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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multimodal X-ray nano-spectromicroscopy analysis of chemically heterogeneous systems

Abstract Understanding the nanoscale chemical speciation of heterogeneous systems in their native environment is critical for several disciplines such as life and environmental sciences, biogeochemistry, and materials science. Synchrotron-based X-ray spectromicroscopy tools are widely used to understand the chemistry and morphology of complex material systems owing to their high penetration depth and sensitivity. The multidimensional (4D+) structure of spectromicroscopy data poses visualization and data-reduction challenges. This paper reports the strategies for the visualization and analysis of spectromicroscopy data. We created a new graphical user interface and data analysis platform named XMIDAS (X-ray multimodal image data analysis software) to visualize spectromicroscopy data from both image and spectrum representations. The interactive data analysis toolkit combined conventional analysis methods with well-established machine learning classification algorithms (e.g. nonnegative matrix factorization) for data reduction. The data visualization and analysis methodologies were then defined and optimized using a model particle aggregate with known chemical composition. Nanoprobe-based X-ray fluorescence (nano-XRF) and X-ray absorption near edge structure (nano-XANES) spectromicroscopy techniques were used to probe elemental and chemical state information of the aggregate sample. We illustrated the complete chemical speciation methodology of the model particle by using XMIDAS. Next, we demonstrated the application of this approach in detecting and characterizing nanoparticles associated with alveolar macrophages. Our multimodal approach combining nano-XRF, nano-XANES, and differential phase-contrast imaging efficiently visualizes the chemistry of localized nanostructure with the morphology. We believe that the optimized data-reduction strategies and tool development will facilitate the analysis of complex biological and environmental samples using X-ray spectromicroscopy techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2016

In vivo dynamics of GFRα1-positive spermatogonia stimulated by GDNF signals using a bead transplantation assay

In mouse testes, spermatogonial stem cells (SSCs), a subpopulation of GFRα1 (GDNF family receptor-α1)-positive spermatogonia, are widely distributed along the convoluted seminiferous tubules. The proliferation and differentiation of the SSCs are regulated in part by local expression of GDNF (glial cell-derived neurotorphic factor), one of major niche factors for SSCs. However, the in vivo dynamics of the GDNF-stimulated GFRα1-positive spermatogonia remains unclear. Here, we developed a simple method for transplanting DiI-labeled and GDNF-soaked beads into the mouse testicular interstitium. By using this method, we examined the dynamics of GFRα1-positive spermatogonia in the tubular walls close to the transplanted GDNF-soaked beads. The bead-derived GDNF signals were able to induce the stratified aggregate formation of GFRα1-positive undifferentiated spermatogonia by day 3 post-transplantation. Each aggregate consisted of tightly compacted A{sub single} and marginal A{sub paired}–A{sub aligned} GFRα1-positive spermatogonia and was surrounded by A{sub aligned} GFRα1-negative spermatogonia at more advanced stages. These data not only provide in vivo evidence for the inductive roles of GDNF in forming a rapid aggregation of GFRα1-positive spermatogonia but also indicate the usefulness of this in vivo assay system of various growth factors for the stem/progenitor spermatogonia in mammalian spermatogenesis. - Highlights: • A novel bead transplantation assay was developed to examine the in vivo effects of growth factors on spermatogonia. • A rapid aggregation of GFRα1-positive spermatogonia was induced by the transplanted GDNF-soaked beads. • Tightly-compacted A{sub single} and marginal A{sub paired}–A{sub aligned} spermatogonia were formed in each GFRα1-positive aggregate.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization

In this paper, we propose a novel hardware accelerator for GCN inference called I-GCN that significantly improves data locality and reduces unnecessary computation through a new online graph restructuring algorithm we refer to as islandization. The proposed algorithm finds clusters of nodes with strong internal but weak external connections. The islandization process yields two major benefits. First, by processing islands rather than individual nodes, there is better on-chip data reuse and fewer off-chip memory accesses. Second, there is less redundant computation as aggregation for common/shared neighbors in an island can be reused. The parallel search, identification, and leverage of graph islands are all handled purely in hardware at runtime working in an incremental pipelined manner. This is done without any preprocessing of the graph data or adjustment of the GCN model structure.

Geng, Tong↗
DOE OSTI2022

The Exploitation of Data Reduction for Visualization

The disparity between the computational speed and storage bandwidth, as demonstrated in Figure 1, is a well known problem that grows with each successive generation. The visualization community is principally responding to this issue by using in situ to reduce which data must be written to storage. However, other communities are taking different, possibly complementary approaches. In particular, data compression is a common general approach to reduce storage demands. Data compression technologies are typically not designed with post processing in mind. The principal metrics measured are compression ratio, the improved bandwidth to storage, and the error introduced. It is assumed that data is inflated to its full size before any post processing can happen. Although when talking about bandwidth disparities, HPC’s dirty little secret is that no part of the memory nor interconnect hardware is increasing at the rate of computation. For example, the Summit supercomputer has a peak computation rate almost 10 times its predecessor, Titan, but only about 4 times the memory, less than twice the aggregate memory bandwidth, and almost no improvement in the interconnect bisection bandwidth. Naively inflating data for post processing does not help with limitations in the memory and interconnect systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Developing and Evaluating Metrics for Demand Flexibility in Buildings: Comparing Simulations and Field Data

Building demand flexibility (DF) has attracted significant attention in recent years among researchers, technology developers and control companies, aggregators, utilities, and many others. There are numerous challenges with today’s electricity systems such as managing peak demand capacity and integrating variable renewables into the grid. Flexible building loads can provide various grid services to help reduce electricity costs, smooth out renewables intermittency and balance supply and demand. Recognizing this, the US DOE is leading the Grid-interactive Efficient Buildings (GEB) initiative which includes research to evaluate the potential, availability and timing of flexible loads. In this paper we present load shed metrics for three building types – medium office, large office and retail store – and compare prototype simulation results with measured data from 12 actual buildings that participated in hot summertime utility demand response (DR) events. The DR strategies included varying zone temperature and reducing light levels. The magnitude of a key DF metric, “demand decrease intensity” (or “shed intensity”) (W/ft2), between the simulation results and field data are similar (14-32% differences) for both mean and median values, though the field data show much larger variation among DR events. The coefficient p-values from linear regression model tests showed that outside air temperature is a significant variable for the whole building shed intensity when the resetting zone temperature strategy is deployed. These findings support the concept of using prototype building simulation to estimate building DF and expanding future simulation research to additional building types and climate zones.

Liu, Jingjing↗
DOE OSTI2015

Kinetic modeling of particle size distribution of soot in a premixed burner-stabilized stagnation ethylene flame

A detailed model of soot formation is proposed, which consists of a gas-phase kinetic model for the pyrolysis and oxidation of selected hydrocarbon fuels and a kinetic mechanism of soot nucleation and mass/size growth through coagulation and surface reactions. The gas-phase model (Ranzi et al., 2012) was expanded to include the chemistry of Polycyclic Aromatic Hydrocarbons (PAHs) up to four-to-five ring PAHs, with a modular and hierarchical approach. The discrete sectional approach was employed to solve the size evolution of the particle size distribution function (PSDF). Analogy and similarity rules were employed to describe heterogeneous reaction kinetics of soot surface reactions. A variable collision efficiency was assumed for the coalescence of small soot particles. Larger particles were assumed to undergo aggregation. The predicted PSDFs are found to be in reasonably good agreement with the experimental data for nascent soot measured in an atmospheric-pressure premixed ethylene–oxygen–argon flame in the burner-stabilized stagnation flame configuration. Sensitivity analyses of the PSDF, number density, and volume fraction were carried out with respect to the rate parameters of addition reactions of acetylene, PAHs, resonantly stabilized radical reactions, and coalescence and aggregation. The findings demonstrate that the reaction of PAHs and acetylene with soot surfaces and the kinetics of coalescence and aggregation exhibit dominant effects on the detailed and global soot properties for the flame studied, in agreement with conclusions of a large range of previous modeling studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Aggregate attack surface management for network discovery of operational technology

Interconnectivity has become a substratum of technology as the benefits of data-driven functionality are being realized in nearly all industries. Increased connectivity of Operational Technology (OT) exacerbates cyber risks because Industrial Control Systems (ICS) are becoming exposed to the Internet. These exposures are often done inadvertently through misconfigurations as additional network devices come online. Attack surface management (ASM) platforms can be used to identify vulnerabilities by performing external network discovery over the Internet using web spiders. These web spiders enable big data analytics of Internet of Things (IoT) devices as identifiable information of Internet-exposed equipment are archived in searchable databases that are made publicly available. There are a multitude of ASM service providers on the market. Here, this study was conducted to evaluate several commonly known tools to determine the aggregate attack surface of control systems. Queries were crafted by targeting commonly known manufacturers and communication protocols found in OT networks. Identified devices were that categorized based on technology types. Each query was replicated between several tools to target identical ICS equipment. Findings in this paper suggested a significant variance in the exposures discovered by each tool, but unique contributions were identified for each tool when a merged attack surface was derived. Therefore, all tools should be used in aggregate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Using National-Scale Data to Inform Coal Power Plant Redevelopment and Coal Community Revitalization Planning

In the United States, individual coal plants have high variation in their construction and operation. Coal plants commonly have multiple units, with different commissioning ages, operational position, and maintenance conditions – even multiple fuels. These units may be in changing states of utilization, with variation in productivity depending on economic contexts. Coal plants themselves may be temporarily offline, mothballed, retired, or decommissioned due to owner/operator portfolios or market conditions, and the current posture of the plant may be difficult to ascertain without local news sources (Tarekegne et al. 2021). Anticipated dates of plant closures are prospectively represented to regulators and to the Energy Information Administration (EIA), but may also experience uncertainty due to the engineering, environmental, economic and social complexity of closing a very large power plant. As noted in this report, closure of the first unit to the last within a single plant may span more than 20 years. Yet tracking and predicting the position of our nation’s coal fleets on a national scale is deeply important. In developing a case for federal datasets and an approach to building and curating these data, this report identified that data must have high fidelity at the plant-level to be meaningful in aggregate. Plant-scale detail is also a match for its application: national priorities in federal investment range from economic revitalization for coal plant communities to maintaining reliable electric grid operations. This document is organized into a statistical review of coal plant retirements, past and prospective, in the United States; trends in the relationship between closures and existing federal investment programs for community revitalization; and potential methods for accelerating site redevelopment through advanced geospatial analysis based on federal data.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Discovering the Multisectoral Impacts of Global Energy Sector Outcomes Through Multiple Ensemble Aggregation Measures

Understanding complex human-Earth system interactions often involves analyzing large scenario ensembles that encompass a wide range of plausible futures. These ensembles often require aggregation to summarize information based on specific criteria or conditions. However, previous research using global change scenario ensembles has largely overlooked how the choice of aggregation method influences the interpretation of results. To address this gap, we leverage a large ensemble data set designed to capture broad energy system dynamics generated using the Global Change Analysis Model. We first explore how energy-related uncertainties are propagated to both global and regional water-energy-food sectors. We then conduct a rank correlation analysis across seven ensemble aggregation measures and demonstrate the need to consider multiple measures in global change scenarios. Our results suggest that global water and food sector outcomes in the 21st century vary widely depending on different scenario assumptions. The global energy productivity is projected to improve by the end of the century across all scenarios. Moreover, regions facing water scarcity challenges in 2100 do not always overlap with those facing extreme energy and food sector outcomes. Although rank correlations across seven aggregation measures are relatively stable across sectors, we identify cases where relying on a single measure leads to losing critical information in the full ensemble. Reliance on a single aggregation measure can distort the interpretation of global change scenario outcomes. Instead, adopting multiple ensemble aggregation measures provides a more holistic understanding of global change scenario ensembles.

Kim, Gijoo↗
DOE OSTI2026

Xanthos-Lake Dataset

The Xanthos-Lake v1.0 dataset provides the input data, trained machine-learning models, and simulation outputs needed to characterize lake water balance, snow and ice conditions, and mixing-layer temperature within the Xanthos global hydrological modeling framework. The dataset supports lake representation across a wide range of lake sizes and hydroclimatic conditions by combining xLSIM, a basin-specific machine-learning emulator of lake snow, ice, ice-cover fraction, and mixing-layer temperature, with the Xanthos-Lake water-balance model. The archive contains NetCDF datasets used to train and evaluate xLSIM, trained model weights, processed meteorological and lake-property inputs, and basin- and lake-category-specific simulation outputs. These materials are organized into four primary data groups, described below. Snowice_model_inputs: Contains the NetCDF input data used to train xLSIM. The xLSIM machine-learning framework uses three lake-based datasets. The meteorological forcing dataset provides monthly relative humidity, specific humidity, surface wind speed, maximum and minimum air temperature, downward longwave and shortwave radiation, snowfall, surface air pressure, and total precipitation. Lake surface area is included as an additional static predictor. The target-state dataset provides lake ice thickness, snow depth, snow cover, and lake mixing-layer temperature, while a companion lake-surface dataset provides the lake ice-cover fraction. Before training, ice thickness and snow depth are converted from meters to centimeters, mixing-layer temperature is converted from kelvin to degrees Celsius and constrained to nonnegative values, and ice-cover fraction is converted from a fraction to a percentage. The predictor variables are normalized using statistics calculated across the selected lakes and time steps. Snowice_model_outputs: Contains the NetCDF outputs generated by xLSIM. For each basin, xLSIM produces a file containing observed and predicted lake-state variables for the training, validation, and testing periods. The modeled variables include lake ice thickness, snow depth, snow cover, mixing-layer temperature, and lake ice-cover fraction. For basins without a sufficiently persistent snow-and-ice signal, the emulator predicts only mixing-layer temperature. The outputs also include training and validation loss histories, the selected model configuration, identifiers of the lakes used in training, and SHAP-based feature-importance information at the global, lake, and seasonal-regime levels. The trained machine-learning model weights are provided separately within the dataset archive. Together, these files support model evaluation and subsequent coupling with the Xanthos-Lake water-balance framework. XanthosLAKES: Contains the NetCDF input data used by the Xanthos-Lake framework. Monthly meteorological inputs include relative and specific humidity, downward shortwave and longwave radiation, mean, maximum, and minimum air temperature, wind speed, precipitation, snowfall, and surface air pressure. Static lake-property datasets provide lake identifiers, geographic locations, surface area, volume, mean depth, elevation, drainage area, fetch, outlet-routing information, and associated Xanthos grid-cell attributes. Separate bathymetric datasets provide the coefficients of the area–depth and volume–depth relationships for each aggregated lake unit. GLEV-based records provide observed lake surface area and evaporation data used to initialize lake states, define reference conditions, and calibrate and evaluate the model. Xanthos-Lake Outputs: Contains the basin- and lake-category-specific NetCDF outputs generated by Xanthos-Lake. Monthly variables include lake surface area, storage volume, outlet discharge, evaporation rate, evaporation volume, lake–groundwater exchange, lake inflow, ice thickness, snow depth, snow-cover fraction, ice-cover fraction, and mixing-layer temperature. The files also contain lake-specific calibration and validation statistics, including normalized root-mean-square error, mean absolute error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias. Stored calibrated and derived parameters include the weir discharge coefficient, fractional freeboard, groundwater exchange coefficient, reference water level, corresponding reference surface area and storage volume, weir-width adjustment factor, and the fraction of routed inflow entering the lake. Basin identifiers, lake category, simulation period, calibration and validation periods, and parameter-schema information are retained as NetCDF metadata.

Abeshu, Guta [Pacific Northwest National Laborator↗
DOE OSTI2023

High-bandwidth density silicon photonic resonators for energy-efficient optical interconnects

The growth of artificial intelligence applications demands ever larger and more complex deep learning models, dominating today's—and tomorrow's—data center and high-performance computing systems. While traditional electronics are failing to keep pace with application demands, silicon photonic (SiPh) interconnects have emerged as a necessary technology to support these systems. SiPh-driven wavelength-division multiplexing (WDM) offers a particularly promising path toward supporting incredibly high-aggregate link bandwidth in a compact and efficient form factor. One of the basic building blocks of these integrated WDM interconnects is the SiPh resonator. Their inherent wavelength selectivity and compact footprint allow for efficient data transmission multiplexed across dozens of carrier wavelengths. Used as add-drop (AD) filters, SiPh resonators are critical to constructing integrated tunable wavelength-selective optical circuit switches as well as for demultiplexing the different carrier wavelengths toward independent wavelength-insensitive photodiodes in a dense wavelength-division multiplexing receiver. Resonators in the all-pass (AP) configuration are widespread as well, allowing for wavelength-selective modulation to drive aggregate link bandwidths far beyond the individual channel data rate. Unlike SiPh Mach–Zehnder modulators (MZM), resonant modulators can be driven using low, complementary metal-oxide-semiconductor drive voltages, allowing for tight co-integration between photonic integrated circuits, fabricated with larger process node technologies, and electronic integrated circuits, designed to exploit the advantages of the latest node. To push toward practical peta-scale interconnects, a comprehensive review of SiPh resonators is required, addressing bottlenecks and design constraints at both the architecture and device levels. We first describe the predominant integrated link architectures and identify their limits. We then discuss the device-level design considerations that can be made for both AD and AP configuration resonators to overcome the system level limits with novel resonator device designs. Analytical models and numerical simulation of resonators are validated by experimental measurement of devices fabricated in a commercial 300-mm foundry, showing a clear path toward volume manufacturing. The demonstrated resonant modulators and filters support the feasibility of increasing the aggregate bandwidth of resonator-driven SiPh interconnects into the peta-scale regime.

42 ENGINEERING↗
DOE OSTI2019

Predicting the mechanical response of oligocrystals with deep learning

In this study, we employ data-driven homogenization approaches to predict the particular mechanical evolution of polycrystalline aggregates with tens of individual crystals. In these oligocrystals the differences in stress response due to microstructural variation is pronounced. Shell-like structures produced by metal-based additive manufacturing and the like make the prediction of the behavior of oligocrystals technologically relevant. The predictions of traditional homogenization theories based on grain volumes are not sensitive to variations in local grain neighborhoods. Direct simulation of the local response with crystal plasticity finite element methods is more detailed, but the computations are expensive. To represent the stress-strain response of a polycrystalline sample given its initial grain texture and morphology we have designed a novel neural network that incorporates a convolution component to observe and reduce the information in the crystal texture field and a recursive component to represent the causal nature of the history information. This model exhibits accuracy on par with crystal plasticity simulations at minimal computational cost per prediction.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Design and impact assessment of watershed investments: An approach based on ecosystem services and boundary work

Watershed investments, whose main aim is to secure water for cities, represent a promising opportunity for large-scale sustainability transitions in the near future. If properly designed, they promote activities in the watershed that enhance ecosystem services while protecting nature and biodiversity, as well as achieving other societal goals. In this paper, we build on the concepts of ecosystem services and boundary work, to develop and test an operative approach for designing and assessing the impact of watershed investments. The approach is structured to facilitate negotiations among stakeholders. Its strategic component includes setting the agenda; defining investment scenarios; and assessing the performance of watershed investments as well as planning for a follow-up. Its technical component concerns data processing; tailoring spatially explicit ecosystem service models; hence their application to design a set of “investment portfolios”, generate future land use scenarios, and model impacts on selected ecosystem services. A case study illustrates how the technical component can be developed in a data scarce context in sub-Saharan Africa in a way that is functional to support the steps of the strategic component. The case study addresses soil erosion and water scarcity-related challenges affecting Asmara, a medium-sized city in Eritrea, and considers urban water security and rural poverty alleviation as two illustrative objectives, within a ten-year planning horizon. The case study results consist in spatially explicit data (investment portfolio, land use scenario, impact on ecosystem services), which were aggregated to quantitatively assess the performance of different watershed investments scenarios, in terms of changes in soil erosion control. By addressing stakeholders' concerns of credibility, saliency, and legitimacy, the approach is expected to facilitate negotiation of objectives, definition of scenarios, and assessment of alternative watershed investments, ultimately, to contribute to implementing an adaptive watershed management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Integrated computational materials engineering of corrosion resistant alloys

Structure, composition and surface properties dictate corrosion resistance in any given environment. The degrees of freedom in alloy design are too numerous in emerging materials such as high entropy alloys and bulk metallic glasses for the use of high-throughput methods or trial and error. We review three domains of knowledge that can be applied towards the goal of corrosion resistant alloy (CRA) design: (a) the aggregation of knowledge gained through experience in developing CRAs empirically, (b) data-driven approaches that use descriptive metrics for alloy composition optimization, and (c) first-principles models of elementary processes that regulate corrosion informed by theory and inspired by phenomenological models in the literature. A path forward for integrated computational materials engineering (ICME) of CRAs that unites these three knowledge domains is introduced.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Incorporation of 16 O-rich anhydrous silicates in the protolith of highly hydrated asteroid Ryugu

The abundant phyllosilicate and carbonate minerals characterizing most of the returned particles from asteroid Ryugu suggest a history of extensive aqueous alteration on its parent body, similar to the rare mineralogically altered, but chemically primitive, CI (Ivuna-type) chondrite meteorites. Particle C0009 differs mineralogically from other Ryugu particles examined so far by containing anhydrous silicates at a level of ~0.5 vol%, and thus can help shed light on the unaltered original materials that constituted Ryugu’s protolith. In situ oxygen isotope measurements of the most Mg-rich olivine and pyroxene in C0009 reveal two populations of Δ 17 O: –25‰ to –15‰ and –8‰ to –3‰. The former and the latter populations correlate well with silicate morphologies similar to those seen in amoeboid olivine aggregates and chondrule phenocrysts, respectively, both of which are abundant in less aqueously altered carbonaceous chondrites. Here, this result also highlights the presence of olivine with Δ 17 O close to the solar value in either a CI chondrite or an asteroid with CI-chondrite characteristics, and provides strong evidence that amoeboid olivine aggregates and Mg-rich chondrules accreted into Ryugu’s protolith. Our data also raise the possibility that the protoliths of CI and other carbonaceous chondrites incorporated similar anhydrous silicates.

58 GEOSCIENCES↗
DOE OSTI2017

New donor polymer with tetrafluorinated blocks for enhanced performance in perylenediimide-based solar cells

The synthesis of a new tetrafluorinated semiconducting donor polymer, poly[(4,8-bis(5-(2-ethylhexyl)-4-fluorothiophene-2-yl)-benzo[1,2-b:4,5-b']dithiophene)-alt-(5,6-difluoro-4,7-(4-(2-ethylhexyl)-dithien-2-yl-2,1,3-benzothiadiazole)] (PBTZF4), and its photovoltaic performance in bulk heterojunction (BHJ) blends with the non-fullerene molecular acceptor [1,2:3,4]-bis-[N,N'-bis-1-pentylhexyl-perylenediimide-1,12-yl]-benzene (bPDI2P), are reported. PBTZF4:bPDI2P solar cells exhibit a high open circuit voltage (V oc ) of 1.118 V, a short circuit current density (J sc ) of 10.02 mA cm –2 , and a fill factor (FF) of 49.5%, affording a power conversion efficiency (PCE) of 5.55%. Interestingly, a lower PCE of 3.68% is obtained with the difluorinated analogue, poly[(4,8-bis(5-(2-ethylhexyl)-thiophene-2-yl)-benzo[1,2-b:4,5-b']dithiophene)- alt -(5,6-difluoro-4,7-(4-(2-ethyl-hexyl)-dithien-2-yl-2,1,3-benzothiadiazole)] (PBTZF2). Both PBTZF4:bPDI2P and PBTZF2:bPDI2P cells benefit from complementary (donor/acceptor) light absorption and very low geminate recombination, with bimolecular recombination being the dominant loss mechanism, as established by femtosecond transient absorption spectroscopy. DFT computation and physicochemical characterization data argue that the “additional” tetrafluorination planarizes the PBTZF4 backbone and enhances aggregation versus PBTZF2, affording superior charge carrier transport as assayed by field-effect mobility. In addition, fluorine-originated HOMO stabilization, –5.41 eV for PBTZF4 versus –5.33 eV for PBTZF2, and a superior blend microstructure afford a higher PBTZF4:bPDI2P solar cell PCE versus PBTZF2:bPDI2P.

36 MATERIALS SCIENCE↗
DOE OSTI2016

TU-D-201-04: Veracity of Data Elements in Radiation Oncology Incident Learning Systems

Purpose: Incident learning systems encompass volumes, varieties, values, and velocities of underlying data elements consistent with the V’s of big data. Veracity, the 5th V however exists only if there is high inter-rater reliability (IRR) within the data elements. The purpose of this work was to assess IRR in the nationally deployed RO-ILS: Radiation Oncology-Incident Learning System (R) sponsored by the American Society for Radiation Oncology (ASTRO) and the American Association of Physicists in Medicine (AAPM). Methods: Ten incident reports covering a wide range of scenarios were created in standardized narrative and video formats and disseminated to 67 volunteers of multiple disciplines from 26 institutions along with two published narratives from the International Commission of Radiological Protection to assess IRR on a nationally representative level. The volunteers were instructed to independently enter the associated data elements in a test version of RO-ILS over a 3-week period. All responses were aggregated into a spreadsheet to assess IRR using free-marginal kappa metrics. Results: 48 volunteers from 21 institutions completed all reports in the study period. The average kappa score for all raters across all critical data elements was 0.659 [range 0.326–1.000]. Statistically significant differences (p <0.05) were noted between reporters of different disciplines and raters with varying levels of experience. Kappa scores were high for event classification (0.781) and contributory factors (0.777) and low for likelihood-of-harm (0.326). IRR was highest among AAPM-ASTRO members (0.672) and lowest among trainees (0.463). Conclusion: A moderate-to-substantial level of IRR in RO-ILS was noted in this study. Although the number of events reviewed in this study was small, opportunities for improving the taxonomy for the lower scoring data elements as well as specific educational targets for training were identified by assessing data veracity quantitatively. This is expected to improve the quality of the data garnered from RO-ILS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

A Retrospective Analysis of the Benefits and Impacts of U.S. Renewable Portfolio Standards

This report, the second in the series, analyzes historical benefits and impacts of all state RPS policies, in aggregate, employing a consistent and well-vetted set of methods and data sets. The analysis focuses on three specific benefits: greenhouse gas emissions, air pollution, and water use. It also analyzes three other impacts: gross job additions, wholesale electricity market price suppression, and natural gas price suppression. These are an important subset, but by no means a comprehensive set, of all possible effects associated with RPS policies. These benefits and impacts are also subject to many uncertainties, which are described and, to the extent possible, quantified within the report.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗