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Objective. The effect of vaginal microbiota on spontaneous preterm birth (sPTB) has not been fully addressed, and few studies have explored the associations between vaginal taxa and sPTB in the gestational diabetes mellitus (GDM) and non-GDM groups, respectively. Study Design. To minimize external interference, a total of 41 pregnant women with sPTB and 308 controls (pregnant women without sPTB) from same regain were enrolled in this case-cohort study. Controls were randomly selected at baseline. With the exception of GDM, other characteristics were not significantly different between the two groups. Vaginal swabs were collected at early second trimester. Using 16S amplicon sequencing, the main bioinformatics analysis was performed on the platform of QIIME 2. Vaginal microbiota traits of the sPTB group were compared with controls. Finally, the effects of binary taxa on sPTB in the GDM group and the non-GDM group were analyzed, respectively. Results. The proportion of GDM in the sPTB (19.51%) was higher than the controls (7.47%, P = 0.018 ). The vaginal microbiota of pregnant women with sPTB exhibited higher alpha diversity metrics (observed features, P = 0.001 ; Faith’s phylogenetic diversity, P = 0.013 ) and different beta diversity metrics (unweighted UniFrac, P = 0.006 ; Jaccard’s distance, P = 0.004 ), compared with controls. The presence of Lactobacillus paragasseri/gasseri (aOR: 3.12, 95% CI: 1.24-7.84), Streptococcus (aOR: 3.58, 95% CI: 1.68-7.65), or Proteobacteria (aOR: 3.39, 95% CI: 1.55-7.39) was associated with an increased risk of sPTB in the non-GDM group ( P < 0.05 ). However, the relative abundance of novel L. mulieris (a new species of the L. delbrueckii group) was associated with a decreased risk of sPTB (false discovery rate, 0.10) in all pregnant women. Conclusion. GDM may modify the association of vaginal taxa with sPTB, suggesting that maternal GDM should be considered when using vaginal taxa to identify pregnant women at high risk of sPTB.

Anti-swing control of bridge cranes has been widely studied to improve the efficiency of industrial transportation. However, in practice, the performance of bridge crane control methods is reduced by external disturbances and internal uncertainties. Therefore, an enhanced coupled nonlinear control strategy based on feedforward compensation has been proposed in this study. Appropriate composite signals were introduced to enhance the coupling between the system states and improve the transient performance of the controller. The design of the composite signal takes into account the characteristics of crane model. The unknown disturbance was then treated as a state variable, and the crane dynamic model was transformed accordingly. Finally, the state of the model was estimated by the extended state observer, and the disturbance estimate was compensated to improve the robustness of the control system. The stability of the controller was verified through a rigorous mathematical analysis. The simulation and experimental results verified the effectiveness of the proposed method.

During the transference of a ladle by the casting crane, the antiswing control of the ladle is particularly difficult due to liquid sloshing, so we designed a model based on a radial spring damper. During the ladle swings, the centrifugal force causes the spring damper to move radially, thereby generating a Coriolis force that inhibits the sloshing of the liquid. In addition, a sloshing analysis of the liquid in the ladle is carried out, and a double-pendulum casting crane model based on viscous damping and radial spring damper is established. On the basis of this model, the Enhanced Coupled Adaptive Sliding Mode Control (ECASMC) method is proposed. By introducing an enhanced coupling variable and constructing a new coupling deviation signal, we enhance the relationship among state quantities. Then, a new type of sliding surface is designed based on the enhanced coupling deviation signal, and adaptive technology is used to adjust the sliding mode parameters, which enhances the system's robustness for system parameter variations and external disturbances. Using LaSalle's invariance principle and Lyapunov theorem, we prove that the casting crane system is asymptotically stable near the desired equilibrium point. The simulation results verify the effectiveness and superior control performance of the proposed method even in the presence of uncertainties.

Underground pipeline networks suffer from severe damage by earth-moving devices due to rapid urbanization. Thus, designing a round-the-clock intelligent surveillance system has become crucial and urgent. In this study, we develop an acoustic signal-based excavation device recognition system for underground pipeline protection. The front-end hardware system is equipped with an acoustic sensor array, an Analog-to-Digital Converter (ADC) module (ADS1274), and an industrial processor Advanced RISC Machine (ARM) cortex-A8 for signal collection and algorithm implementation. Then, a novel Statistical Time-Frequency acoustic Feature (STFF) is proposed, and a fast Extreme Learning Machine (ELM) is adopted as the classifier. Experiments on real recorded data show that the proposed STFF achieves better discriminative capability than the conventional acoustic cepstrum features. In addition, the surveillance platform is applicable for encountering big data owing to the fast learning speed of ELM.

One-class classification achieves anomaly/outlier detection by exploiting the characteristics of target data. As a local similarity measure defined in kernel space, correntropy is generally more robust than the mean square error (MSE) based criterion in dealing with large outliers. In this paper, the maximum mixture correntropy criterion (MMCC) with multiple kernels are applied to the shallow and hierarchical one-class extreme learning machine (OC-ELM) to enhance the model robustness and learning speed. Experiments on benchmark UCI classification datasets, urban acoustic classification (UAC) dataset and 4 synthetic datasets are carried out to show the effectiveness and comparisons with several state-of-the-art methods are provided to demonstrate the superiority of the proposed algorithms.