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The formation of inorganic fine particulate matter (i.e., iPM2.5) is controlled by the thermodynamic equilibrium partitioning of NH3-NH4+. To develop effective control strategies of PM2.5, we aim to understand the impacts of changes in different precursor gases on iPM2.5 concentrations and partitioning of NH3-NH4+. To understand partitioning of NH3-NH4+ in the southeastern U.S., responses of iPM2.5 to precursor gases in four seasons were investigated using field measurements of iPM2.5, precursor gases, and meteorological conditions. The ISORROPIA II model was used to examine the effects of changes in total ammonia (gas + aerosol), total sulfuric acid (aerosol), and total nitric acid (gas + aerosol) on iPM2.5 concentrations and partitioning of NH3-NH4+. The results indicate that reduction in total H2SO4 is more effective than reduction in total HNO3 and total NH3 to reduce iPM2.5 especially under NH3-rich condition. The reduction in total H2SO4 may change partitioning of NH3-NH4+ towards gas-phase and may also lead to an increase in NO3− under NH3-rich conditions, which does not necessarily lead to full neutralization of acidic gases (pH < 7). Thus, future reduction in iPM2.5 may necessitate the coordinated reduction in both H2SO4 and HNO3 in the southeastern U.S. It is also found that the response of iPM2.5 to the change in total H2SO4 is more sensitive in summer than winter due to the dominance of SO42− salts in iPM2.5 and the high temperature in summer. The NH3 emissions from Animal Feeding Operations (AFOs) at an agricultural rural site (YRK) had great impacts on partitioning of NH3-NH4+. The Multiple Linear Regression (MLR) model revealed a strong positive correlation between cation-NH4+ and anions-SO42− and NO3−. This research provides an insight into iPM2.5 formation mechanism for the advancement of PM2.5 control and regulation in the southeastern U.S. 

We demonstrate an application of finding target persons on a surveillance video. Each visually detected participant is tagged with a smartphone ID and the target person with the query ID is highlighted. This work is motivated by the fact that establishing associations between subjects observed in camera images and messages transmitted from their wireless devices can enable fast and reliable tagging. This is particularly helpful when target pedestrians need to be found on public surveillance footage, without the reliance on facial recognition. The underlying system uses a multi-modal approach that leverages WiFi Fine Timing Measurements (FTM) and inertial sensor (IMU) data to associate each visually detected individual with a corresponding smartphone identifier. These smartphone measurements are combined strategically with RGB-D information from the camera, to learn affinity matrices using a multi-modal deep learning network. 

Sea ice growth and decay are critical processes in the Arctic climate system, but comprehensive observations are very sparse. We analyzed data from 23 sea ice mass balance buoys (IMBs) deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020 to investigate the seasonality and timing of sea ice thermodynamic mass balance in the Arctic Transpolar Drift. The data reveal four stages of the ice season: (I) onset of ice basal freezing, mid-October to November; (II) rapid ice growth, December–March; (III) slow ice growth, April–May; and (IV) melting, June onward. Ice basal growth ranged from 0.64 to 1.38 m at a rate of 0.004–0.006 m d–1, depending mainly on initial ice thickness. Compared to a buoy deployed close to the MOSAiC setup site in September 2012, total ice growth was about twice as high, due to the relatively thin initial ice thickness at the MOSAiC sites. Ice growth from the top, caused by surface flooding and subsequent snow-ice formation, was observed at two sites and likely linked to dynamic processes. Snow reached a maximum depth of 0.25 ± 0.08 m by May 2, 2020, and had melted completely by June 25, 2020. The relatively early onset of ice basal melt on June 7 (±10 d), 2019, can be partly attributed to the unusually rapid advection of the MOSAiC floes towards Fram Strait. The oceanic heat flux, calculated based on the heat balance at the ice bottom, was 2.8 ± 1.1 W m–2 in December–April, and increased gradually from May onward, reaching 10.0 ± 2.6 W m–2 by mid-June 2020. Subsequently, under-ice melt ponds formed at most sites in connection with increasing ice permeability. Our analysis provides crucial information on the Arctic sea ice mass balance for future studies related to MOSAiC and beyond. 

Intrinsic size effects in nanoglass plasticity have been connected to the structural length scales imposed by the interfacial network, and control over this behavior is critical to designing amorphous alloys with improved mechanical response. In this paper, atomistic simulations are employed to probe strain delocalization in nanoglasses with explicit correlation to the interfacial characteristics and length scales of the amorphous grain structure. We show that strength is independent of grain size under certain conditions, but scales with the excess free volume and degree of short-range ordering in the interfaces. Structural homogenization upon annealing of the nanoglasses increases their strength toward the value of the bulk metallic glass; however, continued partitioning of strain to the interfacial regions inhibits the formation of a primary shear band. Intrinsic size effects in nanoglass plasticity thus originate from biased plastic strain accumulation within the interfacial regions, which will ultimately govern strain delocalization and homogenous flow in nanoglasses. 

Sea ice thickness is a key parameter in the polar climate and ecosystem. Thermodynamic and dynamic processes alter the sea ice thickness. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provided a unique opportunity to study seasonal sea ice thickness changes of the same sea ice. We analyzed 11 large-scale (∼50 km) airborne electromagnetic sea thickness and surface roughness surveys from October 2019 to September 2020. Data from ice mass balance and position buoys provided additional information. We found that thermodynamic growth and decay dominated the seasonal cycle with a total mean sea ice thickness increase of 1.4 m (October 2019 to June 2020) and decay of 1.2 m (June 2020 to September 2020). Ice dynamics and deformation-related processes, such as thin ice formation in leads and subsequent ridging, broadened the ice thickness distribution and contributed 30% to the increase in mean thickness. These processes caused a 1-month delay between maximum thermodynamic sea ice thickness and maximum mean ice thickness. The airborne EM measurements bridged the scales from local floe-scale measurements to Arctic-wide satellite observations and model grid cells. The spatial differences in mean sea ice thickness between the Central Observatory (<10 km) of MOSAiC and the Distributed Network (<50 km) were negligible in fall and only 0.2 m in late winter, but the relative abundance of thin and thick ice varied. One unexpected outcome was the large dynamic thickening in a regime where divergence prevailed on average in the western Nansen Basin in spring. We suggest that the large dynamic thickening was due to the mobile, unconsolidated sea ice pack and periodic, sub-daily motion. We demonstrate that this Lagrangian sea ice thickness data set is well suited for validating the existing redistribution theory in sea ice models. Our comprehensive description of seasonal changes of the sea ice thickness distribution is valuable for interpreting MOSAiC time series across disciplines and can be used as a reference to advance sea ice thickness modeling.