Regulating the activity of specific neurons is essentially important in neurocircuit dissection and neuropathy therapy. As a recently developed strategy, nanomaterial‐enabled nongenetic neuromodulations that realize remote physical stimuli have made vast progress and shown great clinical potential. However, minimal invasiveness and high spatiotemporal resolution are still challenging for nongenetic neuromodulation. Herein, a second near‐infrared (NIR‐II)‐light‐induced transcranial nongenetic neurostimulation via bioinspired nanovesicles is reported. The rationally designed vesicles are obtained from vesicle‐membrane‐confined enzymatic reactions. This study demonstrates that the vesicle‐enabled NIR‐II photothermal stimuli can elicit neuronal signaling dynamics with precise spatiotemporal control and thus evoke defined neural circuits in nontransgenic mice. Moreover, the vesicle‐mediated NIR‐II optical stimulation can regulate mouse motor behaviors with minimal invasiveness by eliminating light‐emitting implants. Furthermore, the biological modulation is integrated with photoacoustic brain imaging, realizing navigational, and efficient neuromodulation. Such transcranial and precise NIR‐II optical neuromodulation mediated by bioinspired vesicles shows the potential for the optical‐theranostics of neurological diseases in nontransgenic organisms.

Time history testing using a shaking table is one of the most widely used methods for assessing the dynamic response of structures. In shaking-table experiments and on-site monitoring, acceleration sensors are facing problems of missing data due to the fact of measurement point failures, affecting the validity and accuracy of assessing the structural dynamic response. The original measured signals are decomposed by ensemble empirical mode decomposition (EEMD), and the widely used deep neural networks (DNNs), gated recurrent units (GRUs), and long short-term memory networks (LSTMs) are used to predict the subseries of the decomposed original measured signal data to help model and recover the irregular, periodic variations in the measured signal data. The raw acceleration data of a liquefied natural gas (LNG) storage tank in shaking-table experiments were used as an example to compare and discuss the method’s performance for the complementation of missing measured signal data. The results of the measured signal data recovery showed that the hybrid method (EEMD based) proposed in this paper had a higher complementary performance compared with the traditional deep learning methods, while the EEMD-LSTM exhibited the best missing data complementary accuracy among all models. In addition, the effect of the number of prediction steps on the prediction accuracy of the EEMD-LSTM model is also discussed. This study not only provides a method to fuse EEMD and deep learning models to predict measured signal’ missing data but also provides suggestions for the use of EEMD-LSTM models under different conditions.

On the beach in midsummer, one of the favorite recreational activities for adults and children is to build the castle of the sea sand. However, the castles carefully built by the artists are often ruined by the inflow of waves or rising tides. We built the best 3D model of the sandcastle foundation. Moreover, we found the optimal essential moisture content. Due to the frequent precipitation in summer, our model also provides some reinforcement schemes. we compared the bearing capacity coefficients of several common foundation shapes and obtained the optimal shape on a plane. Then we compared the various indexes of the round table with different stacking angles in the vertical direction. We constructed the evaluation function using hierarchical analysis and then found the optimal stacking angle at the point where the first derivative of the function was zero. When calculating the thickness of the foundation based on a sandcastle with specific gravity, we chose the ordinary column foundation bearing capacity formula as the bearing capacity formula of the circular table foundation. Furthermore, we use this formula to find the corresponding thickness of the foundation in combination with the optimal stacking angle previously obtained.

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