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Results for “integrate”
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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Intracellular metabolism analysis of Clostridium cellulovorans via modeling integrating proteomics, metabolomics and fermentation
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Thermodynamic evaluation of solar integration into a natural gas combined cycle power plant
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The challenge of integrating offshore wind power in the U.S. electric grid. Part I: Wind forecast error
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MATPLAN: A probability-based planning tool for cost-effective grid integration of renewable energy
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Integrating land surface phenology with cluster density and size improves spatially explicit models of animal density
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Understanding the relationship between vegetation greenness and productivity across dryland ecosystems through the integration of PhenoCam, satellite, and eddy covariance data
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Outdoor urban nanomaterials: The emergence of a new, integrated, and critical field of study
Engineered nanomaterials (ENMs) are currently widely incorporated in the outdoor urban environmental fabric and numerous new applications and products containing ENMs are expected in the future. As has been shown repeatedly, products containing ENMs have the potential, at some point in their lifetime, to release ENMs into their surrounding environment. However, the expanding body in environmental nanomaterial research has not yet shifted toward ENMs in the context of the complex outdoor urban environment. This is especially surprising because the world's human populations are on a steady march toward more and more urbanization and technological development, accompanied with increased applications for ENMs in the outdoor urban environment. Our objective for this paper is therefore to review, assess, and provide new information in this emerging field. We provide an overview of nanomaterials (NMs, encompassing both ENMs and incidental nanomaterials, INMs) that are likely to be released in the urban environment from outdoor sources by discussing 1) the applications of ENMs that may lead to release of ENMs in urban areas, 2) the recently published data on the release of ENMs from novel nano-enabled applications in the outdoor urban environment, 3) the available literature on the occurrence of INMs in the atmosphere and within/on dust particles, and 4) the potential pathways and fate of NMs in the outdoor urban environment. This review is then followed by three case studies demonstrating the importance of NMs in the outdoor urban environment. The first and second case studies illustrate the occurrence of NMs in urban dust and stormwater ponds, respectively, whereas the third case study discusses the lessons learned from the release of NMs (e.g. Pt, ph and Rh) from automotive vehicle catalytic convertors. This article ends with a discussion of the research priorities needed to advance this emerging field of “outdoor urban nanomaterials” and to assess the potential risks of NMs in the context of urban environments.
Integral microalgae-bacteria model (BIO_ALGAE): Application to wastewater high rate algal ponds
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Quantitative mineralogy for facies definition in the Marcellus Shale (Appalachian Basin, USA) using XRD-XRF integration
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Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning
A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.
The GaAs/GaAs/Si solar cell – Towards current matching in an integrated two terminal tandem
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Modeling the integrated expansion of the Canadian and US power sectors
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