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The 2011 Mw9.0 Tohoku-Oki earthquake may be representative of “maximum”earthquakes: it ruptured the entire seismogenic depth range of the Japan megathrust, including the shallowest segment that reaches the trench where the displacement grew to 60 m and spawned a catastrophic tsunami. Models and direct seafloor measurements imply a comparably large initial relative motion and sustained long-period oscillations between sediment and water at the seafloor above the shallowest megathrust segment. This motion may develop enough shear to re-suspend sediment, but exclusively for the maximum earthquakes. This new co-seismic sediment-entrainment process should leave a recognizable sedimentary fingerprint of these earthquakes. Our physical experiments are testing effects of this shear between sediment and water and its interaction with high-frequency vertical shaking. We also investigate the impact of sediment properties and slope on the entrainment. We worked on several synthetic mixtures, defined according to the grain size distribution, clay mineralogy and water content with either freshwater or sea water. The grain size distribution is simplified but matches those of sediment cores from different subduction zones. For each mixture, we built matrices of the erosion rates according to the flow velocities, which shows the role of water content and vertical shaking. We have also identifi ed different mechanism during the runs:grain-by-grain or clasts entrainment, stripping, motion of the sediment interface, and formation of a dense sediment layer above the surface. These observations maybe recorded in the associated deposit, suggesting different fingerprinting by the tsunamigenic earthquakes depending on the characteristics of each subduction zone. 

Abstract Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the liver. This manuscript develops a generalizable deep learning model to segment the liver on T1-weighted MR images. In particular, three distinct deep learning architectures (nnUNet, PocketNet, Swin UNETR) were considered using data gathered from six geographically different institutions. A total of 819 T1-weighted MR images were gathered from both public and internal sources. Our experiments compared each architecture’s testing performance when trained both intra-institutionally and inter-institutionally. Models trained using nnUNet and its PocketNet variant achieved mean Dice-Sorensen similarity coefficients>0.9 on both intra- and inter-institutional test set data. The performance of these models suggests that nnUNet and PocketNet liver segmentation models trained on a large and diverse collection of T1-weighted MR images would on average achieve good intra-institutional segmentation performance. 

Porous polymers have interesting acoustic properties including wave dampening and acoustic impedance matching and may be used in numerous acoustic applications, e.g., waveguiding or acoustic cloaking. These materials can be prepared by the inclusion of gas-filled voids, or pores, within an elastic polymer network; therefore, porous polymers that have controlled porosity values and a wide range of possible mechanical properties are needed, as these are key factors that impact the sound-dampening properties. Here, the synthesis of acoustic materials with varying porosities and mechanical properties that could be controlled independent of the pore morphology using emulsion templated polymerizations is described. Polydimethylsiloxane-based ABA triblock copolymer surfactants were prepared using reversible addition−fragmentation chain transfer polymerizations to control the emulsion template and act as an additional crosslinker in the polymerization. Acoustic materials prepared with reactive surfactants possessed a storage modulus of ∼300 kPa at a total porosity of 71% compared to materials prepared using analogous nonreactive surfactants that possessed storage modulus values of ∼150 kPa at similar porosities. These materials display very low longitudinal sound speeds of ∼35 m/s at ultrasonic frequencies, making them excellent candidates in the preparation of acoustic devices such as metasurfaces or lenses. 

Polymer-based acoustic metamaterials possess properties including acoustic wave manipulation, cloaking, and sound dampening. Here, PDMS-based elastomers were prepared using thiol–ene “click reactions” with emulsion templating. Acoustic analysis showed these materials achieved sound speed values of ∼ 40 m s −1 , close to the predicted minimum of ∼25 m s −1 attainable. 

Using a Lewis acid-quenched CF2Ph- reagent, we show C–C bond formation through nucleophilic addition reactions to prepare molecules containing internal –CF2– linkages. We demonstrate C(sp2)–C(sp3) coupling using both SNAr reactions and Pd-catalysis. Finally, C(sp3)–C(sp3) bonds are forged using operationally simple SN2 reactions that tolerate medicinally-relevant motifs. 

We propose a new family of depth measures called the elastic depths that can be used to greatly improve shape anomaly detection in functional data. Shape anomalies are functions that have considerably different geometric forms or features from the rest of the data. Identifying them is generally more difficult than identifying magnitude anomalies because shape anomalies are often not distinguishable from the bulk of the data with visualization methods. The proposed elastic depths use the recently developed elastic distances to directly measure the centrality of functions in the amplitude and phase spaces. Measuring shape outlyingness in these spaces provides a rigorous quantification of shape, which gives the elastic depths a strong theoretical and practical advantage over other methods in detecting shape anomalies. A simple boxplot and thresholding method is introduced to identify shape anomalies using the elastic depths. We assess the elastic depth’s detection skill on simulated shape outlier scenarios and compare them against popular shape anomaly detectors. Finally, we use hurricane trajectories to demonstrate the elastic depth methodology on manifold valued functional data.