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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Medical Data Architecture (MDA) Project Status

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions. This gap identifies that the current in-flight medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are a variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable a more medically autonomous crew than the current paradigm. The medical system requirements are being developed in parallel with the exploration mission architecture and vehicle design. ExMC has recognized that in order to make informed decisions about a medical data architecture framework, current methods for medical data management must not only be understood, but an architecture must also be identified that provides the crew with actionable insight to medical conditions. This medical data architecture will provide the necessary functionality to address the challenges of executing a self-contained medical system that approaches crew health care delivery without assistance from ground support. Hence, the products supported by current prototype development will directly inform exploration medical system requirements.In fiscal year 2018, the MDA project developed Test Bed 2, the second iteration in a series of prototypes with functionality focused on data security through role-based access control and encryption, integration with One Portal exercise software and ingestion of an ultrasound Digital Imaging and Communications in Medicine (DICOM) file and image display. Test Bed 2 advances the medical data system architecture framework by providing these functionalities in a scalable system that maintained a layered, modular design. The architecture framework uses a data services approach with role-based access to data in a customized medical record system suitable for space exploration. These functionalities were demonstrated as part of the Next Space Technologies for Exploration Partnerships (NextSTEP) ground test demonstrated at the NASA Johnson Space Center Integrated Power, Avionics and Software (iPAS) facility. Interfacing to a Core Flight Software (CFS) system, the MDA system, using Consultative Committee for Space Data Systems (CCSDS) protocol, transferred an exercise file from the simulated flight MDA system to a mirrored MDA system on the ground through the CFS system. The selection of data sources and demonstrations enabled the team to address stakeholder concerns throughout the development process. In the next iteration, the MDA team will work with stakeholders to identify additional relevant functionalities to further advance system data models, standards and principles that will inform the medical system requirements development.

medical data architecture↗
NASA NTRS2020

Medical Data Architecture Platform and Recommended Requirements for a Medical Data System for Exploration Missions

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically- relevant information to support medical operations during exploration missions. This gap identifies that the current in-flight medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are a variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable a more medically autonomous crew than the current paradigm of medical data management on the International Space Station. ExMC has recognized that in order to make informed decisions about a medical data architecture framework, current methods for medical data management must not only be understood, but an architecture must also be identified that provides the crew with actionable insight to medical conditions. This medical data architecture will provide the necessary functionality to address the challenges of executing a self-contained medical system that approaches crew health care delivery without assistance from ground support. Hence, the products derived from the third MDA prototype development will directly inform exploration medical system requirements for Level of Care IV in Gateway missions. In fiscal year 2019, the MDA project developed Test Bed 3, the third iteration in a series of prototypes, that featured integrations with cognition tool data, ultrasound image analytics and core Flight Software (cFS). Maintaining a layered architecture design, the framework implemented a plug-in, modular approach in the integration of these external data sources. An early version of MDA Test Bed 3 software was deployed and operated in a simulated analog environment that was part of the Next Space Technologies for Exploration Partnerships (NextSTEP) Gateway tests of multiple habitat prototypes. In addition, the MDA team participated in the Gateway Test and Verification Demonstration, where the MDA cFS applications was integrated with Gateway-in-a-Box software to send and receive medically relevant data over a simulated vehicle network. This software demonstration was given to ExMC and Gateway Program stakeholders at the NASA Johnson Space Center Integrated Power, Avionics and Software (iPAS) facility. Also, the integrated prototypes served as a vehicle to provide Level 5 requirements for the Crew Health and Performance Habitat Data System for Gateway Missions (Medical Level of Care IV). In the upcoming fiscal year, the MDA project will continue to provide systems engineering and vertical prototypes to refine requirements for medical Level of Care IV and inform requirements for Level of Care V.

Krihak, M.↗
NASA NTRS2020

Medical Data Architecture Platform and Recommended Requirements for A Medical Data System for Exploration Missions

Minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically relevant information to support medical operations during exploration missions. This gap identifies that the current in-flight medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are a variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable a more medically autonomous crew than the current paradigm of medical data management on the International Space Station. ExMC has recognized that in order to make informed decisions about a medical data architecture framework, current methods for medical data management must not only be understood, but an architecture must also be identified that provides the crew with actionable insight to medical conditions. This medical data architecture will provide the necessary functionality to address the challenges of executing a self-contained medical system that approaches crew health care delivery without assistance from ground support. Hence, the products derived from the third MDA prototype development will directly inform exploration medical system requirements for Level of Care IV in Gateway missions.In fiscal year 2019, the MDA project developed Test Bed 3, the third iteration in a series of prototypes, that featured integrations with cognition tool data, ultrasound image analytics and core Flight Software (cFS). Maintaining a layered architecture design, the framework implemented a plug-in, modular approach in the integration of these external data sources. An early version of MDA Test Bed 3 software was deployed and operated in a simulated analog environment that was part of the Next Space Technologies for Exploration Partnerships (NextSTEP) Gateway tests of multiple habitat prototypes. In addition, the MDA team participated in the Gateway Test and Verification Demonstration, where the MDA cFS applications was integrated with Gateway-in-a-Box software to send and receive medically relevant data over a simulated vehicle network. This software demonstration was given to ExMC and Gateway Program stakeholders at the NASA Johnson Space Center Integrated Power, Avionics and Software (iPAS) facility. Also, the integrated prototypes served as a vehicle to provide Level 5 requirements for the Crew Health and Performance Habitat Data System for Gateway Missions (Medical Level of Care IV). In the upcoming fiscal year, the MDA project will continue to provide systems engineering and vertical prototypes to refine requirements for medical Level of Care IV and inform requirements for Level of Care V.

Krihak, M.↗
NASA NTRSDate not supplied

Pathfinder Technology Demonstrator (PTD) Status

This paper will provide the status of the NASA Small Spacecraft Technology (SST) program’s Pathfinder Technology Demonstrator (PTD) series. Details on the development, launch, and operations of the recently completed PTD-1, as well as the planned operations of the upcoming PTD-3 and PTD-4 flights will be presented. Significant advances in the capabilities of nano-spacecraft over the past 15 years, coupled with improved access to space for CubeSats, have created new opportunities for scientific exploration using these high-value platforms. Continued development and demonstration of key technologies are improving the performance of the 1-unit (U) and 3U class CubeSats while expanding the reach of nano-spacecraft technologies into larger platforms, such as the 12-kilogram class “6U” buses. NASA’s PTD series is demonstrating a variety of new technologies on-orbit, providing proof of the maturity of these significant new technologies, and enhancing the performance of future CubeSats. Each of the four PTD flights consists of one 6U CubeSat weighing approximately 12 kilograms and measuring approximately 36 centimeters x 25 centimeters x 10 centimeters. Each flight is planned to characterize its payload within 90 days of insertion into low-Earth orbit. The PTD flights will demonstrate key technologies such as novel nano-spacecraft compatible propulsion systems, which enable deep space and maneuverable CubeSat flights; optical communications systems to facilitate high data rate collection and communications; and highly integrated systems that combine power and communication system elements to enable high power generation and novel integrated communication systems. The first PTD spacecraft, PTD-1, was placed in orbit by a SpaceX Falcon 9 launch in January of 2021 and demonstrated the functionality of the HYDROS propulsion system, developed by Tethers Unlimited of Bothell, Washington, over a period of six months. The PTD-3 flight, scheduled for launch in summer 2022, will demonstrate the TeraByte InfraRed Delivery, or TBIRD, optical communications payload developed by Massachusetts Institute of Technology Lincoln Laboratory in Lexington. The PTD-4 flight will demonstrate the Lightweight Integrated Solar Array and anTenna, or LISA-T, a payload that consists of an integrated solar array and transceiver developed by NASA’s Marshall Spaceflight Center in Huntsville, Alabama. The PTD spacecraft bus, integration and test, and flight operations services are provided by Tyvak Nano-Satellite Systems, Inc. (“Tyvak”) of Irvine, California. NASA’s SST program within the agency’s Space Technology Mission Directorate funds the PTD demonstration flights. The SST program rapidly develops and demonstrates capabilities for small spacecraft applicable to exploration, science, and the commercial sector. The program is based at NASA’s Ames Research Center.

Small Spacecraft↗
NASA NTRS1981

The validation of LIMS ozone profiles using correlative rocket, ECC, and Dobson data sets

The Limb Infrared Monitor of the Stratosphere (LIMS) experiment is a limb scanning infrared sounder designed to measure vertical temperature profiles and the concentrations of key chemical compounds which are important in the stratospheric ozone-nitrogen photochemistry. Preliminary ozone profiles are being intercompared with correlative rocket and balloonsonde ozone data sets as part of the Nimbus 7 LIMS data validation activity. The LIMS ozone retrievals extend from the lower mesosphere to the tropopause (or cloud-top altitudes). In this paper, preliminary statistics of comparisons of ECC (electrochemical concentration cell) ozonesonde and rocket ozone profiles with LIMS ozone profiles are presented. ECC ozonesonde data taken at times of LIMS overpasses are also used to extend the LIMS retrieval down to ground level. Each combined profile is then integrated to obtain total column ozone. These integrated results are compared to Dobson data obtained at approximately the same time and location.

Remsberg, E. E.↗
DOE OSTI2022

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

Abstract The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub‐daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state‐of‐the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model‐data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge‐guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision‐relevant predictions of riverine water quality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Machine Learning-based False Data Injection Attack Detection and Localization in Power Grids

Cyberattacks on critical infrastructures can be catastrophic and bring nations to their knees. Therefore, detecting these attacks is crucial and challenging. This paper presents a novel approach for detecting and locating cyberattacks affecting an electrical power system. The adversary employs a man-in-the-middle technique to inject false data into the communication between distributed energy resources (DER) and Microgrid Controller (MGC) with the goal of disrupting power delivery. The approach for detection and localization is based on integrating multiple machine learning-based anomaly detection models that combine network traffic data and grid measurements. Experiments are performed to assess the method's performance using a hardware-in-the-loop real-time simulation testbed which includes Modbus TCP/IP communication. Power system topology and operating conditions are based on actual topology and real-world data provided by the Holy Cross Energy utility network. Results confirm that the method can be successfully employed for detecting and localizing cyberattacks.

Leao, Bruno P.↗
NASA NTRS2016

Effects of Zoledronate and Mechanical Loading during Simulated Weightlessness on Bone Structure and Mechanical Properties

Space flight modulates bone remodeling to favor bone resorption. Current countermeasures include an anti-resorptive drug class, bisphosphonates (BP), and high-force loading regimens. Does the combination of anti-resorptives and high-force exercise during weightlessness have negative effects on the mechanical and structural properties of bone? In this study, we implemented an integrated model to mimic mechanical strain of exercise via cyclical loading (CL) in mice treated with the BP Zoledronate (ZOL) combined with hindlimb unloading (HU). Our working hypothesis is that CL combined with ZOL in the HU model induces additive structural and mechanical changes. Thirty-two C57BL6 mice (male,16 weeks old, n8group) were exposed to 3 weeks of either HU or normal ambulation (NA). Cohorts of mice received one subcutaneous injection of ZOL (45gkg), or saline vehicle, prior to experiment. The right tibia was axially loaded in vivo, 60xday to 9N in compression, repeated 3xweek during HU. During the application of compression, secant stiffness (SEC), a linear estimate of slope of the force displacement curve from rest (0.5N) to max load (9.0N), was calculated for each cycle once per week. Ex vivo CT was conducted on all subjects. For ex vivo mechanical properties, non-CL left femurs underwent 3-point bending. In the proximal tibial metaphysis, HU decreased, CL increased, and ZOL increased the cancellous bone volume to total volume ratio by -26, +21, and +33, respectively. Similar trends held for trabecular thickness and number. Ex vivo left femur mechanical properties revealed HU decreased stiffness (-37),and ZOL mitigated the HU stiffness losses (+78). Data on the ex vivo Ultimate Force followed similar trends. After 3 weeks, HU decreased in vivo SEC (-16). The combination of CL+HU appeared additive in bone structure and mechanical properties. However, when HU + CL + ZOL were combined, ZOL had no additional effect (p0.05) on in vivo SEC. Structural data followed this trend with ZOL not modulating trabecular thickness in CL + NAHU mice. In summary, our integrated model simulates the combination of weightlessness, exercise-induced mechanical strain, and anti-resorptive treatment that astronauts experience during space missions. Based on these results, we conclude that, at the structural and stiffness level, zoledronate treatment during simulated spaceflight does not impede the skeletal response to axial compression. In contrast to our hypothesis, our data show that zoledronate confers no additional mechanical or structural benefit beyond those gained from cyclical loading.

Mechanical Loading↗
DOE OSTI2024

Search for photons above 10 18 eV by simultaneously measuring the atmospheric depth and the muon content of air showers at the Pierre Auger Observatory

The Pierre Auger Observatory is the most sensitive instrument to detect photons with energies above 1 0 17 eV . It measures extensive air showers generated by ultrahigh energy cosmic rays using a hybrid technique that exploits the combination of a fluorescence detector with a ground array of particle detectors. The signatures of a photon-induced air shower are a larger atmospheric depth of the shower maximum ( X max ) and a steeper lateral distribution function, along with a lower number of muons with respect to the bulk of hadron-induced cascades. In this work, a new analysis technique in the energy interval between 1 and 30 EeV ( 1 EeV = 1 0 18 eV ) has been developed by combining the fluorescence detector-based measurement of X max with the specific features of the surface detector signal through a parameter related to the air shower muon content, derived from the universality of the air shower development. No evidence of a statistically significant signal due to photon primaries was found using data collected in about 12 years of operation. Thus, upper bounds to the integral photon flux have been set using a detailed calculation of the detector exposure, in combination with a data-driven background estimation. The derived 95% confidence level upper limits are 0.0403, 0.01113, 0.0035, 0.0023, and 0.0021 km − 2 sr − 1 yr − 1 above 1, 2, 3, 5, and 10 EeV, respectively, leading to the most stringent upper limits on the photon flux in the EeV range. Compared with past results, the upper limits were improved by about 40% for the lowest energy threshold and by a factor 3 above 3 EeV, where no candidates were found and the expected background is negligible. The presented limits can be used to probe the assumptions on chemical composition of ultrahigh energy cosmic rays and allow for the constraint of the mass and lifetime phase space of super-heavy dark matter particles. Published by the American Physical Society 2024

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling

Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Toward a Seamless Integration of Computing, Experimental, and Observational Science Facilities: A Blueprint to Accelerate Discovery

The Department of Energy, Office of Science operates world-leading facilities for experimental, observational, and computational science. DOE supercomputing facilities will reach performance at the scale of ExaFLOPs in the coming years, enabling new vistas of scale and precision for large scale simulations and data analysis. Experimental scientific facilities are undergoing similar upgrades that will lead to higher data rates and correspondingly larger computational demands, and will increase the need for near-real-time processing and resilient support for more complex workflows. A transformation of science is underway, with workloads at supercomputing facilities increasingly driven by this explosion of data from instruments and experimental facilities, as well as the accelerating use of Artificial Intelligence (AI) as a tool for scientific discovery. A seamless integration of computing, networking, instruments, and experimental facilities is required to support these emerging workloads and open up a new frontier of U.S. leadership in scientific discovery. We propose to accomplish this by providing frictionless access to the ASCR supercomputing facilities. We describe our vision of combining the power of ASCR supercomputers and networking infrastructure into an integrated scalable fabric, available to end user scientists via interfaces that aim to automate and simplify access to high performance computing systems. This will enable unprecedented computational science capabilities for experimental and observational facilities, and will create new opportunities to combine large simulations and modeling with experimental facility data analysis. This blueprint for creating an integrated network of computational and experimental facilities will provide an enriched discovery environment and open doors for new scientific communities to access the DOE’s world-leading computing and networking capabilities.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Browsing Space Weather Data and Models with the Integrated Space Weather Analysis (iSWA) System

The Integrated Space Weather Analysis (iSWA) System is a comprehensive web-based platform for space weather information that combines data from solar, heliospheric and geospace observatories with forecasts based on the most advanced space weather models. The iSWA system collects, generates, and presents a wide array of space weather resources in an intuitive, user-configurable, and adaptable format - thus enabling users to respond to current and future space weather impacts as well as enabling post-impact analysis. iSWA currently provides over 200 data and modeling products, and features a variety of tools that allow the user to browse, combine, and examine data and models from various sources. This presentation will consist of a summary of the iSWA products and an overview of the customizable user interfaces, and will feature several tutorial demonstrations highlighting the interactive tools and advanced capabilities.

Maddox, Marlo M.↗
DOE OSTI2025

A case study in contrastive learning information combination: Application to technical forensics of additive manufacturing filament source identification

Combination of information from disparate data sources into a single decision is a core challenge in many fields, including the field of technical forensics. Technical forensics (TF) utilizes technical characterization of questioned samples to determine properties of that sample; these properties are then used to infer information of forensic interest, such as provenance, age, or attribution. TF is utilized in traditional forensic applications, such as the attribution of material fragments from an explosive, and in nuclear forensic applications, such as the attribution of actinides which have been interdicted out of regulatory control. The challenge of combining information from disparate sources, described alternately by many terms including “Data Fusion” and “Data Integration”, is exacerbated in the technical forensics domain due to at least two factors: the challenge of interpreting each information source singularly, and the relatively small data set sizes available. Extensive literature exists attempting to combine technical forensics information sources, both in manual and automated processes. These attempts are often bespoke to the specific information sources (such as the bi-, tri-, or quad-isotope chart (Moody, Grant, and Hutcheon 2005)), with some emerging examples of simple early- and late- fusion (, respectively). Simultaneous to the information combination efforts described in the previous paragraph, the field of natural language processing attempted (and largely succeeded) in combining information from multiple non-technical information sources. The ecosystem of “multi-modal” language models, which can take text and images as input, and generate text and images as output, became large and diverse by 2025 (Khan et al. 2025). In a generalized sense, many of these methods are trained by learning neural networks which can convert raw text or images into a vector of numbers describing the text or image, hereafter called “embeddings” and the neural networks performing the conversion are called “embedders”. By using a separate embedder for text and images, finding coincident text and images (such as images with their captions), and optimizing the parameters of the embedders such that the embeddings for the text and the image are similar, the field has found a bridge between text and images (Girdhar et al. 2023). It is the contention of the authors of this report that this insight is not limited to text and images but instead can be extended to any modality which can be found coincidently. The subject of the rest of this report is the application of this method to example multi-modal technical forensic data. Some details about the data used in this report are not appropriate for this report, and are included in a companion report (PNNL-38669).

36 MATERIALS SCIENCE↗
DOE OSTI2022

Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence

For leveraging wearable technologies to advance precision medicine, personalized and learning-based analysis of continuously acquired health data is indispensable, for which neuromorphic computing could provide the most efficient implementation of artificial intelligence (AI) data processing. For realizing on-body neuromorphic computing, skin-like stretchability is required, but yet to be combined with the suite of desired neuromorphic metrics, including linear, symmetric weight update, and sufficient state retention, for achieving high computing efficiency. Here, we report an intrinsically stretchable neuromorphic device based on an electrochemical transistor, which provides a large number (>800) of states, linear/symmetric weight update, excellent switching endurance (>100 million), good state retention (>10 4 s), together with high stretchability of 100% strain. Further integration into a prototype array successfully realized the implementation of vector-matrix multiplication even at 100% strain. Finally, we demonstrate the feasibility of implementing AI-based classification of health signals (as exemplified by electrocardiograms) with a high accuracy that is minimally influenced by the stretched state of the neuromorphic hardware. Finally, this work breaks the ground for combining AI data analysis into skin-like wearable electronics for achieving human-integrated/mimetic intelligent systems.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1988

Performance of a class of parallel spread-spectrum code acquisition schemes in the presence of data modulation

An acquisition procedure for data-modulated direct-sequence spread-spectrum systems is investigated. The correlation time is partitioned into subintervals, and the integration results in these subintervals are noncoherently combined for detection. The tradeoff between noncoherent combining loss and data modulation degradation guides the optimum choice of the number of subintervals. Two forms of data modulation are considered, namely, the alternate-data and random-data cases. The parallel acquisition schemes discussed allow multiple code-phase offsets to be examined at each test. The circular state diagram approach is used to analyze the performances of these schemes. The theory presented is valid for a class of such parallel schemes.

Cheng, Unjeng↗
DOE OSTI2022

On the integration of molecular dynamics, data science, and experiments for studying solvent effects on catalysis

Computational workflows that combine molecular dynamics (MD) simulations and emerging data-centric (DC) methods can accelerate the screening and analysis of solvent systems experimentally and computationally. Here, MD simulations provide atomic positions and velocities of reactant, solvent, and catalyst materials that can be manipulated into data representations that in turn can be used by DC techniques to conduct predictive modeling, feature extraction, and experimental design. For liquid-phase catalytic applications, emerging DC techniques such as Convolutional and Graph Neural Networks (CNN/GNN), Topological Data Analysis (TDA), and Active Learning (AL) can leverage MD and experimental data to quickly predict solvent effects on reaction outcomes. For instance, in recent studies, 3D solvent environments obtained with MD have been exploited by CNNs to predict experimental reaction rates for homogeneous acid-catalyzed lignocellulosic processes. In this perspective, we discuss basic principles of DC methods and how these can be combined with MD to enable high-throughput screening of solvent selection for diverse catalysis applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Photometry as a Proxy for Stellar Activity in Radial Velocity Analyses

Stellar activity remains a limiting factor in measuring precise planet parameters from radial velocity spectroscopy, not least in the search for Earth-mass planets orbiting in the habitable zones of Sun-like stars. One approach to mitigate stellar activity is to use combined analyses of both radial velocity and time-series photometry. We present an analysis of simultaneous disk-integrated photometry and radial velocity data of the Sun in order to determine the useful limits of a combined analysis. We find that simple periodogram or autocorrelation analysis of solar photometry give the correct rotation period <50% of the time. We therefore use a Gaussian process to investigate the time variability of solar photometry and to directly compare simultaneous photometry with radial velocity data. We find that the hyperparameter posteriors are relatively stable over 70 yr of solar photometry and the amplitude tracks the solar cycle. We observe good agreement between the hyperparameter posteriors for the simultaneous photometry and radial velocity data. Our primary conclusion is a recommendation to include an additional prior in Gaussian process fits to constrain the evolutionary timescale to be greater than the recurrence timescale (i.e., the rotation period) to recover more physically plausible and useful results. Our results indicate that such simultaneous monitoring may be a useful tool in enhancing the precision of radial velocity surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1989

Combining multispectral and ancillary data in remote sensing using interval-valued probabilities

This paper presents a method for classifying multisource data in remote sensing and geographic information systems using interval-valued probabilities. In this method, each data source is considered as an information source which provides a body of statistical evidence. In order to integrate information obtained from multiple data sources, the method adopts Dempster's rule for combining multiple bodies of evidence. Preliminary experiments have been undertaken to illustrate the use of the method in a supervised ground-cover classification on multispectral data combined with digital elevation data. They demonstrate the ability of the method in capturing information provided by inexact and incomplete evidence when there are not enough training samples to estimate statistical parameters.

Kim, H.↗