Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data aggregation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Dynamics of Organophosphate‐Induced Structural Changes in Acetylcholinesterase Revealed by Time‐Resolved Small‐Angle X‐Ray Scattering and Inelastic Neutron Scattering

Organophosphates (OPs), the active compounds in nerve agents and insecticides such as paraoxon (POX), exert their toxic effects by irreversibly inhibiting acetylcholinesterase (AChE). Currently available antidotes for OP toxicity can reactivate AChE, but reactivation rates are slow which can lead to irreversible nervous system damage or death. We hypothesize that better antidotes with accelerated reactivation rates can be developed by understanding the structural dynamics of OP‐AChE conjugates. Previous steady‐state small‐angle X‐ray scattering (SAXS) experiments showed that POX and certain other OPs induce quaternary structural changes in human AChE (hAChE) that were reversible by oxime antidotes. To characterize the dynamics of these OP‐induced structural changes we have used time‐resolved SAXS (TR‐SAXS) and inelastic neutron scattering (INS). Stopped‐flow TR‐SAXS experiments were performed at BL‐4 at the Stanford Synchrotron Radiation Lightsource by mixing a molar excess of POX (500 μM) with hAChE (50 μM). Scattering data were collected every 20 msec during the first 1000 msec after mixing. Changes in the solution structure of hAChE during this period occurred with k obs = 0.13(±0.01) s −1 . This rate of structural change is comparable to the rate of hAChE inactivation observed using biochemical assays. INS data were collected at the Cold Neutron Chopper Spectrometer at the Spallation Neutron Source at Oakridge National Laboratory using 100 mg samples of hAChE and POX‐hAChE that were lyophilized and rehydrated in D 2 O. INS data collected at 120 K (to prevent translational motions) were used to compare the vibrational density states of native and POX‐conjugated hAChE. The POX‐conjugated hAChE exhibited an increased number of vibrational states, particularly those associated with regional motions such as loop dynamics, whereas global collective motions remained unchanged. Molecular dynamics simulations were used to simulate the vibrational spectra and identify the region(s) in hAChE that contributed to the differences in the INS experimental data. In aggregate, these studies provide experimental evidence for OP conjugate‐induced changes in hAChE dynamics that has to be considered in the design of antidotes for OP poisoning that have improved efficacy and patient outcomes. Support or Funding Information This work was supported by the CounterACT Program, National Institutes of Health Office of the Director, Grant 1U01NS083451 from NINDS, and by grants to SSRL and ORNL from the U.S. Department of Energy, and the National Institutes of Health. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Blumenthal, Donald K.↗
DOE OSTI2014

SU-D-BRD-04: A Logical Organizational Approach to Clinical Information Management

Purpose: To develop a clinical information management system (CIMS) that collects, organizes physician inputs logically and supports analysis functionality. Methods: In a conventional electronic medical record system (EMR), the document manager component stores data in a pool of standalone .docx or .pdf files. The lack of a content-based logical organization makes cross-checking, reference or automatic inheritance of information challenging. We have developed an information-oriented clinical record system that addresses this shortcoming. In CIMS, a parent library predefines a set of available questions along with the data types of their expected answers. The creation of a questionnaire template is achieved by selecting questions from this parent library to form a virtual group. Instances of the same data field in different documents are linked by their question identifier. This design allows for flexible data sharing and inheritance among various forms using a longitudinal lineage of data indexed according to the modification time stamps of the documents. CIMS is designed with a web portal to facilitate querying, data entry and modification, aggregate report generation, and data adjudication. The current implementation addresses diagnostic data, medical history, vital signs, and various quantities in consult note and treatment summaries. Results: CIMS is currently storing treatment summary information of over 1,000 patients who have received treatment at UCLA Radiation Oncology between March 1, 2013 and January 31, 2014. We are in the process of incorporating a DICOM-RT dosimetry parser and patient reporting applications into CIMS, as well as continuing to define document templates to support additional forms. Conclusion: We are able to devise an alternative storage paradigm which results in an improvement in the accuracy and organizational structure of clinical information.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

WE-G-17A-03: MRIgRT: Quantification of Organ Motion

Purpose: To develop an MRI-based methodology and tools required for the quantification of organ motion on a dedicated MRI-guided radiotherapy system. A three-room facility, consisting of a TrueBeam 6X linac vault, a 1.5T MR suite and a brachytherapy interventional room, is currently under commissioning at our institution. The MR scanner can move and image in either room for diagnostic and treatment guidance purposes. Methods: A multi-imaging modality (MR, kV) phantom, featuring programmable 3D simple and complex motion trajectories, was used for the validation of several image sorting algorithms. The testing was performed on MRI (e.g. TrueFISP, TurboFLASH), 4D CT and 4D CBCT. The image sorting techniques were based on a) direct image pixel manipulation into columns or rows, b) single and aggregated pixel data tracking and c) using computer vision techniques for global pixel analysis. Subsequently, the motion phantom and sorting algorithms were utilized for commissioning of MR fast imaging techniques for 2D-cine and 4D data rendering. MR imaging protocols were optimized (e.g. readout gradient strength vs. SNR) to minimize the presence of susceptibility-induced distortions, which were reported through phantom experiments and numerical simulations. The system-related distortions were also quantified (dedicated field phantom) and treated as systematic shifts where relevant. Results: Image sorting algorithms were validated for specific MR-based applications such as quantification of organ motion, local data sampling, and 4D MRI for pre-RT delivery with accuracy better than the raw image pixel size (e.g. 1 mm). MR fast imaging sequences were commissioning and imaging strategies were developed to mitigate spatial artifacts with minimal penalty on the image spatial and temporal sampling. Workflows (e.g. liver) were optimized to include the new motion quantification tools for RT planning and daily patient setup verification. Conclusion: Comprehensive methods were developed and validated for the quantification of organ motion with applications in MRI-guided RT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-206-11: The Effect of Table Height On CTDIvol and SSDE in CT Scanning: A Phantom Study

Purpose: Localizer projection radiographs acquired prior to CT scans are used to estimate patient size, affecting the function of Automatic Tube Current Modulation (ATCM) and calculation of the Size Specific Dose Estimate (SSDE). Due to geometric effects, the projected patient size varies with scanner table height and with the orientation of the localizer (AP versus PA). Consequently, variations in scanner table height may affect both CTDIvol and the calculated size-corrected dose index (SSDE). This study sought to characterize these effects. Methods: An anthropomorphic phantom was imaged using an AP localizer, followed by a diagnostic scan using ATCM and our institution’s routine abdomen protocol. This was repeated at various scanner table heights, recording the scanner-reported CTDIvol for each diagnostic scan. The width of the phantom was measured from the localizer and diagnostic images using in-house software. The measured phantom width and scanner-reported CTDIvol were used to calculate SSDE. This was repeated using PA localizers followed by diagnostic scans. Results: 1) The localizer-based phantom width varied by up to 54% of the nominal phantom width between minimum and maximum table heights. 2) Changing the table height caused a variation in scanner-reported CTDIvol of a factor greater than 4.6 when using a PA localizer and almost 2 when using an AP localizer. 3) SSDE, calculated from measured phantom size and scanner-reported CTDIvol, varied by a factor of more than 2.8 when using a PA localizer and almost 1.5 when using an AP localizer. Conclusion: Our study demonstrates that off-center patient positioning affects the efficacy of ATCM, more severely when localizers are acquired in the PA rather than AP projection. Further, patient positioning errors can cause a large variation in the calculated SSDE. This hinders interpretation of SSDE for individual patients and aggregate SSDE data when evaluating CT protocols and clinical practices.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Carbon nanoscrolls on the surface of nanocrystalline graphite and diamond films

Nanocrystalline graphite and diamond films with needlelike nanostructures on their surface have been obtained by plasma-enhanced chemical vapor deposition. According to the experimental data, these aggregates have the same nature for films of both types: they are tubular carbon nanoscrolls with a polygonal cross section. Nanoscrolls are formed by a helically folded graphene sheet; they look like twisted prisms. The needlelike prismatic structures have an average diameter in the range of 50‒500 nm, and their length reaches several micrometers. Possible mechanisms of formation of carbon nanostructures are discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Soil properties and root characteristics across four lowland Panamanian forests from 0 - 1 m soil depths

Objectives:Fine roots significantly influence ecosystem-scale cycling of nutrients, carbon (C), and water, yet there is limited understanding of how fine root traits vary across and within tropical forests, some of Earth's most C-rich ecosystems. The biomass of fine roots can impact soil carbon storage, as root mortality is a primary source of new carbon to soils. A positive relationship has been observed between fine root biomass and soil carbon stocks in Panama (Cusack et al 2018). Beyond biomass, root characteristics like specific root length (SRL) could also influence soil carbon, as roots with higher SRL are less dense and thinner, potentially decomposing more easily or promoting soil aggregation. Understanding the effects of root morphology and tissue quality on soil carbon storage and with soil properties in general can improve predictions of landscape-scale carbon patterns. We aggregated new data of root biomass, morphology and nutrient content at 0-10 cm, 10-20 cm, 20-50 cm and 50-100 cm depth increments across four distinct lowland Panamanian forests and paired with already published datasets (Cusack et al 2018; Cusack and Turner 2020) of soil chemistry from the same sites and soil depths to explore relationship between soil carbon stocks and root characteristics.Datasets included:The datasets provided include .csv and .xlsx files for fine root characteristics and soil chemistry from four different forests across 0-10 cm, 10-20 cm, 20-50 cm, and 50-100 cm depth increments. Root characteristics include live fine root biomass, dead fine root biomass, coarse root biomass, specific root length, root diameter, root tissue density, specific root area, root %N, root %C, and root C/N ratio. Soil chemistry data includes total carbon (TC), dissolved organic carbon (DOC), bulk density, total phosphorus (TP), available phosphorus (AEM Pi), and various Mehlich-extractable elements such as aluminum, calcium, iron, potassium, manganese, phosphorus, and zinc. Nitrogen content measures include ammonium, nitrate, total dissolved nitrogen (TDN), dissolved inorganic nitrogen (DIN), and dissolved organic nitrogen (DON). The dataset also includes total exchangeable bases (TEB) and effective cation exchange capacity (ECEC) in both centimoles of charge per kilogram and micromoles of charge per gram. The soil chemistry data was obtained from Cusack et al (2018) and Cusack and Turner (2020) and paired with root characteristics data for the same depth increments and sites. Additionally, a .kml file is provided with coordinates for all 32 plots included in the study across four forests (n = 8 plots per site). Root data was averaged across these 8 plots per site and soil data was collected in one pit in each site. This dataset serves as baseline data before a throughfall exclusion experiment, Panama Rainforest Changes with Experimental Drying (PARCHED), was implemented. No special software is needed to open these files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Mitigation Strategy for the Prediction Inconsistency of Neural Phase Pickers

Neural phase pickers—neural networks designed and trained to pick seismic phase arrivals—have proven to be a powerful tool for developing earthquake catalogs. However, these pickers suffer from prediction inconsistency in which the results they produce change, sometimes substantially, even under a small perturbation to the input waveform. This problem has not been addressed by the developers and users of these pickers. In this study, we show how prediction inconsistency can negatively affect the completeness of earthquake catalogs developed using neural phase pickers. Further, we show that simply using a small step size for the sliding window when processing continuous waveform data and aggregating the results significantly mitigates this problem. We also highlight the importance of training datasets for increasing the consistency and other performance metrics.

58 GEOSCIENCES↗
DOE OSTI2014

Anomaly Detection in Dynamic Networks

Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. A second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the communication counts. In a sequential analysis, anomalous behavior is then identified from outlying behavior with respect to the fitted predictive probability models. Seasonality is again incorporated into the model and is treated as a changepoint model on the transition probabilities of a discrete time Markov process. Second stage analytics are then developed which combine anomalous edges to identify anomalous substructures in the network.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

State Government Revenue Vulnerability Index and Local Government Revenue Vulnerability Index

The State Government Revenue Vulnerability Index (SGRVI) and Local Government Revenue Vulnerability Index (LGRVI) measure the vulnerability of government revenues by estimating monthly changes relative to a January 2020 baseline. Revenues included in both indices include: taxes on products and sales, transportation and housing revenues, individual income taxes, severance taxes and royalties, and property taxes. Local government revenues also include a local revenue from state revenue sharing component. In addition, the main index for the LGRVI for county-level governments includes revenue estimates for sub-county government units, including municipalities, school districts, and special districts. This revenue data is aggregated to produce the indices.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

MAPSTER: Automated Geospatial Data Sharing – Version 1.4.0

The US Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL) developed MAPSTER which is a geospatial data management tool that aggregates, organizes, and shares data from dispersed sources such as unmanned aerial systems (UAS). Built specifically for use in environments where communications may be limited, MAPSTER utilizes two key technologies to effectively manage data in the field and enable easy data sharing with authorized partners: Observer and Checkpoint. Observer is a lightweight software package on an edge device, such as a laptop, that automatically detects newly processed UAS data and sends to a central server called Checkpoint. Checkpoint is a centralized server at ORNL that receives and manages data from all Observer instances. Even in a very low bandwidth environment, Observer can still send information about the UAS data product almost instantly as it generates its own metadata package on the size of KB (kilobytes). MAPSTER is not only for UAS data but for any geospatial data collected at the austere edge and dispersed sources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

NREL Price Series Developed for the ARPA-E FLECCS Program

The price data for four regions (CAISO, ERCOT, MISO-W, and PJM-W) are developed using the ReEDS to PLEXOS conversion as described in (Gagnon et al. 2020). The reference ReEDS case chosen is based on the 2020 Standard Scenario Mid-case, which uses the 2020 ReEDS model version (Cole et al. 2020; Ho et al. 2021). All ReEDS model inputs use 2020 Standard Scenarios Mid-case assumptions except for CO2 prices, which are implemented as linearly increasing CO2 price trajectories beginning at $0/tCO2 in 2020 and ending at either $100/tCO2 or $150/tCO2 in 2035 to dive capacity expansion towards a low-carbon system that could support CCS deployment. However, these scenarios also prohibit CCS deployment in this time frame so that resulting price data are not influenced by the deployment and operation of CCS itself. Implementing the ReEDS to PLEXOS conversion tool, PLEXOS is then simulated using the 2035 ReEDS infrastructure for both CO2 price scenarios, with the following model version and setup: PLEXOS Version: 8.2 Solver: Xpress-MP 35.01.01 Mixed integer optimization relative gap 1% System configuration: • Total number of nodes: 134 (consistent with ReEDS balancing areas) • Line losses enforced using piecewise linear approximation • Energy dump was enabled Price data is aggregated to the ISO/RTO level using load-weighted averages. References: Cole, Wesley, Sean Corcoran, Nathaniel Gates, Daniel Mai, Trieu, and Paritosh Das. 2020. “2020 Standard Scenarios Report: A U.S. Electricity Sector Outlook.” NREL/TP-6A20-77442. Golden, CO: National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy21osti/77442.pdf. Gagnon, Pieter, Will Frazier, Elaine Hale, and Wesley Cole. 2020. “Cambium Documentation: Version 2020.” NREL/TP-6A20-78239. National Renewable Energy Lab. (NREL), Golden, CO (United States). https://doi.org/10.2172/1734551. Ho, Jonathan, Jonathon Becker, Maxwell Brown, Patrick Brown, Ilya (ORCID:0000000284917814) Chernyakhovskiy, Stuart Cohen, Wesley (ORCID:000000029194065X) Cole, et al. 2021. “Regional Energy Deployment System (ReEDS) Model Documentation: Version 2020.” NREL/TP-6A20-78195. Golden, CO: National Renewable Energy Laboratory. https://doi.org/10.2172/1788425.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Monitoring Extreme-scale Lustre Toolkit

We discuss the design and ongoing development of the Monitoring Extreme-scale Lustre Toolkit (MELT), a unified Lustre performance monitoring and analysis infrastructure that provides continuous, low-overhead summary information on the health and performance of Lustre, as well as on-demand, in-depth problem diagnosis and root-cause analysis. The MELT infrastructure leverages a distributed overlay network to enable monitoring of center-wide Lustre filesystems where clients are located across many network domains. We preview interactive command-line utilities that help administrators and users to observe Lustre performance at various levels of resolution, from individual servers or clients to whole filesystems, including job-level reporting. Finally, we discuss our future plans for automating the root-cause analysis of common Lustre performance problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Implications of Successes and Failures of BEV-Focused Incentive Support for PEVs in the U.S., Canada and Europe

An international comparative analysis of causes of variation of PEV sales rates per capita in selected U.S. states, Canadian provinces, and European Nations is conducted. 2014-15 light-duty PEV registrations/sales by make and model are examined, drawing heavily on 2014 data for aggregate comparisons. States, provinces, and nations with PEV success, but with widely varying PEV purchase incentives and charging infrastructure installations are examined. The paper focuses particularly presence or absence of long daily distance charging options for PEVs. Four questions are addressed. (1) European evidence is that PHEV powertrains are a very marketable option for large family vehicles. For small BEVs BMW i3 sales patterns indicate that range extension beyond 120 km but less than 240 km via gasoline significantly increases market share. BEV inter-city functionality provided by aggressive installation of DC fast charging was critical to overall PEV success in Norway. Norway, like Northern Europe and Canada has a utility network that is winter peaking, which allows provision of peak summer BEV long-distance charging needs without difficulty. This is not the case for the U.S., which is summer peaking. The reviewed states, provinces, and nations vary considerably in seasonal peak temperature extremes. These climate differences have a significant bearing on the local viability of PHEVs vs. BEVs. The long distance DC fast charging infrastructure investments needed to support BEV market success are not as large when PHEVs are preferred by consumers.

Battery electric vehicle↗
DOE OSTI2019

Grid Mix Explorer Version 4

The NETL Grid Mix Explorer allows users to browse regional electricity grid mixes and resulting generation and consumption life cycle inventory and impact data, as well as configure customized mixes of technologies and results. Data is aggregated to the Federal Energy Regulatory Commission (FERC) Market Regions (10) or Balancing Authority Area (BA) region (68). This generation mix represents the amount of electricity produced by each power plant type in that region. Note that this can be different than the mix that the region actually uses for electricity (consumption mix). The consumption mix includes the net imports from the various BAs into the selected region. Finally, the tool provides a comparison of the generation and consumption mixes of each of the subregions. As noted above, there can be a significant differences between the two, and the consumption mix is a more accurate reflection of the electricity actually being consumed in an area.

Source record↗
DOE OSTI2018

Data-Driven Network Reduction and Aggregation with DERs for Outage Management

A novel approach exploiting the growing number of Distributed Energy Resources (DERs) to enhance distribution system resiliency is presented. After a power outage, DERs and loads are aggregated into switch-level equivalent models, preserving, at the same time, controllability information to enable real-time outage management analysis. Pre-fault smart-meters measurements are used as inputs for Artificial Neural Networks and Cold Load Pick-Up (CLPU) modeling to obtain short-term load forecast, while an optimal restoration analysis is carried out to determine restored network topology and utility-controlled loads and DERs set-points

Menazzi, Matteo↗
DOE OSTI2019

Identification of aggregate building thermal dynamic model and unmeasured internal heat load from data

This paper is on the problem of simultaneously identifying the parameters of an aggregate thermal dynamic model of a multi-zone building and unknown disturbances from input-output data. An aggregate model is a single-zone equivalent of a multi-zone building, and is useful for many purposes, including model based control of large heating, ventilation and air conditioning (HVAC) equipment that delivers thermal energy to the entire building. A key challenge in identification is the presence of unknown disturbance since it is not measurable but non-negligible.We first present a principled method to aggregate a multizone building model into a single zone model. We then provide a method to identify thermal parameters and the unknown disturbance for this aggregate (single-zone) model. Finally, we test our proposed identification algorithm to data generated from a virtual building. A key insight provided by the aggregation method allows us to recognize under what conditions the estimation of the disturbance signal will be necessarily poor and uncertain.

Guo, Zhong↗
DOE OSTI2023

Developing and tuning a community scale energy model for a disadvantaged community

This work describes the development of a community-scale energy model for a mixed-use low-income community located in Huntington Beach, CA. An accurate community-scale energy model is useful for evaluating the use of limited capital resources used to invest in clean energy technologies. This work lays out the process of developing such a model while relying primarily on publicly available data and highlighting critical partnerships necessary for model development success. The primary contribution of this work is the demonstration of the process used to develop an accurate energy model for a disadvantaged community when minimal building and energy use data is available. The heart of the model is the physics-based community scale energy modeling platform URBANopt. Using a bottom-up load modeling approach, energy simulated energy use falls within 3% or less of aggregate annual utility data, and within 10% or less aggregate monthly utility data. The demonstrated model development and tuning process can be used by others to characterize other atypical communities, which may differ significantly from prototypical models.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗