All‐solid‐state lithium metal batteries (ASSLMBs) stand out for the next generation of energy storage system. However, the further realization is severely hampered by the lithium dendrite formation in solid state electrolytes (SSEs), by mechanisms that remain controversial. Herein, with the aid of experimental and theoretical approaches, the origin of dendrite formation in representative LiBH4 SSE, which is thermodynamically stable with the Li metal, suppressing the side reaction between Li and SSE is elucidated. It is demonstrated that upon diffusion, Li+ encounters an electron, and is subsequently reduced to Li0 within the grain boundary/pore of SSE, eventually leading to short circuit. Thus, introducing LiF with the ability of interstitial filling and low electronic conductivity into SSE is the effective countermeasure, and as expected, with the addition of LiF, the critical current density (CCD) increases by 235% compared to the value of pure LiBH4. The TiS2|LiBH4–LiF|Li ASSLMBs manifest a reversible capacity of 137 mAh g−1 at 0.4 C upon 60 cycles. These findings not only unravel critical issues in Li dendrite formation in SSE, but also propose the countermeasure.

Chronic obstructive pulmonary disease (COPD) has become one of the most fatal diseases of the century considering mortality and morbidity levels worldwide.

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Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) inherently suffers from various errors. Many previous works employed Geoscience Laser Altimeter System onboard the Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) data to assess and enhance SRTM DEM accuracy. Nevertheless, data coregistration between the two datasets was commonly neglected in their studies. In this paper, an automated and simple three dimensional (3D) coregistration method (3CM) was introduced to align the 3-arc-second SRTM (SRTM3) DEM and ICESat/GLAS data over Jiangxi province, China. Then, accuracy evaluation of the SRTM3 DEM using ICESat/GLAS data with and without data coregistration was performed on different classes of terrain factors and different land uses, with the purpose of evaluating the importance of data coregistration. Results show that after data coregistration, the root mean square error (RMSE) and mean bias of the SRTM3 DEM are reduced by 14.4% and 97.1%, respectively. Without data coregistration, terrain aspects with a sine-like shape are strongly related to SRTM3 DEM errors; nevertheless, this relationship disappears after data coregistration. Among the six land uses, SRTM3 DEM produces the lowest accuracy in forest areas. Finally, by incorporating land uses, terrain factors and ICESat/GLAS data into the correction models, the SRTM3 DEM was enhanced using multiple linear regression (MLR), back propagation neural network (BPNN), generalized regression NN (GRNN), and random forest (RF), respectively. Results exhibit that the four enhancement models with data coregistration obviously outperform themselves without the coregistration. Among the four models, RF produces the best result, and its RMSE is about 3.1%, 2.7% and 11.3% lower than those of MLR, BPNN, and GRNN, respectively. Moreover, 146 Global Navigation Satellite System (GNSS) points over Ganzhou city of Jiangxi province were used to assess the accuracy of the RF-derived SRTM3 DEM. It is found that the DEM quality is improved and has a similar error magnitude to that relative to the ICESat/GLASS data.

The fault diagnosis of the gearbox is a complex and important work. In this paper, a multilayer gated recurrent unit (MGRU) method is proposed for spur gear fault diagnosis, that is, three-layer gated recurrent unit (GRU). The vibration signals are firstly monitored on the test bench, and then extracted in both time domain and time-frequency domain. Finally, MGRU is used to learn representation and classification. The MGRU can improve the representation of information and identify the features of fault types more precisely with the increasing number of layers. The proposed method was tested by two spur gears with 10 state modes. To evaluate the method's classification accuracy, four methods were utilized for comparison, i.e., the GRU, long short-term memory (LSTM), multilayer LSTM (MLSTM), and support vector machine (SVM), respectively. In addition, the separability and robustness analysis are also discussed for the proposed MGRU performance. All of the results exhibited that the proposed MGRU approach is effective for spur gear fault diagnosis.