Hybrid Fault Diagnosis Method Based on Mechanical-Electrical Intersectional Characteristics for Generators

In this chapter, a new hybrid fault diagnosis method based on the mechanical-electrical intersectional characteristics for turbo-generators is proposed. Different from other studies, this method not only employs the rotor vibration characteristics but also uses the stator vibration features and the circulating current properties inside the parallel branches of the same phase. Detailed theoretical analysis, as well as the experimental verification study, is carried out to demonstrate the proposed method. It is shown that in the proposed criterion for the method, the combining faulty characteristics for the single rotor eccentricity fault, the single rotor interturn short circuit fault, and the composite fault composed of the rotor eccentricity and the rotor interturn short circuit are all unique. The running conditions can be accurately and quickly identified by the proposed method. The work proposed in this chapter offers a new thought for the condition monitoring and the fault diagnosis of generators

Excessive neutrophil extracellular trap formation induced by Porphyromonas gingivalis lipopolysaccharide exacerbates inflammatory responses in high glucose microenvironment

IntroductionNeutrophil extracellular trap (NET) is a novel defense strategy of neutrophils and found to be induced by Porphyromonas gingivalis (P. gingivalis) lipopolysaccharide (LPS) or high glucose. The aim of this study was to investigate the roles and mechanisms of NET formation in high glucose inflammatory microenvironment.MethodsNETs induced by 1 μg/ml P. gingivalis LPS and/or 25 mM glucose were visualized using a fluorescence microscopy and the levels of extracellular DNA were determined by a microplate reader. The bactericidal efficiency of NETs was assessed by quantifying the survival P. gingivalis in neutrophils. The levels of NLRP3 and IL-1β in THP-1 derived-macrophages, and the expressions of p-PKC βII, p-MEK1/2, p-ERK1/2, ORAI1 and ORAI2 in neutrophils were detected by Western blot. Moreover, levels of intracellular Ca2+ and reactive oxygen species (ROS) in neutrophils were explored by flow cytometry.ResultsP. gingivalis LPS enhanced the formation of NETs and increased the levels of extracellular DNA in high glucose microenvironment (p < 0.05). Compared with normal glucose inflammatory microenvironment, quantities of extra- and intracellular viable P. gingivalis in neutrophils exposed to NETs induced in high glucose inflammatory one were increased (p < 0.05) and the expressions of NLRP3 and IL-1β were dramatically increased in macrophages co-cultured with NETs from high glucose inflammatory microenvironment (p < 0.05). In addition, levels of ROS, intracellular Ca2+, p-PKC βII, p-MEK1/2, p-ERK1/2, ORAI1 and ORAI2 were increased in neutrophils stimulated with both high glucose and P. gingivalis LPS compared with the single stimulus groups (p < 0.05).DiscussionIn high glucose inflammatory microenvironment, formation of NETs was enhanced via oxidative stress, which failed to reverse the decreased bactericidal capacity in high glucose microenvironment, and instead aggravated the subsequent inflammatory responses

SCFM: Social and crowdsourcing factorization machines for recommendation

With the rapid development of social networks, the exponential growth of social information has attracted much attention. Social information has great value in recommender systems to alleviate the sparsity and cold start problem. On the other hand, the crowd computing empowers recommender systems by utilizing human wisdom. Internal user reviews can be exploited as the wisdom of the crowd to contribute information. In this paper, we propose social and crowdsourcing factorization machines, called SCFM. Our approach fuses social and crowd computing into the factorization machine model. For social computing, we calculate the influence value between users by taking users’ social information and user similarity into account. For crowd computing, we apply LDA (Latent Dirichlet Allocation) on people review to obtain sets of underlying topic probabilities. Furthermore, we impose two important constraints called social regularization and domain inner regularization. The experimental results show that our approach outperforms other state-of-the-art methods.This project is supported by the National Natural Science Foundation of China (Nos. 61672340, 61472240, 61572268)