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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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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Ultraviolet C ii and Si iii transit spectroscopy and modeling of the evaporating atmosphere of GJ436B

Hydrogen gas evaporating from the atmosphere of the hot-Neptune GJ436b absorbs over 50% of the stellar Lyα emission during transit. Given the planet’s atmospheric composition and energy-limited escape rate, this hydrogen outflow is expected to entrain heavier atoms such as C and O. We searched for C and Si in the escaping atmosphere of GJ436b using far-ultraviolet Hubble Space Telescope COS G130M observations made during the planet’s extended H i transit. These observations show no transit absorption in the C ii 1334,1335 Å and Si iii 1206 Å lines integrated over [−100, 100] km s{sup −1}, imposing 95% (2σ) upper limits of 14% (C ii) and 60% (Si iii) depth on the transit of an opaque disk and 22% (C ii) and 49% (Si iii) depth on an extended highly asymmetric transit similar to that of H i Lyα. C{sup +} is likely present in the outflow according to a simulation we carried out using a spherically symmetric photochemical-hydrodynamical model. This simulation predicts an ∼2% transit over the integrated bandpass, consistent with the data. At line center, we predict the C ii transit depth to be as high as 19%. Our model predicts a neutral hydrogen escape rate of 1.6×10{sup 9} g s{sup −1} (3.1×10{sup 9} g s{sup −1} for all species) for an upper atmosphere composed of hydrogen and helium.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

In-situ microscale through-silicon via strain measurements by synchrotron x-ray microdiffraction exploring the physics behind data interpretation

In-situ microscale thermomechanical strain measurements have been performed in combination with synchrotron x-ray microdiffraction to understand the fundamental cause of failures in microelectronics devices with through-silicon vias. The physics behind the raster scan and data analysis of the measured strain distribution maps is explored utilizing the energies of indexed reflections from the measured data and applying them for beam intensity analysis and effective penetration depth determination. Moreover, a statistical analysis is performed for the beam intensity and strain distributions along the beam penetration path to account for the factors affecting peak search and strain refinement procedure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Characterization of Contaminants and Mobility in WMA A-AX Boreholes D0006 and D0008

This study was initiated to determine if subsurface contaminants in the A-AX vadose zone at the Hanford site (boreholes D0006 and D0008) caused corrosion of nearby stainless steel well casings (299-E-24-19, 299-E25-46, and 299-E25-236) and, based on the type and distribution of contaminants, determine if contaminants are from one or more sources including a) the A-104 and/or A-105 tanks, b) the 242A evaporator, and c) the 200-E-286 ditch. A tiered approach was used for characterizing contaminants in vadose zone sediments starting with mobile and total contaminant concentrations (Tier 1), characterizing contaminant speciation (Tier 2), and measuring contaminant leaching (Tier 3). Total and mobile contaminant measurements at seven depths showed elevated sulfate and nitrate in all samples (130 to 280 ft depth), elevated chloride in five depths, and no elevated radionuclides except I-129 in D0006 at 206 to 208 ft depth. If these contaminants were initially acidic, stainless-steel corrosion would occur. However, because sediments likely have a high acid neutralization capacity, an acidic spill would have been neutralized within tens of feet, so casing corrosion would likely occur in shallow sediments. GEL labs data also shows low (but above natural) chromate from shallow to 170 ft depth, then higher chromate to 280 ft, suggesting a surface Cr spill or casing corrosion. GEL labs data also showed evidence of more than one plume, with deep nitrate, sulfate, and chloride migration, but only shallow tritium migration. Tritium should migrate nearly unretarded in the subsurface like nitrate and chloride. Elevated aqueous cations and anions in the D0006 206 to 208' depth pore water (SO 4 2- , NO 3 - , Na + , K + , Ca 2+ , Mg 2+ , Si) could be the result of acid disposal and calcite dissolution at shallow depth, with deeper migration of SO 4 2- , NO 3 - from acids and Ca 2+ and Mg 2+ from calcite. Elevated Si (160 mg/L) indicates dissolution of clays or other silicates, which can also occur in acids. Acid extractable metals did show elevated Cr (but not Fe, Ni, Mo, and Mn) above natural levels, which may indicate stainless-steel well corrosion or transport of metals from a shallow spill. The elevated I-129 in D0006 at 206 to 208' depth was low with 0.34 μg/g in pore water and 1.5 μg/g total extractable I-129 in the sediment, which indicates significant fraction of I-129 was bound in one or more precipitates. Although total I-129 was below detection limits in most extractions due to high molybdate interference, I-127 analysis provided some insight into potential precipitate phases. Total I-127 was mainly extracted by acetate or acetic acid (67%), which may be iodine in calcite. Inorganic carbon measurement identified 1.65% calcite, and total organic carbon (TOC) was below detection limits, so iodine was not associated with organic matter. Nearly all the I-127 was present as iodide. In 1-D leach experiments, I-127 as iodide leached from the sediment within 2 pore volumes. Due to high and dynamic molybdate leaching from the sediment and interference with I-129 analysis, there were few quantifiable I-129 measurements. The calculated molybdate release rates from the sediment suggested Mo was being released from the same precipitate phase, such as molybdate incorporated into calcite. Overall, because there was little radioactive contamination measured in D0006 and D0008 boreholes, potential releases from A-104 and A-105 did not reach this location south of the A tank farm. Elevated cations and anions found in D0006 and D0008 at depth as the result of acidic disposal could cause shallow stainless steel casing corrosion. The nearby 242A evaporator generally contained alkaline waste and tritium, but the nearby 200-E-286 ditch generally contained high Cl - liquid effluent, so these are unlikely to be acidic sources.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

In-depth analysis on parallel processing patterns for high-performance Dataframes

The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are bringing more complexities to data engineering applications, which are now integrated into data processing pipelines to process terabytes of data. Typically, a significant amount of time is spent on data preprocessing in these pipelines, and hence improving its efficiency directly impacts the overall pipeline performance. The community has recently embraced the concept of Dataframes as the de-facto data structure for data representation and manipulation. However, the most widely used serial Dataframes today (R, pandas) experience performance limitations while working on even moderately large data sets. We believe that there is plenty of room for improvement by taking a look at this problem from a high-performance computing point of view. In a prior publication, we presented a set of parallel processing patterns for distributed dataframe operators and the reference runtime implementation, Cylon. In this paper, we are expanding on the initial concept by introducing a cost model for evaluating the said patterns. Furthermore, we evaluate the performance of Cylon on the ORNL Summit supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

HERO WEC 2024 Hydraulic Configuration Deployment Data

The following submission includes raw and processed data from the in water deployment of NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC), in the form of parquet files, TDMS files, CSV files, bag files and MATLAB workspaces. This dataset was collected in March 2024 at the Jennette's pier test site in North Carolina. This submission includes the following: - Data description document (HERO WEC FY24 Hydraulic Deployment Data Descriptions.doc) - This document includes detailed descriptions of the type of data and how it was processed and/or calculated. - Processed MATLAB workspace - The processed data is provided in the form of a single MATLAB workspace containing data from the full deployment. This workspace contains data from all sensors down sampled to 10 Hz along with all array Value Added Products (VAPs). - MATLAB visualization scripts - The MATLAB workspaces can be visualized using the file "HERO_WEC_2024_Hydraulic_Config_Data_Viewer.m/mlx". The user simply needs to download the processed MATLAB workspaces, specify the desired start and end times and run this file. Both the .m and .mlx file format has been provided depending on the user's preference. - Summary Data - The fully processed data was used to create a summary data set with averages and important calculations performed on 30-minute intervals to align with the intervals of wave resource data reported from nearby CDIP ocean observing buoys located 20km East of Jennette's pier and 40km Northeast of Jennette's pier. The wave resource data provided in this data set is to be used for reference only due the difference in water depth and proximity to shore between the Jennette's pier test site and the locations of the ocean observing buoys. This data is provided in the Summary Data zip folder, which includes this data set in the form of a MATLAB workspace, parquet file, and excel spreadsheet. - Processed Parquet File - The processed data is provided in the form of a single parquet file containing data from all HERO WEC sensors collected during the full deployment. Data in these files has been down sampled to 10 Hz and all array VAPs are included. - Interim Filtered Data - Raw data from each sensor group partitioned into 30-minute parquet files. These files are outputs from an intermediate stage of data processing and contain the raw data with no Quality Control (QC) or calculations performed in a format that is easier to use than the raw data. - Raw Data - Raw, unprocessed data from this deployment can be found in the Raw Data zip folder. This data is provided in the form of TDMS, CSV, and bag files in the original format output by the MODAQ system. - Python Data Processing Script - This links to an NREL public github repository containing the python script used to go from raw data to fully processed parquet files. Additional documentation on how to use this script is included in the github repository. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗
DOE OSTI2019

Estimated Darcy Velocities Using Temperature Time Series for Meander A of East River, Colorado

Heat was used as a tracer to quantify seasonal changes in vertical Darcy velocity through the streambed at three different locations about Meander A of East River, Colorado. The file Raw_Temp.csv represents raw temperature data (in degrees Celsius) collected from the three locations, A, B, and C, at depths of 5, 10, 20, and 35 cm below the sediment-water interface. Darcy velocity estimates were calculated from these data using 1DTempPro and are shown in 1DTempPro_estimates.csv file. Positive values represent upwelling conditions, while negative values indicate downwelling conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Methods and Models for Analyzing Methane Sorption Capacity of Coal Based on Its Physicochemical Characteristics

The authors study the influence of physicochemical parameters on methane adsorption capacity of coal and offer the analytical method for the methane adsorption capacity for three-phased condition of methane. It is found that in the depth interval to 300 m below surface, methane adsorption capacity measured in lab can exceed natural gas content of coal obtained from geological exploration data by 30%, and the change in the thermodynamic condition of coal–methane system brings irreversible physicochemical consequences in terms of the altered ratios of physical states of the main components. There is no linear connection between natural gas content of a coal bed and its methane adsorption capacity with respect to occurrence depth. The application of Big Data in treatment and interpretation of large data flows is described. The theoretical data predicted using the proposed method and the experimental data on methane content of Kuzbass coal agree.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network

Abstract Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein–protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision–recall curve of 0.63 and a Cohen’s Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network’s information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.

Wang, Xiaowen↗
DOE OSTI2023

CORE: A Global Aggregation Service for Open Access Papers

This paper introduces CORE, a widely used scholarly service, which provides access to the world’s largest collection of open access research publications, acquired from a global network of repositories and journals. CORE was created with the goal of enabling text and data mining of scientific literature and thus supporting scientific discovery, but it is now used in a wide range of use cases within higher education, industry, not-for-profit organisations, as well as by the general public. Through the provided services, CORE powers innovative use cases, such as plagiarism detection, in market-leading third-party organisations. CORE has played a pivotal role in the global move towards universal open access by making scientific knowledge more easily and freely discoverable. In this paper, we describe CORE’s continuously growing dataset and the motivation behind its creation, present the challenges associated with systematically gathering research papers from thousands of data providers worldwide at scale, and introduce the novel solutions that were developed to overcome these challenges. The paper then provides an in-depth discussion of the services and tools built on top of the aggregated data and finally examines several use cases that have leveraged the CORE dataset and services.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

Planck intermediate results: XLVI. Reduction of large-scale systematic effects in HFI polarization maps and estimation of the reionization optical depth

This study describes the identification, modelling, and removal of previously unexplained systematic effects in the polarization data of the Planck High Frequency Instrument (HFI) on large angular scales, including new mapmaking and calibration procedures, new and more complete end-to-end simulations, and a set of robust internal consistency checks on the resulting maps. These maps, at 100, 143, 217, and 353 GHz, are early versions of those that will be released in final form later in 2016. The improvements allow us to determine the cosmic reionization optical depth τ using, for the first time, the low-multipole EE data from HFI, reducing significantly the central value and uncertainty, and hence the upper limit. Two different likelihood procedures are used to constrain τ from two estimators of the CMB E- and B-mode angular power spectra at 100 and 143 GHz, after debiasing the spectra from a small remaining systematic contamination. These all give fully consistent results. A further consistency test is performed using cross-correlations derived from the Low Frequency Instrument maps of the Planck 2015 data release and the new HFI data. For this purpose, end-to-end analyses of systematic effects from the two instruments are used to demonstrate the near independence of their dominant systematic error residuals. The tightest result comes from the HFI-based τ posterior distribution using the maximum likelihood power spectrum estimator from EE data only, giving a value 0.055 ± 0.009. Finally, in a companion paper these results are discussed in the context of the best-fit PlanckΛCDM cosmological model and recent models of reionization.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data

Deep learning (DL) models trained on hydrologic observations can perform extraordinarily well, but they can inherit deficiencies of the training data, such as limited coverage of in situ data or low resolution/accuracy of satellite data. In this work, we propose a novel multiscale DL scheme learning simultaneously from satellite and in situ data to predict 9 km daily soil moisture (5 cm depth). Based on spatial cross-validation over sites in the conterminous United States, the multiscale scheme obtained a median correlation of 0.901 and root-mean-square error of 0.034 m 3 /m 3 . It outperformed the Soil Moisture Active Passive satellite mission's 9 km product, DL models trained on in situ data alone, and land surface models. Our 9 km product showed better accuracy than previous 1 km satellite downscaling products, highlighting limited impacts of improving resolution. Not only is our product useful for planning against floods, droughts, and pests, our scheme is generically applicable to geoscientific domains with data on multiple scales, breaking the confines of individual data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Untargeted, tandem mass spectrometry (LC/MS-MS) metaproteomes from soil samples in control and warming plots in Blodgett Forest, CA (2014-2021)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of Lawrence Berkeley National Laboratory (LBNL) Terrestrial Ecosystem Science (TES) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization. This package contains soil metaproteomics data in the context of site specific metagenomes from soil depth profiles in three paired control and warming plots from a temperate mixed forest in Northern California. Each paired plot had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. These metaproteomes were collected in 2018 after 4.5 years of warming from five depth intervals (0-10 cm, 10-30 cm, 30-45 cm, 45-60 cm, 60-80 cm). For protein identification, the collected spectra were searched following a target-decoy search strategy against a database of metagenome predicted proteins (covering 96 samples from 2014 to 2021) representing the complete sequence diversity at the site. Data was searched with mass spectrometry database search tool (MS-GF+) using Pacific Northwest National Laboratory (PNNL)'s Data Management System (DMS) Processing pipeline. The metagenomes are published as part of another data package. Raw metaproteomic data and the data products from MS-GF+ are deposited in the Mass Spectrometry Interactive Virtual Environment (MassIVE) database under accession no. MSV000097826. Here we present a dataset that includes spectral counts for the detected proteins across samples (EMSL50964_BrodieAllMAGs_Globals_SC.txt), the sequences of the detected proteins, and sample metadata file that contains site information for the soil metaproteome samples.

Belowground Biogeochemistry Science Focus Area↗
DOE OSTI2015

SU-E-T-676: Reproducibility and Consistency of Two SunNuclear 3D Scanning Tanks

Purpose: To determine if two Sun Nuclear 3D Scanning (SNC 3DS) tanks collect reproducible and consistent data and test the precision of the SNC Dosimetry auto-setup. Methods: Percent depth doses (PDDs) and profiles were collected on two SNC 3DS tanks with a Varian TrueBeam linear accelerator. SNC Dosimetry auto-setup application was used with CC13 ionization chambers. After auto-setup, collimator light field was checked manually against the position of the chamber. Comparing measured data for repeated measurements with tank 1 allowed evaluation of SNC-3DS auto-setup and tank reproducibility. Comparing measured data between tanks 1 and 2 allowed evaluation of consistency between tanks. Results: Preliminary results showed reproducibility of depth of maximum dose (Dmax) of 0.38mm for a 10cmx10cm field and 0.67mm for 30cmx30cm on a single tank. PDD values at 5cm, 10cm, and 20cm depths were reproducible within 0.26%. Consistency of Dmax between tanks was 0.17mm for a 10cmx10cm field and 0.44mm for 30cmx30cm. PDD values at 5cm, 10cm, and 20cmwere consistent within 0.06%. Profiles showed reproducibility in field width within 0.4mm for a 10cmx10cm field and 0.7mm for a 30cmx30cm field. Profiles showed consistency in field width within 0.2mm for 10cmx10cm and 30cmx30cm field sizes. Penumbra width was reproducible and consistent to under 0.5mm except for 30cmx30cm field size at 30cm depth where the reproducibility was 2.2mm and the consistency was 2.6mm. Conclusion: In conclusion, the SNC 3DS tank shows good reproducibility in measured data. Since the tank to tank variation in measured data is within the uncertainty of repeated single tank measurements the tanks also perform consistently.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2018

The Dark Energy Survey Data Release I

We describe the first public data release of the Dark Energy Survey, DES DR1, consisting of reduced single-epoch images, co-added images, co-added source catalogs, and associated products and services assembled over the first 3 yr of DES science operations. DES DR1 is based on optical/near-infrared imaging from 345 distinct nights (2013 August to 2016 February) by the Dark Energy Camera mounted on the 4 m Blanco telescope at the Cerro Tololo InterAmerican Observatory in Chile. We release data from the DES wide-area survey covering similar to 5000 deg(2) of the southern Galactic cap in five broad photometric bands, grizY. DES DR1 has a median delivered point-spread function of g = 1.12, r = 0.96, i = 0.88, z = 0.84, and Y = 0.'' 90 FWHM, a photometric precision of <1% in all bands, and an astrometric precision of 151 mas. The median co-added catalog depth for a 1.'' 95 diameter aperture at signal-to-noise ratio (S/N) = 10 is g = 24.33, r = 24.08, i = 23.44, z = 22.69, and Y = 21.44 mag. DES DR1 includes nearly 400 million distinct astronomical objects detected in similar to 10,000 co-add tiles of size 0.534 deg(2) produced from similar to 39,000 individual exposures. Benchmark galaxy and stellar samples contain similar to 310 million and similar to 80 million objects, respectively, following a basic object quality selection. These data are accessible through a range of interfaces, including query web clients, image cutout servers, jupyter notebooks, and an interactive co-add image visualization tool. DES DR1 constitutes the largest photometric data set to date at the achieved depth and photometric precision.

Abbott, T. M. C.↗
DOE OSTI2023

New method for level-lifetime measurements with thick scintillators

Level lifetimes provide key insight into the structure of atomic nuclei and serve as stringent tests of theoretical descriptions. Though several methods for determining level lifetimes exist for both reaction measurements and decay studies, here the focus is on techniques involving the direct measurement of time differences between population and subsequent depopulation of excited states. The techniques presented herein are broadly applicable across multiple timing ranges, but the approach is specifically described for the βγ timing method. A multi-step, amplitude-dependent time walk correction was employed to address the data analysis complications that arise from using thick scintillators for electron detection. Further, additional corrections for the depth of the interaction must also be performed when the parent isotope has been implanted into the detector. Techniques are presented for performing these time walk and depth of interaction corrections. Subsequently, a new Monte Carlo method utilizing measured detector responses obtained from the data, coupled with chi-square minimization, is presented for extracting excited state lifetimes ≳ 100 picoseconds. The framework of this Monte Carlo method is developed for the decay of a state in 68 Zn with a known 1.6 ps half life, which is considered prompt given the detection sensitivity of the technique, and then benchmarked using two other excited states in neutron-rich Ni isotopes with 120(34) ps and 1.05(3) ns half lives. Using this new method which takes into account the thick scintillator used, these same half lives were measured to be 135(10) ps and 1.04(24) ns, respectively. The overall good agreement demonstrates the validity of the technique.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Projected sensitivity of the SuperCDMS SNOLAB experiment

SuperCDMS SNOLAB will be a next-generation experiment aimed at directly detecting low-mass particles (with masses ≤ 10 GeV/c^2) that may constitute dark matter by using cryogenic detectors of two types (HV and iZIP) and two target materials (germanium and silicon). The experiment is being designed with an initial sensitivity to nuclear recoil cross sections ~1×10^-43 cm^2 for a dark matter particle mass of 1 GeV/c^2, and with capacity to continue exploration to both smaller masses and better sensitivities. The phonon sensitivity of the HV detectors will be sufficient to detect nuclear recoils from sub-GeV dark matter. A detailed calibration of the detector response to low-energy recoils will be needed to optimize running conditions of the HV detectors and to interpret their data for dark matter searches. Low-activity shielding, and the depth of SNOLAB, will reduce most backgrounds, but cosmogenically produced H-3 and naturally occurring Si-32 will be present in the detectors at some level. Even if these backgrounds are 10 times higher than expected, the science reach of the HV detectors would be over 3 orders of magnitude beyond current results for a dark matter mass of 1 GeV/c^2. The iZIP detectors are relatively insensitive to variations in detector response and backgrounds, and will provide better sensitivity for dark matter particles with masses ≳5 GeV/c^2. The mix of detector types (HV and iZIP), and targets (germanium and silicon), planned for the experiment, as well as flexibility in how the detectors are operated, will allow us to maximize the low-mass reach, and understand the backgrounds that the experiment will encounter. Upgrades to the experiment, perhaps with a variety of ultra-low-background cryogenic detectors, will extend dark matter sensitivity down to the “neutrino floor,” where coherent scatters of solar neutrinos become a limiting background.

Agnese, R.↗
DOE OSTI2014

WE-G-9A-01: Radiation Oncology Outcomes Informatics

The construction of databases and support software to enable routine and systematic aggregation, analysis and reporting of patient outcomes data is emerging as an important area. “How have results for our patients been affected by the improvements we have made in our practice and in the technologies we use?” To answer this type of fundamental question about the overall pattern of efficacy observed, it is necessary to systematically gather and analyze data on all patients treated within a clinic. Clinical trials answer, in great depth and detail, questions about outcomes for the subsets of patients enrolled in a given trial. However, routine aggregation and analysis of key treatment parameter data and outcomes information for all patients is necessary to recognize emergent patterns that would be of interest from a public health or practice perspective and could better inform design of clinical trials or the evolution of best practice principals. To address these questions, Radiation Oncology outcomes databases need to be constructed to enable combination essential data from a broad group of data types including: diagnosis and staging, dose volume histogram metrics, patient reported outcomes, toxicity metrics, performance status, treatment plan parameters, demographics, DICOM data and demographics. Developing viable solutions to automate aggregation and analysis of this data requires multidisciplinary efforts to define nomenclatures, modify clinical processes and develop software and database tools requires detailed understanding of both clinical and technical issues. This session will cover the developing area of Radiation Oncology Outcomes Informatics. Learning Objectives: Audience will be able to speak to the technical requirements (software, database, web services) which must be considered in designing an outcomes database. Audience will be able to understand the content and the role of patient reported outcomes as compared to traditional toxicity measures. Audience will be understand approaches, clinical process changes, consensus building efforts and standardizations which must be addressed to succeed in a multi-disciplinary effort to aggregate data for all patients. Audience will be able to discuss technical and process issues related to pooling data among institutions in the context of collaborative studies among the presenting institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Subsoil Potassium in Central Iowa Soils: Status and Future Challenges

A reevaluation of plant-available K within central Iowa soils is needed, especially if crop residues are harvested for animal feed or cellulosic feedstock. This study evaluated plant-available K concentrations to depths of 120 cm (n = 2433) within soil profiles at 10 experimental sites. Data from 0- to 5-, 5- to 15-, 0- to 15-, 15- to 30-, 30- to 60-, 60- to 90-, and 90- to 120-cm depths were compiled and evaluated. Based on over 400 samples collected from each depth increment below 15 cm, a reference value of 100 mg kg –1 appears common for central Iowa subsoils. Sixty-four percent of all samples had soil-test K levels below the 120 mg kg –1 threshold for very low nutrient status for crop production in Iowa. Only 28% of surface soil samples (<15-cm depth) were considered to have at least optimum nutrient status with concentrations greater than 160 mg kg –1 . As K is not an environmentally sensitive nutrient, its management in central Iowa soils has generally been neglected in recent years, particularly when compared with N and P management initiatives. Building awareness of soil K profile concentrations, rather than focusing only on surface concentrations, will be necessary to meet nutrient requirements for 21st century agricultural production.

54 ENVIRONMENTAL SCIENCES↗