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Abstract Soil microorganisms play vital roles in regulating multiple ecosystem functions. Recent studies have revealed that the rare microbial taxa (with extremely low relative abundances, which are still largely ignored) are also crucial in maintaining the health and biodiversity of the soil and may respond differently to environmental pressure. However, little is known about the soil community structures of abundant and rare taxa and their assembly processes in different soil layers on the Qinghai‐Tibet Plateau (QTP). The present study investigated the community structure and assembly processes of soil abundant and rare microbial taxa on the northeastern edge of the QTP. Soil microbial abundance was defined by abundant taxa, whereas rare taxa contributed to soil microbial diversity. The results of null model show that the stochastic process ruled the assembly processes of all sub‐communities. Dispersal limitation contributed more to the assembly of abundant microbial taxa in the different soil layers. In contrast, drift played a more critical role in the assembly processes of the rare microbial taxa. In addition, in contrast to previous studies, the abundant taxa played more important roles in co‐occurrence networks, most likely because of the heterogeneity of the soil, the sparsity of amplicon sequencing, the sampling strategy, and the limited samples in the present study. The results of this study improve our understanding of soil microbiome assemblies on the QTP and highlight the role of abundant taxa in sustaining the stability of microbial co‐occurrence networks in different soil layers.

Abstract Subsect. Hirculoideae Engl. & Irmsch., belonging to Saxifraga sect. Ciliatae Haw., has high species richness. It can be divided into S. diversifolia, S. pseudohirculus, and S. sinomontana complexes based on morphological characteristics. The species with prominent leaf veins on the posterior leaf edge were placed in the S. diversifolia complex, which is mainly distributed on the eastern and southern margins of the Qinghai‐Tibetan Plateau. In this study, 53 samples, representing 15 of the 33 described species in the S. diversifolia complex, were sequenced using the Restriction‐site Associated DNA Sequence (RAD‐seq) technique. A total of 111,938 high‐quality SNP loci were screened to investigate the phylogenetic relationships within the S. diversifolia complex. The result of the neighbor‐joining (NJ) tree shows that the S. diversifolia complex is a paraphyletic group. Despite of some inconsistencies as revealed by genetic structural analysis, clustering results of representative species reconstructed by both NJ and principal component analysis analyses support previous biogeographic and morphological evidences. In addition, long‐distance gene flow events for 11 taxa were detected in the S. diversifolia complex, respectively from S. implicans 1 to S. implicans 2, S. diversifolia and S. maxionggouensis, and from S. maxionggouensis to S. nigroglandulifera. These findings may improve our comprehension of the phylogeny, classification, and evolution of the S. diversifolia complex.

This paper presents a hybrid control scheme based on a super-twisting sliding mode control and radial basis function (RBF) neural network control method for micro gyroscopes with unknown model uncertainties and external disturbances. In consideration of unknown model of micro gyroscope system, this paper utilizes the RBF neural network to realize the adaptive approximation of the unknown part of the model and ensure that the controller does not rely on the precise mathematical model of the controlled system. The neural network adaptive law is obtained by using the Lyapunov method, and the stability and convergence of the closed-loop system are guaranteed by adjusting the adaptive weight. The super-twisting sliding mode control method is adopted in order to improve the convergence speed and weaken the chattering. Finally, the simulation results verify the superiority and validity of the proposed method and compare it with the super-twisting sliding mode control without RBF neural network. Simulation results indicate that the control method proposed in this paper is very effective and feasible, achieving the required dynamic and static performance. It not only effectively weakens the chattering of the control system but also ensures the convergence of the system in limited time and improves the property of the control system.

This paper proposes a novel adaptive fuzzy super-twisting sliding mode control scheme for microgyroscopes with unknown model uncertainties and external disturbances. Firstly, an adaptive algorithm is used to estimate the unknown parameters and angular velocity of microgyroscopes. Secondly, in order to improve the performance of the system and the superiority of the super-twisting algorithm, this paper utilizes the universal approximation characteristic of the fuzzy system to approach the gain of the super-twisting sliding mode controller and identify the gain of the controller online, realizing the adaptive adjustment of the controller parameters. Simulation results verify the superiority and the effectiveness of the proposed approach, compared with adaptive super-twisting sliding mode control without fuzzy approximation; the proposed method is more effective.

This paper proposes a novel adaptive Super-Twisting sliding mode control for a microgyroscope under unknown model uncertainties and external disturbances. In order to improve the convergence rate of reaching the sliding surface and the accuracy of regulating and trajectory tracking, a high order Super-Twisting sliding mode control strategy is employed, which not only can combine the advantages of the traditional sliding mode control with the Super-Twisting sliding mode control, but also guarantee that the designed control system can reach the sliding surface and equilibrium point in a shorter finite time from any initial state and avoid chattering problems. In consideration of unknown parameters of micro gyroscope system, an adaptive algorithm based on Lyapunov stability theory is designed to estimate the unknown parameters and angular velocity of microgyroscope. Finally, the effectiveness of the proposed scheme is demonstrated by simulation results. The comparative study between adaptive Super-Twisting sliding mode control and conventional sliding mode control demonstrate the superiority of the proposed method.

Here, a silicon nanopillar array (Si-NPA) was fabricated. It was studied as a room-temperature organic vapour sensor, and the ethanol and acetone gas sensing properties were detected with I-V curves. I-V curves show that these Si-NPA gas sensors are sensitive to ethanol and acetone organic vapours. The turn-on threshold voltage is about 0.5 V and the operating voltage is 3 V. With 1% ethanol gas vapour, the response time is 5 s, and the recovery time is 15 s. Furthermore, an evaluation of the gas sensor stability for Si-NPA was performed. The gas stability results are acceptable for practical detections. These excellent sensing characteristics can mainly be attributed to the change of the overall dielectric constant of Si-NPA caused by the physisorption of gas molecules on the pillars, and the filling of the gas vapour in the voids.