Predicting the currency exchange rate is crucial for financial agents,risk managers,and policymakers.Traditional approaches use publicly announced news on macroeconomic and financial variables as predictors of currenc...Predicting the currency exchange rate is crucial for financial agents,risk managers,and policymakers.Traditional approaches use publicly announced news on macroeconomic and financial variables as predictors of currency exchange.However,the rise of social media may have changed the source of information.For instance,tweets can help investors make informed decisions about the foreign exchange(FX)market by reflecting market sentiment and opinion.From another aspect,changes in currency exchange may incite agents to post tweets.Are tweets good predictors of currency exchange?Is the relationship between tweets and currency exchange bidirectional?We investigate the comovement/causality between the number of#dolar(“enflasyon”resp.)tweets and USDTRY currency exchange using wavelet coherence and transfer entropy(TE)to answer these questions.Wavelet coherence allows us to determine the relationship between the number of tweets and the USDTRY rate by considering the time–frequency domain.TE enables us to quantify the net information flow between the number of tweets and USDTRY.Data from October 2020 to March 2022 were used.The obtained results remain robust regardless of the frequency of retained data(daily or hourly)and the methods used(wavelet or TE).Based on our results,USDTRY is correlated with the number of#dolar tweets(#inflation)mainly in the short run and a few times in the medium run.These relationships change through time and frequency(wavelet analysis results).However,the results from TE indicate a bidirectional relationship between the#dolar(#inflation)tweets number and the USDTRY exchange rate.The influence of the exchange rate on the number of tweets is highly pronounced.Financial agents,risk managers,policymakers,and investors should then pay moderate attention to the number of#dolar(#inflation)tweets in trading/forecasting the USD–TRY exchange rate.展开更多
Relationship between sea level change and a single climate indicator has been widely discussed.However,few studies focused on the relationship between monthly mean sea level(MMSL)and several key impact factors,includi...Relationship between sea level change and a single climate indicator has been widely discussed.However,few studies focused on the relationship between monthly mean sea level(MMSL)and several key impact factors,including CO_(2) concentration,sea ice area,and sunspots,on various time scales.In addition,research on the independent relationship between climate factors and sea level on various time scales is lacking,especially when the dependence of climate factors on Nino 3.4 is excluded.Based on this,we use wavelet coherence(WC)and partial wavelet coherence(PWC)to establish a relationship between MMSL and its influencing factors.The WC results show that the influence of climate indices on MMSL has strong regional characteristics.The significant correlation between Southern Hemisphere sea ice area and MMSL is opposite to that between Northern Hemisphere sea ice area and MMSL.The PWC results show that after removing the influence of Nino 3.4,the significant coherent regions of the Pacific Decadal Oscillation(PDO),Dipole Mode Index(DMI),Atlantic Multidecadal Oscillation(AMO),and Southern Oscillation Index(SOI)decrease to varying degrees on different time scales in different regions,demonstrating the influence of Nino 3.4.Our work emphasizes the interrelationship and independent relationship between MMSL and its influencing factors on various time scales and the use of PWC and WC to describe this relationship.The study has an important reference significance for selecting the best predictors of sea level change or climate systems.展开更多
This research sheds light on the causal link between commodity price indexes,i.e.,the Agricultural Raw Materials Price Index,Industry Input Price Index,Metal Price Index,and Energy Price Index,in the global market,usi...This research sheds light on the causal link between commodity price indexes,i.e.,the Agricultural Raw Materials Price Index,Industry Input Price Index,Metal Price Index,and Energy Price Index,in the global market,using wavelet coherence,Toda–Yamamoto causality,and gradual shift causality tests over the period 1992M1 to 2019M12.Findings from the wavelet power spectrum and partial wavelet coherence reveal that:(1)there was significant volatility in the Agricultural Raw Materials Price Index,Industry Input Price Index,Metal Price Index,and Energy Price Index between 2004 and 2014 at different frequencies;and(2)commodity price indexes significantly caused the energy price index at different time periods and frequencies.It is noteworthy that the outcomes of the Toda–Yamamoto causality and gradual-shift causality tests are in line with the results of wavelet coherence.展开更多
By applying multiple wavelet coherence (MWC) to data from human body movements in triadic interaction, this study quantified triadic synchrony, rhythmic similarity among three interactants. Thirty-nine Japanese underg...By applying multiple wavelet coherence (MWC) to data from human body movements in triadic interaction, this study quantified triadic synchrony, rhythmic similarity among three interactants. Thirty-nine Japanese undergraduates were randomly assigned in a triad, and engaged in a brain-storming task. Triadic synchrony was quantified by calculating MWC to the time-series movement data collected by Kinect v2 sensor. The existence of synchrony was statistically tested by using a pseudo-synchrony paradigm. Results showed that the averaged value of MWC was higher in the experimental participant trio than in those of the pseudo trio in the frequency band of 0.5 - 1 Hz. The result supports the possible utility of applying multiple wavelet coherence to evaluate triadic synchrony in a small group interaction.展开更多
Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces ...Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces a novel FD method to improve both the accuracy and reliability of detecting potential faults in such pumps.Theproposed method combinesWaveletCoherent Analysis(WCA)and Stockwell Transform(S-transform)scalograms with Sobel and non-local means filters,effectively capturing complex fault signatures from vibration signals.Using Convolutional Neural Network(CNN)for feature extraction,the method transforms these scalograms into image inputs,enabling the recognition of patterns that span both time and frequency domains.The CNN extracts essential discriminative features,which are then merged and passed into a Kolmogorov-Arnold Network(KAN)classifier,ensuring precise fault identification.The proposed approach was experimentally validated on diverse datasets collected under varying conditions,demonstrating its robustness and generalizability.Achieving classification accuracy of 100%,99.86%,and 99.92%across the datasets,this method significantly outperforms traditional fault detection approaches.These results underscore the potential to enhance CP FD,providing an effective solution for predictive maintenance and improving overall system reliability.展开更多
Objective:To examine and compare the synchronization of different brain regions during the Chinese and English Stroop tasks.Methods.Ten native Chinese speakers with a moderate command of English participated in this s...Objective:To examine and compare the synchronization of different brain regions during the Chinese and English Stroop tasks.Methods.Ten native Chinese speakers with a moderate command of English participated in this study,and event-related potentials were recorded while participants performed the Stroop task.Then wavelet-based estimation of instantaneous EEG coherence was applied to investigate the synchronization of different brain regions during Stroop task.Results:A greater negativity for the in- congruent situation than congruent situation appeared from 350ms to 600ms post-stimulus onset over frontal,central,and parietal regions in Chinese Stroop task,while the negativity was absent in English Stroop task.However,not only in Chinese Stroop task but also in English Stroop task was it found signif- icantly higher EEG coherences for the incongruent situation than congruent situation over frontal,pari- etal,and frontoparietal regions before 400ms post stimulus onset atβ(13-30 Hz) frequency band.Conclu- sion:This finding indicated that wavelet-based coherence is more exquisite tool to analyze brain electro- physiological signals associated with complex cognitive task than ERP component,and that functional syn- chronization indexed by EEG coherence is enhanced at the earlier stage while processing the conflicting in- formation for the incongruent stimulus.展开更多
This study investigates how the uncertainty surrounding cryptocurrency affects cryptocurrency returns(CR)by employing various wavelet techniques.We concentrate on the recently published cryptocurrency uncertainty inde...This study investigates how the uncertainty surrounding cryptocurrency affects cryptocurrency returns(CR)by employing various wavelet techniques.We concentrate on the recently published cryptocurrency uncertainty index(UCRY)and the top eight cryptocurrencies by market capitalization from December 30,2013,to June 30,2023.Our results showed that the UCRY index strongly predicted CR.In particular,the UCRY index has a leading position at all frequencies for all cryptocurrencies in our sample.Additionally,when the impacts of economic policy uncertainty and the volatility index are eliminated,the significant comovement of UCRY-CR remains unchanged for the short-,medium-,and long-term investment horizons.Therefore,we conclude that the UCRY-CR relationship is both persistent and pervasive.Our study contributes toward the literature on the relationships between cryptocurrencies and market uncertainties,as well as toward investors who use uncertainty indices to design investment strategies for their portfolios.展开更多
The asymmetries of factors influencing the return of cryptocurrencies have already been well documented;however,in the case of NFTs,only information asymmetries and hedging properties related to asymmetries were studi...The asymmetries of factors influencing the return of cryptocurrencies have already been well documented;however,in the case of NFTs,only information asymmetries and hedging properties related to asymmetries were studied.Therefore,the present study examines factors affecting NFT returns,from market-related factors(cryptomarket index return and stock market index return)to the Amihud illiquidity ratio and Google search trends during different market conditions.The wavelet coherences-based methodology was applied separately during the boom,bust,normal,and turbulent periods identified by structural breakpoints.Based on 14 NFT projects between April 2019 and July 2022,results show two fundamental asymmetries influencing these NFT returns.First,there is an asymmetry in the behavior of the factors in different periods;second,there is an asymmetry in how illiquidity manifests itself over NFTs that do or do not possess cash flow-generating potential.展开更多
Geomagnetic observatory data are crucial for all branches of geophysics because they can contribute to earthquake research by detecting anomalies in the Earth’s magnetic field.Recently,data records from the Misallat(...Geomagnetic observatory data are crucial for all branches of geophysics because they can contribute to earthquake research by detecting anomalies in the Earth’s magnetic field.Recently,data records from the Misallat(MLT)and Abu Simbel(ABS)Egyptian geomagnetic observatories were processed and found to be of good quality.In this study,Egyptian observatory data were tested during both quiet and disturbed events and compared with data from INTERMAGNET observatories worldwide at different latitudes and within a narrow range of longitudes in both hemispheres.This study investigated the relationships between magnetic field components from Egyptian observatories and those from INTERMAGNET observatories using graphical representations of the X components;Pearson’s correlation for the X,Y,Z,and F components;cross-correlation for the X component;and wavelet coherence for the F component.The results of this study showed a high correlation between Egyptian observatories and all utilized INTERMAGNET stations,except those located at high latitudes,during both quiet and disturbed events.Additionally,the study confirmed the observed consistency between Egyptian observatories and selected INTERMAGNET stations.Therefore,Egyptian observatories can feasibly fill the gap in the Middle East and North Africa.展开更多
We examine technology ETF and uncertainty index(VIX,GVZ,and OVZ)spillover dynamics and quantile frequency interconnectedness across market states.This study is the first to use quantile-frequency spillover,quadruple w...We examine technology ETF and uncertainty index(VIX,GVZ,and OVZ)spillover dynamics and quantile frequency interconnectedness across market states.This study is the first to use quantile-frequency spillover,quadruple wavelet coherence,and wavelet quantile correlation methodologies to facilitate these analyses.The total connectedness index value is 70%,which is much higher in both the upper and lower quantiles.Under normal market conditions,short-term connectedness significantly exceeds long-term connectedness.Levels of ETF-uncertainty indicator connectedness increase under extreme market conditions;most technology ETFs are net spillover transmitters and uncertainty indices net spillover receivers,indicating the contagion risk of ETF investments.We show that while greater ETF-uncertainty index connectedness may benefit portfolio diversification,large fluctuations in technology EFTs can result in financial instability due to high market volatility.In the long term,the joint effects of uncertainty indices on ETFs are significant,with negative correlations between ETFs and uncertainties at different frequencies,supporting the potential role of uncertainty indices in hedging technology ETF portfolio risks.Dynamic portfolio rebalancing,scenario analysis,and stress testing may help to manage the effects of high connectedness.展开更多
Flash drought is characterized by a period of rapid drought intensification with impacts on agriculture,water resources,ecosystems,and human environment.In the Qilian Mountains,northwestern China,flash droughts are be...Flash drought is characterized by a period of rapid drought intensification with impacts on agriculture,water resources,ecosystems,and human environment.In the Qilian Mountains,northwestern China,flash droughts are becoming more frequently due to the global climate warming.However,the spatiotemporal variations and their driving factors of flash droughts are not clear in this region.In this study,the European Centre for Medium-range Weather Forecasts(ECMWF)Reanalysis v5-Land(ERA5-Land)dataset was utilized to identify two types of flash drought events(heatwave-induced and water scarcity-induced flash drought events)that occurred in the growing season(April‒September)during 1981-2020 in this area.The results showed that the frequency of heatwave-induced flash droughts has decreased since 2010,while the frequency of water scarcity-induced flash droughts has declined markedly.Spatially,heatwave-induced flash droughts were predominantly concentrated in the western Qilian Mountains,whereas water scarcity-induced flash droughts were primarily concentrated in the central and eastern Qilian Mountains.A significantly increasing temporal trend in both types of flash droughts in the eastern Qilian Mountains was found.Meanwhile,there was a decreasing temporal trend of heatwave-induced flash droughts in the southwestern part of the region.Additionally,the influence of two major atmospheric modes,i.e.,the El Niño‒Southern Oscillation(ENSO)and North Atlantic Oscillation(NAO),on these two types of flash droughts was explored by the Superposed Epoch Analysis.The ENSO mainly influences flash droughts in the central and eastern parts of the Qilian Mountains by altering the strength of the East Asian monsoon,while the NAO mainly affects flash droughts in the entire parts of the Qilian Mountains by inducing anomalous westerlies activity.Our findings have important implications for predicting the evolution of flash drought events in the Qilian Mountains region under continued climate warming.展开更多
River runoff plays an important role in watershed ecosystems and human survival,and it is controlled by multiple environmental factors.However,the synergistic effects of various large-scale circulation factors and met...River runoff plays an important role in watershed ecosystems and human survival,and it is controlled by multiple environmental factors.However,the synergistic effects of various large-scale circulation factors and meteorological factors on the runoff on different time-frequency scales have rarely been explored.In light of this,the underlying mechanism of the synergistic effects of the different environmental factors on the runoff variations was investigated in the Yellow River Basin of China during the period 1950-2019 using the bivariate wavelet coherence(WTC)and multiple wavelet coherence(MWC)methods.First,the continuous wavelet transform(CWT)method was used to analyze the multiscale characteristics of the runoff.The results of the CWT indicate that the runoff exhibited significant continuous or discontinuous annual and semiannual oscillations during the study period.Scattered inter-annual time scales were also observed for the runoff in the Yellow River Basin.The meteorological factors better explained the runoff variations on seasonal and annual time scales.The average wavelet coherence(AWC)and the percent area of the significant coherence(PASC)between the runoff and individual meteorological factors were 0.454 and 19.89%,respectively.The circulation factors mainly regulated the runoff on the inter-annual and decadal time scales with more complicated phase relationships due to their indirect effects on the runoff.The AWC and PASC between the runoff and individual circulation factors were 0.359 and 7.31%,respectively.The MWC analysis revealed that the synergistic effects of multiple factors should be taken into consideration to explain the multiscale characteristic variations of the runoff.The AWC or MWC ranges were 0.320-0.560,0.617-0.755,and 0.819-0.884 for the combinations of one,two,and three circulation and meteorological factors,respectively.The PASC ranges were 3.53%-33.77%,12.93%-36.90%,and 20.67%-39.34%for the combinations one,two,and three driving factors,respectively.The combinations of precipitation,evapotranspiration(or the number of rainy days),and the Arctic Oscillation performed well in explaining the variability in the runoff on all time scales,and the average MWC and PASC were 0.847 and 28.79%,respectively.These findings are of great significance for improving our understanding of hydro-climate interactions and water resources prediction in the Yellow River Basin.展开更多
Changes in surface temperature extremes have become a global concern.Based on the daily lowest temperature(TN)and daily highest temperature(TX)data from 2138weather stations in China from 1961 to 2020,we calculated 14...Changes in surface temperature extremes have become a global concern.Based on the daily lowest temperature(TN)and daily highest temperature(TX)data from 2138weather stations in China from 1961 to 2020,we calculated 14 extreme temperature indices to analyze the characteristics of extreme temperature events.The widespread changes observed in all extreme temperature indices suggest that China experienced significant warming during this period.Specifically,the cold extreme indices,such as cold nights,cold days,frost days,icing days,and the cold spell duration index,decreased significantly by-6.64,-2.67,-2.96,-0.97,and-1.01 days/decade,respectively.In contrast,we observed significant increases in warm extreme indices.The number of warm nights,warm days,summer days,tropical nights,and warm spell duration index increased by 8.44,5.18,2.81,2.50,and 1.66d/decade,respectively.In addition,the lowest TN,highest TN,lowest TX,and highest TX over the entire period rose by 0.47,0.22,0.26,and 0.16℃/decade,respectively.Furthermore,using Pearson's correlation and wavelet coherence analyses,this study identified a strong association between extreme temperature indices and atmospheric circulation factors,with varying correlation strengths and resonance periods across different time-frequency domains.展开更多
After defining landslide and debris flow, human activity, and precipitation indices, using with landslide and debris flow disaster data in low-latitude plateau of China, reflecting human activity and precipitation dat...After defining landslide and debris flow, human activity, and precipitation indices, using with landslide and debris flow disaster data in low-latitude plateau of China, reflecting human activity and precipitation data, the influence of human activity and precipitation on mid-long term evolution of landslide and debris flow was studied with the wavelet technique. Results indicate that mid-long evolution of landslide and debris flow disaster trends to increase 0.9 unit every year, and presents obvious stage feature. The abrupt point from rare to frequent periods took place in 1993. There is significant in-phase resonance oscillation between human activity and landslide and debris flow frequency on a scale of 11-16 years, in which the variation of human activity occurs about 0.2-2.8 years before landslide and debris flow variation. Thus, the increase of landslide and debris flow frequency in low latitude plateau of China may be mainly caused by geo-environmental degradation induced by human activity. After the impact of human activity is removed, there is sig- nificant in-phase resonance oscillation between landslide and debris flow frequency and summer rainfall in low-latitude plateau of China in quasi-three-year and quasi-six-year scales, in which the variation of summer precipitation occurs about 0.0-0.8 years before landslide and debris flow variation. Summer precipitation is one of important external causes which impacts landslide and debris flow frequency in low-latitude plateau of China. The mid-long term evolution predicting model of landslide and debris flow disasters frequency in low-latitude plateau region with better fitting and predicting ability was built by considering human activity and summer rainfall.展开更多
We introduce a novel approach to multifractal data in order to achieve transcended modeling and forecasting performances by extracting time series out of local Hurst exponent calculations at a specified scale.First,th...We introduce a novel approach to multifractal data in order to achieve transcended modeling and forecasting performances by extracting time series out of local Hurst exponent calculations at a specified scale.First,the long range and co-movement dependencies of the time series are scrutinized on time-frequency space using multiple wavelet coherence analysis.Then,the multifractal behaviors of the series are verified by multifractal de-trended fluctuation analysis and its local Hurst exponents are calculated.Additionally,root mean squares of residuals at the specified scale are procured from an intermediate step during local Hurst exponent calculations.These internally calculated series have been used to estimate the process with vector autoregressive fractionally integrated moving average(VARFIMA)model and forecasted accordingly.In our study,the daily prices of gold,silver and platinum are used for assessment.The results have shown that all metals do behave in phase movement on long term periods and possess multifractal features.Furthermore,the intermediate time series obtained during local Hurst exponent calculations still appertain the co-movement as well as multifractal characteristics of the raw data and may be successfully re-scaled,modeled and forecasted by using VARFIMA model.Conclusively,VARFIMA model have notably surpassed its univariate counterpart(ARFIMA)in all efficacious trials while re-emphasizing the importance of comovement procurement in modeling.Our study’s novelty lies in using a multifractal de-trended fluctuation analysis,along with multiple wavelet coherence analysis,for forecasting purposes to an extent not seen before.The results will be of particular significance to finance researchers and practitioners.展开更多
The pressure and temperature significantly influence precipitable water vapor(PWV) retrieval. Global Navigation Satellite System(GNSS) PWV retrieval is limited because the GNSS stations lack meteorological sensors. Fi...The pressure and temperature significantly influence precipitable water vapor(PWV) retrieval. Global Navigation Satellite System(GNSS) PWV retrieval is limited because the GNSS stations lack meteorological sensors. First, this article evaluated the accuracy of pressure and temperature in 68 radiosonde stations in China based on ERA5 Reanalysis data from 2015 to 2019 and compared them with GPT3model. Then, the accuracy of pressure and temperature calculated by ERA5 were estimated in 5 representative IGS stations in China. And the PWV calculated by these meteorological parameters from ERA5(ERA5-PWV) were analyzed. Finally, the relation between ERA5-PWV and precipitation was deeply explored using wavelet coherence analysis in IGS stations. These results indicate that the accuracy of pressure and temperature of ERA5 is better than the GPT3 model. In radiosonde stations, the mean BIAS and MAE of pressure and temperature in ERA5 are-0.41/1.15 hpa and-0.97/2.12 K. And the mean RMSEs are 1.35 hpa and 2.87 K, which improve 74.77% and 40.58% compared with GPT3 model. The errors of pressure and temperature of ERA5 are smaller than the GPT3 model in bjfs, hksl and wuh2, and the accuracy of ERA5-PWV is improved by 18.77% compared with the GPT3 model. In addition, there is a significant positive correlation between ERA5-PWV and precipitation. And precipitation is always associated with the sharp rise of ERA5-PWV, which provides important references for rainfall prediction.展开更多
We have recently introduced a new technique,coherent hemodynamics spectroscopy(CHS),which aims at characterizing a specic kind of tissue hemodynamics that feature a high level of covariation with a given physiological...We have recently introduced a new technique,coherent hemodynamics spectroscopy(CHS),which aims at characterizing a specic kind of tissue hemodynamics that feature a high level of covariation with a given physiological quantity.In this study,we carry out a detailed analysis of the signicance of coherence and phase synchronization between oscillations of arterial blood pressure(ABP)and total hemoglobin concentration([Hbt]),measured with near-infrared spectroscopy(NIRS)during a typical protocol for CHS,based on a cyclic thigh cuffocclusion and release.Even though CHS is based on a linear time invariant model between ABP(input)and NIRS measurands(outputs),for practical reasons in a typical CHS protocol,we inducenite“groups”of ABP oscillations,in which each group is characterized by a different frequency.For this reason,ABP(input)and NIRS measurands(output)are not stationary processes,and we have used wavelet coherence and phase synchronization index(PSI),as a metric of coherence and phase synchronization,respectively.PSI was calculated by using both the wavelet cross spectrum and the Hilbert transform.We have also used linear coherence(which requires stationary process)for comparison with wavelet coherence.Themethod of surrogate data is used tond critical values for the signicance of covariation between ABP and[Hbt].Because we have found similar critical values for wavelet coherence and PSI by usingve of the most used methods of surrogate data,we propose to use the data-independent Gaussian random numbers(GRNs),for CHS.By using wavelet coherence and wavelet cross spectrum,and GRNs as surrogate data,we have found the same results for the signicance of coherence and phase synchronization between ABP and[Hbt]:on a total set of 20 periods of cuffoscillations,we have found 17 coherent oscillations and 17 phase synchronous oscillations.Phase synchronization assessed with Hilbert transform yielded similar results with 14 phase synchronous oscillations.Linear coherence and wavelet coherence overall yielded similar number of signicant values.We discuss possible reasons for this result.Despite the similarity of linear and wavelet coherence,we argue that wavelet coherence is preferable,especially if one wants to use baseline spontaneous oscillations,in which phase locking and coherence between signals might be only temporary.展开更多
Phytoplankton blooms are complex environmental phenomena driven by multiple factors. Understanding their relationships with meteorological factors and climate oscillations is essential for advancing data-driven and hy...Phytoplankton blooms are complex environmental phenomena driven by multiple factors. Understanding their relationships with meteorological factors and climate oscillations is essential for advancing data-driven and hybrid statistical-dynamical models. However, these relationships have rarely been investigated across different temporal scales. This study employs wavelet transform coherence and multiple wavelet coherence to examine the multiscale and multivariate relationships between phytoplankton blooms, meteorological factors, and climate oscillations in eight large marine ecosystems of the western North Pacific. The results reveal that all phytoplankton blooms in the studied ecosystems exhibit significant annual oscillations, while seasonal climate patterns demonstrate either unimodal or bimodal distributions. A comparison of the wavelet transform coherence and multiple wavelet coherence results indicates that meteorological factors primarily drive shortperiod variations in phytoplankton blooms, whereas climate oscillations exert more influence on long-term changes. The explanation of phytoplankton blooms increases as the driver factors increase, but there are also some decreasing due to the collinearity between different factors. The sea-air temperature difference emerges as the most significant driving factor, with its mechanisms varying across marine ecosystems: one type influences mixed-layer depth, while the other arises from interspecific differences in temperature sensitivity. Furthermore,the results underscore the importance of integrating non-dominant large-scale circulation indices with predominant meteorological factors for a more comprehensive understanding.展开更多
We used daily return series for three pairs of datasets from the crude oil markets(WTI and Brent),stock indices(the Dow Jones Industrial Average and S&P 500),and benchmark cryptocurrencies(Bitcoin and Ethereum)to ...We used daily return series for three pairs of datasets from the crude oil markets(WTI and Brent),stock indices(the Dow Jones Industrial Average and S&P 500),and benchmark cryptocurrencies(Bitcoin and Ethereum)to examine the connections between various data during the COVID-19 pandemic.We consider two characteristics:time and frequency.Based on Diebold and Yilmaz’s(Int J Forecast 28:57-66,2012)technique,our findings indicate that comparable data have a substantially stronger correlation(regarding return)than volatility.Per Baruník and Křehlík’(J Financ Econ 16:271-296,2018)approach,interconnectedness among returns(volatilities)reduces(increases)as one moves from the short to the long term.A moving window analysis reveals a sudden increase in correlation,both in volatility and return,during the COVID-19 pandemic.In the context of wavelet coherence analysis,we observe a strong interconnection between data corresponding to the COVID-19 outbreak.The only exceptions are the behavior of Bitcoin and Ethereum.Specifically,Bitcoin combinations with other data exhibit a distinct behavior.The period precisely coincides with the COVID-19 pandemic.Evidently,volatility spillover has a long-lasting impact;policymakers should thus employ the appropriate tools to mitigate the severity of the relevant shocks(e.g.,the COVID-19 pandemic)and simultaneously reduce its side effects.展开更多
In recent years,various information and communication technology(ICT)devices measuring three-dimensional(3D)point cloud data have been developed and widely used for the application of pavement surface investigation.Ho...In recent years,various information and communication technology(ICT)devices measuring three-dimensional(3D)point cloud data have been developed and widely used for the application of pavement surface investigation.However,ICT devices have generally been developed not only for measuring road surface profiles but for various geo-reference point clouds.In this background,the validation of surface profiles acquired with ICT devices fulfils an important role in proving the reliability of measurement result composed by point clouds.This study proposes a wavelet transform agreement(WTA)which involves a normalization factor of profile amplitude for further improvement in the wavelet-based coherence technique.The WTA analysis allows evaluating similarity/dissimilarity of two profiles considering both the location and wavelength simultaneously.For this purpose,a terrestrial laser scanner(TLS),a mobile mapping system(MMS),and an unmanned aerial vehicle(UAV)are employed to prove the advantage of WTA in practical applications.As a result,the advantages of WTA analysis are clearly recognized in the optimization for the measurement interval of TLS,the multi-line measurement of MMS for ride quality improvement of a pavement,and the efficient operation of UAV in terms of the flight altitude.This paper also shows the identification of aging development for surface roughness over time in terms of locations and wavelengths.These findings help not only to guarantee the accuracy of profile measurements but to realize the sophisticated way of using 3D point clouds acquired with ICT devices.The outcomes of this study contribute to the increase of productivity for pavement works with improving the quality of surface profile measurement.展开更多
文摘Predicting the currency exchange rate is crucial for financial agents,risk managers,and policymakers.Traditional approaches use publicly announced news on macroeconomic and financial variables as predictors of currency exchange.However,the rise of social media may have changed the source of information.For instance,tweets can help investors make informed decisions about the foreign exchange(FX)market by reflecting market sentiment and opinion.From another aspect,changes in currency exchange may incite agents to post tweets.Are tweets good predictors of currency exchange?Is the relationship between tweets and currency exchange bidirectional?We investigate the comovement/causality between the number of#dolar(“enflasyon”resp.)tweets and USDTRY currency exchange using wavelet coherence and transfer entropy(TE)to answer these questions.Wavelet coherence allows us to determine the relationship between the number of tweets and the USDTRY rate by considering the time–frequency domain.TE enables us to quantify the net information flow between the number of tweets and USDTRY.Data from October 2020 to March 2022 were used.The obtained results remain robust regardless of the frequency of retained data(daily or hourly)and the methods used(wavelet or TE).Based on our results,USDTRY is correlated with the number of#dolar tweets(#inflation)mainly in the short run and a few times in the medium run.These relationships change through time and frequency(wavelet analysis results).However,the results from TE indicate a bidirectional relationship between the#dolar(#inflation)tweets number and the USDTRY exchange rate.The influence of the exchange rate on the number of tweets is highly pronounced.Financial agents,risk managers,policymakers,and investors should then pay moderate attention to the number of#dolar(#inflation)tweets in trading/forecasting the USD–TRY exchange rate.
基金Supported by the National Key R&D Program of China (No.2021YFC3001000)the National Natural Science Foundation of China (Nos.U1911204,51861125203)。
文摘Relationship between sea level change and a single climate indicator has been widely discussed.However,few studies focused on the relationship between monthly mean sea level(MMSL)and several key impact factors,including CO_(2) concentration,sea ice area,and sunspots,on various time scales.In addition,research on the independent relationship between climate factors and sea level on various time scales is lacking,especially when the dependence of climate factors on Nino 3.4 is excluded.Based on this,we use wavelet coherence(WC)and partial wavelet coherence(PWC)to establish a relationship between MMSL and its influencing factors.The WC results show that the influence of climate indices on MMSL has strong regional characteristics.The significant correlation between Southern Hemisphere sea ice area and MMSL is opposite to that between Northern Hemisphere sea ice area and MMSL.The PWC results show that after removing the influence of Nino 3.4,the significant coherent regions of the Pacific Decadal Oscillation(PDO),Dipole Mode Index(DMI),Atlantic Multidecadal Oscillation(AMO),and Southern Oscillation Index(SOI)decrease to varying degrees on different time scales in different regions,demonstrating the influence of Nino 3.4.Our work emphasizes the interrelationship and independent relationship between MMSL and its influencing factors on various time scales and the use of PWC and WC to describe this relationship.The study has an important reference significance for selecting the best predictors of sea level change or climate systems.
文摘This research sheds light on the causal link between commodity price indexes,i.e.,the Agricultural Raw Materials Price Index,Industry Input Price Index,Metal Price Index,and Energy Price Index,in the global market,using wavelet coherence,Toda–Yamamoto causality,and gradual shift causality tests over the period 1992M1 to 2019M12.Findings from the wavelet power spectrum and partial wavelet coherence reveal that:(1)there was significant volatility in the Agricultural Raw Materials Price Index,Industry Input Price Index,Metal Price Index,and Energy Price Index between 2004 and 2014 at different frequencies;and(2)commodity price indexes significantly caused the energy price index at different time periods and frequencies.It is noteworthy that the outcomes of the Toda–Yamamoto causality and gradual-shift causality tests are in line with the results of wavelet coherence.
文摘By applying multiple wavelet coherence (MWC) to data from human body movements in triadic interaction, this study quantified triadic synchrony, rhythmic similarity among three interactants. Thirty-nine Japanese undergraduates were randomly assigned in a triad, and engaged in a brain-storming task. Triadic synchrony was quantified by calculating MWC to the time-series movement data collected by Kinect v2 sensor. The existence of synchrony was statistically tested by using a pseudo-synchrony paradigm. Results showed that the averaged value of MWC was higher in the experimental participant trio than in those of the pseudo trio in the frequency band of 0.5 - 1 Hz. The result supports the possible utility of applying multiple wavelet coherence to evaluate triadic synchrony in a small group interaction.
基金supported by the Technology Innovation Program(20023566,‘Development and Demonstration of Industrial IoT and AI-Based Process Facility Intelligence Support System in Small and Medium Manufacturing Sites’)funded by the Ministry of Trade,Industry,&Energy(MOTIE,Republic of Korea).
文摘Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces a novel FD method to improve both the accuracy and reliability of detecting potential faults in such pumps.Theproposed method combinesWaveletCoherent Analysis(WCA)and Stockwell Transform(S-transform)scalograms with Sobel and non-local means filters,effectively capturing complex fault signatures from vibration signals.Using Convolutional Neural Network(CNN)for feature extraction,the method transforms these scalograms into image inputs,enabling the recognition of patterns that span both time and frequency domains.The CNN extracts essential discriminative features,which are then merged and passed into a Kolmogorov-Arnold Network(KAN)classifier,ensuring precise fault identification.The proposed approach was experimentally validated on diverse datasets collected under varying conditions,demonstrating its robustness and generalizability.Achieving classification accuracy of 100%,99.86%,and 99.92%across the datasets,this method significantly outperforms traditional fault detection approaches.These results underscore the potential to enhance CP FD,providing an effective solution for predictive maintenance and improving overall system reliability.
基金Supported by the National Natural Science Foundation of China(No. 60375037 and 60543003).
文摘Objective:To examine and compare the synchronization of different brain regions during the Chinese and English Stroop tasks.Methods.Ten native Chinese speakers with a moderate command of English participated in this study,and event-related potentials were recorded while participants performed the Stroop task.Then wavelet-based estimation of instantaneous EEG coherence was applied to investigate the synchronization of different brain regions during Stroop task.Results:A greater negativity for the in- congruent situation than congruent situation appeared from 350ms to 600ms post-stimulus onset over frontal,central,and parietal regions in Chinese Stroop task,while the negativity was absent in English Stroop task.However,not only in Chinese Stroop task but also in English Stroop task was it found signif- icantly higher EEG coherences for the incongruent situation than congruent situation over frontal,pari- etal,and frontoparietal regions before 400ms post stimulus onset atβ(13-30 Hz) frequency band.Conclu- sion:This finding indicated that wavelet-based coherence is more exquisite tool to analyze brain electro- physiological signals associated with complex cognitive task than ERP component,and that functional syn- chronization indexed by EEG coherence is enhanced at the earlier stage while processing the conflicting in- formation for the incongruent stimulus.
文摘This study investigates how the uncertainty surrounding cryptocurrency affects cryptocurrency returns(CR)by employing various wavelet techniques.We concentrate on the recently published cryptocurrency uncertainty index(UCRY)and the top eight cryptocurrencies by market capitalization from December 30,2013,to June 30,2023.Our results showed that the UCRY index strongly predicted CR.In particular,the UCRY index has a leading position at all frequencies for all cryptocurrencies in our sample.Additionally,when the impacts of economic policy uncertainty and the volatility index are eliminated,the significant comovement of UCRY-CR remains unchanged for the short-,medium-,and long-term investment horizons.Therefore,we conclude that the UCRY-CR relationship is both persistent and pervasive.Our study contributes toward the literature on the relationships between cryptocurrencies and market uncertainties,as well as toward investors who use uncertainty indices to design investment strategies for their portfolios.
文摘The asymmetries of factors influencing the return of cryptocurrencies have already been well documented;however,in the case of NFTs,only information asymmetries and hedging properties related to asymmetries were studied.Therefore,the present study examines factors affecting NFT returns,from market-related factors(cryptomarket index return and stock market index return)to the Amihud illiquidity ratio and Google search trends during different market conditions.The wavelet coherences-based methodology was applied separately during the boom,bust,normal,and turbulent periods identified by structural breakpoints.Based on 14 NFT projects between April 2019 and July 2022,results show two fundamental asymmetries influencing these NFT returns.First,there is an asymmetry in the behavior of the factors in different periods;second,there is an asymmetry in how illiquidity manifests itself over NFTs that do or do not possess cash flow-generating potential.
文摘Geomagnetic observatory data are crucial for all branches of geophysics because they can contribute to earthquake research by detecting anomalies in the Earth’s magnetic field.Recently,data records from the Misallat(MLT)and Abu Simbel(ABS)Egyptian geomagnetic observatories were processed and found to be of good quality.In this study,Egyptian observatory data were tested during both quiet and disturbed events and compared with data from INTERMAGNET observatories worldwide at different latitudes and within a narrow range of longitudes in both hemispheres.This study investigated the relationships between magnetic field components from Egyptian observatories and those from INTERMAGNET observatories using graphical representations of the X components;Pearson’s correlation for the X,Y,Z,and F components;cross-correlation for the X component;and wavelet coherence for the F component.The results of this study showed a high correlation between Egyptian observatories and all utilized INTERMAGNET stations,except those located at high latitudes,during both quiet and disturbed events.Additionally,the study confirmed the observed consistency between Egyptian observatories and selected INTERMAGNET stations.Therefore,Egyptian observatories can feasibly fill the gap in the Middle East and North Africa.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2024S1A5A2A01028034).
文摘We examine technology ETF and uncertainty index(VIX,GVZ,and OVZ)spillover dynamics and quantile frequency interconnectedness across market states.This study is the first to use quantile-frequency spillover,quadruple wavelet coherence,and wavelet quantile correlation methodologies to facilitate these analyses.The total connectedness index value is 70%,which is much higher in both the upper and lower quantiles.Under normal market conditions,short-term connectedness significantly exceeds long-term connectedness.Levels of ETF-uncertainty indicator connectedness increase under extreme market conditions;most technology ETFs are net spillover transmitters and uncertainty indices net spillover receivers,indicating the contagion risk of ETF investments.We show that while greater ETF-uncertainty index connectedness may benefit portfolio diversification,large fluctuations in technology EFTs can result in financial instability due to high market volatility.In the long term,the joint effects of uncertainty indices on ETFs are significant,with negative correlations between ETFs and uncertainties at different frequencies,supporting the potential role of uncertainty indices in hedging technology ETF portfolio risks.Dynamic portfolio rebalancing,scenario analysis,and stress testing may help to manage the effects of high connectedness.
基金supported by the National Natural Science Foundation of China(42477481,42477483)the Science and Technology Program in Gansu Province(23JRRA599)the Chinese Academy of Sciences(CAS)"Light of West China"Program.
文摘Flash drought is characterized by a period of rapid drought intensification with impacts on agriculture,water resources,ecosystems,and human environment.In the Qilian Mountains,northwestern China,flash droughts are becoming more frequently due to the global climate warming.However,the spatiotemporal variations and their driving factors of flash droughts are not clear in this region.In this study,the European Centre for Medium-range Weather Forecasts(ECMWF)Reanalysis v5-Land(ERA5-Land)dataset was utilized to identify two types of flash drought events(heatwave-induced and water scarcity-induced flash drought events)that occurred in the growing season(April‒September)during 1981-2020 in this area.The results showed that the frequency of heatwave-induced flash droughts has decreased since 2010,while the frequency of water scarcity-induced flash droughts has declined markedly.Spatially,heatwave-induced flash droughts were predominantly concentrated in the western Qilian Mountains,whereas water scarcity-induced flash droughts were primarily concentrated in the central and eastern Qilian Mountains.A significantly increasing temporal trend in both types of flash droughts in the eastern Qilian Mountains was found.Meanwhile,there was a decreasing temporal trend of heatwave-induced flash droughts in the southwestern part of the region.Additionally,the influence of two major atmospheric modes,i.e.,the El Niño‒Southern Oscillation(ENSO)and North Atlantic Oscillation(NAO),on these two types of flash droughts was explored by the Superposed Epoch Analysis.The ENSO mainly influences flash droughts in the central and eastern parts of the Qilian Mountains by altering the strength of the East Asian monsoon,while the NAO mainly affects flash droughts in the entire parts of the Qilian Mountains by inducing anomalous westerlies activity.Our findings have important implications for predicting the evolution of flash drought events in the Qilian Mountains region under continued climate warming.
基金This research was financially supported by the National Natural Science Foundation of China-Shandong Joint Fund(U2006227,U1906234)the National Natural Science Foundation of China(51279189).
文摘River runoff plays an important role in watershed ecosystems and human survival,and it is controlled by multiple environmental factors.However,the synergistic effects of various large-scale circulation factors and meteorological factors on the runoff on different time-frequency scales have rarely been explored.In light of this,the underlying mechanism of the synergistic effects of the different environmental factors on the runoff variations was investigated in the Yellow River Basin of China during the period 1950-2019 using the bivariate wavelet coherence(WTC)and multiple wavelet coherence(MWC)methods.First,the continuous wavelet transform(CWT)method was used to analyze the multiscale characteristics of the runoff.The results of the CWT indicate that the runoff exhibited significant continuous or discontinuous annual and semiannual oscillations during the study period.Scattered inter-annual time scales were also observed for the runoff in the Yellow River Basin.The meteorological factors better explained the runoff variations on seasonal and annual time scales.The average wavelet coherence(AWC)and the percent area of the significant coherence(PASC)between the runoff and individual meteorological factors were 0.454 and 19.89%,respectively.The circulation factors mainly regulated the runoff on the inter-annual and decadal time scales with more complicated phase relationships due to their indirect effects on the runoff.The AWC and PASC between the runoff and individual circulation factors were 0.359 and 7.31%,respectively.The MWC analysis revealed that the synergistic effects of multiple factors should be taken into consideration to explain the multiscale characteristic variations of the runoff.The AWC or MWC ranges were 0.320-0.560,0.617-0.755,and 0.819-0.884 for the combinations of one,two,and three circulation and meteorological factors,respectively.The PASC ranges were 3.53%-33.77%,12.93%-36.90%,and 20.67%-39.34%for the combinations one,two,and three driving factors,respectively.The combinations of precipitation,evapotranspiration(or the number of rainy days),and the Arctic Oscillation performed well in explaining the variability in the runoff on all time scales,and the average MWC and PASC were 0.847 and 28.79%,respectively.These findings are of great significance for improving our understanding of hydro-climate interactions and water resources prediction in the Yellow River Basin.
基金National Key Research and Development Program of China,No.2021YFB3900900。
文摘Changes in surface temperature extremes have become a global concern.Based on the daily lowest temperature(TN)and daily highest temperature(TX)data from 2138weather stations in China from 1961 to 2020,we calculated 14 extreme temperature indices to analyze the characteristics of extreme temperature events.The widespread changes observed in all extreme temperature indices suggest that China experienced significant warming during this period.Specifically,the cold extreme indices,such as cold nights,cold days,frost days,icing days,and the cold spell duration index,decreased significantly by-6.64,-2.67,-2.96,-0.97,and-1.01 days/decade,respectively.In contrast,we observed significant increases in warm extreme indices.The number of warm nights,warm days,summer days,tropical nights,and warm spell duration index increased by 8.44,5.18,2.81,2.50,and 1.66d/decade,respectively.In addition,the lowest TN,highest TN,lowest TX,and highest TX over the entire period rose by 0.47,0.22,0.26,and 0.16℃/decade,respectively.Furthermore,using Pearson's correlation and wavelet coherence analyses,this study identified a strong association between extreme temperature indices and atmospheric circulation factors,with varying correlation strengths and resonance periods across different time-frequency domains.
基金supported by National Natural Science Foundation of China(Grant No.U0933603)National Science and Technology Sup-port Program(Grant No.2011BAC09B07)
文摘After defining landslide and debris flow, human activity, and precipitation indices, using with landslide and debris flow disaster data in low-latitude plateau of China, reflecting human activity and precipitation data, the influence of human activity and precipitation on mid-long term evolution of landslide and debris flow was studied with the wavelet technique. Results indicate that mid-long evolution of landslide and debris flow disaster trends to increase 0.9 unit every year, and presents obvious stage feature. The abrupt point from rare to frequent periods took place in 1993. There is significant in-phase resonance oscillation between human activity and landslide and debris flow frequency on a scale of 11-16 years, in which the variation of human activity occurs about 0.2-2.8 years before landslide and debris flow variation. Thus, the increase of landslide and debris flow frequency in low latitude plateau of China may be mainly caused by geo-environmental degradation induced by human activity. After the impact of human activity is removed, there is sig- nificant in-phase resonance oscillation between landslide and debris flow frequency and summer rainfall in low-latitude plateau of China in quasi-three-year and quasi-six-year scales, in which the variation of summer precipitation occurs about 0.0-0.8 years before landslide and debris flow variation. Summer precipitation is one of important external causes which impacts landslide and debris flow frequency in low-latitude plateau of China. The mid-long term evolution predicting model of landslide and debris flow disasters frequency in low-latitude plateau region with better fitting and predicting ability was built by considering human activity and summer rainfall.
文摘We introduce a novel approach to multifractal data in order to achieve transcended modeling and forecasting performances by extracting time series out of local Hurst exponent calculations at a specified scale.First,the long range and co-movement dependencies of the time series are scrutinized on time-frequency space using multiple wavelet coherence analysis.Then,the multifractal behaviors of the series are verified by multifractal de-trended fluctuation analysis and its local Hurst exponents are calculated.Additionally,root mean squares of residuals at the specified scale are procured from an intermediate step during local Hurst exponent calculations.These internally calculated series have been used to estimate the process with vector autoregressive fractionally integrated moving average(VARFIMA)model and forecasted accordingly.In our study,the daily prices of gold,silver and platinum are used for assessment.The results have shown that all metals do behave in phase movement on long term periods and possess multifractal features.Furthermore,the intermediate time series obtained during local Hurst exponent calculations still appertain the co-movement as well as multifractal characteristics of the raw data and may be successfully re-scaled,modeled and forecasted by using VARFIMA model.Conclusively,VARFIMA model have notably surpassed its univariate counterpart(ARFIMA)in all efficacious trials while re-emphasizing the importance of comovement procurement in modeling.Our study’s novelty lies in using a multifractal de-trended fluctuation analysis,along with multiple wavelet coherence analysis,for forecasting purposes to an extent not seen before.The results will be of particular significance to finance researchers and practitioners.
基金supported by Open Fund of Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources (Grant MEMI-2021-2022-27)funded by the National Natural Science Foundation of China (Grants 41904031,42374040,42061077)+2 种基金the Jiangxi Provincial Natural Science Foundation (Grants 20202BABL213033)the State Key Laboratory of Geodesy and Earth's Dynamics (Grants SKLGED2021-2-2)the Graduate Innovation Foundation of East China University of Technology (Grants YC2022-s604,YC2022-s609)。
文摘The pressure and temperature significantly influence precipitable water vapor(PWV) retrieval. Global Navigation Satellite System(GNSS) PWV retrieval is limited because the GNSS stations lack meteorological sensors. First, this article evaluated the accuracy of pressure and temperature in 68 radiosonde stations in China based on ERA5 Reanalysis data from 2015 to 2019 and compared them with GPT3model. Then, the accuracy of pressure and temperature calculated by ERA5 were estimated in 5 representative IGS stations in China. And the PWV calculated by these meteorological parameters from ERA5(ERA5-PWV) were analyzed. Finally, the relation between ERA5-PWV and precipitation was deeply explored using wavelet coherence analysis in IGS stations. These results indicate that the accuracy of pressure and temperature of ERA5 is better than the GPT3 model. In radiosonde stations, the mean BIAS and MAE of pressure and temperature in ERA5 are-0.41/1.15 hpa and-0.97/2.12 K. And the mean RMSEs are 1.35 hpa and 2.87 K, which improve 74.77% and 40.58% compared with GPT3 model. The errors of pressure and temperature of ERA5 are smaller than the GPT3 model in bjfs, hksl and wuh2, and the accuracy of ERA5-PWV is improved by 18.77% compared with the GPT3 model. In addition, there is a significant positive correlation between ERA5-PWV and precipitation. And precipitation is always associated with the sharp rise of ERA5-PWV, which provides important references for rainfall prediction.
基金the US National Institutes of Health,Grant Nos.R21-EB020347 and R01-NS095334.
文摘We have recently introduced a new technique,coherent hemodynamics spectroscopy(CHS),which aims at characterizing a specic kind of tissue hemodynamics that feature a high level of covariation with a given physiological quantity.In this study,we carry out a detailed analysis of the signicance of coherence and phase synchronization between oscillations of arterial blood pressure(ABP)and total hemoglobin concentration([Hbt]),measured with near-infrared spectroscopy(NIRS)during a typical protocol for CHS,based on a cyclic thigh cuffocclusion and release.Even though CHS is based on a linear time invariant model between ABP(input)and NIRS measurands(outputs),for practical reasons in a typical CHS protocol,we inducenite“groups”of ABP oscillations,in which each group is characterized by a different frequency.For this reason,ABP(input)and NIRS measurands(output)are not stationary processes,and we have used wavelet coherence and phase synchronization index(PSI),as a metric of coherence and phase synchronization,respectively.PSI was calculated by using both the wavelet cross spectrum and the Hilbert transform.We have also used linear coherence(which requires stationary process)for comparison with wavelet coherence.Themethod of surrogate data is used tond critical values for the signicance of covariation between ABP and[Hbt].Because we have found similar critical values for wavelet coherence and PSI by usingve of the most used methods of surrogate data,we propose to use the data-independent Gaussian random numbers(GRNs),for CHS.By using wavelet coherence and wavelet cross spectrum,and GRNs as surrogate data,we have found the same results for the signicance of coherence and phase synchronization between ABP and[Hbt]:on a total set of 20 periods of cuffoscillations,we have found 17 coherent oscillations and 17 phase synchronous oscillations.Phase synchronization assessed with Hilbert transform yielded similar results with 14 phase synchronous oscillations.Linear coherence and wavelet coherence overall yielded similar number of signicant values.We discuss possible reasons for this result.Despite the similarity of linear and wavelet coherence,we argue that wavelet coherence is preferable,especially if one wants to use baseline spontaneous oscillations,in which phase locking and coherence between signals might be only temporary.
基金The National Natural Science Foundation of China under contract Nos 42230406, 42130103 and 42376223。
文摘Phytoplankton blooms are complex environmental phenomena driven by multiple factors. Understanding their relationships with meteorological factors and climate oscillations is essential for advancing data-driven and hybrid statistical-dynamical models. However, these relationships have rarely been investigated across different temporal scales. This study employs wavelet transform coherence and multiple wavelet coherence to examine the multiscale and multivariate relationships between phytoplankton blooms, meteorological factors, and climate oscillations in eight large marine ecosystems of the western North Pacific. The results reveal that all phytoplankton blooms in the studied ecosystems exhibit significant annual oscillations, while seasonal climate patterns demonstrate either unimodal or bimodal distributions. A comparison of the wavelet transform coherence and multiple wavelet coherence results indicates that meteorological factors primarily drive shortperiod variations in phytoplankton blooms, whereas climate oscillations exert more influence on long-term changes. The explanation of phytoplankton blooms increases as the driver factors increase, but there are also some decreasing due to the collinearity between different factors. The sea-air temperature difference emerges as the most significant driving factor, with its mechanisms varying across marine ecosystems: one type influences mixed-layer depth, while the other arises from interspecific differences in temperature sensitivity. Furthermore,the results underscore the importance of integrating non-dominant large-scale circulation indices with predominant meteorological factors for a more comprehensive understanding.
文摘We used daily return series for three pairs of datasets from the crude oil markets(WTI and Brent),stock indices(the Dow Jones Industrial Average and S&P 500),and benchmark cryptocurrencies(Bitcoin and Ethereum)to examine the connections between various data during the COVID-19 pandemic.We consider two characteristics:time and frequency.Based on Diebold and Yilmaz’s(Int J Forecast 28:57-66,2012)technique,our findings indicate that comparable data have a substantially stronger correlation(regarding return)than volatility.Per Baruník and Křehlík’(J Financ Econ 16:271-296,2018)approach,interconnectedness among returns(volatilities)reduces(increases)as one moves from the short to the long term.A moving window analysis reveals a sudden increase in correlation,both in volatility and return,during the COVID-19 pandemic.In the context of wavelet coherence analysis,we observe a strong interconnection between data corresponding to the COVID-19 outbreak.The only exceptions are the behavior of Bitcoin and Ethereum.Specifically,Bitcoin combinations with other data exhibit a distinct behavior.The period precisely coincides with the COVID-19 pandemic.Evidently,volatility spillover has a long-lasting impact;policymakers should thus employ the appropriate tools to mitigate the severity of the relevant shocks(e.g.,the COVID-19 pandemic)and simultaneously reduce its side effects.
基金A part of this research was supported by the Grant-in-aid for Scientific Research(C)grant number 19K04634 of the Japan Society for the Promotion of Science(JSPS).
文摘In recent years,various information and communication technology(ICT)devices measuring three-dimensional(3D)point cloud data have been developed and widely used for the application of pavement surface investigation.However,ICT devices have generally been developed not only for measuring road surface profiles but for various geo-reference point clouds.In this background,the validation of surface profiles acquired with ICT devices fulfils an important role in proving the reliability of measurement result composed by point clouds.This study proposes a wavelet transform agreement(WTA)which involves a normalization factor of profile amplitude for further improvement in the wavelet-based coherence technique.The WTA analysis allows evaluating similarity/dissimilarity of two profiles considering both the location and wavelength simultaneously.For this purpose,a terrestrial laser scanner(TLS),a mobile mapping system(MMS),and an unmanned aerial vehicle(UAV)are employed to prove the advantage of WTA in practical applications.As a result,the advantages of WTA analysis are clearly recognized in the optimization for the measurement interval of TLS,the multi-line measurement of MMS for ride quality improvement of a pavement,and the efficient operation of UAV in terms of the flight altitude.This paper also shows the identification of aging development for surface roughness over time in terms of locations and wavelengths.These findings help not only to guarantee the accuracy of profile measurements but to realize the sophisticated way of using 3D point clouds acquired with ICT devices.The outcomes of this study contribute to the increase of productivity for pavement works with improving the quality of surface profile measurement.
