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All sources NASA NTRS DOE OSTI

DOE OSTI2019

Data-driven efficient network and surveillance-based immunization

Given a contact network and coarse-grained diagnostic information such as electronic Healthcare Reimbursement Claims (eHRC) data, can we develop efficient intervention policies from data to control an epidemic? Immunization is an important problem in multiple areas, especially epidemiology and public health. However, most existing studies rely on assuming prior epidemiological models to develop pre-emptive strategies, which may fail to adapt to the change in new epidemiological patterns and the availability of rich data such as eHRC. In practice, disease spread is usually complicated, hence assuming an underlying model may deviate from true spreading patterns, leading to possibly inaccurate interventions. Additionally, the abundance of health care surveillance data (such as eHRC) makes it possible to study data-driven strategies without too many restrictive assumptions. Therefore, such a data-driven intervention approach can help public-health experts take more practical decisions. In this work, we take into account propagation log and contact networks for controlling propagation. Furthermore, different from previous model-based approaches, our solutions are solely data driven in a sense that we develop immunization strategies directly from the network and eHRC without assuming classical epidemiological models. In particular, we formulate the novel and challenging data-driven immunization problem. To solve it, we first propose an efficient sampling approach to align surveillance data with contact networks, then develop an efficient algorithm with the provably approximate guarantee for immunization. Finally, we show the effectiveness and scalability of our methods via extensive experiments on multiple datasets, and conduct case studies on nation-wide real medical surveillance data.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

QuadConv: Quadrature-based convolutions with applications to non-uniform PDE data compression

We present a new convolution layer for deep learning architectures which we call QuadConv — an approximation to continuous convolution via quadrature. Our operator is developed explicitly for use on non-uniform, mesh-based data, and accomplishes this by learning a continuous kernel that can be sampled at arbitrary locations. Moreover, the construction of our operator admits an efficient implementation which we detail and construct. As an experimental validation of our operator, we consider the task of compressing partial differential equation (PDE) simulation data from fixed meshes. Here, we show that QuadConv can match the performance of standard discrete convolutions on uniform grid data by comparing a QuadConv autoencoder (QCAE) to a standard convolutional autoencoder (CAE). Further, we show that the QCAE can maintain this accuracy even on non-uniform data. In both cases, QuadConv also outperforms alternative unstructured convolution methods such as graph convolution.

Compression↗

NASA NTRS1985

New method for determining heats of combustion of gaseous hydrocarbons

As a spin off of a system developed for monitoring and controlling the oxygen concentration in the Langley 8-foot High Temperature Tunnel, a highly accurate on-line technique was developed for determining heats of combustion of natural gas samples. It is based on measuring the ratio m/n, where m is the (volumetric) flowrate of oxygen required to enrich the carrier air in which the test gas flowing at the rate n is burned, such that the mole fraction of oxygen in the combustion product gases equals that in the carrier air. The m/n ratio is directly related to the heats of combustion of the saturated hydrocarbons present in the natural gas. A measurement of the m/n ratio for the test gas can provide a direct means of determination of its heat of combustion by using the calibration graph relating the m/n values for pure saturated hydrocarbons with their heats of combustion. The accuracy of the technique is determine solely by the accuracy with which the flowrates m and n can be measured and is of the order of 2 percent in the present study. The theoretical principles and experimental results are discussed.

Singh, J. J.↗

NASA NTRSDate not supplied

NASA Life Sciences Portal (NLSP): Supporting Scientific Transparency and Reproducibility

NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles [1]. Some of these improvements will at the same time support the twin pillars of Open Science [2]: transparency of methods and reproducibility of results. Scientific transparency is marked by the easily intelligible communication of what has been investigated: what were the procedures for collecting sample and the characteristics of samples collected? what kinds of measurements were made, what were the environmental conditions of the measurements? What were the analysis techniques of the collected data? Reproducibility of the results and findings from the investigation requires a high level of transparency for all but the simplest investigations; the slightest deviation in communicating and replicating complex experimental procedures or data analyses can often yield quite different data and even findings, thwarting their validation. One of the ways the NLSP is aiming to improve the communication of scientific information is through the use of ontology-driven metadata. Ontologies are powerful, graph-based knowledge representation structures, which can be leveraged to increase data interoperability, the area of the FAIR principles in which many data systems most lack compliance. Over the past decade, there has been a concerted effort in the biomedical community to develop modular and narrowly focused domain and application-specific ontologies in a common, open-source framework, the Open Biological and Biomedical Ontology (OBO) Foundry [3]. The open sharing and modular nature of this effort promises huge increases in harmonized data sharing for systems that leverage these models. Which is in line with the FAIR Data Principles of Findability, Accessibility, Interoperability, and Reuse for scientific data management and stewardship. 1. Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. 2. National Academies of Sciences, E. and Medicine, Open Science by Design: Realizing a Vision for 21st Century Research. 2018, Washington, DC: The National Academies Press. 232. 3. Smith, B., et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007. 25(11): p. 1251-5.

Life Sciences data↗

NASA NTRSDate not supplied

NASA Life Sciences Portal (NLSP): Supporting Scientific Transparency and Reproducibility

Life Sciences data↗

DOE OSTI2026

Data Summarization and Inference at Scale

This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Sampling-Based Motion Planning Algorithms for Replanning and Spatial Load Balancing

The common theme of this dissertation is sampling-based motion planning with the two key contributions being in the area of replanning and spatial load balancing for robotic systems. Here, we begin by recalling two sampling-based motion planners: the asymptotically optimal rapidly-exploring random tree (RRT*), and the asymptotically optimal probabilistic roadmap (PRM*). We also provide a brief background on collision cones and the Distributed Reactive Collision Avoidance (DRCA) algorithm. The next four chapters detail novel contributions for motion replanning in environments with unexpected static obstacles, for multi-agent collision avoidance, and spatial load balancing. First, we show improved performance of the RRT* when using the proposed Grandparent-Connection (GP) or Focused-Refinement (FR) algorithms. Next, the Goal Tree algorithm for replanning with unexpected static obstacles is detailed and proven to be asymptotically optimal. A multi-agent collision avoidance problem in obstacle environments is approached via the RRT*, leading to the novel Sampling-Based Collision Avoidance (SBCA) algorithm. The SBCA algorithm is proven to guarantee collision free trajectories for all of the agents, even when subject to uncertainties in the knowledge of the other agents’ positions and velocities. Given that a solution exists, we prove that livelocks and deadlock will lead to the cost to the goal being decreased. We introduce a new deconfliction maneuver that decreases the cost-to-come at each step. This new maneuver removes the possibility of livelocks and allows a result to be formed that proves convergence to the goal configurations. Finally, we present a limited range Graph-based Spatial Load Balancing (GSLB) algorithm which fairly divides a non-convex space among multiple agents that are subject to differential constraints and have a limited travel distance. The GSLB is proven to converge to a solution when maximizing the area covered by the agents. The analysis for each of the above mentioned algorithms is confirmed in simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗

NASA NTRS1998

Fourier Transform Infrared (FT-IR) Spectroscopy of Atmospheric Trace Gases HCl, NO and SO2

Fourier Transform Infrared (FT-IR) spectral data have been recorded in the spectral region 400-4000/cm of hydrogen chloride and sulfur dioxide with I/cm resolution and of nitric oxide with 0.25 cm-i resolution, under quasi-static conditions, when the sample gas was passed through tubings of aluminum, copper, stainless steel and teflon. The absorbance was measured for the rotational lines of the fundamental bands of (1)H(35)Cl and (1)H(37)Cl for pressures in the range 100-1000 Torr and for the (14)N(16)O molecule in the range 100-300 Torr. The absorbance was also measured for individual rotational lines corresponding to the three modes of vibrations (upsilon(sub 1) - symmetric stretch, upsilon(sub 2) - symmetric bend, upsilon(sub 3) - anti-symmetric stretch) of the SO2 molecule in the pressure range 25-150 Torr. A graph of absorbance versus pressure was plotted for the observed rotational transitions of the three atmospherically significant molecules, and it was found that the absorbance was linearly proportional to the pressure range chosen, thereby validating Beer's law. The absorption cross-sections were determined from the graphical slopes for each rotational transition recorded for the HCl, NO and SO2 species. Qualitative and quantitative spectral changes in the FT-IR data will be discussed to identify and characterize various tubing materials with respect to their absorption features.

Haridass, C.↗

DOE OSTI2024

Infrared-safe energy weighting does not guarantee small nonperturbative effects

Infrared and collinear (IRC) safety has long been used a proxy for robustness when developing new jet substructure observables. This guiding philosophy has been carried into the deep learning era, where IRC-safe neural networks have been used for many jet studies. For graph-based neural networks, the most straightforward way to achieve IRC safety is to weight particle inputs by their energies. However, energy-weighting by itself does not guarantee that perturbative calculations of machine-learned observables will enjoy small nonperturbative corrections. Here, in this paper, we demonstrate the sensitivity of IRC-safe networks to nonperturbative effects, by training an energy flow network (EFN) to maximize its sensitivity to hadronization. We then show how to construct Lipschitz energy flow networks (L-EFNs), which are both IRC safe and relatively insensitive to nonperturbative corrections. We demonstrate the performance of L-EFNs on generated samples of quark and gluon jets, and showcase fascinating differences between the learned latent representations of EFNs and L-EFNs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2021

Identifying Molecules as Biosignatures with Assembly Theory and Mass Spectrometry

The search for evidence of life elsewhere in the universe is hard because it is not obvious what signatures are unique to life. Here we postulate that complex molecules found in high abundance are universal biosignatures as they cannot form by chance. To explore this, we developed the first intrinsic measure of molecular complexity that can be experimentally determined, and this is based upon a new approach called assembly theory which gives the molecular assembly number (MA) of a given molecule. MA allows us to compare the intrinsic complexity of molecules using the minimum number of steps required to construct the molecular graph starting from basic objects, and a probabilistic model shows how the probability of any given molecule forming randomly drops dramatically as its MA increases. To map chemical space, we calculated the MA of ca. 2.5 million compounds, and collected data which showed the complexity of a molecule can be experimentally determined by using three independent techniques including infra-red spectroscopy, nuclear magnetic resonance, and by fragmentation in a mass spectrometer, and this data has an excellent corelation with the values predicted from our assembly theory. We then set out to see if this approach could allow us to identify molecular biosignatures with a set of diverse samples from around the world, outer space, and the laboratory including prebiotic soups. The results show that there is a non-living to living threshold in MA complexity and the higher the MA for a given molecule, the more likely that it had to be produced by a biological process. This work demonstrates it is possible to use this approach to build a life detection instrument that could be deployed on missions to extra-terrestrial locations to detect biosignatures, map the extent of life on Earth, and be used as a molecular complexity scale to quantify the constraints needed to direct prebiotically plausible processes in the laboratory. Such an approach is vital if we are going to find new life elsewhere in the universe or create de-novo life in the lab.

Stuart M Marshall↗

DOE OSTI2022

Mass Detection for Heavy-Duty Vehicles using Gaussian Belief Propagation

Predicting vehicle mass is critical to accurately estimate energy use and emissions of commercial trucks. However, data from vehicle telematics is often not at sufficient temporal resolution or accuracy for use in model-based detection methods. In this work, a new statistical mass prediction technique is described for heavy-duty vehicles that incorporates the use Gaussian Belief Propagation (GBP) for probabilistic inference. Similar to Bayesian inference models, the GBP model typically requires less labeled training data than other contemporary machine learning techniques. First, a factor graph is constructed, and a set of Gaussian belief nodes with associated means and variances are fitted to the training data. To better handle noisy input data, the GBP mass prediction model utilizes a k-nearest factors (kNF) algorithm for probabilistic inference on unseen testing data. The proposed method is compared with a classical weighted k-nearest neighbors (kNN) regressor. This statistical kNF-GBP model works even with low-quantity, low-quality initial training data, while being capable of realtime mass estimation. Unlike the kNN regressor, the GBP model produces a measure of uncertainty with its predictions. The proposed method is validated using curve-sampled driving data collected from multiple cloud-connected Class 8 regional haul diesel trucks. Both the kNN regressor and the kNF-GBP mass prediction model were able to predict payload mass with coefficients of determination above 0.97 with minimal data preprocessing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1985

The Einstein soft X-ray survey of the Pleiades

The results of a 0.1-4.5-keV X-ray survey of a 2 x 2-deg area centered on the Pleiades open cluster, performed using the imaging proportional counter and high-resolution imager of the Einstein Observatory on four days in 1980-1981, are presented in extensive tables, graphs, maps, histograms, and finding charts and characterized. A total of 61 sources are detected, and 44 of these are identified with cluster members of spectral types B-M. Findings discussed include Lx/Lbol of 10 the -7th for early-type stars; F-star mean Lx like that of F stars in the Hyades and in the field (denying time evolution of Lx); mean G-star Lx = 3.7 x 10 to the 29th erg/sec; G-star activity decay more gradual than 1/sq rt t, with sharp fall for t greater than 1 Gyr; and no evidence for X-ray-emissivity/rotational-velocity correlation in a homogeneous sample of K dwarfs or a sample of K and M stars with established V sin i data.

Caillault, J.-P.↗

NASA NTRSDate not supplied

Influence of Thermophysical Property Variability on Thermal Predictions of Ti-6Al-4V

The additive manufacturing (AM) industry has experienced rapid growth in recent decades as industrial interest in the process has grown. Because of this, there are many groups interested in simulations of the AM process. However, the influence of thermophysical property variability on the melt pool geometry during processing of various AM alloys is unclear. The goal of my work at NASA Langley Research Center (LaRC) was to characterize this influence on Ti-6Al-4V during AM processing alongside developing a tool to enable equivalent studies on other AM materials. To facilitate this process, a database tool was developed to contain and organize thermophysical data previously reported by primary sources. The specific thermophysical properties of interest for this study were density, specific heat capacity, and conductivity. Thermal diffusivity was calculated using the other three properties. This data was then fit with a Gaussian distribution and then randomly sampled from using Monte Carlo random value sampling. Using the Rosenthal equation, this sample data was used to simulate the temperature field during additive manufacturing and extract the melt pool geometry. Specifically, the melt pool’s length, width, and depth. This process was repeated an arbitrary number of times, with the default being 1000. Once this information was obtained, histograms were made showing the distributions of the sizes of the melt pool’s length, width, and depth. Representative statistical metrics of the distributions were calculated (mean, standard deviation, and coefficient of variance). This work found that at the simulated processing values, the melt pool’s width and depth had a 5.7% variation while the length had a 3.2% variation. Additionally, a tool was constructed to graph a thermal color map representation of the melt pool for specific, arbitrary values of density, specific heat, and conductivity. This tool allowed for more efficient plotting of individual queries of the thermophysical properties. The results that were observed in the research were that values reported in literature vary and this variance can have a significant impact on the simulation. Predictions of the melt pool’s dimensions show that length has a standard deviation of approximately 6 μm, width has a standard deviation of approximately 5 μm, and depth can vary by approximately 3 μm. Considering the mean sizes of length, width, and depth are 185 μm, 88 μm, and 44 μm respectively, such a deviation is significant. This shows that depth, for example, could be more than 10% larger or smaller than expected. This demonstrates that variability in the reported thermophysical properties are not negligible and should be expected to have an influence on the results of the laser powder bed fusion additive manufacturing process. When simulating this process in the future, measures should be taken to account for this discrepancy and the uncertainty involved

Justin Martin↗

NASA NTRS1983

The elemental and isotopic composition of galactic cosmic ray nuclei

A directly accessible sample of matter which originates outside the solar system is provided by galactic cosmic rays. The present investigation is primarily concerned with progress related to questions raised regarding the similarity or difference between solar system matter and matter coming from outside the solar system. The investigation takes into account U.S. contributions to this topic over the period from 1979 to 1982. The cosmic ray (CR) abundances of all the elements from H to Ni (atomic number Z=1 to 28) have now been measured. Cosmic ray source (CRS) and solar system (SS) elemental compositions are listed in a table, and the ratio of CRS to SS abundance for 21 elements is shown in a graph. There is now clear evidence from CR isotope studies that the nucleosynthesis of CRS material has differed from that of SS material.

Mewaldt, R. A.↗

NASA NTRSDate not supplied

Software Development for SonicSonde Instrumentation Suite

The SonicSonde is an innovative weather instrumentation suite for boundary layer sampling. Software development included creating a graphical user interface (GUI) to receive, process, archive, and display data in real-time. Targeted at the Windows operating system but with multi-platform extensibility in mind, the GUI was developed using Qt 5.14 in C++. A key design consideration was to allow for tailored displays and analysis while also permitting additional sensors to be added to the platform. As such, the application relies heavily on flexible interfaces and multithreading processing for capturing, modelling, and plotting data. The object-oriented nature of Qt made it ideal for this purpose. The application supports input through live serial streams and static .csv or .dat files, custom meteorological graphs, and user-dictated panel views and output intervals. Additionally, the GUI displays real-time information about the state of the instrument. The versatility of the SonicSonde will allow meteorologists and researchers to perform or better support a variety of aeronautic and atmospheric missions.

meterology↗

DOE OSTI2021

MCS+: An Efficient Algorithm for Crawling the Community Structure in Multiplex Networks

In this article, we consider the problem of crawling a multiplex network to identify the community structure of a layer-of-interest. A multiplex network is one where there are multiple types of relationships between the nodes. In many multiplex networks, some layers might be easier to explore (in terms of time, money etc.). We propose MCS+, an algorithm that can use the information from the easier to explore layers to help in the exploration of a layer-of-interest that is expensive to explore. We consider the goal of exploration to be generating a sample that is representative of the communities in the complete layer-of-interest. This work has practical applications in areas such as exploration of dark (e.g., criminal) networks, online social networks, biological networks, and so on. For example, in a terrorist network, relationships such as phone records, e-mail records, and so on are easier to collect; in contrast, data on the face-to-face communications are much harder to collect, but also potentially more valuable. We perform extensive experimental evaluations on real-world networks, and we observe that MCS+ consistently outperforms the best baseline—the similarity of the sample that MCS+ generates to the real network is up to three times that of the best baseline in some networks. We also perform theoretical and experimental evaluations on the scalability of MCS+ to network properties, and find that it scales well with the budget, number of layers in the multiplex network, and the average degree in the original network.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS1999

Flow Effects on the Flammability Diagrams of Solid Fuels: Microgravity Influence on Ignition Delay

The possibility of an accidental fire in space-based facilities is a primary concern of space exploration programs. Spacecraft environments generally present low velocity air currents produced by ventilation and heating systems (of the order of 0.1 m/s), and fluctuating oxygen concentrations around that of air due to CO2 removal systems. Recent experiments of flame spread in microgravity show the spread rate to be faster and the limiting oxygen concentration lower than in normal-gravity. To date, there is not a material flammability-testing protocol that specifically addresses issues related to microgravity conditions. The present project (FIST) aims to establish a testing methodology that is suitable for the specific conditions of reduced gravity. The concepts underlying the operation of the LIFT apparatus, ASTM-E 1321-93, have been used to develop the Forced-flow Ignition and flame-Spread Test (FIST). As in the LIFT, the FIST is used to obtain the flammability diagrams of the material, i.e., graphs of ignition delay time and flame spread rate as a function of the externally applied radiant flux, but under forced flow rather than natural convection conditions, and for different oxygen concentrations. Although the flammability diagrams are similar, the flammability properties obtained with the FIST are found to depend on the flow characteristics. A research program is currently underway with the purpose of implementing the FIST as a protocol to characterize the flammability performance of solid materials to be used in microgravity facilities. To this point, tests have been performed with the FIST apparatus in both normal-gravity and microgravity conditions to determine the effects of oxidizer flow characteristics on the flammability diagrams of polymethylmethacrylate (PMMA) fuel samples. The experiments are conducted at reduced gravity in a KC- 135 aircraft following a parabolic flight trajectory that provides up to 25 seconds of low gravity. The objective of the experiments is to obtain data of ignition delay and flame spread rate at low flow velocities (0.1 to 0.2 m/s), which cannot be obtained under normal gravity because of the natural convection induced flows (approx. 0.5 m/s). Due to the limited reduced gravity time, the data can only be obtained for high radiant fluxes, and are consequently limited in scope. These tests do, however, provide insight into the flammability diagram characteristics at low velocity and reduced gravity, and also into the implications of the flow-dependence of the flammability properties under environments similar to those encountered in space facilities.

Cordova, J. L.↗