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As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID-19 case numbers were very high for three Florida counties, Miami-Dade, Broward, and Palm Beach counties. To investigate patterns, as well as drivers, related to changes in mobility across the tri-county region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID-19 positive case data. Mobility patterns declined in each county when new COVID-19 infections began to rise, beginning in mid-June 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county),as well as social distancing or travel-related factors (e.g., staying at home behaviors) over the two time periods prior to and after the spike of COVID-19 cases. 

Cyanobacterial harmful algal blooms (CyanoHABs) are an increasingly common feature of large, eutrophic lakes. Non-N2-fixing CyanoHABs (e.g., Microcystis) appear to be proliferating relative to N2-fixing CyanoHABs in systems receiving increasing nutrient loads. This shift reflects increasing external nitrogen (N) inputs, and a[50-year legacy of excessive phosphorus (P) and N loading. Phosphorus is effectively retained in legacy-impacted systems, while N may be retained or lost to the atmosphere in gaseous forms (e.g., N2, NH3, N2O). Biological control on N inputs versus outputs, or the balance between N2 fixation versus denitrification, favors the latter, especially in lakes undergoing accelerating eutrophication, although denitrification removal efficiency is inhibited by increasing external N loads. Phytoplankton in eutrophic lakes have become more responsive to N inputs relative to P, despite sustained increases in N loading. From a nutrient management perspective, this suggests a need to change the freshwater nutrient limitation and input reduction paradigms; a shift from an exclusive focus on P limitation to a dual N and P colimitation and management strategy. The recent proliferation of toxic non-N2-fixing CyanoHABs, and ever-increasing N and P legacy stores, argues for such a strategy if we are to mitigate eutrophication and CyanoHAB expansion globally. 

In 2019, all attendees were encouraged to submit their work to the TMS journals Integrating Materials and Manufacturing Innovation and Metallurgical and Materials Transactions A, which will be publishing topical collections on Integrated Computational Materials Engineering (ICME). These collections take the place of a traditional conference proceedings publication. Only submissions from the 5th World Congress on Integrated Computational Materials Engineering (ICME 2019) attendees were considered for these collections. Participants in ICME 2019 have been strongly encouraged to contribute to this effort. 

Abstract The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/ c charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1 $$\pm 0.6$$ ± 0.6 % and 84.1 $$\pm 0.6$$ ± 0.6 %, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation. 

Abstract Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.