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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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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Arginase inhibition reduces interleukin-1β-stimulated vascular smooth muscle cell proliferation by increasing nitric oxide synthase-dependent nitric oxide production

Highlights: •Arginase inhibition suppressed proliferation of IL-1β-stimulated VSMCs in dose-dependent manner. •NO production from IL-1β-induced iNOS expression was augmented by arginase inhibition, reducing VSMC proliferation. •Incubation with cGMP analogues abolished IL-1β-dependent proliferation of VSMCs. -- Abstract: We investigated whether arginase inhibition suppressed interleukin (IL)-1β-stimulated proliferation in vascular smooth muscle cells (VSMCs) and the possible mechanisms involved. IL-1β stimulation increased VSMC proliferation, while the arginase inhibitor BEC and transfection of the antisense (AS) oligonucleotide against arginase I decreased VSMC proliferation and was associated with increased protein content of the cell cycle regulator p21Waf1/Cip1. IL-1β incubation induced inducible nitric oxide synthase (iNOS) mRNA expression and protein levels in a dose-dependent manner, but did not affect arginase I and II expression. Consistent with this data, IL-1β stimulation resulted in increase in NO production that was significantly augmented by arginase inhibition. The specific iNOS inhibitor 1400W abolished IL-1β-mediated NO production and further accentuated IL-1β-stimulated cell proliferation. Incubation with NO donors GSNO and DETA/NO in the presence of IL-1β abolished VSMCs proliferation and increased p21Waf1/Cip1 protein content. Furthermore, incubation with the cGMP analogue 8-Br-cGMP prevented IL-1β-induced VSMCs proliferation. In conclusion, arginase inhibition augmented iNOS-dependent NO production that resulted in suppression of IL-1β-induced VSMCs proliferation in a cGMP-dependent manner.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

TRAF2 regulates the cytoplasmic/nuclear distribution of TRAF4 and its biological function in breast cancer cells

Highlights: •TRAF2 appears to interact with TRAF4 in breast cancer cell lines. •TRAF2 affects the localization and function of TRAF4 in breast cancer cell lines. •TRAF4 may play an important role in the activation of NF-κB via TRAF2. -- Abstract: Although numerous studies have shown that tumor necrosis factor receptor-associated factor 4 (TRAF4) plays an important role in the carcinogenesis of many tumor types, its exact molecular mechanism remains elusive. In this study, we examined the regulation function of TRAF2 to the cytoplasmic/nuclear distribution of TRAF4 in the breast cancer cell line. Using cell immunofluorescent staining, we found that TRAF2 and TRAF4 were co-localized to the cytoplasm in MCF-7 cells. Co-immunoprecipitation showed that TRAF2 could interact with TRAF4 in MCF-10A, MCF-7 and MDA-MB-231 cell lines. Western blotting showed TRAF2 depletion by targeted siRNA in MDA-MB-231 cells led to reduced TRAF4 expression in the cytoplasm and augmented TRAF4 expression in the nucleus. Cytoplasmic expression of TRAF4 was augmented and nuclear expression was reduced when MCF-7 cells were transfected with hTRAF2pLPCX-HA-Flag/P874. MCF-7 cells expressing hTRAF2pLPCX-HA-Flag/P874 had enhanced cell proliferation rates. The nuclear expression of NF-κB significantly increased after TNF-α treatment. When hTRAF2pLPCX-HA-Flag/P874 and the siRNA-TRAF4 plasmid were cotransfected, the nuclear expression of NF-κB was significantly reduced compared with cells transfected with hTRAF2pLPCX-HA-Flag/P874 only. In conclusion, TRAF2 appears to interact with TRAF4 and affect the localization of TRAF4 in breast cancer cell lines. The overexpression of TRAF2 augmented the cytoplasmic expression of TRAF4 which promoted cell proliferation and inhibited cell apoptosis by activating NF-κB nuclear transcription. TRAF4 may play an important role in the activation of NF-κB via TRAF2.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Wound healing potential of adipose tissue stem cell extract

Adipose tissue stem cells (ATSCs) are considered as a promising source in the field of cell therapy and regenerative medicine. In addition to direct cell replacement using stem cells, intercellular molecule exchange by stem cell secretory factors showed beneficial effects by reducing tissue damage and augmentation of endogenous repair. Delayed cutaneous wound healing is implicated in many conditions such as diabetes, aging, stress and alcohol consumption. However, the effects of cell-free extract of ATSCs (ATSC-Ex) containing secretome on wound healing process have not been investigated. In this study, ATSC-Ex was topically applied on the cutaneous wound and healing speed was examined. As a result, wound closure was much faster in the cell-free extract treated wound than control wound at 4, 6, 8 days after application of ATSC-Ex. Dermal fibroblast proliferation, migration and extracellular matrix (ECM) production are critical aspects of wound healing, and the effects of ATSC-Ex on human dermal fibroblast (HDF) was examined. ATSC-Ex augmented HDF proliferation in a dose-dependent manner and migration ability was enhanced by extract treatment. Representative ECM proteins, collagen type I and matrix metalloproteinase-1, are significantly up-regulated by treatment of ATSC-Ex. Our results suggest that the ATSC-Ex have improving effect of wound healing and can be the potential therapeutic candidate for cutaneous wound healing. - Highlights: • Topical application of ATSC-Ex results in faster wound closure than normal wound in vivo. • ATSC-Ex enhances dermal fibroblast proliferation, migration and extracellular matrix production. • This study suggests that ATSC-Ex is an effective source to augment wound healing.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

The role of N-terminal segment and membrane association in MyD88-mediated signaling

Highlights: • 21 AA MYD88 deletion mutant fails to support MyD88-mediated signaling. • WT MyD88 and 21 AA NTD peptide bind to the phosphatidic acid (PA). • Anchoring of MyD88 to cell membrane and addition of PA augments cell signaling. MyD88 is a central signaling mediator of innate immunity, composed of the N-terminal death (DD) and C-terminal Toll/interleukin-1 receptor (TIR) domain linked by an intermediary (INT) domain. We showed that the N-terminal domain (NTD), composed of apparently unstructured 21 amino-acid residues, is involved in localization and clustering of MyD88 and is required for the efficient signaling, since the deletion mutant is unable to reconstitute MyD88-dependent signaling. Furthermore, we found that the NTD peptide interacts with phosphatidic acid, which potentiates MyD88-mediated signaling through TLRs. Propranolol and expression of lysophosphatidyl acid acyltransferase 1, which increase the level of phosphatidic acid augment cell activation via MyD88. Moreover, anchoring of MyD88 to the cell membrane augments signaling supporting the importance of membrane localization in MyD88-mediated signaling.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Ziram, a dithiocarbamate fungicide, exhibits pseudo-cytoprotective actions against oxidative stress in rat thymocytes: Possible environmental risks

Highlights: • Ziram exerted reciprocal actions on H{sub 2}O{sub 2}-induced cell death. • Ziram augmented H{sub 2}O{sub 2}-induced decrease in cellular glutathione levels. • Ziram enhanced H{sub 2}O{sub 2}-induced increase in intracellular Zn{sup 2+} levels. • Ziram attenuated H{sub 2}O{sub 2}-induced depolarization of mitochondrial membrane potential. • Ziram exhibited pseudo-cytoprotective actions against oxidative stress induced by H{sub 2}O{sub 2}. Ziram, a dithiocarbamate fungicide, protects various vegetables and fruits against infections by fungus. Recently, there have been increasing anxieties about the risks in the use of dithiocarbamate fungicides. Our previous studies showed that Zn{sup 2+} was a determinant of Ziram cytotoxicity. In addition, Zn{sup 2+} is linked to H{sub 2}O{sub 2} cytotoxicity. Therefore, in this study, we aimed to test the hypothesis that Ziram could augment the cytotoxicity of H{sub 2}O{sub 2} by examining the changes induced by Ziram in some cellular parameters in rat thymic lymphocytes subjected to H{sub 2}O{sub 2}-induced oxidative stress using flow-cytometric methods with fluorescent dyes. Ziram significantly attenuated H{sub 2}O{sub 2}-induced cell death at sublethal concentrations. However, in the cells under oxidative stress elicited by H{sub 2}O{sub 2}, Ziram promoted the changing over from intact cells to living cells with exposed phosphatidylserine (PS) on plasma membranes, whereas it inhibited the transition from PS-exposed living cells to dead cells. Ziram significantly augmented H{sub 2}O{sub 2} actions, including reduction of cellular glutathione levels and elevation of intracellular Zn{sup 2+} concentrations. Conversely, it attenuated H{sub 2}O{sub 2}-induced depolarization of mitochondrial membrane potential. Ziram at sublethal concentrations seems to exhibit promotive and suppressive actions on the process of cell death caused by H{sub 2}O{sub 2}. Ziram increased the number of living cells with exposed PS, a phenomenon characteristic of early stages of apoptosis. Thus, it is concluded that Ziram exhibits pseudo-cytoprotective actions against H{sub 2}O{sub 2}-induced oxidative stress. Ziram at sublethal concentrations exerts promotive and suppressive actions on the process of cell death caused by oxidative stress.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Efficient sensitivity analysis method for chaotic dynamical systems

The direct differentiation and improved least squares shadowing methods are both developed for accurately and efficiently calculating the sensitivity coefficients of time averaged quantities for chaotic dynamical systems. The key idea is to recast the time averaged integration term in the form of differential equation before applying the sensitivity analysis method. An additional constraint-based equation which forms the augmented equations of motion is proposed to calculate the time averaged integration variable and the sensitivity coefficients are obtained as a result of solving the augmented differential equations. The application of the least squares shadowing formulation to the augmented equations results in an explicit expression for the sensitivity coefficient which is dependent on the final state of the Lagrange multipliers. The LU factorization technique to calculate the Lagrange multipliers leads to a better performance for the convergence problem and the computational expense. Numerical experiments on a set of problems selected from the literature are presented to illustrate the developed methods. The numerical results demonstrate the correctness and effectiveness of the present approaches and some short impulsive sensitivity coefficients are observed by using the direct differentiation sensitivity analysis method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

Cadmium-induced oxidative stress and histological damage in the myocardium. Effects of a soy-based diet

Cd exposure has been associated to an augmented risk for cardiovascular disease. We investigated the effects of 15 and 100 ppm of Cd on redox status as well as histological changes in the rat heart and the putative protective effect of a soy-based diet. Male Wistar rats were separated into 6 groups and treated during 60 days as follows: groups (1), (2) and (3) were fed a casein-based diet; groups (4), (5) and (6), a soy-based diet; (1) and (4) were given tap water; (2) and (5) tap water containing 15 ppm of Cd{sup 2+}; and (3) and (6) tap water containing 100 ppm of Cd{sup 2+}. Serum lipid peroxides increased and PON-1 activity decreased in group (3). Lipoperoxidation also increased in the heart of all intoxicated groups; however protein oxidation only augmented in (3) and reduced glutathione levels diminished in (2) and (3). Catalase activity increased in groups (3) and (6) while superoxide dismutase activity increased only in (6). Glutathione peroxidase activity decreased in groups (3) and (6). Nrf2 expression was higher in groups (3) and (6), and MTI expression augmented in (3). Histological examination of the heart tissue showed the development of hypertrophic and fusion of cardiomyocytes along with foci of myocardial fiber necrosis. The transmission electron microscopy analysis showed profound ultra-structural damages. No protection against tissue degeneration was observed in animals fed the soy-based diet. Our findings indicate that even though the intake of a soy-based diet is capable of ameliorating Cd induced oxidative stress, it failed in preventing cardiac damage. -- Highlights: ► Cd intoxication produces extracellular and ultrastructural damage in the myocardium. ► The intake of a soy-based diet ameliorated Cd-induced oxidative stress. ► Cd-induced myocardial damage wasn't prevented by the intake of a soy-based diet. ► Cd-induced myocardial degeneration may not be caused by oxidative stress generation. ► Histology evaluation is needed to establish the extent of Cd-induced cardiac damage.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

25-Hydroxycholesterol promotes fibroblast-mediated tissue remodeling through NF-κB dependent pathway

Abnormal structural alterations termed remodeling, including fibrosis and alveolar wall destruction, are important features of the pathophysiology of chronic airway diseases such as chronic obstructive pulmonary disease (COPD) and asthma. 25-hydroxycholesterol (25-HC) is enzymatically produced by cholesterol 25-hydorxylase (CH25H) in macrophages and is reported to be involved in the formation of arteriosclerosis. We previously demonstrated that the expression of CH25H and production of 25HC were increased in the lungs of COPD. However, the role of 25-HC in lung tissue remodeling is unknown. In this study, we investigated the effect of 25-HC on fibroblast-mediated tissue remodeling using human fetal lung fibroblasts (HFL-1) in vitro. 25-HC significantly augmented α-smooth muscle actin (SMA) (P<0.001) and collagen I (P<0.001) expression in HFL-1. 25-HC also significantly enhanced the release and activation of matrix metallaoproteinase (MMP)-2 (P<0.001) and MMP-9 (P<0.001) without any significant effect on the production of tissue inhibitor of metalloproteinase (TIMP)-1 and TIMP-2. 25-HC stimulated transforming growth factor (TGF)-β{sub 1} production (P<0.01) and a neutralizing anti-TGF-β antibody restored these 25-HC-augmented pro-fibrotic responses. 25-HC significantly promoted the translocation of nuclear factor (NF)-κB p65 into the nuclei (P<0.01), but not phospholylated-c-jun, a complex of activator protein-1. Pharmacological inhibition of NF-κB restored the 25-HC-augmented pro-fibrotic responses and TGF-β{sub 1} release. These results suggest that 25-HC could contribute to fibroblast-mediated lung tissue remodeling by promoting myofibroblast differentiation and the excessive release of extracellular matrix protein and MMPs via an NF-κB-TGF-β dependent pathway.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Benchmark Theoretical Study of the π–π Binding Energy in the Benzene Dimer

We establish a new estimate for the binding energy between two benzene molecules in the parallel-displaced (PD) conformation by systematically converging (i) the intra- and intermolecular geometry at the minimum, (ii) the expansion of the orbital basis set, and (iii) the level of electron correlation. The calculations were performed at the second-order Møller–Plesset perturbation (MP2) and the coupled cluster including singles, doubles, and a perturbative estimate of triples replacement [CCSD(T)] levels of electronic structure theory. At both levels of theory, by including results corrected for basis set superposition error (BSSE), we have estimated the complete basis set (CBS) limit by employing the family of Dunning’s correlation-consistent polarized valence basis sets. The largest MP2 calculation was performed with the cc-pV6Z basis set (2772 basis functions), whereas the largest CCSD(T) calculation was with the cc-pV5Z basis set (1752 basis functions). The cluster geometries were optimized with basis sets up to quadruple-ζ quality, observing that both its intra- and intermolecular parts have practically converged with the triple-ζ quality sets. The use of converged geometries was found to play an important role for obtaining accurate estimates for the CBS limits. Our results demonstrate that the binding energies with the families of the plain (cc-pVnZ) and augmented (aug-cc-pVnZ) sets converge [within <0.01 kcal/mol for MP2 and <0.15 kcal/mol for CCSD(T)] to the same CBS limit. In addition, the average of the uncorrected and BSSE-corrected binding energies was found to converge to the same CBS limit much faster than either of the two constituents (uncorrected or BSSE-corrected binding energies). Due to the fact that the family of augmented basis sets (especially for the larger sets) causes serious linear dependency problems, the plain basis sets (for which no linear dependencies were found) are deemed as a more efficient and straightforward path for obtaining an accurate CBS limit. We considered extrapolations of the uncorrected (ΔE) and BSSE-corrected (ΔE cp ) binding energies, their average value (ΔE ave ), as well as the average of the latter over the plain and augmented sets (Δ~E ave ) with the cardinal number of the basis set n. Our best estimate of the CCSD(T)/CBS limit for the π–π binding energy in the PD benzene dimer is D e = -2.65 ± 0.02 kcal/mol. The best CCSD(T)/cc-pV5Z calculated value is -2.62 kcal/mol, just 0.03 kcal/mol away from the CBS limit. For comparison, the MP2/CBS limit estimate is -5.00 ± 0.01 kcal/mol, demonstrating a 90% overbinding with respect to CCSD(T). Finally, the spin-component-scaled (SCS) MP2 variant was found to closely reproduce the CCSD(T) results for each basis set, while scaled opposite spin (SOS) MP2 yielded results that are too low when compared to CCSD(T).

Environmental Molecular Sciences Laboratory↗
DOE OSTI2024

Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification

Abstract To bolster the accuracy of existing methods for automated phase identification from X-ray diffraction (XRD) patterns, we introduce a machine learning approach that uses a dual representation whereby XRD patterns are augmented with simulated pair distribution functions (PDFs). A convolutional neural network is trained directly on XRD patterns calculated using physics-informed data augmentation, which accounts for experimental artifacts such as lattice strain and crystallographic texture. A second network is trained on PDFs generated via Fourier transform of the augmented XRD patterns. At inference, these networks classify unknown samples by aggregating their predictions in a confidence-weighted sum. We show that such an integrated approach to phase identification provides enhanced accuracy by leveraging the benefits of each model’s input representation. Whereas networks trained on XRD patterns provide a reciprocal space representation and can effectively distinguish large diffraction peaks in multi-phase samples, networks trained on PDFs provide a real space representation and perform better when peaks with low intensity become important. These findings underscore the importance of using diverse input representations for machine learning models in materials science and point to new avenues for automating multi-modal characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine-learning for designing nanoarchitectured materials by dealloying

Machine learning-augmented materials design is an emerging method for rapidly developing new materials. It is especially useful for designing new nanoarchitectured materials, whose design parameter space is often large and complex. Metal-agent dealloying, a materials design method for fabricating nanoporous or nanocomposite from a wide range of elements, has attracted significant interest. Here, a machine learning approach is introduced to explore metal-agent dealloying, leading to the prediction of 132 plausible ternary dealloying systems. A machine learning-augmented framework is tested, including predicting dealloying systems and characterizing combinatorial thin films via automated and autonomous machine learning-driven synchrotron techniques. This work demonstrates the potential to utilize machine learning-augmented methods for creating nanoarchitectured thin films.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Simulation of Channel Flow with Square Ribs for Blanket First-Wall Cooling: Geometry-Specific Tuning of k-ω Model Using Adjoint Method

Cooling of the plasma-facing first wall is challenging in the design of blanket components because of the high heat flux (on the order of 𝑀𝑊/𝑚2) from the plasma, especially when a low thermal mass medium like helium is chosen as the coolant. Therefore, heat transfer enhancement in which the convective heat transfer rate is augmented by the addition of turbulence-promoting structures becomes a key initiative for providing sufficient cooling capability with helium. Previously, computational fluid dynamics simulations had been performed on pipe flows with different transverse and longitudinal ribbed geometries at Oak Ridge National Laboratory to compare the enhancement performance among different ribbed geometries. Rib shape morphing had been conducted to obtain an optimized rib profile. In the work presented here, the adjoint method is adopted in the ANSYS Fluent solver for turbulence model augmentation, and the Generalized k-ω (GEKO) turbulence model is employed because of its ability of tuning the turbulence model. The Nusselt number and pressure drop obtained from the channel flow with bottom ribbed wall experiments are used as the targets. Sensitivity analysis provides information as guidance to improve the turbulence model accuracy. The augmented GEKO model is tuned for the studied ribbed channel geometry and flow conditions, providing improved predictive accuracy within this context. Extension to other configurations offers potential but may require additional tuning and validation.

Xu, Tracy [ORNL] (ORCID:0009000193700887)↗
DOE OSTI2016

Leptogenesis scenarios for natural SUSY with mixed axion-higgsino dark matter

Supersymmetric models with radiatively-driven electroweak naturalness require light higgsinos of mass ∼100–300 GeV. Naturalness in the QCD sector is invoked via the Peccei-Quinn (PQ) axion leading to mixed axion-higgsino dark matter. The SUSY DFSZ axion model provides a solution to the SUSY μ problem and the Little Hierarchy μ≪m{sub 3/2} may emerge as a consequence of a mismatch between PQ and hidden sector mass scales. The traditional gravitino problem is now augmented by the axino and saxion problems, since these latter particles can also contribute to overproduction of WIMPs or dark radiation, or violation of BBN constraints. We compute regions of the T{sub R} vs. m{sub 3/2} plane allowed by BBN, dark matter and dark radiation constraints for various PQ scale choices f{sub a}. These regions are compared to the values needed for thermal leptogenesis, non-thermal leptogenesis, oscillating sneutrino leptogenesis and Affleck-Dine leptogenesis. The latter three are allowed in wide regions of parameter space for PQ scale f{sub a}∼10{sup 10}–10{sup 12} GeV which is also favored by naturalness: f{sub a}∼√(μM{sub P}/λ{sub μ})∼10{sup 10}–10{sup 12} GeV. These f{sub a} values correspond to axion masses somewhat above the projected ADMX search regions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Testing approximate predictions of displacements of cosmological dark matter halos

We present a test to quantify how well some approximate methods, designed to reproduce the mildly non-linear evolution of perturbations, are able to reproduce the clustering of DM halos once the grouping of particles into halos is defined and kept fixed. The following methods have been considered: Lagrangian Perturbation Theory (LPT) up to third order, Truncated LPT, Augmented LPT, MUSCLE and COLA. The test runs as follows: halos are defined by applying a friends-of-friends (FoF) halo finder to the output of an N-body simulation. The approximate methods are then applied to the same initial conditions of the simulation, producing for all particles displacements from their starting position and velocities. The position and velocity of each halo are computed by averaging over the particles that belong to that halo, according to the FoF halo finder. This procedure allows us to perform a well-posed test of how clustering of the matter density and halo density fields are recovered, without asking to the approximate method an accurate reconstruction of halos. We have considered the results at z =0,0.5,1, and we have analysed power spectrum in real and redshift space, object-by-object difference in position and velocity, density Probability Distribution Function (PDF) and its moments, phase difference of Fourier modes. We find that higher LPT orders are generally able to better reproduce the clustering of halos, while little or no improvement is found for the matter density field when going to 2LPT and 3LPT. Augmentation provides some improvement when coupled with 2LPT, while its effect is limited when coupled with 3LPT. Little improvement is brought by MUSCLE with respect to Augmentation. The more expensive particle-mesh code COLA outperforms all LPT methods, and this is true even for mesh sizes as large as the inter-particle distance. This test sets an upper limit on the ability of these methods to reproduce the clustering of halos, for the cases when these objects are reconstructed at the object-by-object level.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Coarse-to-fine Task-driven Inpainting for Geoscience Images

The processing and recognition of geoscience images have wide applications. Most of existing researches focus on understanding the high-quality geoscience images by assuming that all the images are clear. However, in many real-world cases, the geoscience images might contain occlusions during the image acquisition. This problem actually implies the image inpainting problem in computer vision and multimedia. As far as we know, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images, and they never consider the following geoscience task when developing inpainting methods. Here, this paper aims to repair the occluded regions for a better geoscience task performance and advanced visualization quality simultaneously, without changing the current deployed deep learning based geoscience models. Because of the complex context of geoscience images, we propose a coarse-to-fine encoder-decoder network with the help of designed coarse-to-fine adversarial context discriminators to reconstruct the occluded image regions. Due to the limited data of geoscience images, we propose a MaskMix based data augmentation method, which augments inpainting masks instead of augmenting original images, to exploit the limited geoscience image data. The experimental results on three public geoscience datasets for remote sensing scene recognition, cross-view geolocation and semantic segmentation tasks respectively show the effectiveness and accuracy of the proposed method. The code is available at: https://github.com/HMS97/Task-driven-Inpainting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Visualizing an Exascale Data Center Digital Twin: Considerations, Challenges and Opportunities

Digital twins are an excellent tool to model, visualize, and simulate complex systems, to understand and optimize their operation. In this work, we present the technical challenges of real-time visualization of a digital twin of the Frontier supercomputer.We show the initial prototype and current state of the twin and highlight technical design challenges of visualizing such a large High Performance Computing (HPC) system. The goal is to understand the use of augmented reality as a primary way to extract information and collaborate on digital twins of complex systems. This leverages the spatio-temporal aspect of a 3D representation of a digital twin, with the ability to view historical and real-time telemetry, triggering simulations of a system state and viewing the results, which can be augmented via dashboards for details. Finally, we discuss considerations and opportunities for augmented reality of digital twins of large-scale, parallel computers.

Maiterth, Matthias↗
DOE OSTI2023

FLASH: FPGA-Accelerated Smart Switches with GCN Case Study

Some communication switches, e.g., the Mellanox SHArP and those in the IBM BlueGene clusters, are augmented to process packets at the application level with fixed-function collectives. This approach, however, lacks flexibility, which limits their applicability in diverse and dynamic workloads. Recently, a new type of programmable packet processor, which uses high-level languages, e.g., P4, has emerged as possible candidates. P4-based switches, however, fall short in certain applications, including machine learning, where capabilities not currently supported by P4 are needed. These include more complex calculation, such as sparse computation and fused multiply-accumulate, data-intensive floating point operations, data reuse, and significant memory. The problem addressed here is that such a switch augmentation needs to support: a large amount of state, significant flexible compute capability, and ease of programming, all while maintaining full functionality, including ensuring high throughput, and demonstrating utility. In this work, we propose a programmable look-aside-type accelerator that can be embedded into, or attached to, existing communication switch pipelines and that is capable of processing packets at line-rate. The proposed in-switch accelerator is based on mixing an ISA (subset of RISC-V instructions) with dataflow graphs (found in CGRAs). To augment performance, vector instructions are also supported. To facilitate usability, we have developed a complete toolchain to compile user-provided C/C++ codes to appropriate back-end instructions for configuring the accelerator. While this approach is flexible enough to support various workloads, in this paper, we consider Graph Convolutional Networks (GCNs) as a case study. Experimental results show that this approach considerably improves the performance of distributed GCN applications.

Haghi, Pouya↗