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Full-Text Articles in Physical Sciences and Mathematics
From The Household To Watershed: A Cross-Scale Analysis Of Residential Intention To Adopt Green Stormwater Infrastructure, Sarah Coleman, Stephanie Hurley, Donna Rizzo, Christopher Koliba, Asim Zia
From The Household To Watershed: A Cross-Scale Analysis Of Residential Intention To Adopt Green Stormwater Infrastructure, Sarah Coleman, Stephanie Hurley, Donna Rizzo, Christopher Koliba, Asim Zia
College of Engineering and Mathematical Sciences Faculty Publications
Improved stormwater management for the protection of water resources requires bottom-up stewardship from landowners, including adoption of Green Stormwater Infrastructure (GSI). We use a statewide survey of Vermont paired with a cross-scale and spatial analysis to evaluate the influence of interacting spatial, social, and physical factors on residential intention to adopt GSI across a complex social-ecological landscape. Specifically, we focus on how three GSI practices, (“rain garden (bio retention),” “infiltration trenches,” and “actively divert roof runoff to a rain barrel/lawn/garden instead of the street/sewer”) vary with barriers to adoption, and household attributes across stormwater contexts from the household to watershed …
Erratum: Reducing Cascading Failure Risk By Increasing Infrastructure Network Interdependence, Mert Korkali, Jason G. Veneman, Brian F. Tivnan, James P. Bagrow, Paul D.H. Hines
Erratum: Reducing Cascading Failure Risk By Increasing Infrastructure Network Interdependence, Mert Korkali, Jason G. Veneman, Brian F. Tivnan, James P. Bagrow, Paul D.H. Hines
College of Engineering and Mathematical Sciences Faculty Publications
This corrects the article DOI: 10.1038/srep44499.
Nets: Extremely Fast Outlier Detection From A Data Stream Via Set-Based Processing, Susik Yoon, Jae Gil Lee, Byung Suk Lee
Nets: Extremely Fast Outlier Detection From A Data Stream Via Set-Based Processing, Susik Yoon, Jae Gil Lee, Byung Suk Lee
College of Engineering and Mathematical Sciences Faculty Publications
This paper addresses the problem of efficiently detecting outliers from a data stream as old data points expire from and new data points enter the window incrementally. The proposed method is based on a newly discovered characteristic of a data stream that the change in the locations of data points in the data space is typically very insignificant. This observation has led to the finding that the existing distance-based outlier detection algorithms perform excessive unnecessary computations that are repetitive and/or canceling out the effects. Thus, in this paper, we propose a novel set-based approach to detecting outliers, whereby data points …
Energy And Complexity, Zofia Lukszo, Ettore Bompard, Paul Hines, Liz Varga
Energy And Complexity, Zofia Lukszo, Ettore Bompard, Paul Hines, Liz Varga
College of Engineering and Mathematical Sciences Faculty Publications
No abstract provided.