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 & Galaxy. The organization has 8749 authors who have published 20843 publications receiving 795706 citations. The organization is also known as: UMBC.

Optical Properties of Atmospheric Aerosol in Maritime Environments

Abstract: Systematic characterization of aerosol over the oceans is needed to understand the aerosol effect on climate and on transport of pollutants between continents. Reported are the results of a comprehensive optical and physical characterization of ambient aerosol in five key island locations of the Aerosol Robotic Network (AERONET) of sun and sky radiometers, spanning over 2‐5 yr. The results are compared with aerosol optical depths and size distributions reported in the literature over the last 30 yr. Aerosol found over the tropical Pacific Ocean (at three sites between 208S and 208N) still resembles mostly clean background conditions dominated by maritime aerosol. The optical thickness is remarkably stable with mean value of ta(500 nm) 5 0.07, mode value at tam

Assessing PM2.5 Exposures with High Spatiotemporal Resolution across the Continental United States.

Abstract: A number of models have been developed to estimate PM2.5 exposure, including satellite-based aerosol optical depth (AOD) models, land-use regression, or chemical transport model simulation, all with both strengths and weaknesses. Variables like normalized difference vegetation index (NDVI), surface reflectance, absorbing aerosol index, and meteoroidal fields are also informative about PM2.5 concentrations. Our objective is to establish a hybrid model which incorporates multiple approaches and input variables to improve model performance. To account for complex atmospheric mechanisms, we used a neural network for its capacity to model nonlinearity and interactions. We used convolutional layers, which aggregate neighboring information, into a neural network to account for spatial and temporal autocorrelation. We trained the neural network for the continental United States from 2000 to 2012 and tested it with left out monitors. Ten-fold cross-validation revealed a good model performance with a total R2 of 0....

Managing technical debt in software-reliant systems

Abstract: Delivering increasingly complex software-reliant systems demands better ways to manage the long-term effects of short-term expedients. The technical debt metaphor is gaining significant traction in the agile development community as a way to understand and communicate such issues. The idea is that developers sometimes accept compromises in a system in one dimension (e.g., modularity) to meet an urgent demand in some other dimension (e.g., a deadline), and that such compromises incur a "debt": on which "interest" has to be paid and which the "principal" should be repaid at some point for the long-term health of the project. We argue that the software engineering research community has an opportunity to study and improve this concept. We can offer software engineers a foundation for managing such trade-offs based on models of their economic impacts. Therefore, we propose managing technical debt as a part of the future research agenda for the software engineering field.