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1. Object-Based Comparison of Data-Driven and Physics-Driven Satellite Estimates of Extreme Rainfall

   https://doi.org/10.1175/JHM-D-20-0041.1  

   Li, Zhe; Wright, Daniel B.; Zhang, Sara Q.; Kirschbaum, Dalia B.; Hartke, Samantha H. (December 2020, Journal of Hydrometeorology)

   null (Ed.)

   Abstract The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional “grid-by-grid analysis,” the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG’s accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for “hybrid” data-driven and physics-driven estimates in order to make optimal usage of satellite observations. 

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2. Plasmid-Driven Evolution of Bacteria

   https://doi.org/10.1016/b978-0-443-15750-9.00144-0  

   Ridenhour, BJ; Kosterlitz, O; Top, EM (January 2025, Elsevier)

   Plasmids are extrachromosomal DNA molecules that can be transferred vertically as well as horizontally between bacteria. They have been shown to code for numerous bacterial phenotypes, the most concerning of which is resistance to commonly used antibiotics. Plasmid-mediated transfer of drug resistance genes has notably led to the rapid spread of resistant strains, which now presents a human health crisis. While this rapid evolution of bacteria due to horizontal plasmid transfer has historically dominated this research field, increasing attention is being paid to the (co-)evolutionary interactions that shape the long-term population dynamics of bacteria and plasmids. 

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3. Demonstration of Decentralized Physics-Driven Learning

   https://doi.org/10.1103/PhysRevApplied.18.014040  

   Dillavou, Sam; Stern, Menachem; Liu, Andrea J.; Durian, Douglas J. (July 2022, Physical Review Applied)
4. Dissolution-driven propulsion of floating solids

   https://doi.org/10.1073/pnas.2301947120  

   Chaigne, Martin; Berhanu, Michael; Kudrolli, Arshad (August 2023, Proceedings of the National Academy of Sciences)

   We show that unconstrained asymmetric dissolving solids floating in a fluid can move rectilinearly as a result of attached density currents which occur along their inclined surfaces. Solids in the form of boats composed of centimeter-scale sugar and salt slabs attached to a buoy are observed to move rapidly in water with speeds up to 5 mm/s determined by the inclination angle and orientation of the dissolving surfaces. While symmetric boats drift slowly, asymmetric boats are observed to accelerate rapidly along a line before reaching a terminal velocity when their drag matches the thrust generated by dissolution. By visualizing the flow around the body, we show that the boat velocity is always directed opposite to the horizontal component of the density current. We derive the thrust acting on the body from its measured kinematics and show that the propulsion mechanism is consistent with the unbalanced momentum generated by the attached density current. We obtain an analytical formula for the body speed depending on geometry and material properties and show that it captures the observed trends reasonably. Our analysis shows that the gravity current sets the scale of the body speed consistent with our observations, and we estimate that speeds can grow slowly as the cube root of the length of the inclined dissolving surface. The dynamics of dissolving solids demonstrated here applies equally well to solids undergoing phase change and may enhance the drift of melting icebergs, besides unraveling a primal strategy by which to achieve locomotion in active matter. 

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5. Data-driven pricing of demand response

   https://doi.org/10.1109/SmartGridComm.2016.7778765  

   Khezeli, Kia; Bitar, Eilyan (November 2016, 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm))

   We consider the setting in which an electric power utility seeks to curtail its peak electricity demand by offering a fixed group of customers a uniform price for reductions in consumption relative to their predetermined baselines. The underlying demand curve, which describes the aggregate reduction in consumption in response to the offered price, is assumed to be affine and subject to unobservable random shocks. Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the utility, we investigate the extent to which the utility might dynamically adjust its offered prices to maximize its cumulative risk-sensitive payoff over a finite number of T days. In order to do so effectively, the utility must design its pricing policy to balance the tradeoff between the need to learn the unknown demand model (exploration) and maximize its payoff (exploitation) over time. In this paper, we propose such a pricing policy, which is shown to exhibit an expected payoff loss over T days that is at most O(√T), relative to an oracle who knows the underlying demand model. Moreover, the proposed pricing policy is shown to yield a sequence of prices that converge to the oracle optimal prices in the mean square sense. 

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