An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning.

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge. To extract CP health-sensitive features autonomously from the vibration signals, the proposed approach initially selects a healthy baseline signal. The wavelet coherence analysis is then computed between the healthy baseline signal and the signal obtained from a CP under different operating conditions, yielding coherograms. WCA is a signal processing technique that is used to measure the degree of linear correlation between two signals as a function of frequency. The coherograms carry information about the CP vulnerability towards the faults as the color intensity in the coherograms changes according to the change in CP health conditions. To utilize the changes in the coherograms due to the health conditions of the CP, they are provided to a Convolution Neural Network (CNN) and a Convolution Autoencoder (CAE) for the extraction of discriminant CP health-sensitive information autonomously. The CAE extracts global variations from the coherograms, and the CNN extracts local variations related to CP health. This information is combined into a single latent space vector. To identify the health conditions of the CP, the latent space vector is classified using an Artificial Neural Network (ANN). The proposed method identifies faults in the CP with higher accuracy as compared to already existing methods when it is tested on the vibration signals acquired from real-world industrial CPs.

COVID-19 and risk spillovers of China's major financial markets: Evidence from time-varying variance decomposition and wavelet coherence analysis.

COVID-19 has influenced financial markets drastically; however, this influence has received little attention, particularly in China. This study investigates risk spillovers across China's financial and shipping markets through dynamic spillover measures based on time-varying parameter vector autoregression and generalized forecast error variance decompositions. Stock, fund, and futures markets are identified as major risk senders, whereas other markets are identified as major risk receivers. Surprisingly, bonds, gold, and shipping are safe havens that facilitate portfolio optimization. Furthermore, using wavelet coherence analysis, we find that the coherence between dynamic total spillover and COVID-19 varies across time and frequency domains.

Assessing the responses of vegetation to meteorological drought and its influencing factors with partial wavelet coherence analysis.

The increase in drought frequency in recent years is considered as an important factor affecting vegetation diversity. Understanding the responses of vegetation dynamics to drought is helpful to reveal the behavioral mechanisms of terrestrial ecosystems and propose effective drought control measures. In this study, long time series of Normalized Difference Vegetation Index (NDVI) and Solar-induced chlorophyll fluorescence (SIF) were used to analyze the vegetation dynamics in the Pearl River Basin (PRB). The relationship between vegetation and meteorological drought was evaluated, and the corresponding differences among different vegetation types were revealed. Based on an improved partial wavelet coherence (PWC) analysis, the influences of teleconnection factors (i.e., large-scale climate patterns and solar activity) on the response relationship between meteorological drought and vegetation were quantitatively analyzed to determine the roles of factors. The results indicate that (a) vegetation in the PRB showed an increasing trend from 2001 to 2019, and the SIF increased more than that of NDVI; (b) the vegetation response time (VRT) based on NDVI (VRTN) was typically 4-6 months, while the VRT based on SIF (VRTS) was typically 2-4 months. The VRT was shortest in the woody savannas and longest in the evergreen broadleaf forests. (c) The relationship between the SIF and meteorological drought was more significant than that between the NDVI and meteorological drought. (d) There was a significant positive correlation between meteorological drought and vegetation in the period of 8-20 years. The El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and sunspots were important driving factors affecting the response relationship between drought and vegetation. Specifically, the PDO had the greatest impacts among these factors.

Industry volatility and economic uncertainty due to the COVID-19 pandemic: Evidence from wavelet coherence analysis.

This study investigates the impact of economic uncertainty due to the coronavirus (COVID-19) pandemic on the industrial economy in the US in terms of the interdependence and causality relationship. We apply wavelet coherence analysis to economic policy uncertainty (EPU) data and monthly sector volatility of the S&P 500 index from January 2008 to May 2020. The results reveal that EPU in terms of COVID-19 has influenced the sector volatility more than the global financial crisis (GFC) for all sectors. Furthermore, EPU leads the volatility of all sectors during COVID-19 pandemic, while some sector's volatilities lead EPU during the GFC.

The short-term effects of air pollutants on influenza-like illness in Jinan, China.

BACKGROUND: There is valid evidence that air pollution is associated with respiratory disease. However, few studies have quantified the short-term effects of six air pollutants on influenza-like illness (ILI). This study explores the potential relationship between air pollutants and ILI in Jinan, China. METHODS: Daily data on the concentration of particulate matters < 2.5 µm (PM 2.5), particulate matters < 10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) and ILI counts from 2016 to 2017 were retrieved. The wavelet coherence analysis and generalized poisson additive regression model were employed to qualify the relationship between air pollutants and ILI risk. The effects of air pollutants on different age groups were investigated. RESULTS: A total of 81,459 ILI counts were collected, and the average concentrations of PM2.5, PM10, O3, CO, SO2 and NO2 were 67.8 µg/m3, 131.76 µg/ m3, 109.85 µg/ m3, 1133 µg/ m3, 33.06 µg/ m3 and 44.38 µg/ m3, respectively. A 10 µg/ m3 increase in concentration of PM2.5, PM10, CO at lag0 and SO2 at lag01, was positively associated with a 1.0137 (95% confidence interval (CI): 1.0083-1.0192), 1.0074 (95% CI: 1.0041-1.0107), 1.0288 (95% CI: 1.0127-1.0451), and 1.0008 (95% CI: 1.0003-1.0012) of the relative risk (RR) of ILI, respectively. While, O3 (lag5) was negatively associated with ILI (RR 0.9863; 95%CI: 0.9787-0.9939), and no significant association was observed with NO2, which can increase the incidence of ILI in the two-pollutant model. A short-term delayed impact of PM2.5, PM10, SO2 at lag02 and CO, O3 at lag05 was also observed. People aged 25-59, 5-14 and 0-4 were found to be significantly susceptible to PM2.5, PM10, CO; and all age groups were significantly susceptible to SO2; People aged ≥60 year, 5-14 and 0-4 were found to be significantly negative associations with O3. CONCLUSION: Air pollutants, especially PM2.5, PM10, CO and SO2, can increase the risk of ILI in Jinan. The government should create regulatory policies to reduce the level of air pollutants and remind people to practice preventative and control measures to decrease the incidence of ILI on pollution days.

Spatial evaluation and zoning strategy of land use elemental conflicts in heavy industrial zones: evidence from central Liaoning Province in Northeast China.

The matching imbalance of resource factors leads to land use elemental conflicts (LUECs), which has become the bottleneck restricting high-quality social and economic development. The heavy industrial zones (HIZ) have become the focus area of LUECs due to the high-resource consumption. Taking the urban group of central Liaoning Province, the area of industrial revitalization in northeast China as a case study area, the study proposed a wavelet coherence approach to identifying the influencing indicators and indicators weight of LUECs for spatial evaluation. Two-dimensional graph theory is used to cluster the evaluation results of LUECs at the plot scale and controls the main indicators to put forward the zoning strategies of LUECs. The results showed that the main indicators affecting LUECs in the western part of the HIZ are mainly human indicators, while the fierce conflicts in the east mainly come from natural indicators. The zoning strategies of LUECs in the HIZ should prevent excessive energy consumption from increasing carbon emissions in intense conflict zone and moderate conflict zone and strengthen the rural settlement arrangement and soil erosion control in mild conflict zone and structure ecological security early warnings in potential conflict zone. This study provides an important reference for land use conflicts in the global heavy industrial urban agglomeration.

A wavelet coherence approach to zoning supply-demand matching of carbon sequestration service.

Carbon sequestration (CS) is an important regulating service provided by natural ecosystem which plays an important role in mitigating global climate change. However, there is often spatial mismatch between the carbon sequestration supply and demand (CSSD), which makes it difficult to reduce carbon emissions and increase carbon sinks to achieve local carbon balance. Therefore, it is important to clarify the optimal scale to explore spatial matches and mismatches between CSSD and delimit spatial units for implementing effective carbon-focused management policies. Taking Hunan Province, China as an example, we evaluated CSSD in 2001 and 2017, and identified the optimal scale of spatial matching based on wavelet coherence analysis. The results showed that from 2001 to 2017, CS supply in Hunan Province increased by 6.45 %, while CS demand increased by 261.11 %. 8.40 km was identified as the optimal scale of CSSD spatial matches and mismatches, and Hunan Province could be divided into 3231 spatial units including four types according to the combination of CSSD, i.e. High supply-High demand, Low supply-Low demand, High supply-Low demand and Low supply-High demand. Based on the type changes of spatial units from 2001 to 2017, it was found that the key areas in need of ecological restoration were located in the east side of Xuefeng Mountains and the west side of Luoxiao Mountains, which could support accurate ecosystem monitoring and management under the background of improving the 'one map' of territorial space in Hunan Province. Based on wavelet coherence analysis, this study provided a spatial zoning approach for sustainable land use management, with a special focus on carbon sequestration supply and demand.

Wavelet-based neurovascular coupling can predict brain abnormalities in neonatal encephalopathy.

BACKGROUND: Hypoxic-ischemic encephalopathy (HIE) is a leading cause of morbidity and mortality in neonates, but quantitative methods to predict outcomes early in their course of illness remain elusive. Real-time physiologic biomarkers of neurologic injury are needed in order to predict which neonates will benefit from therapies. Neurovascular coupling (NVC) describes the correlation of neural activity with cerebral blood flow, and the degree of impairment could predict those at risk for poor outcomes. OBJECTIVE: To determine if neurovascular coupling (NVC) calculated in the first 24-hours of life based on wavelet transform coherence analysis (WTC) of near-infrared spectroscopy (NIRS) and amplitude-integrated electroencephalography (aEEG) can predict abnormal brain MRI in neonatal HIE. METHODS: WTC analysis was performed between dynamic oscillations of simultaneously recorded aEEG and cerebral tissue oxygen saturation (SctO2) signals for the first 24 h after birth. The squared cross-wavelet coherence, R2, of the time-frequency domain described by the WTC, is a localized correlation coefficient (ranging between 0 and 1) between these two signals in the time-frequency domain. Statistical analysis was based on Monte Carlo simulation with a 95% confidence interval to identify the time-frequency areas from the WTC scalograms. Brain MRI was performed on all neonates and classified as normal or abnormal based on an accepted classification system for HIE. Wavelet metrics of % significant SctO2-aEEG coherence was compared between the normal and abnormal MRI groups. RESULT: This prospective study recruited a total of 36 neonates with HIE. A total of 10 had an abnormal brain MRI while 26 had normal MRI. The analysis showed that the SctO2-aEEG coherence between the group with normal and abnormal MRI were significantly different (p = 0.0007) in a very low-frequency (VLF) range of 0.06-0.2 mHz. Using receiver operating characteristic (ROC) curves, the use of WTC-analysis of NVC had an area under the curve (AUC) of 0.808, and with a cutoff of 10% NVC. Sensitivity was 69%, specificity was 90%, positive predictive value (PPV) was 94%, and negative predictive value (NPV) was 52% for predicting brain injury on MRI. This was superior to the clinical Total Sarnat score (TSS) where AUC was 0.442 with sensitivity 61.5%, specificity 30%, PPV 75%, and NPV 31%. CONCLUSION: NVC is a promising neurophysiological biomarker in neonates with HIE, and in our prospective cohort was superior to the clinical Total Sarnat score for prediction of abnormal brain MRI.

Large-scale climate variability controls on climate, vegetation coverage, lake and groundwater storage in the Lake Urmia watershed using SSA and wavelet analysis.

Lake Urmia has shrunk by 88% since 1995 and is an outstanding example of an environmental tragedy in the Middle East, and the lake plays a critical role in the environment, economics, and society in the north-western part of Iran. It has been hypothesized that the drying of Lake Urmia has caused by climate variation and a climate-derived increase in droughts. Therefore, it is necessary to understand the teleconnections between the interannual to multidecadal climate variability and Lake Urmia because of the tangible implications for water resource management and policy decisions in the region. In this study, we use singular spectrum analysis (SSA), wavelet coherence analysis, and lag correlation calculations to analyze and quantify the impacts of the El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) on hydro-climate variables of precipitation, temperature, lake level, groundwater fluctuations, soil moisture, vegetation coverage, and insolation clearness index in the Lake Urmia watershed. Overall, the results indicate that climate oscillations attributed to the Pacific Ocean (i.e., ENSO and PDO) have a more powerful influence than Atlantic Ocean oscillations (NAO and AMO) on the variability in the water level of Lake Urmia as well as on other hydro-climate variables, except for temperature that appears influenced by the Atlantic Ocean oscillations, particularly AMO. PDO is the first dominant mode of variability in all the hydro-climate variables (63.46% on average), except for the temperature. Overall, the wavelet coherence analysis findings indicate relatively greater PDO influence than ENSO on variability in the precipitation, soil moisture, vegetation coverage, and insolation clearness index. Furthermore, hydro-climate variables in the area have a relatively highest statistical correlation with PDO (0.69 on average, ranging from 0.54 to 0.78) compared to ENSO, NAO, and AMO. Moreover, a moderate coherence between PDO and the groundwater levels in most adjacent aquifers has occurred at the >8-year period from ~1980 to 2015. In general, the hydro-climate variables statistically have a weak lag correlation with NAO (0.19 on average, ranging from 0.13 to 0.24). AMO comprises the first mode variability in temperature (71.77%), and its coherence with temperature is moderate (~0.5) at >16-year period for the time earlier than 2000. The lag correlation between AMO and temperature (0.66) is relatively near strong. These findings have important implications for decision-makers and scientists to improve water resources planning and operations in Lake Urmia under future climate uncertainty.

Optimization of wavelet coherence analysis as a measure of neural synchrony during hyperscanning using functional near-infrared spectroscopy.

Significance: The expanding field of human social interaction is enabled by functional near-infrared spectroscopy (fNIRS) that acquires hemodynamic signals during live two-person interactions. These advances call for development of methods to quantify interactive processes. Aim: Wavelet coherence analysis has been applied to cross-brain neural coupling. However, fNIRS-specific computations have not been explored. This investigation determines the effects of global mean removal, wavelet equation, and choice of oxyhemoglobin versus deoxyhemoglobin signals. Approach: We compare signals with a known coherence with acquired signals to determine optimal computational approaches. The known coherence was calculated using three visual stimulation sequences of a contrast-reversing checkerboard convolved with the canonical hemodynamic response function. This standard was compared with acquired human fNIRS responses within visual cortex using the same sequences. Results: Observed coherence was consistent with known coherence with highest correlations within the wavelength range between 10 and 20 s. Removal of the global mean improved the correlation irrespective of the specific equation for wavelet coherence, and the oxyhemoglobin signal was associated with a marginal correlation advantage. Conclusions: These findings provide both methodological and computational guidance that enhances the validity and interpretability of wavelet coherence analysis for fNIRS signals acquired during live social interactions.

Wavelet coherence analysis: A new approach to distinguish organic and functional tremor types.

OBJECTIVE: To distinguish tremor subtypes using wavelet coherence analysis (WCA). WCA enables to detect variations in coherence and phase difference between two signals over time and might be especially useful in distinguishing functional from organic tremor. METHODS: In this pilot study, polymyography recordings were studied retrospectively of 26 Parkinsonian (PT), 26 functional (FT), 26 essential (ET), and 20 enhanced physiological (EPT) tremor patients. Per patient one segment of 20 s in duration, in which tremor was present continuously in the same posture, was selected. We studied several coherence and phase related parameters, and analysed all possible muscle combinations of the flexor and extensor muscles of the upper and fore arm. The area under the receiver operating characteristic curve (AUC-ROC) was applied to compare WCA and standard coherence analysis to distinguish tremor subtypes. RESULTS: The percentage of time with significant coherence (PTSC) and the number of periods without significant coherence (NOV) proved the most discriminative parameters. FT could be discriminated from organic (PT, ET, EPT) tremor by high NOV (31.88 vs 21.58, 23.12 and 10.20 respectively) with an AUC-ROC of 0.809, while standard coherence analysis resulted in an AUC-ROC of 0.552. CONCLUSIONS: EMG-EMG WCA analysis might provide additional variables to distinguish functional from organic tremor. SIGNIFICANCE: WCA might prove to be of additional value to discriminate between tremor types.

Enhanced inter-subject brain computer interface with associative sensorimotor oscillations.

Electroencephalography (EEG) captures electrophysiological signatures of cortical events from the scalp with high-dimensional electrode montages. Usually, excessive sources produce outliers and potentially affect the actual event related sources. Besides, EEG manifests inherent inter-subject variability of the brain dynamics, at the resting state and/or under the performance of task(s), caused probably due to the instantaneous fluctuation of psychophysiological states. A wavelet coherence (WC) analysis for optimally selecting associative inter-subject channels is proposed here and is being used to boost performances of motor imagery (MI)-based inter-subject brain computer interface (BCI). The underlying hypothesis is that optimally associative inter-subject channels can reduce the effects of outliers and, thus, eliminate dissimilar cortical patterns. The proposed approach has been tested on the dataset IVa from BCI competition III, including EEG data acquired from five healthy subjects who were given visual cues to perform 280 trials of MI for the right hand and right foot. Experimental results have shown increased classification accuracy (81.79%) using the WC-based selected 16 channels compared to the one (56.79%) achieved using all the available 118 channels. The associative channels lie mostly around the sensorimotor regions of the brain, reinforced by the previous literature, describing spatial brain dynamics during sensorimotor oscillations. Apparently, the proposed approach paves the way for optimised EEG channel selection that could boost further the efficiency and real-time performance of BCI systems.

Acute effects of air pollution on influenza-like illness in Nanjing, China: A population-based study.

Influenza-like illness causes substantial morbidity and mortality. Air pollution has already been linked to many health issues, and increasing evidence in recent years supports an association between air pollution and respiratory infections. It is a pioneer study in China to quantify the effects of air pollution on influenza-like illness. This study used wavelet coherence analysis and generalized additive models to explore the potential association between air pollution (including particulate matter with aerodynamic diameter ≦2.5 µm (PM2.5), particulate matter with aerodynamic diameter ≦10 µm (PM10) and nitrogen dioxide (NO2)) and influenza-like illness (a total of 59860 cases) in Nanjing, China from January 1, 2013 to December 31, 2013. The average concentrations of PM2.5, PM10 and NO2 were 77.37 µg/m(3), 135.20 µg/m(3) and 55.80 µg/m(3). An interquartile range increase in PM2.5 concentration was associated with a 2.99% (95% confidence interval (CI): 1.64%, 4.36%) increase in daily influenza-like cases on the same day, while the corresponding increase in NO2 was associated with a 3.77% (95% CI: 2.01%, 5.56%) increase in daily cases. People aged 0-4 were proved to be significantly susceptible to PM10 and NO2; 5-14 ages were significantly susceptible to PM2.5 and PM10; and 15-24 ages were significantly susceptible to all the analyzed air pollutants. Air pollution effects tended to be null or negative for patients aged over 25, which might be due to the small number of influenza-like cases in this age group. This study can be useful for understanding the adverse health effects of air pollution and the cause of influenza-like illness.