Time-frequency analysis (TFA) technique is an effective approach to capture the changing dynamic in a nonstationary signal. However, the commonly adopted TFA techniques are inadequate in dealing with ...signals having a strong nonstationary characteristic or multicomponent signals having close frequency components. To overcome this shortcoming, a new TFA technique applying a polynomial chirplet transform (PCT) in association with a synchroextracting transform (SET) is proposed in this paper. It is shown that the energy concentration of the time-frequency representation (TFR) of a strong frequency-modulated signal from a PCT transform can be further enhanced by an SET transform. The technique can also be employed to accurately extract the signal components of a multicomponent nonstationary signal with close frequency components by adopting an iterative process. It is found that the TFR calculated from the proposed technique matches well with the ideal TFR, which demonstrates the superiority of the current technique in dealing with nonstationary signals having rapidly changing dynamics. Results from the analysis of the experimental data under varying speed conditions confirm the validity of the proposed technique in dealing with nonstationary signals from practical sources.
An artificial intelligent bearing fault and hierarchical severity diagnosis framework is proposed in this study. The framework utilizes a combined deep belief networks (DBNs) and Dempster–Shafer ...(D-S) theory fault diagnosis scheme and adopts a two-stage approach in classifying (1) bearing fault conditions and (2) fault severities. The combined fault diagnostic scheme first employs two parameter-optimized DBNs to process the horizontal and vertical vibration data acquired from the bearing house of a test rig, where the parameters of the DBNs are optimized using a hybrid genetic algorithm and particle swarm optimization algorithm proposed in this study. The classification results from the two DBNs are fused further using the D-S theory to improve the diagnostic accuracy. The fault diagnosis scheme is used first to classify the bearing fault conditions in Stage 1 from a bulk dataset containing all bearing operation conditions under study. The same diagnosis scheme is applied once more to classify the hierarchical fault severities for each fault condition in Stage 2 using the pre-classified data from Stage 1. The effectiveness of the framework is then evaluated on a set of bearing condition monitoring data. A comparison study between the results obtained using the current method and those from existing published work is also presented in the article. It is shown that the accuracy for bearing fault and severity diagnosis can be substantially improved by using the current framework.
•Bearing fault features is obtained using SVD decomposition and EEMD spatial condition matrix.•Gath Geva ckustering is used to train and calculate the cluster center for each bearing ...condition.•Hamming approach degree is used for pattern recognition of the four bearing fault conditions.
This paper employs a combined ensemble empirical mode decomposition (EEMD) and singular value decomposition (SVD) technique to extract useful fault features from the condition monitoring data of rolling element bearings. The fault features is then classified by a Fuzzy clustering method, Gath-Geva (GG) clustering, to obtain the cluster center and membership matrix of each bearing condition for pattern recognition. The bearing fault recognition is realized by calculating the hamming approach degree between the test samples and the known fault clustering centers from the GG clustering. The proposed algorithm is then evaluated first on several sets of simulated bearing defect data with different signal to noise ratios (SNRs) to represent bearing defects with various degrees of severities. Satisfying diagnosis outcome can be obtained from this set of simulation when the SNR is greater than 1. The algorithm is further evaluated using a set of experimental data from a bearing fault test rig. It is found that the proposed algorithm can diagnose all bearing operation conditions accurately based on the experimental data.
Glioblastoma (GBM) is a prevalent and highly lethal form of glioma, with rapid tumor progression and frequent recurrence. Excessive outgrowth of pericytes in GBM governs the ecology of the ...perivascular niche, but their function in mediating chemoresistance has not been fully explored. Herein, we uncovered that pericytes potentiate DNA damage repair (DDR) in GBM cells residing in the perivascular niche, which induces temozolomide (TMZ) chemoresistance. We found that increased pericyte proportion correlates with accelerated tumor recurrence and worse prognosis. Genetic depletion of pericytes in GBM xenografts enhances TMZ-induced cytotoxicity and prolongs survival of tumor-bearing mice. Mechanistically, C-C motif chemokine ligand 5 (CCL5) secreted by pericytes activates C-C motif chemokine receptor 5 (CCR5) on GBM cells to enable DNA-dependent protein kinase catalytic subunit (DNA-PKcs)-mediated DDR upon TMZ treatment. Disrupting CCL5-CCR5 paracrine signaling through the brain-penetrable CCR5 antagonist maraviroc (MVC) potently inhibits pericyte-promoted DDR and effectively improves the chemotherapeutic efficacy of TMZ. GBM patient-derived xenografts with high CCL5 expression benefit from combined treatment with TMZ and MVC. Our study reveals the role of pericytes as an extrinsic stimulator potentiating DDR signaling in GBM cells and suggests that targeting CCL5-CCR5 signaling could be an effective therapeutic strategy to improve chemotherapeutic efficacy against GBM.
Ophthalmic manifestations have recently been observed in acute and post-acute complications of COVID-19 caused by SARS-CoV-2 infection. Our precious study has shown that host RNA editing is linked to ...RNA viral infection, yet ocular adenosine to inosine (A-to-I) RNA editing during SARS-CoV-2 infection remains uninvestigated in COVID-19. Herein we used an epitranscriptomic pipeline to analyze 37 samples and investigate A-to-I editing associated with SARS-CoV-2 infection, in five ocular tissue types including the conjunctiva, limbus, cornea, sclera, and retinal organoids. Our results revealed dramatically altered A-to-I RNA editing across the five ocular tissues. Notably, the transcriptome-wide average level of RNA editing was increased in the cornea but generally decreased in the other four ocular tissues. Functional enrichment analysis showed that differential RNA editing (DRE) was mainly in genes related to ubiquitin-dependent protein catabolic process, transcriptional regulation, and RNA splicing. In addition to tissue-specific RNA editing found in each tissue, common RNA editing was observed across different tissues, especially in the innate antiviral immune gene MAVS and the E3 ubiquitin-protein ligase MDM2. Analysis in retinal organoids further revealed highly dynamic RNA editing alterations over time during SARS-CoV-2 infection. Our study thus suggested the potential role played by RNA editing in ophthalmic manifestations of COVID-19, and highlighted its potential transcriptome impact, especially on innate immunity.
An analytical solution is presented in this study for the vibro-acoustic analysis of a cavity coupled with a ribbed panel due to an internal point sound source excitation. The solution is validated ...by comparing the result with that obtained using finite element analysis. Generally good agreements are found between the results. The model is then used to examine the sound transmission either through a single ribbed–panel or multiple ribbed–panels separated by air gaps. Results demonstrate that the rib enhancement is effective to reduce the energy transmission controlled by the panel control modes. Whereas the attenuation of the energy transmission to the panels is more effective for the cavity control modes when multiple ribbed panels with air gaps are used. The result also shows that the depth of the air gaps will also play a part on the sound attenuation across the panel system where a larger air gap will lead to a better sound attenuation.
This paper presents a maintenance optimisation method for a multi-state series–parallel system considering economic dependence and state-dependent inspection intervals. The objective function ...considered in the paper is the average revenue per unit time calculated based on the semi-regenerative theory and the universal generating function (UGF). A new algorithm using the stochastic ordering is also developed in this paper to reduce the search space of maintenance strategies and to enhance the efficiency of optimisation algorithms. A numerical simulation is presented in the study to evaluate the efficiency of the proposed maintenance strategy and optimisation algorithms. The simulation result reveals that maintenance strategies with opportunistic maintenance and state-dependent inspection intervals are more cost-effective when the influence of economic dependence and inspection cost is significant. The study further demonstrates that the optimisation algorithm proposed in this paper has higher computational efficiency than the commonly employed heuristic algorithms.
•An automated bearing remaining useful life (RUL) prediction technique is developed in this study.•Both spatial and temporal information contained in bearing data are utilized to render an accurate ...RUL prediction.•A smoothing technique is employed in the approach to reduce the fluctuation in the RUL prediction.
An automated remaining useful life (RUL) prediction technique based on a deep learning network is proposed in this study for an end-to-end RUL prediction of rolling element bearings. The technique utilizes a Convolutional Neural Network (CNN) to learn the spatial features from the bearing condition monitoring data, and then employs a stack of Bidirectional Gate Recurrent Units (BGRU) to extract the temporal degrading trend from the data for a more accurate RUL prediction. A weighted average method is employed to smooth out the trend of the RUL prediction. The effectiveness of the proposed technique is validated using two bearing degradation datasets, and the advantage of the proposed technique is examined by comparing the predicted RUL with those predicted using other commonly employed deep learning techniques. It is shown that the proposed technique can yield a much more accurate result for the bearing RUL prediction than other commonly employed deep learning techniques.
Novel 3D metal formate frameworks {Ba4ClM3(HCO2)13} n (M = Mn for 1, Co for 2, and Mg for 3) were successfully assembled via microwave-assisted synthesis. The complexes are rare coordination ...polymers crystallized at space group P4cc with the polar point group C 4v . In the structure, the MII ions are bridged by two types of anti-anti formate in forming a 3D pcu framework, and additional formates coordinate to the unsaturated sites of the MII ions in the framework, giving an anionic M–formate net. Ba4Cl clusters take the cavities of the net as charge balance, in which the chloride ion deviates from the center of the barium ions. The asymmetric Ba4Cl structure is transmitted throughout the crystal resulting in polar structure, which is further confirmed by nonlinear optical and piezoelectric test. Nonlinear optical activity tests of 1 and 3 show SHG signals 0.32 and 0.28 times that of KDP, while 2 has a piezoelectric coefficient d 33 of 6.8 pC/N along polar axis. Magnetic studies reveal antiferromagnetic coupling between MII ions in 1 and 2. Spin canting was found only in 2 with anisotropic CoII ions, and 2 is a canted antiferromagnetically with T N = 5 K. Further field-induced spin flop was also found in 2 with a critical field 0.9 T.
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![[Ba4Cl] Cations Directed Pe...](http://api.summon.serialssolutions.com/2.0.0/image/issn/XR4PS5YY4K/0020-1669/small "[Ba4Cl] Cations Directed Perovskite-like Polar Metal Formate Frameworks {[Ba4Cl][M3(HCO2)13]} n (M = Mn, Co, and Mg): Microwave-Assisted Synthesis, Structures, and Properties")