Double Graphs Regularized Multi-view Subspace Clustering

Recent years have witnessed a growing academic interest in multi-view subspace clustering. In this paper, we propose a novel Double Graphs Regularized Multi-view Subspace Clustering (DGRMSC) method, which aims to harness both global and local structural information of multi-view data in a unified framework. Specifically, DGRMSC firstly learns a latent representation to exploit the global complementary information of multiple views. Based on the learned latent representation, we learn a self-representation to explore its global cluster structure. Further, Double Graphs Regularization (DGR) is performed on both latent representation and self-representation to take advantage of their local manifold structures simultaneously. Then, we design an iterative algorithm to solve the optimization problem effectively. Extensive experimental results on real-world datasets demonstrate the effectiveness of the proposed method

Fault identification technology for gear tooth surface wear based on MPE method by MI and improved FNN algorithm

Multiscale Permutation Entropy (MPE) is a presented nonlinear dynamic technology for measuring the randomness and detecting the nonlinear dynamic change of time sequences and can be used effectively to extract the nonlinear dynamic wear fault feature of gear tooth surface from vibration signals of gear set. To solve the subjectivity drawback of threshold parameter selection process in MPE method, a joint calculation method based on the Mutual Information (MI) and improved False Nearest Neighbor (FNN) principle for calculating threshold parameters for MPE method was presented in this article. Then, the influence of threshold parameters on the identification accuracy of fault features with the MPE was studied by analyzing simulation data. Through the simulation analysis, the effectiveness of the proposed MPE method is validated. Finally, the wear failure test of spur gear was carried out, and the proposed method was applied to analyze the experimental data of fault signal. Meanwhile, the vibration characteristics of the fault signal are acquired. The analysis results show that the proposed method can effectively realize the fault diagnosis of gear box and has higher fault identification accuracy than the existing methods

Identification of Novel Mutations Associated with Bedaquiline Resistance in Mycobacterium Marinum

Because infections caused by nontuberculous mycobacteria (NTM) are rapidly increasing globally, a need exists for developing novel antibiotics and discovering the mechanism of resistance. This research was aimed at understanding the mechanism of bedaquiline resistance in the model NTM species Mycobacterium marinum ( M. marinum ). The Mycobacterium marinum M strain was subjected to mutant selection with different concentrations of BDQ. After three rounds of evolution, 58 BDQ-resistant mutants were isolated and subjected to WGS. The results were confirmed through PCR and Sanger sequencing. We identified seven genetic mutations among these mutants. The highest drug resistance (6–10× MIC) was associated with a mutation in AtpB, the primary biochemical target of BDQ in Mtb. Numerous mutations and insertions mapped to the gene MMAR_1007 (46/58), which encodes the homolog of Rv0678 (MmpR) in Mtb. More than 93% of mutants (54/58) contained a single mutation (G563A) in MMAR_4049 , which encodes the integral membrane protein YrbE3A-1. Both target-based and efflux-based actions contribute to BDQ resistance in M. marinum. Our findings may aid in developing novel potent anti-NTM (BDQ-based) drug regimens and diagnostic assays for the detection of BDQ-resistant M. marinum

Multi-task unscented Kalman inversion (MUKI): a derivative-free joint inversion framework and its application to joint inversion of geophysical data

In the geophysical joint inversion, the gradient and Bayesian Markov Chain Monte Carlo (MCMC) sampling-based methods are widely used owing to their fast convergences or global optimality. However, these methods either require the computation of gradients and easily fall into local optimal solutions, or cost much time to carry out the millions of forward calculations in a huge sampling space. Different from these two methods, taking advantage of the recently developed unscented Kalman method in computational mathematics, we extend an iterative gradient-free Bayesian joint inversion framework, i.e., Multi-task unscented Kalman inversion (MUKI). In this new framework, information from various observations is incorporated, the model is iteratively updated in a derivative-free way, and a Gaussian approximation to the posterior distribution of the model parameters is obtained. We apply the MUKI to the joint inversion of receiver functions and surface wave dispersion, which is well-established and widely used to construct the crustal and upper mantle structure of the earth. Based on synthesized and real data, the tests demonstrate that MUKI can recover the model more efficiently than the gradient-based method and the Markov Chain Monte Carlo method, and it would be a promising approach to resolve the geophysical joint inversion problems.Comment: 13 pages, 4 figure

End water content determines the magnitude of N2_{2}O pulse from nitrifier denitrification after rewetting a fluvo-aquic soil

Large nitrous oxide (N$_{2}$O) emissions pulses have been observed after rewetting dry soil. However, few studies have uncoupled the effects of drought severity from the degree to which the soil is saturated. In this study, we conducted three aerobic incubation experiments to investigate the effects of soil rewetting on N$_{2}$O emissions from a dryland soil. The results showed that, at constant soil moisture, total N$_{2}$O emissions in soil with 90% water-holding capacity (WHC) were significantly higher than those in 30%, 45%, 60% and 75% WHC treatments. In the dry–wet group, the soil moisture content was adjusted from 30%, 45% and 60% WHC to the end content of 75% and 90% WHC, respectively; the cumulative N$_{2}$O emissions in the 30–90%, 45–90% and 60–90% WHC nitrogen (N) treatments were significantly higher than those in the 30–75%, 45–75% and 60–75% WHC N treatments. Regarding fertilizer N types, there was no significant difference in N$_{2}$O emissions from soil at 90% WHC when (NH$_{4}$)$_{2}$SO$_{4}$ or urea was applied. Nitrification inhibitor significantly reduced N$_{2}$O emissions in soil applied with NH$_{4}$$^{+}$-N fertilizer, indicating that nitrification played a major role in N$_{2}$O emissions from soils. The contribution of denitrification was negligible, according to the low emission rate of soils with only NO$_{3}$$^{-}$ additions. High N$_{2}$O emissions occurred in soil treated with NO$_{2}$$^{-}$, accounting for about 83.6% of those of the NH$_{4}$$^{+}$ treatment. Therefore, in this study we concluded that the end water content of soil was more important than the role of drought severity in the dry-wet process and that nitrifier denitrification was probably the main pathway of N$_{2}$O production under the condition of 90% WHC moisture after rewetting soil