruction and sparse image interpolation that exploits the nonlocal redundancy in images. We adapt a framework, termed plug-and-play priors, to solve these imaging problems in a regularized inversion setting. The power of the plug-and-play approach is that it allows a wide array of modern denoising algorithms to be used as a “prior model” for tomography and image interpolation. We also present sufficient mathematical conditions that ensure convergence of the plug-and-play approach, and we use these insights to design a new nonlocal means denoising algorithm. Finally, we demonstrate that the algorithm produces higher quality reconstructions on both simulated and real electron microscope data, along with improved convergence properties compared to other methods.
47 OTHER INSTRUMENTATION↗
Algorithm 1049: The Delaunay Density Diagnostic
Accurate approximation of a real-valued function depends on two aspects of the available data: the density of inputs within the domain of interest and the variation of the outputs over that domain. There are few methods for assessing whether the density of inputs is sufficient to identify the relevant variations in outputs—i.e., the “geometric scale” of the function—despite the fact that sampling density is closely tied to the success or failure of an approximation method. In this article, we introduce a general purpose, computational approach to detecting the geometric scale of real-valued functions over a fixed domain using a deterministic interpolation technique from computational geometry. The algorithm is intended to work on scalar data in moderate dimensions (2–10). Our algorithm is based on the observation that a sequence of piecewise linear interpolants will converge to a continuous function at a quadratic rate (in L 2 norm) if and only if the data are sampled densely enough to distinguish the feature from noise (assuming sufficiently regular sampling). We present numerical experiments demonstrating how our method can identify feature scale, estimate uncertainty in feature scale, and assess the sampling density for fixed (i.e., static) datasets of input–output pairs. Finally, we include analytical results in support of our numerical findings and have released lightweight code that can be adapted for use in a variety of data science settings.
97 MATHEMATICS AND COMPUTING↗
Rapid simulations of hyperspectral near-field images of three-dimensional heterogeneous surfaces – part II
The modeling of the near-field interaction in the scattering-type scanning near-field optical microscope (s-SNOM) is rapidly advancing, although an accurate yet versatile modeling framework that can be easily adapted to various complex situations is still lacking. In this work, we propose a time-efficient numerical scheme in the quasi-electrostatic limit to capture the tip-sample interaction in the near field. This method considers an extended tip geometry, which is a significant advantage compared to the previously reported method based on the point-dipole approximation. Using this formalism, we investigate, among others, nontrivial questions such as uniaxial and biaxial anisotropy in the near-field interaction, the relationship between various experimental parameters (e.g. tip radius, tapping amplitude, etc.), and the tip-dependent spatial resolution. The demonstrated method further sheds light on the understanding of the contrast mechanism in s-SNOM imaging and spectroscopy, while also representing a valuable platform for future quantitative analysis of the experimental observations.
Chen, Xinzhong (ORCID:0000000261033848)↗
Leaf gas exchange and fitted parameters, San Lorenzo, Panama, 2020
This data package contains photosynthetic CO2 response curves (ACi curves), light response curves (AQ curves), dark adapted dark respiration, conductance curves, and survey measurements for leaves measured at the San Lorenzo forest canopy crane site, Panama (PA-SLZ) from January to March 2020. Leaves were sampled from both the top of the canopy and multiple heights within the canopy from 10 vertical profiles and included around 50 species. In addition, six core species were also measured, from the top of the canopy. Fitted maximum carboxylation rate (Vcmax), maximum electron transport rate (Jmax), and triose phosphate utilization rate (TPU) were derived from measured data as well as the convexity, light use efficiency, apparent quantum yield, and Rdark. The measurements can also be used to derive the conductance parameters g0 and g1 of stomatal model. All the gas exchange data and metadata are presented in .csv files and complete instrument output are included in .zip folders. Data and metadata meet the ESS-DIVE leaf-level gas exchange reporting format requirements. The protocol details are provided as pdf documents. In addition to gas exchange data described here these samples were also used for measurement of leaf optical properties, carbon and nitrogen content, and leaf mass per unit leaf area (LMA) these data can be cross linked using the unique sample ID and are provided in separate related data packages. Sample information including canopy elevation and leaf area index (LAI) can be found in the related “Leaf and canopy traits” data package.
54 ENVIRONMENTAL SCIENCES↗
Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys
The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a novice but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an active learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL-based on Gaussian process (GP) and random forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e., ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials.
Chemistry↗
ASGarD: Adaptive Sparse Grid Discretization
Many areas of science exhibit physical processes that are described by high dimensional partial differential equations (PDEs), e.g., the 4D, 5D and 6D models describing magnetized fusion plasmas, models describing quantum chemistry, or derivatives pricing. Such problems are affected by the so-called “curse of dimensionality” where the number of degrees of freedom (or unknowns) required to be solved for scales as N D where N is the number of grid points in any given dimension D. A simple, albeit naive, 6D example is demonstrated in the left panel of Figure 1. With N = 1000 grid points in each dimension, the memory required just to store the solution vector, not to mention forming the matrix required to advance such a system in time, would exceed an exabyte - and also the available memory on the largest of supercomputers available today. The right panel of Figure 1 demonstrates potential savings for a range of problem dimensionalities and grid resolution. While there are methods to simulate such high-dimensional systems, they are mostly based on Monte-Carlo methods, which rely on a statistical sampling such that the resulting solutions include noise. Since the noise in such methods can only be reduced at a rate proportional to $\sqrt{N_p}$ where N p is the number of Monte-Carlo samples, there is a need for continuum, or grid/mesh-based methods for high-dimensional problems, which both do not suffer from noise and bypass the curse of dimensionality. We present a simulation framework that provides such a method using adaptive sparse grids.
97 MATHEMATICS AND COMPUTING↗
A Methodology for determining optimal milling parameters for low-taper microtensile sample production
Femtosecond lasers are beginning to see an increase in interest for industrial and high throughput microsample milling applications. Despite this, much of the literature regarding removal rate studies for ultrashort pulse laser milling continues to focus on single-pulse or similar experiments that have a very small time on target. The material volume removal rates of femtosecond milling can vary wildly depending on parameters like laser scan speed, hatch spacing, z-axis step size, and pulses on the material. Currently, there is no established methodology for determining the optimal values of these parameters for micromilling applications. This methodology was developed in this study. The methodology consists of a pulse study, a repeat study, and a z-step study that will return the milling parameters that resulted in the largest volume removal rate for the laser used for this research. The methodology was created for the laser system used in this study, and was tested on 316 Stainless Steel, but was developed in such a way that it may be adapted to any material, and with some tweaking of variables might be used in other laser systems. The laser system used in this study utilizes a novel positioning stage that introduces 6 degrees of freedom to the translation of laser samples, being able to tilt and move in all three axes. Using both the developed methodology and the unique capabilities of the laser microtensile samples were produced with taper angles of <1° in 316 Stainless Steel.
Barker, Zachary Wayne↗
Sandwich Hybridization Assay for In Situ Real-Time Cyanobacterial Detection and Monitoring: A Review
As cyanobacterial harmful algal bloom (cHAB) events increase in scale, severity, frequency, and duration around the world, rapid and accurate monitoring and characterization tools have become critically essential for regulatory and management decision-making. The composition of cHAB-forming cyanobacteria community can change significantly over time and space and be altered by sample preservation and transportation, making in situ monitoring necessary to obtain real-time and localized information. Sandwich hybridization assay (SHA) utilizes capture oligonucleotide probes for sensitive detection of target-specific nucleic acid sequences. As an amplification-free molecular biology technology, SHA can be adapted for in-situ, real-time or near real-time detection and qualitatively or semi-quantitatively monitoring of cHAB-forming cyanobacteria, owing to its characteristics such as being rapid, portable, inexpensive, and amenable to automation, high sensitivity, specificity and robustness, and multiplexing (i.e., detecting multiple targets simultaneously). Despite its successful application in the monitoring of marine and freshwater phytoplankton, there is still room for improvement. The ability to identify a cHAB community rapidly would decrease delays in cyanotoxin analyses, reduce costs, and increase sample throughput, allowing for timely actions to improve environmental and human health and the understanding of short- and long-term bloom dynamics. Real-time detection and quantitation of HAB-forming cyanobacteria is essential for improving environmental and public health and reducing associated costs. We review and propose to apply SHA for in situ cHABs monitoring.
59 BASIC BIOLOGICAL SCIENCES↗
*-DCC: A platform to collect, annotate, and explore a large variety of sequencing experiments
Background: Over the past few years the variety of experimental designs and protocols for sequencing experiments increased greatly. To ensure the wide usability of the produced data beyond an individual project, rich and systematic annotation of the underlying experiments is crucial. Findings: We first developed an annotation structure that captures the overall experimental design as well as the relevant details of the steps from the biological sample to the library preparation, the sequencing procedure, and the sequencing and processed files. Through various design features, such as controlled vocabularies and different field requirements, we ensured a high annotation quality, comparability, and ease of annotation. The structure can be easily adapted to a large variety of species. We then implemented the annotation strategy in a user-hosted web platform with data import, query, and export functionality. Conclusions: We present here an annotation structure and user-hosted platform for sequencing experiment data, suitable for lab-internal documentation, collaborations, and large-scale annotation efforts.
59 BASIC BIOLOGICAL SCIENCES↗
A probabilistic inverse prediction method for predicting plutonium processing conditions
In the past decade, nuclear chemists and physicists have been conducting studies to investigate the signatures associated with the production of special nuclear material (SNM). In particular, these studies aim to determine how various processing parameters impact the physical, chemical, and morphological properties of the resulting special nuclear material. By better understanding how these properties relate to the processing parameters, scientists can better contribute to nuclear forensics investigations by quantifying their results and ultimately shortening the forensic timeline. This paper aims to statistically analyze and quantify the relationships that exist between the processing conditions used in these experiments and the various properties of the nuclear end-product by invoking inverse methods. In particular, these methods make use of Bayesian Adaptive Spline Surface models in conjunction with Bayesian model calibration techniques to probabilistically determine processing conditions as an inverse function of morphological characteristics. Not only does the model presented in this paper allow for providing point estimates of a sample of special nuclear material, but it also incorporates uncertainty into these predictions. This model proves sufficient for predicting processing conditions within a standard deviation of the observed processing conditions, on average, provides a solid foundation for future work in predicting processing conditions of particles of special nuclear material using only their observed morphological characteristics, and is generalizable to the field of chemometrics for applicability across different materials.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Output-based mesh optimization for hybridized and embedded discontinuous Galerkin methods
Not provided.
Engineering↗
Temperature sensitivity of extracellular enzymes differs with peat depth but not with season in an ombrotrophic bog
Peatlands contain a large portion of Earth's terrestrial soil organic matter in part due to a reduction in decomposition rates. Organic matter decomposition is initially mediated by extracellular enzyme activity, which is in turn controlled by temperature, moisture, and substrate availability; and all are subject to seasonal variation. As depth increases in peatlands, temperature variability and labile carbon inputs decrease. We hypothesized that the more stable recalcitrant subsurface would contain a smaller less diverse enzyme pool, that is better adapted to a narrow temperature range. Thus temperature dependence would be diminished at depth compared to superficial peat. Potential enzyme activity rates were determined across seasons and with depth in peat samples collected from the Marcell Experimental Forest in northern Minnesota, USA. The temperature dependence, assessed by activation energy, was quantified for three hydrolytic enzymes involved in nutrient cycling at up to 15 temperature points ranging from 2 °C to 65 °C. Potential enzyme activity decreased with peat depth as expected and corresponded with changes in peat composition and microbial biomass from the acrotelm to the catotelm. In an environmentally relevant temperature range (2–23 °C), activation energy decreased with depth for β-glucosidase as predicted and leucine amino peptidase activation energy was the lowest of all enzymes. Stable temperatures at depth appear to result in a microbial community containing enzymes that have lower sensitivity to temperature increases. Surprisingly, there was no significant seasonal effect on enzyme temperature dependence observed in our study. Based on these results, and without shifts in microbial community composition, warming of peat could result in increased carbon and phosphorus cycling at the surface but little change at depth. As a result, differences in enzyme temperature sensitivity suggest nitrogen cycling could remain constant with warming, potentially resulting in proteolytic nitrogen cycling being decoupled from carbon and phosphorus cycling.
59 BASIC BIOLOGICAL SCIENCES↗
Trajectory design via unsupervised probabilistic learning on optimal manifolds
Abstract This article illustrates the use of unsupervised probabilistic learning techniques for the analysis of planetary reentry trajectories. A three-degree-of-freedom model was employed to generate optimal trajectories that comprise the training datasets. The algorithm first extracts the intrinsic structure in the data via a diffusion map approach. We find that data resides on manifolds of much lower dimensionality compared to the high-dimensional state space that describes each trajectory. Using the diffusion coordinates on the graph of training samples, the probabilistic framework subsequently augments the original data with samples that are statistically consistent with the original set. The augmented samples are then used to construct conditional statistics that are ultimately assembled in a path planning algorithm. In this framework, the controls are determined stage by stage during the flight to adapt to changing mission objectives in real-time.
42 ENGINEERING↗
Colorimetric-Luminance Readout for Quantitative Analysis of Fluorescence Signals with a Smartphone CMOS Sensor
Smartphones have shown promise as an enabling technology for portable and distributed point-of-care diagnostic tests. The CMOS camera sensor can be used for detecting optical signals, including fluorescence for applications such as isothermal nucleic acid amplification tests. However, such analysis is typically limited mostly to end point detection of single targets. In this paper, we present a smartphone-based image analysis pipeline that utilizes the CIE xyY (chromaticity-luminance) color space to measure the luminance (in lieu of RGB intensities) of fluorescent signals arising from nucleic acid amplification targets, with a discrimination sensitivity (ratio between the positive to negative signals), which is an order of magnitude more than traditional RGB intensity based analysis. Furthermore, the chromaticity part of the analysis enables reliable multiplexed detection of different targets labeled with spectrally separated fluorophores. We apply this chromaticity-luminance formulation to simultaneously detect Zika and chikungunya viral RNA via end point RT-LAMP (Reverse transcription Loop-Mediated isothermal amplification). We also show real time LAMP detection of Neisseria gonorrhoeae samples down to a copy number of 3.5 copies per 10 μL of reaction volume in our smartphone-operated portable LAMP box. Finally, our chromaticity-luminance analysis is readily adaptable to other types of multiplexed fluorescence measurements using a smartphone camera.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
De Novo Sequencing of Peptides from Top-Down Tandem Mass Spectra
De novo sequencing of proteins and peptides is one of the most important problems in mass spectrometry-driven proteomics. A variety of methods have been developed to accomplish this task from a set of bottom-up tandem (MS/MS) mass spectra. However, a more recently emerged top-down technology, now gaining more and more popularity, opens new perspectives for protein analysis and characterization, implying a need in efficient algorithms for processing this kind of MS/MS data. Here we describe a method that allows to retrieve from a set of top-down MS/MS spectra long and accurate sequence fragments of the proteins contained in a sample. To this end, we outline a strategy for generating high-quality sequence tags from top-down spectra, and introduce the concept of a T-Bruijn graph by adapting to the case of tags the notion of an A-Bruijn graph widely used in genomics. The output of the proposed approach represents the set of amino acid strings spelled out by optimal paths in the connected components of a T-Bruijn graph. We illustrate its performance on top-down datasets acquired from carbonic anhydrase 2 (CAH2) and the Fab region of alemtuzumab.
Environmental Molecular Sciences Laboratory↗