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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

COVID-19: Spatiotemporal social data analytics and machine learning for pandemic exploration and forecasting

This task focused on developing a preliminary approach to use machine learning (ML) to explore the relationship between county-level societal variables and COVID-19 parameters, including COVID-19 cases rates and counts and COVID-19 death rates and counts. The objective was to develop and test a prototype approach for linking COVID-19 and county-level data. The task focused on enhancing and applying existing LANL ML techniques to COVID-19. Our novel ML methods have been a subject of a recently approved U.S. patent. The codes based on these methods are already open-source released. Our ML tools (NMFk/NTFk) are applied to extract hidden features (signals, waves) in the analyzed datasets and automatically identify their optimal number. The features are extracted by identifying counties that have similarities between the county-level societal variables and the COVID-19 parameters. These demonstration analyses will facilitate the ongoing pandemic simulations and predictions performed by Los Alamos other institutions, as well as lay the groundwork for future work.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection

Deep neural networks (DNNs) have achieved state-of-the-art performance across a variety of traditional machine learning tasks, e.g., speech recognition, image classification, and segmentation. The ability of DNNs to efficiently approximate high-dimensional functions has also motivated their use in scientific applications, e.g., to solve partial differential equations and to generate surrogate models. In this paper, we consider the supervised training of DNNs, which arises in many of the above applications. We focus on the central problem of optimizing the weights of the given DNN such that it accurately approximates the relation between observed input and target data. Devising effective solvers for this optimization problem is notoriously challenging due to the large number of weights, nonconvexity, data sparsity, and nontrivial choice of hyperparameters. To solve the optimization problem more efficiently, we propose the use of variable projection (VarPro), a method originally designed for separable nonlinear least-squares problems. Our main contribution is the Gauss--Newton VarPro method (GNvpro) that extends the reach of the VarPro idea to nonquadratic objective functions, most notably cross-entropy loss functions arising in classification. These extensions make GNvpro applicable to all training problems that involve a DNN whose last layer is an affine mapping, which is common in many state-of-the-art architectures. In our four numerical experiments from surrogate modeling, segmentation, and classification, GNvpro solves the optimization problem more efficiently than commonly used stochastic gradient descent (SGD) schemes. Finally, GNvpro finds solutions that generalize well, and in all but one example better than well-tuned SGD methods, to unseen data points.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Centroidal Voronoi tessellation based methods for optimal rain gauge location prediction

With more satellite and model precipitation data becoming available, new analytical methods are needed that can take advantage of emerging data patterns to make well informed predictions in many hydrological applications. Herein we propose a new strategy where we extract precipitation variability patterns and use correlation map to build the resulting density map that serves as an input to centroidal Voronoi tessellation construction that optimizes placement of precipitation gauges. We provide results of numerical experiments based on the data from the Alto-Adige region in Northern Italy and Oklahoma and compare them against actual gauge locations. This method provides an automated way for choosing new gauge locations and can be generalized to include physical constraints and to tackle other types of resource allocation problems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Depot-Based Vehicle Data for National Analysis of Medium- and Heavy-Duty Electric Vehicle Charging

Medium- and heavy-duty vehicles (MHDVs) are a major source of greenhouse gases and local criteria air pollutants. Electrifying MHDVs may reduce these harmful emissions, which disproportionately impact disadvantaged communities. Due to their relatively high per-vehicle energy needs, consistent fleet operations, and frequent colocation of multiple vehicles at depots, MHDVs may have more spatially and temporally concentrated charging demands than light-duty passenger electric vehicles. That charging concentration means their electrification may require careful advance planning and coordination to manage potential impacts to the electrical grid via charge management or infrastructure upgrades. However, MHDV duty cycles and parking schedules are highly variable across vocations of operation, and there is a shortage of nationally representative, vocationally diverse public data describing typical MHDV operations. This report summarizes the methodology - designed with national representativeness in mind - used to create a new set of data describing typical daily driving distances, dwell durations, and normalized electric vehicle depot charging load curves for MHDVs. The dataset reflects the subset of MHDV operating patterns that may originate from a consistent depot each day and rely on the same depot for charging. In addition to trucks with depot-centric vocational patterns, the data describes operations of transit buses and school buses, each with a depot-centric focus. The dataset is available to the public and suitable for national analysis. It can inform research, infrastructure planning, and policymaking regarding the electrification of MHDVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites

In tropical forests, leaf phenology signals leaf-on/off status and exhibits considerable variability across scales from a single tree-crown to the entire forest ecosystem. Such phenology signals importantly regulate large-scale biogeochemical cycles and regional climate. PlanetScope CubeSats data with a 3-m resolution and near-daily global coverage provide an unprecedented opportunity to monitor both fine- and ecosystem-scale phenology variability along large environmental gradients. However, a scalable method that accurately characterizes leaf phenology from PlanetScope with biophysically meaningful metrics remains lacking. Here we developed an index-guided, ecologically constrained autoencoder (IG-ECAE) method to automatically derive a deciduousness metric (i.e. percentage of tree canopies with leaf-off status within an image pixel) from PlanetScope. The IG-ECAE first estimated the reflectance spectra of leafy/leafless canopies based on their spectral indices characteristics, then used the derived reflectance spectra to guide an autoencoder deep learning method with additional ecological constraints to refine the reflectance spectra, and finally used linear spectral unmixing to estimate the relative abundance of leafless canopies (or deciduousness) per PlanetScope image pixel. We tested the IG-ECAE method at 16 tropical forest sites spanning multiple continents and a large precipitation gradient (1470-2819 mm year -1 ). Among these sites, we evaluated the PlanetScope-derived deciduousness with corresponding measures derived from WorldView-2 (n=9 sites) and local phenocams (n=9 sites). Our results show that PlanetScope-derived deciduousness agrees: 1) with that derived from WorldView-2 at the patch level (90m×90m) with r 2 =0.89 across all sites; and 2) with that derived from phenocams to quantify ecosystem-scale seasonality with r 2 ranging from 0.62-0.96. These results demonstrate the effectiveness and scalability of IG-ECAE in characterizing the wide variability in deciduousness across scales from pixels to forest ecosystems, and from a single date to the full annual cycle, indicating the potential for using high-resolution satellites to track the large-scale phenological patterns and response of tropical forests to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Identifying schools at high-risk for elevated lead in drinking water using only publicly available data

Estimating the risk of lead contamination of schools' drinking water at the State level is a complex, important, and unexplored challenge. Variable water quality among water systems and changes in water chemistry during distribution affect lead dissolution rates from pipes and fittings. In addition, the locations of lead-bearing plumbing materials are uncertain. We tested the capability of six machine learning models to predict the likelihood of lead contamination of drinking water at the schools' taps using only publicly available datasets. The predictive features used in the models correspond to those with a proven correlation to the dominant, but commonly unavailable, factors that govern lead leaching: the presence of lead-bearing plumbing materials and water quality conducive to lead corrosion. By combining water chemistry data from public reports, socioeconomic information from the US census, and spatial features using Geographic Information Systems, we trained and tested models to estimate the likelihood of lead contaminated tap water in over 8,000 schools across California and Massachusetts. Our best-performing model was a Random Forest, with a 10-fold cross validation score of 0.88 for Massachusetts and 0.78 for California using the average Area Under the Receiver Operating Characteristic Curve (ROC AUC) metric. The model was then used to assign a lead leaching risk category to half of the schools across California (the other half was used for training). There was good agreement between the modeled risk categories and the actual lead leaching outcomes for every school; however, the model overestimated the lead leaching risk in up to 17% of the schools. This model is the first of its kind to offer a tool to predict the risk of lead leaching in schools at the State level. Further use of this model can help deploy limited resources more effectively to prevent childhood lead exposure from school drinking water.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2021

Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield

This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

ARM Aerial Facility (AAF) Merged Value-Added Product Report for Historical G-1 Field Campaigns

For 30 years, the U.S. Department of Energy (DOE) Office of Science supported an instrumented Grumman Gulfstream-1 (G-1) aircraft for atmospheric field campaigns. Data from the final decade of G-1 operations were archived by the Atmospheric Radiation Measurement (ARM) user facility Data Center (ADC) and made publicly available at no cost to all registered users. To ensure a consistent data format and to improve the accessibility of the ARM airborne data, an integrated data set was recently developed covering the final six years of G-1 operations (2013 to 2018). The integrated data set includes data collected from 236 flights (766.4 hours). Four of the seven field campaigns were based in the U.S. One campaign collected data from the wildfires in the U.S. Pacific Northwest and agricultural burns in the lower Mississippi River valley as part of the Biomass Burning Observation Project (BBOP) in 2013. In 2015, the ARM Cloud Aerosol Precipitation Experiment provided data on atmospheric rivers and associated aerosol-cloud interactions that produce heavy precipitation on the U.S. west coast during the early spring. Research data from Airborne Carbon Measurements-V (ACME-V), collected during the summer of 2015, gave scientists insight into trends and variability of trace gases in the atmosphere over the North Slope of Alaska to improve arctic climate models. In the early summer and autumn of 2016, the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) campaign provided an extensive data set geared toward coupled processes that affect the life cycle of shallow clouds through the interaction among aerosol, cloud, land surface, and ecosystems. In 2014 (March and October), the airborne sampling moved outside of the U.S. to the city of Manaus in central Amazonia, Brazil, where residential and industrial emissions were extensively characterized by flights of the G-1. The GoAmazon2014/15 aircraft campaign data are being integrated with aquatic and terrestrial ecosystem measurements to quantify anthropogenic perturbations to a usually pristine tropical environment. Another international airborne mission was carried out in the Eastern North Atlantic region. The Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) campaign saw the G-1 aircraft fly from Terceira Island in the Azores during the summer of 2017 and the winter of 2018. The campaign studied both seasons to measure key aerosol and cloud processes under various meteorological and cloud conditions with different aerosol sources. Then the G-1 deployed to the Sierras de Córdoba range in central Argentina from October to November 2018 for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign to study orographic convective cloud interactions with their surrounding environment. These comprehensive datastreams provide much-needed insight into spatiotemporal variability of thermodynamic quantities, aerosol and cloud states, and properties for addressing essential science questions in Earth system process studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Considering the Variability of Soiling in Long-Term PV Performance Forecasting: Preprint

This study presents the development of a methodology for evaluating the variability associated with soiling on long-term PV forecasting. Independent engineering firms typically build P50 forecasts for large PV plants through the use of the PVsyst software, where monthly soiling losses are one of many inputs to the P50 model. Subsequently, long-term performance distributions, or Pvalues, are constructed through a Monte Carlo analysis that includes various factors such as: satellite irradiance modeling uncertainty, uncertainty in the PVsyst model, and long-term irradiance variability. Often the PVsyst model uncertainty is increased to account for sites with significant soiling concerns but no systematic method has been presented in the literature to specifically include soiling variability within Pvalues. In this work soiling information from 16 sites in the U.S. Southwest are combined with 20 years of rainfall data to generate 20 years of energy production with soiling losses and then subsequently generate Pvalues. The results show that the spread of Pvalues (P1-P99) can increase from 0-13% when interannual soiling variability is included.

interannual variability↗
DOE OSTI2025

Machine learning inversion from scattering for mechanically driven polymers

A machine learning inversion method is developed for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include energy parameters and conformation variables. The polymer is modeled as a chain of fixed-length bonds constrained by bending energy, and it is subject to external forces such as stretching and shear. We generate a data set consisting of random combinations of energy parameters, including bending modulus, stretching and shear force, along with Monte Carlo-calculated scattering functions and conformation variables such as end-to-end distance, radius of gyration and off-diagonal component of the gyration tensor. The effects of the energy parameters on the polymer are captured by the scattering function, and principal component analysis ensures the feasibility of the machine learning inversion. Finally, we train a Gaussian process regressor using part of the data set as a training set and validate the trained regressor for inversion using the rest of the data. The regressor successfully extracts the feature parameters.

Gaussian process regressors↗
DOE OSTI2024

An Evaluation of Dynamical Downscaling Methods Used to Project Regional Climate Change

In the past decade, dynamical downscaling using “pseudo-global-warming” (PGW) techniques has been applied frequently to project regional climate change. Such techniques generate signals by adding mean global climate model (GCM)-simulated climate change signals in temperature, moisture, and circulation to lateral and surface boundary conditions derived from reanalysis. An alternative to PGW is to downscale GCM data directly. This technique should be advantageous, especially for simulation of extremes, since it incorporates the GCM's full spectrum of changing synoptic-scale dynamics in the regional solution. Here, we test this assumption, by comparing simulations in Europe and Western North America. We find that for warming and changes in temperature extremes, PGW often produces similar results to direct downscaling in both regions. For mean and extreme precipitation changes, PGW generally also performs surprisingly well in many cases. Moisture budget analysis in the Western North America domain reveals why. Large fractions of the downscaled hydroclimate changes arise from mean changes in large-scale thermodynamics and circulation, that is, increases in temperature, moisture, and winds, included in PGW by design. The one component PGW may have difficulty with is the contribution from changes in synoptic-scale variability. When this component is large, PGW performance could be degraded. Global analysis of GCM data shows there are regions where it is large or dominant. Hence, our results provide a road map to identify, through GCM analyses, the circumstances when PGW would not be expected to accurately regionalize GCM climate signals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Emission Inventories from Natural Gas Storage Facilities using Regional Frequency Comb Laser Monitoring and Aircraft Flyovers. Final Report

The project centers around a ground-based regional methane sensor developed by the CU/NIST team members under the DOE ARPA-E MONITOR program, and sophisticated aircraft measurement techniques developed by the UCD/Scientific Aviation team (responsible for a recent Science publication quantifying the Aliso Canyon storage field release). The dual frequency-comb spectrometer is an invisible, eye-safe laser capable of measuring atmospheric methane concentrations along beam paths 1+ miles in length with high precision and stability (<1 ppb methane over 1 mile). The CU/NIST team has developed a unique approach to using a single, central spectrometer to locate and size methane emissions as small as 6 scfh from specific gas field structures within the 1+ mile range of the laser. Deployment involves the stationing of small cubic mirrors at strategic locations across the gas storage site. The mirrors direct light from the spectrometer back to a detector, where methane concentrations are recorded based on light absorption at specific wavelengths. The measurements are coupled with high resolution gas transport models based on meteorological measurements (wind, etc.) to determine the precise location and emission rate of methane sources. The calibration-free and continuous nature of dual frequency-comb spectrometer measurements means that a large area can be monitored continuously for temporal variability of emissions over long periods of time. The aircraft measurements involve spiral flights around storage facilities with a sensitive ethane/methane measurement package. Upwind/downwind concentration comparisons and wind data provide total facility emissions data. During this project, a new micrometeorological package was integrated onto the Scientific Aviation aircraft to provide even greater emissions quantification capability. In this project, the ground-based measurement system was deployed for 11 months (to capture seasonal variability) at a West Coast storage facility, together with a campaign of twice monthly aircraft total facility emissions flights. The data from these two modalities was combined to quantify the methane emissions from the facility with first-of-its-kind temporal and spatial detail. Concurrent with this deployment, the aircraft team performed flights to quantify emissions at a wider array of previously un-surveyed storage sites. The ground-based measurement system was then moved to an additional site of differing total storage and delivery capacity. Aircraft mass balance flights continued, repeatedly surveying (twice monthly) this site, as well as surveying a large number of previously un-assessed sites spanning a variety of size, reservoir type and age. Assessment of ground-based, temporally and spatially detailed methane emissions information alongside aircraft-derived methane total facility emissions information led to an improved understanding of emissions, relevant for inventories of the natural gas storage sector. In particular, this work provided temporal detail (seasonal of emissions and frequency of leaks) to stakeholders and personnel responsible for updates to the EPA’s GHGI. The impact of the proposed work has been a dramatic improvement in the temporal, spatial, and site- and type- specific detail and quantification of emissions inventories for the natural gas storage sector. This work directly benefits the EPA’s GHGI, directly ameliorates the environmental impacts and public health and safety of the natural gas storage sector, and aids policymakers and industry to make sound choices with respect to management and regulation.

03 NATURAL GAS↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2020

TDCOSMO: II. Six new time delays in lensed quasars from high-cadence monitoring at the MPIA 2.2m telescope

In this paper we present six new time-delay measurements obtained from $R_c$-band monitoring data acquired at the Max Planck Institute for Astrophysics (MPIA) 2.2 m telescope at La Silla observatory between October 2016 and February 2020. The lensed quasars HE 0047–1756, WG 0214–2105, DES 0407–5006, 2M 1134–2103, PSJ 1606–2333, and DES 2325–5229 were observed almost daily at high signal-to-noise ratio to obtain high-quality light curves where we can record fast and small-amplitude variations of the quasars. We measured time delays between all pairs of multiple images with only one or two seasons of monitoring with the exception of the time delays relative to image D of PSJ 1606–2333. The most precise estimate was obtained for the delay between image A and image B of DES 0407–5006, where $τ_{AB} = –128.4_{–3.8}^{+3.5}$ d (2.8% precision) including systematics due to extrinsic variability in the light curves. For HE 0047–1756, we combined our high-cadence data with measurements from decade-long light curves from previous COSMOGRAIL campaigns, and reach a precision of 0.9 d on the final measurement. The present work demonstrates the feasibility of measuring time delays in lensed quasars in only one or two seasons, provided high signal-to-noise ratio data are obtained at a cadence close to daily.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Identification and Characterization of ten Escherichia coli Strains Encoding Novel Shiga Toxin 2 Subtypes, Stx2n as Well as Stx2j, Stx2m, and Stx2o, in the United States

The sharing of genome sequences in online data repositories allows for large scale analyses of specific genes or gene families. This can result in the detection of novel gene subtypes as well as the development of improved detection methods. Here, we used publicly available WGS data to detect a novel Stx subtype, Stx2n in two clinical E. coli strains isolated in the USA. During this process, additional Stx2 subtypes were detected; six Stx2j, one Stx2m strain, and one Stx2o, were all analyzed for variability from the originally described subtypes. Complete genome sequences were assembled from short- or long-read sequencing and analyzed for serotype, and ST types. The WGS data from Stx2n- and Stx2o-producing STEC strains were further analyzed for virulence genes pro-phage analysis and phage insertion sites. Nucleotide and amino acid maximum parsimony trees showed expected clustering of the previously described subtypes and a clear separation of the novel Stx2n subtype. WGS data were used to design OMNI PCR primers for the detection of all known stx1 (283 bp amplicon), stx2 (400 bp amplicon), intimin encoded by eae (221 bp amplicon), and stx2f (438 bp amplicon) subtypes. These primers were tested in three different laboratories, using standard reference strains. An analysis of the complete genome sequence showed variability in serogroup, virulence genes, and ST type, and Stx2 pro-phages showed variability in size, gene composition, and phage insertion sites. The strains with Stx2j, Stx2m, Stx2n, and Stx2o showed toxicity to Vero cells. Stx2j carrying strain, 2012C-4221, was induced when grown with sub-inhibitory concentrations of ciprofloxacin, and toxicity was detected. Taken together, these data highlight the need to reinforce genomic surveillance to identify the emergence of potential new Stx2 or Stx1 variants. The importance of this surveillance has a paramount impact on public health. Per our description in this study, we suggest that 2017C-4317 be designated as the Stx2n type-strain.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Comparative Analyses of Bioequivalence Assessment Methods for In Vitro Permeation Test Data

ABSTRACT For topical, dermatological drug products, an in vitro option to determine bioequivalence (BE) between test and reference products is recommended. In particular, in vitro permeation test (IVPT) data analysis uses a reference‐scaled approach for two primary endpoints, cumulative penetration amount (AMT) and maximum flux ( J max ), which takes the within donor variability into consideration. In 2022, the Food and Drug Administration (FDA) published a draft IVPT guidance that includes statistical analysis methods for both balanced and unbalanced cases of IVPT study data. This work presents a comprehensive evaluation of various methodologies used to estimate critical parameters essential in assessing BE. Specifically, we investigate the performance of the FDA draft IVPT guidance approach alongside alternative empirical and model‐based methods utilizing mixed‐effects models. Our analyses include both simulated scenarios and real‐world studies. In simulated scenarios, empirical formulas consistently demonstrate robustness in approximating the true model, particularly in effectively addressing treatment–donor interactions. Conversely, the effectiveness of model‐based approaches heavily relies on precise model selection, which significantly influences their results. The research emphasizes the importance of accurate model selection in model‐based BE assessment methodologies. It sheds light on the advantages of empirical formulas, highlighting their reliability compared to model‐based approaches and offers valuable implications for BE assessments. Our findings underscore the significance of robust methodologies and provide essential insights to advance their understanding and application in the assessment of BE, employed in IVPT data analysis.

Leon, Sami↗
DOE OSTI2023

Do people spend travel time the way they think they would? a comparative study of generic and trip-specific travel time allocation using hybrid multiple discrete continuous (MDC) framework

Unlike driving alone, public transportation allows one to engage in extraneous activities while traveling – often referred to as travel-based multitasking. Research involving travel-based multitasking often relies on either generic (i.e. not related to an actual trip) or revealed (i.e. related to an executed trip) data. The data collected for this study provided a unique opportunity to compare the generic and trip-specific preferences while traveling on public transportation. To this end, the study develops two integrated choice and latent variable models with multiple discrete-continuous (MDC) kernels to simultaneously model the activity selection and the time allocation for generic and trip-specific data, respectively. According to the results the trip-specific data could identify more nuances in the travel-based multitasking behavior than the generic data. Finally, the heterogeneity identified by the developed models will be helpful for the transit operators in providing appropriate facilities (e.g. internet access, reading lights) along various transit corridors.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Detecting Long-period Variability in the SDSS Stripe 82 Standards Catalog

We report the results of a search for long-period (100 < P < 600 days) periodic variability in the SDSS Stripe 82 standards catalog. The SDSS coverage of Stripe 82 enables such a search because there are on average 20 observations per band in ugriz bands for about one million sources, collected over about 6 yr, with a faint limit of r ~ 22 mag and precisely calibrated 1%–2% photometry. We calculated the periods of variable source candidates in this sample using the Lomb–Scargle periodogram and considered the three highest periodogram peaks in each of the gri filters as relevant. Only those sources with gri periods consistent within 0.1% were later studied. We use the Kuiper statistic to ensure uniform distribution of data points in phased light curves. We present five sources with the spectra consistent with quasar spectra and plausible periodic variability. This SDSS-based search bodes well for future sensitive large-area surveys, such as the Rubin Observatory Legacy Survey of Space and Time, which, due to its larger sky coverage (about a factor of 60) and improved sensitivity (~2 mag), will be more powerful for finding such sources.

79 ASTRONOMY AND ASTROPHYSICS↗