In this paper, we develop a mixture of Gaussians-evidential hidden Markov model (MoG-EHMM) to fuse expert knowledge and condition monitoring information for RUL prediction under the belief function theory framework. The evidential Expectation-Maximization algorithm is implemented in the offline phase to train the MoG-EHMM based on historical data. In the online phase, the trained model is used to recursively update the health state and reliability of a particular individual system. The predicted RUL is, then, represented in the form of its probability mass function. A numerical metric is defined based on the Bhattacharyya distance to measure the RUL prediction accuracy of the developed methods. We applied the developed methods on a simulation experiment and a real-world dataset from a bearing degradation test. The results demonstrate that despite imprecisions in expert knowledge, the performance of RUL prediction can be substantially improved by fusing expert knowledge with condition monitoring information. IndexTerms-Belief function theory, expert knowledge, Mixture of Gaussians-evidential hidden Markov model (MoG-EHMM), remaining useful life (RUL) I. INTRODUCTION Prognostics and health management (PHM) has been widely recognized as a useful tool to provide early failure warnings and prevent industrial equipment from unexpected shutdowns. One of the core tasks in PHM

Sepsis is a major cause of patient mortality and morbidity from bacterial infections. Although neutrophils are known to be important in the development of sepsis, how distinctive neutrophil subtypes regulate inflammatory processes involved in septicemia remains unclear. Preconditioning protects organisms against subsequent higher-dose exposures to the same, or even different, stimuli. Several studies have reported various effects of preconditioning on immune cells. However, the detailed mechanisms underlying neutrophil-mediated protection through preconditioning in sepsis remain unknown.Methods: Flow cytometry was conducted to sort the mice peritoneal lavage cells and the blood samples from patients with sepsis. Western blotting and ELISA were carried out to elucidate the expression of TLR9 signal transduction pathway proteins. Histological analysis was used to assess the effect of InP on intestine and liver structure in tlr9-/- and cav-1-/- mice. Fluorescence microscopy, Co-IP, and FRET were carried out to determine the association of TLR9 with Cav-1.Results: We show that membrane toll-like receptor-9 positive (mTLR9+) neutrophils exert a protective effect against fatal bacterial infections through the process of inflammatory preconditioning (InP). InP, which occurs in the setting of a low-dose bacterial challenge, active ingredient is Monophosphoryl lipid A (MPLA), triggers the membrane translocation of TLR9 from the neutrophil cytosol, where it binds to Cav-1. Our findings showed that InP enables TLR9 to facilitate MyD88-mediated TRAF3 and IRF3 signal transduction. Depletion of either TLR9 or Cav-1 largely eliminates the neutrophil-mediated InP effect in sepsis models in vitro and in vivo. Further, examination of clinical samples from patients with sepsis showed that clinical outcomes and likelihood of recovery are closely correlated with mTLR9 and Cav-1 expression in circulating neutrophils.Conclusion: These results demonstrate that the TLR9-Cav-1 axis is a critical signaling pathway involved in the regulation of neutrophil-dependent MPLA mediated InP, and the presence of mTLR9+ neutrophils could be an attractive indicator of clinical outcomes in bacterial sepsis that could be further explored as a potential therapeutic target.

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Abstract-A RF directional modulation technique using a switched antenna array is proposed for communication and direction-finding applications. The main idea is that a baseband modulation signal is transmitted by the switched antenna array. The phase center of the transmit signal is moved by the feeding line of each element from the left to the right. In this way, the data information and Doppler frequency shift information are modulated into a transmit signal constellation simultaneously. Therefore, this constellation is a scrambled constellation compared with traditional baseband modulation signal, which varies with the azimuth angle information of the receiver. For the receiver with a single antenna, a differential correlation algorithm is employed to demodulate the data information, and an azimuth angle estimation algorithm is also developed to extract the azimuth angle information from this scrambled constellation. Simulation results show that this RF directional modulation technique offers a comprehensive scheme for communication and direction-finding from the point view of RF modulation technique.

The fault tree analysis has been extensively implemented in failure analysis of engineered systems. In most cases, the probabilities of basic events, e.g. components’ failures, are represented by crisp values in the fault tree analyses. However, due to lack of knowledge, scarcity of failure data, or vague judgments from experts, it may produce parameter uncertainty associated with degradation models of components/systems, and such model parameter uncertainty can be quantified by the epistemic uncertainty. In addition, the common cause failure, related to the simultaneous failures of two or more components caused by physical interactions or shared environments, often exists in advanced engineered systems and computing systems. In this paper, by considering both the common cause failure and the epistemic uncertainty associated with model parameters, an evidential network model embedded with common cause failure is proposed to facilitate system failure analysis. The detailed transformations from some logic gates of a fault tree to an evidential network model are given. Moreover, the conditional belief mass tables are constructed to quantify the dependency between the states of components and the entire system. An engineering case of an aero-engine oil system, together with comparative results, is presented to demonstrate the effectiveness of the proposed evidential network model.