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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Utilizing Earth Observations to Understand Landscape Patterns and Assist in Wildlife Management in Iona National Park, Angola

Following the end of the Angolan Civil War (1975-2002), human habitation in Iona National Park has grown exponentially, as has the livestock population. An ongoing drought beginning in 2017 has brought people, livestock, and wildlife into increasing competition for resources within the park. This study used Earth observation data, primarily Landsat and Sentinel imagery, to examine landscape trends to improve wildlife preservation approaches in Iona National Park, Angola. In collaboration with the NGO African Parks, we developed a robust land use and land cover (LULC) classification model using remote sensing data to augment sparse ground-based data in this arid land region. We used Google Earth Engine and a random forest classifier to map vegetation types, water bodies, and potential wildlife habitats. This analysis resulted in a high spatial resolution LULC time-series between 1984-2023, highlighting critical periods of socioecological change over the past 40 years. These results increased the partner’s ability to make scientifically grounded decisions about resource allocation and conservation priorities. This analysis supports the feasibility of applying remote sensing techniques coupled with machine learning models in dry regions, where standard survey methods are frequently limited by accessibility and resource availability. However, we identified limitations in ground-truth data and the difficulty of recognizing certain vegetation types in arid areas. Despite these limitations, the study demonstrated Earth observations' ability to transform wildlife management techniques in distant and data-scarce locations, providing a reproducible foundation for similar ecosystems around the world.

Emmanuel Aklie↗
DOE OSTI2023

Graphitized Mesoporous Engineered Carbon Support for Fuel Cell Applications

As proton exchange membrane fuel cells mature into commercial devices capable of powering a wide range of stationary and automotive applications, they need materials with tunable properties to improve their performance and durability. Carbon supports used for platinum (Pt) nanoparticle dispersion is typically based on a furnace black-type material with a random structure, thereby hindering progress in catalyst development. To address this challenge, engineered carbons with a tunable mesoporous structure offer an opportunity to maximize catalyst performance and durability. In this article, we report on the development of a graphitized, mesoporous carbon support with a high degree of ordering, labeled here as ECS4005, for the dispersion of Pt nanoparticles. Pt/ECS4005 shows significantly improved kinetic activity due to its mesoporous structure that mitigates Pt poisoning by sulfonate functional groups in the ionomer while simultaneously enabling favorable accessibility to reactants.

25 ENERGY STORAGE↗
DOE OSTI2011

Automated Planning of Tangential Breast Intensity-Modulated Radiotherapy Using Heuristic Optimization

Purpose: To present an automated technique for two-field tangential breast intensity-modulated radiotherapy (IMRT) treatment planning. Method and Materials: A total of 158 planned patients with Stage 0, I, and II breast cancer treated using whole-breast IMRT were retrospectively replanned using automated treatment planning tools. The tools developed are integrated into the existing clinical treatment planning system (Pinnacle{sup 3}) and are designed to perform the manual volume delineation, beam placement, and IMRT treatment planning steps carried out by the treatment planning radiation therapist. The automated algorithm, using only the radio-opaque markers placed at CT simulation as inputs, optimizes the tangential beam parameters to geometrically minimize the amount of lung and heart treated while covering the whole-breast volume. The IMRT parameters are optimized according to the automatically delineated whole-breast volume. Results: The mean time to generate a complete treatment plan was 6 min, 50 s {+-} 1 min 12 s. For the automated plans, 157 of 158 plans (99%) were deemed clinically acceptable, and 138 of 158 plans (87%) were deemed clinically improved or equal to the corresponding clinical plan when reviewed in a randomized, double-blinded study by one experienced breast radiation oncologist. In addition, overall the automated plans were dosimetrically equivalent to the clinical plans when scored for target coverage and lung and heart doses. Conclusion: We have developed robust and efficient automated tools for fully inversed planned tangential breast IMRT planning that can be readily integrated into clinical practice. The tools produce clinically acceptable plans using only the common anatomic landmarks from the CT simulation process as an input. We anticipate the tools will improve patient access to high-quality IMRT treatment by simplifying the planning process and will reduce the effort and cost of incorporating more advanced planning into clinical practice.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Parallel sorting algorithm classification: is manual instrumentation necessary?

Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We present an approach to learn algorithm classes from parallel performance data directly in order to classify algorithms without access to the source code. We extend previous work to enable classifying parallel sorting algorithms using automatic instrumentation instead of requiring manual region annotations in the source code. In this work, we design and demonstrate a study for classification of parallel sorting algorithms using parallel performance data collected from automatic instrumentation, and evaluate the performance of our new methodology on classification. We leverage Caliper to collect the performance data, Thicket for our exploratory data analysis (EDA), and PyTorch and Scikit-learn to evaluate the effectiveness of random forests, support vector machines (SVMs), decision trees, neural networks, and logistic regressions on parallel performance data. Additionally, we study noise in parallel performance data, whether the removal of noise and pre-processing of the data is necessary to accurately classify parallel sorting algorithms, and determine the effectiveness of features created from performance data. In conclusion, we demonstrate classification accuracy for these five different models of up to 97.7% across four different parallel algorithm classes.

Algorithm Classification↗
DOE OSTI2022

Large Scale Caching and Streaming of Training Data for Online Deep Learning

The training of deep neural network models on large data remains a difficult problem, despite progress towards scalable techniques. In particular, there is a mismatch between the random but predetermined order in which AI flows select training samples and the streaming I/O patterns for which traditional HPC data storage (e.g., parallel file systems) are designed. In addition, as more data are obtained, it is feasible neither simply to train learning models incrementally, due to catastrophic forgetting (i.e., bias towards new samples), nor to train frequently from scratch, due to prohibitive time and/or resource constraints. In this paper, we study data management techniques that combine caching and streaming with rehearsal support in order to enable efficient access to training samples in both offline training and continual learning. We revisit state-of-art streaming approaches based on data pipelines that transparently handle prefetching, caching, shuffling, and data augmentation, and discuss the challenges and opportunities that arise when combining these methods with data-parallel training techniques. We also report on preliminary experiments that evaluate the I/O overheads involved in accessing the training samples from a parallel file system (PFS) under several concurrency scenarios, highlighting the impact of the PFS on the design of the data pipelines.

data pipelines↗
DOE OSTI2024

Advanced Data Science Model for Detecting Intelligent Malware

This study focused on developing a robust artificial intelligence (AI) model capable of detecting and characterizing advanced malware in Internet of Things (IoT) devices using network data. By analyzing network traffic with various machine learning (ML) models, our AI model can identify and characterize malicious activities to significantly improve malware detection accuracy and reliability as compared to traditional methods. The developed AI/ML model was trained using network data from IoT devices, leveraging classifiers such as Random Forest, Gradient Boosting, AdaBoost, and others to optimize detection performance. This project demonstrates a scalable framework for real-time malware detection and characterization in IoT networks, capable of identifying infected devices and facilitating the necessary steps to remove or isolate them, thereby preventing further infections. Although digital twin (DT) integration is not yet implemented in the current model, it represents a promising future enhancement. By creating a virtual replica of physical IoT devices, DT technology would allow for real-time monitoring and analysis without directly accessing operational technology, thus reducing the risk of compromising or reducing the performance of actual devices. This integration would further enhance the security of IoT ecosystems, combining AI technology to better flag and detect indications of malware-infected devices within a nuclear system environment.

42 ENGINEERING↗
NASA NTRS2004

NASA Tech Briefs, November 2004

Topics include: Multifunction Imaging and Spectroscopic Instrument; Position-Finding Instrument Built Around a Magnetometer; Improved Measurement of Dispersion in an Optical Fiber; Probe for Sampling of Interstitial Fluid From Bone; Neuropsychological Testing of Astronauts; Method of Calibration for a Large Cathetometer System; Four-Channel PC/104 MIL-STD-1553 Circuit Board; Improved Method of Locating Defects in Wiring Insulation; Strobe Traffic Lights Warn of Approaching Emergency Vehicles; Improved Timing Scheme for Spaceborne Precipitation Radar; Concept for Multiple-Access Free-Space Laser Communications; Variable Shadow Screens for Imaging Optical Devices; Verifying Diagnostic Software; Initial Processing of Infrared Spectral Data; Activity-Centric Approach to Distributed Programming; Controlling Distributed Planning; New Material for Surface-Enhanced Raman Spectroscopy; Treated Carbon Nanofibers for Storing Energy in Aqueous KOH; Advanced Infant Car Seat Would Increase Highway Safety; Development of Biomorphic Flyers; Second-Generation Six-Limbed Experimental Robot; Miniature Linear Actuator for Small Spacecraft; Process for Making Single-Domain Magnetite Crystals; A New Process for Fabricating Random Silicon Nanotips; Resin-Transfer-Molding of a Tool Face; Improved Phase-Mask Fabrication of Fiber Bragg Gratings; Tool for Insertion of a Fiber-Optic Terminus in a Connector; Nanofluidic Size-Exclusion Chromatograph; Lightweight, Low-CTE Tubes Made From Biaxially Oriented LCPs; Using Redundancy To Reduce Errors in Magnetometer Readings; Compact Instrument for Measuring Profile of a Light Beam; Multilayer Dielectric Transmissive Optical Phase Modulator; Second-Generation Multi-Angle Imaging Spectroradiometer; Real-Time Adaptive Color Segmentation by Neural Networks; Research and Development in Optical Communications; Tests of Multibeam Scintillation Mitigation on Laser Uplinks; and Spaceborne Infrared Atmospheric Sounder.

Source record↗
DOE OSTI2013

An advanced understanding of the specific effects of xylan and surface lignin contents on enzymatic hydrolysis of lignocellulosic biomass

A deep understanding of biomass recalcitrance has been hampered by the intricate and heterogeneous nature of pretreated biomass substrates obtained from random deconstruction methods. In this study, we established a unique methodology based on chemical pulping principles to create "reference substrates" with intact cellulose fibers and controlled morphological and chemical properties that enable us to investigate the individual effect of xylan, bulk, and surface lignin content on enzymatic hydrolysis. We also developed and demonstrated an X-ray photoelectron spectroscopy (XPS) technique for quantifying surface lignin content on biomass substrates. The results from this study show that, apart from its hindrance effect, xylan can facilitate cellulose fibril swelling and thus create more accessible surface area, which improves enzyme and substrate interactions. Surface lignin has a significant impact on enzyme adsorption kinetics and hydrolysis rate. Advanced understanding of xylan, bulk, and surface lignin effects provides critical information for an effective biomass conversion process.

biomass↗
DOE OSTI2015

Multi-Level Anomaly Detection on Time-Varying Graph Data

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.

anomaly detection↗
NASA NTRS1985

On the probability density function of the squared envelope of a sum of random phase vectors

Weber et al. (1980) have found that a generic problem arises in the study of multiuser FHMA/MFSK systems. According to this problem, the presence of the other channel users acts as interference to reception of the desired user's transmission. The present investigation is concerned with a recursive solution in which the recursion parameter is the number of vectors in the sum. A mathematical characterization of such a solution is presented, taking into account the simple two-vector case in which a well-known closed form result is possible. Some numerical results are also discussed. It is pointed out that the considered recursive solution provides a break in the bottleneck associated with the exact computation of the error probability performance of multiuser FHMA/MFSK systems and single access MFSK systems corrupted by multitone jamming.

Simon, M. K.↗
DOE OSTI2021

Widespread co‐occurrence of Sebacinales and arbuscular mycorrhizal fungi in switchgrass roots and soils has limited dependence on soil carbon or nutrients

Societal Impact Statement This work addresses a novel group of Sebacinales mycorrhizal fungi being considered for development as inoculants in managed ecosystems because of their potential access to nutrients in soil organic matter. By comparing the diversity, distributions, and abundances of the Sebacinales with the more common arbuscular mycorrhizal fungi in switchgrass, a biofuel crop, we demonstrate that current suggestions for a Sebacinales revolution in agriculture should be tempered by their ecology. In particular, Sebacinales are rare compared to arbuscular mycorrhizal fungi, and are only weakly associated with soil carbon, suggesting that ideas about improved soil carbon cycling associated with Sebacinales need to be sufficiently studied across a range of environmental conditions prior to their consideration for broad‐spectrum soil inoculants. Summary Arbuscular mycorrhizal (AM) fungi are widespread and important root symbionts, but recent work suggests that Sebacinales fungi may play an equally important role in both plant success and ecosystem carbon and nutrient cycling based on their worldwide occurrence and putative access to organic matter. However, the ecological impacts of Sebacinales will depend on their abundance and distribution relative to AM fungi and environmental soil carbon and nutrient patterns, which remain unexplored. We characterized Glomeromycota and Sebacinales fungi in switchgrass ( Panicum virgatum L.) roots and soils across 14 sites with diverse soil conditions. We examined group richness differences, co‐occurrence patterns, and how the relative abundance of these fungi related to soil carbon, nutrient stoichiometry, and host size. Sebacinales were widespread, but less diverse, common, and abundant than Glomeromycota. Moreover, co‐occurrences were predominantly random, suggesting relatively few interactions between these groups. Sebacinales increased relative to Glomeromycota in soils with more carbon, but explanatory power was limited. Based on our findings, we suggest that Sebacinales are likely complementary to AM fungi in roots. Expectations that Sebacinales have large effects on soil carbon and nutrient cycling may need to be reconsidered, at least based on their limited abundances relative to AM fungi in switchgrass. This is an important consideration as Sebacinales are candidates for use as inoculants in managed ecosystems.

Lee, Marissa R.↗
DOE OSTI2020

Creation of discrete active site domains via mesoporous silica poly(styrene) composite materials for incompatible acid–base cascade reactions

This work highlights the design and synthesis of bifunctional mesoporous silicate – polymer composite dual acid–base supported cascade catalysts. Compartmentalization of the two incompatible active sites is sought by segregating acid sites on the silica surface, and base sites within polymer chains and/or polymer domains. The ability to isolate and segregate active sites via control of the mesoporous silica pore size and polymer molecular weight is probed with silica samples functionalized by a grafting-to process. Supplemental activator and reducing agent (SARA) atom transfer radical polymerization is used to synthesize random copolymers containing protected primary amines. Thiol–ene ‘click’ chemistry facilitates silica functionalization via a convergent approach, with the ene-functionalized polymer end group and silica-grafted thiols forming SBA/MCM-SH-poly(styrene-co-2-(4-vinylbenzyl)isoindoline-1,3-dione). Polymer deprotection and thiol oxidation produces primary amine/sulfonic acid containing composite catalysts. With the polymer supported Lewis base and silica grafted Brønsted acid, the two-step deacetalization – Knoevenagel condensation cascade is explored to assess the ability of these polymer/silica hybrids to segregate active sites, allowing both acid and base site accessibility. Six composite catalysts are synthesized and tested in individual and cascade reactions with kinetic results demonstrating that lower molecular weight SBA-15-P1 and MCM-41-P1 catalysts outperform (higher turnover frequencies and initial rates) their higher molecular weight analogues, as well as a polymer-free system containing molecular active sites dispersed on the silica surface. Higher molecular weight composite catalysts perform more poorly due to limited chain solubility, mass transfer limitations, and poor catalyst accessibility. In many cases, the polymer chains effectively thread into the mesopores, with higher molecular weight polymers leading to pore blockage and inhibited mass transfer.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

In this study, we develop a machine-learning (ML)-enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds, with a particular focus on snow-soil–plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant to the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, United States), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Application of the Diminishing Returns Concept in the Hydroecologic Restoration of Riverscapes

Principles for optimizing the number and placement of ecological restoration actions on rivers and coasts would be useful in project engineering design and program planning. This study demonstrated that the yield of inundated floodplain habitat area from dike breaching conforms to a diminishing returns model. The aggregate effects of establishing hydrologic connections between a tidally influenced main stem river and the floodplain were experimentally examined using a hydrodynamic model. Restoration clusters of size 1, 4, 8, and more, replicated and randomized within the landscape, yielded average wetted floodplain area conforming with an exponential rise to maximum curve2(0.99)r=. Analysis of the average incremental change in floodplain inundation produced per breach showed that opening 25 % of the channels crossing the dike provided the maximum return on investment as measured by wetted area. Midstream breaches yielded 60 % and upstream breaches 2 % of the wetted area produced by downstream breaches. Dike-breach restoration programs therefore can be optimized by strategic determination of the spatial configuration and number of demolitions, though biological factors such as the accessibility of floodplain habitat and total length of channels connected also need to be considered. These findings have implications for cost-benefit analyses in restoration program planning.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2012

Tidal Control of Jet Eruptions Observed by Cassini ISS

Observations by Cassini's Imaging Science Subsystem (ISS) of Enceladus' south polar region at high phase angles has revealed jets of material venting into space. Observations by Cassini's Composite Infrared Spectrometer (CIRS) have also shown that the south polar region is anomalously warm with hotspots associated with geological features called the Tiger Stripes. The Tiger Stripes are large rifts near the south pole of Enceladus, which are typically about 130 km in length, 2 km wide, with a trough 500 m deep, and are l1anked on each side by 100m tall ridges. Preliminary triangulation of jets as viewed at different times and with different viewing geometries in Cassini ISS images taken between 2005 and 2007 have constrained the locations of eight major eruptions of material and found all of them associated with the south polar fractures unofficially the 'Tiger Stripes', and found four of them coincident with the hotspots reported in 2006 by CIRS. While published ISS observations of jet activity suggest that individual eruption sites stay active on the timescale of years, any shorter temporal variability (on timescales of an orbital period, or 1.3 Earth days, for example) is more difficult to establish because of the spotty temporal coverage and the difficulty of visually isolating one jet from the forest of many seen in a typical image. Consequently, it is not known whether individual jets are continuously active, randomly active, or if they erupt on a predictable, periodic schedule. One mechanism that may control the timing of eruptions is diurnal tidal stress, which oscillates between compression/tension as well as right and left lateral shear at any given location throughout Enceladus' orbit and may allow the cracks to open and close regularly. We examine the stresses on the Tiger Stripe regions to see how well diurnal tidal stress caused by Enceladus' orbital eccentricity may possibly correlate with and thus control the observed eruptions. We then identify possible mechanisms by which tidal stress can provide access to the surface for volatile material and implications for observed jet activity.

Hurford, T. A.↗
DOE OSTI2025

Next-Level Energy Management in Manufacturing: Facility-Level Energy Digital Twin Framework Based on Machine Learning and Automated Data Collection

This research introduces an energy prediction framework at the facility level supported by automated data collection and machine learning models. It investigates whether reducing the prediction time scale allows for applying more complex machine learning techniques and if those techniques improve the prediction accuracy. The primary advantages of this framework lie in its automation of the energy prediction process and its provision of real-time energy data suitable for use in energy dashboards or digital twins. A sitewide dataset was created by combining 15 min energy and daily production data of five shops—assembly, battery, body (electric), body (gas), and paint—from a globally recognized electric vehicle manufacturer. Various machine learning models were evaluated on daily, weekly, and monthly datasets, including, in increasingly complex order: naïve, simple linear regression, net regularized generalized linear regression, principal component regression, k-nearest neighbor, random forest, and Bayesian regularized neural network. Compared to the current state-of-the-art energy consumption prediction for the industrial facility level, this research investigates more complex models and smaller time intervals for higher accuracy. The findings revealed that the more complex monthly models require a minimum of a year and a half of data to operate, while weekly models demand a year of data to achieve improved accuracy. Daily models can operate with only six months of data but exhibit poor performance due to reduced prediction accuracy of production. Key challenges identified include access to reliable, high-quality energy and production data and the initial demand for human labor.

digital twin↗
DOE OSTI2015

WE-EF-210-05: Diagnosis and Quantification of Liver Steatosis with Quantitative Ultrasound Backscatter Technique

Purpose: Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in the United States, affects 30% of adult Americans and may progress to more serious diseases. Liver biopsy is the standard method for diagnosing NAFLD. MRI can accurately diagnose and quantify hepatic steatosis but is expensive. Sonography with qualitative interpretation by radiologists is lower cost, more accessible but less sensitive for detection. The objective of this study, using MRI proton density fat fraction (PDFF) as reference, is to assess the accuracy for diagnosing and quantifying steatosis with two quantitative US parameters-- backscatter coefficient (BSC) and attenuation coefficient (AC)--derived from RF signals using the calibration phantom technique. Methods: We performed a prospective, cross-sectional analysis of a cohort of adults (n=204) with NAFLD (MRI-PDFF≥5%) and without NAFLD (controls). Subjects underwent MRI-PDFF and BSC and AC US analyses of the liver on the same day. Patients were randomly assigned to training (n=102, mean age 51±17 years, mean body mass index 31±7 kg/m{sup 2}) and validation (n=102, mean age 49±17 years, body mass index 30±6 kg/m{sup 2}) groups; 69% of patients in each group had NAFLD. Results: BSC provided AUC 0.98 (95% CI 0.95–1.00, p<0.0001) for diagnosis of NAFLD; the optimal BSC cut-off provided sensitivity, specificity, positive and negative predictive values (PPV, NPV) of 87%, 91%, 95%, and 76%, respectively. AC provided AUC 0.89 (95% CI 0.81–0.96, p<0.0001) for diagnosis of steatosis; the optimal AC cut-off provided sensitivity, specificity, PPV, NPV of 80%, 84%, 92%, and 66%, respectively. BSC and AC both correlated significantly with MRI-PDFF (P<0.0001). Conclusion: QUS BSC and AC can accurately diagnose and quantify hepatic steatosis, using MRI-PDFF as reference. With further validation, QUS may emerge as an inexpensive, widely available tool for NAFLD assessment. General support: NIH R01 CA111289, K23 -DK090303, AmerGastroAssoc Found, TF Williams Scholarship, S3000 scanner loaned by Siemens, Sucampo, JA Hartford Found, Atlantic Philanthropies Amer Gastroenterological Assoc. Agencies had no role in design/conduct of study, collection, management, analysis or interpretation of the data; preparation, review, or approval of the manuscript.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Model Exploration of an Information-Based Healthcare Intervention Using Parallelization and Active Learning

This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local community resources to meet basic and self-care needs. Additionally, the diffusion of information about community resources and their use is modeled via networked interactions and their subsequent effect on agents' use of community resources across an urban population. A random forest model is iteratively fitted to model evaluations to characterize the model parameter space with respect to observed empirical data. We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces; by partitioning the parameter space into potentially viable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data. We argue that such methods are necessary to enable model exploration in complex computational models that incorporate increasingly available micro-level behavior data. We provide public access to the model and high-performance computing experimentation code.

97 MATHEMATICS AND COMPUTING↗