The effect of Turbulence Models on Numerical Prediction of Air Flow within Street Canyons

November 15-17, Belgrad

The Very Massive Star Content of the Nuclear Star Clusters in NGC 5253

The blue compact dwarf galaxy NGC 5253 hosts a very young starburst containing twin nuclear star clusters, separated by a projected distance of 5 pc. One cluster (#5) coincides with the peak of the H-alpha emission and the other (#11) with a massive ultracompact H II region. A recent analysis of these clusters shows that they have a photometric age of 1+/-1 Myr, in apparent contradiction with the age of 3-5 Myr inferred from the presence of Wolf-Rayet features in the cluster #5 spectrum. We examine Hubble Space Telescope ultraviolet and Very Large Telescope optical spectroscopy of #5 and show that the stellar features arise from very massive stars (VMS), with masses greater than 100 Msun, at an age of 1-2 Myr. We further show that the very high ionizing flux from the nuclear clusters can only be explained if VMS are present. We investigate the origin of the observed nitrogen enrichment in the circum-cluster ionized gas and find that the excess N can be produced by massive rotating stars within the first 1 Myr. We find similarities between the NGC 5253 cluster spectrum and those of metal poor, high redshift galaxies. We discuss the presence of VMS in young, star-forming galaxies at high redshift; these should be detected in rest frame UV spectra to be obtained with the James Webb Space Telescope. We emphasize that population synthesis models with upper mass cut-offs greater than 100 Msun are crucial for future studies of young massive star clusters at all redshifts.Comment: 11 pages, 7 figures, accepted for publication in Astrophysical Journa

Optimal Stopping of Multi-Robot Exploration for Unknown, Bounded Environments

Limited resources and uncertainty pose a substantial problem for multi-robot exploration of unknown environments. This research paper looks to determine the optimal time to terminate robot exploration while maximizing information gathered. Whilst making this determination, the system\u27s resources and capabilities must be taken into account. To see if our strategy works, we ran many simulations in varying environments. The results of this research are important for real-world uses like robot exploration, search and rescue missions, and automated surveillance. Determining when to stop exploring can help the system save resources, explore faster, and make better decisions

Integrating Management Zones and Canopy Sensing to Improve Nitrogen Recommendation Algorithms

Fertilizer nitrogen use efficiency (NUE) in maize (Zea mays L.) production is historically inefficient, presenting significant environmental and economic challenges. Low NUE can be attributed to poor synchrony between soil N supply and crop demand, applying uniform rates of N fertilizer to spatially variable landscapes, and failure to account for temporal variability in crop response to N. Innovative N management strategies, including crop canopy sensing and management zones (MZ), are tools that have proven useful in increasing NUE. Several researchers have proposed that the integration of these two approaches may result in further improvements in NUE and in profitability by synthesizing both crop- and soil-based information for more robust N management. The objectives of this research were to identify soil and topographic variables that could be used to delineate MZ that appropriately characterize areas with differential crop response to N fertilizer and then to test a sensor-based N application algorithm and evaluate the potential of an integrated MZ- and sensor-based approach compared to uniform N management and to sensor-based N management alone. Management zones delineated with a field-specific approach were able to appropriately characterize the spatial variability in in-season crop response to N in all eight fields and in yield response to N in three of six fields. Sensor-based application resulted in significantly increased NUE compared to uniform N management in six of eight fields, and marginal net return was significantly increased in four of eight fields. Delineated MZ appropriately classified areas of differing NUE in six of eight fields. Results from these studies indicate that integrating field-specific MZ and sensor-based N application has potential to increase NUE and profitability compared to sensor-based or MZ-based N management approaches alone. Additional research is needed to explore how to best incorporate static soil information into a sensor-based algorithm that can be generalized for a variety of soil, climatic, and managerial factors. Advisors: Richard B. Ferguson and Joe D. Luc