Landslide Surface Displacement Prediction Based on VSXC-LSTM Algorithm

Landslide is a natural disaster that can easily threaten local ecology, people's lives and property. In this paper, we conduct modelling research on real unidirectional surface displacement data of recent landslides in the research area and propose a time series prediction framework named VMD-SegSigmoid-XGBoost-ClusterLSTM (VSXC-LSTM) based on variational mode decomposition, which can predict the landslide surface displacement more accurately. The model performs well on the test set. Except for the random item subsequence that is hard to fit, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the trend item subsequence and the periodic item subsequence are both less than 0.1, and the RMSE is as low as 0.006 for the periodic item prediction module based on XGBoost\footnote{Accepted in ICANN2023}

A novel fault diagnosis method for rotating machinery based on S transform and morphological pattern spectrum

With the continuing expansion of the applications of rotating machinery, an earlier and more accurate fault diagnosis method is required. In this paper, a novel characterization method based on S transform and morphological pattern spectrum (ST-MPS) was put forward. In order to verify the application of the method, ST-MPS was applied to a set of experimental signals obtained in a bearing test bench, and the results verified that the proposed feature extraction method is an effective approach to accurately classify the types of bearing fault

Detection and differentiation of influenza viruses with glycan-functionalized gold nanoparticles

Accurate diagnosis of influenza viruses is difficult and generally requires a complex process because of viral diversity and rapid mutability. In this study, we report a simple and rapid strategy for the detection and differentiation of influenza viruses using glycan-functionalized gold nanoparticles (gGNPs). This method is based on the aggregation of gGNP probes on the viral surface, which is mediated by the specific binding of the virus to the glycans. Using a set of gGNPs bearing different glycan structures, fourteen influenza virus strains, including the major subtypes currently circulating in human and avian populations, were readily differentiated from each other and from a human respiratory syncytial virus in a single-step colorimetric procedure. The results presented here demonstrate the potential of this gGNP-based system in the development of convenient and portable sensors for the clinical diagnosis and surveillance of influenza viruses.</p