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DOE OSTI2023

Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensing

Deciding how to optimally deploy sensors in a large, complex, and spatially extended structure is critical to ensure that the surface pressure field is accurately captured for subsequent analysis and design. In some cases, reconstruction of missing data is required in downstream tasks such as the development of digital twins. Here, this paper presents a data-driven sparse sensor selection algorithm, aiming to provide the most information contents for reconstructing aerodynamic characteristics of wind pressures over tall building structures parsimoniously. The algorithm first fits a set of basis functions to the training data, then applies a computationally efficient QR algorithm that ranks existing pressure sensors in order of importance based on the state reconstruction to this tailored basis. The findings of this study show that the proposed algorithm successfully re- constructs the aerodynamic characteristics of tall buildings from sparse measurement locations, generating stable and optimal solutions across a range of conditions. As a result, this study serves as a promising first step toward leveraging the success of data-driven and machine learning algorithms to supplement traditional genetic algorithms currently used in wind engineering.

42 ENGINEERING↗

DOE OSTI2022

DIVA/DeviceEditor (DIVA) v6.0.0

The DIVA software interfaces a process in which researchers design their DNA with a web-based graphical user interface (DeviceEditor), submit their designs to a central queue, and a few weeks later receive their sequence-verified clonal constructs. Each researcher independently designs the DNA to be constructed with a web-based BioCAD tool, and presses a button to submit their designs to a central queue. Researchers have web-based access to their DNA design queues, and can track the progress of their submitted designs as they progress from "evaluation", to "waiting for reagents", to "in progress", to "complete". Researchers access their completed constructs through the central DNA repository. Along the way, all DNA construction success/failure rates are captured in a central database. Once a design has been submitted to the queue, a small number of dedicated staff evaluate the design for feasibility and provide feedback to the responsible researcher if the design is either unreasonable (e.g., encompasses a combinatorial library of a billion constructs) or small design changes could significantly facilitate the downstream implementation process. The dedicated staff then use DNA assembly design automation software to optimize the DNA construction process for the design, leveraging existing parts from the DNA repository where possible and ordering synthetic DNA where necessary. Once all requisite process inputs are available, the design progresses from "waiting for reagents" to "in progress" in the design queue. Human-readable and machine-parseable DNA construction protocols output by the DNA assembly design automation software are then executed by the dedicated staff exploiting lab automation devices wherever possible. Since the all employed DNA construction methods are sequence-agnostic, standardized (utilize the same enzymatic master mixes and reaction conditions), completely independent DNA construction tasks can be aggregated into the same multi-well plates and pursued in parallel. The resulting sets of cloned constructs can then be screened by high-throughput next-gen sequencing platforms for sequence correctness. A combination of long read-length (e.g., PacBio) and paired-end read platforms (e.g., Illumina) would be exploited depending the particular task at hand (e.g., PacBio might be sufficient to screen a set of pooled constructs with significant gene divergence). Post sequence verification, designs for which at least one correct clone was identified will progress to a "complete" status, while designs for which no correct clones were identified will progress to a "failure" status. Depending on the failure mode (e.g., no transformants), and how many prior attempts/variations of assembly protocol have been already made for a given design, subsequent attempts may be made or the design can progress to a "permanent failure" state. All success and failure rate information will be captured during the process, including at which stage a given clonal construction procedure failed (e.g., no PCR product) and what the exact failure was (e.g. assembly piece 2 missing). This success/failure rate data can be leveraged to refine the DNA assembly design process.

Plahar, Hector↗

DOE OSTI2016

DIVA V2.0

The DIVA software interfaces a process in which researchers design their DNA with a web-based graphical user interface, submit their designs to a central queue, and a few weeks later receive their sequence-verified clonal constructs. Each researcher independently designs the DNA to be constructed with a web-based BioCAD tool, and presses a button to submit their designs to a central queue. Researchers have web-based access to their DNA design queues, and can track the progress of their submitted designs as they progress from "evaluation", to "waiting for reagents", to "in progress", to "complete". Researchers access their completed constructs through the central DNA repository. Along the way, all DNA construction success/failure rates are captured in a central database. Once a design has been submitted to the queue, a small number of dedicated staff evaluate the design for feasibility and provide feedback to the responsible researcher if the design is either unreasonable (e.g., encompasses a combinatorial library of a billion constructs) or small design changes could significantly facilitate the downstream implementation process. The dedicated staff then use DNA assembly design automation software to optimize the DNA construction process for the design, leveraging existing parts from the DNA repository where possible and ordering synthetic DNA where necessary. SynTrack software manages the physical locations and availability of the various requisite reagents and process inputs (e.g., DNA templates). Once all requisite process inputs are available, the design progresses from "waiting for reagents" to "in progress" in the design queue. Human-readable and machine-parseable DNA construction protocols output by the DNA assembly design automation software are then executed by the dedicated staff exploiting lab automation devices wherever possible. Since the all employed DNA construction methods are sequence-agnostic, standardized (utilize the same enzymatic master mixes and reaction conditions), completely independent DNA construction tasks can be aggregated into the same multi-well plates and pursued in parallel. The resulting sets of cloned constructs can then be screened by high-throughput next-gen sequencing platforms for sequence correctness. A combination of long read-length (e.g., PacBio) and paired-end read platforms (e.g., Illumina) would be exploited depending the particular task at hand (e.g., PacBio might be sufficient to screen a set of pooled constructs with significant gene divergence). Post sequence verification, designs for which at least one correct clone was identified will progress to a "complete" status, while designs for which no correct clones wereidentified will progress to a "failure" status. Depending on the failure mode (e.g., no transformants), and how many prior attempts/variations of assembly protocol have been already made for a given design, subsequent attempts may be made or the design can progress to a "permanent failure" state. All success and failure rate information will be captured during the process, including at which stage a given clonal construction procedure failed (e.g., no PCR product) and what the exact failure was (e.g. assembly piece 2 missing). This success/failure rate data can be leveraged to refine the DNA assembly design process.

CHEN, JOANNA↗

DOE OSTI2018

A Kriging based spatiotemporal approach for traffic volume data imputation

Along with the rapid development of Intelligent Transportation Systems, traffic data collection technologies have progressed fast. The emergence of innovative data collection technologies such as remote traffic microwave sensor, Bluetooth sensor, GPS-based floating car method, and automated license plate recognition, has significantly increased the variety and volume of traffic data. Despite the development of these technologies, the missing data issue is still a problem that poses great challenge for data based applications such as traffic forecasting, real-time incident detection, dynamic route guidance, and massive evacuation optimization. A thorough literature review suggests most current imputation models either focus on the temporal nature of the traffic data and fail to consider the spatial information of neighboring locations or assume the data follow a certain distribution. These two issues reduce the imputation accuracy and limit the use of the corresponding imputation methods respectively. As a result, this paper presents a Kriging based data imputation approach that is able to fully utilize the spatiotemporal correlation in the traffic data and that does not assume the data follow any distribution. A set of scenarios with different missing rates are used to evaluate the performance of the proposed method. The performance of the proposed method was compared with that of two other widely used methods, historical average and K-nearest neighborhood. Comparison results indicate that the proposed method has the highest imputation accuracy and is more flexible compared to other methods.

Science & Technology - Other Topics↗

DOE OSTI2026

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025

This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: v{x}-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_v{x}-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_v{x}-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_v{x}-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_v{x}-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Performance Year 1 Technical Report - OPEN COG Grid: Extendable Coherent Models-Datasets for Cognitive Power Grids

The OPEN COG Grid project is a collaborative effort between LLNL, NREL, and Texas A&M University (TAMU) to develop synthetic power system datasets that (i) contain all technical information that would be available in a real system, allowing to conduct studies ranging from dynamic simulation to long term planning studies; ii) are accessible to researchers from the broader data sciences community, as oppossed to power system experts only; and (iii) This report summarizes the work conducted during the first 15 months of execution of the project. These activities encompassed: 1. Conduct a survey of existing open data sets and open source power systems simulators, their supported use cases, and accessibility (Chapter 1). 2. Define a new extensible specification for power system data, covering all parameters necessary for most computational use cases (Chapter 2). 3. Collecting real technical system data to complete missing parameters in existing open source datasets (Chapter 3). 4. Develop models that capture the behavior of emergent actors in power grids, neglected by existing datasets; aggregated residential demand response (Chapter 4) and demand response of cryptocurrency miners (Chapter 5). 5. Collect detailed spatial information on distributed energy resources, particular, solar photovoltaic facilities (Chapter 6). The following chapters provide detailed descriptions of these tasks, the assumptions taken, and their findings. In conducting these tasks, the project team produced: two (accepted) conference papers; one journal paper under submission; one draft journal paper pending submission; released one repository with the developed power system data specification, with documentation and examples; and one extended dataset for the Texas power grid under review for release. The team hopes these contributions will enhance access to power system data and remove barriers to the development of new computational techniques for power systems, particularly, those inspired by cognitive sciences.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Informing Nature-based Climate Solutions for the United States with the best-available science

Nature-based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem-scale carbon fluxes, for example, by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state-of-the-art remote sensing products and land-surface models are not yet being widely used to inform NbCS policymaking or implementation. As a result, there is a critical mismatch between the point- and tree-scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policymaking. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. Furthermore, we outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre-existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Multinational Prospective Study of Patient-Reported Outcomes After Prostate Radiation Therapy: Detailed Assessment of Rectal Bleeding

Purpose: The new short Expanded Prostate Cancer Index Composite for Clinical Practice (EPIC-CP) patient-reported health-related quality of life (HRQOL) tool has removed the rectal bleeding question from the previous much longer version, EPIC-26. Herein, we assess the impact of losing the dedicated rectal bleeding question in 2 independent prospective multicenter cohorts. Methods and Materials: In a prospective multicenter test cohort (n=865), EPIC-26 patient-reported HRQOL data were collected for 2 years after treatment from patients treated with prostate radiation therapy from 2003 to 2011. A second prospective multicenter cohort (n=442) was used for independent validation. A repeated-effects model was used to predict the change from baseline in bowel summary scores from longer EPIC instruments using the change in EPIC-CP bowel summary scores with and without rectal bleeding scores. Results: Two years after radiation therapy, 91% of patients were free of bleeding, and only 2.6% reported bothersome bleeding problems. Correlations between EPIC-26 and EPIC-CP bowel scores were very high (r{sup 2}=0.90-0.96) and were statistically improved with the addition of rectal bleeding information (r{sup 2}=0.94-0.98). Considering all patients, only 0.2% of patients in the test cohort and 0.7% in the validation cohort reported bothersome bleeding and had clinically relevant HRQOL changes missed with EPIC-CP. However, of the 2.6% (n=17) of men with bothersome rectal bleeding in the test cohort, EPIC-CP failed to capture 1 patient (6%) as experiencing meaningful declines in bowel HRQOL. Conclusions: Modern prostate radiation therapy results in exceptionally low rates of bothersome rectal bleeding, and <1% of patients experience bothersome bleeding and are not captured by EPIC-CP as having meaningful HRQOL declines after radiation therapy. However, in the small subset of patients with bothersome rectal bleeding, the longer EPIC-26 should strongly be considered, given its superior performance in this patient subset.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2020

Provenance infrastructure for multimodal x-ray experiments and reproducible analysis

Exploring relationships between material structures and desired properties for material discovery and synthesis requires mining machine-readable databases and linking experimental data with sufficiently rich annotations. There are vast gaps both in these data sources and in the connections between them. We present a provenance-based data management and analysis framework that enables capturing, persisting, and reanalyzing experimental data from the BNL National Synchrotron Light Source II (NSLS-II). Our system leverages NSLS-II Bluesky for experimental data acquisition, captures analysis parameters, and enriches the search space with results from the scientific literature. We describe in detail the challenges and benefits of data-driven approaches when comparing computational and experimental data, such as the multiple possible scenarios of interest in often ill-posed inverse problems and missing data. Our infrastructure utilizes the NSLS-II event model and includes discovery mechanisms across multiple beamlines. A text and data mining module using natural language processing classification techniques extracts relevant information from the literature that is added to data sources. We describe a provenance acquisition and analysis rerun workflow based on a directed acyclic graph that keeps track of process and data provenance and allows to replay analysis.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Study of Coronal Heating in Solar Active Regions Using Wide-Field Imaging Spectroscopy: Hinode EIS Slot Observations

Understanding the frequency of heating events that keep the coronal plasma at several million Kelvin above the photospheric temperature of~ 6000K, is one of the most important problems in solar astrophysics. Spectroscopic observations of the Sun in the extreme ultraviolet (EUV) indicate that the coronal plasma reaches temperatures from 1 to 5 MK in active regions. It is also established that temperature in active regions can vary strongly with time and, moreover, contain sub-regions that evolve and develop separately. Tracking the spatio-temporal evolution of temperature requires continuous observation of the entire active region via imaging and spectroscopy. Traditional slit imaging spectroscopy probes plasma heating in solar active regions through observations of diagnostic emission lines and the resulting data are spectrally pure. Here, imaging is performed through rastering process, which severely limits co-temporal observations and often can be slow to miss events that evolve at other portions of the active region. In contrast, wide-field imaging spectroscopy offer simultaneous coverage of a large field of view as well as obtain spectral information in the same direction. This data suffers from spatial-spectral confusion, and are called spectroheliograms. Using the state-of-the-art inversion techniques that are developed recently, now spectroheliogram data can be unfolded to yield spectrally pure maps of large fields over long duration of observations. We use wide slit data, usually referred as ‘slot’, from the EUV Imaging Spectrometer (EIS) onboard Hinode satellite, focusing on active region observations. Here, we present our study of coronal heating in an active region using a long duration Hinode EIS slot observation.

Active region heating↗

DOE OSTI2019

A Structured Quasi-Newton Algorithm for Optimizing with Incomplete Hessian Information

We present a structured quasi-Newton algorithm for unconstrained optimization problems that have unavailable second-order derivatives or Hessian terms. We provide a formal derivation of the well-known Broyden--Fletcher--Goldfarb--Shanno (BFGS) secant update formula that approximates only the missing Hessian terms, and we propose a linesearch quasi-Newton algorithm based on a modification of Wolfe conditions that converges to first-order optimality conditions. We also analyze the local convergence properties of the structured BFGS algorithm and show that it achieves superlinear convergence under the standard assumptions used by quasi-Newton methods using secant updates. In conclusion, we provide a thorough study of the practical performance of the algorithm on the CUTEr suite of test problems and show that our structured BFGS-based quasi-Newton algorithm outperforms the unstructured counterpart(s).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

MPS dose reconstruction for internal emitters: some site-specific issues and approaches

Background: As part of the Million Person Study (MPS), dose reconstructions for internal emitters have been performed for several U.S. facilities where large quantities of radionuclides were handled. The main challenges and dominant sources of potential error in retrospective dose estimates for internally exposed workers have been found to vary from site to site. Here in this paper, we discuss some important issues encountered in dose reconstructions performed for selected MPS sites and the approaches used to address those issues. The focus is on some foundational components of retrospective dose assessments that have received little attention in the literature. Methods: The discussion is built around illustrative exposure data and dose reconstructions for workers at selected facilities addressed in the MPS. Related findings at some non-MPS sites are also discussed. Results: Each of the following items has been found to be a major source of potential error in reconstructed tissue doses for some MPS sites: identification of all dosimetrically important internal emitters; the time pattern of intake; the mode(s) of intake; reliability of bioassay measurements; application of surrogate (coworker) information in lieu of, or in conjunction with, worker-specific monitoring data; the chemical and physical forms of inhaled radionuclide; and the relation of air monitoring data to actual intake. Conclusions: (1) Much of the dose reconstruction effort for internal emitters should be devoted to development of best feasible exposure scenarios. (2) Coworker data should be used to assign exposure scenarios or dose estimates to workers with missing exposure data only if there is compelling evidence of similar coworker exposure. (3) Bioassay data for some radionuclides and periods of operation at MPS sites are of questionable reliability due to sizable uncertainties associated with contamination, recovery, or background issues. (4) Dose estimates derived solely from air monitoring data should be treated as highly uncertain values in the absence of site-specific information demonstrating that the data are reasonably predictive of intake. (5) For intakes known or assumed to be via inhalation, the uncertainty in lung dose typically is much greater than the uncertainty in dose to systemic tissues, when dose estimates are based on urinary excretion data. (6) The lung dose estimate often can be improved through development of site-specific respiratory absorption parameter values. (7) There is generally insufficient site-specific information to justify development of site-specific systemic models.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2018

Generating high-order optical and spin harmonics from ferromagnetic monolayers

High-order harmonic generation (HHG) in solids has entered a new phase of intensive research, with envisioned band-structure mapping on an ultrashort time scale. This partly benefits from a flurry of new HHG materials discovered, but so far has missed an important group. HHG in magnetic materials should have profound impact on future magnetic storage technology advances. Here we introduce and demonstrate HHG in ferromagnetic monolayers. We find that HHG carries spin information and sensitively depends on the relativistic spin–orbit coupling; and if they are dispersed into the crystal momentum k space, harmonics originating from real transitions can be k-resolved and carry the band structure information. Geometrically, the HHG signal is sensitive to spatial orientations of monolayers. Different from the optical counterpart, the spin HHG, though probably weak, only appears at even orders, a consequence of SU(2) symmetry. Our findings open an unexplored frontier—magneto-high-order harmonic generation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Optimal mass variables for semivisible jets

Strongly coupled hidden sector theories predict collider production of invisible, composite dark matter candidates mixed with standard model hadrons in the form of semivisible jets. Classical mass reconstruction techniques may not be optimal for these unusual topologies, in which the missing transverse momentum comes from massive particles and has a nontrivial relationship to the visible jet momentum. We apply the artificial event variable network, a semisupervised, interpretable machine learning technique that uses an information bottleneck, to derive superior mass reconstruction functions for several cases of resonant semivisible jet production. We demonstrate that the technique can extrapolate to unknown signal model parameter values. We further demonstrate the viability of conducting an actual search for new physics using this method, by applying the learned functions to standard model background events from quantum chromodynamics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

Management Observation Facility Fume Hoods

I reviewed all the hoods used for AS&I work in buildings 253, 254, and 255. All have had the minimum annual surveys conducted and some have had semi-annual reviews. The operating parameter labels are posted consistent to the right or above right (with the only exception the perchloric acid hood in 1734- on the left) which facilitates users knowing where to check for information. All hoods with sash doors (except the SE one in 1734 have a note on the Operating Parameters label to "keep sash doors closed". Again, this was checking consistency. No surveys were missed.

61 RADIATION PROTECTION AND DOSIMETRY↗

NASA NTRS2015

Informal Statistics Help Desk

Back by popular demand, the JSC Biostatistics Lab is offering an opportunity for informal conversation about challenges you may have encountered with issues of experimental design, analysis, data visualization or related topics. Get answers to common questions about sample size, repeated measures, violation of distributional assumptions, missing data, multiple testing, time-to-event data, when to trust the results of your analyses (reproducibility issues) and more.

Ploutz-Snyder, R. J.↗

NASA NTRS2011

NLSI Focus Group on Missing ALSEP Data Recovery: Progress and Plans

On the six Apollo landed missions, the Astronauts deployed the Apollo Lunar Surface Experiments Package (ALSEP) science stations which measured active and passive seismic events, magnetic fields, charged particles, solar wind, heat flow, the diffuse atmosphere, meteorites and their ejecta, lunar dust, etc. Today's scientists are able to extract new information and make new discoveries from the old ALSEP data utilizing recent advances in computer capabilities and new analysis techniques. However, current-day investigators are encountering problems trying to use the ALSEP data. In 2007 archivists from NASA Goddard Space Flight Center (GSFC) National Space Science Data Center (NSSDC) estimated only about 50 percent of the processed ALSEP lunar surface data-of-interest to current lunar science investigators were in the NSSDC archives. The current-day lunar science investigators found most of the ALSEP data, then in the NSSDC archives. were extremely difficult to use. The data were in forms often not well described in the published reports and rerecording anomalies existed in the data which could only be resolved by tape experts. To resolve this problem, the DPS Lunar Data Node was established in 2008 at NSSDC and is in the process of successfully making the existing archived ALSEP data available to current-day investigators in easily useable forms. In July of 2010 the NASA Lunar Science Institute (NLSI) at Ames Research Center established the Recovery of Missing ALSEP Data Focus Group in recognition of the importance of the current activities to find the raw and processed ALSEP data missing from the NSSDC archives.

Lewis, L. R.↗

DOE OSTI2017

Imputation for multisource data with comparison and assessment techniques

Missing data are prevalent issue in analyses involving data collection. The problem of missing data is exacerbated for multisource analysis, where data from multiple sensors are combined to arrive at a single conclusion. In this scenario, it is more likely to occur and can lead to discarding a large amount of data collected; however, the information from observed sensors can be leveraged to estimate those values not observed. We propose two methods for imputation of multisource data, both of which take advantage of potential correlation between data from different sensors, through ridge regression and a state-space model. These methods, as well as the common median imputation, are applied to data collected from a variety of sensors monitoring an experimental facility. Performance of imputation methods is compared with the mean absolute deviation; however, rather than using this metric to solely rank themethods,we also propose an approach to identify significant differences. Imputation techniqueswill also be assessed by their ability to produce appropriate confidence intervals, through coverage and length, around the imputed values. Finally, performance of imputed datasets is compared with a marginalized dataset through a weighted k-means clustering. In general, we found that imputation through a dynamic linearmodel tended to be the most accurate and to produce the most precise confidence intervals, and that imputing the missing values and down weighting them with respect to observed values in the analysis led to the most accurate performance.

97 MATHEMATICS AND COMPUTING↗