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1. Surface Topography Hinders Bacterial Surface Motility

   https://doi.org/10.1021/acsami.7b16715  

   Chang, Yow-Ren; Weeks, Eric R; Ducker, William A (March 2018, ACS Applied Materials & Interfaces)

   We demonstrate that the surface motility of the bacterium, Pseudomonas aeruginosa, is hindered by a crystalline hemispherical topography with wavelength in the range of 2–8 μm. The motility was determined by the analysis of time-lapse microscopy images of cells in a flowing growth medium maintained at 37 °C. The net displacement of bacteria over 5 min is much lower on surfaces containing 2–8 μm hemispheres than on flat topography, but displacement on the 1 μm hemispheres is not lower. That is, there is a threshold between 1 and 2 μm for response to the topography. Cells on the 4 μm hemispheres were more likely to travel parallel to the local crystal axis than in other directions. Cells on the 8 μm topography were less likely to travel across the crowns of the hemispheres and were also more likely to make 30°–50° turns than on flat surfaces. These results show that surface topography can act as a significant barrier to surface motility and may therefore hinder surface exploration by bacteria. Because surface exploration can be a part of the process whereby bacteria form colonies and seek nutrients, these results help to elucidate the mechanism by which surface topography hinders biofilm formation. 

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2. Surface Networks

   Kostrikov, Ilya; Jiang, Zhongshi; Panozzo, Daniele; Zorin, Denis; Bruna, Joan (June 2018, IEEE Conference on Computer Vision and Pattern Recognition)

   We study data-driven representations for three- dimensional triangle meshes, which are one of the prevalent objects used to represent 3D geometry. Recent works have developed models that exploit the intrinsic geometry of manifolds and graphs, namely the Graph Neural Networks (GNNs) and its spectral variants, which learn from the local metric tensor via the Laplacian operator. Despite offering excellent sample complexity and built-in invariances, intrinsic geometry alone is invariant to isometric deformations, making it unsuitable for many applications. To overcome this limitation, we propose several upgrades to GNNs to leverage extrinsic differential geometry properties of three-dimensional surfaces, increasing its modeling power. In particular, we propose to exploit the Dirac operator, whose spectrum detects principal curva- ture directions — this is in stark contrast with the classical Laplace operator, which directly measures mean curvature. We coin the resulting models Surface Networks (SN). We prove that these models define shape representations that are stable to deformation and to discretization, and we demonstrate the efficiency and versatility of SNs on two challenging tasks: temporal prediction of mesh deformations under non-linear dynamics and generative models using a variational autoencoder framework with encoders/decoders given by SNs. 

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3. Lake Ice Surface Type Correlation with GNSS Surface Reflectivity

   https://doi.org/10.1109/TGRS.2025.3603893  

   Datta-Barua, Seebany; Parvizi, Roohollah; Khan, Shahrukh; Baverel, Arthur; Johnson, Rosemary; Banwell, Alison F (August 2025, IEEE Transactions on Geoscience and Remote Sensing)
4. The Viscous Surface Wave Problem with Generalized Surface Energies

   https://doi.org/10.1137/18M1195851  

   Remond-Tiedrez, Antoine; Tice, Ian (January 2019, SIAM Journal on Mathematical Analysis)
5. Surface Runoff Discrepancy in Urban‐PLUMBER Land Surface Models

   https://doi.org/10.1029/2026GL122048  

   Jongen, H J; Lipson, M; Teuling, A J; Grimmond, S; Best, M; Baik, J‐J; Demuzere, M; Fortuniak, K; Huang, Y; De_Kauwe, M; et al (April 2026, Geophysical Research Letters)

   Abstract Enhanced surface runoff in urban environments reduces water availability and limits the evaporative cooling potential. We evaluate surface runoff in 18 urban land surface models (ULSM) in Urban‐PLUMBER for 6,570 rainfall events across 21 urban sites. Surface runoff occurs when rainfall exceeds the infiltration, saturation, or interception capacity. Ten models omit at least one of these processes, while seven fail to increase runoff with increasing imperviousness. Surprisingly, some models lack any runoff during intense (>) or prolonged (>20 mm) rainfall. Urban land surface models (ULSMs) turn 0%–86% rainfall into runoff. Most models produce runoff in agreement with an empirical comparison offered by the CN method, especially for high imperviousness. However, ULSM runoff exceeds CN runoff estimates for low impervious fraction, particularly by models with incomplete process description and for low‐intensity rainfall. The large discrepancy between ULSMs calls for advancing the urban hydrology representation in ULSMs, which is essential for correct simulation of evaporative cooling in cities. 

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