Variance Distribution of the First Three PCs of the Six Moss Data Subsets.

<p>Variance Distribution of the First Three PCs of the Six Moss Data Subsets.</p

3D Plot of FTIR Spectra of the 6 Species of Mosses (5 Replicates for Each Sample) Based on PCA.

<p>3D Plot of FTIR Spectra of the 6 Species of Mosses (5 Replicates for Each Sample) Based on PCA.</p

Classification and identification of <i>Rhodobryum roseum</i> Limpr. and its adulterants based on fourier-transform infrared spectroscopy (FTIR) and chemometrics

<div><p>Fourier-transform infrared spectroscopy (FTIR) with the attenuated total reflectance technique was used to identify <i>Rhodobryum roseum</i> from its four adulterants. The FTIR spectra of six samples in the range from 4000 cm⁻¹ to 600 cm⁻¹ were obtained. The second-derivative transformation test was used to identify the small and nearby absorption peaks. A cluster analysis was performed to classify the spectra in a dendrogram based on the spectral similarity. Principal component analysis (PCA) was used to classify the species of six moss samples. A cluster analysis with PCA was used to identify different genera. However, some species of the same genus exhibited highly similar chemical components and FTIR spectra. Fourier self-deconvolution and discrete wavelet transform (DWT) were used to enhance the differences among the species with similar chemical components and FTIR spectra. Three scales were selected as the feature-extracting space in the DWT domain. The results show that FTIR spectroscopy with chemometrics is suitable for identifying <i>Rhodobryum roseum</i> and its adulterants.</p></div

FSD FT-IR Spectra in the 1 800–600 cm⁻¹ Region in the Whole Plants of the 6 Samples.

<p>The characteristic absorption bands are indicated at the top.</p

Results of the Multi-Resolution Decomposition for the FTIR Spectra with DWT.

<p><i>Rhodobryum roseum</i> collected from different areas (a): <i>Rhodobryum roseum</i> 1; (b): <i>Rhodobryum roseum</i> 2; Two Replicates of <i>Rhodobryum roseum</i>; (c): <i>Rhodobryum roseum</i> 2.1; (d): <i>Rhodobryum roseum</i> 2.2.</p

FTIR–ATR Spectra Obtained at 4000–600 cm⁻¹ for Six Samples.

<p>The Main Peaks are Labeled at the Top of the Spectra.</p

DataSheet1_Seismicity-based 3D model of ruptured seismogenic faults in the North-South Seismic Belt, China.zip

The North–South Seismic Belt produces the most frequent strong earthquakes in the Chinese continental region, such as the MS 8.0 Wenchuan earthquake on 12 May 2008 and Ms 7.0 Lushan earthquake on 20 April 2013. This seismicity results in significant hazards. Fault geometry modeling is crucial for analyzing earthquake preparation and trigger mechanisms, simulating and predicting strong earthquakes, inverting fault slip rates, etc. In this study, a novel method for obtaining geometric models of ruptured seismogenic faults over a large area is designed based on datasets from surface fault traces, fault orientations, focal mechanism solutions, and earthquake relocations. This method involves three steps. 1) An initial model of the fault geometry is constructed from the focal mechanism solution data. This initial model is used to select the earthquake relocation data related to the target fault. 2) Next, a fine model of the fault geometry with a higher resolution than that of the initial model is fitted based on the selected earthquake relocation data. 3) The minimum curvature interpolation method (Briggs, 2012) is adopted to build a 3D model of the subsurface fault geometry according to the three-dimensional coordinates of nodes on all profiles of each fault/segment. Based on this method and data collected in the North–South Seismic Belt, the fine morphologies of different faults along 1,573 transverse profiles were fitted, and a 3D model of 263 ruptured seismogenic faults or fault segments in the North–South Seismic Belt was built using the minimum curvature spatial interpolation method. Since the earthquake number decreases with increasing depth, the model uncertainty increases with increasing depth. Different ruptured faults have different degrees of seismicity, so different fault models may have different uncertainties. The overall fitting error of the model is 0.98 km with respect to the interpreted results, from six geophysical exploration profiles.</p

Mean Second-Derivative Spectra of Six Samples.

<p>Mean Second-Derivative Spectra of Six Samples.</p

Description of the Geographic Coordinates and Altitudes of the Aample Sources.

<p>Description of the Geographic Coordinates and Altitudes of the Aample Sources.</p

Dendrogram of the Relationship between 6 Moss Samples (5 Replicates for Each Sample).

<p>The dendrogram clustered by the hierarchical cluster analysis based on the FTIR data.</p