Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Data staging”

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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Survival Impact of Adjuvant Radiation Therapy in Masaoka Stage II to IV Thymomas: A Systematic Review and Meta-analysis

Purpose: To evaluate the survival impact of postoperative radiation therapy (PORT) in stage II to IV thymomas, using systematic review and meta-analysis. Methods and Materials: A database search was conducted with EMBASE, PubMed, Web of Science, Cochrane Library, and Ovid from inception to August 2015. Thymic carcinomas were excluded, and studies comparing overall survival (OS) with and without PORT in thymomas were included. The hazard ratios (HRs) of OS were extracted, and a random-effects model was used in the pooled analysis. Results: Seven retrospective series with a total of 1724 patients were included and analyzed. Almost all of the patients underwent macroscopically complete resection, and thymoma histology was confirmed by the World Health Organization criteria. In the overall analysis of stage II to IV thymomas, OS was not altered with the receipt of PORT (HR 0.79, 95% confidence interval [CI] 0.58-1.08). Although PORT was not associated with survival difference in Masaoka stage II disease (HR 1.45, 95% CI 0.83-2.55), improved OS was observed with the addition of PORT in the discrete pooled analysis of stage III to IV (HR 0.63, 95% CI 0.40-0.99). Significant heterogeneity and publication bias were not found in the analyses. Conclusions: From the present meta-analysis of sole primary thymomas, we suggest the potential OS benefit of PORT in locally advanced tumors with macroscopically complete resection, but not in stage II disease. Further investigations with sufficient survival data are needed to establish detailed treatment indications.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Passive and active peer effects in the spatial diffusion of residential solar panels: A case study of the Las Vegas Valley

This research analyzes the role of peer influences on the adoption of residential rooftop solar photovoltaic panels (PV) within the context of the Diffusion of Innovation Theory. PV literature indicates that adopters are influenced by word of mouth (WOM) information exchange with peers, i.e., active peer effects, while other studies suggest that living near households with visible rooftop PV installations influences adoption, i.e., passive peer effects. We bridge the gap in this literature by conducting a mixed methods analysis. We develop and administer a survey to Las Vegas Valley (LVV) residents to identify current and potential PV adopters' perceptions of peer-effects and consumer intention variables. We conduct a spatial analysis of Google's Project Sunroof data to identify LVV neighborhoods in the later stages of the PV diffusion process, i.e., those with the highest PV adoption rates. Key results show that current PV adopters living in early diffusion areas report significantly higher active and passive peer effects compared to adopters in later diffusion areas. Potential adopters in later diffusion areas report higher passive peer effects than those in early diffusion areas. Overall, because LVV has a low PV adoption rate (<3%), short term strategies aimed at increasing PV adoption should emphasize WOM active peer effects. Here, we caution against long-term green marketing strategies focusing solely on peer-effects as the PV market matures.

14 SOLAR ENERGY↗
DOE OSTI2025

Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions

Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall R 2 values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4% to 8.4% for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. In conclusion, these findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.

Environmental drivers↗
DOE OSTI2026

Life Cycle Analysis of Natural Gas Supply Chain and End Use Applications in the United States

Natural gas (NG) plays a crucial role in current and future energy systems in the United States due to its abundance and affordability. In this study a life cycle analysis of the NG supply chain in the United States was conducted using Argonne's R&D GREET model, examining stages from recovery to distribution using reported field data processed and documented by National Energy Technology Laboratory. Supply chain emissions were evaluated across multiple spatial scales, including national average, overall regional production, region-to-region, and basin-to-region scenarios. The GHG intensity of the U.S. average NG supply chain was estimated at 10.3 kg CO 2 e/MMBtu (lower heating value), with a range across regions from 7.8 kg CO 2 e/MMBtu (Northeast) to 15.1 kg CO 2 e/MMBtu (Pacific). The analysis further assessed how upstream NG emissions influence the life cycle GHG emissions of key end-use applications, including electricity generation (0.044–0.086 kg CO 2 e/kWh from upstream NG in combined cycle facilities), hydrogen production (1.04–2.20 kg CO 2 e/kg H 2 for steam methane reforming [SMR] and 1.06–2.23 kg CO 2 e/kg for autothermal reforming [ATR]), and transit bus operation utilizing compressed natural gas fuel (0.19–0.37 kg CO 2 e/mile) and hydrogen fuel (0.12–0.25 kg CO 2 e/mile for hydrogen produced in SMR and ATR).

compression↗
DOE OSTI2015

An improved model to estimate trapping parameters in polymeric materials and its application on normal and aged low-density polyethylenes

Trapping parameters can be considered as one of the important attributes to describe polymeric materials. In the present paper, a more accurate charge dynamics model has been developed, which takes account of charge dynamics in both volts-on and off stage into simulation. By fitting with measured charge data with the highest R-square value, trapping parameters together with injection barrier of both normal and aged low-density polyethylene samples were estimated using the improved model. The results show that, after long-term ageing process, the injection barriers of both electrons and holes is lowered, overall trap depth is shallower, and trap density becomes much greater. Additionally, the changes in parameters for electrons are more sensitive than those of holes after ageing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

An updated technique to obtain explosive kinetics data on microsecond timescales

There are few techniques available for chemists to obtain time-to-explosion data with known temperature inputs at the early stages of the design and synthesis of new explosives. In the 1960s, a technique was developed to rapidly heat milligram-quantities of confined explosives to ~1000 K on microsecond timescales. Wenograd loaded explosives inside stainless steel hypodermic needles, connected them to a fireset and rapidly discharged a capacitor through the steel. He obtained the temperature by measuring the needle resistance in a Wheatstone bridge arrangement and the time to explosion from a needle rupture. However, owing to the narrow-gauge needles used in the original research, the experiment was only possible with melt-castable explosives; it was never replicated, and modern diagnostics are now available with advances beyond the 1960s. Here, we report the development of the High Explosives Initiation Time (HEIT) test, which utilizes a 250 J pulsed power system to heat the needles. This work extends the Wenograd approach by using optical diagnostics, computational modeling, and advanced techniques to measure needle resistance and needle rupture. Preliminary rate information for pentaerythritol tetranitrate (PETN) will be presented.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2011

PRESENT-DAY MASS FUNCTION OF SIX SMALL MAGELLANIC CLOUD INTERMEDIATE-AGE AND OLD STAR CLUSTERS

We determined the present-day mass functions (PDMFs) of the five intermediate-age star clusters Lindsay 1, Kron 3, NGC 339, NGC 416, and Lindsay 38 and the old star cluster NGC 121 in the Small Magellanic Cloud (SMC) based on observations with the Hubble Space Telescope Advanced Camera for Surveys. The global PDMFs are well matched by Salpeter-like power laws from their main-sequence turnoffs to {approx}0.6 M{sub sun} with a power-law exponent {alpha} ranging from 1.51 {+-} 0.11 (Lindsay 1) to 2.29 {+-} 0.15 (NGC 339). We derive total stellar masses of {approx}10{sup 5} M{sub sun}, except for Lindsay 38, whose mass is of the order of {approx}10{sup 4} M{sub sun}. Differences between the PDMFs most likely reflect the varying stages of dynamical evolution of the clusters. These SMC clusters do not follow the {alpha} versus concentration parameter c correlation as found for Galactic globular clusters of similar mass. This might be an age effect or due to their location in a galaxy where bulge and disk crossings do not play a role. No correlation is found between {alpha} and the cluster core and tidal radii (r{sub c} and r{sub t} , respectively), the half-light radii r{sub h} , age, central surface brightness, metallicity, and galactocentric radius r{sub gc}. All six clusters mass-segregated to different degrees. The two clusters Lindsay 1 and Kron 3 barely show signs for mass segregation, but have low-mass star deficient global PDMFs and might be the remnants of star clusters whose outer parts were stripped. A trend exists between the degree of mass segregation and the ratio age/relaxation time t{sub r,h}, which indicates the stage of dynamical evolution for a cluster. Our data thus suggest that the SMC clusters in the present sample had a range of initial densities and presumably different amounts of mass loss that led to different rates of dynamical evolution. The clusters' positions in the r{sub h,m}/r{sub t} versus r{sub 0}/r{sub h,m} plane imply that all of the clusters are tidally filled. Our SMC clusters with projected distances larger than 3 kpc from the SMC center should have Jacobi radii significantly larger than their observed King tidal radii. The clusters also have higher mean densities than the estimated central density of the SMC. It is possible that these clusters formed in a denser overall environment of the younger SMC, or that the cluster structures were unusually strongly influenced by encounters with giant molecular clouds.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Real-Time Dispatch With Secondary Frequency Regulation: A Pathway to Consider Intra-Interval Fluctuations

Real-time dispatch balances the power demand with minimized operating costs. For the current dispatch model, the demand is assumed to be constant within a time interval, while the intra-interval power balance is left to frequency regulation. Based on practical experience and simulations, this behavior may lead to insufficient frequency regulation and uneconomic regulation costs considering the increase in intra-interval fluctuations caused by renewables. Here, a real-time dispatch method with secondary frequency regulation behaviors is proposed. Without changing the interval of the real-time dispatch command, the system regulation mileage and intra-interval generation adjustment are explicitly formulated in a mixed-integer optimization model. To reduce the computational burden, an efficient two-stage calculation method is proposed. With practical utility data, this article finds that the proposed method can effectively improve the system frequency performance with the subminute net load forecasting curve obtained by an off-the-shelf forecasting approach. Time-domain simulations in IEEE and practical utility systems validate the effectiveness of the proposed method in terms of the frequency performance and total operating costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Scalable FPGA Accelerator for Deep Convolutional Neural Networks with Stochastic Streaming

Here, FPGA-based heterogeneous computing platform, due to its extreme logic reconfigurability, emerges to be a strong contender as computing fabric in modern AI. As a result, various FPGA-based accelerators for deep CNN—the key driver of modern AI—have been proposed due to their advantages of high performance, reconfigurability, and fast development round, etc. In general, the consensus among researchers is that, although FPGA-based accelerator can achieve much higher energy efficiency, its raw computing performance lags behind when compared with GPUs with similar logic density. In this paper, we develop an alternative methodology to efficiently implement CNNs with FPGAs that outperform GPUs in terms of both power consumption and performance. Our key idea is to design a scalable hardware architecture and circuit design for large-scale CNNs that leverages a stochastic-based computing principle. Specifically, there are three major performance advantages. First, all key components of our deep learning CNN are designed and implemented to compute stochastically, thus achieving excellent computing performance and energy efficiency. Second, because our proposed CNN architecture enables a stream-mode computing, all of its stages can process even the partial results from preceding stages, therefore not incurring unnecessary latency due to data dependency. Finally, our FPGA-based deep CNN also provides a superior hardware scalability when compared with conventional FPGA implementations by reducing the bandwidth requirement between layers. The results show that our proposed CNN architecture significantly outperforms all previous FPGA-based deep CNN implementation approaches. It achieves 1.58x more GOPS, 6.42x more GOPS/Slice, and 10.92x more GOPS/W when compared with state-of-the-art CNN architecture. The top-5 accuracy of stochastic VGG-16 CNN is 86.77 percent with 18.91 fps frame rate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

High-precision measurements of the compressibility of chalcogenide glasses at a hydrostatic pressure up to 9 GPa

The volumes of glassy germanium chalcogenides GeSe{sub 2}, GeS{sub 2}, Ge{sub 17}Se{sub 83}, and Ge{sub 8}Se{sub 92} are precisely measured at a hydrostatic pressure up to 8.5 GPa. The stoichiometric GeSe{sub 2} and GeS{sub 2} glasses exhibit elastic behavior in the pressure range up to 3 GPa, and their bulk modulus decreases at pressures higher than 2–2.5 GPa. At higher pressures, inelastic relaxation processes begin and their intensity is proportional to the logarithm of time. The relaxation rate for the GeSe{sub 2} glasses has a pronounced maximum at 3.5–4.5 GPa, which indicates the existence of several parallel structural transformation mechanisms. The nonstoichiometric glasses exhibit a diffuse transformation and inelastic behavior at pressures above 1–2 GPa. The maximum relaxation rate in these glasses is significantly lower than that in the stoichiometric GeSe{sub 2} glasses. All glasses are characterized by the “loss of memory” of history: after relaxation at a fixed pressure, the further increase in the pressure returns the volume to the compression curve obtained without a stop for relaxation. After pressure release, the residual densification in the stoichiometric glasses is about 7% and that in the Ge{sub 17}Se{sub 83} glasses is 1.5%. The volume of the Ge{sub 8}Se{sub 92} glass returns to its initial value within the limits of experimental error. As the pressure decreases, the effective bulk moduli of the Ge{sub 17}Se{sub 83} and Ge{sub 8}Se{sub 92} glasses coincide with the moduli after isobaric relaxation at the stage of increasing pressure, and the bulk modulus of the stoichiometric GeSe{sub 2} glass upon decreasing pressure noticeably exceeds the bulk modulus after isobaric relaxation at the stage of increasing pressure. Along with the reported data, our results can be used to draw conclusions regarding the diffuse transformations in glassy germanium chalcogenides during compression.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Bayesian And Human Reliability Analysis (hra)-aided Method For The Reliability Analysis Of Software (bahamas)

The purpose of the BAHAMAS code is to provide a simplified process for performing quantitative evaluations of software reliability. The Bayesian and Human Reliability Analysis (HRA)-Aided method for the Reliability Analysis of software (BAHAMAS) was developed specifically to perform quantification under limited data conditions, i.e., when limited testing or operational data are available, such as during early development stages. BAHAMAS essentially examines the quality of a software development life cycle to determine the probability of specific types of software failure. BAHAMAS will have modules to support user input for detailed and simplified analyses. The user interface will also support software common cause failure analysis.

Wang, Congjian (0000000207789927)↗
DOE OSTI2025

The Impact of Demographic Lifecycle States on Time to Vehicle Purchase: Insights from the Panel Study of Income Dynamics

This study examines the impact of demographic lifecycle stages on the timing of vehicle purchases, using data from the Panel Study of Income Dynamics from 1999 to 2021. Survival analysis was employed to model the duration until households purchase vehicles, incorporating key lifecycle variables such as age, employment status, marital status, childbirth, home ownership, and the presence of school-going children. The life table results indicate that early adulthood (ages 20–35) is the prime period for vehicle acquisition, with significant peaks around ages 25 to 30. Additionally, the instantaneous hazard of purchasing a vehicle is highest in the late 40s and early 50s. According to the Cox proportional hazards model, employment, marital status, and home ownership significantly increase the likelihood of purchasing a vehicle, while living in multi-unit dwellings decreases it. Interaction effects reveal that married individuals with employed spouses are substantially more likely to purchase vehicles. In conclusion, this study serves as a steppingstone toward integrating demographic lifecycle analysis into car ownership modeling that better reflects real-world scenarios and increases the accuracy of policy and strategic planning.

Car ownership↗
DOE OSTI2024

The microbiome of a Pacific moon jellyfish Aurelia coerulea

The impact of microbiome in animal physiology is well appreciated, but characterization of animal-microbe symbiosis in marine environments remains a growing need. This study characterizes the microbial communities associated with the moon jellyfish Aurelia coerulea, first isolated from the East Pacific Ocean and has since been utilized as an experimental system. We find that the microbiome of this Pacific Aurelia culture is dominated by two taxa, a Mollicutes and Rickettsiales. The microbiome is stable across life stages, although composition varies. Mining the host sequencing data, we assembled the bacterial metagenome-assembled genomes (MAGs). The bacterial MAGs are highly reduced, and predict a high metabolic dependence on the host. Analysis using multiple metrics suggest that both bacteria are likely new species. We therefore propose the names Ca. Mariplasma lunae (Mollicutes) and Ca. Marinirickettsia aquamalans (Rickettsiales). Finally, comparison with studies of Aurelia from other geographical populations suggests the association with Ca. Mariplasma lunae occurs in Aurelia from multiple geographical locations. The low-diversity microbiome of Aurelia provides a relatively simple system to study host-microbe interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era

The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical models have begun to adapt --- e.g., via the onset of artificial intelligence --- which itself presents new challenges and opportunities for growth. This white paper aims to offer guidance and ideas for how we can evolve our technical and collaborative frameworks to promote efficient algorithmic development and take advantage of opportunities for scientific discovery in the petabyte era. We discuss challenges for discovery in large and complex data sets; challenges and requirements for the next stage of development of statistical methodologies and algorithmic tool sets; how we might change our paradigms of collaboration and education; and the ethical implications of scientists' contributions to widely applicable algorithms and computational modeling. We start with six distinct recommendations that are supported by the commentary following them. This white paper is related to a larger corpus of effort that has taken place within and around the Petabytes to Science Workshops (https://petabytestoscience.github.io/).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

DeepDiagnostics: A Software Package for Streamlined Posterior Evaluation

Automated prediction techniques like simulation-based inference (SBI) are important tasks for science experiments that produce large amounts of complex, raw data. However, their development remains in its early stages because the uncertainties of these techniques lack sufficient trustworthiness and interpretability. Packages for SBI provide a growing set of diagnostics; however, the software requirements are substantial, as they are tied to the inference technology itself, and the APIs lack adaptability. We introduce the DeepDiagnostics package for diagnosing posteriors from analytic likelihood-based methods and SBI methods, such as neural posterior estimation. DeepDiagnostics produces a comprehensive set of high-quality visualizations and metrics in a highly accessible, easy-to-use, and flexible package. We address all of these goals by providing a command-line inference tool and a Python API that is controlled through a configuration file. The package includes common diagnostics, such as parity plots, corner (covariance) plots, simulation-based calibration (SBC) diagnostics (including posterior coverage and rank histograms), Lemos et al. s PQMass and TARP, Masserano et al. s WALDO, Linhart et al. s LC2ST, as well as credible region diagnostics developed by our group.

Voetberg, Maggie [Fermilab]↗
DOE OSTI2022

microTrait: A Toolset for a Trait-Based Representation of Microbial Genomes

Remote sensing approaches have revolutionized the study of macroorganisms, allowing theories of population and community ecology to be tested across increasingly larger scales without much compromise in resolution of biological complexity. In microbial ecology, our remote window into the ecology of microorganisms is through the lens of genome sequencing. For microbial organisms, recent evidence from genomes recovered from metagenomic samples corroborate a highly complex view of their metabolic diversity and other associated traits which map into high physiological complexity. Regardless, during the first decades of this omics era, microbial ecological research has primarily focused on taxa and functional genes as ecological units, favoring breadth of coverage over resolution of biological complexity manifested as physiological diversity. Recently, the rate at which provisional draft genomes are generated has increased substantially, giving new insights into ecological processes and interactions. From a genotype perspective, the wide availability of genome-centric data requires new data synthesis approaches that place organismal genomes center stage in the study of environmental roles and functional performance. Extraction of ecologically relevant traits from microbial genomes will be essential to the future of microbial ecological research. Here, we present microTrait , a computational pipeline that infers and distills ecologically relevant traits from microbial genome sequences. microTrait maps a genome sequence into a trait space, including discrete and continuous traits, as well as simple and composite. Traits are inferred from genes and pathways representing energetic, resource acquisition, and stress tolerance mechanisms, while genome-wide signatures are used to infer composite, or life history, traits of microorganisms. This approach is extensible to any microbial habitat, although we provide initial examples of this approach with reference to soil microbiomes.

Karaoz, Ulas↗
DOE OSTI2019

A global R&D storage platform implementing distributed Quality of Service [PowerPoint]

The objectives of the program include: Provide facility R&D to match software R&D; Providing a federated system for the optimization of the various storage types (QoS) and reduction of the required manpower to operate storage; and, ''Data lake'': Hiding complexity behind common interfaces and leave it to the distributed lake infrastructure to solve those issues, being: Transpareent change of storage technologies (time, price); and, Data available for low latency access when needed (staging) and safely archived if configured (Long term archive).

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Investigation of ion kinetic effects in direct-drive exploding-pusher implosions at the NIF

Measurements of yield, ion temperature, areal density (rhoR), shell convergence, and bang time have been obtained in shock-driven, D2 and D3He gas-filled “exploding-pusher” inertial confinement fusion (ICF) implosions at the National Ignition Facility (NIF) to assess the impact of ion kinetic effects. These measurements probed the shock convergence phase of ICF implosions, a critical stage in hot-spot ignition experiments. The data complement previous studies of kinetic effects in shock-driven implosions. Ion temperature and fuel rhoR inferred from fusion-product spectroscopy are used to estimate the ion-ion mean free path in the gas. A trend of decreasing yields relative to the predictions of 2D draco hydrodynamics simulations with increasing Knudsen number (the ratio of ion-ion mean free path to minimum shell radius) suggests that ion kinetic effects are increasingly impacting the hot fuel region, in general agreement with previous results. The long mean free path conditions giving rise to ion kinetic effects in the gas are often prevalent during the shock phase of both exploding pushers and ablatively-driven implosions, including ignition-relevant implosions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗