Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “sparse factorization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

The Improved Simple Corner Balance Method and Efforts to Enhance its Computational Performance

The diamond difference (DD) spatial discretization has long been a work horse of the neutron transport community. For nearly as long as DD has been used in neutron transport, it has been observed that DD produces undamped, non-physical angular fluxes in spatially under resolved problems and can fail in the thick diffusion limit. A long list of alternative spatial discretizations, all with better physics properties: accuracy, robustness, and/or preservation of the thick diffusion limit, have been proposed as alternatives to DD. Unfortunately, these alternative spatial discretizations also require significantly more floating point operations (FLOPs) per unknown compared to DD. Historically, neutron transport problems of interest have been able to be resolved sufficiently that DD is accurate enough and FLOPs have been the computational performance bottleneck controlling time to solution. Thus, in practice DD has been and remains a preferred spatial discretization for neutron transport simulations. Trends in computing architecture are leading to a new paradigm where data movement, not FLOPs will control time to solution. This presents an opportunity to improve neutron transport physics by reconsidering more FLOP intensive spatial discretization than DD without incurring a time to solution penalty. In this work, we consider the improved simple corner balance (ISCB) spatial discretization, a unique alternative to DD that has the same memory footprint as DD, and detail efforts to improve its computational performance. The ISCB spatial discretization is a more FLOP intensive spatial discretization than DD with the same memory footprint as DD. Though designed to maximize FLOP intensity, third party libraries such as LAPACK are not tuned for the unique, frequent, small linear algebra needs of neutron transport problems. While hand coding a sparse Gauss elimination is likely to be faster than a dense matrix solve (CM vs. HGE), were LAPACK suitable for small linear algebra problems, we would not have seen a performance increase with decreased numbers of LAPACK calls for the Schur complement solve (SC4 vs. SC1). Significant effort has reduced the relative compute time of ISCB to DD, but on current computer architectures DD is still faster than ISCB. Future work comparing the overall efficiency of ISCB to DD may reveal that while ISCB is slower than DD on current computers, ISCB is more accurate than DD by a factor greater than the compute time slowdown. As new computer architectures become available, we will test our hypothesis that neutron transport simulations will be bandwidth limited, ideally rendering the additional FLOPs required by ISCB to be 'free' [in terms of time to solution]. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

SU-E-I-45: Reconstruction of CT Images From Sparsely-Sampled Data Using the Logarithmic Barrier Method

Purpose: To develop and investigate whether the logarithmic barrier (LB) method can result in high-quality reconstructed CT images using sparsely-sampled noisy projection data Methods: The objective function is typically formulated as the sum of the total variation (TV) and a data fidelity (DF) term with a parameter λ that governs the relative weight between them. Finding the optimized value of λ is a critical step for this approach to give satisfactory results. The proposed LB method avoid using λ by constructing the objective function as the sum of the TV and a log function whose augment is the DF term. Newton's method was used to solve the optimization problem. The algorithm was coded in MatLab2013b. Both Shepp-Logan phantom and a patient lung CT image were used for demonstration of the algorithm. Measured data were simulated by calculating the projection data using radon transform. A Poisson noise model was used to account for the simulated detector noise. The iteration stopped when the difference of the current TV and the previous one was less than 1%. Results: Shepp-Logan phantom reconstruction study shows that filtered back-projection (FBP) gives high streak artifacts for 30 and 40 projections. Although visually the streak artifacts are less pronounced for 64 and 90 projections in FBP, the 1D pixel profiles indicate that FBP gives noisier reconstructed pixel values than LB does. A lung image reconstruction is presented. It shows that use of 64 projections gives satisfactory reconstructed image quality with regard to noise suppression and sharp edge preservation. Conclusion: This study demonstrates that the logarithmic barrier method can be used to reconstruct CT images from sparsely-amped data. The number of projections around 64 gives a balance between the over-smoothing of the sharp demarcation and noise suppression. Future study may extend to CBCT reconstruction and improvement on computation speed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Pion and kaon structure functions at an electron-ion collider

Pions and kaons are, along with protons and neutrons, the main building blocks of nuclear matter. They are connected to the Goldstone modes of dynamical chiral symmetry breaking, the mechanism thought to generate all hadron mass in the visible universe. The distribution of the fundamental constituents, the quarks and gluons, is expected to be different in pions, kaons, and nucleons. However, experimental data are sparse. As a result, there has been persistent doubt about the behaviour of the pion's valence quark structure function at large Bjorken-$x$ and virtually nothing is known about the contribution of gluons. The Electron-Ion Collider (EIC) with an acceptance optimized for forward physics could provide access to structure functions over a larger kinematic region. This would allow for measurements testing if the origin of mass is encoded in the differences of gluons in pions, kaons, and nucleons, and measurements that could serve as a test of assumptions used in the extraction of structure functions and the pion and kaon form factors. Furthermore, measurements at an EIC would also allow to explore the effect of gluons at high x.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1994

Tensor-GMRES method for large sparse systems of nonlinear equations

This paper introduces a tensor-Krylov method, the tensor-GMRES method, for large sparse systems of nonlinear equations. This method is a coupling of tensor model formation and solution techniques for nonlinear equations with Krylov subspace projection techniques for unsymmetric systems of linear equations. Traditional tensor methods for nonlinear equations are based on a quadratic model of the nonlinear function, a standard linear model augmented by a simple second order term. These methods are shown to be significantly more efficient than standard methods both on nonsingular problems and on problems where the Jacobian matrix at the solution is singular. A major disadvantage of the traditional tensor methods is that the solution of the tensor model requires the factorization of the Jacobian matrix, which may not be suitable for problems where the Jacobian matrix is large and has a 'bad' sparsity structure for an efficient factorization. We overcome this difficulty by forming and solving the tensor model using an extension of a Newton-GMRES scheme. Like traditional tensor methods, we show that the new tensor method has significant computational advantages over the analogous Newton counterpart. Consistent with Krylov subspace based methods, the new tensor method does not depend on the factorization of the Jacobian matrix. As a matter of fact, the Jacobian matrix is never needed explicitly.

Feng, Dan↗
DOE OSTI2023

Quantum Search Approaches to Sampling-Based Motion Planning

In this paper, we present a novel formulation of traditional sampling-based motion planners as database-oracle structures that can be solved via quantum search algorithms. We consider two complementary scenarios: for simpler sparse environments, we formulate the Quantum Full Path Search Algorithm (q-FPS), which creates a superposition of full random path solutions, manipulates probability amplitudes with Quantum Amplitude Amplification (QAA), and quantum measures a single obstacle free full path solution. For dense unstructured environments, we formulate the Quantum Rapidly Exploring Random Tree algorithm, q-RRT, that creates quantum superpositions of possible parent-child connections, manipulates probability amplitudes with QAA, and quantum measures a single reachable state, which is added to a tree. As performance depends on the number of oracle calls and the probability of measuring good quantum states, we quantify how these errors factor into the probabilistic completeness properties of the algorithm. We then numerically estimate the expected number of database solutions to provide an approximation of the optimal number of oracle calls in the algorithm. We compare the q-RRT algorithm with a classical implementation and verify quadratic run-time speedup in the largest connected component of a 2D dense random lattice. We conclude by evaluating a proposed approach to limit the expected number of database solutions and thus limit the optimal number of oracle calls to a given number.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Geomechanical Framework for Secure CO 2 Storage in Fractured Reservoirs and Caprocks for Sedimentary Basins in theMidwest United States

This report presents final technical results for the project Geomechanical Framework for Secure CO 2 Storage in Fractured Reservoirs and Caprocks for Sedimentary Basins in the Midwest United States (DE-FE0023330). The project was a three-year effort consisting of seven technical tasks focused on defining geomechanical factors for CO 2 storage applications in deep saline rock formations in Ohio and the Midwest United States, because geomechancial issues have been identified as a significant risk factor for large-scale CO 2 storage applications. A basin-scale stress-strain analysis was completed to describe the geomechanical setting for rock formations of Ordovician-Cambrian age in Ohio and adjacent areas of the Midwest United States in relation to geologic CO 2 storage applications. The tectonic setting, stress orientation-magnitude, and geomechanical and petrophysical parameters for CO 2 storage zones and caprocks in the region were cataloged. Ten geophysical image logs were analyzed for natural fractures, borehole breakouts, and drilling-induced fractures. The logs indicated mostly less than 10 fractures per 100 vertical feet in the borehole, with mostly N65E principal stress orientation through the section. Geophysical image logs and other logs were obtained for three wells located near the sites where specific models were developed for geomechanical simulations: Arches site in Boone County, Kentucky; Northern Appalachian Basin site in Chautauqua County, New York; and E-Central Appalachian Basin site in Tuscarawas County, Ohio. For these three wells, 9,700 feet of image logs were processed and interpreted to provide a systematic review of the distribution within each well of natural fractures, wellbore breakouts, faults, and drilling induced fractures. There were many borehole breakouts and drilling-induced tensile fractures but few natural fractures. Concentrated fractures were present at the Rome-basal sandstone and basal sandstone-Precambrian contacts at the Arches and East-Central Appalachian Basin sites. Geophysical logs were utilized to develop local-scale geologic models by determining geomechanical and petrophysical parameters within the geologic formations. These data were ported to coupled fluid-flow and reservoir geomechanics multi-phase CO 2 injection simulations. The models were developed to emphasize the geomechanical layers within the CO 2 storage zones and caprocks. A series of simulations were completed for each site to evaluate whether commercial-scale CO 2 could be safely injected into each site, given site-specific geologic and geomechanical controls. This involved analyzing the simulation results for the integrity of the caprock, intermediate, and reservoir zones, as well quantifying the areal uplift at the surface. Simulation results were also examined to ensure that the stress-stress perturbations were isolated within the subsurface, and that there was only limited upward migration of the CO 2 . Simulations showed capacity to inject more than 10 million metric tons of CO 2 in a single well at the Arches and East Central Appalachian Basin sites without excessive geomechanical risks. Low-permeability rock layers at the Northern Appalachian Basin study area well resulted in very low CO 2 injection capacity. Fracture models developed for the sites suggests that the sites have sparse fracture network in the deeper Cambrian rocks. However, there were indicators in image logs of a moderate fracture matrix in the Rose Run Sandstone at the Northern Appalachian Basin site. Dual permeability fracture matrix simulations suggest the much higher injection rates may be feasible in the fractured interval. Guidance was developed for geomechanical site characterization in the areas of geophysical logging, rock core testing, well testing, and site monitoring. The guidance demonstrates that there is a suitable array of options for addressing geomechanical issues at CO 2 storage sites. Finally, a review of Marcellus and Utica-Point Pleasant shale gas wells and CO 2 storage intervals indicates that these items are vertically separated, except for the Oriskany sandstone and Marcellus wells in southwest Pennsylvania and northern West Virginia. Together, project results present a more realistic portrayal of geomechanical risk factors related to CO 2 storage for existing and future coal-fired power plants in Ohio.

58 GEOSCIENCES↗
DOE OSTI2014

TH-A-18C-04: Ultrafast Cone-Beam CT Scatter Correction with GPU-Based Monte Carlo Simulation

Purpose: Scatter artifacts severely degrade image quality of cone-beam CT (CBCT). We present an ultrafast scatter correction framework by using GPU-based Monte Carlo (MC) simulation and prior patient CT image, aiming at automatically finish the whole process including both scatter correction and reconstructions within 30 seconds. Methods: The method consists of six steps: 1) FDK reconstruction using raw projection data; 2) Rigid Registration of planning CT to the FDK results; 3) MC scatter calculation at sparse view angles using the planning CT; 4) Interpolation of the calculated scatter signals to other angles; 5) Removal of scatter from the raw projections; 6) FDK reconstruction using the scatter-corrected projections. In addition to using GPU to accelerate MC photon simulations, we also use a small number of photons and a down-sampled CT image in simulation to further reduce computation time. A novel denoising algorithm is used to eliminate MC scatter noise caused by low photon numbers. The method is validated on head-and-neck cases with simulated and clinical data. Results: We have studied impacts of photo histories, volume down sampling factors on the accuracy of scatter estimation. The Fourier analysis was conducted to show that scatter images calculated at 31 angles are sufficient to restore those at all angles with <0.1% error. For the simulated case with a resolution of 512×512×100, we simulated 10M photons per angle. The total computation time is 23.77 seconds on a Nvidia GTX Titan GPU. The scatter-induced shading/cupping artifacts are substantially reduced, and the average HU error of a region-of-interest is reduced from 75.9 to 19.0 HU. Similar results were found for a real patient case. Conclusion: A practical ultrafast MC-based CBCT scatter correction scheme is developed. The whole process of scatter correction and reconstruction is accomplished within 30 seconds. This study is supported in part by NIH (1R01CA154747-01), The Core Technology Research in Strategic Emerging Industry, Guangdong, China (2011A081402003)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Enhancing gene regulatory network inference through data integration with markov random fields

Here, a gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1998

Cell contact as an independent factor modulating cardiac myocyte hypertrophy and survival in long-term primary culture

Cardiac myocytes maintained in cell culture develop hypertrophy both in response to mechanical loading as well as to receptor-mediated signaling mechanisms. However, it has been shown that the hypertrophic response to these stimuli may be modulated through effects of intercellular contact achieved by maintaining cells at different plating densities. In this study, we show that the myocyte plating density affects not only the hypertrophic response and features of the differentiated phenotype of isolated adult myocytes, but also plays a significant role influencing myocyte survival in vitro. The native rod-shaped phenotype of freshly isolated adult myocytes persists in an environment which minimizes myocyte attachment and spreading on the substratum. However, these conditions are not optimal for long-term maintenance of cultured adult cardiac myocytes. Conditions which promote myocyte attachment and spreading on the substratum, on the other hand, also promote the re-establishment of new intercellular contacts between myocytes. These contacts appear to play a significant role in the development of spontaneous activity, which enhances the redevelopment of highly differentiated contractile, junctional, and sarcoplasmic reticulum structures in the cultured adult cardiomyocyte. Although it has previously been shown that adult cardiac myocytes are typically quiescent in culture, the addition of beta-adrenergic agonists stimulates beating and myocyte hypertrophy, and thereby serves to increase the level of intercellular contact as well. However, in densely-plated cultures with intrinsically high levels of intercellular contact, spontaneous contractile activity develops without the addition of beta-adrenergic agonists. In this study, we compare the function, morphology, and natural history of adult feline cardiomyocytes which have been maintained in cultures with different levels of intercellular contact, with and without the addition of beta-adrenergic agonists. Intercellular contact, communication, and transmission of contractile forces between myocytes appears to play a primary role in remodeling the 2-dimensional cell layer into a parallel alignment of elongated myocytes with highly developed intercalated disk-like junctions. This highly differentiated state is very stable, and cultures which achieve this state exhibit significantly greater longevity than more sparsely plated myocytes. These myocytes typically continue beating, and survive from 6 to more than 12 weeks in culture. When this level of contact and differentiation are not achieved, even among beta-adrenergic stimulated myocytes, contractile activity is not sustained, myofibrils atrophy, there is little or no development of junctional complexes, and the period of myocyte viability is typically no more than 5 weeks in vitro.

Non-NASA Center↗
DOE OSTI2018

Dependence of Mechanical Stresses in Silicon Nitride Films on the Mode of Plasma-Enhanced Chemical Vapor Deposition

Films of silicon nitride SiN{sub x}, obtained by plasma-enhanced chemical vapor deposition from the monosilane SiH{sub 4} and ammonia NH{sub 3} gases, are widely used in microelectronics and micro- and nanoelectromechanical systems. Residual mechanical stresses and film composition are important characteristics for many applications. The properties of SiN{sub x} films, particularly mechanical stresses and composition, depend largely on the conditions of production, e.g., the ratio of the reacting gas flow rates, the composition of the gas mixture, the power and frequency of the plasma generator, and the temperature and pressure during deposition. Despite the great volume of works on the subject, data regarding the dependence of the properties and composition of SiN{sub x} films on the conditions of production remain sparse. This work considers the effect the ratio of the reacting gas flow rates has on the mechanical stresses and composition of silicon nitride films SiN{sub x} obtained by plasma-enhanced chemical vapor deposition from gaseous mixtures of SiH{sub 4} monosilane and NH{sub 3} ammonia using low-frequency plasma. It is found that when the ratio of the gas flow rates of SiH{sub 4} and NH{sub 3} is raised from 0.016 to 0.25, the compressive mechanical stresses are reduced by 31%, the stoichiometric coefficient falls from 1.40 to 1.20, the refractive index rises from 1.91 to 2.08, the concentration of N–H bonds is reduced by a factor of 7.4, the concentration of Si–H bonds grows by a factor of 8.7, and the concentration of hydrogen atoms is reduced by a factor of 1.5. These results can be used for the controlled production of SiN{sub x} films with such specified characteristics as residual mechanical stresses, refractive index, stoichiometric coefficient, and the concentration of hydrogen-containing bonds.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

$\overline{TKE}$ Parameterization and $\bar{v}$ Uncertainty Analysis for CGMF

Previous work was performed on tuning CGMF parameters for 235 U, 238 U, and Plutonium isotopes. Now work is being done to tune minor uranium isotopes. However, uranium isotopes like 232 U and 236 U have almost no experimental data. We are applying cross-isotope models to extrapolate and tune CGMF on isotopes that lack experimental data. There exist several internal CGMF physics quantities that affect the output of CGMF—multi-chance fission probability, excitation energy sharing, spin-cutoff factor, spin scaling, and fragment total kinetic energy to name a few. The mean fragment total kinetic energy, $\overline{TKE}$, is particularly interesting because of its strong anti-correlation with $\bar{v}$. We are most interested in the mean fragment total kinetic energy before neutron emissions. $\overline{TKE}$ is assumed to be pre-neutron emission unless otherwise stated. Currently in CGMF, the $\overline{TKE}$ model for 233,234,235,238 U are tuned independently to reproduce ν for the associated isotopes. In this report, we will tune a cross-isotope $\overline{TKE}$ model to experimental $\overline{TKE}$ data for 232,233,234,235,236,238 U. Because of the unreliable and sparse nature of $\overline{TKE}$ experimental data, future work will use more reliable experimental $\bar{v}$ data to infer the $\overline{TKE}$ model (and likely other internal CGMF parameters) for uranium isotopes. Such work has been performed previously using a sensitivity analysis and Kalman filter methods.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Analyzing Hardware and Software Common Cause Failures in Digital Instrumentation and Control Systems using Dual Error Propagation Method

This paper develops a methodology for quantifying software common cause failures (CCFs) in digital instrumentation and control (I&C) systems of nuclear power plants. To support the transition of analog I&C systems to digital in nuclear power plants, probabilistic risk assessment (PRA) techniques are used. The hardware components of the I&C systems have reliability databases that can be used in the PRA studies. However, the failure data for redundant software components of the systems is sparse. Failure of components constitutes a CCF, wherein two or more components or systems fail due to a single shared cause and coupling mechanism. This paper proposes a quantification approach that can simultaneously model hardware and software components, incorporate the CCFs of software systems in the models, and bridge the gap between the failure quantification of models and the development of CCF parametric databases. We demonstrate the dual error propagation method (DEPM) by developing I&C systems failure models for a representative digital reactor trip system. The DEPM models are built to simulate the control and data flows within the systems and can accommodate failure states. By expanding DEPM to software CCFs, we generated alpha factor parameter estimates for each of the modeled error propagation mechanisms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Iterative methods in GPU-resident linear solvers for nonlinear constrained optimization

Linear solvers are major computational bottlenecks in a wide range of decision support and optimization computations. The challenges become even more pronounced on heterogeneous hardware, where traditional sparse numerical linear algebra methods are often inefficient. For example, methods for solving ill-conditioned linear systems have relied on conditional branching, which degrades performance on hardware accelerators such as graphical processing units (GPUs). To improve the efficiency of solving ill-conditioned systems, our computational strategy separates computations that are efficient on GPUs from those that need to run on traditional central processing units (CPUs). Our strategy maximizes the reuse of expensive CPU computations. Iterative methods, which thus far have not been broadly used for ill-conditioned linear systems, play an important role in our approach. In particular, we extend ideas from Arioli et al., (2007) to implement iterative refinement using inexact LU factors and flexible generalized minimal residual (FGMRES), with the aim of efficient performance on GPUs. In conclusion, we focus on solutions that are effective within broader application contexts, and discuss how early performance tests could be improved to be more predictive of the performance in a realistic environment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Detecting False Data Injection Attacks Against Power System State Estimation With Fast Go-Decomposition Approach

State estimation is a fundamental function in modern energy management system, but its results may be vulnerable to false data injection attacks (FDIAs). FDIA is able to change the estimation results without being detected by the traditional bad data detection algorithms. In this paper, we propose an accurate and computational attractive approach for FDIA detection. In this work, we first rely on the low rank characteristic of the measurement matrix and the sparsity of the attack matrix to reformulate the FDIA detection as a matrix separation problem. Then, four algorithms that solve this problem are presented and compared, including the traditional augmented Lagrange multipliers (ALMs), double-noise-dual-problem (DNDP) ALM, the low rank matrix factorization, and the proposed new “Go Decomposition (GoDec).” Numerical simulation results show that our GoDec algorithm outperforms the other three alternatives and demonstrates a much higher computational efficiency. Furthermore, GoDec is shown to be able to handle measurement noise and applicable for large-scale attacks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2006

Detection of Ice Polar Stratospheric Clouds from Assimilation of Atmospheric Infrared Sounder Data

A novel technique is presented for detection of ice polar stratospheric clouds (PSCs) that form at extremely low temperatures in the lower polar stratosphere during winter. Temperature is a major factor in determining abundance of PSCs, which in turn provide surfaces for heterogeneous chemical reactions leading to ozone loss and radiative cooling. The technique infers the presence of ice PSCs using radiances from the Atmospheric Infrared Sounder (AIRS) in the Goddard Earth Observing System version 5 (GEOS-5) data assimilation system. Brightness temperatures are computed from short-term GEOS-5 forecasts for several hundred AIRS channels, using a radiation transfer module. The differences between collocated AIRS observations and these computed values are the observed-minus-forecast (O-F) residuals in the assimilation system. Because the radiation model assumes clear-sky conditions, we hypothesize that these O-F residuals contain quantitative information about PSCs. This is confirmed using sparse data from the Polar Ozone and Aerosol Measurement (POAM) III occultation instrument. The analysis focuses on 0-F residuals for the 6.79pm AIRS moisture channel. At coincident locations, when POAM III detects ice clouds, the AIRS O-F residuals for this channel are lower than -2K. When no ice PSCs are evident in POAM III data, the AIRS 0-F residuals are larger. Given this relationship, the high spatial density of AIRS data is used to construct maps of regions where 0-F residuals are lower than -2K, as a proxy for ice PSCs. The spatial scales and spatio-temporal variations of these PSCs in the Antarctic and Arctic are discussed on the basis of these maps.

Stajner, Ivanka↗
DOE OSTI2016

Quantitative Analysis of {sup 18}F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy

Purpose: To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis. Methods and Materials: In this institutional review board–approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT {sup 18}F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162 robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy. Results: The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e-6). Conclusion: Quantitative analysis identified novel {sup 18}F-fluorodeoxyglucose positron emission tomography image features that showed improved prognostic value over conventional imaging metrics. If validated in large, prospective cohorts, the new prognostic signature might be used to identify patients for individualized risk-adaptive therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Fast-decaying plant litter enhances soil carbon in temperate forests but not through microbial physiological traits

Abstract Conceptual and empirical advances in soil biogeochemistry have challenged long-held assumptions about the role of soil micro-organisms in soil organic carbon (SOC) dynamics; yet, rigorous tests of emerging concepts remain sparse. Recent hypotheses suggest that microbial necromass production links plant inputs to SOC accumulation, with high-quality (i.e., rapidly decomposing) plant litter promoting microbial carbon use efficiency, growth, and turnover leading to more mineral stabilization of necromass. We test this hypothesis experimentally and with observations across six eastern US forests, using stable isotopes to measure microbial traits and SOC dynamics. Here we show, in both studies, that microbial growth, efficiency, and turnover are negatively (not positively) related to mineral-associated SOC. In the experiment, stimulation of microbial growth by high-quality litter enhances SOC decomposition, offsetting the positive effect of litter quality on SOC stabilization. We suggest that microbial necromass production is not the primary driver of SOC persistence in temperate forests. Factors such as microbial necromass origin, alternative SOC formation pathways, priming effects, and soil abiotic properties can strongly decouple microbial growth, efficiency, and turnover from mineral-associated SOC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Spatial Mapping of Riverbed Grain-Size Distribution Using Machine Learning

Recent alluvial sediments in riverbeds play a significant role in controlling hydrologic exchange flows (HEFs) in river systems. The alluvial layer is usually associated with strong heterogeneity in physical properties (e.g., permeability and hydraulic conductivity), which affects local HEFs and therefore biogeochemical processes. The spatial distribution of these physical properties needs to be determined to inform the numerical models used to reveal the realistic hydro-biogeochemical behaviors. Such information can be obtained based on the intrinsic link between sediment grain-size distribution and hydraulic properties where sediment texture information is available. However, grain-size measurements are usually spatially sparse and do not have adequate coverage and resolution, particularly for a relatively large domain such as the Hanford Reach of the Columbia River. In this paper, we adopted machine learning (ML) approaches for categorizing and mapping the spatial distributions of riverbed substrate grain size and filling in missing areas of substrate data using the ML models along the reach. Such ML models for substrate size mapping were trained at 13,372 locations using measured substrate sizes along with observed and simulated attributes, including bathymetric attributes (e.g., elevation, slope, and aspect ratio) from LIDAR and bathymetric surveys, and hydrodynamic properties (e.g., water depth, velocity, shear stress, and their statistical moments). An ensemble bagging-based ML technique, Random Forest, was adopted to identify the most influential factors as predictors to develop the predictive models with over-fitting issues addressed. The models were evaluated with respect to each individual substrate size class and the lumped group, and then used to generate the final substrate size maps covering all the grid cells in the numerical modeling domain.

54 ENVIRONMENTAL SCIENCES↗