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Reliable short‐time prediction of thermospheric mass density along the satellite orbit is always essential but challenging for the operation of Low‐Earth orbit satellites. In this paper, three machine‐learning prediction algorithms are investigated, including the Bidirectional Long Short‐Term Memory, the Transformer, and the Light Gradient Boosting Machine (LightGBM) ensemble model of the above models. We use satellite data from CHAMP, GOCE, and SWARM‐C to evaluate the robustness and accuracy of different density variations. The comparison demonstrates that all models achieve compelling predictions and are much better than NRLMSISE‐00. The LightGBM ensemble model (LE‐model) consistently outperforms others in accuracy and stability. Furthermore, when the obtained density data from the newly launched satellites are limited, the trained LE‐model can provide a valid prediction for the new satellite orbit by transfer learning. This study offers a promising insight into the short‐time prediction of thermospheric mass density using ensemble‐transfer learning and may be advantageous to future research on space whether. Plain Language Summary: Low‐Earth orbit (LEO) satellites play an important role in many aspects, such as navigation, aerospace, military industry, and so on. The LEO satellites suffer atmospheric drag caused by thermospheric mass density. Therefore, we present three different machine‐learning algorithms to achieve a robust short‐time prediction for thermospheric mass density. All models can provide effective results from testing with three satellite data, and the ensemble model always outperforms others. Then, when the obtained density data from the newly launched satellites are very limited, the pre‐trained ensemble model is also useful for the new satellite orbit by transfer learning. We offer a good insight into the short‐time prediction of thermospheric mass density and assistance for aerospace digitization and intellectualization. Key Points: Based on three different machine‐learning algorithms, we present a robust short‐time prediction for thermospheric mass densityWe evaluate the model performances using data from CHAMP, GOCE, and SWARM‐C with NRLMSISE‐00 as the referenceThe LightGBM ensemble model of Bidirectional Long Short‐Term Memory and Transformer outperforms others and can provide reliable transfer predictions for new satellites [ABSTRACT FROM AUTHOR]

In order to improve the reaction activity of lignin and increase its utilization rate. Lignin extracted from poplar wood powder by deep eutectic solvent was degraded with hydrogen peroxide (H2O2) and sodium hydroxide (NaOH) to obtain degraded deep eutectic solvent lignin (DES-lignin). The structures of DES-lignin and degraded DES-lignin were characterized by gel permeation chromatography (GPC), elemental analysis (EA), Fourier transform infrared spectroscopy (FT-IR), two-dimensional nuclear magnetic resonance spectroscopy (2D-HSQC NMR), and thermogravimetric analysis (TGA). The results indicated that the phenolic hydroxyl group of degraded DES-lignin increased by 39.62% and Mw and Mn decreased by 49.12% and 47.83%, respectively. C content decreased by 1.13%. The relative strength and S/G increased by 25.76% and 13.5%, respectively. The polymerization degree, β-O-4 linkages, and β-β linkages were decreased by 10.82%, 4.8%, and 4.2%, respectively. The residual residue increased by 8.3% at 800 °C. It showed that degraded DES-lignin had the advantages of strong activity, more reaction sites, narrow molecular weight distribution, less condensation structure, and thermal stability, which was more conducive to the high-value utilization of lignin. [ABSTRACT FROM AUTHOR]

The rolling bearing fault vibration signals are nonlinear and non- stationary and have strong noise background,in order to extract the fault feature effectively,a feature extraction technology which combines wavelet denoising and resonance- based sparse signal decomposition（ RSSD） is proposed. The resonance sparse decomposition is a new method for frequency division based on the tunable quality factor wavelet transform and morphological component analysis,it is different from conventional signal decomposition method based on frequency band partition,it is based on different oscillation forms of the signal components. Firstly,the signal is processed by wavelet threshold denoising,then,the signal is decomposed into two parts with different resonance characteristics by the resonance- based sparse signal decomposition method,The one is high- resonance component which has sustained oscillation characteristics,and the other is low- resonance component which has instantaneous impact characteristics. Finally,the impact fault feature is extracted from the low- resonance component by Hilbert envelope demodulation method. This method is applied to simulation signal and failure examples of impact on bearing test bench,the effectiveness of the proposed method is verified.

In the face of the current energy and environmental problems, the full use of biomass resources instead of fossil energy to produce a series of high-value chemicals has great application prospects. 5-hydroxymethylfurfural (HMF), which can be synthesized from lignocellulose as a raw material, is an important biological platform molecule. Its preparation and the catalytic oxidation of subsequent products have important research significance and practical value. In the actual production process, porous organic polymer (POP) catalysts are highly suitable for biomass catalytic conversion due to their high efficiency, low cost, good designability, and environmentally friendly features. Here, we briefly describe the application of various types of POPs (including COFs, PAFs, HCPs, and CMPs) in the preparation and catalytic conversion of HMF from lignocellulosic biomass and analyze the influence of the structural properties of catalysts on the catalytic performance. Finally, we summarize some challenges that POPs catalysts face in biomass catalytic conversion and prospect the important research directions in the future. This review provides valuable references for the efficient conversion of biomass resources into high-value chemicals in practical applications. [ABSTRACT FROM AUTHOR]

The slope length factor is one of the parameters of the Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE) and is sometimes calculated based on a digital elevation model (DEM). The methods for calculating the slope length factor are important because the values obtained may depend on the methods used for calculation. The purpose of this study was to compare the difference in spatial distribution of the slope length factor between the different methods at a watershed scale. One method used the uniform slope length factor equation (USLFE) where the effects of slope irregularities (such as slope gradient, etc.) on soil erosion by water were not considered. The other method used segmented slope length factor equation(SSLFE) which considered the effects of slope irregularities on soil erosion by water. The Arc Macro Language (AML) Version 4 program for the revised universal soil loss equation(RUSLE).which uses the USLFE, was chosen to calculate the slope length factor. In a parallel analysis, the AML code of RUSLE Version 4 was modified according to the SSLFE to calculate the slope length factor. Two watersheds with different slope and gully densities were chosen. The results show that the slope length factor and soil loss using the USLFE method were lower than those using the SSLFE method, especially on downslopes watershed with more frequent steep slopes and higher gully densities. In addition, the slope length factor and soil loss calculated by the USLFE showed less spatial variation.