In-plane spin excitation of skyrmion bags

Skyrmion bags are spin structures with arbitrary topological charges, each of which is composed of a big skyrmion and several small skyrmions. In this work, by using an in-plane alternating current(AC) magnetic field, we investigate the spinwave modes of skyrmion bags, which behave differently from the clockwise(CW) rotation mode and the counterclockwise(CCW) rotation mode of skyrmions because of their complex spin topological structures. The in-plane excitation power spectral density shows that each skyrmion bag possesses four resonance frequencies. By further studying the spin dynamics of a skyrmion bag at each resonance frequency, the four spin-wave modes, i.e., a CCW-CW mode, two CW-breathing modes with different resonance strengths, and an inner CCW mode, appear as a composition mode of outer skyrmion–inner skyrmions. Our results are helpful in understanding the in-plane spin excitation of skyrmion bags, which may contribute to the characterization and detection of skyrmion bags, as well as the applications in logic devices.

Hourly land surface temperature retrieval over the Tibetan Plateau using Geo-LightGBM framework: Fusion of Himawari-8 satellite, ERA5 and site observations

The Tibetan Plateau(TP)is highly sensitive to even minor fluctuations in land surface temperature(LST),which can result in permafrost melting and degradation of alpine grasslands,leading to serious ecological consequences.Therefore,it is crucial to have high-temporal-resolution and seamless hourly estimating and monitoring of LST for a better understanding of climate change on the TP.Here,we employed Himawari-8 satellite,Digital Elevation Model(DEM),ERA5 reanalysis and meteorological station observations data to develop a new LightGBM framework(called Geo-LightGBM)for estimating LST on the TP,and then analyzed the spatiotemporal variations of those LST.Geo-LightGBM demonstrated excellent LST estimation accuracy,with an R2(coefficient of determination)of 0.971,RMSE(root-mean-square error)of 2.479℃,and MAE(mean absolute error)of 1.510℃.The estimated LST values for the year 2020 agreed well with observed values,with remarkable differences in hourly LST variations.Meanwhile,the estimated LST was more accurate than that from FY-4A.Spatially,there were two high LST centers,located in the Yarlung Zangbo River Basin and the Qaidam Basin,and a low LST center located in the central TP.The SHAP(SHapley Additive exPlanations)and correlation analyses revealed DSCS(the mean ground downward shortwave radiation under clear-sky conditions)to be the most importantly input variable for estimating LST.Spatiotemporal dummy variables(e.g.,longitude,latitude,DEM)were also found to be crucial for model accuracy improvement.Our findings indicate the potential for constructing a high-precision and seamless 24-h LST real-time retrieval and monitoring platform for the TP by combining satellite and China's independently developed CLDAS(China Land Data Assimilation System)data in future.

A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings

Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life(RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network(LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM structure.In this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.

Anisotanols A—D,Four Norsesquiterpenoids with an Unprece-dented Sesquiterpenoid Skeleton from Anisodus tanguticus

Anisotanols A—D(1-4),four new compounds possessing an unprecedented sesquiterpenoid skeleton with a congested tricyclic 6/3/5 ring system,were obtained from Anisodus tanguticus.Their structures were elucidated by comprehensive spectroscopic techniques,and the absolute configurations were confirmed via ECD calculations and single-crystal X-ray diffractions.A putative biosynthetic pathway for these compounds was proposed.Biological evaluation disclosed that compound 3 showed anti-angiogenic activity by inhibiting the viability,migration,and tube formation in HUVECs.

Hybrid Particle Swarm Optimization with Differential Evolution for Numerical and Engineering Optimization

In this paper, a hybrid particle swarm optimization （PSO） algorithm with differential evolution （DE） is proposed for numerical benchmark problems and optimization of active disturbance rejection controller （ADRC） parameters. A chaotic map with greater Lyapunov exponent is introduced into PSO for balancing the exploration and exploitation abilities of the proposed algorithm. A DE operator is used to help PSO jump out of stagnation. Twelve benchmark function tests from CEC2005 and eight real world opti- mization problems from CEC2011 are used to evaluate the performance of the proposed algorithm. The results show that statistically, the proposed hybrid algorithm has performed consistently well compared to other hybrid variants. Moreover, the simulation results on ADRC parameter optimization show that the optimized ADRC has better robustness and adaptability for nonlinear discrete-time systems with time delays.