COVID-19 patient care management would greatly benefit from new tools that enable accurate assessment of disease severity and stage, potentially enabling a personalized medicine approach. Detection of the SARS-CoV-2 virus itself, or even quantitation of viral loads, is not sufficient for accurate assessment of disease state beyond diagnosis of infection [eg, doi:10.1093/cid/ciaa344]. Levels of usual suspect protein biomarkers associated with host response to infection [eg, C-reactive protein (CRP); cytokines like IL-6, TNF-alpha, and IL-10; complement proteins like C3a and C5a], and of individual blood cell types (eg, leukocytes, lymphocytes, and subsets thereof), show limited correlation with disease severity and stage, with high patient-to-patient and study-to-study variability [eg, doi:10.1093/cid/ciaa248]. High-dimensional panels of biomarkers should have greater predictive power and resilience to unavoidable sources of variability; however, their assembly from proteins and cell types is extremely difficult, due to technical limitations in analyte measurement, especially with regard to starting material requirements and detection sensitivity. Host response profiling through Next Generation Sequencing (NGS) of gene expression patterns (ie, RNA-Seq) is a promising approach, but at the time of this project there were only two publicly available datasets of relevance [doi:10.1093/cid/ciaa203, doi:10.1080/22221751.2020.1747363], and close inspection of them revealed that each had at least one major flaw that severely undermined its value in supporting robust analysis of host response to SARS-CoV- 2 infection. However, the first of these studies [doi:10.1093/cid/ciaa203] fortuitously collected NGS data not only from host cells, but also from bacteria present in bronchoalveolar lavage fluid (BALF) recovered from COVID-19 patients; and because the respiratory microbiome (in terms of bacterial species content) is far less complex than the human transcriptome, the NGS data collected were sufficient to provide coverage depth supporting robust analysis. Surprisingly, the authors of the study did not carry out a detailed analysis of these data and their potential for revealing important new information about COVID-19. Therefore, we carried out a meta-analysis of the dataset as a first step in evaluating the potential for profiling of respiratory microbiome dynamics as a means of accurately assessing COVID-19 disease state.
Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the United States Department of Energy (DOE) National Nuclear Security Administration. The National Nuclear Security Administration’s Sandia Field Office administers the contract and oversees contractor operations at Sandia National Laboratories, New Mexico. Activities at the site support research and development programs with a wide variety of national security missions, resulting in technologies for nonproliferation, homeland security, energy and infrastructure, and defense systems and assessments. DOE and its management and operating contractor are committed to safeguarding the environment, assessing sustainability practices, and ensuring the validity and accuracy of the monitoring data presented in this Annual Site Environmental Report. This report summarizes the environmental protection and monitoring programs in place at Sandia National Laboratories, New Mexico, during calendar year 2019. Environmental topics include air quality, ecology, environmental restoration, oil storage, site sustainability, terrestrial surveillance, waste management, water quality, and implementation of the National Environmental Policy Act. This report is prepared in accordance with and as required by DOE O 231.1B, Admin Change 1, Environment, Safety, and Health Reporting, and has been approved for public distribution.
This document presents tests from the Sierra Structural Mechanics verification test suite. Each of these tests is run nightly with the Sierra/SD code suite and the results of the test checked versus the correct analytic result. For each of the tests presented in this document the test setup, derivation of the analytic solution, and comparison of the Sierra/SD code results to the analytic solution is provided. This document can be used to confirm that a given code capability is verified or referenced as a compilation of example problems.
Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the United States Department of Energy (DOE) National Nuclear Security Administration. The National Nuclear Security Administration’s Sandia Field Office administers the contract and oversees contractor operations at Sandia National Laboratories, Tonopah Test Range (SNL/TTR) in Nevada and Sandia National Laboratories, Kaua‘i Test Facility (SNL/KTF) in Hawai‘i. Activities at SNL/TTR are conducted in support of DOE weapons programs and have operated at the site since 1957. SNL/KTF has operated as a rocket preparation launching and tracking facility since 1962. DOE and its management and operating contractor are committed to safeguarding the environment, assessing sustainability practices, and ensuring the validity and accuracy of the monitoring data presented in this Annual Site Environmental Report. This report summarizes the environmental protection, restoration, and monitoring programs in place at SNL/TTR and SNL/KTF during calendar year 2019. Environmental topics include air quality, ecology, environmental restoration, oil storage, site sustainability, terrestrial surveillance, waste management, water quality, and implementation of the National Environmental Policy Act. This report is prepared in accordance with and as required by DOE O 231.1B, Admin Change 1, Environment, Safety, and Health Reporting, and has been approved for public distribution.
Recent observations of seismic events at the subsurface energy exploration sites show that spatial and temporal correlations sometimes do not match the spatial order of the known or detected fault location from the injection well. This study investigates the coupled flow and geomechanical control on the patterns of induced seismicity along multiple basement faults that show an unusual spatiotemporal relation with induced seismicity occurring in the far field first, followed by the near field. Two possible geological scenarios considered are (1) the presence of conductive hydraulic pathway within the basement connected to the distant fault (hydraulic connectivity) and (2) no hydraulic pathway, but the coexistence of faults with mixed polarity (favorability to slip) as observed at Azle, TX. Based on the Coulomb stability analysis and seismicity rate estimates, simulation results show that direct pore pressure diffusion through a hydraulic pathway to the distant fault can generate a larger number of seismicity than along the fault close to the injection well. Prior to pore pressure diffusion, elastic stress transfer can initiate seismic activity along the favorably oriented fault, even at the longer distance to the well, which may explain the deep 2013–2014 Azle earthquake sequences. This study emphasizes that hydrological and geomechanical features of faults will locally control poroelastic coupling mechanisms, potentially influencing the spatiotemporal pattern of injection-induced seismicity, which can be used to infer subsurface architecture of fault/fracture networks.
We describe here diffusion models apt to construct a multipole-based, cable braid time domain model for conducting wires. Implementation details of both a ladder network valid for time-domain signals with all frequency content and an approximate single-stage circuit valid for low-frequency dominated time signals (such as electromagnetic pulses) are reported. This time domain model can be leveraged to treat system-generated electromagnetic pulse events, as well as used to further confirm the correctness of the multipole-based, cable braid frequency domain model.
Graphene plasmons provide a compelling avenue toward chip-scale dynamic tuning of infrared light. Dynamic tunability emerges through controlled alterations in the optical properties of the system defining graphene's plasmonic dispersion. Typically, electrostatic induced alterations of the carrier concentration in graphene working in conjunction with mobility have been considered the primary factors dictating plasmonic tunability. We find here that the surrounding dielectric environment also plays a primary role, dictating not just the energy of the graphene plasmon but so too the magnitude of its tuning and spectral width. To arrive at this conclusion, poles in the imaginary component of the reflection coefficient are used to efficiently survey the effect of the surrounding dielectric on the tuning of the graphene plasmon. By investigating several common polar materials, optical phonons (i.e., the Reststrahlen band) of the dielectric substrate are shown to appreciably affect not only the plasmon's spectral location but its tunability, and its resonance shape as well. In particular, tunability is maximized when the resonances are spectrally distant from the Reststrahlen band, whereas sharp resonances (i.e., high-Q) are achievable at the band's edge. These observations both underscore the necessity of viewing the dielectric environment in aggregate when considering the plasmonic response derived from two-dimensional materials and provide heuristics to design dynamically tunable graphene-based infrared devices.
GPUs are now a fundamental accelerator for many high-performance computing applications. They are viewed by many as a technology facilitator for the surge in fields like machine learning and Convolutional Neural Networks. To deliver the best performance on a GPU, we need to create monitoring tools to ensure that we optimize the code to get the most performance and efficiency out of a GPU. Since NVIDIA GPUs are currently the most commonly implemented in HPC applications and systems, NVIDIA tools are the solution for performance monitoring. The Light-Weight Distributed Metric System (LDMS) at Sandia is an infrastructure widely adopted for large-scale systems and application monitoring. Sandia has developed CPU application monitoring capability within LDMS. Therefore, we chose to develop a GPU monitoring capability within the same framework. In this report, we discuss the current limitations in the NVIDIA monitoring tools, how we overcame such limitations, and present an overview of the tool we built to monitor GPU performance in LDMS and its capabilities. Also, we discuss our current validation results. Most of the performance counter results are the same in both vendor tools and our tool when using LDMS to collect these results. Furthermore, our tool provides these statistics during the entire runtime of the tool as a time series and not just aggregate statistics at the end of the application run. This allows the user to see the progress of the behavior of the applications during their lifetime.
The well-known vulnerability of Deep Neural Networks to adversarial samples has led to a rapid cycle of increasingly sophisticated attack algorithms and proposed defenses. While most contemporary defenses have been shown to be vulnerable to carefully configured attacks, methods based on gradient regularization and out-of-distribution detection have attracted much interest recently by demonstrating higher resilience to a broad range of attack algorithms. However, no study has yet investigated the effect of combining these techniques. In this paper, we consider the effect of Jacobian matrix regularization on the detectability of adversarial samples on the CIFAR-10 image benchmark dataset. We find that regularization has a significant effect on detectability, and in some cases can make an undetectable attack on a baseline model detectable. In addition, we give evidence that regularization may mitigate the known weaknesses of detectors to high-confidence adversarial samples. The defenses we consider here are highly generalizable, and we believe they will be useful for further investigations to transfer machine learning robustness to other data domains.
Impact loads can induce a series of undesirable physical phenomena including vibration, acoustical shock, perforation, fracture and fragmentation, etc. The energy associated with the impact loads can lead to severe structure damage and human injuries. A design approach which effectively reduces these negative impacts through shock/stress wave diversion is highly needed. In this paper, a computational model which predicts stress wave propagation by considering different beam geometries and configurations is developed. A novel concept of wave guide design which modifies the stress wave propagation path without disturbance is also presented. This design approach is not only useful for material property characterization particularly at intermediate or high strain rates, but also allows stress wave propagation in a desired direction as the shock/impact energy can be redistributed in controllable paths. The numerical results are experimentally verified through a Drop-Hopkinson bar apparatus at Sandia National Laboratories.
Researchers have been extensively studying wide-bandgap (WBG) semiconductor materials such as gallium nitride (GaN) with an aim to accomplish an improvement in size, weight, and power of power electronics beyond current devices based on silicon (Si). However, the increased operating power densities and reduced areal footprints of WBG device technologies result in significant levels of self-heating that can ultimately restrict device operation through performance degradation, reliability issues, and failure. Typically, self-heating in WBG devices is studied using a single measurement technique while operating the device under steady-state direct current measurement conditions. However, for switching applications, this steady-state thermal characterization may lose significance since the high power dissipation occurs during fast transient switching events. Therefore, it can be useful to probe the WBG devices under transient measurement conditions in order to better understand the thermal dynamics of these systems in practical applications. In this work, the transient thermal dynamics of an AlGaN/GaN high electron mobility transistor (HEMT) were studied using thermoreflectance thermal imaging and Raman thermometry. Also, the proper use of iterative pulsed measurement schemes such as thermoreflectance thermal imaging to determine the steady-state operating temperature of devices is discussed. These studies are followed with subsequent transient thermal characterization to accurately probe the self-heating from steady-state down to submicrosecond pulse conditions using both thermoreflectance thermal imaging and Raman thermometry with temporal resolutions down to 15 ns.
Improving predictive models for noble gas transport through natural materials at the field-scale is an essential component of improving US nuclear monitoring capabilities. Several field-scale experiments with a gas transport component have been conducted at the Nevada National Security Site (Non-Proliferation Experiment, Underground Nuclear Explosion Signatures Experiment). However, the models associated with these experiments have not treated zeolite minerals as gas adsorbing phases. This is significant as zeolites are a common alteration mineral with a high abundance at these field sites and are shown here to significantly fractionate noble gases during field-scale transport. This fractionation and associated retardation can complicate gas transport predictions by reducing the signal-to-noise ratio to the detector (e.g. mass spectrometers or radiation detectors) enough to mask the signal or make the data difficult to interpret. Omitting adsorption-related retardation data of noble gases in predictive gas transport models therefore results in systematic errors in model predictions where zeolites are present.Herein is presented noble gas adsorption data collected on zeolitized and non-zeolitized tuff. Experimental results were obtained using a unique piezometric adsorption system designed and built for this study. Data collected were then related to pure-phase mineral analyses conducted on clinoptilolite, mordenite, and quartz. These results quantify the adsorption capacity of materials present in field-scale systems, enabling the modeling of low-permeability rocks as significant sorption reservoirs vital to bulk transport predictions.
Incorporating experimental data is essential for increasing the credibility of simulation-aided decision making and design. This paper presents a method which uses a computational model to guide the optimal acquisition of experimental data to produce data-informed predictions of quantities of interest (QoI). Many strategies for optimal experimental design (OED) select data that maximize some utility that measures the reduction in uncertainty of uncertain model parameters, for example the expected information gain between prior and posterior distributions of these parameters. In this paper, we seek to maximize the expected information gained from the push-forward of an initial (prior) density to the push-forward of the updated (posterior) density through the parameter-to-prediction map. The formulation presented is based upon the solution of a specific class of stochastic inverse problems which seeks a probability density that is consistent with the model and the data in the sense that the push-forward of this density through the parameter-to-observable map matches a target density on the observable data. While this stochastic inverse problem forms the mathematical basis for our approach, we develop a one-step algorithm, focused on push-forward probability measures, that leverages inference-for-prediction to bypass constructing the solution to the stochastic inverse problem. A number of numerical results are presented to demonstrate the utility of this optimal experimental design for prediction and facilitate comparison of our approach with traditional OED.
Analog hardware accelerators, which perform computation within a dense memory array, have the potential to overcome the major bottlenecks faced by digital hardware for data-heavy workloads such as deep learning. Exploiting the intrinsic computational advantages of memory arrays, however, has proven to be challenging principally due to the overhead imposed by the peripheral circuitry and due to the non-ideal properties of memory devices that play the role of the synapse. We review the existing implementations of these accelerators for deep supervised learning, organizing our discussion around the different levels of the accelerator design hierarchy, with an emphasis on circuits and architecture. We explore and consolidate the various approaches that have been proposed to address the critical challenges faced by analog accelerators, for both neural network inference and training, and highlight the key design trade-offs underlying these techniques.
Polymorphism in metal−organic frameworks (MOFs) means that the same chemical building blocks (nodes and linkers) can be used to construct isomeric MOFs with different topological networks. The choice of topology can substantially impact the pore network of the MOF, changing the sizes and shapes of the pores, which has implications for adsorption and separation applications. In this work, we look at the influence of topology in 38 polymorphic MOFs on the separation of linear and branched C4–C6 alkane isomers, a separation of great importance to the petrochemical industry. We find that the MOF Cu2(1,4-benzenedicarboxylate) in nbo topology (nbo-Cu2BDC) has particularly high affinity for linear alkanes due to its small pore size, which excludes the branched isomers. Upon studying this MOF in further detail, we find that it can take either of two conformations: a cubic conformation, which is typical of nbo MOFs, and a unique star conformation that contains 1D triangular and hexagonal channels. The determination of which conformation the MOF will adopt depends on steric effects between the nodes and linkers.
Electrochemical techniques were used to investigate the erosion-corrosion of titanium in simulated acidic mineral leaching slurries. Erosion-corrosion of titanium was caused by solid particle impingement. Electrochemical noise revealed that solid particle impacts resulted in localised fracture of the passive film, and erosion-corrosion of titanium proceeded in the form of current transients. As conditions become more abrasive, erosion-corrosion is an increasing threat to titanium equipment exposed to acidic slurries.
Particle characteristics can drastically influence the process-structure-property-performance aspects of granular materials in compression. We aim to computationally simulate the mechanical processes of stress redistribution in compacts including the kinematics of particle rearrangement during densification and particle deformation leading to fragmentation. Confined compression experiments are conducted with three sets of commercial microcrystalline cellulose particles nearly spherical in shape with different mean particle size. Experimentally measured compression curves from tall powder columns are fitted with the Kenkre et al. (J. of American Chemical Society, Vol. 79, No. 12) model. This model provides a basis to derive several common two-parameter literature models and as a framework to incorporate statistical representations of critical particle behaviors. We focus on the low-stress compression data and the model comparisons typically not discussed in the literature. Additional single particle compressions report fracture strength with particle size for comparison to the apparent particle strength extracted from bulk compression data.
The current COVID-19 pandemic has resulted in globally constrained supplies for face masks and personal protective equipment (PPE). Production capacity is limited in many countries and the future course of the pandemic will likely continue with shortages for high quality masks and PPE in the foreseeable future. Hence, expectations are that mask reuse, extended wear and similar approaches will enhance the availability of personal protective measures. Repeated thermal disinfection could be an important option and likely easier implemented in some situations, at least on the small scale, than UV illumination, irradiation or hydrogen peroxide vapor exposure. An overview on thermal responses and ongoing filtration performance of multiple face mask types is provided. Most masks have adequate material properties to survive a few cycles (i.e. 30 min disinfection steps) of thermal exposure in the 75°C regime. Some are more easily affected, as seen by the fusing of plastic liner or warping, given that preferred conditioning temperatures are near the softening point for some of the plastics and fibers used in these masks. Hence adequate temperature control is equally important. As guidance, disinfectants sprayed via dilute solutions maintain a surface presence over extended time at 25 and 37°C. Some spray-on alcohol-based solutions containing disinfectants were gently applied to the top surface of masks. Neither moderate thermal aging (less than 24 h at 80 and 95°C) nor gentle application of surface disinfectant sprays resulted in measurable loss of mask filter performance. Subject to bio-medical concurrence (additional checks for virus kill efficiency) and the use of low risk non-toxic disinfectants, such strategies, either individually or combined, by offering additional anti-viral properties or short term refreshing, may complement reuse options of professional masks or the now ubiquitous custom-made face masks with their often unknown filtration effectiveness.
Aerosol jet printing offers a versatile, high-resolution digital patterning capability broadly relevant to flexible and printed electronic systems. Despite its promise and numerous demonstrations, the theoretical principles driving process outputs have not been thoroughly explored. Here a custom-built, modular printing system is developed to provide a head-to-head comparison of two print nozzle geometries to better understand the technology. Print resolution data from a range of process parameters are analyzed using a support vector machine framework. The linear deposition rate is identified as a key variable, which can confound careful studies of printing performance. Taking this into account, a clear difference is observed between the printheads, corresponding to a difference in resolution of 57% 11% under typical conditions. Models to understand differences in aerodynamic and mass transport effects identify enhanced drying within the NanoJet printhead as a likely cause of this difference. Overall, this study provides improved understanding of the aerosol jet printing process, including valuable insight to inform process optimization, robust data analysis, ink formulation, and printer geometric design.
The self-interstitial atom (SIA) is one of two fundamental point defects in bulk Si. Isolated Si SIAs are extremely difficult to observe experimentally. Even at very low temperatures, they anneal before typical experiments can be performed. Given the challenges associated with experimental characterization, accurate theoretical calculations provide valuable information necessary to elucidate the properties of these defects. Previous studies have applied Kohn-Sham density functional theory (DFT) to the Si SIA, using either the local density approximation or the generalized gradient approximation to the exchange-correlation (XC) energy. The consensus of these studies indicates that a Si SIA may exist in five charge states ranging from -2 to +2 with the defect structure being dependent on the charge state. This study aims to re-examine the existence of these charge states in light of recently derived "approximate bounds"on the defect levels obtained from finite-size supercell calculations and new DFT calculations using both semi-local and hybrid XC approximations. We conclude that only the neutral and +2 charge states are directly supported by DFT as localized charge states of the Si SIA. Within the current accuracy of DFT, our results indicate that the +1 charge state likely consists of an electron in a conduction-band-like state that is coulombically bound to a +2 SIA. Furthermore, the -1 and -2 charge states likely consist of a neutral SIA with one and two additional electrons in the conduction band, respectively.
In this article, we studied the total ionization dose (TID) effects on the multilevel-cell (MLC) 3-D NAND flash memory using Co-60 gamma radiation. We found a significant page-to-page bit error variation within a physical memory block of the irradiated memory chip. Our analysis showed that the origin of the bit error variation is the unique vertical layer-dependent TID response of the 3-D NAND. We found that the memory pages located at the upper and lower layers of the 3-D stack show higher fails compared to the middle-layer pages of a given memory block. We confirmed our findings by comparing radiation response of four different chips of the same specification. In addition, we compared the TID response of the MLC 3-D NAND with that of the 2-D NAND chip, which showed less page-to-page variation in bit error within a given memory block. We discuss the possible application of our findings for the radiation-tolerant smart memory controller design.
Reducing ion beam damage from the focused ion beam (FIB) during fabrication of cross sections is a well-known challenge for materials characterization, especially cross sectional characterization of nanostructures. To address this, a new method has been developed for cross section fabrication enabling high resolution transmission electron microscopy (TEM) analysis of 3-D nanostructures free of surrounding material and free of damage detectable by TEM analysis. Before FIB processing, nanopillars are encapsulated in a sacrificial oxide which acts as a protective layer during FIB milling. The cross sectional TEM lamella containing the nanopillars is then mounted and thinned with some modifications to conventional FIB sample preparation that provide stability for the lamella during the following wet-chemical dip etch. The wet-chemical etch of the TEM lamella removes the sacrificial oxide layer, freeing the nanopillars from any material that would obscure TEM imaging. Both high resolution TEM and aberration corrected scanning TEM images of Si/SiGe pillars with diameters down to 30 nm demonstrate the successful application of this approach.