[Response of vegetation productivity to drought in the Qinling-Daba Mountains, China from 2001 to 2020]. / 2001­2020年秦巴山区植被生产力对干旱的响应.

To quantitatively evaluate the effects of drought on vegetation productivity in the Qinling-Daba Mountains, we analyzed the temporal and spatial characteristics of gross primary productivity (GPP) and drought, identified the fluctuation of negative GPP extremes under different vegetation types, and quantified the drought vulnerability and drought risk of GPP from 2001 to 2020 with MODIS GPP products and standardized precipitation evapotranspiration index (SPEI). The results showed that the annual GPP from 2001 to 2020 had an increasing trend in 98.0% of areas in the Qinling-Daba Mountains. The GPP of all vegetation types except wetlands increased significantly. SPEI decreased in 23.8% of area in the Qinling-Daba Mountains from 2001 to 2020. The number of negative GPP extremes had no significant trend, but abnormal GPP fluctuations had intensified, especially in the cultivated land. After 2011, the proportion of concurrent negative GPP extreme and drought had decreased for all vegetation types, but the spatial and temporal range of drought in these negative GPP extremes showed an expanding trend. Compared with the pattern during 2001-2010, the proportion of area with positive drought vulnerability and drought risk increased by 104.1% and 6.7% after 2011, indicating that the area with drought-induced GPP decline had expanded. Among all the vegetation types, drought caused the largest decrease of GPP in wetlands. The results revealed that drought led to an aggravation of GPP fluctuations and increased frequency of GPP extremes in the Qinling-Daba Mountains from 2001 to 2020, which resulted in GPP decline with different magnitudes in most vegetation types.

Sinusoidal Signal Assisted Multivariate Empirical Mode Decomposition for Brain-Computer Interfaces.

A brain-computer interface (BCI) is a communication approach that permits cerebral activity to control computers or external devices. Brain electrical activity recorded with electroencephalography (EEG) is most commonly used for BCI. Noise-assisted multivariate empirical mode decomposition (NA-MEMD) is a data-driven time-frequency analysis method that can be applied to nonlinear and nonstationary EEG signals for BCI data processing. However, because white Gaussian noise occupies a broad range of frequencies, some redundant components are introduced. To solve this leakage problem, in this study, we propose using a sinusoidal assisted signal that occupies the same frequency ranges as the original signals to improve MEMD performance. To verify the effectiveness of the proposed sinusoidal signal assisted MEMD (SA-MEMD) method, we compared the decomposition performances of MEMD, NA-MEMD, and the proposed SA-MEMD using synthetic signals and a real-world BCI dataset. The spectral decomposition results indicate that the proposed SA-MEMD can avoid the generation of redundant components and over decomposition, thus, substantially reduce the mode mixing and misalignment that occurs in MEMD and NA-MEMD. Moreover, using SA-MEMD as a signal preprocessing method instead of MEMD or NA-MEMD can significantly improve BCI classification accuracy and reduce calculation time, which indicates that SA-MEMD is a powerful spectral decomposition method for BCI.