The current investigation aimed to present an overview of the conservation of biological diversity of rare and endangered plant species. Methods of biodiversity conservation as well as several overview recommendations for the preservation of various rare species have been considered. An overview of the taxa included in the red book has been presented on the example of the Russian Federation. Global and local codes and classifiers of plant rarity were also presented. Future prospects for the conservation of biological diversity and the creation and development of bioresource collections have been considered.

The present study aimed to investigate the possibility of using hyperspectral imaging data to identify the invasive and weed species in agrocenoses ecosystem. The most common weeds in grain agrocenoses, i.e., Ambrosia artemisiifolia L., Euphorbia seguieriana Neck., Atriplex tatarica L., Glycyrrhiza glabra L., Setaria pumila (Poir.) Roem. and Schult, served as objects. The population of weeds, especially Ambrosia artemisiifolia is invasive for the selected region of study. Therefore, the shooting of objects was carried out with a hyperspectral camera, Cubert UHD185, and the values of 100 spectral channels were obtained from hyperspectral images. The values of 80 vegetation indices (VIs) were calculated. The material was processed using mathematical statistics (analysis of variance, t-test) and search methods of data analysis (principal component analysis, decision tree, and random forest). Using statistical methods, the simultaneous use of several VIs differentiated between species more deliberately and precisely. The combination of VIs Derivative index (D1), Chlorophyll content index (Datt3), and Pigment specific normalized difference (PSND) can be used for weeds identification. Using the decision tree method, VIs established a good division of weeds into groups; (1) perennial rhizomatous weeds (Euphorbia seguieriana, and Glycyrrhiza glabra), and (2) annual weeds (A. artemisiifolia, A. tatarica, and S. pumila); These Vis are Chlorophyll index (CI), D1, and Datt3. Using the random forest method, the VIs that have the greatest impact on Mean Decrease Accuracy and Mean Decrease Gini are D1, Datt3, PSND, and Double Peak Index (DPI). The use of spectral channel values for the identification of plant species using the principal component analysis, decision tree, and random forest methods showed worse results than when using VIs. A great similarity of the results was obtained with the help of statistical and search methods of data analysis.

We have built a terahertz dispersive Fourier transform spectrometer [Birch, Microchim. Acta 93, 105–122 (1987)] to study frequency properties of superconducting films used for fabrication of THz detectors. The signal reflected from the tested film is measured in time domain, which allows to separate it from the other reflections. The complex conductivity of the film depends on frequency and determines the reflection coefficient. By comparing the film reflection in the superconducting state (temperature is below T_c) with the reflection of the normal state, we characterize the film quality at terahertz frequencies. The method was applied to 70 and 200 nm thick Nb films on a silicon wafer and to 360 nm thick NbTiN films on silicon and quartz wafers. The strong-coupling coefficient, α, was found to be 3.52 for Nb and 3.71–4.02 for the NbTiN films. The experimental results were fitted using extended Mattis-Bardeen theory [Noguchi et al., Phys. Procedia 36, 318–323 (2012)] and show good agreement.