University of Maryland, Baltimore County

About: University of Maryland, Baltimore County is a education organization based out in Baltimore, Maryland, United States. It is known for research contribution in the topics: Population & Aerosol. The organization has 8749 authors who have published 20843 publications receiving 795706 citations. The organization is also known as: UMBC.

Airborne measurements of western U.S. wildfire emissions: Comparison with prescribed burning and air quality implications

Abstract: Wildfires emit significant amounts of pollutants that degrade air quality. Plumes from three wildfires in the western U.S. were measured from aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC^4RS) and the Biomass Burning Observation Project (BBOP), both in summer 2013. This study reports an extensive set of emission factors (EFs) for over 80 gases and 5 components of submicron particulate matter (PM_1) from these temperate wildfires. These include rarely, or never before, measured oxygenated volatile organic compounds and multifunctional organic nitrates. The observed EFs are compared with previous measurements of temperate wildfires, boreal forest fires, and temperate prescribed fires. The wildfires emitted high amounts of PM_1 (with organic aerosol (OA) dominating the mass) with an average EF that is more than 2 times the EFs for prescribed fires. The measured EFs were used to estimate the annual wildfire emissions of carbon monoxide, nitrogen oxides, total nonmethane organic compounds, and PM_1 from 11 western U.S. states. The estimated gas emissions are generally comparable with the 2011 National Emissions Inventory (NEI). However, our PM_1 emission estimate (1530 ± 570 Gg yr^(−1)) is over 3 times that of the NEI PM_(2.5) estimate and is also higher than the PM_(2.5) emitted from all other sources in these states in the NEI. This study indicates that the source of OA from biomass burning in the western states is significantly underestimated. In addition, our results indicate that prescribed burning may be an effective method to reduce fine particle emissions.

A Balancing Act: What Software Practitioners Have to Say about Technical Debt

Abstract: An interview study involving 35 practitioners from a variety of domains aimed to characterize technical debt at the ground level to find out how software practitioners perceive it. The study also aimed to understand the context in which technical debt occurs, including its causes, symptoms, and effects. In addition, the study focused on how practitioners currently deal with technical debt. This analysis paints a picture of a large, complex balancing act of various short- and long-term concerns. The Web Extra gives the interview questions used by Erin Lim, Nitin Taksande, and Carolyn Seaman.

Imaging time-series to improve classification and imputation

Abstract: Inspired by recent successes of deep learning in computer vision, we propose a novel framework for encoding time series as different types of images, namely, Gramian Angular Summation/ Difference Fields (GASF/GADF) and Markov Transition Fields (MTF). This enables the use of techniques from computer vision for time series classification and imputation. We used Tiled Convolutional Neural Networks (tiled CNNs) on 20 standard datasets to learn high-level features from the individual and compound GASF-GADF-MTF images. Our approaches achieve highly competitive results when compared to nine of the current best time series classification approaches. Inspired by the bijection property of GASF on 0/1 rescaled data, we train Denoised Auto-encoders (DA) on the GASF images of four standard and one synthesized compound dataset. The imputation MSE on test data is reduced by 12.18%-48.02% when compared to using the raw data. An analysis of the features and weights learned via tiled CNNs and DAs explains why the approaches work.

Contributions of natural and sexual selection to the evolution of premating reproductive isolation: a research agenda.

Abstract: Speciation by divergent natural selection is well supported. However, the role of sexual selection in speciation is less well understood due to disagreement about whether sexual selection is a mechanism of evolution separate from natural selection, as well as confusion about various models and tests of sexual selection. Here, we outline how sexual selection and natural selection are different mechanisms of evolutionary change, and suggest that this distinction is critical when analyzing the role of sexual selection in speciation. Furthermore, we clarify models of sexual selection with respect to their interaction with ecology and natural selection. In doing so, we outline a research agenda for testing hypotheses about the relative significance of divergent sexual and natural selection in the evolution of reproductive isolation.