Order-frequency Holo-Hilbert spectral analysis for machinery fault diagnosis under time-varying operating conditions.

Holo-Hilbert spectral analysis (HHSA) has been demonstrated to be an effective instantaneous feature demodulation tool for revealing the coupling relationship between the frequency-modulated (FM) carriers and amplitude-modulated (AM) characteristics within nonlinear and non-stationary mechanical vibration signals. However, it is unable to acquire the time varying AM characteristics from the vibration signals of the equipment operates under variable speed conditions. To decode such signals, inspired by HHSA, a novel angle-time double-layer decomposition structure termed order-frequency HHSA (OFHHSA) is established to demodulate the fault information from the time varying vibration signals in this paper. The corresponding spectrogram, namely, order-frequency Holo-Hilbert spectrum (OFHHS) is acquired for describing the interaction relationship between time and angle domains. Besides, the order AM-marginal spectrum is derived from the OFHHS via integrating the carrier variable to exhibit the fault characteristic-related orders. Moreover, the differences between OFHHSA and angle-time cyclo-stationary framework-based order-frequency spectral correlation (OFSC) are analyzed for time varying machinery fault diagnosis. Finally, from the analyses of simulated and tested data of mechanical equipment, the OFHHSA method has avoided the limitations of the two OFSC estimators on periodic assumption and the maximum cut-off order, and the proposed method obtained a more accurate rate in fault identification and more robust ability of anti-noise.

Association between magnesium, copper, and potassium intakes with risk of rheumatoid arthritis: a cross-sectional study from National Health and Nutrition Examination Survey (NHANES).

BACKGROUND: The relationship between Mg (magnesium), Cu (copper), and K (potassium) intakes and the risk of rheumatoid arthritis (RA) remains limited. The aim of present study was to examine the associations between Mg, Cu and K intakes with RA. METHODS: Using data from the National Health and Nutrition Examination Survey (NHANES) 2003-2018, we examined the association between Mg, Cu and K intakes and the risk of RA among US adults. After adjustment for age, sex, race, BMI, educational level, smoking history, alcohol consumption, family Poverty Income Ratio (PIR), diabetes and total daily energy intake, logistic regression models and smooth curve fitting were applied to examine the associations of Mg, Cu and K intakes with RA. RESULTS: A total of 18,338 participants were included (1,008 participants with RA). The multivariate adjusted ORs (95% CI) of RA were [0.66 (0.51, 0.84)], [0.76 (0.60, 0.97)], and [0.75 (0.58, 0.97)] in the highest versus lowest quartile of magnesium intakes, respectively. A nonlinear association between Cu intakes and RA was found. When Cu intake (ln) was between 0.6-2.2 mg, the risk of RA reduced by 26% for every 1 mg increase of intake in Cu [0.74 (0.58, 0.96)]. CONCLUSIONS: Higher Mg, Cu and K intakes may be inversely associated with the risk of RA among US adults, and an inverse L-shaped association between dietary Cu and RA was found.

MiR-30b-5p attenuates the inflammatory response and facilitates the functional recovery of spinal cord injury by targeting the NEFL/mTOR pathway.

BACKGROUND: Neurofilament light chain (NEFL) has been identified as a biomarker for spinal cord injury (SCI), but its effect and underlying mechanism in SCI remain unclear. METHODS: SCI rat models were established for in vivo studies. Lipopolysaccharide (LPS)-induced cell models were used for in vitro studies. The protein and mRNA expression levels of genes were evaluated by western blotting and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The pathological changes in rats after SCI were subjected to histological examinations. The interaction of NEFL and upstream miRNAs was explored using dual-luciferase reporter gene assays. RESULTS: NEFL was highly expressed in SCI rat spinal cord tissues and LPS-stimulated PC12 cells. NEFL silencing showed an inhibitory effect on the morphological changes of SCI rats and the secretion of inflammatory factors and facilitated functional recovery of SCI rats. MiR-30b-5p was demonstrated to target NEFL and negatively regulate NEFL mRNA and protein levels. Downregulation of miR-30b-5p in SCI cell and rat models was demonstrated. MiR-30b-5p alleviated the inflammatory response in SCI rat models and LPS-stimulated PC12 cells and promoted functional recovery in rats by targeting NEFL. NEFL activated mTOR signaling. MiR-30b-5p inactivated mTOR signaling by negatively regulating NEFL. CONCLUSION: MiR-30b-5p alleviated the inflammatory response and facilitated the functional recovery of SCI rats by targeting NEFL to inactivate the mTOR pathway.

Association between Serum Cotinine Levels and Bone Mineral Density: An Analysis of the National Health and Nutrition Examination Survey (NHANES).

Purpose: To investigate the relationship between serum cotinine and lumbar bone mineral density (BMD) among 7905 participants aged 30 years and over. Method: A total of 3945 men and 3960 women from the National Health and Nutrition Examination Survey 2011-2018 were included in this cross-sectional analysis. Independent variable was serum cotinine, which is a biomarker of cigarette exposure. The outcome variable was lumbar BMD. We investigated the associations of serum cotinine levels and lumbar BMD using multivariable linear regression models. Results: Serum cotinine concentration was negatively associated with lumbar BMD after adjustment of relevant covariables (ß = -0.039, 95% CI: -0.078 to -0.014, P = 0.005). However, in the subgroup analysis stratified by gender, this negative association remained only in women (ß = -0.072, 95% CI: -0.132 to -0.012, P = 0.019). Conclusion: Our study suggested that elevated serum cotinine level correlated with decreased lumbar BMD, especially in women. This finding indicated that reducing cigarette exposure and maintaining serum cotinine at a low level may be beneficial to bone health for adults.

Composite Multivariate Multi-Scale Permutation Entropy and Laplacian Score Based Fault Diagnosis of Rolling Bearing.

As a powerful tool for measuring complexity and randomness, multivariate multi-scale permutation entropy (MMPE) has been widely applied to the feature representation and extraction of multi-channel signals. However, MMPE still has some intrinsic shortcomings that exist in the coarse-grained procedure, and it lacks the precise estimation of entropy value. To address these issues, in this paper a novel non-linear dynamic method named composite multivariate multi-scale permutation entropy (CMMPE) is proposed, for optimizing insufficient coarse-grained process in MMPE, and thus to avoid the loss of information. The simulated signals are used to verify the validity of CMMPE by comparing it with the often-used MMPE method. An intelligent fault diagnosis method is then put forward on the basis of CMMPE, Laplacian score (LS), and bat optimization algorithm-based support vector machine (BA-SVM). Finally, the proposed fault diagnosis method is utilized to analyze the test data of rolling bearings and is then compared with the MMPE, multivariate multi-scale multiscale entropy (MMFE), and multi-scale permutation entropy (MPE) based fault diagnosis methods. The results indicate that the proposed fault diagnosis method of rolling bearing can achieve effective identification of fault categories and is superior to comparative methods.

Generalized refined composite multiscale fuzzy entropy and multi-cluster feature selection based intelligent fault diagnosis of rolling bearing.

Extracting the failure related information from vibration signals is a very important aspect of vibration-based fault detection for rolling bearing Multiscale entropy and its improvement, multiscale fuzzy entropy (MFE), are significant complexity measure tools of time series. They have been successfully applied to extract vibration failure features for rolling bearing condition monitoring . However, MFE over different scales will fluctuate with increase of scale factor. A new nonlinear dynamic parameter termed generalized refined composite multiscale fuzzy entropy (GRCMFE) is firstly developed to enhance the performance of MSE and MFE in data complexity measurement. Then three algorithms are developed and compared with MSE and MFE, as well as two algorithms of generalized MFE to verify the availability and superiority by analyzing two kinds of noise signals. In addition, based on three algorithms of GRCMFE, a novel fault diagnosis approach for rolling bearing is proposed with linking multi-cluster feature selection for supervised learning and the gravitational search algorithm optimized support vector machine for failure pattern recognition. Last, the proposed fault diagnostic approach was utilized to analyze two kinds of bearing test data sets. Analysis results indicate that our proposed fault diagnosis approach could effectively extract nonlinear dynamic complexity information and gets the highest identifying rate and the best performance among the comparative approaches.

Morphological changes of contralateral intervertebral foramen induced by cage insertion orientation after unilateral transforaminal lumbar interbody fusion.

BACKGROUND: This study was performed to investigate the morphological changes of contralateral intervertebral foramen (IVF) based on computed tomography images of patients with lumbar spinal stenosis after unilateral transforaminal lumbar interbody fusion (TLIF) and to compare the influence of different orientation of cage insertion on these changes. METHODS: This is a retrospective cohort study. Sixty-nine patients with lumbar spinal stenosis who had undergone single-level unilateral TLIF were retrospectively analyzed. The patients were divided into two groups according to the cage insertion orientation: the oblique group (o-group, 39 cases) and the transverse group (t-group, 30 cases). The morphological parameters of contralateral IVF were measured before and 6 months after the operation. Changes in these parameters were compared and analyzed between the two groups. The 6-month clinical outcomes of the two groups were also collected and analyzed. RESULTS: There was a significant difference in the rate of increase in the segmental angle (p < 0.01) between the two groups, the mean value of segmental angle increased by an average of 29.08% ± 14.93% in the o-group and 48.63% ± 12.01% in the t-group. Overall, the posterior disc height had a significant positive correlation with the foraminal height and area. In the o-group, however, an increase in the segmental angle resulted in a decrease in the foraminal area. No significant difference in clinical outcomes was found between the two groups. CONCLUSIONS: Compared with oblique cage insertion, transverse cage insertion could achieve greater restoration of segmental lumbar lordosis without decreasing contralateral foraminal dimensions.

Composite Interpolation-Based Multiscale Fuzzy Entropy and Its Application to Fault Diagnosis of Rolling Bearing.

Multiscale fuzzy entropy (MFE), as an enhanced multiscale sample entropy (MSE) method, is an effective nonlinear method for measuring the complexity of time series. In this paper, an improved MFE algorithm termed composite interpolation-based multiscale fuzzy entropy (CIMFE) is proposed by using cubic spline interpolation of the time series over different scales to overcome the drawbacks of the coarse-grained MFE process. The proposed CIMFE method is compared with MSE and MFE by analyzing simulation signals and the result indicates that CIMFE is more robust than MSE and MFE in analyzing short time series. Taking this into account, a new fault diagnosis method for rolling bearing is presented by combining CIMFE for feature extraction with Laplacian support vector machine for fault feature classification. Finally, the proposed fault diagnosis method is applied to the experiment data of rolling bearing by comparing with the MSE, MFE and other existing methods, and the recognition rate of the proposed method is 98.71%, 98.71%, 98.71%, 98.71% and 100% under different training samples (5, 10, 15, 20 and 25), which is higher than that of the existing methods.