Exploration of whole brain networks modulated by acupuncture at analgesia acupoint ST36 using scale-specific wavelet correlation analysis

Background Previous studies have demonstrated that acupuncture could modulate various brain systems in the resting brain networks. Graph theoretical analysis offers a novel way to investigate the functional organization of the large-scale cortical networks modulated by acupuncture at whole brain level. In this study, we used wavelets correlation analysis to estimate the pairwise correlations between 90 cortical and subcortical human brain regions in normal human volunteers scanned during the post-stimulus resting state. Methods Thirty-two college students, all right-handed and acupuncture na＇fve, participated in this study. Every participant received only one acupoint stimulation, resulting in 16 subjects in one group. Both structural functional magnetic resonance imaging （fMRI） data （3D sequence with a voxel size of 1 mm3 for anatomical localization） and functional fMRI data （TR=1500 ms, TE=30 ms, flip angle=90°） were collected for each subject. After thresholding the resulting scale-specific wavelet correlation matrices to generate undirected binary graphs, we compared graph metrics of brain organization following verum manual acupuncture （ACU） and sham acupuncture （SHAM） groups. Results The topological parameters of the large-scale brain networks in ACU group were different from those of the SHAM group at multiple scales. There existed distinct modularity functional brain networks during the post-stimulus resting state following ACU and SHAM at multiple scales. Conclusions The distinct modulation patterns of the resting brain attributed to the specific effects evoked by acupuncture. In addition, we also identified that there existed frequency-specific modulation in the post-stimulus resting brain following ACU and SHAM. The modulation may be related to the effects of verum acupuncture on modulating special disorder treatment. This preliminary finding may provide a new clue to understand the relatively function- oriented specificity of acupuncture effects.

A Method for Incipient Fault Diagnosis of Roller Bearings Based on the Wavelet Transform Correlation Filter and Hilbert Transform

Noise is the biggest obstacle that makes the incipient fault diagnosis results of roller bearings uncorrected; a new method for diagnosing incipient fault of roller bearings based on the Wavelet Transform Correlation Filter and Hilbert Transform was proposed. First, the weak fault information features are picked up from the roller bearings fault vibration signals by use of a de-noising characteristic of the Wavelet Transform Correlation Filter as the preprocessing of the Hilbert Envelope Analysis. Then, in order to get fault features frequency, de-noised wavelet coefficients of high scales which represent high frequency signal were analyzed by Hilbert Envelope Spectrum Analysis. The simulation signals and diagnosing examples analysis results reveal that the proposed method is more effective than the method of direct wavelet coefficients-Hilbert Transform in de-noising and clarifying roller bearing incipient fault.

Damage localization for beams based on the wavelet correlation operator

The continuous wavelet transform(CWT) is one of the crucial damage identification tools in the vibration-based damage assessment. Because of the vanishing moment property, the CWT method is capable of featuring damage singularity in the higher scales, and separating the global trends and noise progressively. In the classical investigations about this issue, the localization property of the CWT is usually deemed as the most critical point. The abundant information provided by the scale-domain information and the corresponding effectiveness are, however, neglected to some extent. Ultimately, this neglect restricts the sufficient application of the CWT method in damage localization, especially in noisy conditions. In order to address this problem,the wavelet correlation operator is introduced into the CWT damage detection method as a post-processing. By means of the correlations among different scales, the proposed operator suppresses noise, cancels global trends, and intensifies the damage features for various mode shapes. The proposed method is demonstrated numerically with emphasis on characterizing damage in noisy environments, where the wavelet scale Teager-Kaiser energy operator is taken as the benchmark method for comparison.Experimental validations are conducted based on the benchmark data from composite beam specimens measured by a scanning laser vibrometer. Numerical and experimental validations/comparisons present that the introduction of wavelet correlation operator is effective for damage localization in noisy conditions.

Asymmetric nexus between commercial policies and consumption‑based carbon emissions:new evidence from Pakistan

The current study extends the previous literature by exploring the effects of a newly discovered driver,i.e.,import taxes(as a proxy for commercial policies),on the consumption-based carbon emissions(CCO2e)for 1990Q1-2017Q4.For empirical analysis,several tests and methods,including Augmented Dickey–Fuller unit root test,Zivot–Andrews unit root test,asymmetric cointegration bound testing approach,non-linear ARDL,Wald-test,Granger causality test and wavelet quantile correlation(WQC)method are utilized.Furthermore,NARDL technique estimates reveal that contractionary commercial policy enhances the environmental quality by disrupting the detrimental effects of CCO2e.However,expansionary commercial policy escalates the environmental pollution by boosting the carbon emissions.Also,the exports and the renewable energy improve the ecological quality;however,GDP deteriorates the atmospheric quality by increasing the CCO2e.Besides,WQC method and the trivariate Granger causality test are deployed to confirm the robustness of the results.Based on the findings,some crucial policies are also recommended for sustainable and green development in Pakistan.

High frequency multiscale relationships among major cryptocurrencies:portfolio management implications

This paper examines the high frequency multiscale relationships and nonlinear multiscale causality between Bitcoin,Ethereum,Monero,Dash,Ripple,and Litecoin.We apply nonlinear Granger causality and rolling window wavelet correlation(RWCC)to 15 min-data.Empirical RWCC results indicate mostly positive co-movements and long-term memory between the cryptocurrencies,especially between Bitcoin,Ethereum,and Monero.The nonlinear Granger causality tests reveal dual causation between most of the cryptocurrency pairs.We advance evidence to improve portfolio risk assessment,and hedging strategies.

WRIST FORCE SENSOR'S DYNAMIC PERFORMANCE CALIBRATION BASED ON NEGATIVE STEP RESPONSE

Negative step response experimental method is used in wrist force sensor＇s dynamic performance calibration. The exciting manner of negative step response method is the same as wrist force sensor＇s load in working. This experimental method needn＇t special experiment equipments. Experiment＇s dynamic repeatability is good. So wrist force sensor＇s dynamic performance is suitable to be calibrated by negative step response method. A new correlation wavelet transfer method is studied. By wavelet transfer method, the signal is decomposed into two dimensional spaces of time-frequency. So the problem of negative step exciting energy concentrating in the low frequency band is solved. Correlation wavelet transfer doesn＇t require that wavelet primary function be orthogonal and needn＇t wavelet reconstruction. So analyzing efficiency is high. An experimental bench is designed and manufactured to load the wrist force sensor orthogonal excitation force/moment. A piezoelectric force sensor is used to setup soft trigger and calculate the value of negative step excitation. A wrist force sensor is calibrated. The pulse response function is calculated after negative step excitation and step response have been transformed to positive step excitation and step response. The pulse response function is transferred to frequency response function. The wrist force sensor＇s dynamic characteristics are identified by the frequency response function.

Application of wavelet scale correlation filtering and its improved algorithm in signal processing with a spark sound source

It is seriously interfered by ship noise when analyzing and extracting broadband spark sound source signal. In the energy concentrated domain which is below 5 kHz, the traditional scale correlation filtering algorithm, which is based on adjacent-scale correlation, has limited anti-interference ability due to the low signal-to-noise ratio （SNR） and similar Lipschitz exponent characteristic of each other. However, because different frequency bands of the broadband electric spark signal have different noise interferences, the filtering algorithm based on adjacent-scale correlation is adapted to high SNR and small-scale high-frequency wavelet coefficients filtering; the filtering algorithm based on cross-scale correlation is adapted to low SNR and large-scale low-frequency wavelet coefficients filtering, and the threshold coefficient selection method had been corrected in the algorithm. It is shown that the filtering algorithm has a good filtering effect and extracts the broadband spark sound source signal effectively; it is applicable to broadband underwater acoustic signM processing in the presence of narrow-band strong interference background noise.

Linking the utilization of mineral resources and climate change:A novel approach with frequency domain analysis

Natural resources,climate change,and sustainable development are critical and simultaneously interrelated issues.This study investigates the interdependence between raw minerals material and sea level rise,considering the role of economic performance and material footprint employing wavelet locale multiple correlations from 1970 to 2019.The results provide strong evidence for cross-correlation of climate change with mineral resources,economic output,and domestic material consumption(biomass-fossil,metal,and non-metal)localized at the high frequency-time domain.However,the outcomes provide weak evidence for the association between bivariate time series at low frequency,which is a limitation in the short term.Based on the results,policymakers should implement effective environmental taxes and invest in cutting-edge technologies to optimize clean energy and mineral resources in a sustainable manner.