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Abstract Actuators utilizing snap‐through instabilities are widely investigated for high‐performance fast actuators and shape reconfigurable structures owing to their rapid response and limited reliance on continuous energy input. However, prevailing approaches typically involve a combination of multiple bistable actuator units and achieving multistability within a single actuator unit still remains an open challenge. Here, a soft actuator is presented that uses shape memory alloy (SMA) and mixed‐mode elastic instabilities to achieve intrinsically multistable shape reconfiguration. The multistable actuator unit consists of six stable states, including two pure bending states and four bend‐twist states. The actuator is composed of a pre‐stretched elastic membrane placed between two elastomeric frames embedded with SMA coils. By controlling the sequence and duration of SMA activation, the actuator is capable of rapid transition between all six stable states within hundreds of milliseconds. Principles of energy minimization are used to identify actuation sequences for various types of stable state transitions. Bending and twisting angles corresponding to various prestretch ratios are recorded based on parameterizations of the actuator's geometry. To demonstrate its application in practical conditions, the multistable actuator is used to perform visual inspection in a confined space, light source tracking during photovoltaic energy harvesting, and agile crawling. 

Abstract Given the role played by the historical and extensive coverage of sea ice concentration (SIC) observations in reconstructing the long‐term variability of Antarctic sea ice, and the limited attention given to model‐dependent parameters in current sea ice data assimilation studies, this study focuses on enhancing the performance of the Data Assimilation System for the Southern Ocean in assimilating SIC through optimizing the localization and observation error estimate, and two assimilation experiments were conducted from 1979 to 2018. By comparing the results with the sea ice extent of the Southern Ocean and the sea ice thickness in the Weddell Sea, it becomes evident that the experiment with optimizations outperforms that without optimizations due to achieving more reasonable error estimates. Investigating uncertainties of the sea ice volume anomaly modeling reveals the importance of the sea ice‐ocean interaction in the SIC assimilation, implying the necessity of assimilating more oceanic and sea‐ice observations. 

Abstract The mixoplankton green Noctiluca scintillans (gNoctiluca) is known to form extensive green tides in tropical coastal ecosystems prone to eutrophication. In the Arabian Sea, their recent appearance and annual recurrence have upended an ecosystem that was once exclusively dominated by diatoms. Despite evidence of strong links to eutrophication, hypoxia and warming, the mechanisms underlying outbreaks of this mixoplanktonic dinoflagellate remain uncertain. Here we have used eco-physiological measurements and transcriptomic profiling to ascribe gNoctiluca’s explosive growth during bloom formation to the form of sexual reproduction that produces numerous gametes. Rapid growth of gNoctiluca coincided with active ammonium and phosphate release from gNoctiluca cells, which exhibited high transcriptional activity of phagocytosis and metabolism generating ammonium. This grazing-driven nutrient flow ostensibly promotes the growth of phytoplankton as prey and offers positive support successively for bloom formation and maintenance. We also provide the first evidence that the host gNoctiluca cell could be manipulating growth of its endosymbiont population in order to exploit their photosynthetic products and meet critical energy needs. These findings illuminate gNoctiluca’s little known nutritional and reproductive strategies that facilitate its ability to form intense and expansive gNoctiluca blooms to the detriment of regional water, food and the socio-economic security in several tropical countries. 

Abstract Convergence analysis of accelerated first-order methods for convex optimization problems are developed from the point of view of ordinary differential equation solvers. A new dynamical system, called Nesterov accelerated gradient (NAG) flow, is derived from the connection between acceleration mechanism andA-stability of ODE solvers, and the exponential decay of a tailored Lyapunov function along with the solution trajectory is proved. Numerical discretizations of NAG flow are then considered and convergence rates are established via a discrete Lyapunov function. The proposed differential equation solver approach can not only cover existing accelerated methods, such as FISTA, Güler’s proximal algorithm and Nesterov’s accelerated gradient method, but also produce new algorithms for composite convex optimization that possess accelerated convergence rates. Both the convex and the strongly convex cases are handled in a unified way in our approach.